METABOLOMICS ALLOWS FOR INSIGHT INTO THE METABOLIC ADAPTATIONS AND PERTURBATIONS ASSOCIATED WITH DIETARY CARBOHYDRATE PROFILES, AGING, AND INSULIN DYSREGULATION IN HORSES By Sarah Ilyse Jacob A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Comparative Medicine and Integrative Biology -- Doctor of Philosophy 2017 ABSTRACT METABOLOMICS ALLOWS FOR INSIGHT INTO THE METABOLIC ADAPTATIONS AND PERTURBATIONS ASSOCIATED WITH DIETARY CARBOHYDRATE PROFILES, AGING, AND INSULIN DYSREGULATION IN HORSES By Sarah Ilyse Jacob Understanding the relationship between age, diet, and glucose and insulin dynamics in horses is important given their role in equine metabolic disorders such as insulin dysregulation, equine metabolic syndrome (EMS), and pituitary pars intermedia dysfunction (PPID). However, our understanding of the pathophysiology of metabolic disorders is limited, which hampers the development of new treatment and management strategies, and identification of reliable clinical diagnostic tests. In human medicine, the advent of technologies for comprehensive metabolic analysis (“metabolomics”) has opened new avenues for understanding metabolic diseases. To date, there has been minimal application of metabolomics for the study of metabolic disorders of horses. Chapter 1 is a literature review that describes the interaction between diet and physiologic state on glucose and insulin dynamics in horses and explores the use of metabolomics to gain insight into the underlying physiology and pathophysiology of healthy and diseased individuals. Chapter 2 describes the effect of age and dietary carbohydrate profiles on glucose and insulin dynamics in healthy horses. Sixteen horses, a combination of Thoroughbred and Standardbred mares and geldings, were divided into two groups by age. Using a balanced Latin square design, horses were fed four isocaloric diets: CONTROL (restricted-starch-and-sugar, fortified pellets), STARCH (control plus kibbled corn), FIBER (control plus unmolassed sugar beet pulp/soybean hull pellets), and SUGAR (control plus dextrose powder). Following dietary adaptation, horses underwent an insulin-modified frequently sampled intravenous glucose tolerance test (FSIGTT), modified oral sugar test (OST), and a dietary meal challenge. Data were analyzed using a multivariable linear mixed regression model. Aged horses had higher insulin responses to both intravenous and oral glucose challenges. However, the effect of diet on glucose and insulin dynamics was variable depending on the method of assessment. Chapter 3 describes the effect of dietary carbohydrate profiles and time of year on adrenocorticotropic hormone (ACTH) and cortisol concentrations in adult and aged horses. Following dietary adaptation, thyrotropin releasing hormone (TRH) stimulation tests and overnight dexamethasone suppression tests were performed in March, May, August, and October. Aged horses had higher baseline ACTH and post-dexamethasone cortisol adapted to the starch-rich diet. After controlling for age and diet, baseline ACTH concentrations were significantly increased in October compared to March, May, and August while post-TRH ACTH was higher in August and October compared to March and May. Postdexamethasone cortisol was significantly higher in October compared to March, May, and August. Diet, age, and time of year are potential confounders on endocrine parameters. Chapter 4 describes the use of untargeted metabolomics for insight into metabolic adaptations associated with age and dietary carbohydrate profiles. The metabolomic analysis was performed on plasma samples before (day 0) and after dietary adaptation (day 42) as well as during a modified oral sugar test (0 minutes and 75 minutes). The metabolomic profile revealed a large number of metabolite ion peaks (> 2000) were significantly different between age groups and diet groups demonstrating changes in cellular metabolism. On-going analysis and improved metabolite identification are needed to fully interpret this dataset. Chapter 5 describes the use of metabolomic approaches for insight into metabolic perturbations in Welsh Ponies with insulin dysregulation, obesity, and history of laminitis. The metabolomic analysis was performed on serum samples obtained at 0 minutes (baseline) and 75 minutes during an oral sugar test (OST). Significant metabolite differences, primarily in the lipid and amino acid pathways, were detected between groups which provides new knowledge regarding the pathophysiology of metabolic perturbations. Chapter 6 focuses on conclusions and future directions based on this research. The effect of age and dietary carbohydrate profiles on glucose and insulin dynamics, ACTH concentrations, and cortisol concentrations are important factors to consider when evaluating hormonal and biochemical parameters. In addition, metabolomics is a powerful tool for defining metabolic changes in different physiologic (age) and pathophysiologic states (insulin dysregulation) and in response to changes in diet. Copyright by SARAH ILYSE JACOB 2017 This dissertation is dedicated to Mom and Dad. Thank you for your endless support, encouragement, and love. v ACKNOWLEDGMENTS A sincere thank you to my guidance committee (Dr. Molly McCue, Dr. Helene Pazak, Dr. Ray Geor, Dr. John Buchweitz, Dr. Bo Norby) for their unwavering support and direction throughout this journey. A heartfelt thank you to Dr. Patty Weber, Dr. Jane Manfredi, and Dr. Kristen Woltman for their help and friendship throughout this journey. A huge thank you to all the veterinary and undergraduate students for their hard work and dedication to making this project a success. vi TABLE OF CONTENTS LIST OF TABLES ........................................................................................................................................ x LIST OF FIGURES ................................................................................................................................... xiii CHAPTER 1 ................................................................................................................................................ 1 Literature Review .......................................................................................................................................... 1 INTRODUCTION ................................................................................................................................... 1 GLUCOSE AND INSULIN DYNAMICS .............................................................................................. 2 INSULIN AND CARBOHYDRATE METABOLISM ........................................................................... 3 INSULIN AND LIPID METABOLISM ................................................................................................. 4 DIETARY DETERMINANTS OF POSTPRANDIAL GLUCOSE AND INSULIN ............................. 4 Feed Type and Processing................................................................................................................... 4 Forage Carbohydrates ......................................................................................................................... 5 Addition of Fiber, Oil, Simple Sugars ................................................................................................ 5 INNATE AND ENVIRONMENTAL DETERMINANTS OF GLUCOSE AND INSULIN ................. 6 MEASUREMENT OF GLUCOSE AND INSULIN DYNAMICS ......................................................... 7 WHAT IS METABOLOMICS? .............................................................................................................. 8 Applications ........................................................................................................................................ 8 Sample Preparation ........................................................................................................................... 10 Separation and Detection of Analytes ............................................................................................... 10 Nuclear Magnetic Resonance (NMR) ............................................................................................... 10 Mass Spectrometry (MS) .................................................................................................................. 11 Gas Chromatography-Mass Spectrometry (GC-MS) ........................................................................ 11 Liquid Chromatography-Mass Spectrometry (LC-MS) .................................................................... 12 Analysis of Metabolomics Data ........................................................................................................ 13 Use of Metabolomics for Understanding Metabolism and Disease .................................................. 14 Metabolomics in Human Metabolic Disease .................................................................................... 14 Metabolomics in Veterinary Medicine ............................................................................................. 16 CONCLUSION ...................................................................................................................................... 17 CHAPTER 2 .............................................................................................................................................. 18 Effect of Age and Dietary Carbohydrate Profiles on Glucose and Insulin Dynamics in Horses ............... 18 SUMMARY ........................................................................................................................................... 18 INTRODUCTION ................................................................................................................................. 19 MATERIALS AND METHODS ........................................................................................................... 20 Horses and Groups ............................................................................................................................ 20 Study Design and Diets ..................................................................................................................... 20 Minimal Model ................................................................................................................................. 21 Oral Sugar Test ................................................................................................................................. 22 Dietary Meal Challenge .................................................................................................................... 23 Sample Collection ............................................................................................................................. 23 Determination of Glucose and Insulin Concentrations ..................................................................... 23 Insulin Assay Validation ................................................................................................................... 24 Endocrine Testing ............................................................................................................................. 24 Statistical Analysis ............................................................................................................................ 25 RESULTS .............................................................................................................................................. 25 Animals and Diets ............................................................................................................................. 25 vii Minimal Model Analysis .................................................................................................................. 25 Assessment of Glucose Deflection Below Baseline ......................................................................... 26 Oral Sugar Test Analysis .................................................................................................................. 27 Dietary Meal Challenge Analysis ..................................................................................................... 27 Breed Differences ............................................................................................................................. 27 DISCUSSION ........................................................................................................................................ 28 FOOTNOTES ........................................................................................................................................ 31 APPENDIX ............................................................................................................................................ 32 CHAPTER 3 .............................................................................................................................................. 46 Effect of Dietary Carbohydrates and Time of Year on Adrenocorticotropic Hormone (ACTH) and Cortisol Concentrations in Adult and Aged Horses.................................................................................................. 46 SUMMARY ........................................................................................................................................... 46 INTRODUCTION ................................................................................................................................. 47 MATERIALS AND METHODS ........................................................................................................... 48 Horses and Groups ............................................................................................................................ 48 Study Design ..................................................................................................................................... 49 Endocrine Testing ............................................................................................................................. 50 Statistical Analysis ............................................................................................................................ 50 RESULTS .............................................................................................................................................. 51 Animals ............................................................................................................................................. 51 ACTH Concentrations ...................................................................................................................... 51 Effect of Age and Time of Year................................................................................................... 51 Effect of Diet................................................................................................................................ 52 Effect of Breed ............................................................................................................................. 52 Cortisol Concentrations .................................................................................................................... 53 Effect of Age and Time of Year................................................................................................... 53 Effect of Diet................................................................................................................................ 53 Effect of Breed ............................................................................................................................. 53 DISCUSSION ........................................................................................................................................ 53 FOOTNOTES ........................................................................................................................................ 56 APPENDIX ............................................................................................................................................ 57 CHAPTER 4 .............................................................................................................................................. 71 Insight into Metabolic Alterations Associated with Aging and Dietary Carbohydrate Profiles................. 71 SUMMARY ........................................................................................................................................... 71 INTRODUCTION ................................................................................................................................. 72 MATERIALS AND METHODS ........................................................................................................... 73 Study Design ..................................................................................................................................... 73 Sample Collection ............................................................................................................................. 73 Metabolomics.................................................................................................................................... 74 Sample Preparation ........................................................................................................................... 74 Mass Spectrometry Analysis............................................................................................................. 75 Compound Identification, Quantification, and Data Curation .......................................................... 75 MS/MS for Metabolite Identification ............................................................................................... 76 Data Quality Control ......................................................................................................................... 77 Statistical Analysis ............................................................................................................................ 77 RESULTS .............................................................................................................................................. 78 Animals ............................................................................................................................................. 78 Metabolomics.................................................................................................................................... 78 Metabolite Differences Before (day 0) and After (day 42) Dietary Adaptation ............................... 79 viii Metabolite Changes During an Oral Sugar Test ............................................................................... 80 DISCUSSION ........................................................................................................................................ 82 FOOTNOTES ........................................................................................................................................ 85 APPENDIX ............................................................................................................................................ 86 CHAPTER 5 ............................................................................................................................................ 102 Insight into Metabolic Perturbations in Welsh Ponies with Insulin Dysregulation, Obesity, and Laminitis ................................................................................................................................................... 102 SUMMARY ......................................................................................................................................... 102 INTRODUCTION ............................................................................................................................... 103 MATERIALS AND METHODS ......................................................................................................... 104 Animals ........................................................................................................................................... 104 Oral Sugar Test ............................................................................................................................... 105 Determination of Insulin and Glucose Measurements .................................................................... 105 Determination of Other Hormonal and Biochemical Measurements .............................................. 105 Metabolomics.................................................................................................................................. 105 Sample Preparation for Global Metabolomics ................................................................................ 106 Mass Spectrometry Analysis........................................................................................................... 106 Compound Identification, Quantification, and Data Curation ........................................................ 107 Statistical Analysis .......................................................................................................................... 107 RESULTS ............................................................................................................................................ 108 Animals ........................................................................................................................................... 108 Insulin and Glucose Concentrations ............................................................................................... 108 Other Hormonal and Biochemical Concentrations ......................................................................... 108 Metabolomics.................................................................................................................................. 109 Metabolite Changes During an Oral Sugar Test ............................................................................. 109 Metabolite Differences Between Insulin Dysregulated and Non-Insulin Dysregulated Ponies...... 110 Metabolite Differences Between Obese and Non-Obese Ponies .................................................... 110 Metabolite Differences Between Ponies With and Without a History of Laminitis ....................... 111 Metabolite Similarities Between Insulin Response, Obesity Status, and Laminitis History .......... 112 Metabolite Correlations to Clinical Parameters .............................................................................. 112 Metabolites as Potential Biomarkers............................................................................................... 112 DISCUSSION ...................................................................................................................................... 113 FOOTNOTES ...................................................................................................................................... 119 APPENDIX .......................................................................................................................................... 120 CHAPTER 6 ............................................................................................................................................ 182 Conclusions and Future Directions .......................................................................................................... 182 CONCLUSIONS.................................................................................................................................. 182 FUTURE DIRECTIONS ..................................................................................................................... 185 REFERENCES ......................................................................................................................................... 187 ix LIST OF TABLES Table 2.1 Key nutrients of the dietary carbohydrate profiles (grass hay + concentrate) on a dry matter basis...................................................................................................................................... 33 Table 2.2 Least squares means estimates for cortisol concentrations (µg/dL) from the overnight dexamethasone suppression test in adult and aged horses following dietary adaptation ..... 34 Table 2.3 Least squares means estimates for weight (kg) and body condition score (BCS) before and after dietary adaptation......................................................................................................... 35 Table 2.4 Least squares means estimates and pairwise significant differences (P ≤ 0.05) for glucose and insulin parameters during the frequently sampled intravenous glucose tolerance test (FSIGTT) and oral sugar test (OST) after controlling for diet ............................................. 36 Table 2.5 Least squares means estimates for glucose and insulin parameters during the frequently sampled intravenous glucose tolerance test (FSIGTT) ........................................................ 37 Table 2.6 Least squares means estimates and pairwise differences for outcome measures used to assess glucose deflection below baseline during the frequently sampled intravenous glucose tolerance test (FSIGTT) ....................................................................................................... 38 Table 2.7 Least squares means estimates in adult and aged horses adapted to each diet (control, starch, fiber, sugar) for outcome measures used to assess glucose deflection below baseline during the frequently sampled intravenous glucose tolerance test (FSIGTT) ................................. 39 Table 2.8 Least squares means estimates for glucose and insulin parameters during the oral sugar test (OST) ................................................................................................................................... 40 Table 2.9 Least squares means estimates for glucose and insulin responses during the dietary meal challenge .............................................................................................................................. 41 Table 2.10 Least squares means estimates and pairwise significant differences (P ≤ 0.05) for glucose and insulin responses during the frequently sampled intravenous glucose tolerance test (FSIGTT) and oral sugar test (OST) after controlling for diet ............................................. 42 Table 2.11 Least squares means estimates for glucose and insulin parameters during the frequently sampled intravenous glucose tolerance test (FSIGTT) ........................................................ 43 Table 2.12 Least squares means estimates for glucose and insulin responses during the oral sugar test (OST) ................................................................................................................................... 44 Table 2.13 Least squares means estimates for glucose and insulin responses during the dietary meal challenge .............................................................................................................................. 45 Table 3.1 Key nutrients for each dietary profile (grass hay + concentrate) on a dry matter basis ....... 58 x Table 3.2 Least squares means estimates and pairwise differences for adrenocorticotropic hormone (ACTH) concentrations (pg/mL) from the thyrotropin releasing hormone (TRH) stimulation test after controlling for diet, time of year, and breed .......................................................... 59 Table 3.3 Least squares means estimates for adrenocorticotropic hormone (ACTH) concentrations (pg/mL) from the thyrotropin releasing hormone (TRH) stimulation test at different times of the year ................................................................................................................................. 60 Table 3.4 Least squares means estimates for adrenocorticotropic hormone (ACTH) concentrations (pg/mL) from the thyrotropin releasing hormone (TRH) stimulation test in adult and aged horses at different times of the year ..................................................................................... 61 Table 3.5 Least squares means estimates for adrenocorticotropic hormone (ACTH) concentrations (pg/mL) from the thyrotropin releasing hormone (TRH) stimulation test for each diet (control, starch, fiber, sugar) ................................................................................................ 62 Table 3.6 Least squares means estimates for adrenocorticotropic hormone (ACTH) concentrations (pg/mL) from the thyrotropin releasing hormone (TRH) stimulation test in adult and aged horses following adaptation to each diet (control, starch, fiber, sugar) ............................... 63 Table 3.7 Least squares means estimates and pairwise differences for cortisol concentrations (µg/mL) from the overnight dexamethasone suppression test (ODST) after controlling for diet, time of year, and breed ................................................................................................................. 64 Table 3.8 Least squares means estimates for cortisol concentrations (µg/mL) from the overnight dexamethasone suppression test (ODST) at different times of the year .............................. 65 Table 3.9 Least squares means estimates for cortisol concentrations (µg/mL) from the overnight dexamethasone suppression test (ODST) in adult and aged horses at different times of the year ....................................................................................................................................... 66 Table 3.10 Least squares means estimates for cortisol concentrations (µg/mL) from the overnight dexamethasone suppression test (ODST) in adult and aged horses following adaptation to each diet (control, starch, fiber, sugar) ................................................................................. 67 Table 4.1 Significant (P ≤ 0.05) amino acid, cofactors and vitamins, lipid, nucleotide, peptide, xenobiotic, and unknown pathway metabolites indicated by higher (↑) concentrations and lower (↓) concentrations before (Day 0) and after (Day 42) dietary adaptation in aged horses compared to adult horses independent of diet and the interaction term (age*diet) .............. 87 Table 4.2 Significant (P ≤ 0.05) amino acid, lipid, nucleotide, xenobiotic, and unknown pathway metabolites indicated by higher (↑) concentrations and lower (↓) concentrations before (Day 0) and after (Day 42) dietary adaptation to the fiber diet compared to the control diet ....... 89 Table 4.3 Significant (P ≤ 0.05) amino acid, lipid, peptide, xenobiotic, and unknown pathway metabolites indicated by higher (↑) concentrations and lower (↓) concentrations before (Day 0) and after (Day 42) dietary adaptation to the starch diet compared to the control diet ..... 90 Table 4.4 Significant (P ≤ 0.05) amino acid, lipid, peptide, xenobiotic, and unknown pathway metabolites indicated by higher (↑) concentrations and lower (↓) concentrations before (Day 0) and after (Day 42) dietary adaptation to the sugar diet compared to the control diet ...... 91 xi Table 4.5 Significant (P ≤ 0.05) amino acid, carbohydrate, cofactors and vitamins, lipid, nucleotide, peptide, xenobiotic, other, and unknown pathway metabolites indicated by higher (↑) concentrations and lower (↓) concentrations before (0 minutes) and during (75 minutes) the oral sugar test in aged horses compared to adult horses independent of diet and the interaction term (age*diet) ................................................................................................... 92 Table 4.6 Significant (P ≤ 0.05) amino acid, lipid, xenobiotic, other pathway metabolites indicated by higher (↑) concentrations and lower (↓) concentrations before (0 minutes) and during (75 minutes) the oral sugar test following adaptation to the fiber diet compared to the control diet .............................................................................................................................................. 95 Table 4.7 Significant (P ≤ 0.05) amino acid, lipid, nucleotide, peptide, xenobiotic, and other pathway metabolites indicated by higher (↑) concentrations and lower (↓) concentrations before (0 minutes) and during (75 minutes) the oral sugar test following adaptation to the starch diet compared to the control diet ................................................................................................. 96 Table 4.8 Significant (P ≤ 0.05) amino acid, carbohydrate, cofactors and vitamins, lipid, xenobiotic, other, and unknown pathway metabolites indicated by higher (↑) concentrations and lower (↓) concentrations before (0 minutes) and during (75 minutes) the oral sugar test following adaptation to the sugar diet compared to the control diet..................................................... 98 Table 5.1 List of measured metabolites ............................................................................................. 121 Table 5.2 Number of metabolites in each metabolic pathway and sub-pathway ............................... 148 Table 5.3 Optimal number of metabolites that distinguish between control and disease status for each variable (insulin response, obesity status, laminitis history) as determined by LASSO analysis ............................................................................................................................... 150 Table 5.4 Uphill (positive) correlations between metabolites and clinical parameter ..... measurements (basal glucose, basal insulin, non-esterified fatty acids (NEFAs), triglycerides, leptin, adiponectin) ........................................................................................................................ 151 Table 5.5 Downhill (negative) correlations between metabolites and clinical parameter measurements (basal glucose, basal insulin, non-esterified fatty acids (NEFAs), triglycerides, leptin, adiponectin) ........................................................................................................................ 153 xii LIST OF FIGURES Figure 3.1 Box and whisker plots of clinical laboratory baseline adrenocorticotropic hormone (ACTH) concentrations in adult and aged horses at different times of the year................................. 68 Figure 3.2 Box and whisker plots of clinical laboratory adrenocorticotropic hormone (ACTH) concentrations, at 10 minutes, following administration of thyrotropin releasing hormone (TRH) in adult and aged horses at different times of the year ............................................. 69 Figure 3.3 Least squares means estimates of the age*diet interaction for baseline adrenocorticotropic hormone (ACTH) concentrations ......................................................................................... 70 Figure 4.1 Metabolite ion peaks before and after standard vector regression normalization in pooled quality control samples and experimental samples. Normalization improved the variability in the samples ......................................................................................................................... 100 Figure 4.2 Metabolite ion peaks before and after standard vector regression normalization in pooled quality control samples and experimental samples. Normalization did not correct for batch effects in the samples ......................................................................................................... 101 Figure 5.1a Tricarboxylic acid (TCA) cycle network pathway at 0 minutes (baseline)........................ 156 Figure 5.1b Tricarboxylic acid (TCA) cycle network pathway at 75 minutes ...................................... 157 Figure 5.2a Branched-chain amino acid (BCAA) pathway at 0 minutes (baseline) ............................. 158 Figure 5.2b Branched-chain amino acid (BCAA) pathway at 75 minutes ............................................ 159 Figure 5.3 Significant (P < 0.05) metabolite differences in the lipid, amino acid, carbohydrate, cofactor and vitamin, energy, nucleotide, peptide, and xenobiotic pathways at 0 minutes and 75 minutes ............................................................................................................................... 160 Figure 5.4 Significant (P < 0.05) metabolite differences in the lipid pathway for ponies with insulin dysregulation compared to non-insulin dysregulated ponies at 0 minutes and 75 minutes ..... ............................................................................................................................................ 163 Figure 5.5 Significant (P < 0.05) metabolite differences in the amino acid pathway for ponies with insulin dysregulation compared to non-insulin dysregulated ponies at 0 minutes and 75 minutes ............................................................................................................................... 164 Figure 5.6 Significant (P < 0.05) metabolite differences in the carbohydrate, cofactor and vitamin, energy, nucleotide, xenobiotic pathways for ponies with insulin dysregulation compared to non-insulin dysregulated ponies at 0 minutes and 75 minutes ........................................... 165 Figure 5.7 Significant (P < 0.05) metabolite differences in the lipid pathway for obese compared to nonobese ponies at 0 minutes and 75 minutes ......................................................................... 