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.