Abstract
Background
Despite the high prevalence of peripheral artery disease (PAD) in women, risk factors for PAD in women are not well understood.
Methods
Gender-specific risk factors for PAD were examined in a prospective cohort study of 1024 patients (184 women and 840 men) with stable coronary artery disease who were recruited between 2000 to 2002. Logistic regression models were used to evaluate associations between traditional and non-traditional risk factors and PAD in both men and women.
Results
11% of women and 13% of men were found to have PAD. Women with PAD had a similar prevalence of traditional risk factors (hypertension, hyperlipidemia, and smoking) compared to women without PAD. Women with PAD were significantly more likely to suffer from depression than women without PAD. Men with PAD were more likely to have hypertension, diabetes mellitus, a history of smoking, a worse lipid profile and higher levels of inflammatory biomarkers compared to men without PAD. In a multivariate model, depression was the only significant factor associated with PAD in women while smoking and elevated fibrinogen were independently associated with PAD in men.
Conclusions
The current findings suggest gender differences in risk factors for the development of PAD. Further research is needed to understand the role of depression in PAD.
INTRODUCTION
Peripheral artery disease (PAD) is a significant cause of morbidity and mortality and has recently been recognized as a global pandemic.1 PAD is under-recognized and under-treated in women, even though there appears to be an increasing population burden of PAD in women.2, 3 In fact, in the 2010 United States Census, the prevalence of PAD was higher in women than in men.4 Despite the high prevalence of PAD in women, women are under-represented in contemporary PAD studies5, 6, and risk factors for PAD in women have not been extensively studied. In view of this, the American Heart Association (AHA) issued a scientific statement calling for further research to study PAD in women4.
Traditional cardiovascular disease (CVD) risk factors are more prevalent in men with PAD compared to women with PAD7, 8, suggesting that other risk factors might be involved in the pathophysiology of PAD in women. It is well known that women are at increased risk for depression9-11 compared to men, and we recently demonstrated that depression was a strong and independent risk factor for PAD.12 Others have shown that among patients with PAD, those with depression have worse functional outcomes, greater need for revascularization, poorer quality of life outcomes following revascularization, and higher risk for adverse events after revascularization.13-16 Prior research has also indicated that women with PAD below the age of 65 years are particularly vulnerable to experiencing depressive symptoms and that these symptoms seem to be accompanied with high rates of smoking.17
The associations between depression and psychosocial factors with PAD have not been extensively investigated. A better understanding of patients’ psychosocial profiles might identify risk factors that can be addressed to mitigate both patients’ depressive symptoms and their cardiovascular risk. The Heart and Soul Study was designed to study the association between psychological disorders and cardiovascular events (including PAD) in outpatients with stable coronary artery disease. In this report, we investigated the gender-specific prevalence of traditional CVD, psychological, and social risk factors for PAD. We hypothesized that women with PAD would have a different risk factor profile (including psychosocial factors) compared to men with PAD.
METHODS
Study Population
The Heart and Soul Study was designed to study the association between psychological disorders and cardiovascular events in outpatients with stable CAD. Detailed methods have been previously described.18 Briefly, the investigators performed a prospective cohort study of 1024 subjects with known CHD who were recruited between 2000 and 2002 and followed for 10 years. At the baseline examination, participants completed a structured diagnostic interview for depression, extensive questionnaire, EKG, 6-minute walk test, and full exercise treadmill testing with stress echocardiography. Participants were instructed to bring their medication bottles to the study appointment, and study personnel recorded all current medications. Participants also completed 24-hour ambulatory Holter monitoring to determine heart rate variability and collected 24-hour urine for measurement of creatinine, free cortisol, and catecholamines. Fasting blood was drawn, and samples of serum, plasma, DNA, and 24-hour urine were stored and remain in a specimen biorepository at –80 degrees Celsius. After 5 years of follow-up, 667 participants (>80% of survivors) completed a repeat examination that included a structured diagnostic interview for depression, questionnaire, EKG, exercise treadmill test, fasting blood draw and 24-hour urine. In addition, participants have been contacted annually to inquire about cardiovascular events, which are confirmed by review of medical records. Follow-up information is available for >99% of study participants.
With regards to inclusion criteria, the investigators used administrative databases to identify outpatients with documented coronary artery disease at two Department of Veterans Affairs Medical Centers (San Francisco VA Medical Center and the VA Palo Alto Health Care System, California), one University medical center (University of California, San Francisco), and nine public health clinics in the Community Health Network of San Francisco. Patients were eligible to participate if they had known CHD documented by at least one of the following: a history of myocardial infarction, angiographic evidence of at least 50% stenosis in one or more coronary vessels, prior evidence of inducible ischemia by treadmill or nuclear testing, or a history of coronary revascularization.
