o , J v ‘3 I'fh,z':v_ . 13- uni-9t" ‘Vw‘n-vr—vnwcv 1“- eff? ” {5.1% ¥Jllv :‘Lfi‘ 3 f. s . . ,\ it": 2. “£2515 ~\ 1". l n' M. n ’91. [fa/5t: 51/5 This is to certify that the thesis entitled FACTORS ASSOCIATED WITH URINARY CATECHOLAMINE LEVELS IN MID-PREGNANCY presented by Anjali Sapkal has been accepted towards fulfillment of the requirements for the Master of degree in Epidemiology Science :42 2 r z g , Majo'r Préfessor’s Signature g/a75//Zflfl/}/ Date MSU is an Affirmative Action/Equal Opportunity Institution -'----a---c--o- LIBRARY Michigan State University PLACE IN RETURN Box to remove this checkout from your record. To AVOID FINES return on or before date due. MAY BE RECALLED with earlier due date if requested. DATE DUE DATE DUE DATE DUE 6/01 cJCIRC/DateDuepes-sz FACTORS ASSOCIATED WITH URINARY CATECHOLAMINE LEVELS IN MID-PREGNANCY By Anjali Sapkal A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Department of Epidemiology 2004 ABSTRACT FACTORS ASSOCIATED WITH URINARY CATECHOLAMINE LEVELS IN MID-PREGNANCY By Anjali Sapkal This study used data on mid-pregnancy urinary catecholamine (norepinephrine, epinephrine and dopamine) levels in 227 pregnant women who delivered at term and participated in the Pregnancy Outcome and Community Health (POUCH) study. For each catecholamine, day-to-day levels and waking and bedtime levels were moderately correlated (correlation coefficients of 0.18 to 0.60). Within each collection time (waking, before bed), norepinephrine and dopamine were highly correlated with each other (correlation coefficients of 0.75 to 0.90) but not with epinephrine. Catecholamine levels were lower in African-American women compared to white and other ethnicities and higher with advancing maternal age after adjusting for creatinine and smoking. Women who collected urine beyond 60 minutes post waking had lower epinephrine levels, but norepinephrine and dopamine levels were not significantly affected by this delay. No association was found between time of urine collection (waking and bedtime), work/non-work day and urinary catecholamine levels. In all the analyses, gestational age at enrollment was not significantly associated with any of the catecholamines levels. ACKNOWLEDGEMENTS I express appreciation towards my advisor, Dr. Claudia Holzman and my committee members, Dr. M. Hossein Rahbar and Dr. Naomi Breslau, whose excellent guidance and instructions have made this work possible. My sincere gratitude to professors, Dr. Nigel Paneth, Dr. Wenjiang Fu, Dr. Jianping He, Dr. Pramod Pathak, Dr. Wilfried Karmaus, Dr. Dorothy Pathak, Dr. Joseph Gardiner, Dr. Terry May and Dr. Mahdi Saeed for sharing their knowledge. I would like to thank my POUCH colleagues, Ms Nikki Jones, Ms Marty Regier, Ms Bertha Bullen, Ms Crista Valentine, Mr. Sean Zhou, and the whole POUCH team for their support and co-operation. I thank my parents, Mr. Shankar and Mrs. Alka Sanas, my sister, Ms Smita Sanas and my husband, Mr. Dnyandeo Sapkal for their constant encouragement, motivation and love. TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES LIST OF ABBREVIATIONS INTRODUCTION Preterm Delivery: A Public Health Problem ............................................. 1 Preterm Delivery and Stress .................................................................. 4 Weakness in Studies on PTD and Stress ............................................... 18 Challenges in Measuring Stress ........................................................... 19 Catecholamines and Stress The Biology of Catecholamines ........................................................ 22 Literature Review on Catecholamine and Stress Non-Pregnant Population Levels of Catecholamines in Non-Pregnant Population ........................ 25 Factors Associated with Catecholamine Levels in Non-Pregnant Population ................................................................................................. 26 Relationship between Catecholamines and Stress in Non-Pregnant Population ................................................................................. 29 Pregnant Population Levels of Catecholamines in Pregnancy ........................................... 62 Factors Associated with Catecholamine Levels in Pregnancy ............... 62 Relationship between Catecholamines and Stress in Pregnancy ........... 64 Discrepant Results among Studies on Catecholamines and Stress ...... 79 Catecholamines and Diseases ............................................................ 81 FACTORS ASSOCIATED WITH URINARY CATECHOLAMINE LEVELS IN MID-PREGNANCY Introduction ..................................................................................... 84 Methods Population .................................................................................... 86 Study Sample ................................................................................ 87 Catecholamine Levels in Urine .......................................................... 89 Diary and Interview Data .................................................................. 89 Analytical Strategy .......................................................................... 90 Results Maternal Characteristics .................................................................. 91 Catecholamine Levels ..................................................................... 91 Relationship between Catecholamine Levels and Timing of collection, Interval from Waking to Collection of Urine sample and Work Status ............ 92 Relationship between Catecholamine Levels and Maternal Characteristics...93 Additional Preliminary Analysis ............................................................ 97 Discussion ....................................................................................... 107 Conclusion ...................................................................................... 1 1 1 REFERENCE ................................................................................... 114 Table 1: Table 2: Table 3: Table 4: Table 5: Table 6: Table 7: Table 8: Table 9: LIST OF TABLES Literature Review on Stress and Preterm Delivery — Positive studies .................................................................................... 9 Literature Review on Stress and Preterm Delivery — Negative studies ................................................................................... 15 Literature Review on Catecholamines in Non-Pregnant Population.....33 Literature Review on Relationship of Perceived Stress and Catecholamines in Non-Pregnant Population — Positive studies ......... 38 Literature Review on Relationship of Perceived Stress and Catecholamines in Non-Pregnant Population - Negative studies ........ 39 Literature Review on Relationship of Anxiety and Catecholamines in Non-Pregnant Population — Positive studies ........ 40 Literature Review on Relationship of Depression and Catecholamines in Non-Pregnant Population — Positive studies ........ 42 Literature Review on Relationship of Work Stress and Catecholamines in Non-Pregnant Population — Positive studies ......... 44 Literature Review on Relationship of Work Stress and Catecholamines in Non-Pregnant Population — Negative studies ....... 48 Table 10: Literature Review on Post-Traumatic Stress Disorder and Catecholamines in Non-Pregnant Population—Positive studies ......... 49 Table 11: Literature Review on Post-Traumatic Stress Disorder and Catecholamines in Non-Pregnant Population- Negative studies ...... 53 Table 12: Literature Review on Relationship of Other Types of Stress and Catecholamines in Non-Pregnant Population— Positive studies ........ 54 Table 13: Literature Review on Relationship of Other Types of Stress and Catecholamines in Non-Pregnant Population—Negative studies ....... 61 Table 14: Literature Review on Catecholamines in Pregnant Population ......... 68 Table 15: Literature Review on Relationship of Stress and Catecholamines in Pregnant Population - Positive studies .................................... 71 Table 16: Literature Review on Relationship of Stress and Catecholamines in Pregnant Population - Negative studies ................................... 78 vi Table 17: Table 18: Table 19: Table 20: Table 21: Table 22: Distribution of maternal characteristics in the study sample of 227 term delivered women: Ethnicity, Age, Medicaid, Education, Parity and Week of pregnancy at enrollment ................. 99 Urinary catecholamine levels‘“ (NE, E, DA)” in waking and bedtime samples on three consecutive days (N=227) ............................... 100 Day to Day urinary catecholamines (NE, E, DA) # correlation coefficients and confidence intervals around the coefficients in waking and bedtime samples (N=227) ....................................... 101 Correlation coefficients for urinary catecholamines between the waking and bedtime sam les and between urinary catecholamines (NE, E, DA) on three consecutive days (N=227)...102 Unadjusted mean waking and bedtime urinary catecholamine" levels (NE, E, DA) #2 Repeated measures analyses I (N=227) ......... 103 Adjusted mean waking and bedtime urinary catecholamine+ levels (NE, E, DA) #2 Repeated measures analyses I (N=227) .................. 105 vii LIST OF FIGURES Figure 1: Leading causes of infant mortality (death during the first year of life) in the US among infants born in 1998 ........................................... 1 Figure 2: Leading cause-specific infant mortality rates by maternal ethnicity in the US in 1998 .......................................................... 2 Figure 3: Preterm birth rate in the US over the period 1985-98 ........................ 3 Figure 4: Schematic presentation of pathway of stress leading to preterm delivery .................................................................... 5 Figure 5: Schematic presentation of pathways from the brain to adrenal systems involved in stress .......................................... 23 Figure 6: Flowchart of study sample for catecholamine study ........................ 88 viii LIST OF ABBREVIATIONS U.S.A ................................................................... United States of America PTD .............................................................................. Preterm Delivery AFAM ............................................................................ African-American CRH ....................................................... Corticotrophin-Releasing Hormone lL-1 ...................................................................................... Interleukin-1 lL-6 ...................................................................................... Interleukin-6 PSS ...................................................................... Perceived Stress Scale GA .................................................................................. Gestational Age SMS ................................................. Sympathetic-adrenal Medullary System HPA ............................................. Hypothalamic-Pituitary Adrenocortical Axis E .......................................................................................... Epinephrine NE ................................................................................... Norepinephrine DA ......................................................................................... Dopamine VMA ........................................................................ Vanillylmandelic Acid SES ....................................................................... Socio-Economic Status BMI ............................................................................... Body Mass Index PS ................................................................................. Perceived Stress PTSD .......................................................... Post-Traumatic Stress Disorder LOD .............................................................................. Limit of Detection PMS ..................................................................... Premenstrual Syndrome PMM ................................................................ Premenstrual Magnification MVA .................................................................... Motor Vehicle Accidents MDD .................................................................. Major Depressive Disorder BP ...................................................................................... Bipolar manic PS ........................................................................ Paranoid Schizophrenia US ............................................................... Undifferentiated Schizophrenia OAD ....................................................................... Over-anxious Disorder CR-PTSD ................................................................ Combat-related PTSD Exp ..................................................................................... Experiment lHV .............................................................................. lntra-hepatic Vein PCI ...................................................................... Placental Cord Insertion BDI .................................................................. Beck Depression Inventory POUCH ..................................... Pregnancy Outcomes and Community Health MSAF P .................................................... Maternal Serum Alpha-fetoprotein ABPM ...................................................... Ambulatory Blood Pressure Monitor LMP ........................................................................ Last Menstrual Period HPLC ............................................ High Performance Liquid Chromatography CV ....................................................................... Coefficients of variations GARS .......................................... Global Assessment of Recent Stress Scale L .................................................................................... limit of detection GLM ....................................................................... General Linear Models INTRODUCTION Preterm Deliveg: A Public Health Problem World Health Organization defines preterm delivery as delivery before 259 days of gestation (i.e. before 37 completed weeks’ gestation) (1). In the United States of America (USA), preterm delivery (PTD) is the leading cause of perinatal mortality and morbidity. Despite a small decline from 1999 to 2000, the rate of PTD is higher now than in 1981, having risen steadily for almost two decades. Birth defects Preterm/LBW SIDS Maternal preg. comp. RDS Placenta, cord comp. Infections Accidents Hypoxia/birth asphyxia Pneumonia/influenza 0 20 40 60 80 100120 140160180 Rate per 100 000 live births Figure 1: Leading causes of infant mortality (death during the first year of life) in the US among infants born in 1998 Source: National Center for health statistics, final natality data. Figure 1 shows that preterm birth along with low birth weight is the second leading cause of infant mortality in the US at the rate of 104 per 100,000 live births. For still unknown reasons, African-American (AFAM) women continue to have higher rates of PTD than the white women. AFAM women have a PTD rate of 16.3% whereas white women have a PTD rate of 8.1% chance (2) and this twofold risk disparity has existed for more than 50 years. 350 - 277.6 300 " - White 250 . 1802 E m o x 200 . 54 137.9 150 ‘ 100 . 71 .3 50~ Rate per 100 000 live blrths 0'- Preterm/LBW Birth defects SIDS Maternal RDS preg. comp. Figure 2: Leading cause-specific infant mortality rates by maternal ethnicity in the US in 1998 Source: National Center for health statistics, final natality data. Figure 2 makes clearer the impact of PTD on AFAM infants. The AFAM infant mortality rate due to prematurity is 277.6 per 100,000 live births, which is substantially higher than congenital malformations, the leading cause of infant mortality in white babies. 20— 15 “ ~.~. ere. ooh. 0.0. 9°. :5 3 o OO CO CO 1"!- v-I- r I- Q ' 1"- v-v- r1— 1-1- F!— ry .O 0 en‘- _——-— 10 _, -—r——I'_— _ (D —— ‘ ‘ L . . A h j n J - 5 u o . 1 In tors coca 91- NM win to 500° co coco coco me: or» ma: or our»? cn can: mm ma: ac» ma: c» 020:6 1- 1-1- 1-1- 1-u- 1-1- F‘- ‘- FF“ 0. I IZZIAII races A White I Black J Figure 3: Preterm birth rate in the US over the period 1985-98 Source: National Center for health statistics, final natality data. As seen in figure 3, the racial disparity has decreased because of an increase in PTD among white women and a small decrease in the PTD rate among African-American women. The figure also shows the goal for the USA set by Healthy People 2010 as 7.6%. PTD is associated with substantial emotional trauma to the family. At the community level it adds to the economic costs due to short and long term sequelae of PTD. Short term sequelae such as high mortality and morbidity and long term neurodevelopmental disabilities such as cerebral palsy, mental retardation and learning disabilities are higher among premature infants (3-5). Although improvement in gestational-age-related survival of preterm infants has occurred as a result of the use of antenatal corticosteroids, neonatal surfactant therapy and regionalization of perinatal care, there has been no reduction in the incidence of preterm birth (6). Until now efforts to decrease PTD have mainly focused on secondary prevention such as use of tocolytic agents and early detection of labor. In spite of vast improvements in the field of medicine, the USA continues to have a high rate of PTD as compared to other industrialized nations. For primary prevention of PTD, information is needed on the underlying biological causes of PTD and their antecedent factors. The etiology of PTD remains poorly understood and when progress is made in this regard, maybe a meaningful reduction in the incidence of PTD is likely. Research into the causes and possible interventions to prevent PTD has important public health implications. PTD is thought to have multiple pathways involving several factors. Some of the risk factors that have been explored include demographic characteristics such as ethnicity and socioeconomic status (7, 8) and biologic risk factors such as infection (9-11). During the past few decades, interest has been expressed in the potential etiologic role of psychosocial factors, including stress with PTD. Preterm Deliveg and Stress Stress may lead to PTD, directly or indirectly. One direct pathway is through increases in maternal cortisol that stimulate increases in placental production of corticotrophin-releasing hormone (CRH), which leads to increased uterine contractility. Placental CRH production increases exponentially in the month preceding labor. CRH might lead to parturition either by stimulating the fetal pituitary-adrenal axis and production of fetal cortisol or by increasing prostaglandin production in the placenta and potentiating action of oxytocin. High levels of perceived stress have been shown to be associated with increased CRH production, which might lead to PTD (12). High levels of stress produce elevated levels of catecholamines, resulting in vasoconstriction and subsequently oxygen and calorie reduction to the fetus. There is also some evidence that peaks of epinephrine precipitate labor and delivery (13-15). Stress can lead to an increase in cytokines (such as lL-1 and IL-6), which may then cause PTD or increase susceptibility to infection, thereby increasing the risk of PTD. Indirectly stress can lead to PTD by inducing risk behaviors such as smoking, alcohol, and drug use or influence the patterns of sexual activity (16, 17). L Stress 4 * Health Behaviors \ CRH lnfectfon —> Cytokines ACTH 5 \\ Glucocorticoid PretermI Birth Figure 4: Schematic presentation of pathway of stress leading to preterm delivery Adapted from Gennaro 2003 (17) There is increasing evidence that stress might be associated with PTD (12, 13, 18-30) though studies have produced mixed results (31-36) (See Table 1 and 2). Studies have been done in different countries such as USA, Australia, and Denmark and in different populations such as low socioeconomic class, African- Americans and nulliparous women. Associations between stress and PTD have produced odds ratios ranging between 1.16 (26) to 3.39 (30). Measurement of perceived stress at 24 and 32 weeks was predictive of as much as 0.23 to 0.27 of the variance in gestational age (GA) at birth (21). Each unit increase in prenatal pregnancy anxiety was associated with a 3-day decrease in GA at birth (23). Pritchard CW et al (22) found the strongest association for the stress measure taken at 20 weeks where the odds ratio for those experiencing high levels of perceived difficulty was 2.86 (95% Cl=1.05-7.76) for preterm birth. Dole et al (24) showed that women in the highest negative life events quartile had the highest risk of PTD (RR=1.8, 95% CI 1.2-2.7). Women having one or more highly stressful life events had a relative risk of PTD 1.76 as compared with those without stressful events with a 95% CI of 1.15-2.71 (25). The change in Perceived Stress Scale (PSS) score from 23-26 weeks of gestation to 31-35 weeks and gestational age at delivery had a correlation of r=0.42, p<0.001 (21). The greater the decrease in PSS scores, the longer was the gestational age. Lobel et al (13) found that stress significantly predicted PTD (standardized logistic regression coefficient=0.30, p<0.03). Copper et al (26) showed a significant association between stress and spontaneous PTD, an OR=1.16 (p=0.003). Odds ratio=2.12 was seen for 5+ life events in first two trimesters of pregnancy (19). Rates of PTD increased with increasing levels of household strain within categories of Socio-economic Status (SES) (22). Greater the decrease in perceived stress score during the 2nd trimester, the longer was the gestational age (21). Copper et al (26) found a 16% increase in spontaneous PTD risk for every unit increase in the stress score. Berkowitz et al (19) provides evidence of significant linear trend between number of life events during the first 2 trimesters and the risk of PTD. Hobel et al (12) showed that maternal plasma levels of corticotrophin-releasing hormone are significantly elevated as early as 18 to 20 weeks' gestation in women who subsequently deliver preterm. In turn these changes in corticotrophin-releasing hormone are associated with maternal age and stress level at 18 to 20 weeks' gestation. Studies have offered preliminary evidence that the effects of prenatal stress are highly specific for different birth outcomes (37). Personal resources (mastery, self-esteem, optimism) have been implicated in processes related to fetal growth but not to the timing of delivery, whereas stress defined as state anxiety was seen to be associated with length of gestation but not with fetal growth (29). Studies have measured stress prospectively (12, 13, 20-30) early in pregnancy, hence time order is better established. Stress was measured in earlier part of pregnancy hence less likely to be due to complications experienced in pregnancy. Rates of PTD are consistently highest among women with social disadvantages, African-Americans, poor, undernourished, single parents, lower education and income (18); while women with these characteristics are also known to have high levels of stress. Maladaptive health behaviors such as smoking, substance abuse and poor weight gain during pregnancy are associated with increased rates of prematurity and it has been reported that these behaviors are more prevalent among stressed women. 