Relationships Between Cardiovascular Disease Risk
Factors and Depressive Symptoms as Predictors
of Cardiovascular Disease Events in Women
Thomas Rutledge, Ph.D., ABPP,1,2Sarah E. Linke, M.S., M.P.H.,3B. Delia Johnson, Ph.D.,4
Vera Bittner, M.D., MSPH,5David S. Krantz, Ph.D.,6Carol E. Cornell, Ph.D.,7Viola Vaccarino, M.D., Ph.D.,8
Carl J. Pepine, M.D.,9Eileen M. Handberg, Ph.D.,9Wafia Eteiba, M.D.,4Leslee J. Shaw, Ph.D.,10
Susmita Parashar, M.D., M.P.H., M.S.,8Jo-Ann Eastwood, Ph.D., R.N.,1 1
Diane A. Vido, M.S.,12and C. Noel Bairey Merz, M.D.13
Background: Modifiable risk factors for cardiovascular disease (CVD) account for much of the variability in CVD
outcomes and are also related to psychosocial variables. There is evidence that depression can undermine the
treatment and advance the progression of CVD risk factors, suggesting that CVD risk factor relationships with
CVD events may differ among those with depression.
Methods: This study tracked CVD events and mortality over a median of 5.9 years among a prospective cohort
of 620 women (mean age 59.6 years [11.6]) completing a diagnostic protocol including coronary angiography
and CVD risk factor assessment. Depressive symptoms were assessed using the Beck Depression Inventory
(BDI). The study outcome was combined cardiovascular mortality and events.
Results: Over the follow-up interval, 16.1% of the sample experienced one or more of the cardiovascular
outcomes. In separate Cox regression models adjusting for age, education history, ethnicity, and coronary
angiogram scores, we observed statistically significant CVD risk factor · BDI score interactions for diabetes,
smoking, and waist–hip ratio factors. Simple effect analyses indicated that diabetes and smoking status were
more strongly associated with cardiovascular outcomes among participants with lower BDI scores, whereas
waist–hip ratio values predicted outcomes only among those with higher BDI scores.
Conclusions: These results suggest that the relationship between modifiable CVD risk factors and CVD out-
comes may vary with depression status in clinical samples of women. This evidence augments prior research by
demonstrating that depression may influence CVD risk jointly with or independent of CVD risk factors. It also
provides further support for the inclusion of depression assessment in cardiovascular clinic settings.
physical inactivity, obesity, and smoking) are the most im-
(CVD; e.g., diabetes, dyslipidemia, hypertension,
portant causes of premature morbidity and mortality, ex-
plaining upwards of 90% of the variation in cardiovascular
those with psychosocial stressors such as depression, low
socioeconomic status, and social isolation.8–12Depression is
1VA San Diego Healthcare System, San Diego, California.
2Department of Psychiatry, University of California, San Diego, California.
3San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California.
4Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania.
5Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama.
6Department of Medical and Clinical Psychology, Uniformed Services University of the Health Sciences, Bethesda, Maryland.
7Department of Health Behavior and Health Education, University of Arkansas for Medical Sciences, Little Rock, Arkansas.
8Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, Georgia.
9University of Florida, Gainesville, Florida.
10Department of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas.
11UCLA School of Nursing, Los Angeles, California.
12Allegheny General Hospital, Pittsburgh, Pennsylvania.
13Women’s Heart Center, Cedars-Sinai Heart Institute, Los Angeles, California.
JOURNAL OF WOMEN’S HEALTH
Volume 21, Number 2, 2012
ª Mary Ann Liebert, Inc.
the most robust psychosocial predictor of CVD outcomes,
with multiple cohort studies suggesting a relationship be-
tween depression and CVD incidence and progression inde-
pendent of established CVD risk factors.13–16However,
whereas most of the existing literature has demonstrated that
associations between depression and CVD outcomes are in-
dependent of established risk factors, depression may also
affect CVD risk in combination with these risk factors; for
example, depression is associated with poorer adherence to
prescribed treatments for conditions such as hyperlipid-
emia,17less successful smoking cessation efforts,18lower
physical activity,19poorer blood pressure control,20and more
rapid progression of diabetes.21Thus, studying the combi-
nation of CVD risk factors and depression may yield insights
into CVD risk not observed from approaches that examine
CVD risk factors and depression independently.