Related to exclusion criteria, a total of 15,438 patients with CHD were identified from administrative databases and mailed an invitation to participate. Of the 2495 patients who returned a form indicating that they would be interested in participating, 505 could not be reached by telephone, and 370 were excluded because they had a history of myocardial infarction in the prior 6 months (treadmill test contra-indicated), deemed themselves unable to walk one block (treadmill test not useful), or were planning to move out of the local area within two years (unavailable for follow-up). Of the 1620 patients who were confirmed eligible, 596 declined to participate, and 1024 (63%) enrolled.
Patients were eligible to participate in the study if they met at least one of the following conditions: a history of myocardial infarction (MI), angiographic evidence of at least 50% stenosis in 1 or more coronary vessels, previous evidence of exercise-induced ischemia using treadmill or nuclear testing, or a history of coronary revascularization. Between September 11, 2000, and December 20, 2002, a total of 1024 participants were enrolled. Participants were followed for 7.2 ± 2.6 years (mean +/− SD). Of those, a total of 124 were found to have PAD (21 women and 113 men), defined by either self-report of this diagnosis on entering the study (n=84; men=8.1%; women=8.7%); diagnosis by a physician during hospitalization (n=56; men=5.8%; women=3.8%); ultrasound or angiographically demonstrated obstruction or ulcerated plaque (>50% of diameter or >75% of x-sectional area) of the iliac arteries or below (n=40; men=4.2%; women=2.7%); surgery, angioplasty, or thrombolysis for PAD (n=40; men=4.3%; women=2.2%); and/or exertional leg pain relieved by rest (n=23; men=2.3%; women 2.2%).
Study Measurements
All participants completed a baseline examination that included an interview, fasting venous blood sample collection, a standardized medical history questionnaire, echocardiography, exercise treadmill testing, 24-hour ambulatory electrocardiography and a 24-hour urine collection. Age, sex, race, education level, and medical history were determined by self-report questionnaire. Height and weight were measured by a standardized protocol, with body mass index calculated as weight in kilograms divided by height in meters squared. Participants were instructed to bring their medication bottles to their enrollment visit, and study personnel recorded all current medications. Medications were categorized using Epocrates Rx (Epocrates Inc, S Mateo, California). None of the data presented more than 5% missing variables. For questionnaires, these were reported as missing if 30% or more of the individual items were missing. Otherwise, the total score was divided by the proportion answered to account for missing data. The protocol was approved by the appropriate institutional review boards, and all participants provided written informed consent for participation in the study.
Blood samples
Fasting blood samples were obtained during the morning of the enrollment visit. Levels of high-sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) were determined from plasma and serum samples. These biomarkers were chosen based on their association with disease severity in PAD19-22. HsCRP levels were measured using the Integra assay (Roche, Indianapolis, Indiana) or (owing to a change at the laboratory) the Extended Range assay (Beckman Coulter Ireland Inc, Galway, Ireland). Prior testing demonstrated high correlation of these two methods23. We used the R&D Systems (Minneapolis, MN) Quantikine HS IL-6 immunoassay to determine the concentration of interleukin IL-6. We used the Human Serum Adipokine Panel B LINCOplex Kit (Linco Research, Inc., St. Charles, MO) to measure TNF-α. Low and high-density lipoprotein cholesterol levels were measured from fasting venous blood samples at baseline.
Behavioral and lifestyle factors
A history of smoking was determined by self-report questionnaire. Alcohol use was evaluated with the validated AUDIT-C questionnaire24. To assess medication adherence, participants were asked, “In the past month, how often did you take your medications as the doctor prescribed?” Possible responses were “all of the time (100%),” “nearly all of the time (90%),” “most of the time (75%),” “about half the time (50%),” or “less than half the time (≤50%).” We defined medication nonadherence as taking prescribed medications 75% of the time or less25. To assess physical activity, the participants were asked, “Which of the following statements best describes how physically active you have been during the past month, that is, done activities such as 15 to 20 minutes of brisk walking, swimming, general conditioning, or recreational sports?” Participants chose one of the following six categories: not at all active, a little active (1-2 times per month), fairly active (3-4 times per month), quite active (1-2 times per week), very active (3-4 times per week), or extremely active (≥5 times per week). Participants who reported that they were not at all or a little active were considered physically inactive. Self-report has been shown to be a reliable, valid, and accurate method of assessing physical activity26, 27.
Mental Health Factors
Depressive symptoms were assessed with the validated nine-item Patient Health Questionnaire (PHQ)28. The PHQ provides a dichotomous measure of depressive symptoms based on a score of 10 or higher. A score of 10 or higher has a sensitivity of 88% and a specificity of 88% for major depressive disorders29. The PHQ-9 has been previously used for diagnosis purposes in patients with PAD15. Anxiety symptoms were assessed by the Hospital Anxiety and Depression Scale (HADS)30. The HADS questionnaire ranges from 0-21 with higher scores representing higher levels of anxiety. A score greater or equal to 8 on the anxiety subscale has a sensitivity and specificity of approximately 80% as a case finder for anxiety disorders31.
