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. Author manuscript; available in PMC: 2014 Jun 16.
Published in final edited form as: Stroke. 2011 May 5;42(7):1813–1820. doi: 10.1161/STROKEAHA.110.607853

Resistin, but not Adiponectin and Leptin, is Associated with the Risk of Ischemic Stroke Among Postmenopausal Women: Results from the Women’s Health Initiative

Swapnil N Rajpathak 1, Robert C Kaplan 1, Sylvia Wassertheil-Smoller 1, Mary Cushman 2, Thomas E Rohan 1, Aileen P McGinn 1, Tao Wang 1, Howard D Strickler 1, Philipp E Scherer 3, Rachel Mackey 4, David Curb 5, Gloria YF Ho 1
PMCID: PMC4059356  NIHMSID: NIHMS296634  PMID: 21546486

Abstract

Background

Adipose tissue is considered an endocrine organ that secretes adipokines which possibly mediate the effects of obesity on risk of cardiovascular disease. However, there are yet limited prospective data on the association between circulating adipokine levels and risk of ischemic stroke. We aimed to examine the associations of three adipokines (adiponectin, leptin and resistin) with risk of ischemic stroke.

Methods and Results

We conducted a prospective nested case-control study (972 stroke cases and 972 matched controls) within the Women’s Health Initiative Observational Study cohort. The controls were matched to cases on age, race/ethnicity, date of study enrollment and follow-up time. Adipokine levels were associated with established stroke risk factors, such as obesity and systolic blood pressure. Adjusted for body mass index, the odds ratios (OR) for incident ischemic stroke comparing the highest (Q4) to the lowest quartile (Q1) were 0.81 (95% confidence intervals [CI]: 0.61–1.08; p-trend: 0.068) for adiponectin, 1.15 (95% CI: 0.83–1.59; p-trend: 0.523) for leptin, and 1.57 (95% CI: 1.18–2.08; p-trend: 0.002) for resistin. The association for resistin remained significant even after accounting for established stroke risk factors (OR: 1.39; 95% CI: 1.01–1.90; p-trend: 0.036). Further adjustment for markers for inflammation, angiogenesis, and endothelial function also did not affect our results.

Conclusions

Circulating levels of resistin, but not those of adiponectin or leptin, are associated with an increased risk of incident ischemic stroke in postmenopausal women, independent of obesity and other CVD risk factors.

Keywords: stroke, adipokines, women

INTRODUCTION

Obesity is associated with increased risk of ischemic stroke.1 It is hypothesized that adipokines (or adipocytokines), proteins secreted by adipocytes, may be involved in the underlying biological mechanism linking obesity to ischemic stroke due to their potential effects on insulin resistance,2 systemic inflammation,3–5 and endothelial function,6–8 which are important pathways involved in atherosclerosis and hence the pathogenesis of cardiovascular disease (CVD).9 Although a few studies have reported an association between circulating adipokine levels and the incidence of coronary heart disease,10–12 there are limited prospective data on their association with the risk of ischemic stroke. Furthermore, the existing studies on adipokine-stroke association are limited by small sample sizes.13–16 Therefore, we evaluated the association of baseline circulating levels of adipokines, including adiponectin, leptin and resistin, with the risk of subsequent development of ischemic stroke in the Hormones and Biomarkers Predicting Stroke (HaBPS) Study, a large nested case-control study of the Women’s Health Initiative Observational Study (WHI-OS).

METHODS

Study population

The WHI-OS is an ongoing, ethnically and geographically diverse prospective study, with long-term follow-up of 93,676 participants at 40 clinical centers in the United States, to examine the risk factors for subsequent development of several health outcomes.17–19 Briefly, postmenopausal women aged 50 to 79 years were enrolled between 1993 to 1998. At baseline, women were queried about lifestyle factors, medical history, and personal habits. A physical examination was performed to obtain height, weight, and blood pressure. Fasting blood samples were collected, centrifuged, frozen on site at −70°C, and later shipped to a central specimen repository. As of September 2005, 4.7% of the original 93,676 WHI-OS subjects had been lost- to-follow-up.

The Hormones and Biomarkers Predicting Stroke (HaBPS) Study

The HaBPS study was a nested case-control study within the WHI-OS that aimed to evaluate if baseline circulating levels of adipokines, novel lipoproteins, inflammatory cytokines, and hemostatis markers, were associated with ischemic stroke risk.20, 21 Women were ineligible if they had a history of myocardial infarction or stroke at baseline.