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PE 0:003 0 02000.00 Rm? < 0.0% 0.00..0> 00000.08 00.00..0> 00 000: <6 0.3000. 00.000 .0000 .000 00 00. ..00.0000.0 00000 0:00 000 000000 6.0000000 .000>0 00 W2 Eh. 0:3 000>0 .000>0 0 5000000 0.0000 003 0:. 0 02000005 0:. 0 000000000 02 000 200 000000.). 00. 00.00000 Eh. mm 00 ..N 0000000“. 503 .0000 0000000 0. 00.0 0~.m 0000m 0000000 3005 0:000m 00000.00; 000005 000000 000.5 0000000. 0000< 00.030 020002 I 002.00 000.00.“. 000 000.5 00 3030". 0000.00... N 0.00 ... 17 Weakness in Studies on PTD and Stress 1. Stress has been measured by various instruments such as life events, daily hassles, perceived stress and psychological distress (often measured by anxiety or depression). Life events counts and measures of objectively weighted life events are inadequate because they rely on the stimuli, without ascertaining whether they were appraised by the individual as stressful or elicited responses such as anxiety (31, 33, 34, 36). In some studies, questions were asked about stressful life events only during pregnancy, thus the effect of prior stressful events might have been missed (12, 19, 31). 2. Retrospective or cross-sectional collection of data on stress may face problems of recall bias, temporality and underreport of stress because subjects may not remember the occurrence or the impact of events over time. There are chances that women with adverse birth outcome may review their past in a more negative way (6, 19, 31 ). 3. Stress is typically measured at one time point during pregnancy(18, 19, 23, 24, 26, 27, 30, 32, 34, 36). It is not know if there is a critical point in pregnancy when experiencing acute stress is more detrimental to pregnancy outcomes than at other times or if chronic or acute stress leads to PTD. 4. Studies often fail to test for the direct as opposed to the indirect effects of stress (21, 36). For example, other risk factors for PTD, such as smoking, poor nutrition, drug use, previous PTD, and biomedical risks could be 18 added to models to test if stress is an antecedent to these potentially mediating factors and how much of the stress-PTD association is explained by these factors. Challenges in Measuring Stress Stress has numerous definitions and various measures by which it is assessed in literature (38, 39). One study defines stress as a psycho- physiological consequence of any event challenging an organism’s capacity to cope (40). Stress has also been defined as a stimulus, as a response, and as the transaction between environmental stimuli and individual responses (37). Three broad ways to measure stress described in literature are- (2) objectively as life events or experiences, (8) subjectively as individuals appraisal and (8) biologically due to the activation of physiological systems. Objectively stress can be assessed as a stimulus (stressor). Measurement of such a stressor is done commonly by adding the number of life events experienced by an individual or based on weighted scores to produce an overall life events index. Life events have been defined as environmental stressors that are threatening or harmful and which have the potential to impact adversely on health, whether it be physically or psychologically when it is associated with high state anxiety (40). High trait (personality-related) anxiety can further amplify the psychological response to life events stress. Life events measure has disadvantages of ignoring the subject’s appraisal to the stressor and contributions of coping mechanisms. 19 Stress as a response can be psychological or physiological. Psychological stress can be measured by a variety of self-report (perceived stress) or interviewer ratings of anxiety, depression, distress, etc. Anxiety is considered as the psychological consequence of exposure to real or imagined stress (40). Perceived stress is a measure of individual’s appraisal of the stressful situation, which is a more sensitive measure of stress than objective measure, since all individuals do not perceive life events as equally stressful. Physiological stress can be measured by various techniques such as cardiovascular response (heart rate, blood pressure, vagal tone), immunologically by antibody response, lymphocytic activity, interferon production and neuroendocrine response, which is based on activation of the sympathetic-adrenal medullary system (SMS) and the hypothalamic-pituitary adrenocortical axis (HPA). SMS and HPA are viewed as the primary indicators of a stress response and are known to release stress hormones such as cortisol and catecholamines. Summary: Studies measuring stress in pregnancy conceptualize stress several different ways, which may explain inconsistencies in linking stress to pregnancy outcomes. As noted above there are numerous definitions of stress in literature and various instruments are available for measuring stress. Whether subjects are queried before or after the outcome of interest is important for obtaining the correct time frame of stress measurement. Retrospective collection of stress data may introduce recall bias since women with adverse pregnancy outcomes may view their past in a more negative manner. Measurements at single time in pregnancy might also be inadequate assessment of stress since 20 impact of some acute events might be missed. For studies on stress and pregnancy it is recommended to use repeated measurements, conduct prospective studies and use multidimensional approach to stress measurement because of its conceptual power and potentially greater predictive value (37). Most of the studies on PTD and stress mainly focus on psychological measures of stress and fail to consider physiological measures of stress. Large-scale epidemiological studies have a great potential to examine the physiological links between stress and PTD, but many have failed to include potential physiological markers because it is unclear which ones to use and the assessment protocols are too burdensome. The difficulty in selecting potential stress biomarkers arises from diversity of markers examined (cortisol, catecholamine), the modes of assessing them (such as urine, saliva, plasma) and the times of sampling (24 hours, overnight, morning, evening). The burden of collecting urine throughout the day generally precludes such measures from being used in large-scale epidemiological studies. Several studies have shown largest differences in salivary cortisol to be at the beginning or end of the day and targeted sampling at those times could serve as a low-cost marker. There is also increasing evidence for the value of catecholamines as potential biomarkers of stress. Catecholamines and Stress It has been shown that urinary levels of catecholamines are associated with the activities, behaviors, attitudes, perceptions and stress responses in humans. The three catecholamines, Epinephrine (E), Norepinephrine (NE) and Dopamine 21 (DA) have been recognized as sensitive indicators of psychophysiological reactions. The Biology of Catecholamines Tyrosine is the precursor of all the catecholamines (41-45). Tyrosine hydroxylase forms dopa from tyrosine, which is decarboxylased into dopamine. Dopamine beta-hydroxylase converts dopamine into norepinephrine which is converted into epinephrine by N-methyl transferase (41-45). Epinephrine (E) is the principal hormone of the human adrenal medulla (approximately 90%) while norepinephrine (N0) is the neurohormone of the sympathetic nerves (41, 42, 45, 46). The distribution of catecholamine (E, NE and DA) within the sympatho- adrenal system is uneven. In the nerve cells of the sympathetic system, the ratio of DA to N0 is about 50:50, E being totally lacking. In the adrenal medulla, on the other hand, there is about 5 times more E than NE, while DA represents only about 2 percent of the total amount of catecholamines present. Deviations in secretion of E may be expected with stimulation of adrenal medulla and changes in secretion of DA associated with stimulation of sympathetic system, while excretion of NE will occur in both instances (46, 47). 22 Environmental Demands Higher Brain Centers /\ Sympathetic Nervous System Adrenal Medulla \ / Release of E, NE, DA Figure 5: Schematic presentation of pathways from the brain to adrenal systems involved in stress Adapted from Frankenhaeuser 1989 (48). E has been recognized as the emergency (“flight and fight” reactions) hormone that prepares one to meet threatening situations by stimulating the heart, dilating the arterioles of the heart and skeletal muscles, mobilizing glucose and demobilizing the gut. NE has a major role in cardiovascular homeostasis; it has a general vasoconstrictor action, with the exception of dilation of the coronary vessels and raises both systolic and diastolic pressures (49). The adrenal medulla directly discharges E into the blood and hence blood levels of E reliably reflect changes in adrenal medullary secretion (41, 46). NE is released into the neuroeffector junctions. Reuptake of NE into the neurons or metabolism by O-methylation after uptake account for a major portion of the released NE and only a fraction of NE released from the sympathetic nerves 23 reaches the blood (41, 45, 46). 