166 Figure 5.8 Significant (P < 0.05) metabolite differences in the amino acid pathway for obese compared to non-obese ponies at 0 minutes and 75 minutes .............................................................. 169 xiii Figure 5.9 Significant (P < 0.05) metabolite differences in the carbohydrate, cofactor and vitamin, nucleotide, peptide, xenobiotic pathways for obese compared to non-obese ponies at 0 minutes and 75 minutes...................................................................................................... 170 Figure 5.10 Significant (P < 0.05) metabolite differences in the lipid pathway in ponies with a history of laminitis compared to ponies without a history of laminitis at 0 minutes and 75 minutes ...... ............................................................................................................................................ 171 Figure 5.11 Significant (P < 0.05) metabolite differences in the amino acid pathway in ponies with a history of laminitis compared to ponies without a history of laminitis at 0 minutes and 75 minutes ............................................................................................................................... 174 Figure 5.12 Significant (P < 0.05) metabolite differences in the carbohydrate, cofactor and vitamin, energy, nucleotide, peptide, and xenobiotic pathways in ponies with a history of laminitis compared to ponies without a history of laminitis at 0 minutes and 75 minutes ............................................................................................................................................ 176 Figure 5.13 The relationship between different phenotypes (insulin response, obesity status, laminitis history) and significant metabolites ................................................................................... 178 Figure 5.14a Principal components analysis (PCA) plot of metabolic profiles for the insulin dysregulated (○) and non-insulin dysregulated (●) phenotype................................................................ 179 Figure 5.14b Principal components analysis (PCA) plot of metabolic profiles for the obese (∆) and nonobese (▲) phenotype ......................................................................................................... 180 Figure 5.14c Principal components analysis (PCA) plot of metabolic profiles for the history of laminitis (□) and no history of laminitis (■) phenotype .................................................................... 181 xiv CHAPTER 1 Literature Review INTRODUCTION Equine endocrine disorders are an ever-growing concern in the horse population. Laminitis, a debilitating and often life-threatening disease of the foot [1,2] affects 15% to 20% of horses over the course of their lifetime [3,4]. Although laminitis has several inciting causes, alterations in insulin dynamics likely play a causal role in the development of the disease. Insulin dysregulation is characterized by an abnormal insulin response to oral glucose and/or feeding which leads to hyperinsulinemia and tissue insulin resistance [5]. Insulin dysregulation is a major health concern as hyperinsulinemia is associated with pastureassociated laminitis and experimentally-induced hyperinsulinemia in healthy horses and ponies results in laminitis [6–8]. In addition, insulin resistance has been linked to equine metabolic syndrome (EMS) [2], pituitary pars intermedia dysfunction (PPID) [9], obesity, and aging. Tissue insulin resistance occurs when the ability of insulin to promote the uptake of glucose from the circulation (primarily tissues such as skeletal muscle and adipose) is diminished due to a decreased responsiveness of the insulin receptors and/or the resulting intracellular signaling [5]. This tissue insulin resistance, together with increased pancreatic sensitivity to glucose, causes the pancreas to produce excessive amounts of insulin in response to dietary nonstructural carbohydrates, leading to hyperinsulinemia. Horses, non-ruminant herbivores, survive primarily on high roughage diets with varying amounts of protein, fat, and fiber; however, these roughage diets are often supplemented with a grain concentrate to meet the animal’s daily energy demand. Previous work has shown that metabolic phenotypes vary due to physiologic factors such as age, sex, breed, and genetics [10–12]. In addition to these innate risk factors, several studies have linked environmental factors such as nutrition [13,14], forage nonstructural carbohydrate content [15], lack of physical activity [16,17], endocrine disrupting chemicals [18], and alterations in the gut microbiome [19] to insulin dysregulation, obesity, and/or laminitis. Obesity may also alter insulin dynamics, although its role is unclear. Of all the factors contributing to insulin dysregulation, 1 diet can be manipulated and has been the focus of multiple studies. Current dietary recommendations for horses at-risk for endocrinopathic laminitis include limiting dietary nonstructural carbohydrates thereby reducing the postprandial insulin response. Although these studies have provided insight into factors affecting insulin sensitivity and response in horses, we lack information on underlying physiologic/pathophysiologic mechanisms affecting this system. In human medicine, the advent of technologies for comprehensive metabolic analysis (“metabolomics”) has opened new avenues for understanding metabolic diseases. To this end, recent studies in humans have used metabolomic profiling to reveal characteristic ‘metabolic signatures’ of type-II diabetes mellitus, obesity, and fatty liver disease. Multiple studies have identified higher concentrations of branched-chain amino acids (and BCAA metabolites), sugar metabolites, and acylcarnitines in type-II diabetes mellitus [20–25]. Moreover, this metabolic signature of type-II diabetes mellitus emerges well in advance (>10 years) of disease onset, highlighting the diagnostic potential of metabolomics. To date, there has been minimal application of metabolomics for the study of metabolic disorders of horses. The overarching goal of this dissertation is to use metabolomic approaches to gain deeper insight into metabolic adaptations and perturbations associated with dietary carbohydrate profiles, aging, and insulin dysregulation. It is hypothesized that changes in tissue metabolism are responsible for changes in tissue insulin sensitivity after dietary adaptation. The following objectives are integral to the long-term goals to understand the physiology and pathophysiology of insulin dysregulation in horses hopefully leading to improved diagnostics, management, and treatment of clinical cases. The objectives of this dissertation are to 1) determine the effect of diet and age on glucose and insulin dynamics; 2) determine the effect of diet and age on the plasma metabolome, and 3) determine the relationship between glucose and insulin dynamics and the plasma metabolome. GLUCOSE AND INSULIN DYNAMICS Each cell in the body requires glucose to function making it a necessary substrate for survival. Fortunately for horses, glucose can be found in plants in the form of a simple monosaccharide in addition 2 to being obtained from carbohydrate metabolism. Serum or plasma measurements of glucose from the peripheral blood reflect the net effect of absorption, liver utilization, and peripheral tissue uptake. However, too much glucose can have a negative effect and must be regulated. Insulin, a polypeptide hormone produced by beta cells in the islets of Langarhans in the pancreas, regulates blood glucose levels by increasing the uptake of glucose into tissues (liver, muscle, adipose) and storage as glycogen or lipid. Insulin consists of a α chain (21 amino acids) and a β chain (30 amino acids) joined by two disulfide bonds [26] and is important in the regulation of carbohydrate and lipid metabolism. Serum or plasma measurements of insulin from the peripheral blood reflect the net effect of pancreatic secretion and insulin clearance. In the liver, insulin increases glucose uptake and formation of glucose-6-phosphate. It also activates a phosphatase enzyme that dephosphorylates and activates glycogen synthetase. The role of insulin in muscle may be the most extensive. Insulin stimulates the uptake of glucose and amino acids, stimulates glycogen and protein synthesis, and increases blood flow and nutrient supply to the muscles through direct vasodilatory mechanisms. The muscles enhanced sensitivity to insulin leads to rapid growth and leanness; however, in the case of decreased insulin sensitivity, a decreased growth rate and increased amount of carcass fat will be present. INSULIN AND CARBOHYDRATE METABOLISM Following consumption of a carbohydrate meal, glucose is absorbed into the portal circulation via the small intestine. An increase in blood glucose triggers the release of insulin thereby causing glucose to enter the liver, muscle, and adipose tissue via the GLUT-4 transporters. In the liver, insulin causes glucose uptake and storage in the form of glycogen via the following mechanisms: inactivation of liver phosphorylase, increased activity of the enzyme glucokinase, and increased activity of glycogen synthase [27]. Liver phosphorylase is an enzyme that splits glycogen into glucose thereby preventing the breakdown of liver glycogen. Glucokinase causes phosphorylation of glucose thus trapping it inside the liver cells. Glycogen synthase promotes glycogen synthesis through polymerization of the monosaccharide units that 3 form glycogen molecules. In addition, in the liver, insulin promotes the conversion of excess glucose (not able to be stored as glycogen) into fatty acids, packaged into triglycerides, and transported to adipose tissue. INSULIN AND LIPID METABOLISM The presence of insulin increases the production and storage of triglycerides in adipose tissue and inhibits hydrolyzation of triglycerides to fatty acids. Insulin promotes fatty acid synthesis by increasing the transport of glucose into liver cells. Glucose is split into pyruvate and subsequently converted to acetyl coenzyme A (acetyl-CoA), the substrate necessary for synthesis of fatty acids. The fatty acids are used to form triglycerides, which is the usual form of storage fat in adipose tissue. Insulin also promotes glucose transport into fat cells where the glucose is synthesized into α-glycerol phosphate. This substance supplies the glycerol, which combined with fatty acids forms triglycerides. A decrease in circulating insulin promotes hydrolyzation of triglycerides and release of glycerol and non-esterified fatty acids (NEFAs) into circulation to be used as energy via gluconeogenesis and β-oxidation. DIETARY DETERMINANTS OF POSTPRANDIAL GLUCOSE AND INSULIN The horse is well-adapted to a high structural carbohydrate (cellulose and hemicellulose) diet but supplementation with nonstructural carbohydrates (starch, sugar, fructans) often occurs. The glycemic index, influenced by the type of carbohydrate, of a feed characterizes the postprandial glycemic response to a measured amount of feed [28]. Hay is often classified as having a low glycemic index while grains have a high glycemic index. Starch and sugar concentrates have a higher glycemic index compared to primarily fat and fiber concentrates [29]. Many factors including feed type, feed amount, and feed processing technique may influence postprandial glycemic and insulinemic responses. Feed Type and Processing Determining the most appropriate concentrate feed for horses to meet daily energy requirements without adverse effects remains a challenge. Several studies have linked dietary components such as meal 4 size, amount of starch, type of grain and method of grain processing to metabolic adaptations and perturbations. Based on meal size alone, when all other components are equal, consumption of a small meal empties the stomach faster than a large meal [30]. In addition, feeds with an increased starch content had a slower gastric emptying rate compared to feeds with a low-starch content. An increase in glucose and insulin responses of horses was seen with an increase in starch intake most likely due to glucose absorption in the small intestine [31,32]. Several studies have evaluated the processing techniques with variable results. Consumption of corn [33] or oats [33] yielded an increase in glucose and insulin concentrations; however, the processing technique did not influence these parameters. In contrast, feeding steam-processed corn resulted in a greater glycemic response compared to cracked or ground corn [34,35]. In addition, the processing technique of barley did play in role in the horses glycemic and insulinemic response; extruded barley yielded the highest concentrations and rolled barley yielded the lowest concentrations [36]. Forage Carbohydrates A forage analysis can be a worthwhile tool when evaluating and designing a nutrition regimen as carbohydrate (starch, sugar, cellulose) concentrations can influence glucose and insulin responses. The nonstructural carbohydrate (NSC) content of pasture and hay, the forages most commonly consumed by horses, are variable. Nonstructural carbohydrate, the percentage of starch plus percentage of water-soluble carbohydrate, concentration greater than 10% (dry matter basis) may have adverse effects on glucose and insulin dynamics, especially in horses with metabolic dysregulation [2]. Addition of Fiber, Oil, Simple Sugars There are several different components that may be added to an equine ration. The addition of fiber to a high starch meal did not alter glycemic and insulinemic responses [37]. However, diets higher in fat and/or fiber and lower in starch may decrease insulin concentrations but findings are inconsistent across studies [38–40]. Feeding glucose and fructose to healthy ponies resulted in higher glucose and insulin concentrations compared to control ponies [41]. 5 INNATE AND ENVIRONMENTAL DETERMINANTS OF GLUCOSE AND INSULIN Alterations in glucose and insulin dynamics may vary due to innate (physiologic) risk factors such as age, sex, breed, pregnancy, and genetics [10–12] and environmental risk factors such as such as nutrition [13,14], forage nonstructural carbohydrate content [15], lack of physical activity [16,17], and endocrine disrupting chemicals [18]. In humans, aging is associated with the development of glucose intolerance and insulin resistance with exaggerated glucose and insulin responses to a carbohydrate challenge [42,43]. Following an oral glucose challenge, aged horses (27 ± 0.4 years) had a greater insulin response compared to middle-aged horses (15.2 ± 0.4 years) and young horses (6.8 ± 0.4 years) [44]. Further, mature horses (14.2 ± 0.5 years) had reduced insulin sensitivity compared to young horses (2.0 ± 0.1 years) [35]. A newborn foal can display transient insulin resistance in the first 48-hours of life as the pancreatic β-cells are maturing [45]. Breed is another factor that may influence glucose and insulin dynamics. Previous studies have shown that ponies have reduced insulin sensitivity compared to horses [46]. Further, donkeys have reduced insulin sensitivity compared to ponies and horses [47]. Certain breeds such as Morgans, Arabians, Warmbloods, and Welsh Ponies are at greater risk for development insulin dysregulation [10–12,48]. Overall, there appears to be a genetic predisposition to alterations in glucose and insulin dynamics. Pregnancy can also influence glucose and insulin parameters. Fowden et al. [49] showed that there are significant changes in carbohydrate metabolism during pregnancy which includes hyperinsulinemia, enhanced β cell secretion to endogenous and exogenous glucose, and exaggerated responses to fasting and feeding. In a cohort of Thoroughbred mares, insulin sensitivity in pregnant animals (25 to 31 weeks gestation) was significantly lower compared to nonpregnant mares [50]. In addition, Hoffman et al. [51] demonstrated that reproductive status influenced glucose and insulin metabolism in grazing mares consuming varying carbohydrate diets. Physical activity may improve glucose and insulin parameters. Previous studies, in a cohort of Standardbred horses, have shown that exercise training resulted in improvement of insulin sensitivity [52,53]. Further, exercise training resulted in a decrease in acute insulin response to glucose (AIRg) and 6 improved insulin sensitivity in both obese and non-obese horses [54]. These findings are consistent with findings in other species as studies in humans and rats demonstrated improved insulin sensitivity following short periods of exercise [55,56]. MEASUREMENT OF GLUCOSE AND INSULIN DYNAMICS Determining insulin sensitivity of an individual has been a focus in both human and equine biomedical research. Collection of a single basal (fasting) blood sample is the simplest non-specific measurement of circulating glucose and insulin levels, but the diagnostic interpretation is difficult. The finding of basal hyperglycemia (> 110 mg/dL), decrease in tissue glucose uptake, does not distinguish between a decrease in tissue insulin sensitivity versus an increase in insulin secretion. These values can fluctuate substantially due to environmental factors (stress, feeding, diurnal and seasonal variation). A basal insulin concentration of > 20 mU/L has been suggested as a cut-off to indicate an increase in insulin secretion due to insulin insensitivity [5]. False-negatives may arise as hyperinsulinemia may be persistent or be present for only a few hours postprandially. These basal values may also be used as proxy estimates for insulin sensitivity and secretion by evaluating the glucose-to-insulin ratio (positive correlation to insulin sensitivity) and the insulin-to-glucose ratio (positive correlation to insulin secretion) [57]. Glucose and insulin concentrations may also be measured via response to an intravenous or oral glucose or feed challenge. These challenges, also known as dynamic tests, approximate glycemic and insulinemic challenges to exogenous glucose and/or insulin. These dynamic tests are generally used to quantify tissue insulin sensitivity, pancreatic islet cell responses, or both. The euglycemic-hyperinsulinemic clamp (EHC), considered to be the reference method (“gold standard”) for measurement of tissue insulin sensitivity, looks to maintain a physiologic glucose concentration by adjusting the intravenous glucose concentration in response to an intravenous constant rate infusion of insulin simulating glucose disposal [57]. The insulin-modified frequently sampled intravenous glucose tolerance test (FSIGTT) aims to assess glucose and insulin dynamics under physiologic insulin concentrations [58]. Minimal model analysis of glucose and insulin measurements during the FSIGTT yields estimates of tissue insulin sensitivity (SI; 7 insulin-mediated glucose disposal), acute insulin response to glucose (AIRg; a measure of the degree of insulin secretory response to glucose), glucose-mediated glucose disposal (Sg), and disposition index (DI = SI x AIRg; describes the pancreatic beta-cell response). The glucose tolerance test involves serial measurement of blood glucose concentrations following intravenous administration of dextrose. Evaluation of the time it takes for the glucose concentration to return to baseline demonstrates the individual’s ability to absorb, utilize, and store glucose. The insulin tolerance test measures serial glucose concentrations following intravenous administration of insulin. In healthy individuals, blood glucose concentration should decline to 50% below baseline within thirty minutes of insulin administration. Exaggerated glucose responses may suggest impaired pancreatic insulin secretion or impaired glucose disposal [57]. The combined glucose insulin tolerance test (CGIT) assesses the effects of simultaneous administration of dextrose and insulin via serial measurement of blood glucose concentrations [59]; however, repeatability of this test is poor [60]. Unlike intravenous challenges, oral challenges approximate postprandial glucose and insulin responses. The oral glucose tolerance test (OGTT) measures glucose and insulin concentrations following administration of a measured amount of oral glucose [61]. The oral sugar test (OST) [62], a modification of an oral glucose tolerance test, measures glucose and insulin concentrations after oral administration of commercially available corn syrup (Karo® light). An in-feed challenge or standardized meal challenge measures an animal’s glucose and insulin dynamics to a predetermined amount of a cereal grain-based meal (such as sweet feed). In addition, a dietary meal challenge may be performed to determine the glycemic and insulinemic response to a particular diet to which the horse is adapted. WHAT IS METABOLOMICS? Applications Metabolomics, the study of molecules involved in cellular metabolism, refers to a global interrogation of the biochemical components in a biological sample (serum, plasma, urine, saliva, cerebrospinal fluid). Metabolite profiles are powerful tools for defining metabolic changes in physiologic 8 and pathophysiologic states and may aid in understanding the mechanism of disease. Small molecule metabolites such as lipids, amino acids, peptides, nucleic acids, organic acids, fatty acids, vitamins, carbohydrates, hormones, and steroids are the end products of cellular regulatory processes. As the ultimate response of biological systems to genetic or environmental changes, the comprehensive measurement of metabolites reflects perturbations in metabolism thus providing insight into biological mechanisms and pathogenesis of disease through an understanding of molecular pathways. Used as a diagnostic tool, metabolomics can detect disease prior to the onset of disease, which allows for earlier intervention into prevention and treatment. In addition, metabolomics gives us the ability to better understand the mechanisms of disease occurrence in both the physiological and pathological states. On the most basic level, metabolomics can differentiate between healthy individuals and diseased individuals [63–65]. However, taking it one step further, it can provide sub-classification of disease types by separating individuals based on the underlying pathophysiology within the same disease category. For example, are all individuals with type-II diabetes the same or are there metabolite differences that detect variation in the underlying pathophysiology and disease mechanisms. This technique allows for exploration of the in-depth interaction between compounds in a biological sample and their role in complex biological systems [66]. It reflects changes downstream of genomic, transcriptomic, and proteomic fluctuations representing an organism in health and disease. Metabolomics involves two complementary approaches, untargeted and targeted analyses. Untargeted metabolomics examines as many metabolites as possible whereas a targeted analysis quantifies discrete groups of chemically related metabolites. Studies in humans have used metabolomic profiling to reveal characteristic metabolic signatures of type-II diabetes, obesity, and fatty liver disease [20–25]. More than 4,000 metabolites have been identified in human serum [67] by high-throughput mass spectrometry and chromatography. The use of metabolomics has the potential to provide information to understand physiology and pathophysiology and can be a useful tool for understanding the impact of genetic and environmental factors. 9 Sample Preparation Analysis of biofluids (serum, plasma, urine, saliva, cerebrospinal fluid) reflects organism-level perturbations in metabolism [63]. Biofluids are often the favored sample type for analysis as they are minimally invasive, representative of organism metabolism, and possess an extensive array of highthroughput experimental protocols for metabolite extraction [68,69]. Protein precipitation and extraction of metabolites from high-molecular-weight macromolecules is required. Three solvent systems (methanol/water, methanol/acetonitrile/water, methanol/chloroform/water) have been shown to extract a wide range of hydrophilic compounds with variable efficiency for mass spectrometry and nuclear magnetic resonance (NMR) [70–73]. The extraction protocol is influenced by study design and sample type. Separation and Detection of Analytes Nuclear magnetic resonance (NMR) and mass spectrometry (MS) are two powerful approaches for the analysis of small molecules in targeted and untargeted experimental designs. The dominance of NMR and MS in metabolomics is facilitated by their capacity to identify metabolites and quantify their relative abundance via high-throughput sampling without significant reductions in sensitivity or resolution [74]. However, these single platform approaches are unable to adequately quantify all metabolites in a sample thus the combination of multiple analytical platforms provides greater coverage of metabolism. Nuclear Magnetic Resonance (NMR) Nuclear magnetic resonance (NMR) spectroscopy is a technique that uses the magnetic properties of atomic nuclei to determine the physical and chemical properties of atoms within a mixture. This technique allows for a high-resolution, rapid analysis; however, it has a low sensitivity with more than one peak per component [75]. A simplified NMR spectrometer is comprised of four components: a sample tube, a superconducting magnet, a radio frequency transmitter, and a radio frequency transmitter and amplifier [76]. A deprotonated sample is pipetted into an NMR tube and placed in the machine. A super conducted magnet creates a magnetic field that orients magnetic atomic nuclei parallel to the field. Nuclei aligned with 10 the magnetic field are in a low-energy state while those aligned against the magnetic field are in a highenergy state. The most common nuclei analyzed are 1H and 13C, whose unique chemical shifts in NMR analysis is representative of the backbone of most biological molecules. Mass Spectrometry (MS) Mass spectrometry is an analytical technique that measures the characteristics of individual molecules by sorting ions based on their mass to charge ratio. A mass spectrometer has three associated components: an ion source, a mass analyzer, and a detector. Ionization is required to induce vaporization of metabolites prior to analysis, converting each compound into a charged ion to be detected in the instrument. Mass analyzers differentiate ions by their mass to charge ratio, selecting predefined masses for analysis or providing a full-scan. Detectors measure the abundance of ions sampled. Mass spectrometry coupled with separation techniques such as liquid or gas chromatography enhance the analysis of complex mixtures while improving the resolving power of the compounds. Tandem spectrometry (MS/MS) facilitates the structural elucidation of compounds through fragmentation. A combination of separation and fragmentation, accompanied with authentic standards, represents the gold standard for compound identification in metabolomics. Gas Chromatography-Mass Spectrometry (GC-MS) Gas chromatography-mass spectrometry allows for the analysis of volatile compounds such as eicosanoids, carotenoids, flavonoids, and lipids in complex samples. Gas chromatography is a separation technique in which the mobile phase is gas. It is a trusted and well-established technique in analytical chemistry as it is robust and sensitive with large commercial and public libraries available for compound identification. However, this technique is slow, often requires derivatization, and many analytes are too large and thermally-unstable for the analysis [75]. Vaporized compounds flow through a capillary column with an inert gas, separating compounds as they interact with the column packing based on biochemical properties. As compounds exit the column they are ionized, typically through electron impact (EI) 11 ionization. This form of ionization, categorized as hard ionization, often fragments the compound during vaporization allowing for compound identification without MS/MS. To avoid misidentification from poor fragmentation or co-eluting compounds, tandem GC-MS/MS or two-dimensional separation GC×GC/MS are utilized to produce the highest confidence identifications. Many consider GC-MS to be the “gold standard” for identification in analytical chemistry because of its sensitivity, specificity, and reproducibility of results. Liquid Chromatography-Mass Spectrometry (LC-MS) Liquid chromatography is a separation technique in which the mobile phase is liquid. This technique has many modes of separation available and can accommodate a large sample capacity; however, it is slow with a limited number of commercial libraries available for compound identification [75]. Highperformance liquid chromatography (HPLC) and ultra-high-performance liquid chromatography (UHPLC) are techniques that utilize pressurized liquid solvents to separate, identify and quantify each component in a mixture. UHPLC-MS allows for the analysis of non-volatile compounds such as amino acids, bile acids, fatty acids, sterols and carboxylic acids in complex samples. Similar to gas chromatography, liquid chromatography flows compounds through a capillary column with an inert liquid, separating compounds as they interact with the column packing based on biochemical properties. The choice of packing material and mobile phase influences the class of compounds analyzed. Normal phase liquid chromatography utilizes a polar stationary phase and nonpolar mobile phases, separating compounds by hydrophilicity. Reverse phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) reverse the separation polarity, separating compounds by hydrophobicity. HILIC allows for identification of polar molecules such as carbohydrates and sugars in complex samples. Targeted and untargeted metabolomics by LC-MS is a very popular approach for metabolic profiling. A wide-breadth of chemical classes may be detected, and open-source and commercial software is available for analysis. 12 Analysis of Metabolomics Data Analysis of metabolomics data requires specialized mathematical and statistical tools. Prior to tackling statistical modeling, the raw data must be sequentially processed in multiple phases: file conversion, feature detection, alignment, normalization, and metabolite identification [77]. Once a data matrix has been created, metabolomics data can be analyzed using standard statistical methods for identifying differences between experimental groups (i.e. ANOVA, linear regression) for individual metabolites. However, due to the complexity of metabolomic datasets, and the correlation between individual metabolites, multivariate analysis is often required to account for the conditions of the study design. In addition to standard regression analyses, unsupervised and supervised statistical and machinelearning algorithms are typically employed to identify patterns across metabolites. Unsupervised methods group data without using information from predefined labels or classifiers. Unsupervised methods include clustering analysis [78] and principal components analysis (PCA) [79]. Unsupervised principal components analysis, often used as a starting point in the analysis, clusters samples based on the variance of signals in the metabolite profile [80]. It is a method to reduce the dimensionality of the data while retaining the maximum amount of information. The first principal component is the linear combination of the original variables that explain the greatest amount of variation, the second component is the linear combination of the original variables that account for the greatest amount of variation uncorrelated to the first component, the third component is the linear combination of the original variables that account for the greatest amount of variation uncorrelated to the first and second component, and so on. Clustering analysis divides the datasets into subclasses (clusters) using hierarchical or nonhierarchical algorithms. In both types of clustering, the similarity between pairs of objects is used to group them into subsets (clusters) that have meaning. However, in nonhierarchical clustering the relationship between clusters is undetermined, whereas in hierarchical clustering the relationship between clusters is determined. Hierarchical clustering shows large-scale differences by determining the similarity of two samples and pairing them together in the same cluster, and then by determining the relationship between two clusters and pairing them together, this 13 continues until all pairs of clusters are linked creating a structure (the hierarchy) that shows the relationships between all samples/clusters. In contrast to unsupervised methods, supervised methods rely on predefined groups or classes of data (e.g., case vs. control). Supervised methods, including partial least squares discriminant analysis (PLSDA) and random forest analysis, are used to identify the metabolite(s) most useful for classification of experimental samples (e.g. horse) into groups (i.e., case vs. control). Partial least squares discriminant analysis constructs predictive models based on the regression of class information [81]. It is a method used to sharpen the separation between groups. Random forest is a technique that distinguishes two groups by assembling a decision tree without dimensional reduction [82]. Finally, interpretation of metabolomics data utilizes heatmaps or pathways to visualize changes and responses of metabolites within the study. Use of Metabolomics for Understanding Metabolism and Disease The measurement of small cellular molecules within a tissue or biofluid provides a footprint of the whole body’s metabolic processes. The quantification of cellular compounds from a biological sample provides information about the disruption of metabolic processes in endogenous and exogenous pathways and insight into physiology and pathophysiology of an individual. Further, these compounds (i.e. metabolites) can serve as biomarkers for early detection of disease thus leading to early treatment intervention. Metabolomics in Human Metabolic Disease Human metabolic diseases are heterogeneous in nature as both innate and environmental factors influence disease development and progression. The use of metabolomics to understand the link between obesity, insulin resistance, and type-II diabetes mellitus has provided valuable information regarding disease onset and pathophysiology. Obesity is at the forefront of human medicine as it predisposes individuals to potentially life-threatening diseases such as type-II diabetes mellitus and cardiovascular disease. Obese individuals also suffer from metabolic alterations characterized by hyperglycemia, 14 hyperlipidemia, and insulin resistance. Type-II diabetes mellitus develops once insulin secretion cannot compensate for insulin resistance. Several studies in humans have identified plasma metabolites associated with obesity, insulin resistance, glucose intolerance and type-II diabetes mellitus. Further, these metabolites indicate that primary pathway disruption occurs in carbohydrate metabolism, tricarboxylic acid cycle, lipid metabolism, and amino acid metabolism. Several metabolites have been identified as potential biomarkers for obesity. Amino acids (tyrosine, phenylalanine, alanine, proline), branched-chain amino acids (leucine, isoleucine, valine), and phospholipids were positively correlated with body mass index [83] and hyperlipidemia [84]. Specifically, branched-chain amino acid concentrations are increased in obese individuals [85–88]. Further, acylcarnitines [85,86,89,90] and lipids such as phospholipids [91] and non-esterified fatty acids [92] are altered in obese individuals. These individuals have an increased availability of lipids; however, free fatty acid oxidation is blunted as evident by increased acylcarnitine concentrations [93]. Metabolites have also been identified as potential biomarkers for insulin resistance and type-II diabetes mellitus. Branched-chain amino acids, amino acids, and acylcarnitines are markers of early insulin resistance [65,94–96]. Further, a consistent pattern of reduced glycine and acylcarnitines [25,97,98] with increased concentrations of valine, isoleucine and α-hydroxybutyrate [99,100] has been associated with both basal and dynamic measures of insulin resistance and higher concentrations of branched-chain amino acids, sugar metabolites, and acylcarnitines are noted in type-II diabetes mellitus [21–25,100]. More specifically, alterations in α-hydroxybutyrate and linoleoylglyceraphosphocholine have been identified as joint markers of insulin resistance in type-II diabetes mellitus individuals [96]. In addition, alterations in carbohydrate metabolism and the tricarboxylic acid cycle, including increases in pyruvate, lactate, and citrate as well as decreases in malate, fumarate, and succinate are seen with type-II diabetes mellitus [101,102]. Importantly, metabolic perturbations characterizing and contributing to type-II diabetes mellitus are evident years before disease onset and comprehensive metabolomic profiling has been used to elucidate alterations in novel metabolic pathways implicated in disease development. 