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A Path Model of Chronic Stress, the Metabolic Syndrome, and Coronary Heart Disease

Departments of Psychiatry and Behavioral Sciences and Medicine, University of Washington (P.P.V., J.M.S., J.Z., M.V.S.), Seattle, Washington, and Department of Psychiatry and Behavioral Sciences, Duke University (I.C.S.), Durham, North Carolina.

Address reprint requests to: Peter P. Vitaliano, PhD, University of Washington, Department of Psychiatry and Behavioral Sciences, Box 356560, Seattle, WA 98195-6560. Email: pvital{at}u.washington.edu

	ABSTRACT

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OBJECTIVE: We tested a theoretical stress model cross-sectionally and prospectively that examined whether relationships of chronic stress, psychophysiology, and coronary heart disease (CHD) varied in older adult men (N = 47), older adult women not using hormone replacement therapy (HRT) (N = 64), and older adult women using HRT (N = 41).

METHOD: Structural equations examined relationships of CHD with 1) chronic stress (caring for a spouse with Alzheimer’s disease and patient functioning), 2) vulnerability (anger and hostility), 3) social resources (supports), 4) psychological distress (burden, sleep problems, and low uplifts), 5) poor health habits (high-caloric, high-fat diet and limited exercise), and 6) the metabolic syndrome (MS) (blood pressure, obesity, insulin, glucose, and lipids).

RESULTS: Caregiver men had a greater prevalence of CHD (13/24) than did noncaregiver men (6/23) (p < .05) 27 to 30 months after study entry. This was influenced by pathways from caregiving to distress, distress to the MS, and the MS to CHD. In men, poor health habits predicted the MS 15 to 18 months later, and the MS predicted new CHD cases over 27 to 30 months. In women, no "caregiving-CHD" relationship occurred; however, 15 to 18 months after study entry women not using HRT showed "distress-MS" and "MS-CHD" relationships. In women using HRT, associations did not occur among distress, the MS, and CHD, but poor health habits and the MS were related.

CONCLUSIONS: In older men, pathways occurred from chronic stress to distress to the metabolic syndrome, which in turn predicted CHD. Older women not using HRT showed fewer pathways than men; however, over time, distress, the MS, and CHD were related. No psychophysiological pathways occurred in older women using HRT.

Key Words: stress, • coronary disease, • gender, • metabolic syndrome, • hormone replacement, • path analysis.

Abbreviations: AD = Alzheimer’s disease;; BP = blood pressure;; BMI = body mass index;; CHD = coronary heart disease;; CVD = cardiovascular disease;; DBP = diastolic blood pressure;; HDLC = high-density lipoprotein cholesterol;; HRT = hormone replacement therapy;; LV = latent variable;; LVPLS = latent variable partial least square;; MLE = maximum likelihood estimation;; MS = metabolic syndrome;; MV = manifest variable;; PLS = partial least squares;; RMS = root mean square;; SBP = systolic blood pressure;; SES = socioeconomic status;; TG = triglycerides.

	INTRODUCTION

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There is extensive empirical support for the hypothesis that chronic stress is associated with cardiovascular disease. Greenwood et al. (1) reviewed 14 prospective studies of humans, and almost all of the studies found relationships of stress with CVD or coronary heart disease. Pickering et al. (2) found that in persons with high job strain, blood pressure increased over time, both at home and at work. Unfortunately, few of these studies focused on men over 65 years of age and still fewer included older women. Because older adults are less resilient to stress and illness than are younger adults and chronic stress may be more related to CHD in older than in younger adults, this is an important area of study. Indeed, in the absence of good health habits, even healthy older adults exhibit greater risks for CVD. These include greater BP (3), body fat (3), and insulin and glucose (4). Hence, 72% of persons over age 65 have CVD, and 25% have CHD (5). Importantly, CHD prevalence in women increases dramatically after age 50, suggesting that without hormone replacement therapy, postmenopausal women may lose much of their CHD "protection" (6).

Our literature review of research on chronic stress and CHD noted the absence of studies that used theoretical models to guide their research and hypotheses. Moreover, few studies used prospective designs with naturally occurring (and ecologically valid) chronic stressors in older men and women. Still fewer studies examined psychosocial, behavioral, and physiological measures to represent predisposing, mediating, and outcome variables within the same investigation. Here, we attempted to narrow this gap. However, to meet the above requirements as well as have a large sample would have been prohibitively expensive. Instead, we used a moderately sized sample and a theoretical model of distress to cross-sectionally and prospectively examine interrelationships of a natural chronic stressor and psychosocial, physiological, and biomedical variables. We did this in older adult men, women not using HRT, and women using HRT. Our model posits that chronic stress, personal vulnerabilities, and personal and social resources lead to psychological distress and poor health habits. Distress and poor health habits, in turn, lead to physiological disregulation (Figure 1, top) (7). Later, we will discuss how the model constructs and pathways from chronic stress, personal vulnerabilities, and personal and social resources may be used to predict psychobehavioral responses (psychological distress and poor health habits). We will also review how these responses can lead to physiological disregulation, which may be followed by CHD.

View larger version (24K): [in this window] [in a new window]   Fig. 1. Top, theoretical path model for chronic stress and CHD. Bottom, path model for chronic stress and CHD with manifest variables.