3-methoxy-4-hydroxymandelic acid (vanillylmandelic acid; VMA) is the major urinary excretion product of E and NE in humans (41, 45). Metanephrine and normetanephrine are important intermediates in the formation of VMA (41, 45, 46). Under normal conditions, the 24 hour urinary output of E is 3 to 25 micrograms and of NE is 25 to 45 micrograms (44, 50). However, this output varies during periods of rest and increases with activity (51). A patient at rest excretes 3 to 5 times less NE and 3 to 6 times less E than the patient engaged in moderate activity (49). Under stressful conditions such as severe muscular exercise, centrifugation, trauma, operation, thermal injury, radiation, emotional stress, etc., there is a marked increase in the urinary output of these hormones reflecting an increase in sympathetic nerve and adrenal medullary activity (50, 51). Urine is the easiest source to analyze for catecholamines. Catecholamine levels in the urine need adjustment for the urinary concentration which can be done by either including total volume of urine, collecting all urine over a fixed time period or by measuring creatinine levels as an estimate of concentration. Creatinine determinations can be done on 24-hour urine samples or on the individual sample and the result equated in micrograms of amine per milligram of creatinine or total volume per unit of time. Literature Review on Catecholamine and Stress The relationship of stress and catecholamine excretion is consistent in both non-pregnant as well as pregnant populations. To be able to interpret these 24 results meaningfully, however, it is necessary to consider the possible contribution of other variables such as collection factors and maternal characteristics variables. There appears to be little consistent information on the relationship between these factors and catecholamine excretions in non-pregnant population and even less information in pregnant population. Non-Pregnant Population Levels of Catecholamines in Non-Pregnant Population: Hansen et al (52, 53) mention about high within and between subject variations in urinary catecholamine excretions for healthy men and women population from Denmark. Within subject variation was found to be more than twice the between-subject variation for E, while for NE the within-subject variation was comparable to the between-subject variation. Urinary E and NE were positively correlated with range of r=0.14 to 0.76 in non-pregnant women (53-55), national guardsmen and psychiatric patients as subjects (56), bereaved elderly persons (57), male bus drivers (58) and male employees (59). In healthy men and women, correlation between urinary E and NE was 0.41, between E and DA was 0.37 and between NE and DA was 0.37 (60). Mean urinary DA excretion pattern seemed to follow mean NE excretion but not E excretion (61 ). There was a reported strong correlation between urinary NE and DA excretion in PTSD group r=0.62, p<0.001 (62) and r=0.77, p=0.001 (63), but not a strong correlation in the control group r=0.11, p=non-significant (ns) (63). Urinary E had weak correlation with the other two catecholamines (62). 25 While yet another study found urinary DA, E and NE to be intercorrelated with Spearman rank correlations ranging from 0.47 to 0.69 (64). Factors Associated with Catecholamine Levels in Non-Pregnant Population: Circadian Rhythm- Studies have reported that urinary catecholamine excretion follows a circadian rhythm, with highest excretion in the afternoon and the lowest in mornings and night (52, 53, 60, 61, 65-70). Only study found reduction in urinary E excretion in the afternoon as compared with the morning (71 ). Ethnicity- There was no consistency found in literature for association between ethnicity and catecholamines. Pratt et al (72) showed normotensive black children to have significantly lower levels of nocturnal urinary NE than that of white children, while Ziegler et al (73) found white hypertensives had elevated plasma NE levels, and black hypertensives had normal levels. De Bellis et al found African-Americans excreted a significantly greater concentration of urinary E and showed a trend for significantly greater concentrations of urinary NE over 24 hrs than Caucasian subjects (74). Another study found significant ethnicity differences in DA levels; with AFAM individuals having DA levels higher than whites (64). Three other studies found no effect of ethnicity on urinary E and NE (75, 76) and plasma NE (77). Ag_e_- The relationship between age and catecholamine excretion varied across studies. Yehuda et al found no significant correlation between plasma NE and age (78). Hansen et al, Pratt et al and Evans et al did not detect any association between age and urinary catecholamines (52, 58, 72). But a few others found 26 weak to strong positive association between age and urinary catecholamines (57, 79-84). While two other researchers found that age was significantly negatively correlated with urinary E and NE in men in their fifties and seventies (85) and in children (86). Education- No significant correlation was found between education and urinary E and NE (58). gm Gender was found by many studies to be an important factor associated with catecholamine excretion. Females tend to have lower excretion of urinary E and NE as compared to men (52, 72, 74, 79, 81, 86-90). Because of sex differences, researchers caution against generalizing results based on men to women. Blood Pressure- Hypertensives were found to have increased urinary and plasma catecholamine excretion (73, 81, 91, 92). No significant correlation between plasma NE and blood pressure (78). Smoking- Smoking was found to increase excretion of urinary E and NE (52, 91, 93, 94). Two studies found urinary E and NE not to be associated with smoking (79, 84). Alcohol- Drinking was positively associated with urinary E (79). gody Weight- Schmitt et al (79) and Pratt et al (72) found body weight was unrelated with urinary E and NE and Yehuda et al (78) found no significant correlation between plasma NE and weight. Hansen et al (52) found inverse relationship between urinary E and NE and body mass index (BMI), while Jenner et al (80) showed that urinary NE tend to be positively related to body weight and 27 van Der Beck et al found positive relationship between urinary NE and BMI. Only urinary NE and DA (not E) was significantly correlated with body weight, r=0.39, p<0.05 and r=0.46, p<0.01, respectively (95). Un'ne flow— Strong and consistent positive correlation was found between urinary E and NE and urine flow (71, 79), while another study found no correlation between urinary E and NE and urine volume (60). Creatinine- Urinary E and NE were positively correlated with urinary creatinine (60, 71). Shift Work- Shift work tends to change the pattern of urinary catecholamine excretion (61, 66). Fujiwara et al (65) found urinary NE was significantly higher in the evening shift workers than in the day shift (p=0.027), while opposite relation was found by Yamasaki et al (75) and Boucsein et al (96) with higher urinary E and NE in day shift workers than evening and night shift workers. While Cavatorta et al (97) did not find any difference in levels of serum catecholamines at both morning and afternoon shifts. Eatrental Status- Though women with at least one child living at home (parental status) reported significantly higher levels of home strain than did women without children at home, an effect that was independent of marital status, home strain and parental status were unrelated to urinary E and NE levels (98). Marital Status- Nurses who were not married showed a significant decrease in urinary NE on off days as compared to work days, whereas the work and off day levels were similar in married nurses (99). 28 Urinary E was found to have weak positive association with age in females while negative association was observed in males (79) and urinary NE and E were found to be lower in healthy women than in men in the age group 539 years of age (i.e. during the first half of life expectancy), while excretion was similar in men and women in the 240 years of age group (i.e. second half of life expectancy) (81). Creatinine excretion was seen to be decreasing with increasing age (80). Low SES /high pessimism women demonstrated higher mean 24 hr E levels (p<0.05) compared with other 3 groups (high SES/high pessimism, low SES/Iow pessimism, high SES/low pessimism) and low SES /high pessimism had significantly higher 24 hr urinary E/NE ratios than any other group (p<0.005) (100). Another study found SES did not predict urinary catecholamine excretion (74). Relationship between Catecholamines and Stress in Non-Pregnant Population: Perceived Stress (PS)- Three studies found that perceived stress had a significant positive association with urinary E and NE (82, 101, 102), while three other studies did not find any such relationship (54, 103, 104). Among the negative studies, one did not use a validated method to measure PS (103), another used short term noise stress for which urine might not be a suitable measure of catecholamine (54) and the third study did find lower levels of urinary E and NE in chronically stressed women but these differences did not achieve statistical significance, probably a result of insufficient power (104). 29 4m Significant correlations are reported between state anxiety and urinary E, NE and DA (56, 85, 88, 105-107). Correlations ranged from 0.18 to 0.50. Degression- Depressed subjects had significantly greater amount of urinary E NE and DA excretion, p<0.01 to p<0.007 (86, 89, 90, 108-110) , while one study found a significant inverse correlation between urinary NE and depression (57). Work-stress- Work period urinary E and NE excretion was significantly higher than rest period excretion (p<0.0001 to p<0.05) (53, 55, 67-69, 76, 83, 92, 96, 98, 99, 106, 111). Only one study by Korunka et al did not find urinary E and NE to be significantly different on work and non-work day and also found only a weak relationship between catecholamine Ievels and subjective strain measurements (59). Higher work stress was associated with higher urinary NE and E excretion (55, 92, 111, 112), while few other studies failed to support this association (76, 83, 98, 99). Physical Stress- High physical stress urinary and plasma E and NE were significantly higher than baseline levels (68, 70, 83, 113-116). Other Types of Stress- Other types of stressors which have been examined in literature are bereavement (57), noise (54, 66, 82, 97, 115, 117, 118), traffic congestion (58, 107), crowdedness(119), premenstrual stress (101), weather conditions (107) and dental procedures (120, 121). Mean 24-hr urinary NE and E output for bereaved subjects were significantly higher than those in previous studies of normals (57). Noise exposed subjects had significantly higher urinary E and NE excretions (54, 66, 82, 97, 115) with correlation between perceived noise and E and NE being 0.30 and 0.24 respectively (54). While two studies did 30 not support this association, first in children exposed to chronic aircraft noise (117), where children with low noise exposure reported significantly more stressful life events, this difference in life experience might account for the lack of noise effects and second in male subjects, probably due to small sample size (118). Traffic congestion had significant positive correlation with on-the-job urinary E and NE elevations (58, 107). Perceived crowdedness was associated with increased urinary NE and E excretion (119). Urinary E was significantly positively correlated and NE significantly negatively correlated with premenstrual turmoil (101). Urinary E and NE excretions were high when weather conditions were stressful such as driving in the fog (107). Urinary E and NE were found to be significantly higher in children after dental procedure and on the experimental day as compared to the control day of dental treatment (120, 121). Increased urinary E and NE excretions were also found with increased mental