15 Metabolomics in Veterinary Medicine A limited number of studies in domestic animals (horses, cows, dogs, cats) have identified metabolites associated with individual variability and dietary profiles. The metabolite profile is truly unique to an individual as Colyer et al. [103] illustrated the variance in metabolite profiles of dogs and cats fed the same diet. While dogs and cats are different species there are metabolite commonalities; however, the majority of the variability occurred within lipid metabolism [103]. Further, within the same species, variability across breeds exists. Breed-specific dietary metabolism can be detected by examining metabolite profiles using urine [104] and plasma [105]. Breed and gender are the main drivers of variance. The understanding of the complex interaction between the individual and the environment, particularly diet, remains elusive; however, nutritional metabolomic studies in dogs and cats begin to provide worthwhile information. In human medicine, obesity is a known risk factor for the development of insulin resistance and diabetes, and metabolomics has been used for identification of biomarkers for dietary assessment. The ability to apply this technique to veterinary medicine may allow for better nutritional management of healthy and diseased animals, but the measurement of metabolites is costly. Obesity in domestic cats is a common nutritional disorder. Overweight cats are at risk for medical abnormalities such as insulin resistance, diabetes, hepatic lipidosis, and reduced lifespan. Obesity most commonly results from an imbalance between energy intake and its expenditure but also may result from hypothyroidism, insulinoma, and hyperadrenocorticism. In a study by Deng et al. [106] changes in the feline blood metabolome were evaluated in response to dietary macronutrient composition (high-fat, high-protein, highcarbohydrate). Examination of the metabolome detected distinct differences between diets with primary changes in amino acid and lipid metabolism. Cats on the high-protein diet had decreased nucleotide catabolism and increased amino acid metabolism and gut microbial metabolism. Cats on the high-fat diet had increased lipid metabolism. The following three potential biomarkers were identified to distinguish between diets: ƴ-glutamylleucine, 3-hydroxyisobutyrate, and 3-indoxyl sulfate [106]. Metabolomics has been used in dairy cattle for improving milk production which could have a significant impact on the agricultural industry. In a study that examined the effect of forage quality on milk 16 production, biofluids yielded metabolites and metabolic pathways that may serve as potential biomarkers for higher milk yield and higher quality milk protein [107]. Further, another study demonstrated an association between somatic cell counts and metabolite profiles in milk. Milk samples with high somatic cell counts (720,000 cells/mL) had increased concentrations of lactate, butyrate, isoleucine, acetate, and βhydroxybutyrate compared to milk samples with low somatic cell counts (14,000 cells/mL) [108]. Equine studies have used metabolomics to gain insight into various aspects of equine medicine and disease such as the bacterial community and volatile organic compounds of healthy Thoroughbred horses [109], detection of steroidal aromatase inhibitors in performance horses [110], impact of exercise [111,112], sepsis [113], and breed differences [114]. CONCLUSION Understanding the effect of age and dietary carbohydrate profiles on glucose and insulin dynamics and the plasma metabolome will improve the welfare of horses. The application of metabolomics has the potential to improve the health and well-being of animals and to provide information about physiology and pathophysiology of aging and insulin dysregulation. Identification of differing metabolites between the healthy and diseased phenotypes has the potential to serve as a diagnostic tool. In addition, the ability to define a metabolomic signature may reveal specific biomarkers that predict and/or diagnose metabolic abnormalities leading to a better understanding of disease processes that may help identify new therapeutic targets. Overall, metabolomic profiling will be a relevant approach for further defining equine metabolic alterations and perturbations. 17 CHAPTER 2 Effect of Age and Dietary Carbohydrate Profiles on Glucose and Insulin Dynamics in Horses SUMMARY Background: Glucose and insulin dynamics may be different in adult and aged horses. Objectives: To determine the effect of age and dietary carbohydrates on glucose and insulin dynamics in healthy horses. Study Design: Balanced Latin square design with four isocaloric diets: control (restricted-starch-and-sugar, fortified pellets), starch (control plus kibbled corn), fiber (control plus unmolassed sugar beet pulp/soybean hull pellets), and sugar (control plus dextrose powder). Methods: Sixteen healthy Thoroughbred and Standardbred horses were divided into two groups by age: adult (8.8 ± 2.9 years; n = 8) and aged (20.6 ± 2.1 years; n = 8). Following dietary adaptation, horses underwent an insulin-modified intravenous glucose tolerance test (FSIGTT), modified oral sugar test (OST), and a dietary meal challenge. Outcome variables included: insulin sensitivity (SI), disposition index (DI), glucose effectiveness (Sg), and acute insulin response to glucose (AIRg) from the FSIGTT; peak glucose, peak insulin, time to peak, area under the curve for glucose (AUCg) and insulin (AUCi) from the OST and dietary meal challenge. Data were analyzed using a multivariable linear mixed regression model. Results: AIRg was higher in aged (582.0 ± 59.1) compared to adult (358.0 ± 62.2; P = 0.03) horses. Adult and aged horses had a higher SI on the sugar (adult: 3.4 ± 0.4; aged: 4.0 ± 0.4) diet compared to the control (adult: 2.0 ± 0.4, P = 0.009; aged: 1.4 ± 0.4, P ≤ 0.001) and fiber (adult: 2.0 ± 0.5, P = 0.014; aged: 2.4 ± 0.4, P = 0.004) diets. Feeding a single starch (adult: 21581.0 ± 3273.0; aged: 35205.0 ± 2996.0) or sugar (adult: 26050.0 ± 3072.0; aged: 25720.0 ± 2963.0) meal resulted in postprandial hyperinsulinemia (AUCi). Main Limitations: The study cohort contained two different insulin-sensitive breeds. Conclusions: Age and diet should both be considered when evaluating glucose and insulin dynamics. 18 INTRODUCTION Understanding the relationship between glucose and insulin dynamics, age, and dietary adaptation in horses is important due to their association with metabolic diseases such as equine metabolic syndrome and/or pituitary pars intermedia dysfunction. However, a number of other innate (breed, sex, adiposity, genetics) and environmental (diet, exercise) factors affect insulin dynamics in equids [10,16,48,52,58,115]. Previous studies have demonstrated that insulin responses are higher in aged horses than young horses. These differences have been demonstrated in studies evaluating insulin responses to intravenous glucose challenge (aged: 22.0 ± 0.7 years; young: 7.3 ± 0.6 years) [54], oral glucose challenge (aged: 14.2 ± 0.5 years; young: 6.8 ± 0.4 years) [44] and to feeding (aged: 14.2 ± 0.5 years; young: 2.0 ± 0.1 years) [35]. Insulin sensitivity and insulin response to feeding are also impacted by dietary composition. In animals kept at pasture, exacerbation of hyperinsulinemia and incident laminitis often coincides with an increase in forage nonstructural carbohydrate content [14]. Other studies have reported that the feeding of a starch-rich diet results in a decrease in insulin sensitivity when compared to feeding low-starch diets that contain higher fat (oil) and/or fiber content [52,58,116,117]. However, there have been few reports on the effect of diet on glucose and insulin dynamics in older horses. Nielson et al. [35] reported that age was positively correlated with the magnitude of glycemic and insulinemic responses to feeding, while Rapson [118] reported higher tissue insulin sensitivity in aged horses adapted to a hay and grain diet when compared to a hay only diet. The latter is a surprising finding in light of earlier studies in adult horses that demonstrated reduced insulin sensitivity after adaptation to starch-rich feeds. Given the association between aging and abnormal response to oral glucose or feeding leading to hyperinsulinemia and/or tissue insulin resistance (insulin dysregulation) [5], and evidence that dietary effects on glucose and insulin regulation may be different in adult and aged horses, it is clear that the impact of diet on glucose and insulin dynamics in aged horses needs further evaluation. Our objectives were to 1) evaluate the effect of adaptation to diets containing varying amounts of starch, sugar, and fiber on glucose and insulin dynamics in healthy adult and aged horses as assessed by minimal model analysis of an insulinmodified frequently sampled intravenous glucose tolerance test (FSIGTT) as well as glucose and insulin 19 concentrations during a modified oral sugar test (OST); and 2) to determine the postprandial glycemic and insulinemic responses to meal consumption for each diet. We hypothesized that adaptation to differing carbohydrate diets would alter glucose and insulin dynamics, and aged horses would have an exaggerated insulin response to intravenous and oral glucose as well as to feeding starch and sugar rich diets when compared to adult horses. MATERIALS AND METHODS Horses and Groups Sixteen healthy Thoroughbred (TB) and Standardbred (STB) mares and geldings were divided into two groups by age: adult (5 to 13 years old; 8.8 ± 2.9 years; n = 9; 4 TB mares, 1 STB mare, 3 TB geldings, 1 STB gelding) and aged (18 to 24 years old; 20.6 ± 2.1 years; n = 9; 3 TB mares, 6 STB mares). Horses originated from a single source. Sixteen horses were used in each dietary period (8 per age group); however, one aged horse and one adult horse had to be replaced during the study due to failure to eat the diet (n = 1) and a colonic torsion, not considered to be associated with diet, that resulted in euthanasia (n = 1). Replacements were the same sex and similar in age. Prior to the study, horses were maintained on pasture for an acclimation period of at least one month. One week prior to the study, horses were acclimated to the dry lot conditions used throughout the study. All animals received routine anthelmintic, vaccination, dental, and farrier treatment as appropriate. Study Design and Diets All methods were approved by the Institutional Animal Care and Use Committee at Michigan State University. The study was performed from February to October. Horses were randomly assigned to groups of four, blocked for age, and fed four isocaloric diets using a balanced Latin square design. The control diet consisted of restricted-starch-and-sugar, fortified pelletsa. For the three remaining diets, a portion of the control diet was removed, and the appropriate energy substrate rich complementary feed added: starch (control plus kibbled corna), fiber (control plus unmolassed sugar beet pulp/soybean hull pelletsa), and sugar 20 (control plus dextrose powderb). All horses received four 7-week dietary treatments, with the total ration being fed at a daily rate of 2.0% to 2.2% of bodyweight (hay: 1.2% and concentrate: 0.8% - 1.0%). During each dietary period, horses were group fed the same batch of grass hay (mean of three determinations during the study nonstructural carbohydrate (dry matter basis) = 13.1% ± 0.4%) once daily and individually fed two meals of one of the above diets at 7:00 and 17:00. Quantity was pre-determined based on the horse’s weight tapec estimated weight and delivered at approximately 0.18 megajoules per kilogram bodyweight digestible energy (DE). For a 500-kg horse this equates to: control (4.0-kg), starch (control: 2.3-kg and kibbled corn: 2.0-kg), fiber (control: 2.0-kg and unmolassed sugar beet pulp/soybean hull pellets: 2.8-kg), and sugar (control: 3.1-kg and dextrose powder: 1.0-kg) per day. Further details of the dietary composition are described in Table 2.1. Horses were gradually introduced to each diet during the first five days of each period. For each meal, horses were given sixty minutes to consume the diet, after which any remaining feed was removed, weighed (orts) and recorded. Dietary periods were separated by a two-week washout period during which all horses received the control diet and free access to the same hay and pasture. Prior to, and following each dietary period, bodyweight of the horses was estimated by use of a weight tapec and body condition score (BCS) determined (1-9; Henneke scale) [119] by two evaluators. Basal insulin measurements (adult 13.3 ± 0.5 mU/L and aged: 13.6 ± 0.6 mU/L) were obtained prior to each dietary period as a crude estimate of insulin status. During the 6th week of each dietary period, the glycemic and insulinemic responses to the diets were assessed. In the 7th (final) week of each dietary period, each horse underwent an insulin-modified FSIGTT and a modified OST with at least 48-hours between each test (randomized, blocked by age and diet). An overnight dexamethasone suppression test [120–122] was performed to evaluate pituitary function at least 24-hours after dynamic testing. Minimal Model An insulin-modified FSIGTT [58] was administered after 10-hours of feed withholding (overnight). An intravenous catheter was placed in the jugular vein one hour prior to the start of testing. A baseline blood sample was taken at -10 and -1 minutes prior to the rapid intravenous administration of 300 mg/kg 21 bodyweight glucose administered as a 50% solution followed, at 20 minutes’ post dextrose, by administration of an intravenous bolus of insulin (20 mU/kg bodyweight) (Humulin Rd). Blood samples for glucose and insulin measurement were collected at 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 19, 22, 23, 24, 25, 27, 30, 35, 40, 50, 60, 70, 80, 100, 120, 150, 180, 210 and 240 minutes’ post glucose administration. Minimal model analysis [123,124] (MINMOD Millennium, version 6.02e and WinSAAMf) of the glucose and insulin data yielded estimates of insulin sensitivity (SI (L·min-1·mU-1); insulin-mediated glucose disposal), acute insulin response to glucose (AIRg (mU/L·min-1); the endogenous insulin secretory response to glucose), glucose effectiveness (Sg (min-1); glucose-mediated glucose disposal), and disposition index (DI = SI x AIRg). The following parameters were calculated to explore the magnitude of the deflection in blood glucose below baseline (hypos): baseline glucose (Gb), lowest glucose concentration below baseline (Gmin), time point at lowest glucose concentration (Tmin), glucose concentration at the sampling endpoint (Ge), percent deflection of glucose below baseline (dGb = ([Gmin – Gb] x 100/Gb), percent deflection of glucose below the sampling endpoint (dGe = [Ge – Gmin] x 100/Ge), area under the curve below baseline glucose (HAUC), and the time point where glucose deflection returned to baseline (Ttmax). Oral Sugar Test A modified OST [62] was performed after 10-hours of feed withholding (overnight), as a standardized challenge to compare the horses’ responses after dietary adaptation. After placement of a catheter in the jugular vein, one-hour prior to the commencement of testing, and collection of baseline blood samples (-10 and -1 minutes’ relative to dosing), a commercially-available corn syrup (Karo® lightg) was administered orally by use of a dose syringe (0.25 mL/kg bodyweight). Additional blood samples were collected at 15, 30, 60, 75, 90, 120, 150, and 180 minutes. The following parameters were calculated (GraphPad Prism, version 6.07h): peak glucose, peak insulin, time to peak, and area under the curve [AUC] for glucose and insulin. 22 A higher dose of Karo® syrup was used, 0.25 mL/kg bodyweight instead of 0.15mL/kg bodyweight [64], because our previous work has demonstrated that the higher dose is needed to elicit a consistent insulinemic response in some light breed horses [12]. Dietary Meal Challenge A dietary meal challenge was performed to evaluate the effect of feeding a single meal on postprandial glucose and insulin dynamics for a particular diet (i.e. control, starch, fiber, or sugar). Following a 10-hour (overnight) fast, horses were given a meal of the diet to which the horse was currently adapted. Blood samples were collected, via an indwelling jugular catheter, at 0, 30, 60, 90, 120, 180, 240, and 300 minutes’ relative to the start of the meal for evaluation of glucose and insulin responses. The hay portion was withheld until completion of sample collection. Sample Collection Blood was collected in lithium heparin tubes for glucose analysis and serum tubes for insulin analysis. Plasma tubes were immediately placed on ice while serum tubes were allowed to clot at room temperature for one hour. Tubes were centrifuged (2000 x g for 15 minutes at 22°C) within two hours of collection, supernatants were collected, aliquoted, and stored at -80°C for future analysis. Determination of Glucose and Insulin Concentrations Glucose concentrations were determined in duplicate by a membrane based glucose oxidase method (YSI 2300 STAT Plus™ Glucose & Lactate Analyzeri). Insulin concentrations were determined in duplicate by radioimmunoassay (ImmuChem™ Coated Tube Insulin 125-I RIAj), validated for horse serum by our group. Intra- and inter- assay coefficients of variability were calculated for low and high equine serum controls using four replicates per assay (Intra: 8.36 (low) and 6.99 (high); Inter: 10.69 (low) and 9.14 (high)). 23 Insulin Assay Validation The in-house validation of the ImmuChem™ Coated Tube 125-I RIAj consisted of three steps: precision, recovery on addition, and dilutional parallelism. Precision was determined by calculating the intra- and inter- assay coefficients of variability (CV) using equine serum samples with low (mean = 24.1 mU/L), medium (mean = 113.5 mU/L), and high (mean = 203.5 mU/L) insulin concentrations. Each sample was run six times in a total of fourteen assays. Intra-assay CVs for the different insulin concentrations were as follows: low (7.6%), medium (5.7%), and high (5.2%). Inter-assay CVs for the different insulin concentrations were as follows: low (8.7%), medium (3.0%), and high (3.0%). Second, recovery on addition, an equine serum sample with an extremely low (7.9 mU/L) insulin concentration was spiked with an equal volume of each of the porcine insulin standards (range: 5.5 – 310 mU/L) provided by the manufacturer. Percent recovery was calculated as the observed (measured) concentration divided by the expected concentration multiplied by 100. The average percent recovery across all samples was 98.2% ± 8.0%. Lastly, dilutional parallelism, six equine serum samples were diluted from 2 to 64 times with the zero standards supplied by the manufacturer. The percent recovery of insulin in the diluted samples was calculated following previously published guidelines [125]. The calculated mean percent recovery was not significantly different from 100%. Endocrine Testing Overnight Dexamethasone Suppression Test (ODST): A baseline blood sample (EDTA plasma) was collected via jugular venipuncture in the early evening followed by administration of dexamethasone (0.04 mg/kg bodyweight) intramuscularly. An additional blood sample was collected at 19-hours post dexamethasone injection. Samples were immediately placed on ice, centrifuged (2000 x g at 4°C for 15 minutes) within one hour of collection, and plasma collected and frozen at -80°C for subsequent analysis. Cortisol concentrations were determined via chemiluminescent technology (Immulite®k), validated for horses, at the Animal Health Diagnostic Center at Cornell University [126]. 24 Statistical Analysis Data were analyzed using a multivariable linear mixed regression model in the statistical program Rl. The model included fixed effects (age group, diet, breed, period), interaction term(s) (age group*diet and breed*diet), and a random effect (horse). Model selection was performed using Akaike information criteria (AIC). All data are reported as least squares means estimates and pairwise differences (P ≤ 0.05). Pairwise differences were determined using the lmerTest package difflsmeans function which gives differences of the least squares means table with p-values and confidence intervals using Satterthwaite’s approximation for the degrees of freedom. RESULTS Animals and Diets Aged and adult horses did not show clinical signs of pituitary pars intermedia dysfunction [9] during the study period. All horses, except one aged Thoroughbred mare during the final period (collected in October), had cortisol suppression following an ODST (Table 2.2). Horses tolerated all diets and refusals were negligible. The mean amount of remaining feed constituted less than 2% of the total concentrate fed to each horse (76.2 ± 0.6 grams/horse/day). No significant differences in weight or body condition score were noted between age groups, diets, or time points (Table 2.3). Minimal Model Analysis Overall, mean tissue insulin sensitivity (SI) did not differ between aged and adult horses across all diets. AIRg least squares means estimates were significantly higher in aged compared to adult horses after controlling for diet (Table 2.4); and AIRg was significantly higher in aged than adult horses after adaptation to the fiber and sugar diets. Basal insulin was higher in aged than adult horses adapted to the starch and sugar diets (Table 2.5). DI least squares means estimates were significantly higher in aged compared to adult horses after controlling for diet; and DI was significantly higher in aged versus adult horses after 25 adaptation to the fiber and sugar diets. Sg was higher in aged compared to adult horses after adaptation to the sugar diet. Within adult horses, SI and DI were higher following adaptation to the starch and sugar diets than the control and fiber diets. In addition, AIRg was higher after adaptation to starch compared to the control and fiber diets. Adult horses had a higher Sg on starch compared to fiber and sugar. Within aged horses, SI, DI, AIRg, and Sg were higher after adaptation to sugar compared to the control, starch, and fiber diets (Table 2.5). Assessment of Glucose Deflection Below Baseline All horses regardless of diet, except one aged horse on the fiber diet, had a deflection of blood glucose below baseline concentrations (hypos). Nineteen of the 64 observations did not return to baseline glucose levels by 240 minutes. Clinical signs of hypoglycemia were not detected as values did not fall below a clinically abnormal level. No significant difference was detected in outcome measures used to assess glucose deflection below baseline between age groups independent of diet (Table 2.6); however, an effect of diet was noted (Table 2.7). Aged horses had a greater area under the curve below baseline glucose (HAUC) than adult horses following adaptation to the sugar diet. No other differences were detected between aged and adult horses on the same diet. Aged horses adapted to the sugar diet needed less time to reach the lowest glucose concentration (Tmin) and a significantly greater HAUC and percent glucose deflection below baseline compared to the control and fiber diet. In addition, aged horses had a larger deflection of glucose below end point (dGe) on the sugar diet compared to the control diet. Adult horses had a higher endpoint glucose (Ge) on the sugar diet compared to the fiber diet. 26 Oral Sugar Test Analysis Age had a significant influence on peak insulin with least squares means estimates higher in aged compared to adult horses after controlling for diet (Table 2.4). Further, peak insulin was significantly higher in aged horses on fiber compared to adult horses adapted to the same diet (Table 2.8). Age had a significant influence on AUCi, with least squares means estimates higher in aged versus adult horses, after controlling for diet. Aged horses had a greater AUCi on the control and fiber diets compared to adult horses adapted to the same diets. Glucose parameters did not differ between age groups. Within age group, adult horses had significantly higher basal insulin following adaptation to the sugar diet compared to the starch diet. Within aged horses, AUCg was significantly greater on the control and fiber diets compared to the sugar diet (Table 2.8). Dietary Meal Challenge Analysis Aged horses had significantly higher basal insulin and lower peak glucose compared to adult horses after feeding sugar. In addition, peak insulin and AUCi were significantly higher in aged horses compared to adult horses after feeding the starch diet. Adult horses had significantly higher basal insulin after feeding starch compared to the sugar diet. Adult horses also had significantly higher peak insulin and AUCi after feeding sugar relative to the other diets. After feeding starch, aged horses had significantly higher peak insulin and AUCi compared to feeding the control, fiber, and sugar diets. Aged horses also had significantly higher AUCi after feeding the sugar diet compared to the control, starch, and fiber diets (Table 2.9). Breed Differences The FSIGTT demonstrated that breed, after controlling for diet, had an influence on basal insulin and DI (Table 2.10). Following adaptation to the fiber diet, Standardbreds had a significantly lower DI and Sg compared to Thoroughbreds. SI and AIRg did not differ between groups (Table 2.11). 27 The OST revealed that Standardbreds had significantly lower AUCi, basal glucose, and AUCg compared to Thoroughbreds after controlling for diet (Table 2.10). Standardbreds also had significantly lower peak insulin, AUCi, basal glucose, peak glucose, and AUCg following adaptation to the fiber diet compared to Thoroughbreds on the same diet (Table 2.12). Assessment of the glycemic and insulinemic responses to the diet profile did not differ between breeds after controlling for diet. Pairwise comparisons demonstrated that Standardbreds had significantly lower basal insulin after feeding the sugar diet compared to Thoroughbreds on the same diet. Standardbreds had lower basal glucose on the starch and fiber diets compared to Thoroughbreds. After feeding the sugar diet, Standardbreds had higher peak glucose compared to Thoroughbreds. Within each breed, there was an effect of diet (Table 2.13). DISCUSSION The effect of age, breed, and diet on glucose and insulin dynamics in healthy non-obese horses is variable depending on the assessment. The responses at the tissue level (FSIGTT) reveal that age influences AIRg, regardless of diet, while adaptation to sugar improves SI in both adult and aged horses. However, at the enteral level (OST), minimal changes in glucose and insulin parameters due to dietary adaptation were detected. In contrast, the dietary meal challenge demonstrated enhanced postprandial hyperinsulinemia in both adult and aged horses, following adaptation to both starch and sugar rich diets. Aged horses appear to have higher insulin secretory responses evidenced by higher AIRg, peak insulin, and AUCi. However, it is difficult to discern whether this occurs due to increased pancreatic secretion or decreased clearance. Aged horses may demonstrate an increased insulin response due to decreased glucose uptake by the liver, skeletal muscle, and adipose tissue, due to reduced tissue insulin sensitivity. In humans, aging results in various processes being disturbed such as cellular senescence, mitochondrial dysfunction, altered intercellular communication, genomic instability, and deregulated nutrient sensing [127]. 28 In this study, feeding sugar (2.0 g/kg bodyweight) twice daily to adult and aged horses resulted in improved tissue insulin sensitivity; however, the biological significance of this finding is unknown. Previous work has demonstrated that feeding sugar (1.5 g/kg bodyweight) once daily improves insulin sensitivity [128]. Other studies, looking at younger horses or that included obesity as a factor, have reported that the feeding of a starch-rich diet results in a decrease in insulin sensitivity when compared to a lowstarch diet that contains higher fat (oil) and/or fiber content [52,58,116,117]. In contrast, our findings demonstrate that feeding starch to healthy non-obese adult horses results in improved insulin sensitivity. Healthy, non-obese aged horses had improved insulin sensitivity following adaptation to starch and sugar rich diets. Within the age groups, adaptation to diet influenced all minimal model (SI, DI, Sg, AIRg) parameters; however, diet did not influence the OST glucose and insulin variables, suggesting an apparent disconnect between the two tests. Specifically, adaptation to starch or sugar diets was associated with an increase in SI but no change in insulin dynamics during the OST. The lack of agreement between the FSIGTT and OST may raise questions about the most appropriate assessment of metabolic/endocrine status. However, the two tests are assessing different aspects of glucose and insulin dynamics; the FSIGTT measures tissue level responses, while the OST evaluates postprandial (enteral) responses. Alternate explanations for this finding are that an OST may not be sensitive enough to detect differences in glucose and insulin dynamics in overtly healthy horses, or that dietary adaptation did not result in changes in postprandial glucose and insulin dynamics that were of large enough magnitude to be detected by the OST. Feeding a sugar-rich diet improved tissue insulin sensitivity (SI) but did not alter OST responses suggesting that a sugar-rich diet could be beneficial to horses. However, both tests ignore the glycemic and (importantly) the insulinemic effects of consuming such diets. Following dietary adaptation and without a dynamic challenge, postprandial response to starch and sugar rich diets resulted in hyperinsulinemia. A causal role for insulin in the development of laminitis has been supported by studies that have documented disease in healthy horses/ponies subjected to experimental sustained hyperinsulinemia [6–8], therefore one should be cautioned away from feeding a diet that results in significant postprandial hyperinsulinemia. 29 Recent studies have demonstrated breed differences in relation to glucose and insulin dynamics. Specifically, Standardbreds were reported to have higher SI values compared to Andalusian horses and mixed-breed ponies [11,128]. Although the ideal study cohort would be restricted to a single breed, these data demonstrate that even insulin-sensitive breeds have important differences in glucose and insulin dynamics. In summary, following dietary adaptation in healthy non-obese horses, when glucose and insulin responses were assessed with different tests, the outcomes were not equivalent. The FSIGTT identified that adaptation to sugar improves tissue level insulin sensitivity in both adult and aged horses, but this did not correlate to the OST responses. Glycemic and insulinemic responses to the starch and sugar rich diets showed relative postprandial hyperinsulinemia. In addition, aged horses appear to have a greater insulinemic response to intravenous and oral glucose and feeding; however, the exact mechanism is unknown. The impact of diet and the glucose and insulin responses to such a diet is an important factor to consider when choosing how to feed horses especially older animals and those with certain clinical conditions. Further research is required to better understand the influence of age and diet on glucose and insulin dynamics thus leading to improved nutritional management of aging and metabolically abnormal equids. 30 FOOTNOTES a MARS Horsecare US Inc, Dalton, Ohio, USA. b Sigma-Aldrich, Saint Louis, Missouri, USA. c The Coburn Company, Whitewater, Wisconsin, USA. d Eli Lilly and Company, Indianapolis, Indiana, USA. e Bergman Laboratory, Los Angeles, California, USA. f University of Pennsylvania, Kennett Square, Pennsylvania, USA. g ACH Food Companies Inc., Cordova, Tennessee, USA. h GraphPad Software Inc., La Jolla, California, USA. i YSI Incorporated Life Sciences, Yellow Springs, Ohio, USA. j MP Biomedicals LLC Santa Ana, California, USA. k Siemens Medical Solutions USA, Inc., Malvern, Pennsylvania, USA. l R Core Team, Vienna, AUSTRIA. This chapter is an accepted manuscript in the Equine Veterinary Journal. The manuscript in its published form may be found at https://doi.org/10.1111/evj.12745 31 APPENDIX 32 Table 2.1 Key nutrients of the dietary carbohydrate profiles (grass hay + concentrate) on a dry matter basis (Dairy One, Ithaca, New York, USA). The value in parentheses is the concentrate profile (without the grass hay). Components a % NDFa % Starch % Sugar % NSCb Control 54.8 (17.8) 5.1 (4.3) 9.4 (2.2) 14.4 (6.6) Starch 48.1 (12.2) 15.7 (14.9) 8.8 (1.9) 24.5 (16.9) Fiber 55.4 (21.2) 2.9 (2.2) 9.7 (3.0) 12.5 (5.2) Sugar 50.3 (13.7) 4.0 (3.3) 18.7 (11.6) 22.7 (14.9) NDF (neutral detergent fiber) = hemicellulose, cellulose, lignin NSC (nonstructural carbohydrate) = starch + water soluble carbohydrates (monosaccharides, disaccharides, polysaccharides) b 33 Table 2.2 Least squares means estimates for cortisol concentrations (µg/dL) from the overnight dexamethasone suppression test in adult and aged horses following dietary adaptation. Adult Aged least squares means 95% confidence interval least squares means 95% confidence interval p-value Baseline 3.2 (2.7 – 3.6) 3.4 (3.0 – 3.9) 0.5 Post-Dexamethasone 0.4 (0.2 – 0.5) 0.4 (0.2 – 0.6) 0.9 34 Table 2.3 Least squares means estimates for weight (kg) and body condition score (BCS) before and after dietary adaptation. Adult Aged Control Starch Fiber Sugar Control Starch Fiber Sugar Weight (Before) 493.8 483.9 486.9 491.2 490.0 487.5 482.1 482.5 Weight (After) 483.0 487.4 481.0 482.3 487.1 479.4 480.2 487.7 BCS (Before) 5.3 5.0 5.2 5.0 5.1 5.0 5.2 5.1 BCS (After) 5.0 5.1 4.9 4.9 5.1 5.2 4.9 5.0 35 Table 2.4 Least squares means estimates and pairwise significant differences (P ≤ 0.05) for glucose and insulin parameters during the frequently sampled intravenous glucose tolerance test (FSIGTT) and oral sugar test (OST) after controlling for diet. SI = insulin sensitivity, DI = disposition index, Sg = glucose effectiveness, AIRg = acute insulin response to glucose, AUCi = area under the curve for insulin, AUCg = area under the curve for glucose. Adult Aged least squares means 95% confidence interval least squares means 95% confidence interval p-value Basal Insulin (mU/L) 16.1 (12.7 – 19.4) 22.3 (19.1 – 25.5) 0.02 SI (L·min-1·mU-1) 2.7 (2.0 – 3.3) 2.6 (2.0 – 3.3) 0.9 DI 909.0 (577.0 – 1241.0) 1467.0 (1151.0 – 1783.0) 0.03 Sg (min-1) 2.0 (1.8 – 2.3) 2.2 (2.0 – 2.5) 0.3 AIRg (mU/L·min-1) 358.0 (224.0 – 491.0) 582.0 (455.0 – 709.0) 0.03 Basal Insulin (mU/L) 20.1 (13.8 – 26.4) 24.8 (18.8 – 30.9) 0.3 Peak Insulin (mU/L) 50.2 (40.5 – 59.8) 65.0 (55.7 – 74.2) 0.04 Time to Peak Insulin (minutes) 69.4 (59.0 – 79.8) 74.4 (64.7 – 84.2) 0.5 AUCi ([mU·L-1]·min) 5330.0 (4287.0 – 6372.0) 7652.0 (6655.0 – 8648.0) 0.006 Basal Glucose (mmol/L) 4.8 (4.6 – 5.0) 4.9 (4.8 – 5.1) 0.2 Peak Glucose (mmol/L) 6.7 (6.4 – 6.9) 6.8 (6.6 – 7.1) 0.4 Time to Peak Glucose (minutes) 71.8 (59.4 – 84.2) 76.1 (64.3 – 87.9) 0.6 AUCg ([mmol·L-1]·min) 997.8 (958.1 – 1037.5) 1038.5 (1001.0 – 1076.0) 0.2 FSIGTT Parameters: OST Parameters: 36 Table 2.5 Least squares means estimates for glucose and insulin parameters during the frequently sampled intravenous glucose tolerance test (FSIGTT). Pairwise significant differences (P ≤ 0.05) within age groups indicated by lowercase letters. Differences between age groups for each diet indicated by *. SI = insulin sensitivity, DI = disposition index, Sg = glucose effectiveness, AIRg = acute insulin response to glucose. Adult Aged Control Starch Fiber Sugar Control Starch Fiber Sugar Basal Insulin (mU/L) 16.9 15.5* 14.7 17.1* 20.9a 23.2* 19.6a 25.5b* SI (L·min-1·mU-1) 2.0a 3.3b 2.0a 3.4b 1.4a 2.8b 2.4ab 4.0c DI 590.0a 1445.0b 396.0a* 1205.0b* 659.0a 1503.0b 1233.0ab* 2473.0c* Sg (min-1) 2.0 2.4a 1.8b 2.0b* 2.2a 2.2a 1.9a 2.7b* AIRg (mU/L·min-1) 307.0a 452.0b 299.0a* 371.0* 504.0a 608.0b 524.0ab* 691.0c* 37 Table 2.6 Least squares means estimates and pairwise differences for outcome measures used to assess glucose deflection below baseline during the frequently sampled intravenous glucose tolerance test (FSIGTT). Gmin = lowest glucose concentration below baseline, Tmin = time point at lowest glucose concentration, Ge = glucose concentration at sampling endpoint, dGb = percent deflection of glucose below baseline, dGe = percent deflection of glucose below the sampling endpoint, HAUC = area under the curve below baseline glucose, Ttmax = time point where glucose deflection returned to baseline. Adult Aged least squares means 95% confidence interval least squares means 95% confidence interval p-value Gmin 61.2 (56.0 – 66.4) 60.5 (55.5 – 65.5) 0.8 Tmin 144.9 (115.2 – 175.0) 121.2 (92.5 – 150.0) 0.3 Ge 80.3 (76.0 – 84.6) 84.3 (80.2 – 88.4) 0.2 dGb -23.5 (-30.9 – [-16.2]) -26.3 (-33.4 – [-19.3]) 0.6 dGe 21.8 (13.3 – 30.2) 28.0 (19.8 – 36.1) 0.3 HAUC 9991.0 (7875.0 – 12108.0) 11663.0 (9616.0 – 13710.0) 0.3 Ttmax 211.3 (195.0 – 227.0) 212.7 (199.0 – 226.0) 0.9 38 Table 2.7 Least squares means estimates in adult and aged horses adapted to each diet (control, starch, fiber, sugar) for outcome measures used to assess glucose deflection below baseline during the frequently sampled intravenous glucose tolerance test (FSIGTT). Pairwise significant differences (P ≤ 0.05) within age groups indicated by different lowercase letters for each variable. Differences between age groups for each diet indicated by *. Gmin = lowest glucose concentration below baseline, Tmin = time point at lowest glucose concentration, Ge = glucose concentration at sampling endpoint, dGb = percent deflection of glucose below baseline, dGe = percent