Demographic and cardiovascular risk factors modify association of fasting insulin with incident coronary heart disease and ischemic stroke (from the Atherosclerosis Risk In Communities Study)

Sex Differences in the Association Between Insulin Resistance and Incident Coronary Heart Disease and Stroke Among Blacks Without Diabetes Mellitus: The Jackson Heart Study

Background

Studies exploring the association between insulin resistance (IR) and cardiovascular disease in blacks have not been conclusive, especially for coronary heart disease (CHD). The McAuley index and homeostasis model assessment of IR (HOMA‐IR) perform differently in predicting cardiovascular disease. We investigated this association in the Jackson Heart Study, a large longitudinal cohort of blacks.

Methods and Results

IR was estimated for 3565 participants without diabetes mellitus and cardiovascular disease at baseline using the McAuley index and HOMA‐IR, and their associations with incident CHD and stroke (composite outcome) were compared. A lower McAuley index and higher HOMA‐IR are indicative of IR. Cox regression analysis was used to estimate adjusted hazard ratios for incident CHD and/or stroke. There were 158 events (89 CHD‐only, 58 stroke‐only, and 11 CHD/stroke) over a median follow‐up of 8.4 years. After adjustment for demographic factors, the risk of the composite outcome decreased with each SD increase in the McAuley index (hazard ratio 0.80; 95% CI: 0.67–0.96), with no attenuation after further accounting for CHD and stroke risk factors. When considered individually, McAuley index and HOMA‐IR were associated with CHD (hazard ratio 0.71, 95% CI: 0.55–0.92 and hazard ratio 1.33, 95% CI: 1.03–1.72, respectively), but not stroke risk. The logHOMA‐IR and CHD association was present in men, but not in women (P_interaction=0.01).

Conclusions

Both HOMA‐IR and the McAuley index demonstrate strong associations with CHD but not stroke risk in blacks. The logHOMA‐IR and CHD association was present in men, but not in women.

Racial differences in the association of insulin resistance with stroke risk: the REasons for Geographic And Racial Differences in Stroke (REGARDS) study

BACKGROUND AND PURPOSE

Insulin resistance is associated with increased stroke risk, but the effect has not been adequately examined separately in white and black populations.

METHODS

The association of baseline insulin resistance with risk of cerebral infarction (CI) and intracerebral hemorrhage (ICH) was assessed in 12 366 white and 6782 black participants from the REasons for Geographic And Racial Differences in Stroke (REGARDS) cohort, recruited between 2003 and 2007 and followed for an average of 5.7 years. Insulin resistance was measured with the homeostasis model assessment-insulin resistance.

RESULTS

There were 364 incident CI and 41 incident ICH events. The risk for CI increased with the log of insulin resistance in whites (hazards ratio [HR]ln(IR)=1.17; 95% confidence interval [CI], 1.00-1.38) but was largely attenuated by adjustment for stroke risk factors (HRln(IR)=1.05; 95% CI, 0.88-1.26). There was no association in blacks (HRln(IR)=1.01; 95% CI, 0.81-1.25). After adjustment for demographic factors and risk factors, there was a significant difference by race in the association of insulin resistance with risk of ICH (P=0.07), with a decrease in the risk of ICH in whites (HRln(IR)=0.61; 95% CI, 0.35-1.04) but a nonsignificant increase in blacks (HRln(IR)=1.20; 95% CI, 0.60-2.39).

CONCLUSIONS

These data support the growing evidence that insulin resistance may play a more important role in stroke risk among white than black individuals and suggest a potentially discordant relationship of insulin resistance on CI and ICH among whites.

Fasting insulin concentrations and incidence of hypertension, stroke, and coronary heart disease: a meta-analysis of prospective cohort studies

BACKGROUND

Insulin resistance is a precursor of numerous chronic diseases, including cardiovascular disease (CVD). The fasting insulin concentration is considered a reasonable surrogate of insulin resistance, especially among nondiabetic individuals.

OBJECTIVE

We aimed to quantitatively summarize the literature on the association of fasting insulin concentrations with risk of hypertension, stroke, and coronary heart disease (CHD) by conducting a meta-analysis of prospective cohort studies.

DESIGN

Eligible studies were identified by searching PubMed and EMBASE through January 2013. Additional information was retrieved through Google Scholar or a hand review of the reference lists from relevant articles. Prospective cohort studies that reported RRs and corresponding 95% CIs for the association of interest were identified. Data were extracted independently by 2 investigators, and the weighted RRs and 95% CIs for the associations were obtained by using a random-effects model.

RESULTS

Of the 22 identified studies, 10 reported results on hypertension (36,617 individuals and 4491 cases), 7 on stroke (27,887 individuals and 1550 cases), and 9 on CHD (22,379 individuals and 1986 cases). Comparison of the highest with the lowest quantile of fasting insulin concentrations showed a pooled RR (95% CI) of 1.63 (1.35, 1.97) for hypertension, 1.18 (0.87, 1.60) for stroke, and 1.50 (1.28, 1.77) for CHD. Each 50-pmol/L increment in fasting insulin was associated with a 25% increase in risk of hypertension [RR: 1.25 (1.14, 1.36)] and a 16% increase in risk of CHD [RR: 1.16 (1.10, 1.22)] but was not associated with risk of stroke [RR: 0.999 (0.99, 1.01)].

CONCLUSIONS

A higher fasting insulin concentration or hyperinsulinemia was significantly associated with an increased risk of hypertension and CHD but not stroke. This meta-analysis suggests that early fasting insulin ascertainment in the general population may help clinicians identify those who are potentially at high risk of CVD.

