Adiposity in schizophrenia: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Objective Although a relationship between schizophrenia (SCZ), antipsychotic (AP) medication, and metabolic dysregulation is now well established, the effect of adiposity is less well understood. By synthesizing findings from imaging techniques that measure adiposity, our systematic review and meta‐analysis (PROSPERO CRD42020192977) aims to determine the adiposity‐related effects of illness and treatment in this patient population. Methods We searched MEDLINE, EMBASE, PsychINFO and Scopus for all relevant case‐control and prospective longitudinal studies from inception until February 2021. Measures of adiposity including percent body fat (%BF), subcutaneous adipose tissue (SAT), and visceral adipose tissue (VAT) were analyzed as primary outcomes. Results Our search identified 29 articles that used imaging methods to quantify adiposity among patients with SCZ spectrum disorders. Analyses revealed that patients have greater %BF (mean difference (MD) = 3.09%; 95% CI: 0.75–5.44), SAT (MD = 24.29 cm 2 ; 95% CI: 2.97–45.61) and VAT (MD = 33.73 cm 2 , 95% CI: 4.19–63.27) compared to healthy controls. AP treatment was found to increase SAT (MD = 31.98 cm 2 ; 95% CI: 11.33–52.64) and VAT (MD = 16.30 cm 2 ; 95% CI: 8.17–24.44) with no effect on %BF. However, change in %BF was higher for AP‐free/AP‐naïve patients compared to treated patients. Conclusion Our findings indicate that patients with SCZ spectrum disorders have greater adiposity than healthy controls, which is increased by AP treatment. Young, AP‐naïve patients may be particularly susceptible to this effect. Future studies should explore the effect of specific APs on adiposity and its relation to overall metabolic health.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".