Supervised machine learning identifies impaired mitochondrial quality control in β cells with development of type 2 diabetes
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Bibliographic record
Abstract
Abstract In type 2 diabetes (T2D), molecular pathways driving β cell failure are difficult to resolve with standard single cell analysis. Here we developed an interpretable, supervised machine learning framework that couples sparse rule-based classification (SnakeClassifier), pathway constrained modelling (BlackSwanClassifier), and β cell mitochondrial fitness stratification (Kolmogorov-Arnold Neural Networks KANN), linking and integrating them into disease mechanisms in single cell RNA sequencing (scRNA-seq) from 52 human donors. SnakeClassifier trained on 50 genes accurately predicted T2D at single cell resolution, outperforming classical ensemble machine learning classifier models, and yielded donor level diabetes scores that correlated with chronic hyperglycemia. The clustering of β cell populations (β1-4) revealed a resilient non-diabetic (ND) β1 subtype characterized by preserved β cell identity genes and lower disease risk, whereas T2D β2-4 subtypes exhibited upregulation of genes involved in cellular and mitochondrial stress and suppression of genes promoting oxidative phosphorylation and insulin secretion. Mitophagy emerged as the dominant program linked to T2D and a mitophagy focused BlackSwanClassifier nominated PINK1, BNIP3 , and FUNDC1 as key regulators. PINK1 was enriched in ND β1, decreased with T2D disease score and connected sex stratified mitophagy. We generated a KANN derived mitochondrial fitness index (MFI) integrating mitophagy, mitochondrial proteostasis, biogenesis and oxidative phosphorylation into a single interpretable score (R 2 = 0.934 vs module-based mitochondria quality index), which identified mitophagy PINK1, SQSTM1, PRKN and BNIP3 as top contributors to T2D progression. These transparent models unify prediction with T2D disease mechanism and identify the mitophagy receptor PINK1 as a central determinant of β cell metabolic fitness
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it