Medical records were obtained for all potential stroke events that were identified through self-report at annual contacts; adjudication was performed locally by trained physicians who assigned a diagnosis according to standardized criteria. Only stroke events that required hospitalization were considered as potential outcomes and transient ischemic attacks were not included. All locally adjudicated stroke events were then sent for central adjudication by a team of study neurologists. Strokes were classified as ischemic or hemorrhagic on review of reports of brain imaging studies. Ischemic stroke was defined as the rapid onset of a persistent neurologic deficit attributed to an obstruction lasting >24 hours without evidence of other causes, unless death supervened or there was a demonstrable lesion compatible with acute stroke on computed tomography or MRI scan. The subtypes of ischemic stroke were classified using Trial of Org 10172 in Acute Stroke Treatment (TOAST) criteria22 as large artery atherothrombosis, cardioembolic, lacunar (small vessel), or undetermined etiology. Biomarker assays were performed at a later time after all ischemic strokes were adjudicated and classified.

A total of 972 incident ischemic stroke cases were identified from study baseline to July 1, 2003. One control was selected from the risk set at the time of the case diagnosis and matched to each case on age at baseline (±2 years), race/ethnicity, date of study enrollment (±3 months), and follow-up time.

Laboratory assays

Using stored plasma samples, the levels of adiponectin and resistin were measured simultaneously by a multiplex assay (Milliplex Human Adipokine Panel A) and those of leptin were measured by Milliplex Human Adipokine Panel B (Millipore, Billerica MA). The inter-assay CVs were 11.3%, 11.4%, and 5.3% for adiponectin, resistin, and leptin, respectively. We previously examined the intra-class correlation coefficients (ICC) of circulating adipokines in a separate study population23 and reported 3 year ICCs of 0.82 (95% CI: 0.66, 0.98) for adiponectin, 0.96 (95% CI: 0.91, 0.99) for resistin, and 0.58 (95% CI: 0.25, 0.91) for leptin, indicating that circulating adipokine levels are relatively stable, and that a single measurement at baseline may reflect an individual’s long term exposure. Laboratory methods for other biomarkers measured in the HaBPS study, such as C reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), vascular cell adhesion molecule-1 (VCAM-1), high molecular weight adiponectin and hepatocyte growth factor (HGF), had been reported previously.20, 24

Statistical analysis

Cases and controls were first compared with respect to the reported risk factors for ischemic stroke at baseline; because of the matched case-control design, univariable conditional logistic regression and paired t-test were used. To assess whether reported risk factors for ischemic stroke at baseline (e.g., obesity, smoking, and hypertension) could be potential confounders, we examined their cross-sectional associations with the three adipokines among controls. Means of the log transformed adipokines were compared across the categories of each risk factor using analysis of variance (ANOVA). To simplify presentation of data, we report the geometric means of adipokines and their 95% tolerance factors, defined as exponential of (1.96 x standard error of the log-transformed adipokine variable), which can readily be manipulated to obtain 95% confidence intervals (CIs) of the geometric means. Conditional logistic regression model was used for multivariable analyses. The adipokine variables were modeled in quartiles derived from distribution among the control population. The odds ratios (ORs) and 95% CIs were estimated using the lowest quartile as the reference category. Tests of linear trend across quartiles were conducted by assigning the median value for each category, fitting this as a continuous variable in the model, and assessing statistical significance using Wald test. The following established stroke risk factors were included in the multivariable model: body mass index (BMI, continuous), waist circumference (continuous), physical activity (METs per week in quartiles), current smoking, use of non-steroidal anti-inflammatory drugs (NSAIDs), use of hypertension medication, systolic blood pressure (continuous), diabetes (self-reported treatment or a fasting glucose level ≥ 126 mg/dL), history of coronary artery disease (none, 1, or ≥ 2 of the following conditions: angina, congestive heart failure, coronary revascularization procedure, atrial fibrillation, or peripheral artery disease), elevated high-density lipoprotein (HDL) cholesterol (≥ 50 mg/dL), and triglyceride (continuous). Certain risk factors suggested in the stroke literature (e.g., low-density lipoprotein (LDL) cholesterol level and postmenopausal hormone therapy) were not significantly associated with risk in the present study after adjusting for the aforementioned covariates, and they were not included in the regression model. In an additional regression model, we further adjusted for two biomarkers that we previously reported to be associated with ischemic stroke risk in the HaBPS study – HGF24 (an angiogenic factor) and CRP20 – as well as other markers for inflammation and endothelial function (IL-6, TNF-α, and VCAM-1). Finally, to evaluate if the associations of adipokines with risk of ischemic stroke were modified by other stroke risk factors (e.g., BMI, hypertension, and CRP, etc.), interaction terms were included and their significance was determined using the Wald test. All statistical analyses were performed using SAS® version 9.1 (SAS Institute, Cary, NC). All statistical tests conducted were two-sided and p-values less than 0.05 were considered statistically significant.

