EXPLORING A MULTI-OMICS APPROACH IN PREDICTING INSULIN RESISTANCE

Abstract

Background: There are notable limitations in the current methods for measuring insulin resistance, which limits research into its pathogenesis and the prevention of its health consequences. Although previous studies have identified several biomarkers associated with insulin resistance with the intent of uncovering the mechanisms behind its development and reliable measurement tools, flawed proxies are frequently used, and certain types of biomarkers remain understudied. Methods: Participants of the Outcome Reduction With Initial Glargine Intervention (ORIGIN) trial self-titrated insulin doses to achieve fasting plasma glucose ≤5.3 mmoL/L (95 mg/dL) and doses were recorded at each visitation. The degree of insulin resistance was calculated as the natural logarithm of the median of all recorded insulin doses in people who achieved normoglycemia with insulin glargine. Normoglycemia was defined as a fasting plasma glucose <5.6mmol/L and HbA1c < 6% at the 2-year visit. The relationships between insulin resistance and clinical traits, protein biomarkers, polygenic risk scores, and methylation scores were characterized, and predictive models were developed using these factors as potential predictors. Results: Two methylation scores and 8 protein biomarkers were associated with insulin resistance after accounting for baseline HbA1c and fasting plasma glucose. The multi-protein (R² = 0.27, 95% CI [0.13, 0.41]), multi-MS (R² = 0.15, 95% CI [0.05, 0.29]), and multi-clinical models (R² = 0.23, 95% CI [0.10, 0.37]) were all predictive of insulin resistance. Combining the three data types in the muti-omics model (R2=0.29, 95% CI [0.15, 0.43]) improved performance marginally. Conclusions: Several methylation and protein biomarkers are associated with insulin resistance and may improve its prediction beyond routinely measured clinical traits.