Interpretable single-cell machine learning identifies PINK1-centred mitophagy as a determinant of metabolic fitness...

Progressive pancreatic β-cell dysfunction constitutes a hallmark of type 2 diabetes (T2D), yet the molecular programmes governing metabolic fitness deterioration remain incompletely characterised at single-cell resolution.

Single-cell transcriptomic data (GSE221156) encompassing pancreatic islet cells from 48 donors (non-diabetic [ND], n = 17; pre-diabetic [PD], n = 14; T2D, n = 17) were analysed using an interpretable machine learning framework that integrated sparse rule-based classification, pathway-constrained modelling, and mitochondrial fitness indexing.

Five transcriptionally distinct β-cell subtypes (β1-β5) were resolved. The β1 subtype exhibited peak proportional representation in PD donors (35.0%) compared with ND (16.7%) and T2D (21.1%), consistent with stress-induced adaptive expansion. Mitochondrial biogenesis and ER stress emerged as the top-ranked programmes discriminating T2D from ND β-cells (importance scores = 0.329 and 0.329). Within the mitophagy gene set, MFN2 exhibited the highest feature importance (|logFC| = 0.472), followed by PINK1 (|logFC| = 0.199). PINK1 expression progressively declined across β1→β5 subtypes. A mitochondrial fitness index integrating mitophagy, proteostasis, biogenesis, and oxidative phosphorylation achieved R² = 0.65 against module-based quality scores. Hypergeometric enrichment analysis of differentially expressed genes between the adaptive β1 subtype and dysfunctional β2-β5 subtypes revealed significant enrichment of insulin secretion (p = 0.0019), endoplasmic reticulum stress (p = 0.0013), and oxidative phosphorylation (p = 0.045) pathways.

PINK1-centred mitophagy represents a critical determinant of β-cell metabolic fitness in T2D, with implications for therapeutic strategies aimed at preserving islet function during disease progression.