Diabetes, a Contemporary Risk for Parkinson’s Disease: Epidemiological and Cellular Evidences
Bibliographic record
Abstract
Diabetes mellitus (DM), a group of diseases characterized by defective glucose metabolism, is the most widespread metabolic disorder affecting over 400 million adults worldwide. This pathological condition has been implicated in the pathogenesis of a number of central encephalopathies and peripheral neuropathies. In further support of this notion, recent epidemiological evidence suggests a link between DM and Parkinson's disease (PD), with hyperglycemia emerging as one of the culprits in neurodegeneration involving the nigrostriatal pathway, the neuroanatomical substrate of the motor symptoms affecting parkinsonian patients. Indeed, dopaminergic neurons located in the mesencephalic substantia nigra appear to be particularly vulnerable to oxidative stress and degeneration, likely because of their intrinsic susceptibility to mitochondrial dysfunction, which may represent a direct consequence of hyperglycemia and hyperglycemia-induced oxidative stress. Other pathological pathways induced by increased intracellular glucose levels, including the polyol and the hexosamine pathway as well as the formation of advanced glycation end-products, may all play a pivotal role in mediating the detrimental effects of hyperglycemia on nigral dopaminergic neurons. In this review article, we will examine the epidemiological as well as the molecular and cellular clues supporting the potential susceptibility of nigrostriatal dopaminergic neurons to hyperglycemia.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".