A shared comparison of diabetes mellitus and neurodegenerative disorders
Bibliographic record
Abstract
Abstract Diabetes mellitus (DM), one of the most prevalent metabolic diseases in the world population, is associated with a number of comorbid conditions including obesity, pancreatic endocrine changes, and renal and cardio‐cerebrovascular alterations, coupled with peripheral neuropathy and neurodegenerative disease, some of these disorders are bundled into metabolic syndrome. Type 1 DM (T1DM) is an autoimmune disease that destroys the insulin‐secreting islet cells. Type 2 DM (T2DM) is diabetes that is associated with an imbalance in the glucagon/insulin homeostasis that leads to the formation of amyloid deposits in the brain, pancreatic islet cells, and possibly in the kidney glomerulus. There are several layers of molecular pathologic alterations that contribute to the DM metabolic pathophysiology and its associated neuropathic manifestations. In this review, we describe the general signature metabolic features of DM and the cross‐talk with neurodegeneration. We will assess the underlying molecular key players associated with DM‐induced neuropathic disorders that are associated with both T1DM and T2DM. In this context, we will highlight the role of tau and amyloid protein deposits in the brain as well in the pancreatic islet cells, and possibly in the kidney glomerulus. Furthermore, we will discuss the central role of mitochondria, oxidative stress, and the unfolded protein response in mediating the DM‐associated neuropathic degeneration. This study will elucidate the relationship between DM and neurodegeneration which may account for the evolution of other neurodegenerative diseases, particularly Alzheimer's disease and Parkinson's disease as discussed later.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 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".