RESULTS

As expected, women who developed ischemic stroke (case patients) had more adverse CVD risk profile – more current smoking, higher BMI, systolic blood pressure and homeostasis model assessment for insulin resistance (HOMA-IR), lower physical activity and HDL-cholesterol, and were likely to have history of diabetes and coronary artery disease – compared to those who remained free of disease (controls) (Table 1). In terms of the adipokines of interest, cases had significantly lower levels of adiponectin and higher levels of leptin and resistin than controls.

Table 1.

Baseline characteristics among cases of ischemic stroke and matched controls*

Case patients (n = 972) Controls (n = 972) P value†
Age, years, mean (SD) 68.7 (6.4) 68.7 (6.4) NA
Race-ethnicity, n (%) NA
 White Non-Hispanic 833 (86) 833 (86)
 Black 80 (8) 80 (8)
 Other 59 (6) 59 (6)
Hormone therapy, n (%)
 Never 309 (32) 323 (34) Reference
 Past 226 (24) 235 (25) 0.902
 Current 416 (44) 395 (41) 0.306
Smoking status, n (%)
 Never 505 (53) 526 (55) Reference
 Former 377 (39) 400 (42) 0.728
 Current 79 (8) 37 (4) <0.001
Physical activity, METs/week, mean (SD) 12.4 (13.8) 14.2 (14.1) 0.004
BMI, kg/m2, mean (SD) 27.7 (5.9) 27.0 (5.3) 0.007
Waist circumference, cm, mean (SD) 87.3 (13.6) 84.7 (12.3) < 0.001
Current NSAID use, n (%) 406 (42) 332 (34) 0.001
Diabetes, n (%) 162 (17) 81 (8) < 0.001
History of coronary & arterial disease, n (%) 201 (21) 126 (13) < 0.001
Current hypertension medication use, n (%) 461 (47) 340 (35) < 0.001
Systolic blood pressure, mmHg, mean (SD) 137.2 (19.4) 130.1 (18.0) < 0.001
HDL-cholesterol, mg/dL, mean (SD) 57.2 (16.2) 59.8 (16.4) <0.001
LDL-cholesterol, mg/dL, mean (SD) 140.8 (37.4) 139.0 (36.7) 0.313
Triglycerides, mg/dL, mean (SD) 179.1 (89.9) 160.9 (80.4) < 0.001
Adiponectin, μg/mL, geometric mean (TF) ‡ 25.6 (1.04) 27.0 (1.04) 0.034
Leptin, ng/mL, geometric mean (TF) ‡ 14.1 (1.06) 12.4 (1.06) 0.002
Resistin, ng/mL, geometric mean (TF) ‡ 12.7 (1.03) 12.0 (1.02) <0.001
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*

The n presented may not add to the total due to missing information.

†

P value was obtained by conditional logistic regression for categorical variables and paired t-test for continuous variables; NA = matched variable, statistics not performed.

‡

TF = tolerance factor, anti-log(1.96 x standard error of log-transformed data).

Table 2 shows the relationships of the three adipokines with various stroke risk factors at baseline among the controls. As expected, all three adipokines were associated with adiposity and insulin resistance; BMI, waist circumference, and HOMA-IR were positively associated with the levels of resistin and leptin and inversely with that of adiponectin. Adiponectin and leptin, but not resistin, were associated with sociodemographic factors. Leptin, but not adiponectin or resistin, was inversely associated with postmenopausal hormone use and physical activity. Although leptin and adiponectin were associated with prevalence of hypertension and diabetes, resistin was not associated with these conditions. Finally, the three adipokines of interest were associated with circulating lipids and inflammatory markers, particularly CRP and IL-6. Both adiponectin and resistin were positively associated with VCAM-1, a marker for endothelial function. Leptin levels increased with those of HGF, an angiogenic factor that is associated with risk of stroke in this study population.24 Among the three adipokines of interest, leptin correlated moderately with adiponectin (r = −0.30; p<0.001) and resistin (r = 0.14; p<0.001), but there was no correlation between adiponectin and resistin levels (r = −0.04; p=0.264).