In this article, we assessed relationships between modifiable
CVD risk factors, depression symptoms, and CVD events and
mortality among women presenting with symptoms of myo-
cardial ischemia and enrolled in the Women’s Ischemia Syn-
drome Evaluation (WISE) study. Prior WISE publications22,23
reported evidence of independent relationships between de-
we examined the combination of depression and CVD risk fac-
tors as event predictors, with the hypothesis that prospective
relationships between CVD risk factors and CVD deaths and
events would vary according to depressive symptom status as
measured by Beck Depression Inventory (BDI) scores.
Materials and Methods
Participant recruitment and entrance criteria
Women (‡18 years old) undergoing a clinically indicated
coronary angiogram for suspected myocardial ischemia were
(University of Alabama at Birmingham; University of Florida,
Gainesville; University of Pittsburgh; and Allegheny General
Hospital, Pittsburgh).24The WISE study was designed to im-
prove the understanding and diagnosis of ischemic heart dis-
ease in women. Exclusion criteria included major comorbidity
compromising follow-up, pregnancy, contraindication to pro-
vocative diagnostic testing, cardiomyopathy, New York Heart
Association class IV heart failure, recent myocardial infarction
or revascularization procedure, significant valvular or con-
data on 620 women with complete data on study variables. All
participants provided written informed consent, and all par-
ticipating sites obtained Institutional Review Board approval.
The WISE Angiographic Core Laboratory (Rhode Island
Hospital, Providence, RI) performed quantitative analysis of
coronary angiograms, with investigators blinded to all other
and at nearby reference segments using an electronic cine
Corporation). Each participant received a continuous coro-
nary artery disease (CAD) severity score based on angiogram
results and a modified Gensini index.26This severity score
of severity of the stenosis (0–19, 20–49, 50–69, 70–89, 90–98,
99–100), adjusting for partial and complete collaterals. Scores
were then adjusted according to lesion location with more
proximal lesions receiving a higher weighting factor.
Women were contacted at 6 weeks post-baseline and an-
nually thereafter for a median of 5.9 years (25th percen-
tile=2.5 years; 75th percentile=6.9 years) to track their
subsequent experiences of CVD events and cardiovascular
mortality. Follow-up consisted of a scripted telephone inter-
view by an experienced research nurse. This data collection
tool was validated previously against medical records.26
Death certificates were obtained and reviewed by the study
cardiologists in the event of participants’ death. For study
purposes, CVD events included myocardial infarction (MI),
congestive heart failure, stroke, and deaths judged to have
resulted probably or definitely from cardiovascular causes.
Modifiable risk factors
All participants completed a baseline evaluation that in-
cluded a physical examination with blood pressure and
physical measurements, clinical interview, and a fasting
blood draw for the measurement of lipids and glucose. Major
CVD risk factors in the WISE protocol included smoking
status, blood pressure, dyslipidemia, diabetes, obesity, and
physical inactivity. We assessed smoking based on self-
reported current versus not current smoker status. Blood
pressure was measured by a trained study nurse using a
standard sphygmomanometer during the physical examina-
tion; however, because approximately two thirds of WISE
participants reported use of one or more antihypertensive
agents in the past week (beta-blockers, calcium antagonists,
angiotensin-converting enzyme inhibitors, etc.), we defined
hypertension status for this report dichotomously based upon
participants’ report of a history of hypertension requiring
treatment (lifestyle or medication). Dyslipidemia status was
also defined dichotomously based upon the participants’ re-
ported history of treatment for the purpose of the current
study due to approximately one third of the sample missing
either one or both lipoprotein blood test results. Fasting blood
glucose readings served as our primary measure of diabetic
status; we also collected self-reported diabetes treatment
histories. We operationally defined obesity in terms of waist–
hip ratio values, with waist circumference measured at the
umbilicus. Both hip and waist values were rounded to the
nearest inch. For the purpose of subgroup analyses, we di-
chotomously defined elevated waist-to-hip ratio values
‡0.85.3Physical inactivity was evaluated with the Post-
menopausal Estrogen-Progestin Intervention questionnaire
(PEPI-Q),27a validated self-report instrument measuring
scale, ranging from inactivity to heavy activity. Prior studies
show that PEPI-Q scores correlate with aerobic fitness levels
determined by treadmill testing.27We further collected in-
formation about CVD risk factor histories and treatments for
use in exploratory analyses.
