High-Molecular-Weight Adiponectin and Incident Ischemic Stroke in Postmenopausal Women
A Women’s Health Initiative Study
Background and Purpose— Although low levels of adiponectin are associated with coronary heart disease and cardiovascular disease risk factors, it is unclear whether adiponectin levels are related to the risk of developing ischemic stroke.
Methods— We examined the relationship between baseline high-molecular-weight (HMW) adiponectin levels and incident ischemic stroke in postmenopausal women using data and specimens from the Hormones and Biomarkers Predicting Stroke Study, a case–control study nested within the Women’s Health Initiative Observational Study. Included were 855 incident ischemic stroke cases and 855 control subjects matched for age, race–ethnicity, date of entry into the cohort, and follow-up time. ORs of incident ischemic stroke associated with baseline HMW adiponectin levels were calculated using conditional logistic regression modeling adjusting for body mass index, type 2 diabetes, hypertension, smoking, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, physical activity, C-reactive protein, and aspirin use.
Results— Lower levels of HMW adiponectin were significantly associated with type 2 diabetes, hypertension, higher body mass index, waist circumference, glucose, and insulin levels and lower high-density lipoprotein cholesterol levels. The distribution of incident stroke cases by HMW adiponectin quartiles was 49.9%, 50.5%, 50.7%, and 48.9%, respectively (P=0.96). Multivariable-adjusted ORs of stroke associated with the top 3 quartiles of HMW adiponectin versus the first quartile were 0.99 (95% CI, 0.71 to 1.37), 1.37 (0.99 to 1.91), and 1.25 (0.88 to 1.79), respectively (P trend=0.14).
Conclusion— Despite moderate associations between HMW adiponectin and cardiovascular disease risk factors, we found no evidence of an association between HMW adiponectin levels and incident ischemic stroke in these postmenopausal women.
Adiponectin is the most abundant plasma protein secreted by adipose tissue and has been found to have protective vascular and myocardial effects.1 Adiponectin resembles the complement factor C1q with anti-inflammatory effects on vascular endothelial cells.2,3 The total amount of adiponectin in blood is composed of low (trimer), medium (hexamer), and high-molecular-weight (HMW, 12- to 18-mer) isoforms.4 Recent evidence suggests that HMW adiponectin might be the most biologically active form; low levels of the HMW form, much more so than total adiponectin, are primarily responsible for associations with obesity, insulin resistance, and coronary artery disease5–7; and the HMW form, but not the trimeric form, has been shown to activate NF-κB transcription factor.8 Lower adiponectin levels have been associated with increased prevalence of common cardiovascular disease (CVD) risk factors, including insulin resistance, higher levels of total cholesterol, low-density lipoprotein cholesterol (LDL-C), and triglycerides and lower levels of high-density lipoprotein cholesterol (HDL-C).9–18 Although adiponectin levels are associated with incident myocardial infarction,3,19–22 it is not well established whether adiponectin is also related to risk of incident stroke.
Limited evidence suggests a potential link between adiponectin and stroke; however, the results are contradictory. Although some studies suggest that lower levels of adiponectin may have a modest protective role in the development of stroke,23,24 a few small prospective studies did not find a significant association between levels of adiponectin and incident stroke.25,26 These contradictory results could arise from case definitions of incident versus recurrent stroke status or lack of statistical power. A recent case–control study of adipocytokines nested within the Women’s Health Initiative Observational Study (WHI-OS) of postmenopausal women did not find total adiponectin to be associated with an increased risk of incident ischemic stroke (Rajpathak, unpublished data). To our knowledge, previous studies have not assessed the HMW form of adiponectin in relation to stroke. We examined the relationship between HMW adiponectin, thought to be the more biologically active isoform, and incident stroke. We hypothesized that higher baseline HMW adiponectin levels in postmenopausal women would be associated with a lower incidence of ischemic stroke.
Participants and Study Design
The current analyses use data and specimens from the Hormones and Biomarkers Predicting Stroke (HaBPS) ancillary study to the WHI-OS. HaBPS, a case–control study nested within the WHI, is designed to examine the relationships between baseline biomarkers and hormones with the subsequent development of ischemic stroke. HaBPS includes 972 ischemic stroke cases and 972 control subjects matched on baseline age (±2 years), race–ethnicity, date of study enrollment (±3 months), and follow-up time (control follow-up time ≥ case follow-up time).