	Pathways From Chronic Stress, Vulnerabilities, and Resources to Psychological Distress and Poor Health Habits

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Personal vulnerabilities and resources also predict distress and poor health habits. Vulnerabilities are defined as relatively enduring and less controllable influences than are resources, which are more mutable and more influenced by the environment than are vulnerabilities. Vulnerabilities such as personality and dispositions are shaped by inherited traits and by propensities acquired through experiences. Stress-diathesis theory suggests that the distress induced by "challenging events" is determined by whether the vulnerability threshold is crossed and maladaptation ensues (12). High levels of anger and hostility are aspects of personality that increase vulnerability to CVD and CHD (13). These dispositions, in turn, are related to and predictive of greater distress and poorer health habits (14). Hostility also influences social supports and vice versa (15). Finally, demographic variables may alter one’s vulnerability to stressors. Women report more distress than men (16), but men may be more physiologically reactive in situations that are inconsistent with their traditional roles (17). Such changes can lead to greater cardiovascular dysfunction (17).

One challenge to understanding gender differences is the fact that aging is associated with a greater atherogenic lipid profile (18), but menopause is related to a greater risk of hypertension (19) and dyslipidemia (18) independent of age. Early natural menopause is associated with greater CHD risk in women who have never used HRT (20), whereas HRT use in postmenopausal women is believed to reduce CHD risk factors (21). Indeed, HRT may be a resource because it is associated with greater high-density lipoprotein cholesterol and less low-density lipoprotein cholesterol, insulin resistance, and central adiposity (21). HRT may also be beneficial because women using the therapy report lower levels of hostility (22). In sum, menopause complicates gender differences in psychophysiological relationships because some women use HRT and some do not. This creates three natural groups with different CHD risk profiles: men, women not using HRT, and women using HRT. By comparing psychophysiological relationships in these groups, one may better understand how gender and HRT act as respective vulnerabilities and resources in older adults.

Just as HRT may act as a biological resource, emotional and instrumental supports (23) may provide social resources. Such resources are related to better health habits (24) and less distress and chronic stress (23, 25). Personal resources include socioeconomic status (education and income) and active coping (direct action and problem solving). Higher SES is associated with better physical health (26). Active coping is inversely associated with distress (27), poor health habits (eg, diet, alcohol, and smoking) (10), and physiological disregulation (28).

	Pathways From Psychobehavioral Variables to Physiological Disregulation

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Chronic stressors can cause physiological changes that increase CHD risk (29). Short-term (laboratory challenges and exams) and long-term (job strain, divorce, and widowhood) stressors are associated with greater fasting glucose (30), BP (31), lipids (31, 32), and insulin levels (33). Under chronic stress, angry and hostile persons are at risk for greater resting BP (34), body mass index (35), fat and caloric intake (35), glucose and insulin levels (36), and waist-hip ratios (37). Psychological distress may exacerbate pathophysiology (38) and lead to elevations in insulin (39), glucose (30), total cholesterol and triglycerides (40), and BP (41). Finally, sleep problems are associated with greater cardiovascular problems (42), and poor health habits (eg, diet and sedentary behavior) are associated with greater coronary risk (43, 44).

Interrelationships of Physiological Variables, the Synergistic Effects of the Metabolic Syndrome (MS), and the Prediction of CVD/CHD
Obesity is positively associated with hypertension (45, 46), dyslipidemia (46), and insulin resistance in skeletal muscle and adipose tissue (47). This leads to elevated insulin, which is related to lower HDLC and elevated levels of TG, resting BP, and BP reactivity (47). Obesity is also related to stroke (48), diabetes (47), and CHD morbidity and mortality (49). However, the primary mover in such relationships may be insulin resistance. Higher insulin levels exist in persons at risk for CHD (50, 47) and in persons with atherosclerosis with and without ischemia (51, 52). Hyperinsulinemia also predicts CHD 5 to 10 years later, independent of diabetes and other risk factors (47, 50); however, the prediction results from insulin resistance and not directly from hyperinsulinemia. The synergistic effects of insulin resistance, obesity, hypertension, and hyperlipidemia form a construct called the "metabolic syndrome" (53), which is a predictor of CHD (47).

Psychosocial Correlates and Predictors of the MS and CHD
Distress combined with overeating and inactivity can lead to greater levels of obesity, insulin, glucose, BP, and plasma lipids (53). Psychosocial correlates and predictors of CHD include greater chronic stress, anger, and hostility (54, 55), higher levels of psychological distress (55, 56), lower social supports (57), lower SES (58), and poorer health habits (59). Finally, the lack of positive experiences is associated with a greater waist-hip ratio (60) and worse glycemic control (61).

	Research Questions

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Given the proposed model and its supporting literature, we examined whether older persons under chronic stress would manifest greater CHD prevalence than older persons not under such stress and whether the model pathways would relate to CHD prevalence. We also examined several associated questions about the pathways in Figure 1, top, and their potential roles as mediators of relationships of chronic stress with CHD:

1. Is variability in distress influenced by chronic stress, vulnerabilities, and resources?
   
2. Is variability in poor health habits influenced by chronic stress and distress?
   
3. Is variability in the MS influenced and/or predicted by distress and poor health habits?
   
4. Is CHD prevalence influenced and/or predicted by elevated MS values?
   
5. Do intermediate pathways mediate relationships between chronic stress and CHD?
   
6. Given the potential compounding of chronic stress across time, do the above relationships become stronger over time?
   
7. Are the above relationships modified by gender and HRT use?
   

To examine these questions, we defined "persons under chronic stress" as persons caring for a spouse with Alzheimer’s disease. Spouse caregivers experience numerous stressors (physical, emotional, and financial) (62), hassles (62), anger (63), and depressed mood (63) relative to matched control subjects, and such variables are associated with hostility (64). The demands of caregiving coupled with the biological vulnerabilities of aging put spouse caregivers at increased risk for CVD and CHD (65) and make them an appropriate group for examining the above questions. We excluded persons with diabetes. Diabetes is associated with insulin-glucose disregulation, increased risk for depression, and cardiovascular complications. Its premorbid presence would have complicated our analyses and subsequent inferences.