stress (70, 115, 122), thermal stress (115), fatigue (123), flying in planes (124) and parachute jumping (125). Plasma E declined significantly in the vulnerable elderly Alzheimer spousal caregivers who received respite, but rose in those who were wait-listed (122). Hansen et al found no relationship between urinary E and NE with scales of cognitive, behavioral and emotional stress (53). Post-traumatic Stress Disorder (PTSD) subjects were found to have significantly higher excretions of urinary catecholamines, E, NE and DA (62-64, 74, 84, 95, 126, 127) and plasma NE (78). Urinary catecholamines were positively correlated with duration of PTSD trauma and severity of PTSD 31 symptoms (62, 74). Two authors did not find association between urinary E and NE and PTSD (128, 129). Perceived control appeared to be an important link in the psycho-physiological stress process. This is seen in three studies where elevated urinary E and NE levels were seen only in those subjects where closing the window could not reduce the perceived disturbance due to traffic noise (82), perceived job control had negative correlation with on-the-job E and NE elevations (58) and parents of children admitted to critical care who appraised their child’s situation as one that they could change had lower anxiety and significantly lower catecholamine excretion than parents who appraised their child’s situation as the one that must be accepted (88). Briefly, the literature reviewed on catecholamines and stress in non-pregnant population points towards a consistent and strong relationship between these two factors. Several measures have been used to assess stress such as self-report, responses to stress (anxiety and depression) and stressors such as physical stress, work-stress, noise, thermal stress and mental stress. 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Urinary NE excretion ranged from 6.2 to 56.4 micrograms (pg) per 24 hrs, while E ranged from 0.18 to 18.1 pg per 24 hrs (131). These ranges were compatible with the levels found by Subrahmanyam (133) in urine of normal pregnant women (NE=36.211.18 pg and E=6.5:l:0.37 pg) and non-pregnant women (NE=33.721.62 pg and E=5.7:tO.54 pg). Lederman et al found plasma values of catecholamines in third trimester to be 8:39.8220 picogram/ml and NE=212.41:60.1 picogram/ml (137). These excretions of E and NE which remain within normal limits during the course of pregnancy start to rise with the onset of labor and drop down gradually during the postpartum period (14, 15, 130, 132, 137). Most of the studies collected urine over a 24 hour period for determination of E and NE excretion (14, 130-133), while two studies were based on plasma levels of E and NE (15, 137). One study (138) found NE and E to be positively correlated with each other (r=0.42, p<0.03). Factors Associated with Catecholamine Levels in Pregnancy: Circadian Rhythm- Urinary catecholamines showed a circadian pattern with a significant rise of NE and DA levels in the afternoon period and lower 8 levels in the night period at p<0.05 (139). 62 Age— The positive correlation seen between urinary E and age r=0.41, p<0.04 (138) could indicate that the older women were experiencing more stress with greater demands from children and other responsibilities, while another study did not find any significant correlations between maternal age and urinary catecholamine (8, NE and DA) measures (140). Education- Education and urinary catecholamines (8, NE and DA) did not have any significant correlations (140). Gestational age- Gestational age was not associated with the rise in plasma NE during physical stressful stimulus of puncturing the fetal trunk for blood sampling or transfusion (141 ). Posture- Posture was shown to influence excretion of catecholamines, in urine samples collected after 40 minutes in recumbent position and 40 minutes of ambulation. NE levels significantly increased (p=0.006) when the lateral recumbent position was compared with post-ambulation pre-stocking period, there was marginal increase in DA and the increase in mean 8 was not statistically significant (142). Blood Pressure- Toxemic patients had increased urinary NE and E excretion when compared with normal pregnant women (133, 134) and normal women (133). The increases in the plasma levels of NE and E after submaximal exercise test showed a positive correlation with the increase in systolic blood pressure, r=0.75, P<0.05 and r=0.75, P<0.05 respectively (143). Nisell et al found plasma NE and 8 levels at rest and after mental stress test were not significantly different 63 between the group of women with pregnancy induced hypertension and healthy pregnant controls (144). Mim—de; Women residing at high altitude tend to have higher levels of catecholamines during pregnancy than women living at lower altitude (140). Relationship between Catecholamines and Stress in Pregnancy: Among the 20 studies reviewed, 17 studies showed a consistent relationship between stress and catecholamine excretion in pregnancy (135-138, 140, 141, 143-152), while 3 studies did not support this relationship (139, 153, 154). Studies have been done on different populations such as low socioeconomic class, African-Americans, adolescent women and physicians. m Field et al (145) found high anxiety pregnant women to have significantly elevated urinary NE (p=0.05), elevated E (not statistically significant) and low DA (p=0.01) levels. While Kemp et al (138) did not find any significant correlations between urinary NE and E with state anxiety. Study by Sanders et al found a significant inverse relationship between urinary NE and composed-anxious scale, r = - 0.52 and p<0.05 (153). High anxiety women were found to have higher scores on depression and anger scales (145). High-risk pregnancy group among low socioeconomic pregnant women had significantly higher levels of 8 levels in urine compared with low risk group, though there were no significant group differences for anxiety scores (138). Degression- While one study found significantly higher excretion of urinary NE, E and DA levels during neonatal period in mothers with depressive symptoms and their infants and significant correlation of Beck depression inventory scores at 6 64 months post-partum with elevated NE at neonatal period ( 146), other two studies found significantly lower levels of plasma NE and E on day with post-partum blues (152) and statistically significant correlation (r=0.76) between decreased urinary NE and increased depressive scores in pregnant women and non- significant increase in E (149). Work Stress- Both E and NE excretion in urine were significantly higher (52 to 93%) on workdays than non-workdays (135, 147, 148). State anxiety test taken midway through work and non-work days showed a median 34% increase on workday compared to non-workday (147). Elevated risk of poor pregnancy outcomes such as preterm delivery, low birth weight are seen in practicing physicians and nurses, which may be related to high levels of psychological stress and long working hours in these professions (155). Studies have shown urinary E and NE to be increased by 64% (p<0.025) in physician/nurse group during workdays over those of a working non-physician control group of similar gestational age (148). Low job latitude was significantly associated with a slight increase in urinary NE and DA levels in the afternoon and night, p<0.05; however no significant increase in catecholamine levels was found in association with the other indices of life stress or work stress, although NE values tend to be higher with most stress measures (139). Physical Stress— Measurements of urinary excretion of vanilmandelic acid (metabolite of E and NE) before and after several forms of exercise found levels to be higher after exercise, though this difference did not achieve statistical significance (134). Rauramo et al (143) were able to support this theory by 65 showing plasma concentrations of NE and E rising rapidly during sub-maximal exercise, from 2.9103 to 6.9212 nmol/l and from 03120.04 to 0.471008 nmol/l, respectively and then declined rapidly thereafter. Other Types of Stress- Four studies examined levels of catecholamines in plasma of pregnant women after stressful events such as hooked to fetal heart rate monitor (151), exposure to thermal stress (136), standardized mental stress test (144) and undergoing fetal blood sampling or transfusion (141). Plasma NE and E rose significantly in pregnant women not in labor hooked on fetal heart rate monitor (Group A), in comparison with the group of pregnant women not hooked on fetal heart rate monitor (Group B), after start of monitoring and began to fall after monitoring was cutoff at 30 minutes. In group B, there was no rise in E and NE. No difference was seen in DA levels between the two groups (151). Fetal heart rate monitor might be a source of stress for group A women. Exposure to thermal stress and mental stress test induced feeling of discomfort, stress and irritation. Both stresses caused levels of plasma NE and E to be significantly elevated during and after exposure (136, 144). Women undergoing fetal transfusion in second and third trimester had substantially elevated plasma NE levels after transfusion (141). Fetal plasma NE levels also showed a significant increase after transfusion, with significantly higher levels in those fetuses with samples obtained via intra-hepatic vein (IHV) than in fetuses in whom samples were obtained via placental cord insertion (PCI) site (141). This difference might be due to the fact that IHV is innervated and PCI is not innervated leading to dissimilar physical stresses felt by the fetuses, supporting the other finding of 66 duration of stimulus (i.e. the time interval from puncturing the fetal trunk to collecting the last sample) being associated with the rise in NE in the IHV group (141). Two intervention studies (146, 150) strengthen association between catecholamines and stress. Depressed mother subjected to intervention of 3 months had significantly lower mean Beck depression inventory scores at 6 and 12 months after intervention and significantly lower urinary E and DA levels than depressed mothers not provided with intervention (146). Pregnant women with massage and relaxation therapy reported feeling less anxious after therapy (150) with significant decrease in urinary NE (p<0.01) for massage group and increase in urinary DA for massage group (p<0.01) and for relaxation group (p<0.005) as compared to catecholamine levels before therapy. The 15 minutes experimental noise stimulation did not induce changes of plasma 8 and NE in pregnant normotensive and hypertensive women (154). Compared to non-pregnant population the range of studies done on pregnant women examining the relation between catecholamines and stress is limited, still consistent associations are seen. 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Sample Size - Some discrepancies remain between results of studies examining the relationship of stress and catecholamines. These might be attributed among other factors to small sample size (59, 104, 118, 153, 154). Small sample sizes may lead to reduced power with failure to detect differences even when present. 