Supervised machine learning identifies impaired mitochondrial quality control in β-cells with development of type 2 diabetes.

Defining molecular pathways driving β-cell failure in type 2 diabetes (T2D) is challenging given donor heterogeneity. We developed an interpretable machine learning framework coupling sparse rule-based classification, pathway constrained modeling, and mitochondrial fitness stratification, applied to single-cell RNAseq from 52 human islet donors. A 50-gene classifier predicted T2D at single-cell resolution, outperforming ensemble models, with donor-level scores correlating with HbA1c. We identified a resilient non-diabetic (ND) β-cell subtype with preserved β-cell identity, while T2D β-cell subtypes showed cellular stress and suppressed oxidative phosphorylation. Mitophagy emerged as the dominant cellular pathway, with PINK1, BNIP3, and FUNDC1 as predictors. At the donor level, PINK1 expression decreased with T2D score and correlated with sex‑specific mitophagy patterns. We developed a mitochondrial fitness index (MFI, R² = 0.934) integrating mitophagy, proteostasis, biogenesis, and respiration, identifying PINK1, SQSTM1, PRKN, and BNIP3 as top T2D contributors. Interpretable machine learning revealed mitophagy as central to β-cell metabolic fitness.