Abstract

Abstract Insulin resistance (IR)/compensatory hyperinsulinemia exert effects on the type and nature of blood lipids that contribute to the increased risk of atherosclerotic cardiovascular disease (ASCVD). Direct measurement of IR is labor-intensive and cannot be performed in a clinical setting. Previously, we modeled the utility of fasting insulin and C-peptide measurements (IR score) to inform the degree of insulin resistance in a large cohort of individuals without diabetes who had undergone direct measurement of whole-body IR. In the current study, we examined the associations of lipoprotein subfractions with IR and determined the usefulness of lipoprotein subfractions to identify insulin resistant individuals. A total of 527 persons (median age 50 years, 65% women, 93% non-Hispanic, and 70% white) underwent measurements of BMI, lipid panel, and lipoprotein subfractions by ion mobility (IM) as well as direct measurement of IR by steady-state plasma glucose (SSPG) concentration during the insulin suppression test. Individuals in the top tertile of SSPG concentration were defined as being insulin resistant. A stepwise linear regression model was used to select a combination of lipoprotein subfractions that remained associated with SSPG after adjusting for BMI, age, sex, ethnicity, race, and triglyceride to HDL cholesterol ratio (TG/HDL-C). An IM score was calculated using linear combinations of the regression coefficients for IM lipoprotein subfractions. Similarly, scores were calculated for TG/HDL-C, the full model excluding the IM lipoprotein subfractions, and the full model that included all variables. The scores were evaluated for predicting the top tertile of SSPG by using area under the receiver operator characteristic curve (AUC) analysis and the positive predictive value (PPV) calculations where the highest 5% value of a score was considered a positive test. Several of the IM lipoprotein sub-fractions were associated with SSPG in a linear regression model after adjusting for BMI, age, sex, ethnicity, race, and TG/HDL-C. When predicting individuals in the top tertile of SSPG, the IM score and TG/HDL-C were similar (AUC=0.68 and 0.70 respectively; PPV=.59 and .59 respectively); however, when used together, they significantly improved the prediction of IR (AUC=0.73; PPV=0.70). Similarly, the score derived from the full stepwise model that included the IM score significantly improved the AUC and PPV when compared with the score from the model that excluded the IM score (AUC=0.84 vs. 0.81; PPV=0.89 vs 0.85). In conclusion, the IM score and TG/HDL-C are comparable in identifying insulin resistant individuals and their combination improves prediction of IR when combined with BMI and demographic data. Among patients who have undergone ion mobility testing, the IM score may assist prioritization of subjects for further testing by the IR score and aid in identification of persons at increased risk of ASCVD associated with IR. Presentation: Saturday, June 11, 2022 11:45 a.m. - 12:00 p.m.

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