Table 2.

Geometric means and 95% tolerance factors (TF)* of adipokines by categories of various stroke risk factors among controls †

Adiponectin, μg/mL Geometric Mean (TF) Leptin, ng/mL Geometric Mean (TF) Resistin, ng/mL Geometric Mean (TF)
Socio-demographic factors
Age P = 0.002 P = 0.067 P = 0.505
 50–59 (n=95) 22.8 (1.11) 13.3 (1.22) 12.2 (1.08)
 60–69 (n=392) 26.2 (1.05) 13.3 (1.09) 11.8 (1.04)
 70–79 (n=485) 28.6 (1.06) 11.6 (1.09) 12.1 (1.04)
Race-ethnicity P < 0.001 P < 0.001 P = 0.746
 White (n=833) 28.5 (1.04) 11.7 (1.06) 11.9 (1.02)
 Black (n=80) 17.7 (1.15) 24.6 (1.16) 12.3 (1.11)
 Other (n=59) 22.1 (1.12) 10.9 (1.27) 11.8 (1.10)
Education P = 0.049 P < 0.001 P = 0.586
 ≤High school (n=226) 25.1 (1.07) 14.6 (1.12) 11.8 (1.05)
 Some college/post secondary training (n=362) 28.4 (1.06) 13.2 (1.10) 12.1 (1.04)
 College graduate or higher (n=381) 26.9 (1.07) 10.6 (1.10) 11.8 (1.04)
Lifestyle factors
Postmenopausal hormone use P = 0.824 P = 0.029 P = 0.065
 Never (n=323) 26.8 (1.07) 13.6 (1.11) 12.4 (1.04)
 Past (n=235) 27.7 (1.10) 12.4 (1.12) 11.8 (1.05)
 Current (n=395) 27.0 (1.05) 11.3 (1.10) 11.6 (1.03)
Smoking status P = 0.449 P = 0.285 P = 0.327
 Never (n=526) 26.5 (1.05) 12.9 (1.08) 12.1 (1.03)
 Former (n=400) 27.8 (1.07) 12.0 (1.09) 11.8 (1.04)
 Current (n=37) 26.0 (1.21) 10.5 (1.42) 11.3 (1.11)
Physical activity, METs per week P = 0.257 P < 0.001 P = 0.100
 Q1 (n=265) 25.6 (1.07) 14.9 (1.12) 12.5 (1.05)
 Q2 (n=218) 26.3 (1.08) 15.2 (1.12) 11.8 (1.05)
 Q3 (n=242) 28.1 (1.09) 10.9 (1.12) 11.9 (1.05)
 Q4 (n=239) 27.8 (1.07) 9.6 (1.12) 11.6 (1.05)
Anthropometric factors
BMI, kg/m2 P < 0.001 P < 0.001 P < 0.001
 <25 (n=390) 31.7 (1.07) 6.2 (1.08) 11.4 (1.04)
 25–29.9 (n=346) 26.0 (1.06) 16.6 (1.07) 11.9 (1.04)
 ≥30 (n=222) 22.1 (1.07) 26.8 (1.08) 12.9 (1.05)
Waist circumference, cm P < 0.001 P < 0.001 P < 0.001
 Q1 (n=244) 35.0 (1.07) 5.7 (1.12) 12.0 (1.05)
 Q2 (n=255) 27.6 (1.09) 10.1 (1.09) 11.1 (1.05)
 Q3 (n=226) 26.0 (1.07) 17.7 (1.09) 12.2 (1.05)
 Q4 (n=241) 21.1 (1.07) 24.9 (1.08) 12.7 (1.05)
Medical History
Diabetes P < 0.001 P < 0.001 P = 0.142
 Yes (n=81) 16.4 (1.15) 20.4 (1.18) 12.7 (1.09)
 No (n=889) 28.2 (1.04) 11.9 (1.06) 11.9 (1.02)
Current antihypertensive medication P < 0.001 P < 0.001 P = 0.304
 Yes (n=340) 24.0 (1.08) 16.4 (1.09) 12.2 (1.04)
 No (n=632) 28.8 (1.04) 10.7 (1.08) 11.9 (1.03)
Systolic blood pressure, mmHg P = 0.029 P < 0.001 P = 0.079
 <120 (n=288) 29.5 (1.06) 9.3 (1.12) 11.7 (1.04)
 120–139 (n=400) 26.2 (1.05) 13.0 (1.09) 11.8 (1.03)
 140–159 (n=222) 25.8 (1.10) 15.8 (1.11) 12.2 (1.06)
 ≥160 (n=62) 25.4 (1.17) 14.5 (1.23) 13.3 (1.11)
Diastolic blood pressure, mmHg P = 0.323 P < 0.001 P = 0.159
 <80 (n=671) 27.5 (1.05) 11.5 (1.07) 11.8 (1.03)
