Clinical management of major depressive disorder with comorbid obesity
Bibliographic record
Abstract
Obesity is one of the most prevalent somatic comorbidities in individuals with major depressive disorder and greatly affects the course and prognosis of that disorder. The bidirectional relationship between major depressive disorder and obesity often creates a feedback cycle that challenges both patients and health-care providers. Gaps in interdisciplinary collaboration and limitations in knowledge transfer hinder the effective management of this patient population. This narrative Review synthesises current evidence from obesity and major depressive disorder research, offering a comprehensive risk stratification and monitoring framework that integrates psychological and metabolic parameters to enhance clinical decision making. We examine the latest evidence on pharmacological and psychotherapeutic interventions as well as lifestyle-based strategies-such as exercise, dietary modifications, and weight-loss medications-with the aim of alleviating depressive symptoms while supporting weight management and improving metabolic health. Bariatric surgery, which is a key component in obesity management, is not covered in this Review. Finally, we highlight the crucial need for an integrated, interdisciplinary treatment approach and provide practical guidance for optimising care to improve outcomes for individuals with major depressive disorder and comorbid obesity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".