A deep cellular atlas of the human ventral substantia nigra in Parkinson’s identifies a genetic and molecular overlap with insulin resistance
Bibliographic record
Abstract
Abstract Parkinson’s disease (PD) is a complex neurodegenerative disorder characterised by selective neuronal loss. We integrate deep full-length single-nuclei sequencing of the human substantia nigra with novel genome-wide association studies (GWAS) identifying genetic and cellular drivers of PD. Genetic risk converges on AGTR1+ dopaminergic neurons and perineuronal oligodendrocytes (pODCs), both reduced in PD, as well as oligodendrocyte precursor cells, enriched among disease-disrupted intercellular interactions. AGTR1+ neurons represent a metabolically stressed state, characterised by renin-angiotensin system (RAS) and MAPK activation, oxidative stress, and mitochondrial dysfunction, rather than a distinct subtype. AGTR1+ neurons and pODCs link PD risk to metabolic traits; in pODCs, this association reflects insulin resistance with downregulated PI3K–AKT signalling. GWAS of comorbid PD and type 2 diabetes (T2D) identifies loci in AGTR1 and TCF7L2, while AGTR1+ neurons specifically upregulate RAS and T2D drug targets. Familial PD genes associated to comorbid PD/T2D associate with non-Lewy body PD, stratifying disease mechanisms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".