Context: Accurate assessment of insulin sensitivity may better identify individuals at increased risk of cardio-metabolic diseases.
Objectives: To examine whether a combination of anthropometric, biochemical and imaging measures can better estimate insulin sensitivity index (ISI) and provide improved prediction of cardio-metabolic risk, in comparison to HOMA-IR.
Design and participants: Healthy male volunteers (96 Chinese, 80 Malay, 77 Indian), 21 to 40 years, body mass index 18−30 kg/m2. Predicted ISI (ISI-cal) was generated using 45 randomly selected Chinese through stepwise multiple linear regression, and validated in the rest using non-parametric correlation (Kendall's tau τ). In an independent longitudinal cohort, ISI-cal and HOMA-IR were compared for prediction of diabetes and cardiovascular disease (CVD), using ROC curves.
Setting: The study was conducted in a university academic medical centre.
Outcome measures: ISI measured by hyperinsulinemic euglycemic glucose clamp, along with anthropometric measurements, biochemical assessment and imaging; incident diabetes and CVD.
Results: A combination of fasting insulin, serum triglycerides and waist-to-hip ratio (WHR) provided the best estimate of clamp-derived ISI (adjusted R2 0.58 versus 0.32 HOMA-IR). In an independent cohort, ROC areas under the curve were 0.77±0.02 ISI-cal versus 0.76±0.02 HOMA-IR (p>0.05) for incident diabetes, and 0.74±0.03 ISI-cal versus 0.61±0.03 HOMA-IR (p<0.001) for incident CVD. ISI-cal also had greater sensitivity than defined metabolic syndrome in predicting CVD, with a four-fold increase in the risk of CVD independent of metabolic syndrome.
Conclusions: Triglycerides and WHR, combined with fasting insulin levels, provide a better estimate of current insulin resistance state and improved identification of individuals with future risk of CVD, compared to HOMA-IR. This may be useful for estimating insulin sensitivity and cardio-metabolic risk in clinical and epidemiological settings.