	METHODS

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Participants
Caregiver couples were recruited from 1) mailings to physicians, 2) the University of Washington Alzheimer’s disease (AD) registry, 3) the AD Association, and 4) printed and electronic media. Criteria for care recipient inclusion were 1) living with a spouse who is the primary caregiver, 2) 60 years old, and 3) diagnosis of possible or probable primary degenerative dementia based on the Diagnostic and Statistical Manual of Mental Disorders (DSM) III (66), the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Associations (NINCDS-ADRDA) (67), and exclusion criteria, which are provided in much greater detail elsewhere (68). Noncaregivers were recruited from senior centers, retirement organizations, and the media. They and their spouses (noncare recipients) had to be 60 years old and functioning independently (as did the caregivers). No exclusion criteria for major illnesses were used a priori for caregivers and noncaregivers. This provided a more representative sample of older adults.

Because Alzheimer’s disease is a progressive degenerative disorder, we used the course of this disease as a chronic stressor for spouse caregivers. We assessed their temporal psychophysiological reactions to such stress relative to noncaregivers at two points in time (time 1 was study entry, and time 2 was 15–18 months later). One year after time 2 (or 27–30 months after study entry), we also obtained these subjects’ medical records. Finally, at times 1 and 2 we examined cognitive and behavioral measures in care recipients and noncare recipients.

At time 1, the samples consisted of 90 spouse caregivers and their spouses (care recipients with AD) and 88 noncaregiver spouses (and their spouses). From time 1 to time 2, we had attrition of 13 caregivers because of the following reasons: 3 "care recipients" did not decline and were thought not to have AD, 5 caregivers died (3 from heart attacks and 2 from strokes), 1 got divorced, 1 noncaregiver moved, and 3 refused to continue. Five noncaregivers were lost to follow-up because of the following reasons: one noncaregiver died of a stroke, two spouses of noncaregivers died (one from cancer and one from CHD), and two noncaregivers moved. Five caregivers and three noncaregivers with verified diabetes (see medical records below) were excluded in the current analyses. Finally, although an attempt was made to group-match the caregivers and noncaregivers on age and gender, the percentage of women using HRT in each group was left to chance. Complete data were available on 72 caregivers (24 men, 28 women not using HRT, and 20 women using HRT) and 80 noncaregivers (23 men, 36 women not using HRT, and 21 women using HRT).

Measures Used to Assess Constructs
Measures of chronic stress.
Caregiver status (caregiver vs. noncaregiver) was assessed according to the above inclusion and exclusion criteria. Care recipient mental status was measured by the Mini-Mental Status Exam (69). It assesses orientation, memory, etc. Care recipient functioning was measured by the Record of Independent Living (70). It assesses competence in maintenance (eg, feeding and washing) and higher functioning (eg, reading and recreation) (Figure 1, top).

Measures of personal resources.
The Revised Ways of Coping Checklist (71) assessed problem-focused coping (eg, came up with a couple of different solutions to the problem). SES was operationalized by education (in years) and income (in $20,000 increments from $10,000 to $100,000 per year).

Measures of vulnerability.
The Anger Expression Scale (72) assessed how one generally acts when one is angry (anger-out, eg, "I lose my temper"; anger-control, eg, "I keep my cool."). The Trait Anger Scale (73) assessed anger proneness and hostility (eg, "I have a fiery temper").

Measures of social resources.
The Interpersonal Support Evaluation List (74) includes four scales: Appraisal ("there is at least one person I know whose advice I trust"); Belonging ("there are several people with whom I enjoy spending time"); Tangible ("if for some reason I was thrown in jail, there is someone I could call on to bail me out"); Self-esteem ("most people I know think highly of me"). The Social Supports Questionnaire assessed the degree of satisfaction with one’s supports (75).

Measures of psychological distress.
The Hassles and Uplifts Scales (76) examined hassles and uplifts in work, family, health, and finances in the past month. To avoid circular inferences, the health domain was not included. The Hamilton Depression Rating Scale (77) assessed depressive symptoms using a structured interview. The Screen for Caregiver Burden (68) assessed caregiver affects and care recipient behaviors. The Sleep Problems Questionnaire, derived from the Sleep Disorders Questionnaire I (78), assessed problems such as "not getting enough sleep."

Measures of poor health habits.
Exercise was assessed using 10 self-report items (eg, light vs. heavy chores and normal vs. brisk walking). Subjects recorded the days per week and time spent each day in each activity. These yielded an ordinal score: aerobically active (0), namely vigorous exercise (eg, brisk walking) for 30 minutes a day 3 times per week; light activity (1); and sedentary or minimal activity (2). Diet (eg, total daily calories and grams of fat) was assessed from 3-day food diaries (1 weekend day and 2 weekdays) using the version 13 nutrient database of the Nutrition Coordinating Center, University of Minnesota (79).

Measures of the metabolic syndrome.
At times 1 and 2, subjects were asked to fast for 12 hours and abstain from smoking cigarettes and consuming alcohol and caffeine before arriving at the University of Washington Medical Center at 9 AM. A nurse used heparinized syringes to collect blood from the hand and forearm of each seated subject. After plasma was separated by centrifugation, it was frozen at -70°C, transported in dry ice, and later analyzed at the Northwest Lipids Laboratory.

