Evidence map›Paper›PMID 42440097›Full record

ArticleActa diabetologica2026

Interpretable single-cell machine learning identifies PINK1-centred mitophagy as a determinant of metabolic fitness in type 2 diabetes.

Bingjun Zeng, Da Qin, Xiaowei Xu, Dayun Tao, Ying Jiang, Hao Zhou, Yanli Wang

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Article in Acta diabetologica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Bingjun ZengDepartment of Postgraduate, Hebei North University, 11 Zuanshi South Road, Zhangjiakou, 075000, Hebei, China.
Da QinDepartment of Cardiology, the Sixth Medical Center of Chinese PLA General, Hospital Fucheng Road, Haidian District, Beijing, China.
Xiaowei XuDepartment of Cardiology, the Sixth Medical Center of Chinese PLA General, Hospital Fucheng Road, Haidian District, Beijing, China.
Dayun TaoDepartment of Cardiology, the Sixth Medical Center of Chinese PLA General, Hospital Fucheng Road, Haidian District, Beijing, China.
Ying JiangDepartment of Cardiology, the Sixth Medical Center of Chinese PLA General, Hospital Fucheng Road, Haidian District, Beijing, China.
Hao ZhouDepartment of Postgraduate, Hebei North University, 11 Zuanshi South Road, Zhangjiakou, 075000, Hebei, China. zhouhao301@outlook.com.ORCID http://orcid.org/0009-0000-2040-1301
Yanli WangDepartment of Cardiovascular Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China. 15201551034@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundProgressive 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.

methodsSingle-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.

resultsFive 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.

conclusionsPINK1-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.

Indexed as

Diabetes Mellitus, Type 2Machine LearningMitophagyProtein KinasesHumansInsulin-Secreting CellsMitochondriaPrediabetic StatePTEN-Induced Putative KinaseSingle-Cell AnalysisTranscriptomeProtein KinasesPTEN-Induced Putative KinaseInterpretable machine learningMitophagyPINK1Single-cell transcriptomicsType 2 diabetesβ-cell heterogeneity

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.