Participants’ self-reported education history served as an
estimate of socioeconomic status. Finally, each woman re-
sponded to a question assessing her marital status (options
included never married, currently married or living together,
separated, divorced, or widowed).
Women completed a validated measure (Beck Depression
Inventory28[BDI]) of depressive symptom severity and re-
134RUTLEDGE ET AL.
assessment. We used continuous BDI scores for all primary
analyses; for comparisons of lower and higher BDI groups, we
categorized BDI scores into those ranging from 0 to 9 versus
‡10, in line with BDI score interpretation guidelines.29
Comparisons of women with lower versus higher BDI
scores on CVD risk factors, demographic factors, and CVD
outcomes were completed using t tests and chi-square ana-
lyses. Separate cardiovascular events and mortality outcomes
did not occur at a frequency to provide adequate statistical
power; therefore, we combined the event and mortality ca-
tegories as the primary outcome. Cox regression methods
were employed to assess time to event relationships between
CVD risk factors, BDI scores, and risk factor · BDI score in-
teraction effects with cardiovascular outcomes. BDI scores
were maintained in continuous form in the primary analyses.
Participant age, race, education history, marital status, and
log-transformed coronary artery disease severity score were
each included as covariates. We examined each of the six
measured CVD risk factors (i.e., fasting glucose/diabetes,
dyslipidemia, hypertension, physical activity, smoking, and
of a significant interaction, we completed simple effect ana-
lyses for CVD risk factors with significant interaction results,
wherein we tested the CVD risk factor relationship with car-
diovascular outcomes among participants with lower and
higher BDI scores. All statistical analyses were completed
using SPSS software, version 17.0 (SPSS Inc.), with statistical
significance declared at p<0.05.
During the WISE recruitment period 1882/8557 women
screened for the resulting 22% meeting eligibility criteria.
From this group, 936 (50%) enrolled in WISE. Among the 936
women initially enrolled in WISE, 292 were missing BDI data
due to delayed implementation of the psychosocial question-
naires into the baseline assessment. An additional 24 partici-
pants were missing CVD risk factor information, follow-up
data, or statuson one ormore covariates, leaving 620forevent
analyses. Within this subsample, a total of 100 women expe-
rienced one or more CVD events for analysis purposes; 30
cardiovascular deaths, 21 myocardial infarctions, 37 conges-
tive heart failure episodes, and 29 strokes occurred over the
follow-up period (events were not independent, accounting
for why the number of CVD outcomes was greater than the
number of women experiencing one or more of theoutcomes).
Depression scores were not associated with follow-up length.
Demographic and CVD risk factor information appears in
Table 1. A total of 278 women (44.8%) had BDI scores ‡10.
Women with BDI scores ‡10 were less likely to report being
married and significantly morelikely to haveless education, a
nonwhite ethnicity increased rates of several measured CVD
risk factors, greater usage of antidepressants, and higher rates
of CVD events (9.1% vs. 15.8% event rates for those with BDI
scores <10 and ‡10, respectively, p=0.01) over follow-up.
CVD risk factors, depression,
and cardiovascular outcomes
Table 2 presents Cox regression results modeling predic-
tors of combined cardiovascular death and events. Results are
separated in Table 2 by CVD risk factor. In each model, cov-
ariate terms including age, education history, ethnicity, and
angiogram-derived CAD severity scores were entered at the
first step of the model, followed by the specific CVD risk
factor and continuous BDI scores and finally the CVD risk
factor · BDI score interaction term. In each model, CAD se-
verity scores were a highly significant predictor (p<0.001) of
cardiovascular events. The main study hypothesis was that
CVD risk factor relationships with cardiovascular outcomes
would vary by depression status (i.e., significant interaction
effects). In partial support of the study hypotheses, statisti-
cally significant interaction effects were observed in three of
the six CVD risk factor models (fasting glucose or diabetes,
smoking, and waist–hip ratio).
