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The pathophysiological link between adiposity and blood pressure is not completely understood, and evidence suggests an influence of sex and genetic determinants. We aimed to identify the relationship between adiposity and blood pressure, independent of a robust set of lifestyle and metabolic factors, and to examine the modulating role of sex and Angiotensin-Converting Enzyme (ACE) insertion/deletion (I/D) polymorphisms. In the Relationship Between Insulin Sensitivity and Cardiovascular Disease (RISC) study cohort, 1211 normotensive individuals, aged 30 to 60 years and followed-up after 3.3 years, were characterized for lifestyle and metabolic factors, body composition, and ACE genotype. Body mass index (BMI) and waist circumference (WC) were independently associated with mean arterial pressure, with a stronger relationship in women than men (BMI: r =0.40 versus 0.30; WC: r =0.40 versus 0.30, both P ID and II ACE genotypes in both sexes ( P ACE genotype only in women ( P =0.03). A 5 cm larger WC at baseline increased the risk of developing hypertension at follow-up only in women (odds ratio, 1.56 [95% CI, 1.15–2.10], P =0.004) and in II genotype carriers (odds ratio, 1.87 [95% CI, 1.09–3.20], P =0.023). The hypertensive effect of adiposity is more pronounced in women and in people carrying the II variant of the ACE genotype, a marker of salt sensitivity.

Aim Insulin resistance needs to be identified as early as possible in its development to allow targeted prevention programmes. Therefore, we compared various fasting surrogate indices for insulin sensitivity using the euglycaemic insulin clamp in an attempt to develop the most appropriate method for assessing insulin resistance in a healthy population. Methods Glucose, insulin, proinsulin, glucagon, glucose tolerance, fasting lipids, liver enzymes, blood pressure, anthropometric parameters and insulin sensitivity (Mffm/I) using the euglycaemic insulin clamp were obtained for 70 normoglycaemic non-obese individuals. Spearman’s rank correlations were used to examine the association between Mffm/I and various fasting surrogate indices of insulin sensitivity. A regression model was used to determine the weighting for each variable and to derive a formula for estimating insulin resistance. The clinical value of the surrogate indices and the new formula for identifying insulin-resistant individuals was evaluated by the use of receiver operating characteristic (ROC) curves. Results The variables that best predicted insulin sensitivity were the HDL-to-total cholesterol ratio, the fasting NEFA and fasting insulin. The use of the lipid-parameter-based formula Mffm/I = 12 × [2.5 × (HDL-c/total cholesterol) − NEFA] – fasting insulin appeared to have high clinical value in predicting insulin resistance. The correlation coefficient between Mffm/I and the new fasting index was higher than those with the most commonly used fasting surrogate indices for insulin sensitivity. Conclusion A lipid-parameter-based index using fasting samples provides a simple means of screening for insulin resistance in the healthy population.