deflection of glucose below the sampling endpoint, HAUC = area under the curve below baseline glucose, Ttmax = time point where glucose deflection returned to baseline. Adult Aged Control Starch Fiber Sugar Control Starch Fiber Sugar Gmin 62.1 58.8 61.2 62.8 63.4 59.3 63.8 55.4 Tmin 144.3 118.4 178.3 138.8 150.4a 125.6 133.8a 75.0b Ge 81.3 81.8 73.6a 84.6b 80.8 87.8 81.2 87.8 dGb -24.8 -24.8 -21.6 -22.9 -20.1a -28.0 -21.4a -35.9b dGe 22.1 26.4 13.4 25.2 21.0a 31.5 22.7 36.7b HAUC 9947.0 11268.0 8327.0 10424.0* 9703.0a 11467.0 10418.0a 15064.0b* Ttmax 227.8 211.9 188.0 217.5 210.2 200.2 230.0 210.6 39 Table 2.8 Least squares means estimates for glucose and insulin parameters during the oral sugar test (OST). Pairwise significant differences (P ≤ 0.05) within age groups indicated by lowercase letters. Differences between age groups for each diet indicated by *. AUCi = area under the curve for insulin, AUCg = area under the curve for glucose. Adult Aged Control Starch Fiber Sugar Control Starch Fiber Sugar Basal Insulin (mU/L) 21.3 18.9a 14.1 26.1b* 25.1 22.4 23.2 28.6* Peak Insulin (mU/L) 53.2 52.3 41.2* 53.9 69.3 61.6 64.3* 64.6 Time to Peak Insulin (minutes) 69.8 74.8 79.9 52.9 79.5 67.0 79.8 71.4 AUCi ([mU·L-1]·min) 6004.0* 4995.0 4901.0* 5418.0 8329.0* 6982.0 7783.0* 7513.0 Basal Glucose (mmol/L) 4.8 4.9 4.7 4.8 5.0 4.9 5.0 4.8 Peak Glucose (mmol/L) 6.8 6.6 6.6 6.7 7.1 6.7 7.1 6.4 Time to Peak Glucose (minutes) 70.0 70.3 83.5 63.3 71.8 80.6 83.7 68.3 AUCg ([mmol·L-1]·min) 1023.3 981.4 1007.9 978.5 1087.0a 1003.3 1096.6a 967.1b 40 Table 2.9 Least squares means estimates for glucose and insulin responses during the dietary meal challenge. Pairwise significant differences (P ≤ 0.05) within age groups indicated by lowercase letters. Differences between age groups for each diet indicated by *. AUCi = area under the curve for insulin, AUCg = area under the curve for glucose. Adult Aged Control Starch Fiber Sugar Control Starch Fiber Sugar Basal Insulin (mU/L) 17.8 22.2a 19.6 14.8b* 22.7 22.9 23.9 23.3* Peak Insulin (mU/L) 76.4ac 105.7ab* 57.7c 130.2b 87.1ac 174.8b* 77.1a 121.3c Time to Peak Insulin (minutes) 105.8 149.4 110.3 144.6 82.7a 103.8 91.1a 147.6b AUCi ([mU·L-1]·min) 15100.0ac 21581.0bc* 11921.0a 26050.0b 15639.0a 35205.0b* 13010.0ac 25720.0d Basal Glucose (mmol/L) 4.6 4.7 4.8 4.7 4.8 4.8 4.8 4.8 Peak Glucose (mmol/L) 6.8ac 7.8bc 6.1a 8.7b* 6.4a 7.9b 6.1a 6.9* Time to Peak Glucose (minutes) 126.8a 130.6a 122.8 93.1b 97.8 99.2 100.7 108.4 AUCg ([mmol·L-1]·min) 1820.4 1859.2 1670.5a 1925.8b 1742.7 1864.8 1687.2 1753.8 41 Table 2.10 Least squares means estimates and pairwise significant differences (P ≤ 0.05) for glucose and insulin responses during the frequently sampled intravenous glucose tolerance test (FSIGTT) and oral sugar test (OST) after controlling for diet. SI = insulin sensitivity, DI = disposition index, Sg = glucose effectiveness, AIRg = acute insulin response to glucose, AUCi = area under the curve for insulin, AUCg = area under the curve for glucose. Standardbred Thoroughbred least squares means 95% confidence interval least squares means 95% confidence interval p-value Basal Insulin (mU/L) 16.0 (12.5 – 19.4) 22.3 (19.2 – 25.5) 0.02 SI (L·min-1·mU-1) 2.2 (1.5 – 2.9) 3.0 (2.4 – 3.7) 0.1 DI 913.0 (571.0 – 1256.0) 1463.0 (1154.0 – 1772.0) 0.03 Sg (min-1) 2.0 (1.7 – 2.3) 2.3 (2.0 – 2.5) 0.2 AIRg (mU/L·min-1) 419.0 (281.0 – 558.0) 520.0 (397.0 – 643.0) 0.3 Basal Insulin (mU/L) 18.9 (12.4 – 25.5) 25.9 (20.1 – 31.8) 0.1 Peak Insulin (mU/L) 51.2 (41.2 – 61.2) 63.9 (54.9 – 72.9) 0.08 Time to Peak Insulin (minutes) 68.0 (57.3 – 78.7) 75.8 (66.2 – 85.4) 0.3 AUCi ([mU·L-1]·min) 5510.0 (4433.0 – 6587.0) 7472.0 (6500.0 – 8443.0) 0.02 Basal Glucose (mmol/L) 4.7 (4.5 – 4.9) 5.0 (4.9 – 5.2) 0.01 Peak Glucose (mmol/L) 6.6 (6.3 – 6.9) 6.9 (6.6 – 7.2) 0.2 Time to Peak Glucose (minutes) 73.1 (60.3 – 85.8) 74.8 (63.3 – 86.3) 0.8 AUCg ([mmol·L-1]·min) 982.2 (941.4 – 1023.1) 1054.1 (1017.3 – 1090.8) 0.02 FSIGTT Parameters: OST Parameters: 42 Table 2.11 Least squares means estimates for glucose and insulin parameters during the frequently sampled intravenous glucose tolerance test (FSIGTT). Pairwise significant differences (P ≤ 0.05) within breed groups indicated by lowercase letters. Differences between breed groups for each diet indicated by *. SI = insulin sensitivity, DI = disposition index, Sg = glucose effectiveness, AIRg = acute insulin response to glucose. Standardbred Thoroughbred Control Starch Fiber Sugar Control Starch Fiber Sugar Basal Insulin (mU/L) 17.3 15.1* 14.3 17.2* 20.4a 23.6* 20.0a 25.4b* SI (L·min-1·mU-1) 1.5b 2.6 1.4b 3.4a 1.9a 3.5bc 2.9b 4.0c DI 613.0ac 1152.0c 348.0a* 1540.0b 636.0a 1796.0bc 1281.0b* 2139.0c Sg (min-1) 2.1a 2.0a 1.5b* 2.3a 2.2 2.5b 2.1a* 2.4 AIRg (mU/L·min-1) 373.0a 483.0b 338.0a 484.0b 439.0a 577.0b 486.0a 578.0b 43 Table 2.12 Least squares means estimates for glucose and insulin responses during the oral sugar test (OST). Pairwise significant differences (P ≤ 0.05) within breed groups indicated by lowercase letters. Differences between breed groups for each diet indicated by *. AUCi = area under the curve for insulin, AUCg = area under the curve for glucose. Standardbred Thoroughbred Control Starch Fiber Sugar Control Starch Fiber Sugar Basal Insulin (mU/L) 21.4 17.3 14.8 22.3* 24.9 24.0 22.5 32.3* Peak Insulin (mU/L) 52.5 58.1 42.0* 52.4 70.0 55.9 63.5* 66.2 Time to Peak Insulin (minutes) 71.8 75.7 58.9* 65.6 77.5 66.1a 100.8b* 58.8a AUCi ([mU·L-1]·min) 5916.0* 5846.0 4905.0* 5371.0* 8418.0a* 6131.0b 7778.0* 7560.0* Basal Glucose (mmol/L) 4.8 4.7 4.5* 4.7 5.0 5.1 5.1* 5.0 Peak Glucose (mmol/L) 6.9 6.8 6.2* 6.5 7.1 6.5b 7.5a* 6.6b Time to Peak Glucose (minutes) 75.1 79.2 77.5 60.4 66.7 71.7 89.7 71.3 AUCg ([mmol·L-1]·min) 1021.8 998.1 984.2* 946.7 1088.5ac 986.6bc 1143.2a* 999.0c 44 Table 2.13 Least squares means estimates for glucose and insulin responses during the dietary meal challenge. Pairwise significant differences (P ≤ 0.05) within breed groups indicated by lowercase letters. Differences between breed groups for each diet indicated by *. AUCi = area under the curve for insulin, AUCg = area under the curve for glucose. Standardbred Thoroughbred Control Starch Fiber Sugar Control Starch Fiber Sugar Basal Insulin (mU/L) 18.1 20.7 20.1 14.7* 22.3 24.4 23.4 23.4* Peak Insulin (mU/L) 85.8a 137.3b 53.6a 130.4b 77.7a 143.2b 81.1a 121.1b Time to Peak Insulin (minutes) 96.1a 166.5b* 128.4 128.3 92.4a 86.7a* 72.9a 163.9b AUCi ([mU·L-1]·min) 16073.0a 29315.0b 11152.0a 26293.0b 14666.0a 27471.0b 13780.0a 25478.0b Basal Glucose (mmol/L) 4.6 4.6* 4.6* 4.7 4.8 4.9* 5.0* 4.8 Peak Glucose (mmol/L) 6.7a 8.2b 5.9a 8.5b* 6.6b 7.6a 6.2b 7.1* Time to Peak Glucose (minutes) 111.0 119.0 120.4a 86.4b 113.7 110.8 103.0 115.1 AUCg ([mmol·L-1]·min) 1748.2 1953.6b 1603.9a 1964.7b 1814.8 1770.4 1753.8 1714.9 45 CHAPTER 3 Effect of Dietary Carbohydrates and Time of Year on Adrenocorticotropic Hormone (ACTH) and Cortisol Concentrations in Adult and Aged Horses SUMMARY Background: Diagnosis of equine pituitary pars intermedia dysfunction (PPID) remains a challenge as multiple factors (stress, exercise, time of year) influence adrenocorticotropic hormone (ACTH) and cortisol concentrations. Objectives: To assess endocrine status in a study designed to evaluate the effects of age and diet on glucose and insulin dynamics. Study Design: Sixteen healthy Thoroughbred and Standardbred horses were grouped by age: adult (mean ± SD; 8.8 ± 2.9 years; n = 8) and aged (20.6 ± 2.1 years; n = 8) and fed grass hay plus four isocaloric concentrate diets (control, starch, fiber, sugar) using a balanced Latin square design. Methods: Thyrotropin releasing hormone (TRH) stimulation tests and overnight dexamethasone suppression tests were performed in March, May, August, and October. Data were analyzed using a multivariable linear mixed regression model. Results: None of the horses showed clinical signs (hypertrichosis, regional adiposity, skeletal muscle atrophy, lethargy) of pituitary pars intermedia dysfunction. Baseline ACTH was significantly higher in aged horses (mean ± SEM; 60.0 ± 10.7 pg/mL) adapted to the starch diet compared to adult horses (15.7 ± 12.0 pg/mL) on the same diet (P = 0.017). After controlling for age and diet, baseline ACTH concentrations were significantly increased in October (57.7 ± 7.1 pg/mL) compared to March (13.2 ± 7.1 pg/mL; P < 0.001), May (12.4 ± 7.1 pg/mL; P < 0.001) and August (24.2 ± 7.1 pg/mL; P < 0.001) while post-TRH ACTH was higher in August (376.6 ± 57.6 pg/mL) and October (370.9 ± 57.5 pg/mL) compared to March (101.9 ± 57.3 pg/mL; P < 0.001) and May (74.5 ± 57.1 pg/mL; P < 0.001). Aged horses had significantly higher post-dexamethasone cortisol on the starch diet (0.6 ± 0.1µg/dL) compared to the sugar diet (0.2 ± 0.1 µg/dL; P = 0.021). Post-dexamethasone cortisol was significantly higher in October (0.6 ± 0.1 µg/dL) 46 compared to March (0.3 ± 0.1 µg/dL; P = 0.005), May (0.2 ± 0.1 µg/dL; P < 0.001), and August (0.3 ± 0.1 µg/dL; P = 0.004). Breed did not influence ACTH or cortisol measurements. Main Limitations: ACTH and cortisol concentrations were not measured prior to dietary adaptation. Conclusions: In addition to age and time of year, diet is a potential confounder as animals on a starch diet may be incorrectly diagnosed with pituitary pars intermedia dysfunction. INTRODUCTION Pituitary pars intermedia dysfunction (PPID) is the most common endocrine disorder of older horses, yet definitive diagnosis remains a challenge. Measurement of plasma adrenocorticotropic hormone (ACTH) concentrations is a commonly used diagnostic test; however, ACTH concentrations are influenced by multiple factors such as stress [129], feeding status (fasted versus fed) [130,131], exercise [132,133], and time of year [121,134–136], therefore several dynamic tests have been proposed to evaluate endocrine responses in older horses. Measurement of ACTH concentration at baseline (9 – 35 pg/mL)a and at 10 minutes (< 110 pg/mL)a, following administration of thyrotropin releasing hormone (TRH), is widely used for the diagnosis of PPID. In cases where clinicians suspect early PPID the TRH stimulation test is recommended [137]. However, from July to October clinically normal horses demonstrate increased ACTH concentrations following administration of TRH [134,135,138]. Further, there is variability in ACTH laboratory reference ranges complicating interpretation of the test results. Adequate reference ranges, especially in clinically normal animals, need to be established. Similar to the TRH stimulation test, cortisol measurement at baseline (2 – 6 µg/dL)a and following an overnight dexamethasone suppression test (< 1 µg/dL)a, suffers from variability due to the time of year [121,138]. The overnight dexamethasone suppression test is also looked at less favorably due to concern about the development of laminitis following corticosteroid administration although evidence is limited [139,140] and that a minimal number of PPID animals may demonstrate hyperadrenocorticism. Like ACTH concentrations, it is recommended that cortisol concentrations be interpreted with caution and in conjunction with the animal’s clinical signs. 47 As part of a study to evaluate the effects of dietary carbohydrates on glucose and insulin dynamics in healthy adult and aged horses, we performed TRH stimulation tests and overnight dexamethasone suppression tests in a group of sixteen healthy horses consuming four different diets at four different times of the year. Our data demonstrate that ACTH and cortisol concentrations in these horses vary due to both diet and time of year. To the authors’ knowledge, although a small sample size, this is the first study to demonstrate an effect of diet on ACTH and cortisol concentrations as well as TRH stimulation and dexamethasone suppression tests. MATERIALS AND METHODS Data presented in this manuscript were collected as part of a larger study designed to evaluate the effects of age and dietary adaptation to diets with varying carbohydrate composition [141]. The study was conducted from February to October. Endocrine testing was performed in March, May, August, and October. The Institutional Animal Care and Use Committee (IACUC) at Michigan State University approved all methods. Horses and Groups Sixteen healthy Thoroughbred (TB) and Standardbred (STB) mares and geldings were divided into two groups by age: adult (5 to 13 years old; 8.8 ± 2.9 years; n = 9; 4 TB mares, 1 STB mare, 3 TB geldings, 1 STB gelding) and aged (18 to 24 years old; 20.6 ± 2.1 years; n = 9; 3 TB mares, 6 STB mares). One horse had to be replaced in each age group due to failure to eat the diet (n = 1, aged Thoroughbred mare replaced by an aged Standardbred mare) and severe colic (unrelated to diet or age) resulting in euthanasia (n = 1, adult Thoroughbred mare replaced by an adult Standardbred mare). Body condition score was determined (1 – 9 Henneke scale) [123] by two evaluators at the start and end of each dietary period (mean ± SEM; adult: 5.2 ± 0.2 (start), 5.0 ± 0.1 (end) and aged: 5.1 ± 0.2 (start), 5.0 ± 0.1 (end)). Baseline (day 0) insulin concentrations were obtained prior to each dietary period; however, no significant difference was noted due 48 to age (mean ± SEM; adult: 11.9 ± 1.0 mU/L and aged: 14.7 ± 0.9 mU/L). All animals received routine anthelmintic, vaccination, dental, and farrier treatment as appropriate. Study Design Dietary periods occurred during the following times: Period 1 (February 9th – March 29th), Period 2 (April 13th – May 31st), Period 3 (June 15th – August 2nd), and Period 4 (August 17th – October 4th). Horses were randomly assigned to groups of four, blocked for age, and fed grass hay plus four isocaloric concentrate diets using a balanced Latin square design. The control diet consisted of restricted-starch-andsugar, fortified pelletsb. For the three remaining diets, a portion of the control diet was removed, and the appropriate energy substrate rich feed added: starch (control plus kibbled cornb), fiber (control plus unmolassed sugar beet pulp/soybean hull pelletsb), and sugar (control plus dextrose powderc). Table 3.1 shows the key nutrients of each diet. Horses were housed on a dry lot with no access to pasture or forced exercise during each dietary period. All horses received four 7-week dietary treatments, with the total ration being fed at a daily rate of 2.0% to 2.2% of bodyweight (hay: 1.2% and concentrate: 0.8% - 1.0%) on a dry matter basis. During each dietary period, horses were group fed the same batch of grass hay (nonstructural carbohydrate (dry matter basis): 13.1% ± 0.4%) once daily and individually fed two meals of one of the above diets at 7:00 and 17:00. Quantity was pre-determined based on the horse’s estimated weight using a weight taped. Dietary periods were separated by a two-week washout period during which all horses received the control diet and free access to the same hay and pasture. In the 7th (final) week of each dietary period, each horse underwent an overnight dexamethasone suppression test followed 36-hours later by a thyrotropin releasing hormone (TRH) stimulation test to evaluate adrenal cortical and pituitary function. Therefore, endocrine tests occurred in early spring (March), mid-to-late spring (May), mid-to-late summer (August), and fall (October). 49 Endocrine Testing Thyrotropin Releasing Hormone (TRH) Stimulation Test: Following a 10-hour (overnight) feed withdrawal, a baseline blood sample (EDTA plasma) was collected via jugular venipuncture followed by intravenous administration of 1 milligram of TRHc. An additional blood sample was collected at 10 minutes post-TRH injection [post-TRH]. Samples were immediately placed on ice, centrifuged (2000 x g at 4°C for 15 minutes) within one hour of collection, and plasma separated and stored at -80°C for subsequent analysis. ACTH concentrations were determined via chemiluminescent technology (Immulite®e) validated for horses at the Animal Health Diagnostic Center at Cornell University [126]. Overnight Dexamethasone Suppression Test: A baseline blood sample (EDTA plasma) was collected via jugular venipuncture, after consumption of the evening meal, followed by administration of dexamethasone (0.04 mg/kg bodyweight) intramuscularly. An additional blood sample was collected at 19hours post-dexamethasone injection. Samples were immediately placed on ice, centrifuged (2000 x g at 4°C for 15 minutes) within one hour of collection, and plasma collected and frozen at -80°C for subsequent analysis. Cortisol concentrations were determined via chemiluminescent technology (Immulite®), validated for horses, at the Animal Health Diagnostic Center at Cornell University [126]. Statistical Analysis Data were analyzed using a multivariable linear mixed regression model in the statistical program Rf. The model included fixed effects (age, diet, breed, time of year), interaction term(s) (age*time of year, age*diet, breed*diet), and a random effect (horse). Model selection was performed using Akaike information criteria (AIC). The full model, including interaction terms, was used for ACTH at baseline, ACTH after TRH stimulation, and cortisol at baseline. The post-dexamethasone cortisol model included the fixed effects (diet, age, time of year) and the random effect (horse) without the interaction terms. Data are reported as least squares means estimates in the tables. Pairwise significant differences (P ≤ 0.05) were determined using the lmerTest package difflsmeans function, which gives differences of the least squares means table with p-values and confidence intervals using Satterthwaite’s approximation for the degrees of 50 freedom. However, figures represent the raw data values, as would be used as a clinical diagnostic test, to illustrate the variability of diet and time of year, and the raw data in relation to currently suggested cut-off values for diagnosis of PPID. RESULTS Animals Adult and aged horses did not show typical clinical signs (hypertrichosis, regional adiposity, skeletal muscle atrophy, lethargy) of PPID [9] during the study period. All diets were well tolerated and refusals (measured by weighing any remaining concentrate at the end of 60 minutes) were insignificant (mean ± SD; 76.2 ± 0.6 grams/horse/day). ACTH Concentrations Effect of Age and Time of Year As expected, ACTH concentrations increased following administration of TRH in both age groups. Baseline ACTH concentrations were significantly higher in aged horses compared to adult horses after controlling for diet, time of year, and breed (Table 3.2). Further, baseline ACTH concentrations were significantly higher in October compared to March, May, and August after controlling for age, diet, and breed (Table 3.3). However, these main effects are qualified by an interaction between age and time of year where aged horses had a significantly greater increase in baseline ACTH concentrations in October compared to adult horses (Table 3.4). No significant difference was detected in post-TRH ACTH concentrations in aged horses compared to adult horses after controlling for diet, time of year, and breed (Table 3.2). Further, post-TRH ACTH concentrations were significantly higher in August and October compared to March and May after controlling for age, diet, and breed (Table 3.3). However, these main effects are qualified by an interaction between age and time of year where aged horses had a significantly greater increase in post-TRH ACTH concentrations in August compared to adult horses (Table 3.4). 51 Variability in clinical laboratory ACTH concentrations at different times of year may lead to false positives. One aged horse (12.5%) in March and May, and two aged horses (25%) in August had baseline ACTH concentrations above the suggested cut-off (< 35 pg/mL). In October, three adult animals (37.5%) and four aged animals (50%) had elevated baseline ACTH concentrations (Figure 3.1). Following administration of TRH, in March, four aged horses (50%) had increased ACTH concentrations above the suggested cut-off (< 110 pg/mL). In May, two adult horses (25%) and four aged horses (50%) would be considered positive for PPID. In addition, in August and October, six adult horses (75%) and all aged horses (100%) had increased ACTH concentrations (Figure 3.2). Effect of Diet Baseline ACTH concentrations were significantly higher after adaptation to the starch diet compared to the control diet after controlling for age, time of year, and breed (Table 3.5). Further, baseline ACTH concentrations were significantly higher in aged horses compared to adult horses after controlling for diet, time of year, and breed (Table 3.2). However, these main effects are qualified by an interaction between age and diet where aged horses had a significantly greater increase in baseline ACTH concentrations after adaptation to the starch diet compared to adult horses adapted to the same diet (Figure 3.3, Table 3.6). Following TRH administration, ACTH concentrations were not different between diets after controlling for age, time of year, and breed (Table 3.5). However, an age*diet interaction showed that aged horses had higher post-TRH ACTH concentrations following adaptation to the control diet compared to adult horses adapted to the same diet (Table 3.6). Effect of Breed Breed did not significantly influence ACTH concentrations in this study. 52 Cortisol Concentrations Effect of Age and Time of Year Age did not influence cortisol concentrations at either time point after controlling for diet, time of year, and breed (Table 3.7). However, while cortisol suppression occurred in both age groups across all times of the year, post-dexamethasone cortisol concentrations were significantly higher in October compared to the other three months after controlling for age, diet, and breed (Table 3.8). An age*time of year interaction demonstrated that aged horses had significantly higher post-dexamethasone cortisol concentrations in October compared to adult horses (Table 3.9). Aged horses had significantly higher baseline cortisol concentrations in March and October compared to May. In addition, aged horses had significantly higher post-dexamethasone cortisol concentrations in October compared to the other three months (Table 3.8). Effect of Diet Diet did not influence baseline cortisol concentrations in adult and aged horses. However, an age*diet interaction demonstrated that aged horses had significantly higher post-dexamethasone cortisol concentrations on the starch diet compared to the sugar diet. The control and fiber diet did not significantly influence post-dexamethasone cortisol concentrations (Table 3.10). Effect of Breed Breed did not significantly influence cortisol concentrations in this study. DISCUSSION These results show that the effect of diet on ACTH and cortisol should be considered when interpreting endocrine results, in addition to effects of age and time of year. As expected, an increase in ACTH occurs in adult and aged horses following administration of TRH [134,135,142,143]; however, the magnitude is greater in aged horses. These data also suggest there is a diet*age interaction where aged 53 animals adapted to a starch-rich diet may have higher resting endogenous (baseline) ACTH concentrations. In addition, regardless of age and diet, time of year variability exists in ACTH concentrations. Diagnosis of pituitary pars intermedia dysfunction using the TRH stimulation test may be unreliable at certain times of the year [121,135,138]. In the present study, using the laboratory cut-off of 110 pg/mL, in March, all adult animals (100%) would be considered negative for PPID. However, during May, August, and October, adult horses demonstrated significantly increased ACTH concentrations in the absence of clinical signs. Aged horses had elevated post-TRH ACTH concentrations at all four times of the year in the absence of clinical signs (Figure 3.1). The exact reason for the time of year variability remains unknown but may be due to photoperiod [136] and/or climate temperature. The currently recommended laboratory cut-offs for the TRH stimulation test are difficult to interpret for late summer to late fall. In August, twentyfive percent (25%) of the aged animals had baseline ACTH concentrations above the suggested cut-off (< 35 pg/mL) (Figure 3.1). In the present study, in October 37.5% of the adult animals and 50% of the aged animals had elevated baseline ACTH concentrations. Following administration of TRH, in August and October, 75% of the adult horses and 100% of the aged horses had increased ACTH concentrations above the suggested cut-off (< 110 pg/mL) (Figure 3.2); however, an elevation in the fall is normal in herbivores. In agreement with the equine endocrinology group recommendation [137], these data provide additional support for adjusted seasonal cut-offs from July to November. It is important to note that in both the adult and aged groups, elevations in ACTH above the laboratory reference range may suggest subclinical disease and pituitary hyperplasia. None of the animals in this study were evaluated by post-mortem examination or histopathology, therefore a true determination of false positives cannot be made. In contrast to the TRH stimulation test, the overnight dexamethasone suppression test appears to identify fewer false positives. In this cohort, suppression of cortisol occurred in both age groups regardless of diet and time of year. Diet may influence ACTH and cortisol concentrations, especially in aged individuals. Aged horses adapted to a starch-rich diet had significantly higher endogenous ACTH concentrations and decreased cortisol suppression compared to the other carbohydrate diets. The unusual finding that aged horses had increased post-TRH ACTH measurements on the control diet compared to adults is difficult to explain but 54 could be an age or diet effect. It would be expected that this change would also be seen on the fiber-rich diet given the similarities in nonstructural carbohydrate content. A possible explanation for the variability may have to do with the higher starch content in the control versus fiber-rich diet thus lending support to dietary starch being a driving mechanism in the manipulation of equine endocrine hormones, although it is appreciated that the difference is relatively small. These are only speculations and the exact mechanism for these differences is unknown. Another explanation to explore is the role of gastrointestinal microbes in the gut-brain communication pathway. Further, a number of studies demonstrate the role of diet in shaping the microbiota [144–146]. Studies in mice indicate that alterations in the gastrointestinal microbiome can affect the regulation of neuroendocrine hormones of the hypothalamic-pituitary-adrenocortical (HPA) axis [147– 152]. The data presented here suggest that changes in the HPA axis and differences in ACTH concentrations in relation to diet in horses needs to be explored further. The data presented here suggest that changes in the HPA axis and differences in ACTH concentrations in relation to diet in horses needs to be explored further. In conclusion, following dietary adaptation in healthy non-obese horses, when endocrine parameters were assessed, the outcomes were influenced by age, time of year, and diet. Aged horses had higher ACTH concentrations at both time points (baseline and 10 minutes [post-TRH]) compared to adult horses. Diet may be another factor affecting the endocrine parameters as animals on a starch-rich diet may be incorrectly diagnosed with pituitary pars intermedia dysfunction. Further research is required to better understand the influence of age, time of year, and diet on ACTH and cortisol concentrations to establish accurate reference ranges thus leading to improved diagnosis and management of equine endocrine disease. These findings suggest the need for reference ranges for different times of the year [136] and possibly the need for different reference ranges for aged horses. Finally, these findings further support the need for a more sensitive diagnostic test for PPID. 55 FOOTNOTES a Animal Health Diagnostic Center at Cornell University b MARS Horsecare US Inc, Dalton, Ohio, USA. c Sigma-Aldrich, Saint Louis, Missouri, USA. d The Coburn Company, Whitewater, Wisconsin, USA. e Siemens Medical Solutions USA, Inc., Malvern, Pennsylvania, USA. f R Core Team, Vienna, AUSTRIA. This chapter is an accepted manuscript in Domestic Animal Endocrinology. The manuscript in its published form may be found at https://doi.org/10.1016/j.domaniend.2017.10.005 56 APPENDIX 57 Table 3.1 Key nutrients for each dietary profile (grass hay + concentrate) on a dry matter basis (Dairy One, Ithaca, New York, USA). Components a % Crude Protein % NDFa % Starch % WSC % NSCb Control 12.4 54.8 5.1 9.4 14.4 Starch 11.1 48.1 15.7 8.8 24.5 Fiber 11.0 55.4 2.9 9.7 12.5 Sugar 10.7 50.3 4.0 18.7 22.7 NDF (neutral detergent fiber) = hemicellulose, cellulose, and lignin NSC (nonstructural carbohydrate) = starch + water soluble carbohydrates (WSC; monosaccharides, disaccharides, polysaccharides) b 58 Table 3.2 Least squares means estimates and pairwise differences for adrenocorticotropic hormone (ACTH) concentrations (pg/mL) from the thyrotropin releasing hormone (TRH) stimulation test after controlling for diet, time of year, and breed. Adult Aged least squares means 95% confidence interval least squares means 95% confidence interval p-value Baseline 14.6 ([-1.09] – 30.1) 39.6 (24.4 – 54.9) 0.04 Post-TRH 141.0 (10.4 – 271.0) 321.0 (197.2 – 446.0) 0.06 59 Table 3.3 Least squares means estimates for adrenocorticotropic hormone (ACTH) concentrations (pg/mL) from the thyrotropin releasing hormone (TRH) stimulation test at different times of the year. For each time point (baseline or post-TRH), pairwise significant differences (P ≤ 0.05) between months indicated by lowercase letters. March May August October least squares means 95% confidence interval least squares means 95% confidence interval least squares means 95% confidence interval least squares means 95% confidence interval Baseline 13.2a ([-1.1] – 27.5) 12.4a ([-1.9] – 26.6) 24.2a (9.9 – 38.6) 57.7b (43.3 – 72.0) Post-TRH 101.9a ([-13.9] – 218.0) 74.5a (40.9 – 190.0) 376.6b (260.2 – 493.0) 370.9b (254.8 – 487.0) 60 Table 3.4 Least squares means estimates for adrenocorticotropic hormone (ACTH) concentrations (pg/mL) from the thyrotropin releasing hormone (TRH) stimulation test in adult and aged horses at different times of the year. For each time point (baseline or post-TRH), pairwise significant differences (P ≤ 0.05) within age groups indicated by lowercase letters. Differences between age groups indicated by *. Adult Aged March May August October March May August October Baseline 7.8ab 4.2a 14.9ab 29.7b* 18.8a 20.5a 33.6a 85.7b* Post-TRH -0.9a 28.6a 157.9a* 376.3b 204.6a 120.4ad 595.2b* 365.5ac 61 Table 3.5. Least squares means estimates for adrenocorticotropic hormone (ACTH) concentrations (pg/mL) from the thyrotropin releasing hormone (TRH) stimulation test for each diet (control, starch, fiber, sugar). For each time point (baseline or post-TRH), pairwise significant differences (P ≤ 0.05) between diets indicated by lowercase letters. Control Starch Fiber Sugar least squares means 95% confidence interval least squares means 95% confidence interval least squares means 95% confidence interval least squares means 95% confidence interval Baseline 20.4a (6.3 – 34.6) 37.9b (23.6 – 52.1) 21.8ab (7.6 – 36.1) 27.4ab (13.3 – 41.5) Post-TRH 236.0 (121.4 – 351.0) 205.0 (89.6 – 321.0) 252.0 (136.7 – 368.0) 230.0 (116.3 – 344.0) 62 Table 3.6 Least squares means estimates for adrenocorticotropic hormone (ACTH) concentrations (pg/mL) from the thyrotropin releasing hormone (TRH) stimulation test in adult and aged horses following adaptation to each diet (control, starch, fiber, sugar). For each time point (baseline or post-TRH), pairwise significant differences (P ≤ 0.05) within age groups indicated by lowercase letters. Differences between age groups indicated by *. Adult Aged Control Starch Fiber Sugar Control Starch Fiber Sugar Baseline 11.8 15.7* 11.7 17.2 29.1a 60.0b* 31.9a 37.6ab Post-TRH 54.0* 110.3 244.6 153.1 418.5* 299.9 259.6 307.7 63 Table 3.7 Least squares means estimates and pairwise differences for cortisol concentrations (µg/dL) from the overnight dexamethasone suppression test (ODST) after controlling for diet, time of year, and breed. Adult Aged least squares means 95% confidence interval least squares means 95% confidence interval p-value Baseline 3.2 (2.7 – 3.6) 3.4 (3.0 – 3.9) 0.4 Post-dexamethasone 0.4 (0.2 – 0.5) 0.4 (0.2 – 0.6) 0.9 64 Table 3.8 Least squares means estimates for cortisol concentrations (µg/dL) from the overnight dexamethasone suppression test (ODST) at different times of the year. For each time point (baseline or post-dexamethasone), pairwise significant differences (P ≤ 0.05) between months indicated by lowercase letters. March May August October least squares means 95% confidence interval least squares means 95% confidence interval least squares means 95% confidence interval least squares means 95% confidence interval Baseline 3.4 (2.8 – 3.9) 3.0 (2.4 – 3.5) 3.4 (2.8 – 4.0) 3.5 (3.0 – 4.1) Postdexamethasone 0.3a (0.2 – 0.5) 0.2a (0.1 – 0.4) 0.3a (0.2 – 0.5) 0.6b (0.5 – 0.8) 65 Table 3.9 Least squares means estimates for cortisol concentrations (µg/dL) from the overnight dexamethasone suppression test (ODST) in adult and aged horses at different times of the year. For each time point (baseline or post-dexamethasone), pairwise significant differences (P ≤ 0.05) within age groups indicated by lowercase letters. Differences between age groups indicated by *. Adult Aged March May August October March May August October Baseline 3.0 3.3 3.6 2.9 3.8a 2.6b 3.2ab 4.1a Postdexamethasone 0.4 0.2 0.3 0.5* 0.3a 0.3a 0.3a 0.8b* 66 Table 3.10 Least squares means estimates for cortisol concentrations (µg/dL) from the overnight dexamethasone suppression test (ODST) in adult and aged horses following adaptation to each diet (control, starch, fiber, sugar). For each time point (baseline or post-dexamethasone), pairwise significant differences (P ≤ 0.05) within age groups indicated by lowercase letters. Adult Aged Control Starch Fiber Sugar Control Starch Fiber Sugar Baseline 2.9 2.8 3.3 3.7 3.7 3.3 3.4 3.4 Postdexamethasone 0.4 0.4 0.4 0.4 0.4ab 0.6a 0.3ab 0.2b 67 Figure 3.1 Box and whisker plots of clinical laboratory baseline adrenocorticotropic hormone (ACTH) concentrations in adult and aged horses at different times of the year. The solid horizontal line represents the median, the box indicates interquartile range, and the bars indicate the range of values. The dashed horizontal line represents the laboratory cut-off (35 pg/mL) for diagnosis of pituitary pars intermedia dysfunction. 250 ACTH (pg/mL) 200 150 Adult Aged 100 50 0 March May August 68 October Figure 3.2 Box and whisker plots of clinical laboratory adrenocorticotropic hormone (ACTH) concentrations, at 10 minutes, following administration of thyrotropin releasing hormone (TRH) in adult and aged horses at different times of the year. The solid horizontal line represents the median, the box indicates interquartile range, and the bars indicate the range of values. The dashed horizontal line represents the laboratory cut-off (110 pg/mL) for diagnosis of pituitary pars intermedia dysfunction. ACTH (pg/mL) 1250 1000 750 Adult 500 Aged 250 0 March May August 69 October Figure 3.3 Least squares means estimates of the age*diet interaction for baseline adrenocorticotropic hormone (ACTH) concentrations. The linear equation y = mx + b represents the relative magnitude of the main effects (age, diet) on the response where m represents the magnitude of the interaction (slope of the line). Each diet (starch, fiber, sugar) is compared to the control diet. A C T H ( p g /m L ) 80 A d u lt 60 y = 3 0 .9 x - 1 .8 A ged 40 20 y = 3 .8 x + 7 .9 0 C o n tr o l S ta r c h A C T H ( p g /m L ) 40 A d u lt 30 y = 2 .8 x + 2 6 .3 A ged 20 10 y = -0 .1 x + 1 1 .9 0 C o n tr o l F ib e r A C T H ( p g /m L ) 40 y = 8 .5 x + 2 0 .6 30 A d u lt A ged 20 y = 5 .4 x + 6 .4 10 0 C o n tr o l Sugar 70 CHAPTER 4 Insight into Metabolic Alterations Associated with Aging and Dietary Carbohydrate Profiles SUMMARY Background: Metabolomics, the study of small-molecule metabolites, can provide information about changes in metabolic processes across the tissues. Objectives: 1) To examine the plasma metabolome of horses before (day 0) and after (day 42) adaptation to dietary carbohydrate profiles. 2) To identify differences in metabolites in horses, following dietary adaptation, before (0 minutes) and during (75 minutes) a modified oral sugar test. Study Design: Balanced Latin square with four isocaloric diets: control (restricted-starch-and-sugar fortified pellets), starch (control plus kibbled corn), fiber (control plus unmolassed sugar beet pulp/soybean hull pellets), and sugar (control plus dextrose powder). Methods: Sixteen healthy Thoroughbred and Standardbred mares and geldings divided into two age groups: adult (8.8 ± 2.9 years; n = 8) and aged (20.6 ± 2.1 years; n = 8). The metabolomic analysis was performed on plasma samples collected before (day 0) and after (day 42) dietary adaptation as well as before (0 minutes) and during (75 minutes) a modified oral sugar test (OST). Data were analyzed using multivariable linear mixed regression modeling with significance set at P ≤ 0.05. Results: A large number of metabolite ion peaks were significantly different between age groups and diet groups; however, to date only 3.4% of the ion peaks have been identified. Main Limitations: An equine-specific spectral library does not exist thus a number of significant metabolite ion peaks remain unknown. The application of stringent quality control parameters has identified several ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) runs that need to be repeated to assure data quality. Conclusions: Data provides insight into physiologic differences between adult and aged horses as well as differences in dietary carbohydrate profiles following adaptation. 