We subsequently performed separate simple effect analy-
ses for the three risk factors with significant interactions, in
which we computed Cox regression models assessing the
covariate-adjusted CVD risk factor at both lower (BDI scores
of 0–9) and higher (BDI scores ‡10) depressive symptom
levels. For the fasting glucose or diabetes factor, simple effect
Table 1. Demographic, Cardiovascular Disease Risk Factor, and Clinical Event Characteristics of Women’s
Ischemia Syndrome Evaluation Participants by Beck Depression Inventory Category (N=620)
BDI score <10 (n=342)a
BDI score ‡10 (n=278) p value for group difference
Age, mean (sd)
CAD score, mean (sd)b
Race (% nonwhite)
Completed high school
Elevated waist–hip ratioc
aBDI, Beck Depression Inventory.
bCAD, coronary artery disease.
cWaist–hip ratio ‡0.85.
PREDICTORS OF CVD EVENTS135
analyses revealed that fasting glucose scores were a highly
participants with lower BDI scores (hazard ratio [HR] 1.009,
95% confidence interval [CI] 1.005–1.013), but not statistically
associated with outcomes among those with higher BDI
scores (HR 1.003, 95% CI 0.99–1.005). The same pattern was
observed replacing fasting glucose values with dichotomous
diabetic status (HR 5.3, 95% CI 2.7–10.2 and HR 1.4, 95% CI
0.7–2.6, respectively). Current smoking status was likewise
more strongly associated with cardiovascular outcomes
among lower BDI participants (HR 1.9, 95% CI 0.9–4.5,
p=0.10) than among higher BDI scorers (HR 1.4, 95% CI 0.7–
2.7). Finally, waist–hip ratio status showed the opposite pat-
tern, with waist–hip ratio values marginally predictive of
outcomes among higher BDI scores (HR 1.9, 95% CI 0.95–3.7,
p=0.07) but not lower scorers (HR 0.74, 95% CI 0.38–1.4). The
decrease in statistical significance patterns in the simple ef-
fects analyses was largely attributable to the reduction in
power resulting from examining results within BDI sub-
The direction of the interaction effects for diabetic and
smoking status—showing stronger relationships with out-
comes among those with lower BDI scores—appears poten-
tially counterintuitive. Therefore, we performed follow-up
analyses in order to provide a clear interpretation of these
patterns. The above hazard ratios indicated relative, not
absolute, prediction of events across risk factor and BDI ca-
tegories. As shown in Table 3, for diabetes, smoking, and
waist–hip ratio factors, the absolute event risk was highest
among those with a combination of the risk factor and higher
BDI scores. From Table 3, we can see that the significant Cox
regression interaction between diabetes and lower BDI was a
result of the relatively greater difference in event risk between
nondiabetics and diabetics among those with lower BDI
scores compared with women with higher BDI scores. Spe-
cifically, for those with lower BDI scores, 9% of nondiabetics
experienced an event over follow-up, compared with 21% of
those with diabetes. This represented a more than twofold
relative risk difference associated with diabetes among those
with lower BDI scores. In contrast, among patients with
higher BDI scores, 32% of nondiabetics experienced a CVD
event, compared with 44% of those with diabetes. This re-
presented a 38% increase in event risk across the nondiabetic
versus diabetic groups. Thus, the significant interaction haz-
ard ratio indicated that the relative difference in event risk
among nondiabetics versus diabetics was larger among those
with lower BDI scores than among those with higher BDI
scores. It does not suggest that higher BDI scores were ‘‘pro-
tective’’ among diabetics. Again, as seen in Table 3, the ab-
solute risk of CVD events was highest among those with both
diabetes and higher BDI scores, indicating that the joint
presence of higher BDI scores was associated with a larger
The same pattern held true for smoking status. Specifically,
among women with lower BDI scores, 12% of nonsmokers
experienced an event compared with 24% of smokers (a 200%
relative risk difference). Among women with higher BDI
scores, however, 23% of nonsmokers and 34% of smokers
experienced a CVD event over follow-up (a 48% relative risk
increased theabsoluteevent riskassociated withsmoking,the
relative risk difference for smoking status as a predictor was
stronger for women with lower BDI scores. Notably, the
pattern observed for diabetes and smoking status was re-
versed for waist–hip ratio. Here, the relative risk difference
associated with a lower versus higher waist–hip ratio was
larger among those with higher BDI scores (an 18% versus
36% rate of events, respectively) than among those with lower
Table 2. Time to Event Rates of Cardiovascular
Mortality and Events Among Women Categorized
by Cardiovascular Risk Factor and Beck Depression
Inventory Scores (N=620)
Risk factor Hazard ratio95% CI
Fasting glucose levels
Diabetes · BDIa
Diabetes at BDI<10
Diabetes at BDI‡10
Dyslipidemia · BDI
Hypertension · BDI
Physical inactivity · BDI
Smoking · BDIa
Smoking at BDI<10
Smoking at BDI‡10
Waist–hip ratio · BDIa
Waist–hip ratio at BDI<10
Waist–hip ratio at BDI‡10
Adjusted for age, education history, ethnicity, and CAD severity
Events included myocardial infarction, stroke, and hospitalization
for congestive heart failure.