 80–89 (n=252) 26.2 (1.08) 14.6 (1.12) 12.2 (1.04)
 ≥90 (n=49) 24.7 (1.16) 16.4 (1.23) 12.9 (1.12)
History of coronary artery disease P =0.900 P =0.420 P = 0.342
 Yes (n=126) 27.1 (1.11) 13.2 (1.17) 12.3 (1.07)
 No (n=815) 26.9 (1.04) 12.3 (1.07) 11.9 (1.03)
NSAID use P = 0.250 P = 0.003 P = 0.333
 Yes (n=332) 26.2 (1.04) 14.0 (1.08) 12.2 (1.03)
 No (n=640) 27.4 (1.07) 11.6 (1.10) 11.9 (1.04)
Laboratory variables
Total cholesterol, mg/dL P = 0.831 P = 0.075 P = 0.013
 <200 (n=202) 26.6 (1.09) 11.0 (1.15) 12.6 (1.05)
 200–239 (n=367) 27.4 (1.07) 12.4 (1.10) 12.2 (1.04)
 ≥240 (n=403) 26.9 (1.05) 13.2 (1.09) 11.5 (1.03)
HDL cholesterol, mg/dL P < 0.001 P < 0.001 P = 0.001
 < 50 (n=286) 21.1 (1.06) 15.3 (1.10) 12.7 (1.04)
 ≥50 (n=684) 30.0 (1.05) 11.3 (1.07) 11.7 (1.03)
LDL cholesterol, mg/dL P = 0.385 P = 0.029 P = 0.162
 < 100 (n=143) 29.3 (1.10) 10.9 (1.19) 12.4 (1.06)
 100 to 129 (n=253) 27.0 (1.07) 11.3 (1.12) 12.3 (1.05)
 130 to 159 (n=285) 27.5 (1.08) 12.8 (1.11) 11.9 (1.05)
 160 to 189 (n=201) 26.5 (1.08) 14.3 (1.12) 11.6 (1.05)
 ≥190 (n=75) 25.0 (1.11) 13.2 (1.20) 11.3 (1.08)
Triglycerides, mg/dL P < 0.001 P < 0.001 P = 0.009
 < 150 (n=522) 31.5 (1.05) 10.5 (1.09) 11.8 (1.03)
 150–199 (n=228) 23.9 (1.09) 15.1 (1.11) 11.7 (1.06)
 ≥200 (n=222) 21.4 (1.07) 15.1 (1.11) 12.8 (1.05)
HOMA-IR P < 0.001 P < 0.001 P < 0.001
 Q1 (n=243) 35.3 (1.06) 5.8 (1.11) 11.3 (1.04)
 Q2 (n=241) 29.8 (1.09) 10.8 (1.11) 11.5 (1.04)
 Q3 (n=243) 26.8 (1.06) 15.7 (1.10) 12.3 (1.05)
 Q4 (n=242) 18.7 (1.07) 24.2 (1.08) 12.8 (1.05)
CRP, mg/L P < 0.001 P < 0.001 P = 0.032
 Q1 (n=245) 31.6 (1.07) 7.9 (1.13 11.7 (1.05)
 Q2 (n=239) 28.3 (1.10) 11.9 (1.12) 11.5 (1.05)
 Q3 (n=227) 26.1 (1.07) 14.3 (1.12) 12.0 (1.04)
 Q4 (n=234) 22.5 (1.07) 18.1 (1.11) 12.7 (1.05)
IL-6, pg/mL P < 0.001 P < 0.001 P < 0.001
 Q1 (n=246) 30.6 (1.07) 8.1 (1.12) 11.4 (1.04)
 Q2 (n=238) 28.8 (1.07) 12.1 (1.11) 11.6 (1.05)
 Q3 (n=240) 25.9 (1.09) 14.1 (1.11) 11.6 (1.05)
 Q4 (n=241) 23.3 (1.08) 17.2 (1.12) 13.3 (1.05)
TNF-α, pg/mL P = 0.394 P = 0.076 P < 0.001
 Q1 (n=233) 28.7 (1.07) 11.0 (1.13 10.6 (1.04)
 Q2 (n=229) 27.4 (1.09) 12.8 (1.13) 12.4 (1.05)
 Q3 (n=229) 27.1 (1.07) 13.7 (1.12) 12.2 (1.05)
 Q4 (n=227) 26.3 (1.08) 12.7 (1.13) 12.9 (1.05)
VCAM-1, ng/mL P = 0.009 P = 0.148 P =0.001
 Q1 (n=235) 24.7 (1.07) 13.5 (1.12) 11.4 (1.05)
 Q2 (n=234) 25.9 (1.09) 13.0 (1.11) 11.6 (1.05)
 Q3 (n=236) 28.9 (1.07) 12.0 (1.13) 12.0 (1.05)
 Q4 (n=234) 28.7 (1.08) 11.3 (1.14) 12.9 (1.05)
HGF, pg/mL P = 0.130 P <0.001 P =0.032
 Q1 (n=242) 28.7 (1.07) 10.3 (1.12 11.8 (1.04
 Q2 (n=242) 26.9 (1.07) 10.8 (1.12) 11.5 (1.05)
 Q3 (n=242) 27.4 (1.10) 12.7 (1.13) 11.8 (1.05)
 Q4 (n=241) 25.2 (1.08) 16.7 (1.12) 12.7 (1.05)
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*

