Obesity, Cardiovascular and Neurodegenerative Diseases: Potential Common Mechanisms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it