One hour after the blood draws, subjects were asked to sit quietly and listen to soft music over headphones for 7 minutes. Two SBP and DBP measures were then obtained 2 minutes apart with the right arm resting at heart level. A Dinamap Adult/Pediatric Vital Signs Monitor Model 845XT (Tampa, FL) was used. From these readings, mean arterial pressure was computed as (SBP + 2 DBP)/3. Lipids were quantified by an Abbott Spectrum Multichromatic Analyzer (Irving, TX) (80). HDLC was separated from the plasma with dextran sulfate magnesium, and the resulting supernatant was assayed for cholesterol (80). Glucose was measured by the combined Abbott Analyzer’s catalytic activities of hexokinase and glucose-6-phosphate dehydrogenase. The between-assay coefficient of variation was <3%. Insulin was assessed using a radioimmunoassay polyethylene glycol-accelerated method with 48-hour incubation. The primary antibody was a guinea pig antibody, the precipitating antibody was a goat anti-guinea pig antibody, and the tracer was mono-iodo-tyr-A14-insulin. To assess body mass index, individuals were weighed in street clothes and height without shoes was assessed with a standard ruler. Obesity was defined as 90th percentile of BMI (weight in kilograms/height in meters squared) on the age and gender norms of the Northwest Lipids Laboratory. These BMI cutoffs varied from 28 to 30.

Measure of CHD.
Puckett’s (81) criteria were used on the subjects’ medical records to obtain date and nature of diagnosis, treatment, prognosis, and medications. The coder was blind to the subject’s status as a caregiver or noncaregiver. A quality control checklist (82) showed that BP was recorded for 4 years in 63% of the records and for 1 year in 30% of the records. In 93% of the records, treatment/ICD-9 codes and/or diagnostic tests and dates for CHD and CVD were listed (eg, arteriosclerosis (ICD-9-CM code 414.0), ischemia (414.9), angina (413.9), other CHD (414.8), atherosclerotic (440) and peripheral vascular disease (440.2), aortic sclerosis (440.0), and stroke (436)). In records without diagnoses, CHD medications were used for documentation (2 of 45 subjects).

Medical records were collected 27 to 30 months after a subject’s entry into the study (time 1). The presence of CHD was determined retrospectively using the dates of the physicians’ records. Documented CHD that occurred up to time 1 was used to estimate CHD prevalence at time 1. However, because the interval between time 1 and time 2 was only 15 to 18 months, we included all CHD diagnoses that occurred between time 1 and the collection of medical records (12 months after time 2) as the point prevalence of CHD at follow-up.

Statistical Analyses
To examine our research questions, we used latent variable partial least squares estimation (83). PLS is a more exploratory way of performing structural equation modeling than the popular LISREL approach. Although LISREL permits more definitive conclusions, it requires a multivariate normal distribution for the observed variables to use maximum likelihood estimation. This may not be tenable and can lead to incorrect inferences about the model. LISREL also requires large samples (N 300). In contrast, PLS uses ordinary least squares, which does not make distributional assumptions and can model skewed and ordinal data (84). Hence, it is more suitable for research with small samples, nonnormal distributions, and categorical variables as in the current study. Because PLS estimates LVs (eg, chronic stress) as exact linear combinations of MVs, it avoids the LISREL indeterminacy problem (85) and reduces measurement error. It also optimizes correlations (r) between LVs, maximizes the total variance predicted by the model, suggests where r might or might not exist, and proposes paths for future tests (84, 85). The least squares approach also avoids the MLE identification problem of LVs with few MVs (85).

In the PLS framework, the constructs in Figure 1, top, are considered unmeasured LVs operationalized by manifest (measured) variables (MVs) (Figure 1, bottom). LVPLS estimates relationships among the LVs and the coefficients that specify relationships of the MVs with their LVs. To compute LV scores, PLS calculates weights for one block of MVs at a time. It then estimates the next block of MVs for another LV (outside approximation). PLS then performs regressions among LVs based on the structural model (inside approximation). At each iteration, structural estimates are used to obtain outside weights, and measurement model estimates are used to obtain inside weights. Hence, PLS maximizes the explained variance in endogenous LVs and minimizes the residual variance in both LVs and MVs. Here, PLS proceeded by blocking relationships between MVs.¹ To address whether LVs were separate constructs, we examined the r values of the residuals. Interpretation of the LVs was based on 1) the r and ß values between LVs, 2) the amounts of variability in the endogenous LVs explained by their predictors, 3) the magnitude of the r values between LVs for which no r was theorized, and 4) an overall nonprobability fit index (ie, unexplained r between LVs).

PLS can handle much smaller sample sizes than LISREL because estimates of parameters are performed within LVs until a solution is reached (84). PLS only analyzes parts of the model, so the sample size required is only for the largest multiple regression done at any one time, namely 1) the LV with the largest number of MVs or 2) the endogenous LV with the largest number of exogenous LVs predicting it. Here, PLS cross-sectional analyses were repeated six times: men, women not using HRT, and women using HRT at times 1 and 2. This was done because gender may be a vulnerability for CHD (17) and HRT may be a protection against CHD (21) and we wished to test their moderating effects on the relationships in the model. We also performed prospective analyses when possible. In the current analyses, social resources and the MS were the largest LVs (ie, five MVs), and distress was the endogenous LV with the most predictors (ie, four LVs). Using a heuristic of 5 to 10 cases per predictor, the required sample size was 5 to 10 times five MVs, or 25 to 50. Below, the prospective analysis of men had the smallest sample size (N = 39). This was closer to the upper (N = 50) than to the lower bound estimate.

