Obesity, Cardiovascular and Neurodegenerative Diseases: Potential Common Mechanisms
Bibliographic record
Abstract
The worldwide increase in the incidence of obesity and cardiovascular and neurodegenerative diseases, e.g. Alzheimer's disease, is related to many factors, including an unhealthy lifestyle and aging populations. However, the interconnection between these diseases is not entirely clear, and it is unknown whether common mechanisms underlie these conditions. Moreover, there are currently no fully effective therapies for obesity and neurodegeneration. While there has been extensive research in preclinical models addressing these issues, the experimental findings have not been translated to the clinic. Another challenge relates to the time of onset of individual diseases, which may not be easily identified, since there are no specific indicators or biomarkers that define disease onset. Hence knowing when to commence preventive treatment is unclear. This is especially pertinent in neurodegenerative diseases, where the onset of the disease may be subtle and occur decades before the signs and symptoms manifest. In metabolic and cardiovascular disorders, the risk may occur in-utero, in line with the concept of fetal programming. This review provides a brief overview of the link between obesity, cardiovascular and neurodegenerative diseases and discusses potential common mechanisms including the role of the gut microbiome.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".