The Role of Adipokines in Understanding the Associations between Obesity and Depression
Bibliographic record
Abstract
Objective. Two major causes of disability, major depression and obesity, share overlapping psychosocial and pathophysiological etiologies. Studies are now focused on biological mechanisms linking the two illnesses, and there is interest in the role that adipokines may have in mediating the association between obesity and depression. We reviewed the literature to look at what is currently known about this association, focusing on the adipokines leptin, adiponectin, and resistin. Methods. A MEDLINE search, citing articles from 1966 onward, supplemented by a review of bibliographies, was conducted to identify relevant studies. Results. This paper identified plausible pathways underlying a link between adipokines and depression. Only a few studies have yet been conducted specifically examining these biomarkers in patients with depression, but the results are intriguing. Conclusion. This paper is one of the first to examine the association between adipokines and depression. It provides an overview of the physiological role of adipokines and summarizes the data suggesting that they may be dysregulated in major depression. This area of research may become increasingly important as new treatment strategies are developed.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| 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".