	RESULTS

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No differences existed in the age and gender of the caregivers (mean age = 70.4 ± 6.5 years; 34% men) and noncaregivers (mean age = 69.0 ± 5.4 years; 30% men). Mean ages (±SD) were 71.5 (±4.8) years for men, 69.5 (±5.8) years for women not using HRT, and 67.5 (±6.4) years for women using HRT. Participants were white except for one black caregiver and one black/Asian noncaregiver couple. At study entry, men caregivers had been providing care for 36.3 (±21.9) months, women caregivers not using HRT had been providing care for 41.9 (±24.9) months, and female caregivers using HRT had been providing care for 55.8 (±35.8) months. The only difference was for men and women using HRT (p < .05). At study entry, male caregivers provided 5 (±4.3) hours of care per day, women caregivers not using HRT provided 7.1 (±5.8) hours of care, and female caregivers using HRT provided 6.1 (±4.6) hours of care. There were no differences among the groups.

Internal consistency coefficient alphas for all measures varied from a low of 0.70 for the Hamilton Depression Scale at time 1 to a high of 0.98 for the Record of Independent Living at time 2. Tables 1 and 2 contain health-related, psychosocial, and demographic data for the MVs used to assess the LVs in Figure 1, bottom, and for other variables of interest. MVs are compared primarily for caregivers and noncaregivers (or for their spouses) stratified by gender and HRT (men, women not using HRT, and women using HRT). At each time, male caregivers were more obese and reported more depressed mood, more burden, fewer uplifts, fewer social supports, less problem-focused coping, and less education than did male noncaregivers. Caregiver women not using HRT reported less exercise than their noncaregiver counterparts as well as more depressed mood, sleep problems, and burden at both times. Caregiver women using HRT reported more depressed mood, sleep problems, and burden and fewer uplifts than their noncaregiver counterparts at both times. Male caregivers reported less burden at time 1 and fewer sleep problems at times 1 and 2 than did each group of female caregivers. Finally, cognitive and functional impairments were greater for AD victims than for spouses of noncaregivers (Table 2).

View larger version (29K): [in this window] [in a new window]   Fig. 2. CHD prevalence in each stratum at study entry and follow-up. *p < .05.

Results of Partial Least Squares: Measurement Model
Figure 1, bottom, contains the measurement and structural models. Rather than presenting the 138 factor loadings in the measurement model, we summarize the r values of the MVs with the LVs.³ Although the psychosocial LVs were generally consistent across MVs, time, and strata, the MS and poor health habits showed more instability. Chronic stress was consistent across MVs, time, and strata (eg, 0.91 to 0.98), as was social resources. Loadings were uniformly high for the interpersonal support and evaluation list (0.63 to 0.90), but satisfaction decreased from time 1 (0.60 to 0.70) to time 2 (0.30 to 0.40) across all strata. Across time, vulnerability showed a consistent upward pattern for anger expression and trait anger in all strata (from 0.60 to 0.97). Distress showed a similar pattern for burden (0.81 to 0.89) and uplifts (-0.50 to -0.78) across time and strata, but sleep problems had high loadings for women (eg, 0.67 to 0.83) and not for men (0.13 to 0.46). At both times, hassles had very weak loadings, therefore it was dropped. Depression was also dropped, but for structural reasons that will be discussed below.

As with the other LVs, the MS was an exact linear combination of the MVs used to estimate it. From time 1 to time 2, loadings decreased for mean arterial pressure (0.63 to 0.47 for men, 0.64 to 0.33 for women not using HRT, and 0.32 to 0.03 for women using HRT). Loadings for obesity also decreased for men (0.74 to 0.64), women not using HRT (0.87 to 0.72), and women using HRT (0.81 to 0.31). For glucose, large increases occurred in men (0.51 to 0.71), women not using HRT (0.34 to 0.69), and women using HRT (0.53 to 0.84). Insulin (0.75 to 0.67) and lipids (0.73 to 0.75) had high loadings for men at each time; however, in women not using HRT, the loadings increased for insulin (0.56 to 0.85) and lipids (0.06 to 0.36); and in women using HRT, the loadings decreased for insulin (0.67 to 0.41) and lipids (0.4 to -0.50). In sum, at both times, all loadings were important for defining the MS in men, but in women who did not use HRT, insulin and obesity were important for defining the MS only at time 2. In contrast, weak loadings occurred for all MVs (except for glucose) in women using HRT. Daily calories and fats were not highly correlated with exercise. Hence, we used an inner-directed block (in which the LV is viewed as an effect rather than a cause of the MVs) to estimate the poor health habits LV. Here, LVPLS estimates the LV as a linear combination of the MVs to maximize the relationship with other LVs(85).⁴ Despite this procedure, poor health habits was still quite variable. Based on the regression weights, poor health habits were defined by diet in men at time 1. However, over time, exercise became increasingly important (from 0.13 at time 1 to 0.80 at time 2) and diet became less important. In women not using HRT, poor health habits was defined by all indicators at time 1, but over time, exercise became more important (0.89 at time 1 and 1.12 at time 2) and diet became less important. In women using HRT, diet became less important from time 1 to time 2, but exercise remained the same at both times (0.43 and 0.47). In sum, as discussed below, relationships between poor health habits and the MS had different meanings across strata because the weights for this LV were different across strata.

Results of Partial Least Squares: Structural Models
Table 3 contains standardized path coefficients (ß values) for the paths in Figure 1, bottom, for all strata at each time. These are partial regression coefficients of the direct paths of one LV to another LV while controlling for the other LVs of the model. This was done after all MVs were standardized (mean = 0, SD = 1). Table 3 also lists the variances in endogenous LVs explained by their predictors (R²). These were obtained by multiplying the ß by the r between pairs of LVs. Significance tests for ß values were conducted, with the required multivariate normal assumption, ⁵ by transforming R² values to F values (84).⁶ Because personal resources had weak relationships with the other LVs across all strata (ß = 0.02–0.05), it was deleted from each model. Finally, depression and the MS were not as strongly related as the other distress MVs were with the MS. Hence, depression was deleted from the distress LV.