Table 3. Percent of Women Experiencing
a Cardiovascular Disease (CVD) Death and Event
Risk Over Follow-Up, Categorized by CVD Risk Factor
and Beck Depression Inventory Status (N=620)
BDI score status
CVD risk factor statusBDI<10BDI‡10
Physically inactive (no)
Physically inactive (yes)
Current smoker (no)
Current smoker (yes)
Elevated waist–hip ratio (no)
Elevated waist–hip ratio (yes)
136RUTLEDGE ET AL.
BDI scores (12% versus 14%). These patterns ofrelative versus
absolute risk explain what might have first appeared as par-
This investigation assessed the interrelationships among
six established CVD risk factors, depressive symptoms, and
CVD-related death and events. Many previous studies have
identified relationships between depression and CVD devel-
opment and prognosis independent of established CVD risk
effects between CVD risk factors and depressive symptoms in
theformofinteractions. The resultsindicated that associations
between several established CVD risk factors—including di-
abetes, smoking, and elevated waist–hip ratio—and CVD
death and events varied according to depressive symptom
severity. This pattern was robust to covariate adjustment that
included demographic characteristics and quantitative an-
giogram-derived CAD severity.
These results carry at least two implications for future re-
search. First, the findings suggest that depression may be
associated with cardiovascular outcomes through different
pathways. Depression, for example, may be related to CVD
independent of CVD risk factors12–14or, as suggested here, in
combination with CVD risk factors. Many studies have de-
scribed depression’s relationships with greater numbers and
severity of CVD risk factors; however, few have examined the
possibility of interactive associations among depression, CVD
risk factors, and cardiovascular events. Second, among wo-
men presenting with suspected myocardial ischemia, ele-
linked toCVDriskfactorstatus,which mayhaveimplications
for managing CVD risk factors in this population. Because co-
occurring depression appears to undermine CVD risk factor
management based on prior research,9,18,19and the potential
interactions between elevated depression and CVD risk factor
implications observed here, depression screening performed
in accordance with American Heart Association (AHA) rec-
ommendations could be a tool to help providers identify pa-
tients that warrant more attention to risk factor management
as a means of improving health outcomes.34
The pattern of interaction effects observed proved difficult
to interpret on the surface. Our results showed that CVD risk
was highest among women with risk factors and higher BDI
scores, a pattern that was consistent across each of the six
measured risk factors. However, in the cases of the significant
interaction tests for diabetes and smoking, the hazard ratios
showed that risk factor status better predicted events among
women with lower BDI scores rather than what may have
of absolute event risk numbers, however, explained this ap-
parent discrepancy, showing that the above two interactions
were based upon relative risk differences that were, in fact,
entirely consistent with the stronger absolute risk associated
with higher BDI scores.
In practice, these results indicate that CVD event risk as-
sociated with established risk factors is consistently higher
among women who also report higher depressive symptoms
in the form of BDI scores. The significant interaction patterns
further suggest that the ability to predict event risk using di-
abetes, smoking, or waist–hip ratio information is enhanced
by the concurrent knowledge of BDI scores. The correlational
nature of these data, however, leaves us to speculate regard-
ing the precise mechanisms underlying these interactions. For
factors such as waist–hip ratio, for example, it is important to
recognize that weight gain is a common depressive symptom,
suggesting that higher waist–hip ratios and depression levels
share similar etiologies and may exacerbate one another. This
conceptual overlap is not true for smoking or diabetes; how-
ever, depression may confound smoking cessation efforts12
and interfere with medication or exercise adherence among
patients with diabetes.13
This study focused on depressive symptoms assessed by
BDI scores. Among women with elevated BDI scores, more
than 25% reported current use of antidepressants; this pattern
could suggest that some antidepressant-treated WISE partici-
pants were experiencing only partial symptom relief from
identify elevated depressive symptom levels overestimated
the prevalence of clinically significant depression. Even with
state-of-the-art treatments, achieving lasting reductions in
depressive symptoms often requires careful monitoring and
follow-up care by a mental health provider.35,36Effective de-
pression treatment is likewise dependent upon the accurate
identification of depression, which evidence suggests is often
under-recognized.37,38Any possible underestimate of true
in an attenuation of the group differences observed. That the
pattern of statistical relationships we observed remained de-
spite this possible treatment-selection factor and moderate
sample sizes foreventanalyses may beseenasfurthersupport
for the observed interrelationships between depressive
symptoms and CVD risk factors as outcome predictors.