Enrollment into the WHI-OS occurred from October 1993 to December 1998, resulting in a total enrollment of 93 676 women. Detailed descriptions of the WHI-OS methods have been previously published.27,28 Inclusion criteria for the HaBPS study were: age 50 to 59 years at baseline, being postmenopausal, absence of medical conditions with an anticipated survival of <3 years, and no history of myocardial infarction or stroke at baseline. The sample of 83 218 women gave rise to 972 incident ischemic stroke cases between baseline and July 1, 2003; all of these were enrolled and then matched to 972 control subjects. One control subject was selected for each case at the time of the case subject’s stroke event matched on the criteria noted previously. Cases were removed from the pool of eligible control subjects before control selection; as such, cases with later event times could not be selected as a control to a case with an earlier event time. All participants provided written informed consent for the WHI as approved by the Institutional Review Boards at each participating WHI-OS site.
Measurement of Demographic, Health Behavior, and Physical Factors
Height was measured with a wall-mounted stadiometer, and weight was measured with participants in light clothing. Body mass index (BMI) was calculated as weight in kilograms divided by height in square meters. Based on their BMI, individuals were classified as being normal weight (BMI of 18.5 to 24.9 kg/m2), overweight (BMI of 25.0 to 29.9 kg/m2), or obese (BMI of ≥30.0 kg/m2). Waist circumference at the natural waist or narrowest part of the torso was measured to the nearest 0.1 cm. Seated systolic and diastolic blood pressures were measured in the right arm using a conventional mercury sphygmomanometer after 5 minutes of rest with an appropriate cuff size based on arm circumference measurement. Two measurements were taken at least 30 seconds apart and were averaged for the current analyses. Information on demographic characteristics, medical history, and lifestyle were obtained by interview or by self-report using questionnaires including smoking status (never, past, current), physical activity (metabolic equivalents were calculated as total energy expenditure from recreational physical activity in kcal/wk/kg with the questionnaire intentionally worded without reference to a specific timeframe such as “last month” or “last year” to collect the “usual” patterns of activity), education, income (total family income from all sources, before taxes), and history of hypertension and diabetes at the time of being not pregnant. Women were asked to bring all of their prescription medications to the baseline visit for entry in a pharmacy database (Master Drug Database, Medi-Span), and this database was used to identify current users of aspirin and antihypertensive, lipid-lowering, and antidiabetic medications.
Human HMW adiponectin was measured in archival serum samples using an enzyme-linked immunosorbent assay kit (Linco Research, a subsidiary of Millipore, St Charles, Mo) according to the manufacturer’s instructions. The percent mean coefficient of variation±SE was 35±2.6% in duplicate samples for 88 of the subjects and the lower limit of detection was 0.5 ng/mL.
Stored blood specimens were sent to the WHI core laboratory for measurement of levels of plasma C-reactive protein, insulin, fasting plasma glucose, and lipids. Lipids were assayed at Liposcience using nuclear magnetic resonance. LDL-C was calculated from triglycerides, HDL-C, and total cholesterol for those women who had a triglyceride value ≤400 mg/dL. LDL-C values were set to missing for those women whose triglyceride value was >400 (n=35) or who were missing HDL-C, total cholesterol, or triglyceride values (n=7).
Stroke Ascertainment and Follow-Up Determination
Annual telephone or mail contact with participants or third-party respondents was used to obtain information on incident stroke events, other vascular events, and deaths. Self-reported or third-party reports of events were confirmed by medical record review. Reported events were initially adjudicated locally by trained physician adjudicators at each site according to standard criteria and were then sent for central adjudication by study neurologists. Strokes that were identified by local adjudication but which were not confirmed by central adjudication were excluded from this study. Ischemic stroke was defined as the rapid onset of a persistent neurological 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 CT or MRI scan. Only stroke events requiring hospitalization were considered outcomes. Strokes were classified as ischemic or hemorrhagic through review of brain imaging study reports; transient ischemic attacks and hemorrhagic strokes were excluded in this study.