Is variability in poor heath habits influenced by chronic stress and distress?
Depending on the stratum and time of study, six of the 12 paths from chronic stress and distress to health habits were significant (total R² = 5.8% to 32.8%) (Table 3).

Is variability in the MS explained by distress and poor health habits?
Depending on the stratum and time of study, six of the 12 paths from distress and poor health habits to the MS were significant (total R² = 6.8% to 23.0%). Figure 3 contains the final models for the strata. Emboldened paths were significant at times 1 and 2, dotted paths were only significant at time 1, dashed paths were only significant at time 2, and deleted paths were nonsignificant at both times. The pathway from distress to the MS was significant at both times in men, significant at time 2 in women not using HRT, and never significant in women using HRT. The relationship of poor health habits with the MS, although insignificant in men, is significant in women not using HRT at time 1 and significant in women using HRT at both times (explaining 15.2% and 20.7% of the variance, respectively).

View larger version (27K): [in this window] [in a new window]   Fig. 3. Path model that results from each stratum. Top, men; middle, women not using HRT; bottom, women using HRT. Bold line = significant path at both time 1 and time 2; dotted line = significant path at time 1; dashed line = significant path at time 2; no line = no significant path at either times.

Do intermediate pathways mediate relationships of chronic stress with CHD?
In men, the relationship of chronic stress with CHD at time 2 (Figure 2, top) may have been mediated because it became insignificant in the presence of significant pathways (Figure 3, top) from chronic stress to distress, distress to the MS, and the MS to CHD.

Do the relationships determined in the previous five research questions become stronger over time?
In women not using HRT, several pathways are significant at time 2 that were not significant at time 1. These include chronic stress to health habits, distress to the MS, and the MS to CHD. In men, pathways from social resources to distress and from the MS to CHD were significant at time 2 but not at time 1.

Are the above relationships modified by gender and HRT use?
Extensive modification occurred for poor health habits with the MS. In men, this relationship was driven primarily by associations of sedentary behaviors with all variables in the MS. In women not using HRT, this relationship was driven primarily by associations of sedentary behaviors with insulin, glucose, and obesity. However, in women using HRT, this relationship was driven primarily by the association of fat intake with glucose.

Evaluation of Overall Cross-Sectional Models
To evaluate models, six criteria are recommended (83, 85): 1) LVs should have more than three MVs—in our models, all LVs had three to five MVs except CHD; 2) measurement loadings should be 0.55—here, 98 of 120 reflective paths met this criterion; 3) the total R² for endogenous LVs (ie, resources, poor health habits, distress, MS, and CHD) should be 0.10—here, 21 of 30 LVs had total R² 0.10 (this is calculated by adding over the R² values for the paths leading to each LV); and 4) each path should explain 1.5% of the variance in a predicted LV—here, 52 of 66 paths met this criterion. Criteria 5 and 6 involve comparing the observed model to the null (no paths among LVs) and saturated (paths between all exogenous and endogenous LVs) models. An observed model should be parsimonious and closer to the saturated than the null model. Criterion 5 uses the average R² values for endogenous LVs. Criterion 6 uses the root mean square Cov(E, U). The RMS of the covariance between the MV residuals compares the relative goodness of fit of the observed to the saturated and null models.

Table 4 shows the average R² values and RMS Cov(E, U) for all strata as well as comparisons of null and saturated models. At time 1, the models for each stratum were similar in overall predictiveness (average R² = 19% to 22%). However, by time 2 the R² for men increased from 19.4% to 32.2%, and the model for men explained 9% more variance than did the time 2 model for women not using HRT. The average R² values and the RMSs of all models were close to the saturated models, except for the time 1 RMS in women using HRT.

View larger version (30K): [in this window] [in a new window]   Fig. 4. Prospective path model for men. For parsimony, vulnerability, and social resources are omitted. Numbers on the paths are path coefficients with percentage of variance explained by the path in parenthesis. White LVs are time 1, the light gray LV is time 2, and the dark gray LVs are time 3.

	DISCUSSION

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In contrast to more moderate differences in psychosocial pathways among the strata, substantial differences occurred in psychophysiological and biomedical pathways. Positive relationships between distress and the MS were 3 to 12 times larger in men (Figure 3, top) than in women (Figure 3, middle and bottom). Also, the MS at time 2 was positively associated with CHD prevalence in men. Hence, an avenue may exist from chronic stress to distress to the MS and then on to CHD. In contrast, no paths occurred directly from vulnerability (anger expression and trait anger) to the MS or CHD in men beyond those influenced by distress (7). This result is inconsistent with research that found that a chain occurred in men from hostility to BMI and waist-hip ratio to insulin, and then on to BP and lipids (37). However, in the latter study (37) and most studies of anger, hostility, and metabolism, distress was not directly examined in the analyses.

In women not using HRT, the patterns and magnitudes of associations were intermediate between those of men and women using HRT, with a tendency at time 2 for women not using HRT to exhibit relationships more like men. Among women not using HRT, positive associations between distress and the MS and between the MS and CHD were absent at study entry; however, they were significant at follow-up (Table 3 and Figure 3, middle). This was evident from the chain that occurred in such women, and in men, from vulnerability to distress to the MS to CHD (Figure 3, top and middle). These results are consistent with research in subjects free from diabetes, which has shown that stress increases glucose levels (30–33) and that physiological disregulation is associated with distress and chronic stressors (11, 88). These results also emphasize the central role played by insulin regulation in health (47). Indeed, the very large loadings of insulin on the MS LV (as well as the path coefficients from the MS to CHD) implicate it as a risk factor for CHD, especially because no subjects in our study had diabetes. In women not using HRT, distress and poor health habits were related to the MS, suggesting that both are important. Interestingly, in women not using HRT, distress was not as consistently associated with the MS as in men, and poor health habits were not as consistently associated with the MS as in women using HRT, again suggesting that women not using HRT had pathways intermediate between men and women using HRT.