Tolerance factor = anti-log(1.96 x standard error of log-transformed data).

†

The total number of subjects may not add up to 972 controls due to missing data.

Table 3 shows the association between quartiles of plasma adipokine levels and risk of ischemic stroke. In the model accounting for only the matching factors (age and race/ethnicity), adiponectin levels were inversely associated with the risk of ischemic stroke (OR comparing quartile 4 [Q4] vs. quartile 1 [Q1]: 0.77; 95% CI: 0.59–1.01; p-trend: 0.023), while levels of leptin (OR: 1.38; 95% CI: 1.05–1.80; p-trend: 0.019) and resistin (OR: 1.61; 95% CI: 1.22–2.13; p-trend: <0.001) were positively associated with this risk. However, when BMI was included in the model, this association remained statistically significant only for resistin (OR comparing Q4 vs. Q1: 1.57; 95% CI: 1.18–2.08; p-trend: 0.002). Furthermore, this association for resistin remained significant even after additional adjustment for established stroke risk factors, (OR between extreme quartiles: 1.39; 95% CI: 1.01–1.90; p-trend: 0.036). Further adjustment with additional markers for inflammation (CRP, IL-6, and TNF-α), angiogenesis (HGF), and endothelial dysfunction (VCAM-1), also did not attenuate the association between resistin and ischemic stroke (OR for Q4 vs. Q1: 1.49; 95% CI: 1.04–2.15; p-trend: 0.037). In additional analysis, we also evaluated the association between leptin:adiponectin ratio and ischemic stroke risk; however the results were null. For the resistin-stroke association, we did not observe any effect modification of the resistin-stroke association by risk factors of ischemic stroke (e.g., obesity, smoking, postmenopausal hormone use, and CRP, etc.). The results by subtype of ischemic stroke were similar (data not shown), although the power for this analysis was limited.

Table 3.

Odds ratios (95% CI) from conditional logistic regression analysis for the associations of adipokines with incident ischemic stroke