HWM adiponectin laboratory measurements were available for 855 of the 972 case–control pairs in HaBPS. Because the HMW adiponectin distribution was significantly right-skewed, data were categorized into quartiles observed in data from the control subjects. Linear trends in demographic, health history, laboratory values and the frequency of HMW cases across quartiles of HMW adiponectin were tested with linear regression. Assessments of trends across HMW categories were made using the median HMW adiponectin value for each category through linear regression. ORs of incident stroke associated with each quartile of HMW adiponectin were tabulated using conditional logistic regression to account for matching factors and incorporate further adjustment for BMI groups (normal weight, overweight, obese), diabetes (self-reported treatment or a fasting glucose level ≥126 mg/dL), smoking behavior, hypertension (self-reported use of hypertension medicine, systolic blood pressure ≥140 mm Hg, or diastolic blood pressure ≥90 mm Hg), LDL-C, HDL-C, physical activity (metabolic equivalents), C-reactive protein, and aspirin use. Although variables such as hypertension (P=0.06), LDL-C levels (P=0.07), aspirin use (P=0.28), and physical activity (P=0.61) did not reach statistical significance for association with quartiles of HMW adiponectin, they were still included in the regression model because of their clinical relevance to CVD risk factors. The P value for linear trend test in the context of conditional logistic regression was calculated by treating a 4-level variable representing the median HMW adiponectin value in each quartile as a continuous variable. Analyses were then repeated using quintiles and deciles of HWM adiponectin (observed in data from the control subjects).
All statistical analyses were performed using the STATA Version 10 software package (StataCorp LP, College Station, Texas). All statistical tests were 2-sided, and probability values <0.05 were considered statistically significant.
The median HMW adiponectin level was 11.0 (25th percentile 5.2, 75th percentile 18.6) μg/mL. Baseline characteristics of control subjects by quartiles of HWM adiponectin are presented in Table 1. Lower levels of HMW adiponectin were significantly associated with age, race–ethnicity, CVD risk factors (smoking, greater prevalence of type 2 diabetes and obesity), anthropometric factors (higher BMI, greater waist circumference), and several CVD biomarkers (higher levels of glucose, insulin, triglycerides, C-reactive protein and lower levels of HDL-C; P<0.01 for all). The distribution of incident stroke cases was similar across quartiles of HMW adiponectin (49.9%, 50.5%, 50.7%, and 48.9%, respectively; P=0.96; Table 2). Table 2 presents ORs for incident stroke from the conditional logistic regression model matched for age and race–ethnicity (Model 1). No significant trend was observed between HMW adiponectin quartiles and incident ischemic stroke. Further adjustment for multiple risk factors did not materially alter these results (Model 2). In additional analyses, no significant trend was revealed between incident ischemic stroke and HMW adiponectin when it was analyzed in quintiles or deciles (data not shown).
To our knowledge, this is the only published study to date examining associations of HMW adiponectin with the risk of incident stroke. In addition, our study examines this question in postmenopausal women, who especially experience an increase in the incidence of stroke and excess adipose tissue compared with their premenopausal counterparts. Although lower levels of HMW adiponectin were found to be significantly associated with a number of CVD risk factors, including smoking, diabetes, obesity, and hypertension, neither univariate nor multivariate conditional logistic regression analyses in our study suggested an association between HMW adiponectin and incident ischemic stroke in these postmenopausal women.