71 INTRODUCTION Metabolomics provides a comprehensive analysis of all small molecules involved in cellular metabolism in a biological sample (serum, plasma, urine, saliva). Metabolomics involves two complementary approaches, untargeted and targeted analyses. With untargeted metabolomics, a qualitative approach examines as many metabolites as possible, whereas a targeted analysis quantifies discrete groups of chemically related metabolites (e.g. amino acids and products of amino acid metabolism). More than 4,000 metabolites have been identified in human serum by use of high-throughput mass spectrometry (gas chromatography [GC]-MS and liquid chromatography [LC]-MS) [67]. Both exogenous factors (diet, drugs, exercise, microbiome) and endogenous factors (age, genetics, body composition, reproductive status, diurnal cycle) likely influence the nutritional metabolome [153]. Understanding the role of nutrition in cellular metabolism is important due to the association with metabolic adaptation and perturbation. Manipulation of diet can have a negative impact on the transition from health to disease as well as serve as a positive treatment option in the transition from disease to health. In humans with metabolic abnormalities, dietary carbohydrate modification altered the serum metabolomic profile especially lipid metabolism and glucose and insulin metabolism [154]. Further, diet-induced weight loss altered lipid metabolites [155,156]. However, few studies have used metabolomics to gain insight into equine medicine, and to the author’s knowledge no studies have used metabolomics to study dietary adaptation in horses [109,113,157]. Metabolomics is a powerful tool for defining metabolic changes in different physiologic and pathophysiologic states specifically the understanding of the effects of diet and physiologic state (age) on glucose and insulin dynamics and metabolic adaptation to diet. The objectives were to use untargeted metabolomics to explore the metabolic adaptation following consumption of various dietary carbohydrate profiles and to characterize the differences in the plasma metabolome before and during a modified oral sugar test (OST) in adult and aged horses. 72 MATERIALS AND METHODS Study Design Sixteen healthy Thoroughbred (TB) and Standardbred (STB) mares and geldings were divided into two groups by age: adult (5 to 13 years old; mean ± SD; 8.8 ± 2.9 years; n = 9; 4 TB mares, 1 STB mare, 3 TB geldings, 1 STB gelding) and aged (18 to 24 years old; 20.6 ± 2.1 years; n = 9; 3 TB mares, 6 STB mares). One aged horse and one adult horse had to be replaced during the study due to failure to eat the diet (n = 1) and a colonic torsion, not considered to be associated with diet, that resulted in euthanasia (n = 1). Replacements were the same sex and similar in age. All animals received routine anthelmintic, vaccination, dental, and farrier treatment as appropriate. All methods were approved by the Institutional Animal Care and Use Committee at Michigan State University. Horses were randomly assigned to groups of four, blocked for age, and fed four isocaloric diets using a balanced Latin square design. The control diet consisted of restricted-starch-and-sugar, fortified pelletsa. For the three remaining diets, a portion of the control diet was removed, and the appropriate energy substrate rich complementary feed added: starch (control plus kibbled corna), fiber (control plus unmolassed sugar beet pulp/soybean hull pelletsa), and sugar (control plus dextrose powderb). All horses received four 7-week dietary treatments, with the total ration being fed at a daily rate of 2.0% to 2.2% of bodyweight (hay: 1.2% and concentrate: 0.8% - 1.0%) on a dry matter basis. During each dietary period, horses were group fed the same batch of grass hay once daily and individually fed two meals of one of the above diets at 7:00 and 17:00. Quantity was pre-determined based on the horse’s weight tapec estimated weight. In the 7th (final) week, all horses underwent a modified oral sugar test. Dietary periods were separated by a two-week washout period during which all horses received the control diet and free access to the same hay and pasture. Sample Collection Blood was collected in lithium heparin tubes via jugular venipuncture prior to the start of each dietary period (day 0) as well as following dietary adaptation (day 42) for metabolomic analysis. Plasma 73 tubes were immediately placed on ice and centrifuged (2000 x g for 15 minutes at 22°C) within thirty minutes of collection, supernatants were collected, aliquoted, and stored at -80°C for future analysis. Following dietary adaptation, a modified oral sugar test (OST) was performed as previously described [62]. Briefly, administration of a commercially-available corn syrup (Karo® lightd) was given orally by use of a dose syringe (0.25 mL/kg bodyweight) [12]. Blood was collected in lithium heparin tubes via an indwelling jugular catheter at 0 minutes (baseline) and 75 minutes for metabolomic analysis. Plasma tubes were immediately placed on ice and centrifuged (2000 x g for 15 minutes at 22°C) within thirty minutes of collection, supernatants were collected, aliquoted, and stored at -80°C for future analysis. Metabolomics A total of 256 samples were analyzed via an untargeted approach using a combination of chromatography and mass spectrometry following sample preparation. One-hundred-twenty-eight (128) samples represented the metabolome before (day 0) and after (day 42) adaptation to each dietary carbohydrate profile (control, starch, fiber, sugar). Further, one-hundred-twenty-eight (128) samples represented the metabolome at 0 minutes and 75 minutes during a modified oral sugar test following adaptation to each diet (control, starch, fiber, sugar). Sample Preparation Samples were divided into four fractions: analysis by ultra-high-performance liquid chromatography-mass spectrometry (reverse phase positive ionization), analysis by ultra-high-performance liquid chromatography-mass spectrometry (reverse phase negative ionization), analysis by hydrophilic interaction liquid chromatography (HILIC), and analysis by gas chromatography-mass spectrometry (GCMS). However, only the liquid chromatography-mass spectrometry analysis will be discussed. Protein was removed from samples prior to analysis. Internal standards (phenylalanine, hippuric acid, cholic acid, glucose, palmitic acid) were added to the plasma sample followed by the addition of cold (-80oC) 10% acetone/90% methanol. Samples were vortexed then incubated at -20oC for 15 minutes. Samples were 74 centrifuged (13,000 x g x 10 minutes), supernatant transferred to a clean tube, and sample dried using vacuum centrifugation. A starting buffer (5% acetone, 95% water, 0.1% formic acid) was added to the dry sample. Mass Spectrometry Analysis The ultra-high-performance liquid chromatography (UHPLC) platform utilized a Thermo Scientific Ultimate 3000 UHPLCf and a Thermo Scientific Q Exactive™e Quadrupole Orbitrap mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source operated at 70,000 mass resolution. Three sample extracts were reconstituted in acidic or basic liquid chromatography compatible solvents. The first aliquot was analyzed using acidic, positive ion-optimized conditions, the second aliquot used basic, negative ion-optimized conditions, and the third aliquot was analyzed via negative ionization following elution from a hydrophilic liquid chromatography (HILIC) column (SeqQuant® ZIC®-pHILICf). The gas chromatography-mass spectrometry platform utilized an Agilent 7200B Quadrupole Time-ofFlight GC/MSg. Samples were randomized prior to metabolomics analysis. Each sample batch consisted of a process blank (water with formic acid), experimental plasma samples, and a pooled sample. Blank samples were used to subtract background variation in metabolite ion peaks prior to statistical analysis. Pooled samples, used as quality control samples, were created by mixing equal volumes (20 µL) from each of the 256 experimental plasma samples. For the negative analysis, pooled samples were run after every ten experimental samples. For the positive analysis, pooled samples were run after every four experimental samples. For the GC-MS analysis, pooled samples were run after every third sample. Compound Identification, Quantification, and Data Curation Raw data files for each analysis method (LC-MS [positive], LC-MS [negative], HILIC, GC-MS) were uploaded separately into the Progenesis® QI softwareh. Retention times were aligned to the most suitable pooled sample based on total chromatogram similarity. Features were selected using absolute ion 75 sensitivity of 500,000 for reverse-phase liquid chromatography and 100,000 for HILIC and normalized to total ion abundance to correct for unwanted systematic variation. Multiple adduct ions were selected for each ion polarity for de-convolution. The positive adducts included: [M+H]+, [M+2H]2+, [M+H-H2O]+, [M+NH4]+, [M+Na]+, and [M+K]+. The negative and HILIC adducts included: [M-H]-, [M-H-H2O]- and [M+Cl]-. Following deconvolution, two-dimensional peak matrices of peak abundance were exported into the statistical program Ri for further processing and statistical analyses. A multivariable mixed linear model was utilized to determine significant features (P < 0.05). The model included fixed effects (diet, age, breed, period), interaction terms (age*diet and breed*diet), and a random effect (horse). MS/MS for Metabolite Identification Inclusion lists were generated for features of statistical significance and subjected to fragmentation (MS/MS) to assist in identification of metabolites of interest. Three distinct inclusion lists were created following statistical analysis of the peak matrices. Missing values were imputed using feature means and log-transformed to assume normality. Predominant features in the blank samples were subtracted from each inclusion list. In addition, similar features within the chromatograph window of six seconds were excluded from the inclusion list. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis was performed from the inclusion lists. Metabolites were identified by automated comparison of accurate mass and fragmentation pattern to an experimental spectral library (National Institute of Standards and Technology (NIST 2014)j) and theoretical spectral libraries (Human Metabolome Database (HMDB)k and LipidBlastl). Three tiers of compound identifications were utilized to discriminate quality and source of the match. For all searches, a liberal precursor and fragment mass tolerance window of 0.01 Da were used to reduce missed compound identifications. Identifications were manually validated. As needed, fragment spectra were compared to the METLINm Metabolomics Database to assess the match quality. 76 Data Quality Control All quality control (QC) analyses were performed in the statistical program Ri. First, data were normalized to remove batch effects and correct for drift within a single batch (standard vector regression [158]). To evaluate if normalization was effective for pooled quality control samples and experimental samples thus removing drift and batch effects, a regression model was fit to the data and an R2 value calculated. Metabolite ion peaks with an R2 value greater than 0.8 in the pooled samples were removed. Metabolite ion peaks with an R2 value greater than 0.5 in the experimental samples were removed. Normalized metabolite ion peaks were visually inspected and metabolites in which normalized sample quantities did not vary around the pooled samples were excluded. Second, a variant of the Dixon’s outlier test was used to test for outliers. Outliers were removed if the metabolite failed the outlier test at a cutoff of 0.3; a maximum of 10% of outliers on each side was allowed. Third, metabolite ion peaks with > 10% missing values were excluded from the dataset and missing values imputed using random forest. Statistical Analysis Data were analyzed using a multivariable linear mixed model fit for each metabolite. All models were mixed regression models containing both fixed and random effects. Full model predictors included: age (adult, aged), diet (control, fiber, starch, sugar), breed (Thoroughbred, Standardbred), and period (first, second, third, fourth) and the interactions between age and period (age*period), age and diet (age*diet), and breed and diet (breed*diet) as fixed effects. A random effect for horse nested within period was included in all models to account for animal-to-animal (inter-animal) variability, to account for the variation and correlation in data due to repeated measures on the same observational unit over time (i.e. repeated measures for horse due to the Latin square design) and to account for any effect of diet sequence. The inclusion of horse as a random effect variable also accounts for missing data from the four horses that were not sampled in the entire study. To evaluate if there was an effect of the previous diet (despite a washout period) on the “Day 0” sample, multivariable linear mixed models were fit for each metabolite for the 77 second, third, and fourth period. The model included fixed effects (age, last diet, breed, period), interaction terms (age*last diet, breed*last diet, age*period), and a random effect (horse). All data are reported as least squares means estimates and pairwise significant differences (P ≤ 0.05) from the full model. Pairwise differences for all models were determined using the lmerTest package difflsmeans function which gives differences of the least squares means table with p-values and confidence intervals using Satterthwaite’s approximation for degrees of freedom. For pairwise comparisons between variables with more than two levels, p-values and confidence intervals were corrected for multiple comparisons using the Tukey Honest Significant Difference (HSD) method. RESULTS Animals Diets were well tolerated by the horses and refusals were insignificant (mean ± SD; 76.2 ± 0.6 grams/horse/day). No significant differences in weight or body condition score were noted between age groups, diets, or time points. Metabolomics Before normalization and removal of outliers, the negative phase metabolomic analysis had 304 samples (16 blank + 32 pooled + 256 experimental) and a total of 2,580 metabolite ion peaks of which 1,000 features were significantly different in initial statistical analyses and were subjected to MS/MS. Four experimental samples and 143 metabolite ion peaks were removed due to > 10% missingness. After MS/MS fragmentation, 73 metabolites were of known identity based on homology with human metabolites. The positive phase metabolomic analysis had 342 samples (16 blank + 70 pooled + 256 experimental) and 3,252 metabolite ion peaks of which 1,000 features were significantly different in initial statistical analyses and were subjected to MS/MS. Two experimental samples and 147 metabolite ion peaks were removed due to > 10% missingness. After MS/MS fragmentation, 100 metabolites were of known identity based on homology with human metabolites. 78 After normalization and removal of outliers, the negative phase metabolomic analysis had a total of 1,996 metabolite ion peaks and the positive phase metabolomic analysis had a total of 2,704 metabolite ion peaks. Figure 4.1 shows a metabolite ion peak that improved following normalization. Figure 4.2 demonstrates a metabolite ion peak in which normalization did not correct for batch effects. An additional 24 samples were excluded from both the positive and negative analyses due to inadequate correction for batch effects after normalization. Known metabolites were classified into eight metabolic pathways (lipid, amino acid, carbohydrate, cofactors and vitamins, energy, nucleotide, peptide, xenobiotics) based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway classification system. Metabolite Differences Before (day 0) and After (day 42) Dietary Adaptation Overall, across both the fixed effects (diet, age, period, breed) and interaction terms (age*period, diet*age, diet*breed), 7,228 metabolite ion peaks (248 known peaks) were significant. Due to the exclusion of 24 experimental samples and issues with the quality control samples, the data presented here only includes the effects of age and diet, the two primary variables of interest. To determine if metabolic alterations were present for the main effects of age and diet, metabolite ion peaks were compared at baseline (day 0) and after adaptation (day 42). There was no effect of the previous diet on the baseline sample. For the main effect of age, a total of 2,177 metabolite ion peaks (71 known peaks) were significant with 982 metabolite ion peaks (23 known peaks) at baseline, 373 metabolite ion peaks (18 known peaks) after adaptation, and 822 metabolite ion peaks (30 known peaks) changed between the timepoints. For the main effect of diet, a total of 1,644 metabolite ion peaks (59 known peaks) were significant with 442 metabolite ion peaks (15 known peaks) at baseline, 742 metabolite ion peaks (28 known peaks) after adaptation, and 460 metabolite ion peaks (16 known peaks) changed between the timepoints. When comparing adult horses versus aged horses, 982 metabolite ion peaks at baseline and 373 metabolite ion peaks following adaptation were statistically different. Known metabolites (n = 41) significantly different between age groups are listed based on their metabolic pathways and sub-pathways: lipid (n = 11), amino acid (n = 9), cofactors and vitamins (n = 1), nucleotide (n = 3), peptide (n = 4), 79 xenobiotics (n = 9), and unknown (n = 4) (Table 4.1). At baseline, 12 metabolites had increased concentrations while 10 metabolites had decreased concentrations in aged horses compared to adult horses. Following dietary adaptation, 18 metabolites had increased concentrations while 1 metabolite had a decreased concentration in aged horses compared to adult horses. When comparing the fiber, starch, and sugar diets versus the control diet, 219 metabolite ion peaks at baseline and 275 metabolite ion peaks following adaptation were statistically different. Known metabolites (n = 39) significantly different between diets are listed based on their metabolic pathways and sub-pathways: lipid (n = 7), amino acid (n = 13), carbohydrate (n = 1), nucleotide (n = 2), peptide (n = 3), xenobiotics (n = 6), other (n = 4), and unknown (n = 3). Following dietary adaptation, compared to the control diet, 4 metabolites had increased concentrations while 3 metabolites had decreased concentrations on the fiber diet (Table 4.2), 5 metabolites had increased concentrations while 3 metabolites had decreased concentrations on the starch diet (Table 4.3), and 4 metabolites had increased concentrations while 5 metabolites had decreased concentrations on the sugar diet (Table 4.4). Metabolite Changes During an Oral Sugar Test A total of 7,325 metabolite ion peaks (248 known peaks) were significant for fixed effects (diet, age, period, breed) and interaction terms (age*period, age*diet, diet*breed) combined. Due to the exclusion of 24 experimental samples and issues with the quality control samples, the data presented here includes only the main effects of age and diet, the two main variables of interest. To determine if metabolic alterations were present for the main effects of age and diet, metabolite ion peaks were compared at 0 minutes (baseline) and 75 minutes. For the main effect of age, a total of 2,954 metabolite ion peaks (108 known peaks) were significant with 944 metabolite ion peaks (48 known peaks) at 0 minutes, 891 metabolite ion peaks (27 known peaks) at 75 minutes, and 1,119 metabolite ion peaks (33 known peaks) changed from 0 to 75 minutes. For the main effect of diet, a total of 1,851 metabolite ion peaks (59 known peaks) were significant with 753 metabolite ion peaks (25 known peaks) at 0 minutes, 833 metabolite ion 80 peaks (29 known peaks) at 75 minutes, and 265 metabolite ion peaks (5 known peaks) changed from 0 minutes to 75 minutes. When comparing adult horses versus aged horses, 944 metabolite ion peaks at 0 minutes (baseline) and 891 metabolite ion peaks at 75 minutes were statistically different. Known metabolites (n = 51) significantly different between age groups are listed based on their metabolic pathways and sub-pathways: lipid (n = 13), amino acid (n = 11), carbohydrate (n = 3), cofactors and vitamins (n = 1), nucleotide (n = 2), peptide (n = 4), xenobiotics (n = 11), other (n = 1), and unknown (n = 5) (Table 4.5). At 0 minutes (baseline), 28 metabolites had increased concentrations while 6 metabolites had decreased concentrations in aged horses compared to adult horses. At 75 minutes, post administration of Karo® syrup, 15 metabolites had increased concentrations while 6 metabolites had decreased concentrations in aged horses compared to adult horses. When comparing the fiber, starch, and sugar diets versus the control diet, 469 metabolite ion peaks at 0 minutes (baseline) and 421 metabolite ion peaks at 75 minutes were statistically different. Known metabolites (n = 39) significantly different between diets are listed based on their metabolic pathways and sub-pathways: lipid (n = 4), amino acid (n = 11), carbohydrate (n = 2), cofactors and vitamins (n = 1), nucleotide (n = 1), peptide (n = 5), xenobiotics (n = 10), other (n = 4), and unknown (n = 1). At 0 minutes (baseline), compared to the control diet, no metabolites had increased concentrations while 3 metabolites had decreased concentrations on the fiber diet (Table 4.6), 2 metabolites had increased concentrations while 14 metabolites had decreased concentrations on the starch diet (Table 4.7), and 4 metabolites had increased concentrations while 2 metabolites had decreased concentrations on the sugar diet (Table 4.8). At 75 minutes, post administration of Karo® syrup, compared to the control diet, 1 metabolite had an increased concentration while 5 metabolites had decreased concentrations on the fiber diet (Table 4.6), no metabolites had increased concentrations while 10 metabolites had decreased concentrations on the starch diet (Table 4.7), and 4 metabolites had increased concentrations while 11 metabolites had decreased concentrations on the sugar diet (Table 4.8). 81 DISCUSSION Metabolomics remains a powerful approach for defining changes in cellular metabolism in relation to age and nutrition. In this study, untargeted metabolomics has identified thousands of significant metabolite ion peaks; however, the identity of a number of these peaks remains unknown. While substantial conclusions cannot be made at this point, this study has provided evidence that metabolomic profiling is a relevant approach for further defining metabolic alterations due to age and diet in horses. Examination of the plasma metabolome demonstrated significant differences in metabolites primarily derived from amino acids, lipids, and xenobiotics. However, these results are likely to change when additional metabolites are identified through additional MS/MS analyses. Application of metabolomics to aging studies identifies metabolites and metabolic pathways that change during the aging process. Aging is a multifaceted process characterized by a general decline in cellular function and homeostasis. In humans, studies have identified changes in sphingolipid metabolism, arachidonic acid metabolism, and glycerophospholipid metabolism during the aging process. Specifically, sphingosine, oleamide, indolelactic acid, and lysophosphatidylcholines were strongly (positively) correlated with age [159]. Despite limitations in metabolite identification, we have identified increases in sphinganine, an arachidonic acid metabolite (15-HETE), and a lysophosphatidylcholine metabolite (phosphatidylethanolamine (20:1/0:0)) in aged horses when compared to adult horses. Further, citric acid cycle intermediates (isocitrate, α-ketoglutarate, malate), nucleotides (allantoin, xanthine, uridine), amino acids (lysine, L-kynurenine), and branched-chain amino acids (leucine, isoleucine, valine) were increased in aged humans [160]. Similarly, in aged horses in this study, we have identified increases in a lysine metabolite (N-epsilon-acetyl-L-lysine) and nucleotide metabolites (allantoic acid, uridine) in aged horses relative to adult horses. Interestingly, we observed a decrease in L-kynurenine in aged horses, which is opposite to the observations in humans. Identification of age-related biomarkers is relevant to understanding underlying metabolic changes to target preventative and therapeutic treatments to delay aging-associated pathological changes. 82 Application of metabolomics to nutrition studies shows that interactions between nutrients and metabolism lead to metabolic alterations. Dietary metabolites play a significant role in an individual’s cellular metabolism by acting as building blocks of macromolecules and cellular membranes, signaling messengers, antioxidants, and sources of energy. For example, feeding a diet enriched with fatty acids alters neural membranes and concentrations of docosahexaenoic acid (DHA) leading to a change in neurotransmission and brain development [161–163]. In addition, dietary sugars enter glycolysis, which generates nucleotide adenosine triphosphate, an energy-rich metabolite; however, dietary sugars may also enter the fatty acid synthesis pathway. In humans, consumption of sugar-rich foods leads to fatty liver disease and diabetes [164]. In horses, consumption of a sugar-rich diet leads to improved tissue insulin sensitivity [128,165]; however, its role in the manifestation of disease is unknown. Understanding the effect of dietary metabolites on cellular metabolism would allow diets to be designed to improve cellular function and overall health. Generation and analysis of metabolomic data present several challenges. Untargeted metabolomics (or discovery metabolomics) creates a comprehensive analysis of a biological sample. While this gives the most complete picture of cellular metabolism, it comes with several challenges such as identification of known metabolites for biological interpretation and data analysis. While metabolomics is a very powerful tool for exploring cellular metabolism, it is a finicky technology and process. The inclusion of quality control samples provides a method for minimizing variation amongst results. First, normalization of the experimental samples against the pooled quality control samples should eliminate unwanted non-biological variation. Unwanted non-biological variation such as signal drift and batch effect in metabolomics experiments pose a challenge when interpreting biological significance. Signal drift refers to measurement fluctuations due to changes in instrument sensitivity, chromatograph retention time, and sample preparation. Batch effect refers to the technical variation that occurs between “batches” of samples analyzed at different times. Quality control samples serve as a measure of repeatability within an analytical batch. These samples aid in the removal of metabolic features with excessive drift in accurate mass, retention time, or signal. Pooled quality control samples serve as technical replicates as theoretically, their biological composition is 83 similar to the experimental samples. The purpose of a quality control sample is to equilibrate the analytical platform before analysis of experimental samples to ensure reproducible data, to calculate technical precision within each batch, and to provide data to use for signal correction within and between batches [68]. Comparison of quality control samples before and after normalization demonstrates variability between batches raising concerns that an error in instrumentation may be present prompting samples to be rerun. The second challenge of performing untargeted metabolomics is the identification of metabolites. In this study, thousands of metabolite ion peaks were generated; however, a limited number of peaks were identified. Initially, significant metabolite ion peaks were separated into inclusion lists for MS/MS fragmentation for metabolite identification. However, following generation of the MS/MS data, an error in the experimental sample master key was identified. This error may have resulted in the inclusion of nonsignificant metabolite ion peaks or exclusion of significant metabolite ion peaks. Further, the lack of an equine-specific library coupled with an outdated human metabolite library contributed to a small proportion of identifications. Additional identifications are expected as the pooled quality control samples will be analyzed by MS/MS and all metabolite ion peaks within those samples will be identified which should lead to a more complete biological interpretation. In addition, an updated National Institute of Standards and Technology (NIST) database should be available by December 2017. Further data analysis is required to overcome these challenges and limitations. First, identification and rerunning of samples with significant variability that cannot be corrected with normalization needs to be performed. Second, all metabolite ion peaks in the pooled quality control samples need to undergo MS/MS fragmentation which will allow for additional metabolite identifications. It is anticipated that once these limitations are overcome, based on human studies, that metabolite differences will provide insight into age-associated and diet-associated changes in cellular metabolism. 84 FOOTNOTES a MARS Horsecare US Inc, Dalton, Ohio, USA. b Sigma-Aldrich, Saint Louis, Missouri, USA. c The Coburn Company, Whitewater, Wisconsin, USA. d ACH Food Companies Inc., Cordova, Tennessee, USA. e Thermo Fisher Scientific, Waltham, Massachusetts, USA. f EMD Millipore Corporation, Billerica, Massachusetts, USA. g Agilent, Santa Clara, California, USA. h Nonlinear Dynamics, Durham, North Carolina, USA. i R Core Team, Vienna, AUSTRIA. j National Institute of Standards and Technology, U.S. Department of Commerce, Gaithersburg, Maryland, USA. k Wishart Research Group, University of Alberta, Edmonton, CANADA. l Fiehn Laboratory, University of California – Davis, Davis, California, USA. m The Scripps Research Institute, La Jolla, California, USA. 85 APPENDIX 86 Table 4.1 Significant (P ≤ 0.05) amino acid, cofactors and vitamins, lipid, nucleotide, peptide, xenobiotic, and unknown pathway metabolites indicated by higher (↑) concentrations and lower (↓) concentrations before (Day 0) and after (Day 42) dietary adaptation in aged horses compared to adult horses independent of diet and the interaction term (age*diet). AMINO ACID Choline Metabolism trimethylamine N-oxide Urea Cycle D-ornithine Lysine Metabolism N-epsilon-acetyl-L-lysine Methionine Metabolism methionine sulfoxide Phenylalanine and Tyrosine Metabolism L-tyrosine Tryptophan Metabolism indolelactic acid L-kynurenine indoxyl sulfate Tyrosine Metabolism retinol DAY 0 COFACTORS AND VITAMINS Hemoglobin, Porphyrin and Chlorophyll Metabolism bilirubin DAY 0 LIPID Arachidonic Acid Metabolism 15-HETE Fatty Acid Metabolism 1-o-hexadecyl-2-o-acetyl-sn-glyceryl-3-phosphorylcholine 1,2-di-(9Z-octadecenoyl)-sn-glycero-3-phospho-N,N-dimethylethanolamine traumatic acid PC-M6 lysophospholipid (16:0) hydroxybutyrylcarnitine 3-hydroxysuberic acid L-carnitine cis-5,8,11-eicosatrienoic acid Sphingolipid Metabolism sphinganine DAY 0 DAY 42 ↑ ↑ ↑ ↑ ↓ ↓ ↓ ↑ ↓ 87 DAY 42 ↓ DAY 42 ↑ ↓ ↑ ↑ ↓ ↓ ↓ ↑ ↑ ↑ ↑ Table 4.1 (cont’d) NUCLEOTIDE Purine Metabolism allantoic acid Pyrimidine Metabolism uridine Uracil Derivative 5-hydroxymethyl-4-methyluracil DAY 0 DAY 42 PEPTIDE Dipeptide prolyl-tyrosine tyrosyl-aspartate valine-glycine DAY 0 XENOBIOTIC Chemical Compound 1-(9Z-octadecenoyl)-sn-glycero-3-phosphoethanolamine 1-heptadecanoyl-sn-glycero-3-phosphocholine 1-pentadecanoyl-sn-glycero-3-phosphocholine 1-hexadecyl-sn-glycero-3-phosphocholine Drug dimethyl sulfoxide Food Component/Plant juzirine 1-hexadecanoyl-sn-glycerol daphniphylline glycerol 1-stearate DAY 0 ↓ ↑ ↑ ↑ ↑ UNKNOWN 1-stearoyl-2-linoleoyl-sn-glycero-3-phosphoethanolamine 3,4-dihydroxyphenylglycol o-sulfate 1,2-di-(9Z-octadecenoyl)-sn-glycero-3-phospho-(1'-myo-inositol) 1-stearoyl-2-linoleoyl-sn-glycero-3-phosphoethanolamine DAY 0 ↑ ↑ DAY 42 ↑ ↑ ↑ DAY 42 ↑ ↑ ↑ 88 ↓ DAY 42 ↑ ↑ ↑ ↑ ↑ ↑ Table 4.2 Significant (P ≤ 0.05) amino acid, lipid, nucleotide, xenobiotic, and unknown pathway metabolites indicated by higher (↑) concentrations and lower (↓) concentrations before (Day 0) and after (Day 42) dietary adaptation to the fiber diet compared to the control diet. AMINO ACID Choline Metabolism trimethylamine N-oxide Tyrosine Metabolism retinol DAY 0 LIPID Fatty Acid Metabolism phosphatidylethanolamine (22:2/0:0) 3-hydroxysuberic acid hydroxybutyrylcarnitine phosphatidylethanolamine (20:1/0:0) Steroid glycoursodeoxycholic acid DAY 0 NUCLEOTIDE Uracil Derivative 5-hydroxymethyl-4-methyluracil DAY 0 DAY 42 ↑ ↓ ↓ ↓ DAY 42 ↓ ↑ ↓ DAY 42 ↓ XENOBIOTIC Chemical Compound 1-(9Z-octadecenoyl)-sn-glycero-3-phosphoethanolamine Food Component/Plant hydrocotarnine UNKNOWN 1-palmitoyl-2-hydroxy-sn-glycero-3-phosphoethanolamine 3-methoxybenzenepropanoic acid o-nitrobenzoic acid 89 ↓ ↑ ↓ ↓ ↑ ↑ Table 4.3 Significant (P ≤ 0.05) amino acid, lipid, peptide, xenobiotic, and unknown pathway metabolites indicated by higher (↑) concentrations and lower (↓) concentrations before (Day 0) and after (Day 42) dietary adaptation to the starch diet compared to the control diet. AMINO ACID Arginine and Proline Metabolism N-acetylornithine Histidine Metabolism L-glutamic acid Lysine Metabolism N-alpha-acetyl-L-lysine N-epsilon-acetyl-L-lysine Methionine Metabolism 5-methylthioribose Tryptophan Metabolism L-kynurenine DAY 0 LIPID Fatty Acid Metabolism lysophospholipid (16:0) phosphatidylethanolamine (20:1/0:0) phosphatidylethanolamine (20:4/0:0) DAY 0 PEPTIDE Dipeptide aspartyl-tryptophan prolyl-tyrosine tyrosyl-aspartate DAY 0 XENOBIOTIC Chemical Compounds 1-(9Z-octadecenoyl)-sn-glycero-3-phosphoethanolamine 1-pentadecanoyl-sn-glycero-3-phosphocholine Food Component/Plant glycerol 1-stearate DAY 0 UNKNOWN 1-palmitoyl-2-hydroxy-sn-glycero-3-phosphoethanolamine DAY 0 ↓ DAY 42 ↓ ↑ ↑ ↓ ↑ ↑ ↓ ↓ ↑ 90 ↓ DAY 42 ↓ ↑ DAY 42 ↑ ↑ DAY 42 ↓ ↑ DAY 42 Table 4.4 Significant (P ≤ 0.05) amino acid, lipid, peptide, xenobiotic, and unknown pathway metabolites indicated by higher (↑) concentrations and lower (↓) concentrations before (Day 0) and after (Day 42) dietary adaptation to the sugar diet compared to the control diet. AMINO ACID Amino Sugar Metabolism L-glutamine D-Arginine and D-Ornithine Metabolism; Urea Cycle D-ornithine Histidine Metabolism L-histidine Phenylalanine and Tyrosine Metabolism p-cresol sulfate Tryptophan Metabolism indoxyl sulfate DAY 0 CARBOHYDRATE Glucose Derivative butyl (S)-3-hydroxybutyrate glucoside DAY 0 LIPID Fatty Acid Metabolism phosphatidylethanolamine (20:4/0:0) DAY 0 NUCLEOTIDE Pyrimidine Metabolism uridine DAY 0 XENOBIOTIC Drug indoleacrylic acid Food Component/Plant isoferulic acid DAY 0 OTHER Dopamine and Norepinephrine Metabolism DOPA sulfate norepinephrine sulfate Polyphenol Metabolite caffeic acid 4-sulfate cholest-4-en-26-oic acid, 7-alpha-hydroxy-3-oxo DAY 0 DAY 42 ↑ ↓ ↑ ↓ ↓ DAY 42 ↑ DAY 42 ↓ DAY 42 ↑ DAY 42 ↑ ↑ DAY 42 ↓ ↓ ↓ 91 Table 4.5 Significant (P ≤ 0.05) amino acid, carbohydrate, cofactors and vitamins, lipid, nucleotide, peptide, xenobiotic, other, and unknown pathway metabolites indicated by higher (↑) concentrations and lower (↓) concentrations before (0 minutes) and during (75 minutes) the oral sugar test in aged horses compared to adult horses independent of diet and the interaction term (age*diet). AMINO ACID Arginine and Proline Metabolism N-acetylornithine citrulline Urea Cycle D-ornithine Lysine Metabolism N-alpha-acetyl-L-lysine Phenylalanine and Tyrosine Metabolism DL-phenylalanine L-tyrosine Pyrimidine Metabolism 2-aminoisobutyric acid Tryptophan Metabolism indolelactic acid L-kynurenine indoxyl sulfate Tyrosine Metabolism retinol 0 MINUTES CARBOHYDRATE Glucose Derivative butyl (S)-3-hydroxybutyrate glucoside Starch and Sucrose Metabolism 2-phenylethanol glucuronide D-lyxose 0 MINUTES COFACTORS AND VITAMINS bilirubin 0 MINUTES ↑ 75 MINUTES ↑ ↑ ↑ ↑ ↓ ↓ ↑ ↓ ↑ ↓ ↑ 75 MINUTES ↓ ↓ ↑ 92 75 MINUTES Table 4.5 (cont’d) LIPID Arachidonic Acid Metabolism 15-HETE Fatty Acid Metabolism L-carnitine lysophospholipid (16:0) lysophosphatidylethanolamine (20:1/0:0) 1-nonadecanoyl-2-(9Z-octadecenoyl)-glycero-3-phosphocholine 1,2-di-(9Z-octadecenoyl)-sn-glycero-3-phospho-N, Ndimethylethanolamine cis-5,8,11-eicosatrienoic acid traumatic acid N-oleoylethanolamine 1-o-hexadecyl-2-o-acetyl-sn-glyceryl-3-phosphorylcholine Fatty Acyls 10,20-dihydroxyeicosanoic acid (E)-2-methyl-2-buten-1-ol o-beta-D-glucopyranoside Sphingolipid Metabolism sphinganine 0 MINUTES NUCLEOTIDE Purine Metabolism allantoic acid Pyrimidine Metabolism uridine 0 MINUTES PEPTIDE Dipeptide prolyl-tyrosine tyrosyl-aspartate valine-glycine isoleucine-arginine 0 MINUTES 75 MINUTES ↑ ↓ ↑ ↑ ↓ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ 75 MINUTES ↑ ↓ ↑ ↑ ↑ 93 75 MINUTES ↑ ↑ ↓ Table 4.5 (cont’d) XENOBIOTIC Chemical Compound 1-heptadecanoyl-sn-glycero-3-phosphocholine 1-hexadecanoyl-sn-glycero-3-phosphoethanolamine 1-hexadecyl-sn-glycero-3-phosphocholine 1-pentadecanoyl-sn-glycero-3-phosphocholine 1-stearoyl-2-hydroxy-sn-glycero-3-phosphocholine 1-(9Z-octadecenoyl)-sn-glycero-3-phosphoethanolamine Drug indoleacrylic acid lidocaine Food Component/Plant daphniphylline 1-hexadecanoyl-sn-glycerol cyclohexylamine 0 MINUTES 75 MINUTES ↑ ↑ ↑ ↑ ↑ ↑ ↑ OTHER Lactone 3-hydroxyadipic acid 3,6-lactone 0 MINUTES UNKNOWN 1-hexadecanoyl-2-sn-glycero-3-phosphate 3-methoxybenzenepropanoic acid 9(10)-EpOME 1-palmitoyl-2-hydroxy-sn-glycero-3-phosphoethanolamine cholest-4-en-26-oic acid, 7-alpha-hydroxy-3-oxo 0 MINUTES ↓ ↑ ↑ ↑ ↑ ↑ ↓ ↑ 75 MINUTES ↑ 94 ↑ 75 MINUTES ↑ Table 4.6 Significant (P ≤ 0.05) amino acid, lipid, xenobiotic, other pathway metabolites indicated by higher (↑) concentrations and lower (↓) concentrations before (0 minutes) and during (75 minutes) the oral sugar test following adaptation to the fiber diet compared to the control diet. AMINO ACID Urea Cycle D-ornithine Tryptophan Metabolism indoxyl sulfate 0 MINUTES 75 MINUTES LIPID Fatty Acid Metabolism lysophosphatidylethanolamine (20:1/0:0) 0 MINUTES XENOBIOTIC Drug indoleacrylic acid salicylic acid 0 MINUTES 75 MINUTES ↓ ↑ ↓ OTHER Benzene Derivative homoveratric acid Dopamine and Norepinephrine Metabolism norepinephrine sulfate 0 MINUTES 75 MINUTES ↓ ↓ 75 MINUTES ↓ ↓ ↓ 95 ↓ Table 4.7 Significant (P ≤ 0.05) amino acid, lipid, nucleotide, peptide, xenobiotic, and other pathway metabolites indicated by higher (↑) concentrations and lower (↓) concentrations before (0 minutes) and during (75 minutes) the oral sugar test following adaptation to the starch diet compared to the control diet. AMINO ACID Arginine and Proline Metabolism N-acetylornithine Leucine, Isoleucine and Valine Metabolism isovalerylglucuronide Lysine Metabolism N-alpha-acetyl-L-lysine N (6)-methyllysine Phenylalanine and Tyrosine Metabolism p-cresol sulfate Tryptophan Metabolism DL-indole-3-lactic acid Tyrosine Metabolism retinol 0 MINUTES 75 MINUTES ↓ ↓ LIPID Fatty Acid Metabolism hydroxybutyrylcarnitine 0 MINUTES 75 MINUTES ↓ ↓ NUCLEOTIDE Pyrimidine Metabolism uridine 0 MINUTES 75 MINUTES PEPTIDE Dipeptide glucose-phenylalanine leucyl-alanine L-gamma-glutamyl-L-isoleucine isoleucine-arginine prolyl-tyrosine 0 MINUTES ↓ ↓ ↓ ↓ ↓ ↓ ↑ ↓ ↓ ↓ ↑ 96 75 MINUTES ↓ Table 4.7 (cont’d) XENOBIOTIC Benzoate Metabolism hippuric acid Drug salicylic acid p-acetaminobenzoic acid Food Component/Plant cyclohexylamine (9Z,12Z,14E)-16-Hydroxy-9,12,14-octadecatrienoic acid 0 MINUTES 75 MINUTES ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ OTHER Dopamine and Norepinephrine Metabolism norepinephrine sulfate 0 MINUTES 75 MINUTES ↓ 97 Table 4.8 Significant (P ≤ 0.05) amino acid, carbohydrate, cofactors and vitamins, lipid, xenobiotic, other, and unknown pathway metabolites indicated by higher (↑) concentrations and lower (↓) concentrations before (0 minutes) and during (75 minutes) the oral sugar test following adaptation to the sugar diet compared to the control diet. AMINO ACID Arginine and Proline Metabolism N-acetylornithine Urea Cycle citrulline D-ornithine Phenylalanine and Tyrosine Metabolism p-cresol sulfate Tryptophan Metabolism L-kynurenine indoxyl sulfate 0 MINUTES 75 MINUTES ↓ ↑ ↓ ↓ ↓ ↑ ↑ ↓ CARBOHYDRATE Starch and Sucrose Metabolism 2-phenylethanol glucuronide tyramine glucuronide 0 MINUTES 75 MINUTES COFACTORS AND VITAMINS Hemoglobin, Porphyrin and Chlorophyll Metabolism bilirubin 0 MINUTES LIPID Fatty Acid Metabolism hydroxybutyrylcarnitine lysophosphatidylethanolamine (20:1/0:0) 0 MINUTES XENOBIOTIC Chemical Compound 1-(9Z-octadecenoyl)-sn-glycero-3-phosphoethanolamine Drug indoleacrylic acid Food Compound/Plant polyethylene (1xi,3xi)-1,2,3,4-tetrahydro-1-methyl-beta-carboline-3-carboxylic acid 0 MINUTES ↑ ↑ 75 MINUTES ↑ 75 MINUTES ↓ ↓ 98 75 MINUTES ↓ ↑ ↑ ↓ Table 4.8 (cont’d) OTHER Aminobenzoate Degradation; Microbial Metabolism o-nitrobenzoic acid Dopamine and Norepinephrine Metabolism DOPA sulfate norepinephrine sulfate 0 MINUTES 75 MINUTES ↓ ↓ ↓ UNKNOWN 1-palmitoyl-2-hydroxy-sn-glycero-3-phosphoethanolamine 0 MINUTES 99 75 MINUTES ↓ Figure 4.1 Metabolite ion peaks before and after standard vector regression normalization in pooled quality control samples and experimental samples. Normalization improved the variability in the samples. The vertical black line separates the analytical batches. 