Quartiles of adipokine P for trend
Q1 Q2 Q3 Q4
Adiponectin
Median level (μg/mL) 14.8 24.0 33.0 46.0
Number of cases 279 261 199 225
Adjusted for
 age + ethnicity Reference 0.91 (0.71–1.17) 0.70 (0.54–0.91) 0.77 (0.59–1.01) 0.023
 age + ethnicity + BMI Reference 0.92 (0.71–1.19) 0.72 (0.55–0.94) 0.81 (0.61–1.08) 0.068
 age + ethnicity + BMI + CVD risk factors* Reference 1.08 (0.81–1.43) 0.89 (0.65–1.22) 1.16 (0.82–1.63) 0.588
Leptin
Median level (ng/mL) 4.1 10.2 18.1 33.9
Number of cases 196 228 268 268
Adjusted for
 age + ethnicity Reference 1.16 (0.89–1.51) 1.35 (1.05–1.75) 1.38 (1.05–1.80) 0.019
 age + ethnicity + BMI Reference 1.12 (0.85–1.48) 1.22 (0.92–1.62) 1.15 (0.83–1.59) 0.523
 age + ethnicity + BMI + CVD risk factors* Reference 1.02 (0.75–1.40) 1.00 (0.73–1.38) 0.94 (0.65–1.35) 0.628
Resistin
Median level (ng/mL) 8.0 10.7 13.3 17.8
Number of cases 181 229 277 277
Adjusted for
 age + ethnicity Reference 1.30 (0.99–1.72) 1.58 (1.21–2.07) 1.61 (1.22–2.13) <0.001
 age + ethnicity + BMI Reference 1.33 (1.00–1.76) 1.59 (1.21–2.09) 1.57 (1.18–2.08) 0.002
 age + ethnicity + BMI + CVD risk factors* Reference 1.24 (0.91–1.68) 1.55 (1.15–2.10) 1.39 (1.01–1.90) 0.036
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*

CVD risk factors included current smoking, physical activity, NSAIDs use, hypertension medication use, systolic blood pressure, history of coronary and artery diseases, HDL cholesterol, triglyceride, diabetes, and waist circumference.

DISCUSSION

We found that high circulating resistin, but not adiponectin or leptin, was independently associated with an increased risk of developing ischemic stroke after accounting for obesity and other stroke risk factors. Our study confirms in a large prospective study a positive association between resistin levels and development of ischemic stroke.

A few potential mechanisms may possibly explain the association between resistin and ischemic stroke. Resistin upregulates pro-inflammatory cytokines, such as IL-6 and TNF-α, in human peripheral blood mononuclear cells via the NF-κB pathway.3 In addition, it promotes endothelial cell activation, including stimulation of endothelin-1 release and upregulation of cellular adhesion molecules, such as VCAM-1 and ICAM-1.25 Therefore, resistin may increase the risk of stroke by promoting systemic inflammation and endothelial dysfunction, both of which play a role in atherosclerosis.9 In a study that conducted a time course of gene expression in the aortas in mice, resistin mRNA levels steadily increased with lesion size.26 Cross-sectional data in humans have also indicated that high levels of serum resistin are independently associated with carotid intima media thickness, a marker of atherosclerosis.27

Consistent with its biological activity28 and findings from previous studies,29, 30 we found in this study that resistin correlated positively with levels of inflammatory markers (CRP, IL-6, TNF-α) and VCAM-1, a biomarker for endothelial function. Resistin levels also increased with adiposity in our study, although this was not a consistent observation in other studies.31, 32 The fact that the association between resistin and ischemic stroke remained significant after we adjusted for obesity as well as markers for inflammation and endothelial dysfunction in multivariable analyses suggests that the effects of resistin on stroke risk cannot be entirely explained by these obesity associated pathways and may involve additional unidentified biological mechanisms.

Among the three adipokines under study, only the effects of resistin on ischemic stroke were independent of obesity. Two factors may explain this observation. As mentioned above, other obesity-independent biomechanisms may link resistin with stroke risk. Moreover, unlike adiponectin and leptin that are mainly secreted by adipocytes, other cell types contribute to the circulating levels of resistin, particularly peripheral blood mononuclear cells such as macrophages.33, 34 Macrophages infiltrating atherosclerotic aneurysms secrete resistin,34 hence resistin may exert its effects on vascular endothelial cells and smooth muscle cells locally in atheromas regardless of adiposity.