Insulin resistance and the risk of stroke and stroke subtypes in the nondiabetic elderly

Insulin resistance, which plays a key role in the development of diabetes mellitus, is a putative modifiable risk factor for stroke. The aim of this study was to investigate if markers of insulin resistance were associated with risk of stroke in the general elderly population. This study was part of the large population-based Rotterdam Study and included 5,234 participants who were aged 55 years or older and stroke free and diabetes free at baseline (1997-2001). Fasting insulin levels and homeostasis model assessment for insulin resistance were used as markers for insulin resistance. Cox regression was used to determine associations between insulin resistance markers and stroke risk, adjusted for age, sex, and potential confounders. During 42,806 person-years of follow-up (median: 8.6 years), 366 first-ever strokes occurred, of which 225 were cerebral infarctions, 42 were intracerebral hemorrhages, and 99 were unspecified strokes. Fasting insulin levels were not associated with risk of any stroke, cerebral infarction, or intracerebral hemorrhage. Homeostasis model assessment for insulin resistance, which almost perfectly correlated with fasting insulin levels, was also not associated with risk of stroke or stroke subtypes. In conclusion, in this population-based cohort study among nondiabetic elderly, insulin resistance markers were not associated with risk of stroke or any of its subtypes.

P wave indices, obesity, and the metabolic syndrome: the atherosclerosis risk in communities study

Atrial fibrillation and obesity are increasing in prevalence and are interrelated epidemics. There has been limited assessment of how obesity and the metabolic syndrome impact P wave indices, established electrocardiographic predictors of atrial fibrillation. We conducted a cross-sectional analysis to determine the association of obesity and the components of the metabolic syndrome with P wave indices in the population-based Atherosclerosis Risk in Communities (ARIC) study. Analyses were adjusted for demographic, anthropometric and clinical variables, and cardiovascular diseases and risk factors. Following relevant exclusions, 14,433 subjects were included (55% women and 24.7% black). In multivariable analyses, we identified significant, progressive increases in PR interval, P wave maximum duration, and P wave terminal force with BMI 25-30 kg/m(2) and BMI ≥30 kg/m(2) compared to the reference group <25 kg/m(2) (P < 0.0001 for trend for all P wave indices). These effects were present in both blacks and whites. Presence of metabolic syndrome was also associated with longer P wave indices. When components of the metabolic syndrome were examined separately, hypertension resulted in significant (P < 0.001) augmentation of the three P wave indices. Similarly, waist circumference was associated with greater P wave maximum duration in both races (P < 0.001). We concluded that P wave indices are significantly associated with obesity and particularly with hypertension and waist circumference. P wave indices may comprise intermediate markers, independent of age and cardiovascular risk, of the pathway linking obesity and with the risk of atrial fibrillation (AF).

Fasting and post-glucose load measures of insulin resistance and risk of ischemic stroke in older adults

BACKGROUND AND PURPOSE

Few studies have assessed post-glucose load measures of insulin resistance and ischemic stroke risk, and data are sparse for older adults. We investigated whether fasting and post-glucose load measures of insulin resistance were related to incident ischemic stroke in nondiabetic, older adults.

METHODS

Participants were men and women in the Cardiovascular Health Study, age 65+ years and without prevalent diabetes or stroke at baseline, followed for 17 years for incident ischemic stroke. The Gutt insulin sensitivity index was calculated from baseline body weight and from fasting and 2-hour postload insulin and glucose; a lower Gutt index indicates higher insulin resistance.

RESULTS

Analyses included 3442 participants (42% men) with a mean age of 73 years. Incidence of ischemic stroke was 9.8 strokes per 1000 person-years. The relative risk (RR) for lowest quartile versus highest quartile of Gutt index was 1.64 (95% CI, 1.24-2.16), adjusted for demographics and prevalent cardiovascular and kidney disease. Similarly, the adjusted RR for highest quartile versus lowest quartile of 2-hour glucose was 1.84 (95% CI, 1.39-2.42). In contrast, the adjusted RR for highest quartile versus lowest quartile of fasting insulin was 1.10 (95% CI, 0.84-1.46).

CONCLUSIONS

In nondiabetic, older adults, insulin resistance measured by Gutt index or 2-hour glucose, but not by fasting insulin, was associated with risk of incident ischemic stroke.

When should one adjust for measurement error in baseline variables in observational studies?

Previously, we showed that in randomised experiments, correction for measurement error in a baseline variable induces bias in the estimated treatment effect, and conversely that ignoring measurement error avoids bias. In observational studies, non-zero baseline covariate differences between treatment groups may be anticipated. Using a graphical approach, we argue intuitively that if baseline differences are large, failing to correct for measurement error leads to a biased estimate of the treatment effect. In contrast, correction eliminates bias if the true and observed baseline differences are equal. If this equality is not satisfied, the corrected estimator is also biased, but typically less so than the uncorrected estimator. Contrasting these findings, we conclude that there must be a threshold for the true baseline difference, above which correction is worthwhile. We derive expressions for the bias of the corrected and uncorrected estimators, as functions of the correlation of the baseline variable with the study outcome, its reliability, the true baseline difference, and the sample sizes. Comparison of these expressions defines a theoretical decision threshold about whether to correct for measurement error. The results show that correction is usually preferred in large studies, and also in small studies with moderate baseline differences. If the group sample sizes are very disparate, correction is less advantageous. If the equivalent balanced sample size is less than about 25 per group, one should correct for measurement error if the true baseline difference is expected to exceed 0.2-0.3 standard deviation units. These results are illustrated with data from a cohort study of atherosclerosis.