The WISE study was not specifically designed to study
depression status or treatment effects. Thus, no information
concerning the type, duration, or effectiveness of past or
present depression treatment was collected. Extending these
results to those with interview-diagnosed mood disorders
and in protocols with more detailed information concerning
patterns of antidepressant use and depression treatment (and
effectiveness) over time will be important future steps. Fur-
ther, women with higher BDI scores may have differed in a
depression severity may vary over time.
Although our analyses demonstrated differential associa-
tions for CVD risk factors and CVD outcomes in relation to
depressive symptoms, we cannot exclude that these associa-
tions may be due to variables such as diet or medication ad-
herence, for which higher BDI scores may be a marker. Due to
the moderate sample size, the current results should be
viewed as preliminary support for interaction testing in a
larger cohort. In some analyses, our results were based upon
dichotomously defined CVD risk factors rather than contin-
uously measured forms of these factors. Although logistical
factors such as high levels of treatment (blood pressure) and
missing data (lipids) favored this approach for assessing
participants standing on these risk factors, the loss of data
from artificial dichotomies is also an important limitation.
Finally, the enrollment criteria for the WISE study sample
limitour abilityto makegeneralizations tomen orto similarly
PREDICTORS OF CVD EVENTS137
aged women with different cardiac risk profiles. WISE par-
ticipants were recruited in tertiary care centers for evaluation
of suspected myocardial ischemia. The clinical characteristics
of the WISE sample were intended to resemble as closely as
possible women undergoing routine coronary assessments.
However, these same characteristics set the WISE sample
apart from asymptomatic women or women with other
medical complications, limiting the ability to generalize our
the impact of several modifiable CVD risk factors (diabetes,
smoking, and waist–hip ratio as a measure of obesity) on
clinical outcomes including CVD death and events (congestive
heart failure, stroke, MI) varied depending on women’s con-
current levels of depressive symptom severity as measured by
the BDI. Although depression is an established independent
predictor of CVD events, these results offer an alternative
perspective regarding the mechanisms by which depression
may affect the development or course of CVD, implying that a
second pathway may be via influencing the event risk associ-
ated with risk factors for CVD. These results reinforce AHA
guidelines for the assessment of depression in coronary heart
disease patients39and suggest that further research should be
depression and traditional CVD risk factors.
This work was supported by contracts from the National
Heart, Lung and Blood Institutes (N01-HV-68161, N01-HV-
68162, N01-HV-68163, N01-HV-68164) and grants (U0164829,
U01 HL649141, U01 HL649241), a General Clinical Research
Center grant (MO1-RR00425) from the National Center for
Research Resources, and grants from the Gustavus and Louis
Pfeiffer Research Foundation, Denville, New Jersey; The
Women’s Guild of Cedars-Sinai Medical Center, Los Angeles,
California; The Ladies Hospital Aid Society of Western
Pennsylvania, Pittsburgh, Pennsylvania; and The Edythe
Broad Endowment for Women’s Heart Research, Los An-
geles, California. The first author takes full responsibility for
to the WISE data. All authors contributed to the development
of this manuscript in important ways, including study design,
data collection, participating in multiple internal drafts for
editorial purposes, group discussion of findings, and data
analysis. The article received approval for publication by the
WISE P&P committee and the participating authors.
The listed authors have no financial disclosures or conflicts
of interest with the findings in this paper.
1. Dawber TR, Meadors GF, Moore FE Jr. Epidemiological
approaches to heart disease: the Framingham Study. Am J
Public Health 1951;41:279–281.