These results are consistent with a number of previous studies that failed to demonstrate a protective role of total adiponectin on ischemic stroke independent of known CVD risk factors. A case–control study using combined data from the Swedish Monitoring Trends and Determinants in Cardiovascular Disease (MONICA) and Vasterbotten Intervention Program (VIP) studies did not find a significant association between total adiponectin levels and incident stroke in either men or women; however, with only 276 cases analyzed, it is possible that the study lacked adequate power.26 Similarly, a case–control study nested within the Jichi Medical School cohort study found no significant difference in the odds of stroke between lowest and highest total adiponectin quartiles nor a significant linear trend toward a reduced risk of stroke at higher total adiponectin levels.25 However, this study, too, may have lacked power because only 179 patients were analyzed. In addition, a recent case–control study nested within PROspective Study of Pravastatin in the Elderly at Risk (PROSPER; n=266 cases) showed that although reduced total adiponectin may have a modest role in development of stroke in older people, total adiponectin did not contribute to the multivariate predictive model and therefore did not have clinical predictive use.24 Finally, a recent case–control study from the HaBPS demonstrated that although levels of total adiponectin (analyzed in quartiles) were inversely associated with the risk of ischemic stroke in univariate analyses, this association was no longer significant after adjusting for BMI and other risk factors for stroke, including smoking, physical activity, hypertension medication use, systolic blood pressure, and diabetes (Rajpathak, unpublished data). However, a case–control study nested within the Physician’s Health Study (n=259 cases) found 2 adiponectin gene variants (rs266729 and rs182052) to be associated with reduction in the conditional odds of ischemic stroke, suggesting a protective role of adiponectin gene variation in the risk of ischemic stroke independent of diabetes.23 This contradicting result could be partially explained by allelic heterogeneity (alternative genes at 1 locus causing similar phenotypes, which cannot be detected by linkage analysis) and case–control selection criteria. In addition, gene variant analyses are prone to multiple comparison problems and often fail to be replicated.
Adiponectin is distinct from other known adipocytokines in that it appears to have protective vascular effects.1 In obese and insulin-resistant individuals, adiponectin levels are reduced, which might contribute to excess CVD risk in this population.11,15 Consistent with improved vascular outcomes from the epidemiological data are mouse and cell culture studies, which suggest that adiponectin has antidiabetic and anti-inflammatory properties. In a mouse model, adiponectin was found to play a protective role in cerebral ischemic stroke by regulating endothelial nitric oxide synthase.29 Adiponectin is believed to decrease vascular inflammation by inhibiting tumor necrosis factor-induced expression of cell adhesion molecules.1 In endothelial cells, adiponectin also enhances production of nitric oxide and suppresses production of reactive oxygen species.1,30 Given these effects, it remains unclear why adiponectin has generally been found to be unrelated to stroke. This could be due to the complexity of adiponectin signaling, suggesting that multiple mechanisms are involved in cardiovascular protection by adipocytokines. More research in this area is needed.
The limitations of our study should be noted. Using a relatively new technique of quantifying HMW adiponectin (enzyme-linked immunosorbent assay kit; Linco Research, a subsidiary of Millipore) resulted in high intra-assay variation. However, any measurement error was likely reduced by analysis of HMW adiponectin in quartiles. In addition, the expected presence of association between HMW adiponectin and CVD risk factors in these analyses provides further evidence that the measurement error is not likely driving these results. An additional limitation is that our findings are only generalizable to postmenopausal women primarily of white race–ethnicity, who evidenced a lower prevalence of overweight and obesity than the general population.
This study has a number of strengths. The nesting of this study within the WHI-OS with long-term follow-up not only allowed us to assess a much larger number of case–control pairs than previous studies, but also allowed us to examine incident rather than prevalent cases of stroke. Finally, unlike other previous studies that looked at total adiponectin levels, we examined HMW adiponectin, which is considered the more biologically active form.
In summary, despite moderate associations between HMW adiponectin and various CVD risk factors, we found no evidence of an association between levels of HMW adiponectin and incident ischemic stroke in our sample of postmenopausal women. Our findings do not suggest adiponectin as an attractive molecular target for therapeutic intervention for stroke prevention among postmenopausal women. More basic science and epidemiological research is needed to understand the seeming lack of a protective effect of adiponectin in ischemic stroke despite favorable associations with stroke risk factors.
WHI Centers and Investigators
Program Office (National Heart, Lung, and Blood Institute, Bethesda, Md): Elizabeth Nabel, Jacques Rossouw, Shari Ludlam, Linda Pottern, Joan McGowan, Leslie Ford, and Nancy Geller.
Clinical Coordinating Center (Fred Hutchinson Cancer Research Center, Seattle, Wash): Ross Prentice, Garnet Anderson, Andrea LaCroix, Charles L. Kooperberg, Ruth E. Patterson, and Anne McTiernan; Wake Forest University School of Medicine, Winston-Salem, NC: Sally Shumaker; Medical Research Labs, Highland Heights, Ky: Evan Stein; University of California at San Francisco, San Francisco, Calif: Steven Cummings.