Two cross-sectional studies conducted in Japan suggested that serum resistin were significantly higher among those with history of ischemic stroke compared to controls.35, 36 Other than our study, only one other prospective study has evaluated the association between resistin and cardiovascular disease.16 In this nested case-control study within the European Investigation into Cancer and Nutrition (EPIC)-Postdam Study, resistin was significantly associated with increased risk of myocardial infarction, but not ischemic stroke after adjusting for age, sex, BMI, waist circumference, alcohol intake, education, lipids, hypertension, and diabetes. However, this study was limited by the small number of 97 stroke cases. Nevertheless, in a Greek study among 211 survivors of ischemic stroke, high resistin levels after stroke were associated with increased 5-year morbidity and mortality.37

Similar to resistin, adiponectin and leptin are associated with inflammation and endothelial dysfunction.8, 38 While the relationship between resistin and insulin resistance in humans is unclear, the association of adiponectin and leptin with insulin resistance provides a third mechanism through which these two adipokines may contribute to the pathogenesis of cardiovascular disease.39 Adiponectin has the opposite effects compared to those of resistin and leptin. It suppresses pro-inflammatory cytokines (e.g., TNF and interferon-γ), induces anti-inflammatory cytokines (e.g, interleukin-10 and interleukin-1 receptor antagonist), improves insulin sensitivity, and stimulates endothelial production of nitric oxide and thereby improves vascular function.40, 41 In our study, we did observe significant associations of circulating levels of adiponectin and leptin with inflammation markers (e.g., CRP and IL-6), HOMA-IR, and various stroke risk factors (e.g., hypertension, diabetes, and dyslipidemia). Although leptin and adiponectin may be involved in the pathogenesis of cardiovascular disease due to their bioactivities, our data showed that their associations with ischemic stroke were much attenuated after accounting for obesity and became trivial after further adjustment with other stroke risk factors. This is not surprising given that adiponectin and leptin are synthesized mainly by adipocytes,38 so that their circulating levels are bona fide biomarkers and surrogates for adiposity. Nevertheless, our results indicate that measuring the circulating levels of adiponectin and leptin do not contribute significantly to risk prediction of ischemic stroke beyond the already established clinical risk factors, such as obesity and hypertension.

Our finding for adiponectin is consistent with two prospective studies that have evaluated this association.13, 15 Furthermore, we recently reported that high molecular weight fraction of adiponectin, considered to be the more bioavailable form, was also not associated with ischemic stroke risk in this study population.42 Limited data exists for the leptin-stroke association. In a cross-sectional analysis within National Health And Nutrition Examination Survey (NHANES), circulating leptin levels were significantly associated with ischemic stroke in women, but not among men.43 However, in a prospective study conducted within the Northern Sweden Monitoring of Trends and Determinants in Cardiovascular Diseases (MONICA) project with 234 cases of ischemic and 42 cases of hemorrhagic stroke, leptin was significantly associated with elevated risk among men, but not among women,15 an observation that is consistent with our results among women.

Our study has a few limitations that warrant discussion. First, we assayed for total levels of resistin and cannot address whether low molecular weight resistin is also important in stroke.44, 45 Secondly, the relationship of circulating levels of adipokines with those within the target tissue is not clear and requires further investigation. The adjustment for obesity in our multivariable analysis may potentially be ‘overadjustment’, however, we aimed to evaluate each adipokine as a risk factor for ischemic stroke independent of obesity while accounting for other obesity effects. In addition, since we evaluated multiple biomarkers in this study, we cannot rule out the possibility of type 1 error for the association observed for resistin. Lastly, this study was conducted among postmenopausal women, and our results need to be confirmed in other populations, particularly premenopausal women and men.

In conclusion, our data suggest that circulating levels of resistin, but not those of adiponectin or leptin, are associated with risk of development of ischemic stroke among postmenopausal women, independent of established stroke risk factors. Future epidemiological studies, including studies in men and in premenopausal women, are needed to confirm this finding, and mechanistic studies should further elucidate the role of resistin in the etiopathogenesis and prevention of ischemic stroke.

Acknowledgments

The complete list of WHI centers and investigators can be found online at http://www.whiscience.org/publications/WHI_investigators_shortlist.pdf. The authors thank Dan Wang for assistance in data analysis and Elaine Cornell and Danielle Parent for assistance in laboratory measurements.

Funding: This research was funded by contract N01-WH-74310 (Dr. Ho) with the National Heart, Lung, and Blood Institute (NHLBI). The Hormones and Biomarkers Predicting Stroke Study was supported by R01NS042618 (Dr. Wassertheil-Smoller) from the National Institutes of Neurological Disorders and Stroke. The Women’s Health Initiative program is funded by the NHLBI through contracts N01WH22110, 24152, 32100–2, 32105–6, 32108–9, 32111–13, 32115, 32118–32119, 32122, 42107–26, 42129–32, and 44221.

Footnotes

CONFLICTS OF INTEREST

None

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