100 Figure 4.2 Metabolite ion peaks before and after standard vector regression normalization in pooled quality control samples and experimental samples. Normalization did not correct for batch effects in the samples. The vertical black line separates the analytical batches. 101 CHAPTER 5 Insight into Metabolic Perturbations in Welsh Ponies with Insulin Dysregulation, Obesity, and Laminitis SUMMARY Background: Metabolomics, the study of small-molecule metabolites, has increased understanding of human metabolic diseases but has not been used to study equine metabolic syndrome. Objectives: 1) To examine the serum metabolome of Welsh Ponies with and without insulin dysregulation before and during an oral sugar test. 2) To identify differences in metabolites in ponies with insulin dysregulation, obesity, or history of laminitis. Study Design: In a case-control study, twenty Welsh Ponies (mean ± SD; 13.8 ± 9.0 years) classified as non-insulin dysregulated [CON] (n = 10, insulin < 30 mU/L) or insulin dysregulated [ID] (n = 10, insulin > 60 mU/L) at 75 minutes post administration of corn syrup (Karo® light), obese (n = 6) or non-obese (n = 14), and history of laminitis (n = 9) or no history of laminitis (n = 11). Methods: Metabolomic analysis was performed on serum obtained at 0 minutes (baseline) and 75 minutes during the oral sugar test. Data were analyzed with multivariable mixed linear models with significance set at P ≤ 0.05. Results: Mean insulin concentrations were significantly higher in insulin dysregulated when compared to non-insulin dysregulated ponies at baseline (ID: 15.4 ± 5.6 mU/L and CON: 4.0 ± 2.6 mU/L; P = 0.001) and 75 minutes (ID: 112.7 ± 29.1 mU/L and CON: 16.3 ± 8.3 mU/L; P < 0.001). Metabolomic analysis revealed a total of 646 metabolites of which 506 were of known identity based on homology with human metabolites. Significant metabolite differences, primarily in the lipid and amino acid pathways, were detected between groups (insulin response, obesity status, laminitis history). Main Limitations: Samples were collected from client-owned Welsh Ponies in five different locations. Conclusions: Data provides insight into a possible distinct pattern of metabolites that may have diagnostic utility for early detection of equine metabolic syndrome and provide new knowledge regarding the pathophysiology of metabolic perturbations associated with this condition. 102 INTRODUCTION Insulin dysregulation, generalized obesity, regional adiposity and a predisposition for laminitis are central features of equine metabolic syndrome (EMS). Insulin dysregulation, defined as an abnormal resting insulin (hyperinsulinemia) and/or abnormal insulin response to intravenous or oral glucose challenge or feeding, is thought to be the central pathophysiologic feature of EMS. Complex multifactorial disease processes such as human metabolic syndrome and EMS result from disruption of metabolic processes across multiple tissues that sum together to create clinical disease [166]. Due to the complex nature, measurement of glycemic and insulinemic responses to oral or intravenous glucose and/or insulin challenges is likely inadequate to distinguish between hyperinsulinemia caused by exaggerated pancreatic responses, tissue insulin resistance, or reduced insulin clearance [167]. Yet, except for studies addressing the lamina during experimentally induced laminitis [6,8] and dynamic assessment of insulin resistance [168,169], few studies have attempted to identify the metabolic derangements of EMS at a tissue or cellular level. Our current understanding of EMS is based on clinical assays that do not directly assess the altered cellular and molecular pathophysiology within major metabolic tissues (muscle, adipose, liver) and are therefore insufficient to unravel EMS pathophysiology. Metabolomics, the study of molecules involved in cellular metabolism (i.e. nucleotides, amino acids, fatty acids, carbohydrates, etc.), refers to the global interrogation of the biochemical components in a biological sample (serum, plasma, urine, saliva, cerebrospinal fluid). Because metabolite abundance in the serum can provide information about disruption in metabolic processes across the tissues, evaluation of the serum metabolome is a logical place to start investigating the molecular perturbations relevant to EMS [170]. More than 4,000 metabolites have been identified in human serum using high-throughput mass spectrometry and chromatography [67]. Several human studies have identified plasma metabolites and distinct metabolomic signatures associated with insulin resistance, glucose intolerance, obesity, and typeII diabetes mellitus [21,25,97]. In addition to elucidating alterations in novel metabolic pathways implicated in disease development, serum metabolomics can be used to identify biomarkers that can effectively pinpoint animals 103 at-risk for EMS and laminitis. Several human studies have demonstrated that metabolite biomarkers identified in cross-sectional data are useful for the detection of subclinical/pre-clinical disease months to years before the onset of clinically identifiable insulin resistance [88,171–174]. Thus, serum metabolites hold promise as potential biomarkers that would allow timely identification of metabolic derangements in animals at-risk for insulin dysregulation. Although metabolomic analysis is a potentially powerful tool to study the complex molecular pathophysiology of EMS, the measurement of metabolites is costly. Therefore, our objectives were to demonstrate the potential of serum metabolomics to explore the pathophysiology of metabolic dysregulation and to differentiate between individuals with and without evidence of insulin dysregulation, obesity and/or history of laminitis by characterizing differences in the serum metabolome before and during an oral sugar test (OST) in a small cohort of Welsh Ponies. To our knowledge, this is the first attempt to examine the pathophysiology of insulin dysregulation in horses using metabolomics. MATERIALS AND METHODS Animals In a case-control study, twenty Welsh Ponies classified as non-insulin dysregulated [CON] (n = 10, insulin < 30 mU/L) or insulin dysregulated [ID] (n = 10, insulin > 60 mU/L) at 75 minutes post administration of Karo® light corn syrupa were used for this study. The cohort was comprised of clientowned ponies located in Virginia, Maryland, Mississippi, Arkansas, and California. Additional information on diet, exercise, management, body condition score [119], laminitis history, and biochemical measures (non-esterified fatty acids (NEFAs), triglycerides, leptin, adiponectin) was obtained for each pony. All methods were approved by the Institutional Animal Care and Use Committee at the University of Minnesota and Michigan State University. 104 Oral Sugar Test An OST was administered to all ponies as previously described [62]. Briefly, oral administration of commercially available corn syrup (Karo® lighta) was given using a 60cc catheter tip syringe (dose: 0.15 mL/kg bodyweight). Blood was collected via an indwelling jugular catheter at 0 minutes (baseline) and 75 minutes. The intravenous catheter was placed one hour prior to commencement of the oral sugar test following subcutaneous administration of lidocaine. Blood was centrifuged, and serum separated and stored at -80°C until analysis. Determination of Insulin and Glucose Measurements Insulin concentrations were determined in duplicate by radioimmunoassay (Coat-A-Count®b) previously validated for equids [125]. Glucose concentrations were determined in duplicate via a membrane-based glucose oxidase method (YSI 2300 STAT Plus™ Glucose & Lactate Analyzerc). Determination of Other Hormonal and Biochemical Measurements NEFA concentrations were determined using an in-vitro quantitative enzymatic colorimetric method assay (NEFA-HRd). Triglyceride concentrations were determined using the Serum Triglyceride Determination Kit (TR0100e). Leptin concentrations were determined using a radioimmunoassay (MultiSpecies Leptin RIAf). Adiponectin concentrations were determined using the Human High Molecular Weight (HMW) Adiponectin ELISAf previously validated for equine serum [175]. Metabolomics Forty serum samples, 0 minutes (baseline) and 75 minutes from each pony, were analyzed at Metabolon® Inc. using a combination of chromatography and mass spectrometry following sample preparation as described previously [176]. 105 Sample Preparation for Global Metabolomics Samples were divided into five fractions: analysis by ultra-performance liquid chromatographytandem mass spectrometry (UPLC-MS/MS; positive ionization), analysis by UPLC-MS/MS (negative ionization), analysis by UPLC-MS/MS polar platform (negative ionization), analysis by gas chromatography-mass spectrometry (GC-MS), and one sample was reserved for repeat analysis. A targeted analysis utilized three types of controls in concert with the experimental samples: 1) samples generated from a small portion of each experimental sample of interest served as a technical replicate throughout the dataset; 2) extracted water samples served as process blanks; and 3) a combination of standards spiked into every analyzed sample allowed instrument performance monitoring. Instrument variability was determined by calculating the median relative standard deviation for the standards that were added to each sample prior to injection into the mass spectrometers. Mass Spectrometry Analysis The UPLC-MS/MS [177] platform utilized a Waters Acquity UPLCg and a Thermo Scientific QExactiveh high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution. Three sample extracts were dried then reconstituted in acidic or basic liquid chromatography-compatible solvents. The first aliquot was analyzed using acidic, positive ion-optimized conditions (n = 254 metabolites), the second aliquot used basic, negative ion-optimized conditions (n = 284 metabolites), and the third aliquot was analyzed via negative ionization following elution from a hydrophilic interaction liquid chromatography (HILIC) column (n = 54 metabolites). Gas chromatography-mass spectrometry (GC-MS) [178] was performed with a Thermo-Finnigan Trace DSQh mass spectrometer with electron impact ionization (EI). Samples were dried, derivatized, and separated on a fused silica column with helium as the carrier gas (n = 54 metabolites). 106 Compound Identification, Quantification, and Data Curation Metabolites were identified by automated comparison of the ion features in the experimental samples to a reference library of chemical standard entries that included retention time, molecular weight, preferred adducts, and in-source fragments as well as associated MS spectra and curated by visual inspection for quality control using software developed at Metabolon® Inc. [179]. Metabolon maintains a library of molecules based on authenticated standards that contain the retention time, mass to charge ratio, and chromatographic data. Identification of known chemical entities was based on comparison to metabolomic library entries of more than 3,300 commercially available purified standards. Peaks were quantified using area-under-the-curve. Raw area counts for each metabolite in each sample were normalized to correct for variation resulting from instrument inter-day tuning differences. Subsequent quality control and curation processes were designed to ensure accurate, consistent identification, and to minimize system artifacts, misassignments, and background noise. Pathways were assigned for each metabolite which allowed for examination of overrepresented pathways. Statistical Analysis Statistical analysis, performed in the statistical program Ri, following log transformation to ensure normality included multivariable mixed linear models with sex as a covariate and examined metabolite differences between insulin dysregulated and non-insulin dysregulated ponies, obese and non-obese ponies, and ponies with and without a history of laminitis. Correlations between metabolites and biochemical parameters (basal glucose, basal insulin, NEFAs, triglycerides, leptin, adiponectin) were explored using mixed linear models. A simple t-test was performed to determine significance between insulin dysregulated ponies and non-insulin dysregulated ponies for each biochemical parameter. Significance was set at P ≤ 0.05. Network pathway interaction diagrams were generated using Metscape [180]. Unsupervised principal components analysis (PCA) was performed to visualize the distribution of metabolic profiles within and between groups. Supervised least absolute shrinkage and selection operator (LASSO) penalized generalized linear models were fitted for optimal feature selection for classification of 107 each group – insulin response, obesity status, and laminitis history. LASSO regressions were fitted using the ‘glmnet’ R package. Model parameters were tuned using leave-one-out cross-validation and the optimal subset of features was selected from the model with minimal mean cross-validation error. No further model diagnostics could be performed due to an insufficient number of observations. RESULTS Animals Non-insulin dysregulated ponies (mean ± SD; 13.8 ± 9.0 years) were a combination of mares (n = 6), geldings (n = 3), and stallions (n = 1) while the insulin dysregulated ponies (11.3 ± 6.1 years) were exclusively mares (n = 10). All ponies were in moderate to obese body condition (median [range]; CON: 5.5 [5.0 – 8.0] and ID: 6.8 [5.0 – 8.5] out of 9). Ponies did not have clinical laminitis at the time of testing; however, a history of laminitis was reported in both non-insulin dysregulated (n = 1) and insulin dysregulated ponies (n = 8). Insulin and Glucose Concentrations Mean insulin concentrations were significantly higher in insulin dysregulated ponies compared to non-insulin dysregulated ponies at 0 minutes (mean ± SD; ID: 15.4 ± 5.6 mU/L and CON: 4.0 ± 2.6 mU/L; P = 0.001) and at 75 minutes (ID: 112.7 ± 29.1 mU/L and CON: 16.3 ± 8.3 mU/L; P < 0.001) whereas mean glucose concentrations did not differ between groups at baseline (ID: 73.9 ± 9.0 mg/dL and CON: 73.9 ± 7.2 mg/dL) or at 75 minutes (ID: 108.1 ± 12.6 mg/dL and CON: 106.3 ± 23.4 mg/dL). Other Hormonal and Biochemical Concentrations Mean non-esterified fatty acid concentrations (ID: 0.4 ± 0.2 mEq/L and CON: 0.3 ± 0.2 mEq/L), triglyceride concentrations (ID: 92.9 ± 101.8 mg/dL and CON: 42.0 ± 56.4 mg/dL), and leptin concentrations (ID: 6.6 ± 3.9 ng/mL and CON: 5.8 ± 5.1 ng/mL) did not differ between insulin dysregulated ponies and non-insulin dysregulated ponies. Adiponectin concentrations (ID: 2692.3 ± 1553.5 ng/mL and 108 CON: 7838.5 ± 3281.5 ng/mL) were significantly lower in insulin dysregulated ponies compared to noninsulin dysregulated ponies. Metabolomics Metabolomic analysis revealed a total of 646 metabolites of which 506 were of known identity based on homology with human metabolites (Table 5.1). The 506 known metabolites were classified into eight metabolic pathways (lipid, amino acid, carbohydrate, cofactors and vitamins, energy, nucleotide, peptide, xenobiotics) and 71 sub-pathways based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway classification system (Table 5.2). Metabolite Changes During an Oral Sugar Test To determine if the oral sugar test elicited metabolite alterations in addition to the expected glycemic and insulinemic responses, metabolites were compared at 0 minutes (baseline) and 75 minutes in all twenty ponies regardless of phenotype (insulin response, obesity status, laminitis history). Seventeen (17) metabolites had increased concentrations while 107 metabolites had decreased concentrations post administration of Karo® light corn syrup when compared to baseline concentrations. Metabolites (n = 124) significantly different between these time points are listed by metabolic pathways and sub-pathways (Figure 5.1): lipid (n = 31), amino acid (n = 46), carbohydrate (n = 5), cofactors and vitamins (n = 6), energy (n = 3), nucleotide (n = 3), peptide (n = 9), and xenobiotics (n = 21). After administration of Karo® syrup, glucose, fructose, and mannose increased, whereas lactate decreased relative to baseline. The tricarboxylic acid (TCA) cycle intermediates (alpha-ketoglutarate, malate, succinate) were also significantly decreased at 75 minutes (Figure 5.2). Thirty-one (31) lipid metabolites were significantly different between time points. Most lipid metabolites (26 / 31), including acylcarnitines, monohydroxy long-chain fatty acids and polyunsaturated fatty acids, were decreased at 75 minutes indicating an overall decrease in fatty acid metabolism after administration of Karo® syrup. Similarly, amino acid metabolism metabolites (44 / 46), including branched-chain amino acids, were reduced (Figure 5.3). The most dramatic 109 decrease in metabolites from baseline to 75 minutes was a decrease in lidocaine from the subcutaneous administration for catheter placement. Metabolite Differences Between Insulin Dysregulated and Non-Insulin Dysregulated Ponies When comparing the insulin dysregulated ponies versus the non-insulin dysregulated ponies, 55 metabolites at 0 minutes (baseline) and 51 metabolites at 75 minutes were statistically different. Metabolites (n = 82) significantly different between insulin dysregulated and non-insulin dysregulated ponies are listed based on their metabolic pathways and sub-pathways: lipid (Figure 5.4; n = 45), amino acid (Figure 5.5; n = 16), and carbohydrate, cofactors and vitamins, energy, nucleotide, peptide, and xenobiotics (Figure 5.6; n = 21). At baseline, 12 metabolites had increased concentrations while 43 metabolites had decreased concentrations in insulin dysregulated ponies compared to non-insulin dysregulated ponies. Further, at baseline, most metabolites with decreased concentrations were in the lipid (n = 22) and amino acid (n = 9) pathways. At 75 minutes, 7 metabolites had increased concentrations and 44 metabolites had decreased concentrations in insulin dysregulated ponies compared to non-insulin dysregulated ponies. Similar to baseline, insulin dysregulated ponies had decreased concentrations of lipid metabolites (n = 22) and amino acid metabolites (n = 7) after administration of Karo® syrup. In addition, at 75 minutes, insulin dysregulated ponies had lower concentrations of TCA cycle intermediates (citrate, fumarate, malate) compared to noninsulin dysregulated ponies. Insulin dysregulated ponies had a significantly higher concentration of serotonin (5HT) relative to non-insulin dysregulated ponies. Metabolite Differences Between Obese and Non-Obese Ponies To determine if the same or similar metabolites were different in obese (body condition score ≥ 7.0) ponies (n = 6) versus non-obese ponies (n = 14), metabolite measurements were compared in these two groups. Overall, 91 metabolites at 0 minutes (baseline) and 102 metabolites at 75 minutes were statistically different between these groups. Metabolites (n = 145) significantly different between groups are listed based on their metabolic pathways and sub-pathways: lipid (Figure 5.7; n = 79), amino acid (Figure 5.8; n = 41), 110 and carbohydrate, cofactors and vitamins, nucleotide, peptide, xenobiotics (Figure 5.9; n = 25). At baseline, 65 metabolites had increased concentrations while 26 metabolites had decreased concentrations in obese ponies compared to non-obese ponies. Obese ponies compared to non-obese ponies had significantly higher baseline concentrations of long-chain fatty acids (n = 8) and acylcarnitines (n = 8). Obese ponies also had significantly higher baseline concentrations of branched-chain amino acids (isoleucine, leucine, valine). At 75 minutes, 57 metabolites had increased concentrations and 45 metabolites had decreased concentrations in obese ponies compared to non-obese ponies. Similar to baseline, obese ponies had elevated concentrations of several acylcarnitines and branched-chain amino acids compared to non-obese ponies following administration of Karo® syrup. Obese ponies also had significantly lower concentrations of polyunsaturated fatty acids at 75 minutes. The most dramatic metabolite increases in obese ponies compared to non-obese ponies was 1,5 – anhydroglucitol. Metabolite Differences Between Ponies With and Without a History of Laminitis To determine if the same or similar metabolites were different due to laminitis history, ponies with a history of laminitis (n = 9) were compared to ponies without a history of laminitis (n = 11). One-hundredthirty-six (136) metabolites at 0 minutes (baseline) and 124 metabolites at 75 minutes were statistically different between previously laminitic and non-laminitic ponies. Metabolites (n = 182) significantly different between these groups are listed based on their metabolic pathways and sub-pathways: lipid (Figure 5.10; n = 91), amino acid (Figure 5.11; n = 50), and carbohydrate, cofactors and vitamins, energy, nucleotide, peptide, xenobiotics (Figure 5.12; n = 41). At baseline, 62 metabolites had increased concentrations while 74 metabolites had decreased concentrations in ponies with a history of laminitis compared to ponies without a history of laminitis. At baseline, long-chain fatty acids (n = 10), polyunsaturated fatty acids (n = 7) and monoacylglycerols (n = 8) were consistently higher in previously laminitic compared to non-laminitic ponies. At 75 minutes, 60 metabolites had increased concentrations and 64 metabolites had decreased concentrations in ponies with a history of laminitis compared to ponies without a history of laminitis. These differences were characterized by greater concentrations of 111 polyunsaturated fatty acids (n = 6) and monoacylglycerols (n = 6) in previously laminitic compared to nonlaminitic ponies. Metabolite Similarities Between Insulin Response, Obesity Status, and Laminitis History The overlaps between the significant metabolites identified when ponies were parsed by insulin response, obesity status, or laminitis history are depicted in Figure 5.13. A total of six (6) metabolites were shared in all three groups. Twenty-five (25) metabolites overlap when ponies were parsed by obesity status or laminitis history, five (5) metabolites overlap when ponies were parsed by laminitis history or insulin response, and three (3) metabolites overlap when ponies were parsed by obesity status or insulin response. Metabolite Correlations to Clinical Parameters Additional analysis revealed subsets of measured metabolites correlated (r ≥ 0.5) to the clinical parameters commonly measured in equids with suspected metabolic dysfunction (basal glucose, basal insulin, NEFAs, triglycerides, leptin, adiponectin; Table 5.4 and Table 5.5). Seven (7) metabolites were correlated to basal glucose, eighty-five (85) metabolites were correlated to basal insulin, seven (7) metabolites were correlated to NEFAs, thirteen (13) metabolites were correlated to triglycerides, fifty-four (54) metabolites were correlated to leptin, and twelve (12) metabolites were correlated to adiponectin. Thirty (30) compounds were correlated to more than one parameter. Regardless of the parameter most of the metabolites arise from the lipid and amino acid pathways. Metabolites as Potential Biomarkers To show the utility of serum metabolites to distinguish between ponies grouped by insulin response, obesity status or laminitis history, unsupervised principal components analysis (PCA) was performed. The first two principal components depicted capture approximately 34.2% (principal component 1 – 22.8%, principal component 2 – 11.4%) of the variation in the data and separate the ponies into two groups. Plots of the first two dimensions from unsupervised PCA allow for visualization of the relationships between the 112 metabolic profiles of each group when ponies are labeled by insulin response (Figure 5.14a), obesity status (Figure 5.14b) or laminitis history (Figure 5.14c). The optimal number of metabolites (i.e. biomarkers) necessary to distinguish individuals based on insulin response, obesity status, and laminitis history was determined using LASSO regression. The number of metabolites needed to differentiate ponies based on insulin response (n = 23), obesity status (n = 14), and laminitis history (n = 21) is listed in Table 5.3. Minimal overlap was observed between these metabolite lists with only three metabolites (2-margaroylGPC, oleoyl-linoleoyl-glycerophosphocholine, phenylcarnitine) shared between the insulin response and laminitis history biomarker lists. DISCUSSION While insulin dysregulation is thought to be the central pathophysiologic mechanism of EMS, the lack of information regarding cellular and molecular pathophysiology make the underlying molecular mechanisms unclear. For complex diseases that span multiple tissues, metabolomics from biologic fluid samples including serum, plasma, and urine, provide an opportunity to obtain additional quantitative biologic information that may help decipher disease mechanisms and identify potentially useful disease biomarkers. Here we demonstrate the utility of serum metabolomics to contribute to our understanding of EMS pathophysiology by 1) establishing the power of serum metabolomics to give insight into the metabolic responses to an oral sugar test beyond measurement of glucose and insulin concentrations, 2) identifying a list of metabolites that differentiates between the three EMS phenotypes (insulin response, obesity status, laminitis history), 3) identifying metabolites that correlate to other typical measures of metabolic dysfunction (basal glucose, basal insulin, NEFAs, triglycerides, leptin, adiponectin), and 4) pinpointing metabolites that can potentially be used as biomarkers for disease. The oral sugar test is a relatively simple test used in horses and ponies to provide an indication of glycemic and insulinemic responses to an oral sugar bolus. Similar to an oral glucose tolerance test (OGTT) performed in humans, an oral sugar test should provide a physiologic stimulus that results in metabolite flux through specific metabolic reactions/pathways. Comparable to findings in humans, the metabolite 113 concentrations in ponies (regardless of phenotype) indicate a switching from a relatively catabolic state (baseline/fasting) to an anabolic state after administration of Karo® syrup. Many of these changes can be attributed to four key areas of insulin action — an increase in glycolysis, and decreases in lipolysis, ketogenesis and proteolysis [181,182]. First, following administration of an oral sugar bolus, the cytosolic pathways of glucose disposal are overloaded as demonstrated by significant increases in glucose, fructose, and mannose. Second, increases in pyruvate concentrations above baseline values indicate increases in glycolysis [182]. In humans, a switch to glycolysis is also indicated by elevations in lactate which occurs approximately 30 minutes after peak insulin values [182]; however, lactate did not significantly increase at 75 minutes in our study. Our previous work has demonstrated that insulin peaks between 60 – 90 minutes post Karo® syrup administration [10,12]; therefore the 75 minute sample may have been too early to detect increases in lactate associated with insulin action. The switch to glycolysis from beta-oxidation is also supported by changes in acylcarnitines. Carnitine conjugation of long-chain fatty acids is a required step for import into the mitochondria prior to beta-oxidation and acylcarnitine accumulation. Acylcarnitine release into the plasma reflects substrate flux through beta-oxidation; decreases in acylcarnitines at 75 minutes relative to baseline suggest a decrease in beta-oxidation [183]. Decreases in saturated and monounsaturated long-chain fatty acids and polyunsaturated fatty acids indicate an inhibition of lipolysis [183]. Inhibition of ketogenesis and decrease TCA cycle flux is evidenced by decreases in ketones (3hydroxybutyrate) and TCA cycle intermediates (alpha-ketoglutarate, malate, succinate). Lastly, a decrease in amino acid concentrations, including branched-chain amino acid concentrations, after administration of Karo® syrup indicates an inhibition of proteolysis and possible usage for protein synthesis. Comparison of ponies with and without insulin dysregulation primarily identified differences in lysolipids, TCA cycle intermediates and urea cycle metabolites. Several glycerophosphocholines, such as oleoyl-linoleoyl-glycerophosphocholine, were decreased in insulin dysregulated ponies at both time points. In humans, individuals with low concentrations of oleoyl-linoleoyl-glycerophosphocholine develop glucose intolerance and type-II diabetes mellitus [184]. In addition, decreases in the TCA cycle intermediates (citrate, malate, fumarate) in insulin dysregulated ponies mirror decreased TCA cycle intermediates in type114 II diabetes mellitus patients [181,182,185]. Similar to humans with insulin resistance and type-II diabetes mellitus, decreases in urea cycle metabolites were present in insulin dysregulated ponies [184,185]. Finally, decreases in polyunsaturated fatty acids (linoleate) and bile acids (cholate), which were both decreased in insulin dysregulated ponies, are important biomarkers of insulin resistance in humans [95,184]. In humans, branched-chain amino acids, non-esterified fatty acids, acylcarnitines, and phospholipids have been identified as potential biomarkers for obesity [89,186,187]. For some of the metabolites, group differences were evident at baseline, while for others the difference was only evident after an oral sugar test which is a similar finding in human studies [181,182]. Analogous to obese humans, obese ponies compared to non-obese ponies have elevated serum concentrations of several long-chain fatty acids at baseline [188]. However, unlike obese humans, long-chain fatty acid concentrations were not different between obese and non-obese ponies at 75 minutes [83]. Obese ponies also had increased concentrations of several long-chain acylcarnitines (C16, C18, C18:1) at both time points when compared to non-obese ponies. These findings parallel findings in obese humans and may indicate lipid oversupply resulting in saturation of the mitochondrial capacity for beta-oxidation and incomplete long-chain fatty acid oxidation [188]. Obese ponies had higher carnitine concentrations at baseline and 75 minutes which has been associated with increased body mass index and waist circumference as well as insulin resistance and elevated triglycerides in humans [189]. Similar to findings in obese humans [190], obese ponies had lower lysolipid concentrations relative to non-obese ponies post administration of an oral sugar bolus. In addition, obese ponies had higher concentrations of branched-chain amino acids (isoleucine, leucine, valine) compared to non-obese ponies, indicating a delayed suppression of BCAA oxidation after an oral sugar test [83,189,190]. The most dramatic metabolite increases in obese ponies compared to non-obese ponies was 1,5 – anhydroglucitol, a metabolite that competes with glucose for filtration and elimination by the kidneys. This metabolite is a recognized marker of postprandial glucose control in humans [191]. The largest number of significant differences in metabolites were identified when ponies with a history of laminitis were compared to ponies without a history of laminitis; however, with the exception of increases in monoacylglycerols and polyunsaturated fatty acids at baseline and 75 minutes, many of the 115 differences in previously laminitic compared to non-laminitic ponies overlapped with important metabolic differences in the insulin dysregulation and obesity analyses, or overlapped between all three phenotypes. The metabolite overlap between phenotypes is not surprising given that these phenotypes often occur concurrently in equine metabolic dysfunction. Elevated circulating free fatty acids and hyperinsulinemia have been reported in obese horses with insulin resistance [192] and triglyceride accumulation in muscle rather than adipose tissue [193] was seen in healthy horses challenged with super-physiologic levels of insulin. Metabolites involved in fatty acid metabolism and amino acid metabolism were elevated in insulin dysregulated ponies, obese ponies, and ponies with a history of laminitis. In addition, the tricarboxylic acid cycle was less efficient in insulin dysregulated ponies and ponies with a history of laminitis as citrate, malate and fumarate, metabolites associated with the mitochondrial use of pyruvate, were lower in these phenotypes post administration of oral sugar suggesting that the mitochondria were less able to remove acetyl-CoA equivalents through energy production. Levels of homoarginine were lower in obese and previously laminitic ponies when compared to non-obese or non-laminitic ponies. In humans, low homoarginine levels are a risk factor for cardiovascular, cerebrovascular, and renal diseases potentially due to effects on nitric oxide and cellular energy metabolism [194]. The overlap in metabolite changes between these three phenotypes is also evident in the principal components analysis. In this analysis, more than 30% of the variation was captured by the first two principal components and plotting the first versus the second principal component separated the ponies into two clusters. However, the two clusters did not align completely with any of the three clinical phenotype groups (insulin response, obesity status, laminitis history), suggesting that these clinical phenotypes alone are inadequate to separate metabolic differences between the ponies. Much focus has been directed towards identification of animals at-risk for EMS and the identification of biomarkers that can provide prognostic information about laminitis risk. An ideal diagnostic test for EMS would be based on measurements at a single time point, would not be impacted by environmental variables, would be minimally confounded by individual factors (gender, age, breed, genetics), and would accurately classify horses and/or predict laminitis risk, allowing appropriate early 116 intervention and disease prevention. In humans, serum metabolites have been identified that predict metabolic diseases up to a decade before clinical onset. In this study, LASSO analysis yielded a subset of compounds that differentiate between disease and healthy individuals that could play a role as diagnostic tests. Identification of differing metabolites between the non-insulin dysregulated and insulin dysregulated phenotypes in a baseline sample may eliminate the need for a dynamic challenge test in the future. In addition, the ability to define a metabolomic signature may reveal specific biomarkers that predict and/or diagnose insulin dysregulation leading to a better understanding of disease processes that may help identify new therapeutic targets. This study has provided evidence that metabolomic profiling is a relevant approach for further defining metabolic alterations due to insulin dysregulation and obesity in horses. Examination of the serum metabolome of this Welsh Pony cohort demonstrated significant differences in metabolites primarily derived from the lipid and amino acid pathways when comparing ponies grouped by each EMS phenotype (insulin response, obesity status, laminitis history). Further, examination of the metabolite list against currently used metabolic dysregulation measurements (basal glucose, basal insulin, NEFAs, triglycerides, leptin, adiponectin) revealed a strong correlation to leptin and triglycerides, suggesting that metabolites may be useful for linking obesity and insulin dysregulation to other components of the EMS phenotype. However, despite the parallel between the findings from this study and findings in humans with insulin resistance, type-II diabetes mellitus and obesity, results from this cohort should be interpreted with caution. First, this is a small cohort restricted to a single breed. Second, ponies were initially included based on insulinemic responses to an oral sugar test thus there is significant overlap between the insulin dysregulation, obesity and laminitis groups limiting our analyses. When ponies were parsed by the three phenotypes (insulin response, obesity status, laminitis history) metabolomics showed that there were similarities and distinct differences which coincides with our understanding that equine metabolic syndrome is complex. The results presented here should be confirmed in a large cohort of animals that will allow for metabolite differences due to pathologic factors such as insulin dysregulation and obesity and physiologic factors such as age, gender, and breed to be differentiated. Despite these limitations, our results 117 clearly demonstrate the potential of serum metabolomics to provide insight into the molecular pathophysiology and to define a metabolomic signature for EMS. 118 FOOTNOTES a ACH Food Companies Inc., Cordova, Tennessee, USA. b Siemens Diagnostics, Los Angeles, California, USA. c YSI Incorporated Life Sciences, Yellow Springs, Ohio, USA. d Wako Chemicals USA, Richmond, Virginia, USA. e Sigma-Aldrich® Company, St. Louis, Missouri, USA. f EMD Millipore Corporation, Billerica, Massachusetts, USA. g Waters Corporation, Milford, Massachusetts, USA. h Thermo Fisher Scientific, Waltham, Massachusetts, USA. i R Core Team, Vienna, AUSTRIA. 