Treating obesity in patients with depression: a narrative review and treatment recommendation
Bibliographic record
Abstract
The high morbidity of obesity and depression pose significant public health concerns, with the prevalence of obesity doubling in the US between 1990 and 2022 and patients frequently presenting with both. Untreated obesity and depression can greatly impact patient health and well-being, as both obesity and depression are associated with a number of comorbidities including sleep apnea, type 2 diabetes mellitus, metabolic syndrome, metabolic dysfunction-associated steatotic liver disease, and cardiovascular disease. This narrative review aims to provide a comprehensive and current overview of the overlapping etiologies between obesity and depression as well as the available treatment options that may be recommended by primary care professionals to treat these patients with concomitant obesity and depression. With the considerable overlap in the population of patients with obesity and depression, as well as the overlap in the neurobiological, hormonal, and inflammatory pathways underlying both diseases, primary care professionals should consider screening patients presenting with obesity for depression. Holistic treatment options, including lifestyle and behavioral modifications, and pharmacotherapy for both depression and obesity and bariatric surgery for obesity are critical to manage both conditions simultaneously. Therefore, due to the overlapping neurobiological pathways and mechanisms responsible for the incidence and progression of both obesity and depression, a holistic treatment plan including strategies with efficacy for both conditions and any additional comorbidities may improve the clinical approach for patients with concomitant obesity and depression.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".