2. Urine Vs Blood Catecholamines - Some studies have measured catecholamines in 24 hour urine collection, some in samples of urine collected over varied periods of times, while others have collected blood samples to determine the catecholamines. Studies examining plasma levels of catecholamines usually tend to look at catecholamines responses to acute stressors such as labor, postpartum period, being hooked to fetal heart rate monitor, exposure to noise stimulus, thermal stress, mental stress, exercise and fetal blood transfusion. While studies hypothesizing association between catecholamines and long term stress such as anxiety, depression, work stress tend to use urine to determine catecholamine levels. This might be because catecholamines have a very short half-life and turnover or decay of circulating levels can occur within a minute or two (46, 156). Hence blood levels of catecholamines reflect acute states and are not particularly useful in studies on effects of chronic responses because of considerable fluctuations and intra-individual variability in catecholamine levels. Urinary catecholamines are excreted slowly over period of hours during which the bladder fills and therefore 79 provide longer, more long term measures of sympathetic nervous system activity than the blood measures (46, 156). . Psychological vs. Physical stress - Catecholamine responses are studied for different types of stresses such as emotional/psychological stress and physical stress. While physical stressors maybe helpful for acute catecholamine response, psychological stress might be associated mainly with chronic response. Catecholamine responses to acute and chronic stress may be different. . Confounders - Factors such as physical activity, drug use, consumption of alcohol and caffeinated beverages may affect catecholamines and need to be controlled for when catecholamines are used to assess stress and are linked to other factors such as adverse pregnancy outcomes (156). Not all studies described above takes these factors into consideration, which might account for varied results. . Gender- Another important factor, “gender”, might play an important role in explaining the inconsistent results found in various studies (52, 72, 74, 79, 81, 86-90). In literature, females were found to have lower excretion of urinary catecholamine as compared to men. Among the studies reviewed some studies have used females as study subjects; some studies have used only male population, while still others used both males and females. As previously mentioned researchers caution against generalizing results from studies based on men population to women population due to the gender differences. 80 Minimal burden of collecting urinary catecholamine levels make it a potentially useful physiological marker of stress in epidemiologic studies. By introducing feasible assessment protocols such as targeted sampling (overnight urine collection, morning/evening samples), such protocols can be introduced into large-scale epidemiological studies. Catecholamines and Diseases Catecholamines have also been implicated in some chronic diseases. Stress causes activation of the sympatho-adrenal system. Signals from the brain cortex are sent to the hypothalamus and via the autonomic nervous system to the adrenal medulla. This leads to increased production of all catecholamine hormones (48, 88). The short term mount in the stress hormones is favorable since it assists the mental and physical adjustment to acute environmental demands. However, the frequent and long term elevations of stress hormones may result in psychosomatic disturbances and structural changes in blood vessels leading to coronary heart disease and hypertension (48, 157). Two other factors- low control (58, 88) and inability to unwind after stressful encounters (98, 99) also play an important role in long term effects of stress hormones. Urinary NE and 8 were found to be lower in healthy women than in men in the age group 539 years of age (i.e. during the first half of life expectancy), while excretion was similar in men and women in the 240 years of age group (i.e. second half of life expectancy). In hypertensive individuals, catecholamine excretion was slightly higher in the first half and significantly higher in the second half of life expectancy. These differences in catecholamines excretion could contribute to 81 the sex-related and age-related differences in incidence of cardiovascular diseases such as hypertension and coronary heart disease (81). Resting levels of urinary catecholamines were seen to be very high in hypertensive smokers (91). Urinary NE and E excretion rates in the workplace were found to be significantly related to blood pressure (92) and to systolic blood pressure means (76), also there was a greater proportion of women with history of hypertension in the work-stressed group, 0.05= 37 weeks) and 78 women who delivered preterm (< 37 weeks). To examine factors associated with urinary catecholamine levels in pregnancy, analyses were restricted to the 227 women who delivered at term and did not work between one AM. and eleven A.M. (i.e. no shift work) during the ‘Part 2’ data collection period. 87 Figure 6: Flowchart of study sample for catecholamine study Enrolled in POUCH as of 6-14-2001 (Removed 5 with anomalies and 5 with unknown pregnancy outcome) N=1200 / \ Opportunity to be in Prior to part 2 protocol 93” 2 N=166 N=1034 Refused ‘0 be i" Enrolled in Part 2 Pa” 2 N=896 (86.7%) N=143 (13.8%) Returned samples and diary Never returned samples and/or N=832 (80.5%) diary N=59 (5.7%) (92.9% of enrollees in Part 2) (6.6 % of enrollees in Part 2) Catecholamines not Catecholamines assessed assessed N= 513 N=314 Term delivery Preterm delivery N=236 N=78 Non night/shift Night/shift work work N=9 N=227 88 Catecholamine Levels in Urine: Urinary catecholamines were measured using an extraction procedure followed by high performance liquid chromatography (HPLC) using a modified method developed by Hollenbach (160). Briefly, pH was adjusted to between 3 and 5, followed by addition of an internal standard (3, 4-dihydroxybenzylamine- hydrobromide). Extraction followed using 2M NH4OH/NH4CI buffer and heptane containing 1% octanol. Following mixing and centrifugation, 3 ml of the organic layer was transferred into polyethylene tubes and octanol and 80mM acetic acid were added. After shaking and centrifuging, the aqueous layer was collected and stored for analysis. The HPLC system (Waters 2695 separations module) included an electrochemical detector (Waters 464). A Waters Symmetry Shield RP 4.6 x 150 mm column as used. The electrode potential was +650 mV versus an Ag/AgCl-reference electrode. The mobile phase consisted of 0.175 mMsodium decanesulfonate, 0.1 mM EDTA, 0.15 mM NaH2PO4 and 5% methanol (pH 5.1) with a flow-rate 1.2 ml/min. Catecholamines assessed were norepinephrine (NE), epinephrine (E) and dopamine (DA). Inter-assay coefficients of variations (CV) for NE, 8, DA and creatinine are 10%, 14%, 10% and 8% respectively and intra-assay CV are 5%, 13%, 6% and 2% respectively. Diary and Interview Data: Along with collection of urine, women completed a daily diary for three consecutive days. This diary collected information on time of urine collection, time of waking and sleeping, whether it was workday or non-workday, time of starting and finishing work and Global Assessment of Recent Stress Scale 89 (GARS). Demographic information and data on psychosocial and behavioral factors were ascertained by in-person and self-administered interviews, which were largely composed of complete or abbreviated versions of previously validated psychosocial instruments (16). Analytical Strategy: During inspection of data it was found that for NE, E and DA values, approximately 5%, 27% and <0.005%, respectively were below the limit of detection (L). Non-detectable values were substituted by L/2 as recommended by Hornung et al and Finkelstein et al (161, 162). The limit of detection for NE and E was 1 andl and for DA was 3 nM/dl, hence all values below ‘L’ for E and NE were replaced by 0.5 and for DA by 1.5. Catecholamine values were transformed to their natural log (log nM/dl) to adjust for right skewness. The log transformed catecholamine value was divided by urine sample creatinine value to adjust for concentration of urine (log nM/dl of catecholamine per mM/L of creatinine). The creatinine adjusted, log transformed catecholamine values were used in all analyses and outliers above or below 4 standard deviations were removed. Relationships between catecholamine levels (waking, bedtime across the 3 days) were assessed using Pearson’s correlations. Sample collection times were dichotomized for waking (at or before 9 AM versus after 9 AM) and bedtime (before 10 PM versus 10 PM and later), and time interval from waking to collection of sample was divided into four categories, 0-15 minutes, 16-30 minutes, 31-60 minutes, and greater than 60 90 minutes. This approach follows the analytic strategy used to assess factors associated with salivary cortisol levels in the same study sample (163). Ethnicity was categorized into two groups African-American,(AFAM) and white and other, age into five groups (< 20, 20 to 24, 25 to 29, 30 to 34 and >=35), Medicaid into two (on Medicaid “yes/no”), education into three groups (< 12, =12 and >12 years), parity into three groups (0, 1, >=2) and gestational age at enrollment into 3 groups (weeks 16 to 21, 22 to 23, >23). Proc GLM was used to describe day-specific relationships between catecholamines levels, collection factors, and maternal characteristics. Repeated measures analysis by Proc Mixed was used for analyses across all 3 days. Proc mixed was chosen over proc GLM for repeated measures analysis, since proc mixed handles observations with missing values and takes into consideration within and between subject variability. All analyses were conducted using the SAS software. .R_682|t_s Maternal Characteristics: Characteristics of the 227 women who delivered at term and are included in this analysis are described in Table 17. Catecholamine Levels: Table 18 shows the mean, median and standard deviation for NE, E and DA separated as waking and bedtime for 3 consecutive days. Day-to-Day correlation coefficients for NE waking ranged from 0.37 to 0.45, NE bedtime from 0.20 to 0.44, E waking from 0.22 to 0.34, E bedtime from 0.33 91 to 0.40, DA waking from 0.30 to 0.33 and DA bedtime from 0.42 to 0.60 (Table 19). Correlations for each catecholamine between waking and bedtime samples and correlations between the three urinary catecholamines are presented on Table 20. There appeared to be only a moderate correlation for each catecholamine across three consecutive days and between their respective waking and evening values, hence it was decided to do further analysis by keeping the catecholamine values separated by day ( Day 1, 2 and 3 of sample collection) and collection time (waking and bedtime). Correlations between NE and E ranged from 0.17 to 0.52, between NE and DA from 0.75 to 0.90 and between E and DA from 0.29 to 0.60. Strong correlation was seen between NE and DA