Clinical Centers: Albert Einstein College of Medicine, Bronx, NY: Sylvia Wassertheil-Smoller; Baylor College of Medicine, Houston, Texas: Jennifer Hays; Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass: JoAnn Manson; Brown University, Providence, R: Annlouise R. Assaf; Emory University, Atlanta, Ga: Lawrence Phillips; Fred Hutchinson Cancer Research Center, Seattle, Wash: Shirley Beresford; George Washington University Medical Center, Washington, DC: Judith Hsia; Los Angeles Biomedical Research Institute at Harbor–UCLA Medical Center, Torrance, Calif: Rowan Chlebowski; Kaiser Permanente Center for Health Research, Portland, Ore: Evelyn Whitlock; Kaiser Permanente Division of Research, Oakland, Calif: Bette Caan; Medical College of Wisconsin, Milwaukee, Wis: Jane Morley Kotchen; MedStar Research Institute/Howard University, Washington, DC: Barbara V. Howard; Northwestern University, Chicago/Evanston, Ill: Linda Van Horn; Rush Medical Center, Chicago, Ill: Henry Black; Stanford Prevention Research Center, Stanford, Calif: Marcia L. Stefanick; State University of New York at Stony Brook, Stony Brook, NY: Dorothy Lane; The Ohio State University, Columbus, Ohio: Rebecca Jackson; University of Alabama at Birmingham, Birmingham, Ala: Cora E. Lewis; University of Arizona, Tucson/Phoenix, Ariz: Tamsen Bassford; University at Buffalo, Buffalo, NY: Jean Wactawski-Wende; University of California at Davis, Sacramento, Calif: John Robbins; University of California at Irvine, Calif: F. Allan Hubbell; University of California at Los Angeles, Los Angeles, Calif: Howard Judd; University of California at San Diego, LaJolla/Chula Vista, Calif; Robert D. Langer; University of Cincinnati, Cincinnati, Ohio: Margery Gass; University of Florida, Gainesville/Jacksonville, Fla: Marian Limacher; University of Hawaii, Honolulu, Hawaii: David Curb; University of Iowa, Iowa City/Davenport, Iowa: Robert Wallace; University of Massachusetts/Fallon Clinic, Worcester, Mass: Judith Ockene; University of Medicine and Dentistry of New Jersey, Newark, NJ: Norman Lasser; University of Miami, Miami, Fla: Mary Jo O’Sullivan; University of Minnesota, Minneapolis, Minn: Karen Margolis; University of Nevada, Reno, Nev: Robert Brunner; University of North Carolina, Chapel Hill, NC: Gerardo Heiss; University of Pittsburgh, Pittsburgh, Pa: Lewis Kuller; University of Tennessee, Memphis, Tenn: Karen C. Johnson; University of Texas Health Science Center, San Antonio, Texas: Robert Brzyski; University of Wisconsin, Madison, Wis: Gloria E. Sarto; Wake Forest University School of Medicine, Winston-Salem, NC: Mara Vitolins; and Wayne State University School of Medicine/Hutzel Hospital, Detroit, Mich: Michael S. Simon.
We thank the key investigators involved in the WHI.
Sources of Funding
The research on which this publication is based was funded by Grant Numbers R01NS042618 (S.W.-S.) and R03NS061114 (R.P.W.) from the National Institutes of Neurological Disorders and Stroke. The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, US Department of Health and Human Services through contracts N01WH22110, 24152, 32100-2, 32105-6, 32108-9, 32111-13, 32115, 32118-32119, 32122, 42107-26, 42129-32, and 44221.
The study sponsors had no role in the design of the study; the collection, analysis, and interpretation of the data; the writing of the manuscript; or the decision to submit the manuscript for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Neurological Disorders and Stroke or the National Institutes of Health. The complete list of WHI centers and investigators can be found online at www.whiscience.org/collaborators/investigators.php.
- Received December 17, 2009.
- Revision received February 8, 2010.
- Accepted March 9, 2010.
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