Quantifying the improvement of surrogate indices of hepatic insulin resistance using complex measurement techniques

Abstract

We evaluated the ability of simple and complex surrogate-indices to identify individuals from an overweight/obese cohort with hepatic insulin-resistance (HEP-IR). Five indices, one previously defined and four newly generated through step-wise linear regression, were created against a single-cohort sample of 77 extensively characterised participants with the metabolic syndrome (age 55.6 ± 1.0 years, BMI 31.5 ± 0.4 kg/m(2); 30 males). HEP-IR was defined by measuring endogenous-glucose-production (EGP) with [6-6(2)H(2)] glucose during fasting and euglycemic-hyperinsulinemic clamps and expressed as EGP*fasting plasma insulin. Complex measures were incorporated into the model, including various non-standard biomarkers and the measurement of body-fat distribution and liver-fat, to further improve the predictive capability of the index. Validation was performed against a data set of the same subjects after an isoenergetic dietary intervention (4 arms, diets varying in protein and fiber content versus control). All five indices produced comparable prediction of HEP-IR, explaining 39-56% of the variance, depending on regression variable combination. The validation of the regression equations showed little variation between the different proposed indices (r(2) = 27-32%) on a matched dataset. New complex indices encompassing advanced measurement techniques offered an improved correlation (r = 0.75, P<0.001). However, when validated against the alternative dataset all indices performed comparably with the standard homeostasis model assessment for insulin resistance (HOMA-IR) (r = 0.54, P<0.001). Thus, simple estimates of HEP-IR performed comparable to more complex indices and could be an efficient and cost effective approach in large epidemiological investigations.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1. Comparison of regression against outcome variable.

a) Hepatic insulin resistance (HIR) Index as described in Vangipurapu et al.; b) HIR Index generated from simple clinical measurements; c) HIR index from the regression on the complex measurement set. d) HOMA-IR Regression index.

Figure 2. Comparison of regression against outcome variable, validation only.

a) Hepatic insulin resistance (HIR) Index as described in Vangipurapu et al.; b) HIR Index generated from simple clinical measurements; c) HIR index from the regression on the complex measurement set. d) HOMA-IR Regression index.