Statistical Analysis
Differences in baseline characteristics by gender were evaluated with t-tests for continuous variables and Chi-square tests for categorical variables (no variables had more than 4% missing data). We log-transformed covariates with severely right-skewed distributions (inflammatory biomarkers). Odd ratios and confidence intervals were calculated using logistic regression models. Variables included in the multivariate models were based on an a-priori determination of significance at <0.10 based on univariate models, in addition to age and activity status. To help interpret negative findings, confidence intervals (CI) were assessed in addition to p-values for logistic regression estimates. A wide CI generally fails to rule out substantial effects in at least one direction for negative findings, while a narrow CI increases the certainty about non-significant findings32. Statistical analyses were performed using Stata/SE 12 (StataCorp, College Station, TX).
RESULTS
21 women (11%) and 113 men (13%) were found to have PAD. Women with PAD had a similar prevalence of hypertension, diabetes mellitus and history of myocardial infarction compared to women without PAD (Table 1). There were no significant differences in the use of aspirin, ACE-inhibitors or statins between the two groups. Women with and without PAD were equally likely to be smokers and had no significant differences in their lipid profile. Women with PAD were significantly more likely to suffer from depression compared to women without PAD (p=0.01).
Table 1.
Characteristics of Women with and without PAD.
| Traditional Risk Factors | Women with PAD (n=21) | Women without PAD (n=163) | p-value |
|---|---|---|---|
| Age, Mean (SD), y | 67 ± 12 | 64 ± 11 | 0.21 |
| Caucasian | 11 (52) | 80 (49) | 0.78 |
| BMI | 28 ± 7 | 30 ± 6 | 0.39 |
| Waist-hip ratio | 0.88± 0.06 | 0.88 ±0.07 | 0.86 |
| Comorbidities and Cardiac Disease Severity | |||
| Hypertension | 16 (76) | 120 (74) | 0.80 |
| Hx of MI | 11 (55) | 72 (45) | 0.38 |
| Past Coronary Revascularization | 9 (43) | 72 (44) | 0.89 |
| LVEF (%) | 64 ± 7 | 64 ± 8 | 0.93 |
| CCHF | 5 (24) | 28 (17) | 0.46 |
| Treadmill Score (METs) | 6 ± 3 | 7 ± 3 | 0.23 |
| Diabetes Mellitus | 7 (33) | 44 (27) | 0.54 |
| Arthritis | 15 (71) | 100 (62) | 0.49 |
| History of Alcoholism | 3 (14) | 8 (5) | 0.01 |
| Systolic Blood Pressure (mm Hg) | 139 ± 21 | 137 ± 22 | 0.82 |
| Diastolic Blood Pressure (mmHg) | 71 ± 11 | 76 ± 11 | 0.07 |
| Medications | |||
| Aspirin | 13 (62) | 115 (71) | 0.42 |
| ACE-inhibitor | 8 (38) | 76 (47) | 0.46 |
| B-Blocker | 8 (38) | 87 (53) | 0.19 |
| Statin | 10 (48) | 89 (55) | 0.55 |
| Anti-depressant | 5 (24) | 40 (25) | 0.94 |
| Traditional Risk Factors, Inflammation and Metabolic Factors | |||
| History of Smoking | 12 (57) | 90 (55) | 0.87 |
| Total Cholesterol | 196 ± 52 | 191 ± 48 | 0.65 |
| LDL | 114 ± 9 | 111 ± 41 | 0.78 |
| HDL | 53 ± 17 | 53 ± 17 | 0.94 |
| Hemoglobin A1c (%) | 6 ± 1 | 6 ± 1 | 0.48 |
| Glucose (mg/dL) | 132 ± 55 | 116 ± 36 | 0.07 |
| Log CRP (mg/L) | 0.7 ±1.3 | 1.1 ± 1.3 | 0.22 |
| Log IL-6 (pg/ml) | 1.0 ± 0.6 | 0.9 ± 0.8 | 0.31 |
| Log TNF-α (pg/ml) | 1.4 ± 1.1 | 1.2 ± 0.8 | 0.52 |
| Log fibrinogen (mg/dL) | 6.0 ± 0.2 | 6.0 ± 0.2 | 0.35 |
| Psychosocial and Behavioral Risk Factors | |||
| Depression by PHQ-9 score | 10 (48) | 37 (23) | 0.01 |
| Current PTSD | 1 (5) | 22 (14) | 0.26 |
| History of General Anxiety Disorder | 5 (24) | 30 (18) | 0.55 |
| Anxiety Score | 7 ± 3 | 7 ± 4 | 0.88 |
| Married | 3 (14) | 45 (28) | 0.19 |
| Poor Social Support | 16 (76) | 113 (69) | 0.52 |
| Physically active | 14 (67) | 86 (53) | 0.24 |
| Adherent to medication | 17 (81) | 139 (87) | 0.41 |
| Regular alcohol use | 4 (19) | 25 (16) | 0.68 |
Men with PAD, compared to men without PAD, were more likely to have hypertension, diabetes mellitus, and past coronary revascularization compared to men without PAD (Table 2). Men with PAD were also more likely to have a history of smoking and had lower high-density lipoprotein cholesterol, and higher inflammatory biomarkers (CRP, IL6, TNF-α and fibrinogen) compared to men without PAD.
