Early metabolic impairment as a contributor to neurodegenerative disease: Mechanisms and potential pharmacological intervention
Bibliographic record
Abstract
The metabolic syndrome comprises a family of clinical and laboratory findings, including insulin resistance, hyperglycemia, hypertriglyceridemia, low high-density lipoprotein cholesterol levels, and hypertension, in addition to central obesity. The syndrome confers a high risk of cardiovascular mortality. Indeed, metabolic dysfunction has been shown to cause a direct insult to smooth muscle and endothelial components of the vasculature, which leads to vascular dysfunction and hyperreactivity. This, in turn, causes cerebral vasoconstriction and hypoperfusion, eventually contributing to cognitive deficits. Moreover, the metabolic syndrome disrupts key homeostatic processes in the brain, including apoptosis, autophagy, and neurogenesis. Impairment of such processes in the context of metabolic dysfunction has been implicated in the pathogenesis of neurodegenerative diseases, including Alzheimer, Parkinson, and Huntington diseases. The aim of this review is to elucidate the role that the metabolic syndrome plays in the pathogenesis of the latter disorders, with a focus on the role of perivascular adipose inflammation in the peripheral-to-central transduction of the inflammatory insult. This review delineates common signaling pathways that contribute to these pathologies. Moreover, the role of therapeutic agents aimed at treating the metabolic syndrome, as well as their risk factors that interfere with the aforementioned pathways, are discussed as potential interventions for neurodegenerative diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".