but NE and DA show only a moderate correlation with 8. Relationship between Catecholamine Levels and Timing of collection, Interval from Waking to Collection of Urine sample and Work Status: Day-specific analysis found interval from waking to collection of urine sample (p-values from 0.309), waking collection time (p-values from 0.1-0.7), bedtime collection time (p-values from 0.2-0.9), and workday/non-workday (p-values from 0.08-1.0) did not have any statistically significant relationship with any of the catecholamines. Unadjusted and adjusted repeated measures analysis across three days also did not to show any associations between catecholamines and waking collection time, bedtime collection time, and workday/non-workday. A borderline significance of E waking was seen only on day 2 with work (p=0.08) 92 which became non-significant when analyzed using unadjusted repeated measures model (p=0.1). ln unadjusted repeated measures model, interval of collection develops a borderline significance with E waking (p=0.08), which achieves statistical significance when adjusted for waking collection time and work (p=0.04). E waking levels increase from the 0-15 minutes interval to 16-30 minutes interval, then gradually decrease over the next 2 intervals (31-60 and >60 minutes). The urine collected later than 60 minutes after waking has the lowest 8 waking levels as compared to the previous 3 intervals. In a sub-analysis done only on women who work (N=123), the unadjusted repeated measures analysis across three days did not show any associations between catecholamines and interval from waking to collection of urine sample, waking collection time, and workday/non-workday. But there was a significant association seen for bedtime collection time with only NE bedtime sample (p=0.04) with collection at and after 10 pm having lower mean NE bedtime values as compared with collection before 10 pm. This association was lost (p=0.5) in the adjusted model with work status. Relationship between Catecholamine Levels and Maternal Characteristics: ln day-specific analysis, ethnicity, Medicaid status and age were important variables associated with catecholamine levels. Ethnicity was significantly associated with all catecholamines (waking and bedtime) on all three consecutive days (p-values <0.0001 to 0.05), except 8 waking on day 1 (p=1.0). AFAM women had lower catecholamine values as compared to white and other. Medicaid was statistically significantly associated with N8 bedtime (only on day 93 2, p=0.008), E waking (p-value from 0.009008), 8 bedtime (only on day 2 and 3, p= 0.005 and 0.06, respectively), DA waking (p=0.02 only on day 2 ), DA bedtime ( only on day 2 and 3, p=0.007 and 0.01) and not with N8 waking ( 0.2 to 0.7).Women insured by Medicaid had lower values of all catecholamines compared to women not insured by Medicaid. The Medicaid insured groups might be confounded by ethnicity with more AFAM women insured by Medicaid. Age achieved significance for NE waking only on day 2 (p=0.07), NE bedtime only on day 2 (p=0.06), E bedtime on day 2 and 3 (p=0.03 and 0.04) and DA bedtime on day 1 and 2 (p=0.06 and 0.04). A trend of increasing levels of urinary catecholamines was found with increasing age. Education was associated with N8 waking on day 1 and 2 (p=0.03 and 0.02), NE bedtime an all 3 days (p-values ranging from 0.03 to 0.06), E waking only on day 2 (p=0.03), DA waking also only on day 2 (p=0.007) and DA bedtime on all 3 days (p-value 0.01-0.02). The group with > 12 years of education tend to have higher values for all catecholamines except for NE bedtime (where < 12 years of education had highest values on day 1 and day 3) and for E waking only on day 2 (where = 12 had highest value). The > 12 years of education group is likely to be confounded by age as women with > 35 of age will more likely be in this education group. Parity did not show any significant association with any catecholamines except on day 1 with N8 waking (p=0.003) and DA waking (p=0.0006). Nulliparous women tend to have higher values particularly for NE and DA (waking as well as bedtime samples). For NE and DA, those women enrolled after 23 weeks of gestation tend to have lower values. 94 This might again be confounded by ethnicity, as more AFAM women tend to fall in the “>23 gestational weeks enrollment” group. Similar means were observed for age groups 20-24 and 25—29 and 30-34 and >=35, and parity groups 1 and >= 2, hence it was decided to combined these groups. Unadjusted repeated measures analysis using proc mixed was used to include data across 3 day (Table 21). This analysis found ethnicity to be statistically significantly related to all catecholamines except E waking, age significantly associated with all catecholamines except for NE waking and DA waking for which borderline significance was found, Medicaid was significantly associated with all catecholamines except NE waking and DA waking, education statistically significant for all catecholamines except 8 waking and E bedtime for which borderline significance was seen, parity was statistically significant only for NE and DA waking and border significant for DA bedtime. The trend seen in day- wise analyses remained consistent; with AFAM women, women on Medicaid and women enrolled after 23 weeks of gestation having lower catecholamine values and women >=30 years of age, having >12 years of education and nulliparous having higher catecholamine values. All the above maternal characteristics were put into a single model to adjust for each other and results are shown in Table 22. Ethnicity and age maintained consistent association with all catecholamines except 8 waking with AFAM women having lower catecholamine levels as compared to white and other ethnicities and increasing catecholamine levels with increasing age groups and 95 women in >=30 age group having the higher catecholamine values as compared to the other two age groups (< 20 and 20 to 29). Medicaid remained significant only for E waking (p=0.05) with women insured by Medicaid having higher NE waking levels as compared to women on Medicaid and NE waking (p=0.04) with women insured by Medicaid having higher NE waking levels as compared to women not insured by Medicaid. Education was significant for NE waking (p=0.04) and DA bedtime (p=0.03) with > 12 year education women having higher values as compared to women from the other 2 groups (<12 and =12 years of education) and NE bedtime (p=0.02) with women of < 12 years education having higher values as compared to women from the other 2 groups. Parity maintained its strong significance for NE waking (p=0.002), DA waking (p<0.0001) and DA bedtime (p=0.07) with nulliparous women (parity = 0) having higher values as compared to other group women (parity >=1). Smoking was found to be associated with N8 bedtime (p=0.04) and E bedtime (p=0.05) when used as a continuous variable but not with the other catecholamine levels. When smoking was added to the above full adjusted model all maternal characteristics maintained their significance as the previous model without smoking. In all the analyses, gestational age at enrollment was not significantly associated with any of the catecholamines. Interactions between the maternal characteristics variables could not be analyzed in the present study due to small number of women in some of the groups. The interactions will be examined when the dataset is enlarged with all POUCH participants. 96 Additional Preliminary Analysis: Creatinine values were used to adjust the catecholamines values for the urine concentration. It was found that creatinine values tended to be higher in AFAM, in women insured by Medicaid and in women enrolled after 23 weeks’ gestation, while creatinine values were lower in the older age group women, women with > 12 years of education and nulliparous women. It was important to assess whether this characteristic distribution of creatinine for maternal characteristics was responsible for the significant relationships seen between catecholamines and maternal characteristics. Creatinine values might vary depending on whether a woman voided during the night before she collected the morning urine sample. It is possible that these women who void during the night might have more dilute urine which could have an effect on the creatinine levels of the morning urine sample. In women who void during the night, their waking urinary catecholamine levels will reflect a shorter time period (only the early morning hours) as compared with women who do not void and urine is stored over the entire night. To answer these questions of effect of creatinine and voiding during the night, additional preliminary analyses was done on data collected on another subset of 130 women. Collection of information on voiding during the night before collecting the waking urine sample was started after the POUCH study had began, hence this data is not available for the 227 women in the catecholamine study. Approximately 59% of the 130 women had voided during the night before collecting the waking urine sample and these women had significantly lower 97 creatinine as compared to the women who had not void during the night (10.35 versus 12.02 mM/L, p=0.03). Voiding/not voiding during the night was found to be unrelated with all maternal characteristics except ethnicity. Repeated measures analysis, as described in the results section, had shown a significant association between catecholamines and ethnicity. When the variable (voided during the night “Yes/No”) was added to this model, the significant relationship between ethnicity and all catecholamines waking samples remained significant. These analyses show that creatinine values and voiding might only partly explain the relationship between catecholamine and ethnicity. Hence we could infer that the relationship between catecholamines, ethnicity, age, education, Medicaid and parity maybe true and not merely a result of confounding by creatinine. This question will be reanalyzed with a larger sample size when all the POUCH “part 2” participants’ data is collected and cleaned. 98 Table 17: Distribution of maternal characteristics in the study sample of 227 term delivered women: Ethnicity, Age, Medicaid, Education, Parity and Week of pregnancy at enrollment Maternal Characteristics % (N) Ethnicity: African-American 29 (66) White and other~ 71 (161) Age (yrs): < 20 13 (30) 20 to 24 31 (71) 25 to 29 29 (66) 30 to 34 20 (45) Medicaid: Yes 50 (113) No 50 (114) Education (yrs)*: < 12 18 (40) = 12 30 (69) > 12 52 (117) Parity: 0 27 (60) 1 31 (71) >= 2 42 (96) Week of pregnancy at enrollment: 16 to 21 40 (91) 22 to 23 35 (80) > 23 25 (56) * Data missing for 1 woman ~ Ethnicity “other" is 6 % (n=14) 99 Table 18: Urinary catecholamine levels+ (NE, 8, DA) # in waking and bedtime samples on three consecutive days (N=227) Day 1 Day 2 Day 3 Mean Median SD Mean Median SD Mean Median SD N8 Waking 0.51 0.44 0.33 0.58 0.49 0.41 0.55 0.49 0.41 NE Bedtime 0.64 0.50 0.53 0.58 0.49 0.47 0.56 0.44 0.48 DA Waking 0.74 0.63 0.42 0.83 0.71 0.49 0.81 0.71 0.53 DA Bedtime 0.86 0.65 0.71 0.81 0.66 0.63 0.79 0.60 0.64 E Waking 0.30 0.27 0.36 0.35 0.31 0.41 0.29 0.26 0.40 E Bedtime 0.31 0.25 0.45 0.36 0.28 .50 0.33 0.26 0.47 " All catecholamine values expressed as log nM/dl of catecholamine per mM/L of creatinine # NE: Norepinephrine, E: Epinephrine, DA: Dopamine 100 02.80000 ”<0 .0222002.0m ”m .0222002.00.0z ”m2 ... .020.2. 