Table 2.
Characteristics of Men with or without PAD
| Traditional Risk Factors | Men with PAD (n=113) | Men without PAD (n=727) | p-value |
|---|---|---|---|
| Age, Mean (SD), y | 68 ± 10 | 67 ± 11 | 0.91 |
| Caucasian | 75 (66) | 449 (62) | 0.36 |
| BMI | 28 ± 5 | 28 ± 5 | 0.99 |
| Waist-hip ratio | 0.98± 0.06 | 0.97 ±0.07 | 0.20 |
| Comorbidities and Cardiac Disease Severity | |||
| Hypertension | 90 (80) | 497 (69) | 0.02 |
| Hx of MI | 68 (60) | 396 (55) | 0.28 |
| Past Coronary Revascularization | 81 (72) | 440 (61) | 0.03 |
| LVEF (%) | 60 ± 10 | 61 ± 10 | 0.43 |
| CHF | 38 (34) | 108 (15) | <0.0001 |
| Treadmill Score (METs) | 6 ± 3 | 8 ± 3 | 0.0003 |
| Diabetes Mellitus | 40 (35) | 174 (24) | 0.01 |
| Arthritis | 64 (57) | 377 (52) | 0.53 |
| History of Alcoholism | 26 (23) | 96 (13) | 0.02 |
| Systolic Blood Pressure (mm Hg) | 134 ± 23 | 132 ± 20 | 0.31 |
| Diastolic Blood Pressure (mmHg) | 73 ± 13 | 75 ± 11 | 0.29 |
| Medications | |||
| Aspirin | 96 (85) | 568 (78) | 0.10 |
| ACE-inhibitor | 68 (60) | 372 (51) | 0.07 |
| B-Blocker | 74 (65) | 424 (58) | 0.15 |
| Statin | 82 (73) | 476 (65) | 0.14 |
| Anti-depressant | 22 (19) | 121 (17) | 0.46 |
| Traditional Risk Factors, Inflammation and Metabolic Factors | |||
| History of smoking | 92 (81) | 515 (71) | 0.02 |
| Total Cholesterol | 171 ± 39 | 176 ± 41 | 0.26 |
| LDL | 100 ± 30 | 103 ± 32 | 0.27 |
| HDL | 41 ± 12 | 45 ± 13 | 0.01 |
| Hemoglobin A1c (%) | 6 ± 1 | 6 ± 1 | 0.48 |
| Glucose (mg/dL) | 132 ± 55 | 116 ± 36 | 0.07 |
| Log CRP (mg/L) | 1.1±1.3 | 0.6 ± 1.3 | 0.0005 |
| Log IL-6 (pg/ml) | 1.3 ± 0.7 | 0.9 ± 0.7 | <0.00001 |
| Log TNF-α (pg/ml) | 1.4 ± 0.9 | 1.2 ± 0.9 | 0.008 |
| Log fibrinogen (mg/dL) | 6.0 ± 0.2 | 5.9 ± 0.2 | 0.0001 |
| Psychosocial and Behavioral Risk Factors | |||
| Depression by PHQ-9 score | 27 (24) | 125 (17) | 0.09 |
| Current PTSD | 10 (9) | 62 (9) | 0.92 |
| History of General Anxiety Disorder | 12 (11) | 60 (8) | 0.41 |
| Anxiety Score | 5 ± 6 | 5 ± 4 | 0.22 |
| Married | 51 (45) | 337 (47) | 0.78 |
| Poor Social Support | 68 (60) | 496 (68) | 0.08 |
| Physically active | 67 (60) | 482 (66) | 0.17 |
| Adherent to medication | 106 (95) | 670 (93) | 0.45 |
| Regular alcohol use | 28 (25) | 236 (33) | 0.10 |
In a multivariate analysis that included diastolic blood pressure, glucose, and depression (p<0.10 for each variable in univariate analysis) in addition to age, only depressive symptoms remained significantly associated with PAD in women (p=0.02; Table 3).
Table 3.
Independent risk factors for PAD in Women using a Multivariate Model.
| Risk Factors | Odds Ratio | Confidence Interval | P-value |
|---|---|---|---|
| Age | 1.04 | 0.99, 1.09 | 0.10 |
| Diastolic blood pressure | 0.97 | 0.93, 1.02 | 0.26 |
| Blood glucose | 1.01 | 1.00, 1.02 | 0.08 |
| Physically activity | 1.17 | 0.86, 1.59 | 0.31 |
| Depression | 3.83 | 1.33, 11.00 | 0.01 |
Univariate analysis demonstrated hypertension (p=0.02), diabetes mellitus (p=0.01), CRP (p<0.0001), fibrinogen (p<0.0001), HDL (p=0.01), smoking (p=0.02), physical activity (p=0.08) and depressive symptoms (p=0.09) to be associated with PAD in men. In the multivariate analysis (Table 4), fibrinogen (p=0.05) and a history of smoking (p=0.03) remained independently associated with PAD in men.