In contrast to men and women not using HRT, at both times, women using HRT showed a complete absence of pathways from distress to the MS and from the MS to CHD (Figure 3, bottom). However, because this study lacked random assignment to HRT, it is unclear whether weak psychophysiological pathways in women using HRT (vs. women not using HRT) resulted from HRT, untapped differences between these women, or both. We do not know why women were using HRT or for how long. We do know, however, that HRT has beneficial consequences for insulin sensitivity (47), body fat regulation (89), cardiovascular health (21), and for reduced sympathetic arousal (90). We also know that the two groups of women were similar in diet, exercise, income, and age, but that women using HRT were slightly better educated, and education was unrelated to the LVs within each stratum. Regardless of whether women using HRT were initially healthier (91) or whether HRT made them healthier, the fact remains that psychophysiological pathways in women using HRT were much weaker. If the pathway between psychological stressors and stress hormones is, in fact, partially "blocked" in women using HRT (90, 92) and such women are also free from Type 1 diabetes, the only way that the MS can still develop is through suboptimal health habits. Indeed, in women using HRT, although there was no path from distress to the MS, the path from poor health habits to the MS was large and significant at both times (Table 3).

It is very important to note that the prospective analyses of CHD incidence in men yielded results that were quite different from the cross-sectional analyses. These results suggest different interpretations than those afforded by cross-sectional work. Cross-sectionally, distress directly influenced variability in the MS, but poor health habits did not. In contrast, distress at time 1 did not directly predict the MS at time 2, but it did predict the MS via poor health habits at time 1. As such, distress may have immediate associations with the MS, but relationships with poor health habits may occur more slowly. Also, although the MS at time 2 was related to CHD prevalence over 27 to 30 months, the MS at time 1 also predicted incident cases of CHD during that time period. Differences in relationships of distress and poor health habits with the MS would not have been observed had we not performed prospective analyses. These results support research in Finland (50) and suggest that distress and health habits are associated with the MS, but that their relative influence differs over time and strata. These results also support the need (as discussed in the Introduction) for prospective psychophysiological studies that examine chronic naturalistic stressors in older adults.

Temporal differences in relationships of distress with the MS may be partially influenced by the fact that at time 2, all caregiver groups reported more distress than did their respective noncaregiver comparisons, but at time 1, distress was not universally greater for caregivers than for noncaregivers. Years of caregiving for a spouse with AD exposes one to stressors that are likely to activate a metabolic cascade, but this may only occur in caregivers who are highly distressed and reach a load threshold (93). Complex dynamics exist among distress, the hypothalamic-pituitary-adrenal axis, metabolic risk factors, and CVD (94–96); however, stressors must be of some duration before they increase visceral fat and chronically elevate insulin levels (97).

Limitations
Several methodological issues must be considered in this research. First, the sample size (N = 152) was reduced by considering each stratum separately. This requires caution to avoid overinterpreting the data. The stratification also adversely affected the subject-to-variable ratio. Also, although PLS is suitable for small subject-to-variable ratios and exploratory analyses (84), it has not been as well studied as LISREL (it does not have as many goodness-of-fit indices), and measurement errors are not completely eliminated from the LVs because they are principal components and not common factors as for MLE. PLS estimates can be biased and inconsistent, but they will be asymptotically consistent when there are large sample sizes and large numbers of MVs per LV (84). A second issue involves the dichotomous CHD outcome. Whereas this restricted the ranges of the correlations for the LVs with CHD, it probably produced more conservative results. A third issue concerns the use of correlations with a "case-control" design, particularly when the correlation matrices and variances for case and control subjects are very different (heterogeneity of variance). Although the correlations of the chronic stress LV with the other LVs were very different (as were the variances) for caregivers and noncaregivers, the intercorrelations and variances of the other LVs were not different for caregivers and noncaregivers. Also, when we re-ran the analyses without the chronic stress LV, we obtained similar results.⁷ This suggested that although chronic stress may have produced an environment conducive to distress, it was actually distress and poor health habits that may have driven the subsequent pathways to CHD. Fourth, because these results come from a naturalistic study, inferences can only be made to populations with similar experiences. We do, however, believe that caregiving is relatively common among the general public, and demand characteristics reflect familial and financial stressors. A final issue concerns the racial characteristics and the age distributions of the men and women in our samples. Our samples were white, and other racial groups may respond differently to caregiving (98) and may vary in behavioral and physiological mechanisms (99). Also, although the mean age was not different for women using HRT and women not using HRT or for caregivers and noncaregivers within these two strata, age was different for men and women using HRT and for male caregivers and male noncaregivers. Because age was not related to the LVs within the men’s stratum,⁸ their age difference probably did not bias our results. However, the greater age in men vs. women using HRT may have contributed to stronger psychophysiological-CHD relationships in men. Alternatively, men who are still alive and married at 70 years of age may represent a censored and hardy cohort relative to men in general. In future, we will study the roles of age and gender as sources of selection biases.

