Factors Associated with the Development of Depression and the Influence of Obesity on Depressive Disorders: A Narrative Review
Bibliographic record
Abstract
Depression affects several aspects of life, including socioeconomic status, relationships, behavior, emotions, and overall health. The etiology of depression is complex and influenced by various factors, with obesity emerging as a significant contributor. This narrative review aims to investigate the factors associated with the development of depression, with a particular focus on the role of obesity. The literature search was conducted on PubMed, Embase, and PsycINFO from May to July 2024. The review highlights the impact of environmental and socioeconomic conditions; lifestyle choices, including physical activity and dietary habits; stress; traumatic experiences; neurotransmitter imbalances; medical and psychological conditions; hormone fluctuations; and epigenetic factors on depression. A key emphasis is placed on the inflammatory processes linked to obesity, which may drive the bidirectional relationship between obesity and depression. The findings suggest that obesity is associated with an increased risk of depression, potentially due to chronic inflammation, neurochemical dysregulation, and the emotional and social challenges related to weight stigma and obesity management. Understanding these interconnected factors is important for developing targeted interventions to address both obesity and depression, leading to improved quality of life for those affected.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".