Table 4.
Independent risk factors for PAD in Men using a Multivariate Model.
| Risk Factors | Odds Ratio | Confidence Interval | P-value |
|---|---|---|---|
| Age | 1.01 | 0.99, 1.03 | 0.33 |
| Hypertension | 1.54 | 0.92, 2.57 | 0.10 |
| Diabetes Mellitus | 1.36 | 0.86, 2.16 | 0.19 |
| CRP | 1.14 | 0.86, 1.50 | 0.36 |
| Fibrinogen | 1.30 | 1.00, 1.70 | 0.05 |
| HDL | 0.80 | 0.63, 1.01 | 0.07 |
| History of Smoking | 1.46 | 1.05, 2.04 | 0.03 |
| Physical Activity | 1.00 | 0.88, 1.15 | 0.92 |
| Depression | 1.43 | 0.84, 2.43 | 0.19 |
DISCUSSION
It is well known that traditional CVD risk factors are strongly associated with the development and progression of PAD. However, few studies examine PAD risk factors separately by gender. In our study, we found traditional risk factors to be similar in prevalence in women with and without PAD; however, these traditional risk factors were more prevalent in men with PAD compared to men without PAD. This suggests that there might be other risk factors that contribute to PAD in women. In women, depression was the strongest risk factor for PAD. In a study of 1932 participants free of four traditional CVD risk factors (smoking, diabetes, hypertension, and dyslipidemia) in the Multi-Ethnic Study of Atherosclerosis (MESA), there was still a significant association between female gender and lower ABI.8 In a cohort of over 15,000 participants, women who had never smoked were more likely to develop PAD compared with male never-smokers, even after adjustment for age, low-density lipoprotein cholesterol, hypertension, and diabetes.33 In our study, depression was more common in women with PAD compared to those without PAD. It is possible that women and men have a different set of risk factors for PAD, and that depression or other psychosocial factors might be more important in women. These risk factors are not commonly evaluated in PAD studies, and further research is needed to better clarify these associations.
We and others have demonstrated that depression is a risk factor for PAD12, 34, 35. In addition to increasing the risk of PAD, depression also appears to impact the functional status and symptoms of patients with PAD, leading to more dramatic annual declines in functional performance13, reduced walking distance14, and reduced quality of life benefit following revascularization15. The findings from this study add to a growing body of literature on behavioral and psychosocial risk factors in the development of cardiovascular disorders. Though not specifically evaluated in patients with PAD, social support has been linked to adverse cardiovascular outcomes in other populations36. In patients at risk or with an atherothrombotic disorder, people living alone were at higher risk of cardiovascular death than those who lived with someone37. In a recent review, low “functional support” increased both cardiac and all-cause mortality38. With regards to other psychosocial risk factors, general stress, work-related stress and the feeling of “lack of control” are now recognized as potential risk factors for cardiovascular diseases39. Overall, it is possible that psychosocial risk factors impact cardiovascular diseases, and influence the development of PAD.
The findings of this study have important implications for clinical practice. Depression may play an important role in the pathophysiology of PAD. It is also possible that depressive symptoms could potentially affect not only the development of disease, but also hinder treatment benefits and rehabilitation. Although previous studies have demonstrated a clear association between depression and cardiovascular disease40, it is still unclear if pharmacological treatment of depression can improve cardiovascular outcomes, particularly those related to PAD. Future studies should focus on the effects of depression treatment on progression and outcomes of patients with PAD .
Limitations
Our study findings must be interpreted in light of several limitations. The first major limitation is that this was a secondary analysis with a limited number of patients (especially women) and subsequently, limited power to detect differences. The fact that the sample size is small and the proportion of women low (<20%) limits our ability to detect sex-specific differences. Because of these limitations, the study should be considered as pilot and hypothesis-generating. Furthermore, since the aim of the Heart and Soul Study was to measure risks of development of cardiac diseases, it is possible that selection bias was introduced in the study, further limiting interpretation of results in the context of PAD.
Second, since the primary objective of assessing the impact of psychological factors in patients with CAD, several PAD measures are missing, including the ABI. This further limits the inferences that can be made from our results and the assessment of PAD-specific measures. In addition, the Heart and Soul Study includes mostly urban men with existing heart disease, which may limit the generalizability of our results.