119 APPENDIX 120 Table 5.1 List of measured metabolites. Metabolic Pathway Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Platform Comp ID KEGG HMDB PUBCHEM glycine LC/MS pos 58 C00037 HMDB00123 750 N-acetylglycine GC/MS 27710 HMDB00532 10972 sarcosine (N-methylglycine) GC/MS 1516 C00213 HMDB00271 1088 3141 C00719 HMDB00043 247 1648 C00065 HMDB00187 5951 HMDB02931 65249 HMDB00167 6288 Sub Pathway Glycine, Serine and Threonine Metabolism Glycine, Serine and Threonine Metabolism Glycine, Serine and Threonine Metabolism Glycine, Serine and Threonine Metabolism Glycine, Serine and Threonine Metabolism Glycine, Serine and Threonine Metabolism Glycine, Serine and Threonine Metabolism Glycine, Serine and Threonine Metabolism Alanine and Aspartate Metabolism Alanine and Aspartate Metabolism Alanine and Aspartate Metabolism Alanine and Aspartate Metabolism Alanine and Aspartate Metabolism Alanine and Aspartate Metabolism Biochemical Name LC/MS pos LC/MS pos LC/MS polar LC/MS pos LC/MS neg LC/MS polar LC/MS polar LC/MS neg LC/MS polar LC/MS pos LC/MS polar LC/MS pos LC/MS pos LC/MS pos LC/MS pos betaine serine N-acetylserine threonine N-acetylthreonine alanine N-acetylalanine aspartate asparagine N-acetylasparagine N-acetylaspartate (NAA) Amino Acid Glutamate Metabolism glutamate Amino Acid Glutamate Metabolism glutamine Amino Acid Glutamate Metabolism N-acetylglutamate Amino Acid Glutamate Metabolism N-acetylglutamine 121 37076 1284 C00188 33939 C01118 1126 C00041 HMDB00161 5950 1585 C02847 HMDB00766 88064 443 C00049 HMDB00191 5960 512 C00152 HMDB00168 6267 HMDB06028 99715 33942 152204 22185 C01042 HMDB00812 65065 57 C00025 HMDB00148 611 53 C00064 HMDB00641 5961 15720 C00624 HMDB01138 70914 33943 C02716 HMDB06029 182230 Table 5.1 (cont’d) Amino Acid Glutamate Metabolism N-acetyl-aspartyl-glutamate (NAAG) Amino Acid Glutamate Metabolism pyroglutamine Amino Acid Histidine Metabolism histidine Amino Acid Histidine Metabolism N-acetylhistidine Amino Acid Histidine Metabolism 1-methylhistidine Amino Acid Histidine Metabolism 3-methylhistidine Amino Acid Histidine Metabolism trans-urocanate Amino Acid Histidine Metabolism cis-urocanate Amino Acid Histidine Metabolism imidazole propionate Amino Acid Histidine Metabolism imidazole lactate Amino Acid Histidine Metabolism 1-methylimidazoleacetate Amino Acid Histidine Metabolism 4-imidazoleacetate Amino Acid Lysine Metabolism lysine Amino Acid Lysine Metabolism N6-acetyllysine Amino Acid Lysine Metabolism N-6-trimethyllysine Amino Acid Lysine Metabolism 2-aminoadipate Amino Acid Lysine Metabolism glutarate (pentanedioate) Amino Acid Lysine Metabolism glutarylcarnitine (C5) Amino Acid Lysine Metabolism 3-methylglutarylcarnitine (1) 122 LC/MS pos LC/MS pos LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS pos LC/MS neg LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS polar LC/MS pos LC/MS pos LC/MS pos LC/MS polar LC/MS pos LC/MS pos 35665 C12270 HMDB01067 46225 5255 134508 59 C00135 HMDB00177 6274 33946 C02997 HMDB32055 75619 30460 C01152 HMDB00001 92105 15677 C01152 HMDB00479 64969 607 C00785 HMDB00301 736715 40410 1549103 40730 HMDB02271 70630 15716 C05568 HMDB02320 440129 32350 C05828 HMDB02820 75810 32349 C02835 HMDB02024 96215 1301 C00047 HMDB00182 5962 36752 C02727 HMDB00206 92832 1498 C03793 HMDB01325 440120 6146 C00956 HMDB00510 469 396 C00489 HMDB00661 743 44664 HMDB13130 71464488 46547 HMDB00552 128145 Table 5.1 (cont’d) Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Lysine Metabolism Phenylalanine and Tyrosine Metabolism Phenylalanine and Tyrosine Metabolism Phenylalanine and Tyrosine Metabolism Phenylalanine and Tyrosine Metabolism Phenylalanine and Tyrosine Metabolism Phenylalanine and Tyrosine Metabolism Phenylalanine and Tyrosine Metabolism Phenylalanine and Tyrosine Metabolism Phenylalanine and Tyrosine Metabolism Phenylalanine and Tyrosine Metabolism Phenylalanine and Tyrosine Metabolism Phenylalanine and Tyrosine Metabolism Phenylalanine and Tyrosine Metabolism Phenylalanine and Tyrosine Metabolism Phenylalanine and Tyrosine Metabolism Phenylalanine and Tyrosine Metabolism Phenylalanine and Tyrosine Metabolism Phenylalanine and Tyrosine Metabolism pipecolate phenylalanine N-acetylphenylalanine phenylpyruvate phenyllactate (PLA) 4-hydroxyphenylacetate phenylacetylglycine tyrosine N-acetyltyrosine 4-hydroxyphenylpyruvate 3-(4-hydroxyphenyl) lactate phenol sulfate p-cresol sulfate o-cresol sulfate 3-methoxytyrosine gentisate phenylpropionylglycine 3-[3-(sulfooxy)phenyl] propanoic acid 3-hydroxy-3-phenylpropionate LC/MS pos LC/MS pos LC/MS neg LC/MS neg LC/MS neg GC/MS LC/MS pos LC/MS pos LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS pos LC/MS neg LC/MS neg LC/MS neg GC/MS 123 1444 C00408 HMDB00070 849 64 C00079 HMDB00159 6140 33950 C03519 HMDB00512 74839 566 C00166 HMDB00205 997 22130 C05607 HMDB00779 3848 541 C00642 HMDB00020 127 33945 C05598 HMDB00821 68144 1299 C00082 HMDB00158 6057 HMDB00866 68310 32390 1669 C01179 HMDB00707 979 32197 C03672 HMDB00755 9378 32553 C02180 HMDB60015 74426 36103 C01468 HMDB11635 4615423 36845 11615528 12017 18280 35434 C00628 HMDB01434 1670 HMDB00152 3469 HMDB00860 152323 45415 187488 43497 92959 Table 5.1 (cont’d) Amino Acid Amino Acid Amino Acid Amino Acid Phenylalanine and Tyrosine Metabolism Phenylalanine and Tyrosine Metabolism Phenylalanine and Tyrosine Metabolism Phenylalanine and Tyrosine Metabolism 3-(3-hydroxyphenyl) propionate 3-(4-hydroxyphenyl) propionate 3-phenylpropionate (hydrocinnamate) thyroxine Amino Acid Tryptophan Metabolism tryptophan Amino Acid Tryptophan Metabolism indolelactate Amino Acid Tryptophan Metabolism indoleacetate Amino Acid Tryptophan Metabolism indolepropionate Amino Acid Tryptophan Metabolism 3-indoxyl sulfate Amino Acid Tryptophan Metabolism kynurenine Amino Acid Tryptophan Metabolism picolinate Amino Acid Tryptophan Metabolism 5-hydroxyindoleacetate Amino Acid Tryptophan Metabolism serotonin (5HT) Amino Acid Tryptophan Metabolism tryptophan betaine Amino Acid Tryptophan Metabolism C-glycosyltryptophan Amino Acid Tryptophan Metabolism indole-3-carboxylic acid Amino Acid Amino Acid Amino Acid Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism LC/MS neg LC/MS neg LC/MS neg LC/MS pos LC/MS pos GC/MS LC/MS pos LC/MS pos LC/MS neg LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS neg LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS neg leucine N-acetylleucine 4-methyl-2-oxopentanoate 124 35635 C11457 HMDB00375 91 39587 C01744 HMDB02199 10394 15749 C05629 HMDB00764 107 46079 C01829 HMDB01918 5819 54 C00078 HMDB00929 6305 18349 C02043 HMDB00671 92904 27513 C00954 HMDB00197 802 32405 HMDB02302 3744 27672 HMDB00682 10258 15140 C00328 HMDB00684 161166 1512 C10164 HMDB02243 1018 437 C05635 HMDB00763 1826 2342 C00780 HMDB00259 5202 37097 C09213 HMDB61115 442106 38116 C19837 HMDB03320 69867 60 C00123 HMDB00687 6106 1587 C02710 HMDB11756 70912 22116 C00233 HMDB00695 70 32675 Table 5.1 (cont’d) Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism isovalerate isovalerylglycine isovalerylcarnitine beta-hydroxyisovalerate beta-hydroxyisovaleroylcarnitine alpha-hydroxyisovaleroyl carnitine 3-methylglutaconate alpha-hydroxyisovalerate methylsuccinate isoleucine allo-isoleucine LC/MS neg LC/MS neg LC/MS pos LC/MS polar LC/MS pos LC/MS pos LC/MS pos LC/MS neg LC/MS polar LC/MS pos GC/MS LC/MS neg LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS neg 3-methyl-2-oxovalerate 2-methylbutyrylcarnitine (c5) 2-methylbutyrylglycine tiglyl carnitine tigloylglycine 2-hydroxy-3-methylvalerate 44656 C08262 HMDB00718 10430 35107 HMDB00678 546304 34407 HMDB00688 6426851 12129 HMDB00754 69362 38667 HMDB00522 1551553 33937 HMDB00407 99823 15745 HMDB01844 10349 HMDB00172 6306 35433 46263 1125 C00407 6950182; 99288 46552 15676 C00671 HMDB03736 47 45095 HMDB00378 6426901 31928 HMDB00339 193872 35428 HMDB02366 22833596 1598 HMDB00959 6441567 36746 HMDB00317 164623 3-hydroxy-2-ethylpropionate GC/MS 32397 HMDB00396 188979 ethylmalonate LC/MS polar 15765 HMDB00622 11756 125 Table 5.1 (cont’d) Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Leucine, Isoleucine and Valine Metabolism Methionine, Cysteine, SAM and Taurine Metabolism Methionine, Cysteine, SAM and Taurine Metabolism Methionine, Cysteine, SAM and Taurine Metabolism Methionine, Cysteine, SAM and Taurine Metabolism Methionine, Cysteine, SAM and Taurine Metabolism Methionine, Cysteine, SAM and Taurine Metabolism Methionine, Cysteine, SAM and Taurine Metabolism Methionine, Cysteine, SAM and Taurine Metabolism Methionine, Cysteine, SAM and Taurine Metabolism Methionine, Cysteine, SAM and Taurine Metabolism Methionine, Cysteine, SAM and Taurine Metabolism Methionine, Cysteine, SAM and Taurine Metabolism LC/MS pos LC/MS neg LC/MS polar LC/MS pos LC/MS neg LC/MS polar LC/MS neg LC/MS pos LC/MS neg LC/MS neg LC/MS pos LC/MS pos valine N-acetylvaline 3-methyl-2-oxobutyrate isobutyrylcarnitine isobutyrylglycine 3-hydroxyisobutyrate alpha-hydroxyisocaproate methionine N-acetylmethionine N-formylmethionine methionine sulfoxide 2-aminobutyrate 1649 C00183 HMDB00883 6287 HMDB11757 66789 HMDB00019 49 33441 HMDB00736 168379 35437 HMDB00730 10855600 1591 44526 C00141 1549 C06001 HMDB00336 87 22132 C03264 HMDB00746 83697 1302 C00073 HMDB00696 6137 1589 C02712 HMDB11745 448580 2829 C03145 HMDB01015 439750 18374 C02989 HMDB02005 158980 42374 C02261 HMDB00650 439691 2-hydroxybutyrate (AHB) GC/MS 21044 C05984 HMDB00008 440864 cysteine GC/MS 31453 C00097 HMDB00574 5862 cystine GC/MS 31454 C00491 HMDB00192 67678 HMDB02108 24417 LC/MS pos LC/MS neg LC/MS polar LC/MS polar S-methylcysteine cysteine s-sulfate hypotaurine taurine 126 39592 22176 C05824 HMDB00731 115015 590 C00519 HMDB00965 107812 2125 C00245 HMDB00251 1123 Table 5.1 (cont’d) Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Amino Acid Methionine, Cysteine, SAM and Taurine Metabolism Urea cycle; Arginine and Proline Metabolism Urea cycle; Arginine and Proline Metabolism Urea cycle; Arginine and Proline Metabolism Urea cycle; Arginine and Proline Metabolism Urea cycle; Arginine and Proline Metabolism Urea cycle; Arginine and Proline Metabolism Urea cycle; Arginine and Proline Metabolism Urea cycle; Arginine and Proline Metabolism Urea cycle; Arginine and Proline Metabolism Urea cycle; Arginine and Proline Metabolism Urea cycle; Arginine and Proline Metabolism Urea cycle; Arginine and Proline Metabolism Urea cycle; Arginine and Proline Metabolism LC/MS neg LC/MS pos LC/MS pos N-acetyltaurine arginine urea ornithine GC/MS proline citrulline homoarginine homocitrulline dimethylarginine (SDMA + ADMA) N-acetylarginine N-delta-acetylornithine N-methyl proline trans-4-hydroxyproline pro-hydroxy-pro Amino Acid Creatine Metabolism creatine Amino Acid Creatine Metabolism creatinine Amino Acid Creatine Metabolism guanidinoacetate Amino Acid Polyamine Metabolism N-acetylputrescine Amino Acid Polyamine Metabolism 4-acetamidobutanoate 127 LC/MS pos LC/MS pos LC/MS polar LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos 48187 159864 1638 C00062 HMDB00517 232 1670 C00086 HMDB00294 1176 1493 C00077 HMDB03374 6262 1898 C00148 HMDB00162 145742 2132 C00327 HMDB00904 9750 22137 C01924 HMDB00670 9085 22138 C02427 HMDB00679 65072 36808 C03626 HMDB01539 123831 33953 C02562 HMDB04620 67427 43249 9920500 37431 557 32306 C01157 35127 HMDB00725 5810 HMDB06695 11673055 27718 C00300 HMDB00064 586 513 C00791 HMDB00562 588 43802 C00581 HMDB00128 763 37496 C02714 HMDB02064 122356 1558 C02946 HMDB03681 18189 Table 5.1 (cont’d) Amino Acid Guanidino and Acetamido Metabolism 4-guanidinobutanoate Amino Acid Glutathione Metabolism cysteine-glutathione disulfide Amino Acid Glutathione Metabolism 5-oxoproline Peptide Gamma-glutamyl Amino Acid gamma-glutamylalanine Peptide Gamma-glutamyl Amino Acid gamma-glutamylglutamine Peptide Gamma-glutamyl Amino Acid gamma-glutamylisoleucine Peptide Gamma-glutamyl Amino Acid gamma-glutamylleucine Peptide Gamma-glutamyl Amino Acid gamma-glutamyllysine Peptide Gamma-glutamyl Amino Acid gamma-glutamylmethionine Peptide Gamma-glutamyl Amino Acid gamma-glutamylphenylalanine Peptide Gamma-glutamyl Amino Acid gamma-glutamylthreonine Peptide Gamma-glutamyl Amino Acid gamma-glutamyltyrosine Peptide Gamma-glutamyl Amino Acid gamma-glutamylvaline Peptide Dipeptide Derivative carnosine Peptide Dipeptide Derivative N-acetylcarnosine Peptide Dipeptide Derivative anserine Peptide Dipeptide alpha-glutamylglutamate Peptide Dipeptide aspartylleucine Peptide Dipeptide aspartylvaline LC/MS pos LC/MS pos LC/MS neg LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS neg LC/MS pos LC/MS neg LC/MS pos LC/MS pos LC/MS pos 128 15681 C01035 46734 1494 C01879 HMDB03464 500 HMDB00656 4247235 HMDB00267 7405 37063 2730 440103 C05283 HMDB11738 150914 34456 HMDB11170 14253342 18369 HMDB11171 151023 33934 HMDB03869 65254; 14284565 44872 7009567 33422 HMDB00594 111299 2734 HMDB11741 94340 43829 HMDB11172 7015683 HMDB00033 439224 HMDB12881 9903482 33364 1768 C00386 43488 15747 C01262 HMDB00194 112072 22166 C01425 HMDB28818 439500 40068 332962 41373 4991131 Table 5.1 (cont’d) Peptide Dipeptide cyclo(gly-pro) Peptide Dipeptide cyclo(leu-pro) Peptide Dipeptide glycylleucine Peptide Dipeptide glycylphenylalanine Peptide Dipeptide glycylvaline Peptide Dipeptide histidylvaline Peptide Dipeptide isoleucylaspartate Peptide Dipeptide isoleucylglycine Peptide Dipeptide leucylleucine Peptide Dipeptide phenylalanylleucine Peptide Dipeptide phenylalanyltryptophan Peptide Dipeptide prolylglycine Peptide Dipeptide pyroglutamylvaline Peptide Dipeptide tryptophylglutamate Peptide Dipeptide cis-Cyclo[L-ala-L-Pro] Carbohydrate Carbohydrate Carbohydrate Carbohydrate Glycolysis, Gluconeogenesis, and Pyruvate Metabolism Glycolysis, Gluconeogenesis, and Pyruvate Metabolism Glycolysis, Gluconeogenesis, and Pyruvate Metabolism Glycolysis, Gluconeogenesis, and Pyruvate Metabolism LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS neg LC/MS pos LC/MS neg LC/MS pos LC/MS pos LC/MS neg 1,5-anhydroglucitol (1,5-AG) 37077 126154 37104 7074739 34398 C02155 HMDB00759 92843 33954 HMDB28848 92953 18357 HMDB28854 97417 42069 7021871 42982 40008 36756 342532 C11332 HMDB28933 40192 76807 4078229 41377 40703 7408076; 6426709 32394 152416 41401 3634442 47098 6428987 20675 C07326 HMDB02712 64960 glucose GC/MS 20488 C00031 HMDB00122 79025 pyruvate LC/MS neg 42582 C00022 HMDB00243 1060 lactate GC/MS 527 C00186 HMDB00190 612 129 Table 5.1 (cont’d) Carbohydrate Glycolysis, Gluconeogenesis, and Pyruvate Metabolism glycerate LC/MS polar 1572 C00258 HMDB00139 752 Carbohydrate Pentose Metabolism ribulose GC/MS 35855 C00309 HMDB00621 151261 Carbohydrate Pentose Metabolism ribose GC/MS 12083 C00121 HMDB00283 5779 Carbohydrate Pentose Metabolism ribitol 15772 C00474 HMDB00508 6912 Carbohydrate Pentose Metabolism ribonate Carbohydrate Pentose Metabolism xylulose GC/MS 18344 C00310 HMDB00654 5289590 Carbohydrate Pentose Metabolism xylonate GC/MS 35638 C05411 HMDB60256 6602431 Carbohydrate Pentose Metabolism xylose GC/MS 15835 C00181 HMDB00098 135191 Carbohydrate Pentose Metabolism xylitol GC/MS 4966 C00379 HMDB02917 6912 Carbohydrate Pentose Metabolism arabinose GC/MS 575 C00216 HMDB00646 66308 Carbohydrate Pentose Metabolism threitol GC/MS 35854 C16884 HMDB04136 169019 Carbohydrate Pentose Metabolism arabitol GC/MS 38075 C01904 HMDB01851 94154 sucrose LC/MS neg 1519 C00089 HMDB00258 5988 fructose GC/MS 577 C00095 HMDB00660 5984 sorbitol GC/MS 15053 C00794 HMDB00247 5780 mannose GC/MS 584 C00159 HMDB00169 18950 mannitol GC/MS 15335 C00392 HMDB00765 6251 15443 C00191 HMDB00127 444791 32377 C00270 HMDB00230 439197 Carbohydrate Carbohydrate Carbohydrate Carbohydrate Carbohydrate Disaccharides and Oligosaccharides Fructose, Mannose and Galactose Metabolism Fructose, Mannose and Galactose Metabolism Fructose, Mannose and Galactose Metabolism Fructose, Mannose and Galactose Metabolism Carbohydrate Aminosugar Metabolism glucuronate Carbohydrate Aminosugar Metabolism N-acetylneuraminate LC/MS polar LC/MS polar LC/MS polar LC/MS polar 130 27731 5460677 Table 5.1 (cont’d) Carbohydrate Aminosugar Metabolism erythronate Energy TCA Cycle citrate Energy TCA Cycle alpha-ketoglutarate Energy TCA Cycle succinylcarnitine Energy TCA Cycle succinate Energy TCA Cycle fumarate Energy TCA Cycle Energy LC/MS polar LC/MS neg LC/MS polar LC/MS pos LC/MS polar 42420 HMDB00613 2781043 1564 C00158 HMDB00094 311 528 C00026 HMDB00208 51 1437 C00042 HMDB00254 1110 GC/MS 1643 C00122 HMDB00134 444972 malate GC/MS 1303 C00149 HMDB00156 525 TCA Cycle tricarballylate LC/MS neg 15729 C19806 HMDB31193 14925 Energy Oxidative Phosphorylation phosphate GC/MS 11438 C00009 HMDB01429 1061 Lipid Short Chain Fatty Acid valerate 33443 C00803 HMDB00892 7991 Lipid Medium Chain Fatty Acid caproate (6:0) 32489 C01585 HMDB00535 8892 Lipid Medium Chain Fatty Acid heptanoate (7:0) 1644 C17714 HMDB00666 8094 Lipid Medium Chain Fatty Acid caprylate (8:0) 32492 C06423 HMDB00482 379 Lipid Medium Chain Fatty Acid pelargonate (9:0) 12035 C01601 HMDB00847 8158 Lipid Medium Chain Fatty Acid 10-undecenoate (11:1n1) Lipid Medium Chain Fatty Acid 5-dodecenoate (12:1n7) HMDB00529 5312378 Lipid Long Chain Fatty Acid myristate (14:0) Lipid Long Chain Fatty Acid myristoleate (14:1n5) Lipid Long Chain Fatty Acid palmitate (16:0) LC/MS neg LC/MS neg LC/MS neg GC/MS LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg 131 37058 32497 33968 1365 C06424 HMDB00806 11005 32418 C08322 HMDB02000 5281119 1336 C00249 HMDB00220 985 Table 5.1 (cont’d) Lipid Long Chain Fatty Acid palmitoleate (16:1n7) Lipid Long Chain Fatty Acid margarate (17:0) Lipid Long Chain Fatty Acid 10-heptadecenoate (17:1n7) Lipid Long Chain Fatty Acid stearate (18:0) Lipid Long Chain Fatty Acid oleate (18:1n9) Lipid Long Chain Fatty Acid cis-vaccenate (18:1n7) Lipid Long Chain Fatty Acid 10-nonadecenoate (19:1n9) Lipid Long Chain Fatty Acid arachidate (20:0) Lipid Long Chain Fatty Acid eicosenoate (20:1n9 or 11) Lipid Long Chain Fatty Acid erucate (22:1n9) Lipid Lipid Lipid Lipid Lipid Lipid Lipid Lipid Lipid Polyunsaturated Fatty Acid (n3 and n6) Polyunsaturated Fatty Acid (n3 and n6) Polyunsaturated Fatty Acid (n3 and n6) Polyunsaturated Fatty Acid (n3 and n6) Polyunsaturated Fatty Acid (n3 and n6) Polyunsaturated Fatty Acid (n3 and n6) Polyunsaturated Fatty Acid (n3 and n6) Polyunsaturated Fatty Acid (n3 and n6) Polyunsaturated Fatty Acid (n3 and n6) LC/MS neg LC/MS neg LC/MS neg LC/MS neg stearidonate (18:4n3) eicosapentaenoate (EPA; 20:5n3) docosapentaenoate (n3 DPA; 22:5n3) docosahexaenoate (DHA; 22:6n3) docosatrienoate (22:3n3) linoleate (18:2n6) linolenate [alpha or gamma; (18:3n3 or 6)] dihomo-linolenate (20:3n3 or n6) arachidonate (20:4n6) 132 33447 C08362 1121 HMDB03229 445638 HMDB02259 10465 33971 5312435 1358 C01530 HMDB00827 5281 GC/MS 1359 C00712 HMDB00207 445639 GC/MS 33970 C08367 HMDB03231 5282761 HMDB13622 5312513 HMDB02212 10467 LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg 33972 1118 C06425 33587 5282768 1552 C08316 HMDB02068 5281116 33969 C16300 HMDB06547 5312508 18467 C06428 HMDB01999 446284 32504 C16513 HMDB01976 6441454 44675 C06429 HMDB02183 445580 32417 C16534 HMDB02823 5312556 1105 C01595 HMDB00673 5280450 34035 C06427 35718 C03242 HMDB02925 5280581 1110 C00219 HMDB01043 444899 5280934 Table 5.1 (cont’d) Lipid Lipid Lipid Polyunsaturated Fatty Acid (n3 and n6) Polyunsaturated Fatty Acid (n3 and n6) Polyunsaturated Fatty Acid (n3 and n6) docosapentaenoate (n6 DPA; 22:5n6) docosadienoate (22:2n6) dihomo-linoleate (20:2n6) Lipid Fatty Acid, Branched 15-methylpalmitate (isobar with 2methylpalmitate) Lipid Fatty Acid, Branched 17-methylstearate Lipid Fatty Acid, Dicarboxylate 2-hydroxyglutarate Lipid Fatty Acid, Dicarboxylate azelate (nonanedioate) Lipid Fatty Acid, Dicarboxylate sebacate (decanedioate) Lipid Fatty Acid, Dicarboxylate dodecanedioate Lipid Fatty Acid, Dicarboxylate octadecanedioate Lipid Fatty Acid, Methyl Ester linoleate, methyl ester Lipid Fatty Acid, Amino 2-aminoheptanoate Lipid Fatty Acid, Amino 2-aminooctanoate Lipid Fatty Acid Synthesis malonylcarnitine Lipid Fatty Acid Synthesis 2-methylmalonyl carnitine Lipid Lipid Lipid Lipid Fatty Acid Metabolism (also BCAA Metabolism) Fatty Acid Metabolism (also BCAA Metabolism) Fatty Acid Metabolism (also BCAA Metabolism) Fatty Acid Metabolism (also BCAA Metabolism) LC/MS pos LC/MS neg LC/MS neg LC/MS neg LC/MS neg GC/MS LC/MS neg LC/MS neg LC/MS neg LC/MS neg GC/MS LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS neg LC/MS pos LC/MS neg butyrylcarnitine butyrylglycine propionylcarnitine propionylglycine 133 37478 C16513 32415 C16533 17805 C16525 HMDB13123 6441454 5282807 HMDB05060 6439848 38768 17903417 38296 3083779 37253 C02630 HMDB00606 43 18362 C08261 HMDB00784 2266 32398 C08277 HMDB00792 5192 32388 C02678 HMDB00623 12736 HMDB00782 70095 36754 36801 5284421 43761 227939 43343 HMDB00991 69522 37059 HMDB02095 22833583 35482 HMDB13133 53481628 HDMB02013 439829 HMDB00808 88412 HMDB00824 107738 HMDB00783 98681 32412 C02862 31850 32452 31932 C03017 Table 5.1 (cont’d) Lipid Lipid Lipid Lipid Lipid Lipid Lipid Lipid Lipid Lipid Lipid Lipid Lipid Lipid Lipid Lipid Fatty Acid Metabolism (also BCAA Metabolism) Fatty Acid Metabolism (Acyl Glycine) Fatty Acid Metabolism (Acyl Glycine) Fatty Acid Metabolism (Acyl Carnitine) Fatty Acid Metabolism (Acyl Carnitine) Fatty Acid Metabolism (Acyl Carnitine) Fatty Acid Metabolism (Acyl Carnitine) Fatty Acid Metabolism (Acyl Carnitine) Fatty Acid Metabolism (Acyl Carnitine) Fatty Acid Metabolism (Acyl Carnitine) Fatty Acid Metabolism (Acyl Carnitine) Fatty Acid Metabolism (Acyl Carnitine) Fatty Acid Metabolism (Acyl Carnitine) Fatty Acid Metabolism (Acyl Carnitine) Fatty Acid Metabolism (Acyl Carnitine) Fatty Acid Metabolism (Acyl Carnitine) LC/MS polar LC/MS neg LC/MS neg LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos methylmalonate (MMA) hexanoylglycine N-octanoylglycine acetylcarnitine hydroxybutyrylcarnitine hexanoylcarnitine octanoylcarnitine decanoylcarnitine cis-4-decenoyl carnitine laurylcarnitine myristoylcarnitine palmitoylcarnitine stearoylcarnitine oleoylcarnitine linoleoylcarnitine myristoleoylcarnitine Lipid Carnitine Metabolism deoxycarnitine Lipid Carnitine Metabolism carnitine Lipid Ketone Bodies 3-hydroxybutyrate (BHBA) GC/MS 134 1496 HMDB00202 487 35436 HMDB00701 99463 43502 HMDB00832 84290 HMDB00201 1 43264 HMDB13127 53481617 32328 HMDB00705 6426853 HMDB00791 123701 HMDB00651 10245190 34534 HMDB02250 10427569 33952 HMDB05066 53477791 HMDB00222 461 34409 HMDB00848 6426855 35160 HMDB05065 6441392; 53477789 46223 HMDB06469 6450015 32198 33936 C02170 C02571 C02838 33941 38178 44681 C02990 48182 36747 C01181 HMDB01161 134 15500 C00318 HMDB00062 10917 542 C01089 HMDB00357 441 Table 5.1 (cont’d) Lipid Fatty Acid, Monohydroxy 4-hydroxybutyrate (GHB) GC/MS Lipid Fatty Acid, Monohydroxy alpha-hydroxycaproate Lipid Fatty Acid, Monohydroxy 2-hydroxyoctanoate Lipid Fatty Acid, Monohydroxy 2-hydroxydecanoate Lipid Fatty Acid, Monohydroxy 2-hydroxypalmitate Lipid Fatty Acid, Monohydroxy 2-hydroxystearate Lipid Fatty Acid, Monohydroxy 3-hydroxydecanoate Lipid Fatty Acid, Monohydroxy 5-hydroxyhexanoate Lipid Fatty Acid, Monohydroxy 16-hydroxypalmitate Lipid Fatty Acid, Monohydroxy 13-HODE + 9-HODE Lipid Eicosanoid 12-HETE Lipid Endocannabinoid oleic ethanolamide Lipid Endocannabinoid N-oleoyltaurine Lipid Endocannabinoid N-stearoyltaurine Lipid Endocannabinoid N-palmitoyltaurine Lipid Inositol Metabolism myo-inositol GC/MS 19934 C00137 HMDB00211 Lipid Inositol Metabolism chiro-inositol GC/MS 37112 C19891 HMDB34220 Lipid Inositol Metabolism inositol 1-phosphate (I1P) GC/MS 1481 C04006 HMDB00213 440194 Lipid Phospholipid Metabolism choline LC/MS pos 15506 C00114 HMDB00097 305 LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg 135 34585 C00989 HMDB00710 10413 37073 HMDB01624 99824 22036 HMDB02264 94180 42489 21488 35675 17945 HMDB31057 C03045 92836 69417 22053 HMDB02203 26612 31938 HMDB00525 170748 HMDB06294 10466 39609 C18218 37752 43013 37536 HMDB06111 5312983 38102 HMDB02088 5283454 39732 6437033 39730 168274 39835 892 Table 5.1 (cont’d) Lipid Phospholipid Metabolism glycerophosphorylcholine (GPC) Lipid Phospholipid Metabolism glycerophosphoethanolamine Lipid Lysolipid 1-myristoylglycerophosphocholine (14:0) Lipid Lysolipid 2-myristoylglycerophosphocholine* Lipid Lysolipid 1-pentadecanoylglycerophosphocholine (15:0) Lipid Lysolipid 1-palmitoylglycerophosphocholine (16:0) Lipid Lysolipid 2-palmitoylglycerophosphocholine Lipid Lysolipid 1-palmitoleoylglycerophosphocholine (16:1) Lipid Lysolipid 2-palmitoleoylglycerophosphocholine Lipid Lysolipid 1-margaroylglycerophosphocholine (17:0) Lipid Lysolipid 2-margaroylglycerophosphocholine Lipid Lysolipid 1-stearoylglycerophosphocholine (18:0) Lipid Lysolipid 2-stearoylglycerophosphocholine Lipid Lysolipid 1-oleoylglycerophosphocholine (18:1) Lipid Lysolipid 2-oleoylglycerophosphocholine Lipid Lysolipid 1-linoleoylglycerophosphocholine (18:2n6) Lipid Lysolipid 2-linoleoylglycerophosphocholine Lipid Lysolipid 1-linolenoylglycerophosphocholine (18:3n3) Lipid Lysolipid 2-linolenoylglycerophosphocholine(18:3n3) 136 LC/MS pos LC/MS polar LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos 15990 C00670 HMDB00086 71920 37455 C01233 HMDB00114 123874 45453 C04230 HMDB10379 460604 35626 37418 33955 86554 35253 15061532 33230 24779461 35819 44682 C04230 HMDB12108 24779463 44683 33961 497299 35255 10208382 48258 16081932 48259 34419 35257 45951 45869 C04100 11988421 Table 5.1 (cont’d) Lipid Lysolipid 1-nonadecanoylglycerophosphocholine (19:0) Lipid Lysolipid 1-dihomo-linoleoylglycerophosphocholine (20:2n6) Lipid Lysolipid 1-arachidoylglycerophosphocholine (20:0) Lipid Lysolipid 2-arachidoylglycerophosphocholine Lipid Lysolipid 1-eicosenoylglycerophosphocholine (20:1n9) Lipid Lysolipid 2-eicosenoylglycerophosphocholine(20:1n9) Lipid Lysolipid 1-eicosatrienoylglycerophosphocholine (20:3) Lipid Lysolipid 2-eicosatrienoylglycerophosphocholine Lipid Lysolipid 1-arachidonoylglycerophosphocholine (20:4n6) Lipid Lysolipid 2-arachidonoylglycerophosphocholine Lipid Lysolipid Lipid Lysolipid Lipid Lysolipid Lipid Lysolipid Lipid Lysolipid Lipid Lysolipid 2-docosahexaenoylglycerophosphocholine Lipid Lysolipid 1-palmitoylplasmenylethanolamine Lipid Lysolipid 1-stearoylplasmenylethanolamine Lipid Lysolipid 1-oleoylplasmenylethanolamine 1-eicosapentaenoylglycerophosphocholine (20:5n3) 1-docosapentaenoylglycerophosphocholine (22:5n3) 2-docosapentaenoylglycerophosphocholine (22:5n3) 1-docosapentaenoylglycerophosphocholine (22:5n6) 1-docosahexaenoylglycerophosphocholine (22:6n3) 137 LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS neg LC/MS neg LC/MS neg 47087 33871 45456 C04230 35623 44560 48119 33821 35884 33228 35256 44563 37231 37366 45675 33822 35883 39270 39271 44621 C05208 HMDB10390 24779473 Table 5.1 (cont’d) Lipid Lysolipid 1-palmitoylglycerophosphoethanolamine Lipid Lysolipid 2-palmitoylglycerophosphoethanolamine Lipid Lysolipid 1-stearoylglycerophosphoethanolamine Lipid Lysolipid 2-stearoylglycerophosphoethanolamine Lipid Lysolipid 1-oleoylglycerophosphoethanolamine Lipid Lysolipid 2-oleoylglycerophosphoethanolamine Lipid Lysolipid 1-palmitoleoylglycerophosphoethanolamine Lipid Lysolipid 1-linoleoylglycerophosphoethanolamine Lipid Lysolipid 2-linoleoylglycerophosphoethanolamine Lipid Lysolipid 1-arachidonoylglycerophosphoethanolamine Lipid Lysolipid 2-arachidonoylglycerophosphoethanolamine Lipid Lysolipid 1-eicosatrienoylglycerophosphoethanolamine Lipid Lysolipid 1-palmitoylglycerophosphoinositol Lipid Lysolipid 1-stearoylglycerophosphoinositol Lipid Lysolipid 2-stearoylglycerophosphoinositol Lipid Lysolipid 1-oleoylglycerophosphoinositol Lipid Lysolipid 1-linoleoylglycerophosphoinositol Lipid Lysolipid 1-arachidonoylglycerophosphoinositol Lipid Lysolipid 1-stearoylglycerophosphoserine 138 LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg 35631 HMDB11503 9547069 HMDB11130 9547068 HMDB11506 9547071 HMDB11507 52925130 HMDB11517 42607465 45452 42398 41220 35628 45455 34565 32635 36593 35186 32815 44630 35305 19324 39223 36602 36594 34214 45966 9547101 Table 5.1 (cont’d) Lipid Lysolipid 1-oleoylglycerophosphoserine Lipid Lysolipid 1-linoleoylglycerophosphoserine Lipid Lysolipid 1-palmitoylglycerophosphate Lipid Lysolipid 1-arachidonoylglyercophosphate Lipid Lysolipid 1-palmitoylglycerophosphoglycerol Lipid Lysolipid 1-oleoylglycerophosphoglycerol Lipid Lysolipid 2-nonadecanoylglycerophosphocholine (19:0) Lipid Lysolipid oleoyl-linoleoyl-glycerophosphoinositol (1) Lipid Lysolipid Lipid Lysolipid Lipid Lysolipid palmitoyl-linoleoyl-glycerophosphocholine (1) Lipid Lysolipid palmitoyl-linoleoyl-glycerophosphocholine (2) Lipid Lysolipid palmitoyl-linoleoyl-glycerophosphoinositol (1) Lipid Lysolipid palmitoyl-oleoyl-glycerophosphocholine (1) Lipid Lysolipid Lipid Lysolipid Lipid Lysolipid Lipid Lysolipid oleoyl-linoleoyl-glycerophosphocholine (1) Lipid Lysolipid oleoyl-linoleoyl-glycerophosphocholine (2) palmitoyl-arachidonoyl-glycerophosphocholine (1) palmitoyl-arachidonoyl-glycerophosphocholine (2) stearoyl-arachidonoyl-glycerophosphocholine (1) stearoyl-arachidonoyl-glycerophosphocholine (2) stearoyl-arachidonoyl-glycerophosphoinositol (1) 139 LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS pos LC/MS polar LC/MS polar LC/MS polar LC/MS polar LC/MS polar LC/MS polar LC/MS polar LC/MS polar LC/MS polar LC/MS polar LC/MS polar LC/MS polar 19260 9547099 43676 34428 C04036 HMDB00327 6419701 46325 45970 45968 47088 48303 48383 48392 48385 48269 48304 48267 48386 48393 48282 48270 48390 3300276 Table 5.1 (cont’d) Lipid Lysolipid palmitoyl-palmitoyl-glycerophosphocholine (1) Lipid Lysolipid palmitoyl-palmitoyl-glycerophosphocholine (2) Lipid Lysolipid stearoyl-linoleoyl-glycerophosphocholine (1) Lipid Lysolipid stearoyl-linoleoyl-glycerophosphocholine (2) Lipid Lysolipid Lipid Lysolipid Lipid Glycerolipid Metabolism glycerol Lipid Glycerolipid Metabolism glycerol 3-phosphate (G3P) Lipid Monoacylglycerol 1-myristoylglycerol (1-monomyristin) Lipid Monoacylglycerol 1-palmitoylglycerol (1-monopalmitin) Lipid Monoacylglycerol 2-palmitoylglycerol (2-monopalmitin) Lipid Monoacylglycerol 1-margaroylglycerol (1-monoheptadecanoin) Lipid Monoacylglycerol 1-stearoylglycerol (1-monostearin) Lipid Monoacylglycerol 2-stearoylglycerol (2-monostearin) Lipid Monoacylglycerol 1-oleoylglycerol (1-monoolein) Lipid Monoacylglycerol 2-oleoylglycerol (2-monoolein) Lipid Monoacylglycerol 1-linoleoylglycerol (1-monolinolein) Lipid Monoacylglycerol 2-linoleoylglycerol (2-monolinolein) Lipid Monoacylglycerol 1-linolenoylglycerol stearoyl-arachidonoylglycerophosphoethanolamine (1) stearoyl-linoleoyl-glycerophosphoethanolamine (1) LC/MS polar LC/MS polar LC/MS polar LC/MS polar LC/MS polar LC/MS polar LC/MS neg GC/MS 140 LC/MS neg LC/MS pos LC/MS neg LC/MS pos GC/MS LC/MS pos LC/MS pos LC/MS neg LC/MS neg LC/MS neg LC/MS pos 48276 48389 48387 48388 48497 48499 15122 C00116 HMDB00131 753 15365 C00093 HMDB00126 754 35625 C01885 HMDB11561 79050 21127 HMDB31074 14900 33419 HMDB11533 123409 34391 21188 107036 D01947 HMDB31075 34059 21184 24699 79075 HMDB11567 5283468 21232 5319879 27447 5283469 32506 HMDB11538 5365676 34393 HMDB11569 53480978 Table 5.1 (cont’d) Lipid Monoacylglycerol 1-dihomo-linolenylglycerol (alpha, gamma) Lipid Sphingolipid Metabolism sphinganine Lipid Sphingolipid Metabolism palmitoyl sphingomyelin Lipid Sphingolipid Metabolism stearoyl sphingomyelin Lipid Sphingolipid Metabolism sphingosine 1-phosphate Lipid Sphingolipid Metabolism sphingosine Lipid Sphingolipid Metabolism myristoyl sphingomyelin Lipid Sphingolipid Metabolism nervonoyl sphingomyelin Lipid Mevalonate Metabolism 3-hydroxy-3-methylglutarate Lipid Sterol cholesterol Lipid Sterol Lipid LC/MS pos LC/MS pos LC/MS polar LC/MS polar LC/MS pos LC/MS pos LC/MS polar LC/MS polar LC/MS polar 48341 17769 C00836 HMDB00269 37506 3126 9939941 19503 C00550 HMDB01348 6453725 34445 C06124 HMDB00277 5283560 17747 C00319 HMDB00252 5353955 42463 11433862 47153 531 C03761 HMDB00355 1662 GC/MS 63 C00187 HMDB00067 11025495 7-alpha-hydroxy-3-oxo-4-cholestenoate (7Hoca) LC/MS neg 36776 C17337 HMDB12458 3081085 Sterol cholestanol GC/MS 21131 C12978 HMDB00908 6665 Lipid Sterol beta-sitosterol GC/MS 27414 C01753 HMDB00852 222284 Lipid Sterol campesterol GC/MS 33997 C01789 HMDB02869 173183 Lipid Steroid 5alpha-pregnan-3alpha,20beta-diol disulfate 1 Lipid Steroid cortisol 1712 C00735 HMDB00063 5754 Lipid Steroid corticosterone 5983 C02140 HMDB01547 5753 Lipid Steroid cortisone 1769 C00762 HMDB02802 222786 Lipid Steroid estrone 3-sulfate 18474 C02538 HMDB01425 3001028 141 LC/MS neg LC/MS pos LC/MS pos LC/MS pos LC/MS neg 37201 Table 5.1 (cont’d) Lipid Primary Bile Acid Metabolism cholate Lipid Primary Bile Acid Metabolism glycocholate Lipid Primary Bile Acid Metabolism taurocholate Lipid Primary Bile Acid Metabolism chenodeoxycholate Lipid Primary Bile Acid Metabolism glycochenodeoxycholate Lipid Primary Bile Acid Metabolism taurochenodeoxycholate Lipid Primary Bile Acid Metabolism tauro-alpha-muricholate Lipid Primary Bile Acid Metabolism tauro-beta-muricholate Lipid Lipid Lipid Lipid Lipid Lipid Lipid