Data-driven Cluster Analysis Reveals Increased Risk for Severe Insulin-deficient Diabetes in Black/African Americans

Abstract

Abstract Context Diabetes is a heterogenic disease and distinct clusters have emerged, but the implications for diverse populations have remained understudied. Objective Apply cluster analysis to a diverse diabetes cohort in the US Deep South. Design Retrospective hierarchical cluster analysis of electronic health records from 89 875 patients diagnosed with diabetes between January 1, 2010, and December 31, 2019, at the Kirklin Clinic of the University of Alabama at Birmingham, an ambulatory referral center. Patients Adult patients with International Classification of Diseases diabetes codes were selected based on available data for 6 established clustering parameters (glutamic acid decarboxylase autoantibody; hemoglobin A1c; body mass index; diagnosis age; HOMA2-B; HOMA2-IR); ∼42% were Black/African American. Main Outcome Measure(s) Diabetes subtypes and their associated characteristics in a diverse adult population based on clustering analysis. We hypothesized that racial background would affect the distribution of subtypes. Outcome and hypothesis were formulated prior to data collection. Results Diabetes cluster distribution was significantly different in Black/African Americans compared to Whites (P < .001). Black/African Americans were more likely to have severe insulin-deficient diabetes (OR, 1.83; 95% CI, 1.36-2.45; P < .001), associated with more serious metabolic perturbations and a higher risk for complications (OR, 1.42; 95% CI, 1.06-1.90; P = .020). Surprisingly, Black/African Americans specifically had more severe impairment of β-cell function (homoeostatic model assessment 2 estimates of β-cell function, C-peptide) (P < .001) but not being more obese or insulin resistant. Conclusion Racial background greatly influences diabetes cluster distribution and Black/African Americans are more frequently and more severely affected by severe insulin-deficient diabetes. This may further help explain the disparity in outcomes and have implications for treatment choice.