CONCLUSIONS
In this study of outpatients with stable CAD, traditional CVD risk factors were associated with PAD in men. Depression was significantly more common in women with PAD compared to those without PAD. Given the heavy burden of PAD in women, our findings highlight the importance of continuing investigations in the field of gender differences in PAD, with a particular focus on mental health. Depression, particularly in women, should become an important consideration to the clinician with regards to its association with cardiovascular diseases.
ACKNOWLEDGEMENTS
This work was supported by start-up funds from the University of California San Francisco and the Northern California Institute for Research and Education. Dr. Cohen was supported by NIH/NIH Heart, Lung and Blood Institute grant K23 HL 094765-01. Dr. Smolderen was supported by the Netherlands Organization for Scientific Research [VENI grant no.: 916.11.179], by an unrestricted grant from W.L. Gore & Associates, Inc. (Flagstaff, AZ), and by the Patient-Centered Outcomes Research Institute [1 IP2 PI00753-01 and CE-1304-6677]. The Heart and Soul Study was funded by the Department of Veterans Affairs, Washington, DC, the National Heart Lung and Blood Institute (R01 HL079235), Bethesda, MD, the American Federation for Aging Research (Paul Beeson Scholars Program), New York, NY, the Robert Wood Johnson Foundation (Faculty Scholars Program), Princeton, NJ, the Ischemia Research and Education Foundation, South San Francisco, CA, and the Nancy Kirwan Heart Research Fund, San Francisco, CA. The project described was supported by Award Number KL2RR024130 from the National Center for Research Resources (Dr. Grenon). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Center for Research Resources or the NIH. The funding organizations were not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review, or approval of the manuscript.
Footnotes
CONFLICT OF INTEREST DISCLOSURES
None
REFERENCES
- 1.Fowkes FG, Rudan D, Rudan I, Aboyans V, Denenberg JO, McDermott MM, et al. Comparison of global estimates of prevalence and risk factors for peripheral artery disease in 2000 and 2010: a systematic review and analysis. Lancet. 2013 doi: 10.1016/S0140-6736(13)61249-0. [DOI] [PubMed] [Google Scholar]
- 2.Sigvant B, Wiberg-Hedman K, Bergqvist D, Rolandsson O, Andersson B, Persson E, et al. A population-based study of peripheral arterial disease prevalence with special focus on critical limb ischemia and sex differences. J Vasc Surg. 2007;45:1185–91. doi: 10.1016/j.jvs.2007.02.004. [DOI] [PubMed] [Google Scholar]
- 3.Moussa ID, Jaff MR, Mehran R, Gray W, Dangas G, Lazic Z, et al. Prevalence and prediction of previously unrecognized peripheral arterial disease in patients with coronary artery disease: the Peripheral Arterial Disease in Interventional Patients Study. Catheter Cardiovasc Interv. 2009;73:719–24. doi: 10.1002/ccd.21969. [DOI] [PubMed] [Google Scholar]
- 4.Hirsch AT, Allison MA, Gomes AS, Corriere MA, Duval S, Ershow AG, et al. American Heart Association Council on Peripheral Vascular D, Council on Cardiovascular N, Council on Cardiovascular R, Intervention, Council on Cardiovascular S, Anesthesia, Council on Clinical C, Council on E, Prevention. A call to action: women and peripheral artery disease: a scientific statement from the American Heart Association. Circulation. 2012;125:1449–72. doi: 10.1161/CIR.0b013e31824c39ba. [DOI] [PubMed] [Google Scholar]
- 5.Conte MS, Bandyk DF, Clowes AW, Moneta GL, Seely L, Lorenz TJ, et al. Results of PREVENT III: a multicenter, randomized trial of edifoligide for the prevention of vein graft failure in lower extremity bypass surgery. J Vasc Surg. 2006;43:742–751. doi: 10.1016/j.jvs.2005.12.058. discussion 751. [DOI] [PubMed] [Google Scholar]
- 6.Hoel AW, Kayssi A, Brahmanandam S, Belkin M, Conte MS, Nguyen LL. Under-representation of women and ethnic minorities in vascular surgery randomized controlled trials. J Vasc Surg. 2009;50:349–54. doi: 10.1016/j.jvs.2009.01.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.McDermott MM, Liu K, Criqui MH, Ruth K, Goff D, Saad MF, et al. Ankle-brachial index and subclinical cardiac and carotid disease: the multi-ethnic study of atherosclerosis. Am J Epidemiol. 2005;162:33–41. doi: 10.1093/aje/kwi167. [DOI] [PubMed] [Google Scholar]