Lipid Nucleotide Nucleotide Nucleotide Secondary Bile Acid Metabolism Secondary Bile Acid Metabolism Secondary Bile Acid Metabolism Secondary Bile Acid Metabolism Secondary Bile Acid Metabolism Secondary Bile Acid Metabolism Secondary Bile Acid Metabolism Secondary Bile Acid Metabolism Purine Metabolism (Hypo)Xanthine/Inosine Purine Metabolism (Hypo)Xanthine/Inosine Purine Metabolism (Hypo)Xanthine/Inosine LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS pos LC/MS neg deoxycholate lithocholate taurolithocholate taurolithocholate 3-sulfate ursodeoxycholate tauroursodeoxycholate taurohyodeoxycholic acid taurocholenate sulfate inosine hypoxanthine xanthine 142 22842 C00695 HMDB00619 221493 18476 C01921 HMDB00138 10140 18497 C05122 HMDB00036 6675 1563 C02528 HMDB00518 10133 32346 C05466 HMDB00637 12544 18494 C05465 HMDB00951 387316 HMDB00932 168408 42605 33983 1114 C04483 HMDB00626 222528 1483 C03990 HMDB00761 9903 31889 C02592 HMDB00722 10595 36850 C03642 HMDB02580 440071 1605 C07880 HMDB00946 31401 HMDB00874 9848818 39378 43588 119046 32807 1123 C00294 HMDB00195 6021 3127 C00262 HMDB00157 790 3147 C00385 HMDB00292 1188 Table 5.1 (cont’d) Nucleotide Nucleotide Nucleotide Nucleotide Nucleotide Nucleotide Nucleotide Nucleotide Nucleotide Nucleotide Nucleotide Nucleotide Nucleotide Nucleotide Nucleotide Nucleotide Nucleotide Nucleotide Nucleotide Purine Metabolism (Hypo)Xanthine/Inosine Purine Metabolism (Hypo)Xanthine/Inosine Purine Metabolism (Hypo)Xanthine/Inosine Purine Metabolism, Adenine containing Purine Metabolism, Adenine containing Purine Metabolism, Adenine containing Purine Metabolism, Adenine containing Purine Metabolism, Guanine containing Pyrimidine Metabolism, Orotate containing Pyrimidine Metabolism, Uracil containing Pyrimidine Metabolism, Uracil containing Pyrimidine Metabolism, Uracil containing Pyrimidine Metabolism, Uracil containing Pyrimidine Metabolism, Uracil containing Pyrimidine Metabolism, Uracil containing Pyrimidine Metabolism, Uracil containing Pyrimidine Metabolism, Cytidine containing Pyrimidine Metabolism, Thymine containing Pyrimidine Metabolism, Thymine containing xanthosine urate allantoin adenosine 5'-monophosphate (AMP) adenosine adenine N1-methyladenosine 7-methylguanine orotate uridine uracil pseudouridine 5-methyluridine (ribothymidine) 3-ureidopropionate beta-alanine LC/MS pos LC/MS neg LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS pos LC/MS polar LC/MS neg LC/MS pos LC/MS neg LC/MS pos LC/MS pos GC/MS LC/MS pos LC/MS pos LC/MS neg LC/MS pos N-acetyl-beta-alanine cytidine thymine 5,6-dihydrothymine 143 15136 C01762 HMDB00299 64959 1604 C00366 HMDB00289 1175 1107 C02350 HMDB00462 204 32342 C00020 HMDB00045 6083 555 C00212 HMDB00050 60961 554 C00147 HMDB00034 190 15650 C02494 HMDB03331 27476 35114 C02242 HMDB00897 11361 1505 C00295 HMDB00226 967 606 C00299 HMDB00296 6029 605 C00106 HMDB00300 1174 33442 C02067 HMDB00767 15047 HMDB00884 445408 35136 3155 C02642 HMDB00026 111 55 C00099 HMDB00056 239 37432 C01073 514 C00475 HMDB00089 6175 604 C00178 HMDB00262 1135 1418 C00906 HMDB00079 93556 76406 Table 5.1 (cont’d) Nucleotide Cofactors and Vitamins Cofactors and Vitamins Cofactors and Vitamins Cofactors and Vitamins Cofactors and Vitamins Cofactors and Vitamins Cofactors and Vitamins Cofactors and Vitamins Cofactors and Vitamins Cofactors and Vitamins Cofactors and Vitamins Cofactors and Vitamins Cofactors and Vitamins Cofactors and Vitamins Cofactors and Vitamins Pyrimidine Metabolism, Thymine containing Nicotinate and Nicotinamide Metabolism Nicotinate and Nicotinamide Metabolism Nicotinate and Nicotinamide Metabolism Riboflavin Metabolism Pantothenate and CoA Metabolism Ascorbate and Aldarate Metabolism Ascorbate and Aldarate Metabolism Ascorbate and Aldarate Metabolism 3-aminoisobutyrate GC/MS quinolinate nicotinamide trigonelline (N'-methylnicotinate) riboflavin (Vitamin B2) pantothenate threonate oxalate (ethanedioate) gulonic acid LC/MS neg LC/MS pos LC/MS pos LC/MS neg LC/MS pos LC/MS polar LC/MS neg LC/MS polar 1566 C05145 HMDB03911 64956 1899 C03722 HMDB00232 1066 594 C00153 HMDB01406 936 32401 C01004 HMDB00875 5570 1827 C00255 HMDB00244 493570 1508 C00864 HMDB00210 6613 27738 C01620 HMDB00943 151152 20694 C00209 HMDB02329 971 46957 9794176 Tocopherol Metabolism alpha-tocopherol GC/MS 1561 C02477 HMDB01893 14985 Tocopherol Metabolism gamma-tocopherol GC/MS 33420 C02483 HMDB01492 14986 Hemoglobin and Porphyrin Metabolism Hemoglobin and Porphyrin Metabolism Hemoglobin and Porphyrin Metabolism Hemoglobin and Porphyrin Metabolism heme bilirubin (Z,Z) bilirubin (E,E) biliverdin Vitamin B6 Metabolism pyridoxate Xenobiotics Benzoate Metabolism hippurate Xenobiotics Benzoate Metabolism 2-hydroxyhippurate (salicylurate) Xenobiotics Benzoate Metabolism 3-hydroxyhippurate 144 LC/MS pos LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg 41754 43807 26945 C00486 HMDB00054 32586 5280352 5315454 2137 C00500 HMDB01008 5353439 31555 C00847 HMDB00017 6723 15753 C01586 HMDB00714 464 18281 C07588 HMDB00840 10253 HMDB06116 450268 39600 Table 5.1 (cont’d) Xenobiotics Benzoate Metabolism 4-hydroxyhippurate Xenobiotics Benzoate Metabolism benzoate Xenobiotics Benzoate Metabolism 3-hydroxybenzoate Xenobiotics Benzoate Metabolism catechol sulfate Xenobiotics Benzoate Metabolism O-methylcatechol sulfate Xenobiotics Benzoate Metabolism 3-methyl catechol sulfate (1) Xenobiotics Benzoate Metabolism 3-methyl catechol sulfate (2) Xenobiotics Benzoate Metabolism 4-methylcatechol sulfate Xenobiotics Benzoate Metabolism methyl-4-hydroxybenzoate Xenobiotics Benzoate Metabolism 2-ethylphenylsulfate Xenobiotics Benzoate Metabolism 4-ethylphenylsulfate Xenobiotics Benzoate Metabolism 4-vinylphenol sulfate Xenobiotics Food Component/Plant 1,6-anhydroglucose Xenobiotics Food Component/Plant 1H-quinolin-2-one Xenobiotics Food Component/Plant 2,3-dihydroxyisovalerate Xenobiotics Food Component/Plant gluconate Xenobiotics Food Component/Plant cinnamoylglycine Xenobiotics Food Component/Plant dihydroferulic acid Xenobiotics Food Component/Plant equol glucuronide LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg GC/MS LC/MS neg LC/MS neg LC/MS neg GC/MS LC/MS pos LC/MS polar LC/MS polar LC/MS neg LC/MS neg LC/MS neg 145 35527 HMDB13678 151012 15778 C00180 HMDB01870 243 15673 C00587 HMDB02466 7420 35320 C00090 HMDB59724 3083879 46111 22473 46165 46164 46146 34386 D01400 HMDB32572 7456 HMDB04072 6426766 HMDB00640 2724705 36847 36099 C13637 36098 C05627 21049 40464 C06415 38276 C04039 HMDB12141 677 587 C00257 HMDB00625 10690 HMDB11621 709625 38637 40481 41948 6038 14340 Table 5.1 (cont’d) LC/MS neg LC/MS neg Xenobiotics Food Component/Plant equol sulfate Xenobiotics Food Component/Plant ergothioneine Xenobiotics Food Component/Plant erythritol Xenobiotics Food Component/Plant ferulic acid 4-sulfate Xenobiotics Food Component/Plant homostachydrine Xenobiotics Food Component/Plant indolin-2-one Xenobiotics Food Component/Plant N-(2-furoyl) glycine Xenobiotics Food Component/Plant quinate Xenobiotics Food Component/Plant stachydrine Xenobiotics Food Component/Plant tartarate Xenobiotics Food Component/Plant thymol sulfate Xenobiotics Food Component/Plant 4-allylphenol sulfate Xenobiotics Food Component/Plant methyl glucopyranoside (alpha + beta) Xenobiotics Bacterial/Fungal tartronate (hydroxymalonate) Xenobiotics Drug 2-hydroxyacetaminophen sulfate Xenobiotics Drug 4-acetaminophen sulfate Xenobiotics Drug 4-acetamidophenol Xenobiotics Drug 4-acetylphenol sulfate Xenobiotics Drug 6-oxopiperidine-2-carboxylic acid GC/MS 146 LC/MS neg LC/MS pos LC/MS pos LC/MS neg LC/MS polar LC/MS pos LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS neg LC/MS pos LC/MS neg LC/MS pos 40478 37459 C05570 HMDB03045 3032311 20699 C00503 HMDB02994 222285 HMDB29200 6305574 HMDB33433 441447 47114 33009 C08283 43374 C12312 31536 321710 HMDB00439 21863 18335 C00296 HMDB03072 6508 34384 C10172 HMDB04827 115244 15336 C00898 HMDB00956 444305 36095 C09908 HMDB01878 C02287 HMDB35227 45 37475 C06804 HMDB59911 83939 12032 C06804 HMDB01859 1983 37181 46144 20693 33173 44620 4684006 43231 3014237 Table 5.1 (cont’d) LC/MS neg LC/MS pos LC/MS neg LC/MS pos LC/MS neg LC/MS neg LC/MS neg LC/MS neg Xenobiotics Drug hydroquinone sulfate Xenobiotics Drug lidocaine Xenobiotics Drug salicylate Xenobiotics Chemical 2-pyrrolidinone Xenobiotics Chemical sulfate Xenobiotics Chemical O-sulfo-L-tyrosine Xenobiotics Chemical 2-aminophenol sulfate Xenobiotics Chemical 2-ethylhexanoate Xenobiotics Chemical 2-hydroxyisobutyrate Xenobiotics Chemical dimethyl sulfone Xenobiotics Chemical phenylcarnitine Xenobiotics Chemical trizma acetate GC/MS 20710 Xenobiotics Chemical N-methylpipecolate LC/MS pos 47101 GC/MS LC/MS pos LC/MS pos 147 35322 C00530 HMDB02434 161220 35661 D00358 HMDB14426 3676 1515 C00805 HMDB01895 338 HMDB02039 12025 HMDB01448 1118 31675 46960 C00059 45413 514186 43266 HMDB61116 181670 1554 HMDB31230 8697 22030 HMDB00729 11671 HMDB04983 6213 43424 C11142 43265 C07182 6503 11862129; 11286529 Table 5.2 Number of metabolites in each metabolic pathway and sub-pathway. METABOLIC PATHWAY NUMBER OF METABOLITES Amino Acid 135 Alanine and Aspartate Metabolism 6 Creatine Metabolism 3 Glutamate Metabolism 6 Glutathione Metabolism 2 Glycine, Serine and Threonine Metabolism 8 Guanidino and Acetamido Metabolism 1 Histidine Metabolism 10 Leucine, Isoleucine and Valine Metabolism 29 Lysine Metabolism 8 Methionine, Cysteine, SAM and Taurine Metabolism 13 Phenylalanine and Tyrosine Metabolism 22 Polyamine Metabolism 2 Tryptophan Metabolism 12 Urea cycle; Arginine and Proline Metabolism 13 Carbohydrate 24 Amino-sugar Metabolism 3 Disaccharides and Oligosaccharides 1 Fructose, Mannose and Galactose Metabolism 4 Glycolysis, Gluconeogenesis, and Pyruvate Metabolism 5 Pentose Metabolism 11 Cofactors and Vitamins 15 Ascorbate and Aldarate Metabolism 3 Hemoglobin and Porphyrin Metabolism 4 Nicotinate and Nicotinamide Metabolism 3 Pantothenate and CoA Metabolism 1 Riboflavin Metabolism 1 Tocopherol Metabolism 2 Vitamin B6 Metabolism 1 Energy 8 Oxidative Phosphorylation 1 TCA Cycle 7 Lipid 216 Carnitine Metabolism 2 Eicosanoid 1 Endocannabinoid 4 Fatty Acid Metabolism (also BCAA Metabolism) 5 Fatty Acid Metabolism (Acyl Carnitine) 13 Fatty Acid Metabolism (Acyl Glycine) 2 148 Table 5.2 (cont’d) Fatty Acid Synthesis 2 Fatty Acid, Amino 2 Fatty Acid, Branched 2 Fatty Acid, Dicarboxylate 5 Fatty Acid, Methyl Ester 1 Fatty Acid, Monohydroxy 10 Glycerolipid Metabolism 2 Inositol Metabolism 3 Ketone Bodies 1 Long Chain Fatty Acid 13 Lysolipid 80 Medium Chain Fatty Acid 6 Mevalonate Metabolism 1 Monoacylglycerol 12 Phospholipid Metabolism 3 Polyunsaturated Fatty Acid (n3 and n6) 12 Primary Bile Acid Metabolism 8 Secondary Bile Acid Metabolism 8 Short Chain Fatty Acid 1 Sphingolipid Metabolism 7 Steroid/Sterol 10 Nucleotide 23 Purine Metabolism, (Hypo)Xanthine/Inosine containing 6 Purine Metabolism, Adenine containing 4 Purine Metabolism, Guanine containing 1 Pyrimidine Metabolism, Cytidine containing 1 Pyrimidine Metabolism, Orotate containing 1 Pyrimidine Metabolism, Thymine containing 3 Pyrimidine Metabolism, Uracil containing 7 Peptide 31 Dipeptide/Dipeptide Derivative 21 Gamma-glutamyl Amino Acid 10 Xenobiotics 54 Bacterial/Fungal 1 Benzoate Metabolism 15 Chemical 10 Drug 8 Food Component/Plant 20 Unknown 140 TOTAL 646 149 Table 5.3 Optimal number of metabolites that distinguish between control and disease status for each variable (insulin response, obesity status, laminitis history) as determined by LASSO analysis. INSULIN RESPONSE 13-HODE + 9-HODE 1-eicosatrienoyl-GPC 1-linoleoyl-GPC 2-margaroyl-GPC 3-(3-hydroxyphenyl) propionate 3-hydroxyisobutyrate 4-imidazoleacetate 5-hydroxyindoleacetate asparagine cinnamoylglycine glucuronate imidazole propionate indole-3-carboxylic acid isovalerylcarnitine N-palmitoyltaurine octadecanedioate oleoyl-linoleoyl-glycerophosphocholine oxalate (ethanedioate) palmitoyl-linoleoyl-glycerophosphocholine phenylcarnitine sphingomyelin stearoyl-arachidonoyl-glycerophosphoinositol OBESITY 1-eicosadienoyl-GPC 1-linoleoyl-GPS arabitol betaine carnosine chenodeoxycholate dodecanedioate equol glucuronide ferulic acid 4-sulfate methylmalonate (MMA) N-6-trimethyllysine octanoylcarnitine palmitoyl-arachidonoyl-glycerophosphocholine ursodeoxycholate 150 LAMINITIS 1-dihomo-linolenylglycerol 1-oleoylglycerol 2-docosahexaenoyl-GPC 2-ethylphenylsulfate 2-margaroyl-GPC 2-palmitoylglycerol 2-pyrrolidinone 4-acetylphenyl sulfate 4-hydroxybutyrate benzoate carnitine homocitrulline N-delta-acetylornithine oleoyl-linoleoyl-glycerophosphocholine orotate phenylcarnitine propionylglycine quinolinate serotonin tartarate tauro-alpha-muricholate Table 5.4 Uphill (positive) correlations between metabolites and clinical parameter measurements (basal glucose, basal insulin, non-esterified fatty acids (NEFAs), triglycerides, leptin, adiponectin). BASAL GLUCOSE BASAL INSULIN NEFAs TRIGLYCERIDES LEPTIN ADIPONECTIN r = 0.7 r = 0.5 r = 0.7 r = 0.7 r = 0.5 hydroquinone sulfate 3-[3-(sulfooxy)phenyl] propanoic acid cinnamoylglycine mannitol arginine asparagine phenylcarnitine tartronate (hydroxymalonate) r = 0.6 stearoyl-arachidonoylglycerophosphoinositol r = 0.6 N-acetylvaline adenine N-acetylleucine 2-ethylhexanoate N-(2-furoyl) glycine arabinose cyclo(leu-pro) taurolithocholate tricarballylate cytidine N-(2-furoyl) glycine taurolithocholate 3-sulfate hydroquinone sulfate isovalerylcarnitine citrulline r = 0.5 4-allylphenol sulfate oxalate (ethanedioate) 4-imidazoleacetate N-oleoyltaurine r = 0.6 guanidinoacetate taurohyodeoxycholic acid xylose indoleacetate 13-HODE + 9-HODE 1-dihomolinoleoylglycerophosphocholine r = 0.5 2-arachidonoylglycerophosphocholine cyclo(gly-pro) methionine sulfoxide N-acetylglycine 1-palmitoylglycerophosphoinositol asparagine 2-ethylphenylsulfate 1H-quinolin-2-one dihydroferulic acid 1-linoleoylglycerophosphoserine 3-(3-hydroxyphenyl) propionate 1-linoleoylglycerophosphoinositol salicylate N-acetylleucine gentisate N-methylpipecolate 1linoleoylglycerophosphoinositol pyroglutamine N-acetylasparagine indolin-2-one 2-stearoylglycerophosphoinositol propionylglycine dodecanedioate isoleucylaspartate 1palmitoylglycerophosphoinositol isoleucylglycine hippurate r = 0.5 151 1linoleoylglycerophosphocholine 2stearoylglycerophosphocholine Table 5.4 (cont’d) N-acetylalanine benzoate 3-hydroxybenzoate salicylate 4-hydroxyhippurate picolinate phenol sulfate gentisate N-acetylglutamate 3-phenylpropionate (hydrocinnamate) 3-hydroxy-3-phenylpropionate 2-hydroxydecanoate equol glucuronide 4-hydroxybutyrate (GHB) 4-acetamidophenol 3-hydroxybenzoate hippurate 2-pyrrolidinone 3-(3-hydroxyphenyl) propionate prolylglycine N-acetylglutamine 2-aminophenol sulfate 152 Table 5.5 Downhill (negative) correlations between metabolites and clinical parameter measurements (basal glucose, basal insulin, non-esterified fatty acids (NEFAs), triglycerides, leptin, adiponectin). BASAL GLUCOSE r = -0.6 BASAL INSULIN r = -0.7 NEFAs r = -0.5 TRIGLYCERIDES r = -0.5 LEPTIN r = -0.5 ADIPONECTIN r = -0.6 valine carnitine 2-hydroxypalmitate betaine 10-heptadecenoate allantoin N1-methyladenosine r = -0.5 homocitrulline corticosterone 10-nonadecenoate asparagine 3-methyl-2-oxobutyrate beta-alanine cortisol 10-undecenoate r = -0.5 Cortisone r = -0.6 trans-urocanate 2-aminobutyrate cholate N1-methyladenosine erucate alanine hypotaurine trans-urocanate 1-eicosapentaenoylglycerophosphocholine carnitine octadecanedioate betaine 10-nonadecenoate cholestanol riboflavin (Vitamin B2) eicosenoate dihomo-linolenate beta-hydroxyisovaleroylcarnitine docosahexaenoate (DHA) oleic ethanolamide docosapentaenoate eicosapentaenoate (EPA) linolenate 5-dodecenoate lysine cholestanol margarate isovalerylcarnitine oleate r = -0.5 palmitoleate 1-margaroylglycerophosphocholine stearidonate phosphate myristoleoylcarnitine 10-heptadecenoate 16-hydroxypalmitate sphinganine palmitate stearidonate indole-3-carboxylic acid margarate 15-methylpalmitate 153 Table 5.5 (cont’d) 4-methyl-2-oxopentanoate 1-palmitoylglycerophosphocholine urate cis-vaccenate isoleucine 7-alpha-hydroxy-3-oxo-4-cholestenoate myristoylcarnitine 1-linolenoylglycerophosphocholine 2-margaroylglycerophosphocholine 1-palmitoleoylglycerophosphocholine glutarate (pentanedioate) palmitoleate dihomo-linolenate myristate 2-hydroxyisobutyrate 2-palmitoleoylglycerophosphocholine palmitoylcarnitine 1-oleoylglycerophosphocholine 1-eicosatrienoylglycerophosphocholine stearate 5alpha-pregnan-3alpha,20beta-diol disulfate 1 2-arachidoylglycerophosphocholine 2-methylbutyrylcarnitine oleate 3-hydroxydecanoate 2-aminobutyrate alpha-hydroxyisovaleroyl carnitine 3-methyl-2-oxovalerate 154 Table 5.5 (cont’d) docosatrienoate stearoylcarnitine oleoylcarnitine N-delta-acetylornithine linolenate ornithine N-stearoyltaurine 155 Figure 5.1a Tricarboxylic acid (TCA) cycle network pathway at 0 minutes (baseline). 156 Figure 5.1b Tricarboxylic acid (TCA) cycle network pathway at 75 minutes. 157 Figure 5.2a Branched-chain amino acid (BCAA) pathway at 0 minutes (baseline). 158 Figure 5.2b Branched-chain amino acid (BCAA) pathway at 75 minutes. 159 Figure 5.3 Significant (P < 0.05) metabolite differences at 0 minutes and 75 minutes during an oral sugar test are displayed for the lipid, amino acid, carbohydrate, cofactor and vitamin, energy, nucleotide, peptide, and xenobiotic pathways. Filled circles (●) represent the least squares means estimates and horizontal lines (—) represent the confidence interval around the least squares means (LS means). For each metabolite, the least squares means estimate for baseline is set to 0 (vertical line). Positive LS means indicate increases in metabolite abundance following administration of Karo® light corn syrup relative to baseline, whereas negative LS means indicate decreases in metabolite abundance following administration of Karo® light corn syrup relative to baseline. All data are represented on a log scale. 160 Figure 5.3 (cont’d) 161 Figure 5.3 (cont’d) 162 Figure 5.4 Significant (P < 0.05) metabolite differences in the lipid pathway for ponies with insulin dysregulation compared to non-insulin dysregulated ponies at 0 minutes and 75 minutes. Filled circles (●) represent the least squares means estimates and horizontal lines (—) represent the confidence interval around the least squares means (LS means) for insulin dysregulated ponies. For each metabolite, the least squares means estimate for non-insulin dysregulated ponies is set to 0 (vertical line). Positive LS means in insulin dysregulated ponies indicate increases in metabolite abundance relative to controls, whereas negative LS means in insulin dysregulated ponies indicate decreases in metabolite abundance relative to controls. All data are represented on a log scale. 163 Figure 5.5 Significant (P < 0.05) metabolite differences in the amino acid pathway for ponies with insulin dysregulation compared to non-insulin dysregulated ponies at 0 minutes and 75 minutes. Filled circles (●) represent the least squares means estimates and horizontal lines (—) represent the confidence interval around the least squares means (LS means) for insulin dysregulated ponies. For each metabolite, the least squares means estimate for non-insulin dysregulated ponies is set to 0 (vertical line). Positive LS means in insulin dysregulated ponies indicate increases in metabolite abundance relative to controls, whereas negative LS means in insulin dysregulated ponies indicate decreases in metabolite abundance relative to controls. All data are represented on a log scale. 164 Figure 5.6 Significant (P < 0.05) metabolite differences in the carbohydrate, cofactor and vitamin, energy, nucleotide, and xenobiotic pathways for ponies with insulin dysregulation compared to non-insulin dysregulated ponies at 0 minutes and 75 minutes. Filled circles (●) represent the least squares means estimates and horizontal lines (—) represent the confidence interval around the least squares means (LS means) for insulin dysregulated ponies. For each metabolite, the least squares means estimate for noninsulin dysregulated ponies is set to 0 (vertical line). Positive LS means in insulin dysregulated ponies indicate increases in metabolite abundance relative to controls, whereas negative LS means in insulin dysregulated ponies indicate decreases in metabolite abundance relative to controls. All data are represented on a log scale. 165 Figure 5.7 Significant (P < 0.05) metabolite differences in the lipid pathway for obese compared to nonobese ponies at 0 minutes and 75 minutes. Filled circles (●) represent the least squares means estimates and horizontal lines (—) represent the confidence interval around the least squares means (LS means) for obese ponies. For each metabolite, the least squares means estimate for non-obese ponies is set to 0 (vertical line). Positive LS means in obese ponies indicate increases in metabolite abundance relative to controls, whereas negative LS means in obese ponies indicate decreases in metabolite abundance relative to controls. All data are represented on a log scale. 166 Figure 5.7 (cont’d) 167 Figure 5.7 (cont’d) 168 Figure 5.8 Significant (P < 0.05) metabolites differences in the amino acid pathway for obese compared to non-obese ponies at 0 minutes and 75 minutes. Filled circles (●) represent the least squares means estimates and horizontal lines (—) represent the confidence interval around the least squares means (LS means) for obese ponies. For each metabolite, the least squares means estimate for non-obese ponies is set to 0 (vertical line). Positive LS means in obese ponies indicate increases in metabolite abundance relative to controls, whereas negative LS means in obese ponies indicate decreases in metabolite abundance relative to controls. All data are represented on a log scale. 169 Figure 5.9 Significant (P < 0.05) metabolite differences in carbohydrate, cofactor and vitamin, nucleotide, peptide, and xenobiotic pathways for obese compared to non-obese ponies at 0 minutes and 75 minutes. Filled circles (●) represent the least squares means estimates and horizontal lines (—) represent the confidence interval around the least squares means (LS means) for obese ponies. For each metabolite, the least squares means estimate for non-obese ponies is set to 0 (vertical line). Positive LS means in obese indicate increases in metabolite abundance relative to controls, whereas negative LS means in obese ponies indicate decreases in metabolite abundance relative to controls. All data are represented on a log scale. 170 Figure 5.10 Significant (P < 0.05) metabolites differences in the lipid pathway in ponies with a history of laminitis compared to ponies without a history of laminitis at 0 minutes and 75 minutes. Filled circles (●) represent the least squares means estimates and horizontal lines (—) represent the confidence interval around the least squares means (LS means) for ponies with a history of laminitis. For each metabolite, the least squares means estimate for ponies without a history of laminitis is set to 0 (vertical line). Positive LS means in ponies with a history of laminitis indicate increases in metabolite abundance relative to controls, whereas negative LS means in ponies with a history of laminitis indicate decreases in metabolite abundance relative to controls. All data are represented on a log scale. 171 Figure 5.10 (cont’d) 172 Figure 5.10 (cont’d) 173 Figure 5.11 Significant (P < 0.05) metabolite differences in the amino acid pathway in ponies with a history of laminitis compared to ponies without a history of laminitis at 0 minutes and 75 minutes. Filled circles (●) represent the least squares means estimates and horizontal lines (—) represent the confidence interval around the least squares means (LS means) for ponies with a history of laminitis. For each metabolite, the least squares means estimate for ponies without a history of laminitis is set to 0 (vertical line). Positive LS means in ponies with a history of laminitis indicate increases in metabolite abundance relative to controls, whereas negative LS means in ponies with a history of laminitis indicate decreases in metabolite abundance relative to controls. All data are represented on a log scale. 174 Figure 5.11 (cont’d) 175 Figure 5.12 Significant (P < 0.05) metabolite differences in carbohydrate, cofactor and vitamin, energy, nucleotide, peptide, and xenobiotic pathways in ponies with a history of laminitis compared to ponies without a history of laminitis at 0 minutes and 75 minutes. Filled circles (●) represent the least squares means estimates and horizontal lines (—) represent the confidence interval around the least squares means (LS means) for ponies with a history of laminitis. For each metabolite, the least squares means estimate for ponies without a history of laminitis is set to 0 (vertical line). Positive LS means in ponies with a history of laminitis indicate increases in metabolite abundance relative to controls, whereas negative LS means in ponies with a history of laminitis indicate decreases in metabolite abundance relative to controls. All data are represented on a log scale. 176 Figure 5.12 (cont’d) 177 Figure 5.13 The relationship between different phenotypes (insulin response, obesity status, laminitis history) and significant metabolites. 1-oleoylglycerophosphoserine 2-hydroxydecanoate guanidinoacetate citrulline carnitine 52 Insulin Response 5 palmitoyl-arachidonoyl-glycerophosphocholine oleoyl-linoleoyl-glycerophosphoinositol 1-linoleoylglycerophosphoserine inositol-1-phosphate dodecanedioate 2-pyrrolidinone 3 6 Laminitis History 25 36 59 10-undecenoate 2-methylbutyrylcarnitine 3-hydroxybenzoate 3-indoxyl sulfate 4-hydroxybutyrate 4-methyl-2-oxopentanoate biliverdin cis-vaccenate valine Obesity Status campesterol docosadienoate dihomo-linoleate cystine ergothioneine ferulic acid 4-sulfate homoarginine isoleucine methylmalonate myristate myristoleate 178 N-acetylputrescine N-deltaacetylornithine N-palmitoyltaurine oleate oleic ethanolamide palmitoleate sorbitol tryptophan betaine Figure 5.14a Principal components analysis (PCA) plot of metabolic profiles for the insulin dysregulated (○) and non-insulin dysregulated (●) phenotype. 179 Figure 5.14b Principal components analysis (PCA) plot of metabolic profiles for the obese (∆) and nonobese (▲) phenotype. 180 Figure 5.14c Principal components analysis (PCA) plot of metabolic profiles for the history of laminitis (□) and no history of laminitis (■) phenotype. 181 CHAPTER 6 Conclusions and Future Directions CONCLUSIONS Dietary adaptation to various carbohydrate profiles altered glucose and insulin dynamics in adult and aged horses; however, the response is variable depending on assessment. The insulin-modified frequently sampled intravenous glucose tolerance test (FSIGTT) showed that adult and aged horses had improved tissue insulin sensitivity (SI) following adaptation to the starch-rich and sugar-rich diet. However, a modified oral sugar test (OST) did not reveal significant changes in glucose and insulin dynamics. In contrast, the dietary meal challenge demonstrated enhanced postprandial hyperinsulinemia (AUCi) in both adult and aged horses following consumption of a single starch-rich or sugar-rich meal. These data would suggest that feeding a starch-rich or sugar-rich diet may be beneficial; however, enhanced postprandial hyperinsulinemia cannot be ignored given its causal role in the induction of laminitis. Further, it is important to note that the dynamic challenge tests were performed following adaptation to the respective diets over seven weeks and consumption of these diets long-term may yield different results. Similar to previously reported studies, glucose and insulin dynamics vary between adult and aged horses. The insulin-modified frequently sampled intravenous glucose tolerance test (FSIGTT) showed that aged horses had a higher acute insulin response to glucose (AIRg) compared to adult horses. Further, a modified oral sugar test (OST) demonstrated that aged horses had higher peak insulin and area-under-thecurve insulin (AUCi). These data suggest that aged horses have higher insulin secretory responses; however, it is unknown whether this occurs due to increased uptake or decreased clearance. Glucose and insulin dynamics also vary between breeds. The insulin-modified frequently sampled intravenous glucose tolerance test (FSIGTT) showed that Thoroughbreds had a higher basal (fasting) insulin compared to Standardbreds. Further, a modified oral sugar test (OST) demonstrated that Thoroughbreds had a higher area-under-the-curve insulin (AUCi) and area-under-the-curve glucose (AUCg). While differences in glucose and insulin dynamics have been previously reported between typically insulin- 182 sensitive breeds and relatively insulin-resistant breeds; these data suggest that insulin-sensitive breeds have important differences in glucose and insulin dynamics. In addition to effects on insulin and glucose, dietary adaptation likely influences ACTH and cortisol concentrations in adult and aged horses. Aged horses had significantly higher baseline ACTH concentrations following adaptation to the starch-rich diet. However, ACTH concentrations following a TRH stimulation test were not affected by dietary adaptation. Further, baseline cortisol concentrations were not influenced by dietary adaption, but post-dexamethasone cortisol concentrations were significantly higher after adaptation to the starch-rich diet. These data suggest that diet is a potential confounder on ACTH and cortisol concentrations and should be considered when interpreting endocrine results. Age influences ACTH and cortisol concentrations. The TRH stimulation test showed that aged horses had significantly higher baseline ACTH concentrations, but no statistical difference between age groups was appreciated for post-TRH ACTH concentrations. In addition, the overnight dexamethasone suppression test did not yield statistical differences between adult and aged horses for baseline cortisol concentrations or post-dexamethasone cortisol concentrations. Time of year also influences ACTH concentrations and cortisol concentrations. Baseline and postTRH ACTH concentrations were significantly higher in October. Post-dexamethasone cortisol was significantly higher in October compared to March, May, and August. Previous studies have shown that ACTH and cortisol concentrations are influenced by age and time of year [121,134–136,138]; however, to the author’s knowledge the finding that diet influences these endocrine parameters is novel information. A possible explanation for this finding may be the role of gastrointestinal microbes in the gut-brain communication pathway as studies in mice indicate that alterations in the gastrointestinal microbiome can affect the regulation of neuroendocrine hormones of the hypothalamic-pituitary-adrenocortical (HPA) axis [147,149–152,195]. Ideally, while none of the horses showed clinical signs (hypertrichosis, regional adiposity, skeletal muscle atrophy, lethargy) of pituitary pars intermedia dysfunction, a post-mortem examination would have been performed to evaluate the gross and histologic appearance of the pituitary gland. 183 Evaluation of the plasma metabolome between adult and aged horses and adaptation to dietary carbohydrate profiles provided initial information regarding molecular and cellular changes. Untargeted metabolomics provided an extensive and qualitative analysis as thousands of significant metabolite ion peaks were identified; however, a number of these peaks remain unknown. Examination of the plasma metabolome demonstrated significant differences in metabolites primarily derived from amino acids, lipids, and xenobiotics, and initial results show promise as aged horses show some differences in metabolites that mirror differences in aged humans. However, these results may change when additional metabolites are identified. Despite the limitations, the data to date suggest that metabolomics is a relevant approach for defining metabolic changes due to age and diet. Evaluation of the serum metabolome between non-insulin dysregulated and insulin dysregulated ponies, obese and non-obese ponies, and ponies with and without a history of laminitis provided further evidence that metabolomic profiling is useful for further defining cellular and molecular physiology and pathophysiology. Comparison of ponies with and without insulin dysregulation primarily identified differences in lysolipids, TCA cycle intermediates, and urea cycle metabolites. Several glycerophosphocholines (oleoyl-linoleoyl-glycerophosphocholine) and TCA cycle intermediates (citrate, malate, fumarate) were decreased in insulin dysregulated ponies. Many of these findings are similar to humans with insulin resistance and type-II diabetes mellitus. Comparison of non-obese and obese ponies primarily identified differences in concentrations of long-chain fatty acids, acylcarnitines, and branchedchain amino acids (isoleucine, leucine, valine). These metabolites were increased in obese ponies. Metabolomic analysis showed similarities and differences between phenotypes with many of the metabolites derived from fatty acid metabolism and amino acid metabolism. The results presented here should be confirmed in a large cohort of animals that will allow for metabolite differences due to pathologic factors such as insulin dysregulation and obesity and physiologic factors such as age, gender, and breed to be differentiated. These data suggest that metabolomics is a relevant approach for understanding complex diseases that span multiple tissues by providing additional quantitative biologic information that may help decipher disease mechanisms and identify potentially useful disease biomarkers. 184 FUTURE DIRECTIONS These studies provide evidence that the application of metabolomics to enable comprehensive metabolic profiling of horse serum and plasma samples is a relevant approach to gain deeper insight into metabolic adaptations and perturbations associated with dietary carbohydrate profiles, aging, and insulin dysregulation in horses. Further, the development of an equine-specific metabolite spectral library will allow for additional identification of biomarkers and development of a metabolomic signature for equine metabolic diseases that may eventually lead to early detection of affected animals. Formulation of a nutrition plan for metabolically abnormal horses remains a challenge as there is a disconnect between tissue insulin sensitivity and postprandial insulin responses. Current dietary recommendations for horses at-risk for endocrinopathic laminitis include limiting dietary nonstructural carbohydrates (NSC) thereby reducing postprandial glucose and insulin responses because hyperinsulinemia has been shown to induce laminitis. The research presented in this dissertation demonstrated that a starch-rich diet and a sugar-rich diet fed for seven weeks improved tissue insulin sensitivity in non-obese horses. In a previous study, a sugar-rich diet fed for twenty weeks resulted in improved tissue insulin sensitivity; however, a starch-rich diet fed for twenty weeks resulted in tissue insulin resistance and induced obesity [128]. In both studies, despite changes in tissue insulin sensitivity, sugarrich and starch-rich diets resulted in greater postprandial insulin concentrations compared to low nonstructural carbohydrate diets. These studies highlight the disconnect between the tissue insulin sensitivity/resistance and postprandial insulin responses; the two components of insulin dysregulation. Further, these findings complicate dietary recommendations for insulin dysregulation in horses -- improved tissue insulin sensitivity would be beneficial, whereas postprandial hyperinsulinemia would be detrimental. Currently, the mechanisms underlying improvements in tissue insulin sensitivity with sugar-rich diets are unknown, and it is unclear why starch-rich diets fed for different lengths of time had opposite effects on tissue insulin sensitivity. Understanding the mechanisms underlying the changes in tissue insulin sensitivity is key to understanding the consequences of different dietary components and to making appropriate dietary recommendations for horses with insulin dysregulation. 185 Changes in glucose and insulin dynamics due to dietary adaptation are likely in part due to alterations in the gut microbiome. The complex microbial population of the equine intestinal tract also plays a key role in health and disease, and the composition and complexity of this population are starting to be revealed. The availability of next-generation sequencing and bioinformatics methods offers an opportunity to improve understanding of the role of the gastrointestinal microbiome in the pathogenesis of metabolic disease and insulin dysregulation. The emerging clinical importance of insulin dysregulation in horses justifies studies that advance understanding of underlying pathophysiology; knowledge that will lead to improved methods for identification of at-risk individuals before the onset of disease and will potentially identify new therapeutic targets. Metabolites identified in the cohort of Welsh Ponies provide new insight into the pathophysiology of insulin dysregulation. Additionally, dietary alterations in tissue metabolism measured through alterations in plasma metabolites provide information regarding the impact of dietary change at the molecular level. 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