The association between depressive symptom severity and metabolic disturbances in major depressive and bipolar disorders: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Persons with depression are differentially affected by metabolic alterations, notably, insulin resistance and dyslipidemia. Metabolic alterations affect acute pharmacotherapy response and predispose risk for cardiovascular diseases. We aimed to extend knowledge pertaining to the depression-metabolic alteration association by evaluating whether depressive symptom severity moderates the association. METHODS: We conducted a systematic search of PubMed, Ovid and Scopus from inception to May 2025. Two reviewers (S.W. and G.H.L.) independently screened the identified studies. Studies were included if they enrolled adults with depression and reported on at least one metabolic parameter (i.e., fasting glucose, insulin, lipid panels). Standardized mean differences of metabolic parameters were pooled across studies. RESULTS: We identified 28 studies for inclusion. Persons with depression exhibited higher fasting glucose (SMD = 0.30, 95 % CI [0.12, 0.48]) and dyslipidemia [i.e., trends of increased low-density lipoprotein (SMD = 0.21, 95 % CI [-0.03, 0.44]) and lower high-density lipoprotein (SMD = -0.72, 95 % CI [-1.41, -0.03])]. Measures of insulin resistance were positively associated with anhedonia severity, sleep disturbances, and suicidal ideation. LIMITATIONS: Between-study methodological differences, including study design and sociodemographics, affects the synthesis of overall trends. CONCLUSION: Herein, we identify an association between depressive symptom severity and dysglycemia, dyslipidemia and insulin resistance. The results augment the conceptual framework implicating metabolic disturbances in depression pathophysiology and indirectly support testing that therapeutics currently in development in the treatment of depression (e.g., GLP-1 receptor agonists) may exhibit differential efficacy as a function of illness severity.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".