- 8.Aboyans V, McClelland RL, Allison MA, McDermott MM, Blumenthal RS, Macura K, et al. Lower extremity peripheral artery disease in the absence of traditional risk factors. The Multi-Ethnic Study of Atherosclerosis. Atherosclerosis. 2011;214:169–73. doi: 10.1016/j.atherosclerosis.2010.10.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Ferrari AJ, Charlson FJ, Norman RE, Flaxman AD, Patten SB, Vos T, et al. The epidemiological modelling of major depressive disorder: application for the global burden of disease study 2010. PloS one. 2013;8:e69637. doi: 10.1371/journal.pone.0069637. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Weissman MM, Klerman GL. Sex differences and the epidemiology of depression. Archives of general psychiatry. 1977;34:98–111. doi: 10.1001/archpsyc.1977.01770130100011. [DOI] [PubMed] [Google Scholar]
- 11.Weissman MM, Bland RC, Canino GJ, Faravelli C, Greenwald S, Hwu HG. Cross-national epidemiology of major depression and bipolar disorder. JAMA : the journal of the American Medical Association. 1996;276:293–9. et alK. [PubMed] [Google Scholar]
- 12.Grenon SM, Hiramoto J, Smolderen KG, Vittinghoff E, Whooley MA, Cohen BE. Association between depression and peripheral artery disease: insights from the heart and soul study. Journal of the American Heart Association. 2012;1:e002667. doi: 10.1161/JAHA.112.002667. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Ruo B, Liu K, Tian L, Tan J, Ferrucci L, Guralnik JM, et al. Persistent depressive symptoms and functional decline among patients with peripheral arterial disease. Psychosomatic medicine. 2007;69:415–24. doi: 10.1097/PSY.0b013e318063ef5c. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Smolderen KG, Aquarius AE, de Vries J, Smith OR, Hamming JF, Denollet J. Depressive symptoms in peripheral arterial disease: a follow-up study on prevalence, stability, and risk factors. Journal of affective disorders. 2008;110:27–35. doi: 10.1016/j.jad.2007.12.238. [DOI] [PubMed] [Google Scholar]
- 15.Smolderen KG, Safley DM, House JA, Spertus JA, Marso SP. Percutaneous transluminal angioplasty: association between depressive symptoms and diminished health status benefits. Vascular medicine. 2011;16:260–6. doi: 10.1177/1358863X11415568. [DOI] [PubMed] [Google Scholar]
- 16.Cherr GS, Zimmerman PM, Wang J, Dosluoglu HH. Patients with depression are at increased risk for secondary cardiovascular events after lower extremity revascularization. Journal of general internal medicine. 2008;23:629–34. doi: 10.1007/s11606-008-0560-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Smolderen KG, Spertus JA, Vriens PW, Kranendonk S, Nooren M, Denollet J. Younger women with symptomatic peripheral arterial disease are at increased risk of depressive symptoms. Journal of vascular surgery. 2010;52:637–44. doi: 10.1016/j.jvs.2010.04.025. [DOI] [PubMed] [Google Scholar]
- 18.Whooley MA, de Jonge P, Vittinghoff E, Otte C, Moos R, Carney RM, et al. Depressive symptoms, health behaviors, and risk of cardiovascular events in patients with coronary heart disease. JAMA : the journal of the American Medical Association. 2008;300:2379–88. doi: 10.1001/jama.2008.711. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Ridker PM, Cushman M, Stampfer MJ, Tracy RP, Hennekens CH. Plasma concentration of C-reactive protein and risk of developing peripheral vascular disease. Circulation. 1998;97:425–8. doi: 10.1161/01.cir.97.5.425. [DOI] [PubMed] [Google Scholar]
- 20.Ridker PM, Stampfer MJ, Rifai N. Novel risk factors for systemic atherosclerosis: a comparison of C-reactive protein, fibrinogen, homocysteine, lipoprotein(a), and standard cholesterol screening as predictors of peripheral arterial disease. Jama. 2001;285:2481–5. doi: 10.1001/jama.285.19.2481. [DOI] [PubMed] [Google Scholar]
- 21.Beckman JA, Preis O, Ridker PM, Gerhard-Herman M. Comparison of usefulness of inflammatory markers in patients with versus without peripheral arterial disease in predicting adverse cardiovascular outcomes (myocardial infarction, stroke, and death). Am J Cardiol. 2005;96:1374–8. doi: 10.1016/j.amjcard.2005.07.041. [DOI] [PubMed] [Google Scholar]
- 22.Tzoulaki I, Murray GD, Lee AJ, Rumley A, Lowe GD, Fowkes FG. C-reactive protein, interleukin-6, and soluble adhesion molecules as predictors of progressive peripheral atherosclerosis in the general population: Edinburgh Artery Study. Circulation. 2005;112:976–83. doi: 10.1161/CIRCULATIONAHA.104.513085. [DOI] [PubMed] [Google Scholar]