002002200 ”.0 + $22.00.... $20.00. 8.2-00.2. .9002... .....o-..~.c. 60.0-8.2. 00.2 .80 3.2 .00 00.0 0.... £020.02 800-000. 3.0-5.0. 80.2-8.0. 3.2-00.0. 60.0-8.2. 80040.0. 0...... 00.0 00.2 8.0 $3 .00 0.8-0.02 .3000... 60.2-8.0. €00-80. .020-2... $00-80. 80.9.0.0. 00.0 ~00 «to 8.0 80 8.2 0.02.28 20 ...00.. ._0 ...00. ._0 ...00. ..0 ...00. 20 ...00. 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The present study has a fairly large sample size of 227 women, which will increase as more samples are analyzed from the POUCH study. Most of the previous studies have collected 24 hour or overnight urine to measure the catecholamine content. A few studies do have similar protocols as the present study of collecting morning urine samples (138, 145, 146) and have been successful in supporting the hypothesis of relationships between stress and catecholamines. A study by White et al compared overnight and 24 hour urine collection to measure urinary catecholamines. They found the correlation between overnight and 24 hour urine catecholamine levels was high when both measures were standardized for urinary creatinine levels. Correlations between overnight and 24 hour urine collections for 8, NE and DA were 0.37, 0.75 and 0.84 respectively. The authors support the practice of using creatinine excretion as a denominator for catecholamine excretion in community-based epidemiological studies (164). Our study uses creatinine to adjust for catecholamine excretions in urine. Only one previous study in pregnant women (138) mentioned about NE and 8 being positively correlated with each other (r=0.42, p<0.03). Other studies done in non-pregnant population showed urinary E and NE to be positively correlated with range of r=0.14 to 0.76 (53-60). Correlation between urinary E and DA was 107 0.37 and between NE and DA was 0.37 to 0.77 (60, 62, 63). lntercorrelation of E, NE and DA was found to range between 0.47 to 0.69 (64). Mean urinary DA excretion pattern seems to follow mean NE excretion but not E excretion (61). Urinary E had weak correlation with the other two catecholamines (62). Our study showed a good correlation between urinary NE and DA of r=0.75 to 0.90, while fair correlations were found between NE and E and E and DA of r=0.17 to 0.52 and r=0.29 to 0.60, respectively. I The present study is unique in examining the collection factors (waking and bedtime collection, interval from waking to collection of urine sample) with respect to catecholamine levels. To our knowledge, previous studies have not examined the influences of these factors in urinary catecholamine levels. Our study shows that collection time (waking and bedtime collection time) are not associated with the levels of urinary catecholamines. In the present study, interval from waking to collection of urine sample was significantly associated with E waking after adjusting for waking urine collection time and work status. The urine collected later than 60 minutes after waking had the lowest E waking levels as compared to the previous 3 intervals. This association might be an influence of whether the woman voided or not during the night before collecting the waking urine sample. Previous studies have consistently shown catecholamines to be higher during the work period than the non-work period. Most of the earlier studies were done in non-pregnant population (53, 55, 67-69, 76, 83, 92, 96, 98, 99, 106, 111) and only three were done in pregnant population (135, 147, 148). All these studies 108 collected urine to assess the catecholamine levels, either by multiple urinary samples on work and non-work day or urine samples collected over extended periods of time. Our study did not detect an association between urinary catecholamines and work/ non-work days. Since our study collects samples of urine once in morning and then in the evening the effect of the work might not be captured. Among the maternal characteristics included in the present study, only age, education and gestational age have been examined in prior studies on pregnant population (138, 140, 141), while ethnicity, age and education have been examined in non-pregnant population (52, 57, 58, 72-76, 78-86, 98). Studies on pregnant and non-pregnant women have found no relationship between catecholamines and education and gestational age (140, 141). However in our study education was significantly related to waking and bedtime NE and bedtime DA . Our study does not find any consistent association between Medicaid insurance status and urinary catecholamines. But in our study parity was significantly associated with NE waking, DA waking and DA bedtime. A few studies failed to find an association between age and plasma and urinary catecholamines (52, 58, 72, 78, 140), but other studies, both in pregnant and non-pregnant population, found a weak to strong positive association between age and urinary catecholamines (57, 79-84). This could indicate that the older women were experiencing more stress with greater demands from children and other responsibilities. Two other researchers found that age was significantly negatively correlated with urinary E and NE in men in fifties and seventies (85) 109 and in children (86). Possibly the relation between age and catecholamines might be reversed in children and elderly persons compared to middle-aged population. Our study done on pregnant women support the findings that catecholamines tend to increase with increasing age. Previous studies about ethnicity and catecholamines were found only in non-pregnant population with no consistency in results. Three studies found no effect of ethnicity on urinary E and NE (75, 76) and plasma NE (77). Pratt et al (72) showed normotensive black children to have significantly lower levels of nocturnal urinary NE, while Ziegler et al (73) found white hypertensives had elevated plasma NE levels and blacks had normal levels. De Bellis et al found African-Americans excreted significantly greater concentration of urinary E and showed a trend for significantly greater concentrations of urinary NE over 24 hrs than Caucasian subjects (74). Our study showed that African American pregnant women had lower urinary catecholamine levels than pregnant women from other ethnicities. There are some limitations to our present study. In our study, we did not collect urine over a period of 24 hours; instead we collected two 10 ml samples of urine twice a day (first morning and before bedtime urine samples). A 24 hour urine sample would have been ideal to collect but was not possible in the POUCH study because of the feasibility of such a collection in a study with a large population of approximately 3000 pregnant women. Women enrolled in the POUCH study included working women who might have posed a compliance problem for a 24 hour urine collection protocol. Another limitation of our study is the compliance with collecting the first morning void. Pregnant women tend to 110 have increased frequency of voiding and are likely to void during the night before collecting the morning urine sample. Such a urine sample will not capture the all night effect in the morning sample as would the sample in the women who do not void during the night. Influence of certain other factors on urinary catecholamines such as physical activity and consumption of alcohol, cigarettes and caffeinated beverages can not be adequately controlled for in the analyses. Though the POUCH study collects data on these factors during the in-person interview, this data is a global measure of a woman’s habits rather than measures on the specific day of urine collection. Conclusion Previous studies on catecholamine levels have shown variability within individuals for urinary catecholamine values. Our study found day-to-day catecholamine levels to be only moderately correlated with each other and therefore multiple measures of catecholamine may be helpful to account for this intra-individual variability. Our study did not find associations between time of urine collection (waking and bedtime) and urinary catecholamine levels. As previously mentioned our interest in examining these factors arises from another study using POUCH data to assess levels of salivary cortisol in pregnant women (163). Literature has found that urinary catecholamine levels are higher in the afternoon and lower in morning and night. Also urinary catecholamines do not have the diurnal pattern seen for salivary cortisol with early morning peaks. So it is reasonable to assume 111 that our urine sampling times did not coincide with the times when catecholamine levels peak (mid day). The significant association found between interval from waking to urine collection and E waking can be examined with a larger sample size from POUCH study in future to assess whether this is an effect of voiding during the night before collecting the waking urine sample. Though our study found no association between urinary catecholamine levels and work/non-workday, studies in the literature have found this association to exist with higher levels of catecholamines found on workdays. A possible reason why our study did not detect this association might be that our study collected samples of urine once in the morning and once in the evening, these samples might not have been able to capture the effect of the work period and maybe collection of urine sample during work or immediately after work are required. Our study found ethnicity to have a consistent and significant association with all urinary catecholamine levels except epinephrine waking. AFAM women were found to have lower levels of urinary catecholamine than white and other ethnicities. The literature is not consistent for associations between catecholamine levels and ethnicity. One reason for lower levels of urinary catecholamine in AFAM women might be alterations in receptor sensitivity along the stress axis after exposure to prolonged stress. It is also possible that study results were due to lack of adjustment for unknown confounders in the urinary catecholamine and ethnicity relationship. 112 Age was also found to have a consistent and significant association with all urinary catecholamine levels except epinephrine waking in our study. The increasing urinary catecholamines levels seen with increase age might indicate that the older women were experiencing more stress with greater demands from children and other responsibilities. Results from this study have implications for future studies and analyses of the POUCH data. Our study found urinary NE and DA to be highly correlated. In future analyses on POUCH data for urinary catecholamine levels, these two biomarkers could be used as correlated variables so as to increase the power in the analyses. Future studies might not need to measure both of these catecholamines. The factors examined in our study in relation to urinary catecholamines are not an exhaustive list. 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