Advances, Conclusions, and Implications
Despite these limitations, we believe this analysis has advantages. The theoretical stress model chain of pathways from psychosocial constructs to the MS and on to CHD has rarely been examined in one sample. It allowed us to assess the relative importance of variables relating chronic stress to psychophysiological variables in older adults. In contrast to checklists of life events, we studied a prototypic chronic stressor (caregiving) that reflects real life experiences and has important relevance to society. Moreover, caregivers and noncaregivers were nondiabetic, thus yielding results unconfounded by this disease. By examining men, women not using HRT, and women using HRT, we were able to compare the potential importance of gender and HRT in psychophysiological processes. Men (N = 47) had the strongest relationship between distress and the MS, and women using HRT (N = 41) had the weakest relationship, with women not using HRT (N = 64) between these extremes. By measuring anger, hostility, and distress, we were able to show that anger and hostility contributed to distress, but that distress had a more direct relationship with metabolism. This suggests that the interface of such effects may be important to cardiovascular and metabolic disregulation. In men, the pathway from distress to the MS was one of the largest pathways from caregiving to CHD. Hence, although caregiving may activate distress, the distress reaction may precipitate metabolic reactions. This was seen when we reanalyzed the model without chronic stress. The use of two time points showed that relationships grew stronger over time and that some variables may have lagged effects. In pathways connecting chronic stress, distress, MS, and CHD, 8 of 9 associations across the three strata showed increases from time 1 to time 2, suggesting that reactions to chronic stress may accumulate in older adults. The cross-sectional and prospective results for men suggest that distress may have immediate associations with both the MS and poor health habits, but health habits may not have an immediate association with the MS. Conversely, prior distress does not directly predict future MS, but it does predict metabolic changes through alterations in health habits measured 15 to 18 months before the MS. The longitudinal design also allowed us to observe that the model predicted CHD in the 27- to 30-month interval after time 1 in men who had no record of CHD at time 1. Therefore, it was less likely that relationships of distress and the MS were due exclusively to preexisting CHD problems.

In the absence of protective behaviors (eg, good diet and exercise), even healthy older adults experience changes that increase their vulnerability to CHD (3, 100), namely, greater insulin resistance from sedentary behavior and greater adipose tissue (47, 101). Aging, poor health habits, and chronic stress may jointly exacerbate pathophysiology and lead to even greater health risks, particularly if chronic stress and CHD have been present for many years. Caregiving is a situation of high demand, low control, and psychological challenges. Such situations may trigger CHD events and/or result in CHD progression (11). In this study, by time 2, spouse caregivers had already provided full-time care for an average of 53 months, and by the time medical records were obtained, they had been caregiving for an average of 5.4 years (53 + 12 = 65 months). In some of these caregivers, a physiological load threshold may have been reached because in only 27 to 30 months, the point prevalence of CHD increased by 19% in caregivers and 8% in noncaregivers (33% in male caregivers and 13% in male noncaregivers). This is provocative because caregivers may be unable to provide home care if they become ill, and in response to this, society will incur tremendous costs. In 1996, approximately 15 billion dollars was spent on AD patients in nursing homes (102). Moreover, in 1996, for every extra month that persons with AD were cared for in the community, $1.35 billion in institutional costs of care nationwide were saved (102). Thus, identifying individuals who are most vulnerable to the ill effects of caregiving may be a first step to targeting interventions with the greatest benefits, both from a humanitarian and fiscal perspective. We hope that the current work will begin to accomplish this goal.

	ACKNOWLEDGMENTS

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Drs. Vitaliano, Scanlan, Savage, and Zhang were supported by National Institute of Mental Health Grant RO1 MH57663, National Institute on Aging Grant RO1 AG10760, and National Institutes of Health, Clinical Research Center Grant M01-RR00037; Dr. Siegler was supported by National Heart, Lung, and Blood Institute Grant RO1 HL55356, National Institute on Aging Grants 1RO1 AG12458 and RO1 AG19605, and National Cancer Institute Grant PO1CA72099. We thank Drs. Karen Moe and Nancy Woods for comments on the manuscript and Roslyn Siegel for clerical support.

	NOTES

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¹ In inner-directed blocks, MVs were estimated as regressed variates (eg, caregiver status and AD patient scores were weighted to maximally predict chronic stress). In outer-directed blocks, MVs were estimated as common variance (eg, vulnerability was estimated by the approximate factor loadings of trait anger, anger-out, and anger-control on a first principal component). Inner- and outer-block diagnostics assessed the adequacy of the MVs in the model.

² Caregiver incidence represents new cases of CHD in all caregivers (N = 14) divided by the number who were still free of CHD at this time (or the total number of caregivers (N = 72) minus the number of caregivers who had CHD at time 1 (N = 14)). This denominator equals 72 - 14, or 58.

³ By squaring each loading (a²), one is able to obtain the communality of shared variance between a MV and an LV. The error variance of MV = 1 - a².

⁴ Because of this, it has been argued that factor loadings or internal consistencies are misleading. Rather, the interpretation should be based on the regression coefficients of MVs on the LV to see which MVs have the largest contributions to the LV.

⁵ To perform inference with PLS, one must either make the multivariate normal assumption or use jack-knife procedures.

⁶ F = (R²/m)/[(1 - R²)/(N - m - 1)], where N = number of subjects and m = number of predictors or m = 1 for a single path. Hence, for p < .05, R² must be 8.3% if N > 47 (for men), R² must be 6.1% if N > 64 (for women not using HRT), and R² must be 9.0% if N > 41 (for women using HRT).

⁷ The PLS program generates paths of all possible pairs of LVs. This allowed us to see that the path for vulnerability did not go directly to the MS or to CHD.

⁸ The ß and R² values are available from the corresponding author.

Received for publication July 27, 2000.

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