2. Yusuf S, Hawken S, Ounpuu S, et al.; INTERHEART Study
Investigators. Effect of potentially modifiable risk factors
associated with myocardial infarction in 52 countries (the
INTERHEART study): case-control study. Lancet 2004;364:
3. Yusuf S, Hawken S, Ounpuu S, et al.; INTERHEART Study
Investigators. Obesity and the risk of myocardial infarction
in 27,000 participants from 52 countries: a case-control
study. Lancet 2005;366:1640–1649.
4. The Multiple Risk Factor Intervention Trial Research Group.
Mortality after 16 years for participants randomized to the
Multiple Risk Factor Intervention Trial. Circulation 1996;94:
5. Oh K, Hu FB, Manson JE, Stampfer MJ, Willett WC. Dietary
fat intake and risk of coronary heart disease in women: 20
years of follow-up of the nurses’ health study. Am J Epi-
6. Khot UN, Khot MB, Bajzer T, et al. Prevalence of conven-
tional risk factors in patients with coronary artery disease.
7. He J, Ogden LG, Bazzano LA, Vupputuri S, Loria C,
Whelton PK. Risk factors for congestive heart failure in US
men and women: NHANES I epidemiologic follow-up
study. Arch Intern Med 2001;161:996–1002.
8. Carnethon MR, Biggs ML, Barzilay JI, et al. Longitudinal
association between depressive symptoms and incident type
2 diabetes mellitus in older adults: the cardiovascular health
study. Arch Intern Med 2007;167:802–807.
9. Georgiades A, Zucker N, Friedman KE, et al. Changes in
depressive symptoms and glycemic control in diabetes
mellitus. Psychosom Med 2007;69:235–241.
10. Kim JY, Oh DJ, Yoon TY, Choi JM, Choe BK. The impacts of
obesity on psychological well-being: a cross-sectional study
about depressive mood and quality of life. J Prev Med Pub
11. Herva A, Rasanen P, Miettunen J, et al. Co-occurrence of
metabolic syndrome with depression and anxiety in young
adults: the Northern Finland 1966 Birth Cohort Study. Psy-
chosom Med 2006;68:213–216.
12. Rozanski A, Blumenthal JA, Kaplan J. Impact of psycho-
logical factors on the pathogenesis of cardiovascular disease
and implications for therapy. Circulation 1999;99:2192–2217.
13. Rumsfeld JS, Ho PM. Depression and cardiovascular dis-
ease: a call for recognition. Circulation 2005;111:250–253.
14. Barth J, Schumacher M, Herrmann-Lingen C. Depression as
a risk factor for mortality in patients with coronary heart
disease: a meta-analysis. Psychosom Med 2004;66:802–813.
15. Ferketich AK, Schwartzbaum JA, Frid DJ, Moeschberger
ML. Depression as an antecedent to heart disease among
men and women in the NHANES I study. Arch Int Med
16. Wassertheil-Smoller S, Shumaker S, Ockene J, et al. De-
pression and cardiovascular sequelae in postmenopausal
women. Arch Intern Med 2004;164:289–298.
17. Le TK, Able SL, Lage MJ. Resource use among patients with
diabetes, diabetic neuropathy, or diabetes with depression.
Cost Eff Resour Alloc 2006;4:18.
18. Levine MD, Marcus MD, Perkins KA. A history of depression
and smoking cessation outcomes among women concerned
19. Nguyen HQ, Koepsell T, Unu ¨tzer J, Larson E, LoGerfo JP.
Depression and use of a health plan-sponsored physical activity
program by older adults. Am J Prev Med 2008;35:111–117.
20. Treiber FA, Kamarck T, Schneiderman N, Sheffield D, Ka-
puku G, Taylor T. Cardiovascular reactivity and develop-
ment of preclinical and clinical disease states. Psychosom
138 RUTLEDGE ET AL.
21. de Groot M, Anderson R, Freedland KE, Clouse RE, Lust- Download full-text
man PJ. Association of depression and diabetes complica-
tions: a meta-analysis. Psychosom Med 2001;63:619–630.
