De Filippis, Eleanna

Abstract Context Metformin is the first-line drug for treating diabetes but has a high failure rate. Objective To identify demographic and clinical factors available in the electronic health record (EHR) that predict metformin failure. Methods A cohort of patients with at least 1 abnormal diabetes screening test that initiated metformin was identified at 3 sites (Arizona, Mississippi, and Minnesota). We identified 22 047 metformin initiators (48% female, mean age of 57 ± 14 years) including 2141 African Americans, 440 Asians, 962 Other/Multiracial, 1539 Hispanics, and 16 764 non-Hispanic White people. We defined metformin failure as either the lack of a target glycated hemoglobin (HbA1c) (<7%) within 18 months of index or the start of dual therapy. We used tree-based extreme gradient boosting (XGBoost) models to assess overall risk prediction performance and relative contribution of individual factors when using EHR data for risk of metformin failure. Results In this large diverse population, we observed a high rate of metformin failure (43%). The XGBoost model that included baseline HbA1c, age, sex, and race/ethnicity corresponded to high discrimination performance (C-index of 0.731; 95% CI 0.722, 0.740) for risk of metformin failure. Baseline HbA1c corresponded to the largest feature performance with higher levels associated with metformin failure. The addition of other clinical factors improved model performance (0.745; 95% CI 0.737, 0.754, P < .0001). Conclusion Baseline HbA1c was the strongest predictor of metformin failure and additional factors substantially improved performance suggesting that routinely available clinical data could be used to identify patients at high risk of metformin failure who might benefit from closer monitoring and earlier treatment intensification.

Skeletal muscle is highly plastic and dynamically regulated by the body’s physical demands. This study aimed to determine the plasticity of skeletal muscle DNA methylation in response to 8 weeks of supervised exercise training in volunteers with a range of insulin sensitivities. We studied 13 sedentary participants and performed euglycemic hyperinsulinemic clamps with basal vastus lateralis muscle biopsies and peak aerobic activity (VO2 peak) tests before and after training. We extracted DNA from the muscle biopsies and performed global methylation using Illumina’s Methylation EPIC 850K BeadChip. Training significantly increased peak aerobic capacity and insulin-stimulated glucose disposal. Fasting serum insulin and insulin levels during the steady state of the clamp were significantly lower post-training. Insulin clearance rates during the clamp increased following the training. We identified 13 increased and 90 decreased differentially methylated cytosines (DMCs) in response to 8 weeks of training. Of the 13 increased DMCs, 2 were within the following genes, FSTL3, and RP11-624M8.1. Of the 90 decreased DMCs, 9 were within the genes CNGA1, FCGR2A, KIF21A, MEIS1, NT5DC1, OR4D1, PRPF4B, SLC26A7, and ZNF280C. Moreover, pathway analysis showed an enrichment in metabolic and actin-cytoskeleton pathways for the decreased DMCs, and for the increased DMCs, an enrichment in signal-dependent regulation of myogenesis, NOTCH2 activation and transmission, and SMAD2/3: SMAD4 transcriptional activity pathways. Our findings showed that 8 weeks of exercise training alters skeletal muscle DNA methylation of specific genes and pathways in people with varying degrees of insulin sensitivity.