- 23.Whooley MA, Caska CM, Hendrickson BE, Rourke MA, Ho J, Ali S. Depression and inflammation in patients with coronary heart disease: findings from the Heart and Soul Study. Biological psychiatry. 2007;62:314–20. doi: 10.1016/j.biopsych.2006.10.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Dawson DA, Grant BF, Stinson FS. The AUDIT-C: screening for alcohol use disorders and risk drinking in the presence of other psychiatric disorders. Comprehensive psychiatry. 2005;46:405–16. doi: 10.1016/j.comppsych.2005.01.006. [DOI] [PubMed] [Google Scholar]
- 25.Gehi AK, Ali S, Na B, Whooley MA. Self-reported medication adherence and cardiovascular events in patients with stable coronary heart disease: the heart and soul study. Archives of internal medicine. 2007;167:1798–803. doi: 10.1001/archinte.167.16.1798. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Kurtze N, Rangul V, Hustvedt BE, Flanders WD. Reliability and validity of self-reported physical activity in the Nord-Trondelag Health Study: HUNT 1. Scandinavian journal of public health. 2008;36:52–61. doi: 10.1177/1403494807085373. [DOI] [PubMed] [Google Scholar]
- 27.Ainsworth BE, Jacobs DR, Jr., Leon AS. Validity and reliability of self-reported physical activity status: the Lipid Research Clinics questionnaire. Medicine and science in sports and exercise. 1993;25:92–8. doi: 10.1249/00005768-199301000-00013. [DOI] [PubMed] [Google Scholar]
- 28.Spitzer RL, Kroenke K, Williams JB. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. Primary Care Evaluation of Mental Disorders. Patient Health Questionnaire. JAMA : the journal of the American Medical Association. 1999;282:1737–44. doi: 10.1001/jama.282.18.1737. [DOI] [PubMed] [Google Scholar]
- 29.Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. Journal of general internal medicine. 2001;16:606–13. doi: 10.1046/j.1525-1497.2001.016009606.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta psychiatrica Scandinavica. 1983;67:361–70. doi: 10.1111/j.1600-0447.1983.tb09716.x. [DOI] [PubMed] [Google Scholar]
- 31.Bjelland I, Dahl AA, Haug TT, Neckelmann D. The validity of the Hospital Anxiety and Depression Scale. An updated literature review. J Psychosom Res. 2002;52:69–77. doi: 10.1016/s0022-3999(01)00296-3. [DOI] [PubMed] [Google Scholar]
- 32.Hoenig JM, Heisey DM. The abuse of power: the pervasive fallacy of power calculations for data analysis. The American Statistician. 2001;55(1):19–24. [Google Scholar]
- 33.Zheng ZJ, Rosamond WD, Chambless LE, Nieto FJ, Barnes RW, Hutchinson RG, et al. Lower extremity arterial disease assessed by ankle-brachial index in a middle-aged population of African Americans and whites: the Atherosclerosis Risk in Communities (ARIC) Study. Am J Prev Med. 2005;29:42–9. doi: 10.1016/j.amepre.2005.07.019. [DOI] [PubMed] [Google Scholar]
- 34.Wattanakit K, Williams JE, Schreiner PJ, Hirsch AT, Folsom AR. Association of anger proneness, depression and low social support with peripheral arterial disease: the Atherosclerosis Risk in Communities Study. Vascular medicine. 2005;10:199–206. doi: 10.1191/1358863x05vm622oa. [DOI] [PubMed] [Google Scholar]
- 35.Arseven A, Guralnik JM, O'Brien E, Liu K, McDermott MM. Peripheral arterial disease and depressed mood in older men and women. Vascular medicine. 2001;6:229–34. doi: 10.1177/1358836X0100600405. [DOI] [PubMed] [Google Scholar]
- 36.Uchino BN, Bowen K, Carlisle M, Birmingham W. Psychological pathways linking social support to health outcomes: a visit with the “ghosts” of research past, present, and future. Soc Sci Med. 2012;74:949–57. doi: 10.1016/j.socscimed.2011.11.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Udell JA, Steg PG, Scirica BM, Smith SC, Jr., Ohman EM, Eagle KA, et al. REduction of Atherothrombosis for Continued Health (REACH) Registry Investigators. Living alone and cardiovascular risk in outpatients at risk of or with atherothrombosis. Arch Intern Med. 2012;172:1086–95. doi: 10.1001/archinternmed.2012.2782. [DOI] [PubMed] [Google Scholar]
- 38.Barth J, Schneider S, von Kanel R. Lack of social support in the etiology and the prognosis of coronary heart disease: a systematic review and meta-analysis. Psychosom Med. 2010;72:229–38. doi: 10.1097/PSY.0b013e3181d01611. [DOI] [PubMed] [Google Scholar]
- 39.Neylon A, Canniffe C, Anand S, Kreatsoulas C, Blake GJ, Sugrue D, McGorrian C. A global perspective on psychosocial risk factors for cardiovascular disease. Prog Cardiovasc Dis. 2013;55:574–81. doi: 10.1016/j.pcad.2013.03.009. [DOI] [PubMed] [Google Scholar]
- 40.Whooley MA, Wong JM. Depression and cardiovascular disorders. Annual review of clinical psychology. 2013;9:327–54. doi: 10.1146/annurev-clinpsy-050212-185526. [DOI] [PubMed] [Google Scholar]