22. Vaccarino V, McClure C, Johnson BD, et al. Depression, the
metabolic syndrome and cardiovascular risk. Psychosom
23. Vaccarino V, Johnson BD, Sheps DS, et al. Depression, in-
flammation, and incident cardiovascular disease in women
with suspected coronary ischemia: the National Heart, Lung,
and Blood Institute-sponsored WISE study. J Am Coll Car-
24. Bairey Merz CN, Kelsey SF, Pepine CJ, et al. The Women’s
Ischemia Syndrome Evaluation (WISE) study: protocol de-
sign, methodology, and feasibility report. J Am Coll Cardiol
25. Sharaf BL, Pepine CJ, Kerensky RA, et al. Detailed angio-
graphic analysis of women with suspected ischemic chest
pain (pilot phase data from the NHLBI-sponsored Women’s
Ischemia Syndrome Evaluation [WISE] study angiographic
core laboratory). Am J Cardiol 2001;87:937–941.
26. Mahoney EM, Jurkovitz CT, Chu H, et al. Cost and cost
effectiveness of an early invasive vs conservative strategy for
the treatment of unstable angina and non-ST-segment ele-
vation myocardial infarction. JAMA 2002;288:1851–1858.
27. The Writing Group for the PEPI trial. Effects of estrogen/pro-
gestin regimens on heart disease risk factors in postmenopausal
women.The Postmenopausal Estrogen/ProgestinInterventions
Trial. JAMA 1995;273:199–208 (erratum, JAMA 1995;274:1676).
28. Beck AT. Depression Inventory. Philadelphia: Center for
Cognitive Therapy, 1978.
29. Beck AT, Rush AJ, Shaw BF, Emery G. Cognitive Therapy of
Depression. New York: Guilford Press, 1979.
30. Rosengren A, Hawken S, Ounpuu S, et al.; INTERHEART
investigators. Association of psychosocial risk factors with
risk of acute myocardial infarction in 11119 cases and 13648
controls from 52 countries (the INTERHEART study): case-
control study. Lancet 2004;364:953–962.
31. Sherwood A, Blumenthal JA, Trivedi R, et al. Relationship of
depression to death or hospitalization in patients with heart
failure. Arch Intern Med 2007;167:367–373.
32. Musselman DL, Evans DL, Nemeroff CB. The relationship of
depression to cardiovascular disease: epidemiology, biology,
and treatment. Arch Gen Psychiatry 1998;55:580–592.
33. van Melle JP, de Jonge P, Spijkerman TA, et al. Prognostic
association of depression following myocardial infarction
with mortality and cardiovascular events: a meta-analysis.
Psychosom Med 2004;66:814–822.
34. Davidson KW, Rieckmann N, Clemow L, et al. Enhanced
depression care for patients with acute coronary syndrome
and persistent depressive symptoms: coronary psychosocial
evaluation studies randomized controlled trial. Arch Intern
35. Kessler RC, Merikangas KR, Wang PS. Prevalence, co-
morbidity, and service utilization for mood disorders in the
United States at the beginning of the twenty-first century.
Annu Rev Clin Psychol 2007;3:137–158.
36. Feinstein RE, Blumenfield M, Orlowski B, Frishman WH,
Ovanessian S. A national survey of cardiovascular physi-
cians’ beliefs and clinical care practices when diagnosing
and treating depression in patients with cardiovascular
disease. Cardiol Rev 2006;14:164–169.
37. Williams JW Jr, Noel PH, Cordes JA, Ramirez G, Pignone
M. Is this patient clinically depressed? JAMA 2002;287:
38. Olfson M, Marcus SC, Druss B, Elinson L, Tanielian T,
Pincus HA. National trends in the outpatient treatment of
depression. JAMA 2002;287:203–209.
39. Lichtman JH, Bigger JT Jr, Blumenthal JA, et al. Depression
and Coronary Heart Disease. Recommendations for screen-
ing, referral, and treatment. A science advisory from the
American Heart Association Prevention Committee of the
Council on Cardiovascular Nursing, Council on Clinical
Cardiology, Council on Epidemiology and Prevention, and
Interdisciplinary Council on Quality of Care and Outcomes
Research. Circulation 2008;118:1768–1775.
Address correspondence to:
Thomas Rutledge, Ph.D., ABPP
Psychology Service 116B
VA San Diego Healthcare System, Medical Center
3350 La Jolla Village Drive
San Diego, CA 92161
PREDICTORS OF CVD EVENTS139