Vitamin D Deficiency as a Risk Factor for Schizophrenia: A meta-analysis of Observational Studies
Bibliographic record
Abstract
Abstract Background: Schizophrenia is a heterogeneous disorder in which there is interaction between genetic and environmental factors. Study have found that vitamin D deficiency is a risk factor for schizophrenia. We conducted a meta-analysis to investigate the relationship between schizophrenia and blood vitamin D level.Methods: A systematic review and meta-analysis have been conducted. All published observational articles have been searched from PubMed, Web of Science, Scopus, Cochrane library and Embase until September 2019. The Newcastle-Ottawa scale was used to examine the quality of the included studies.Results: A total of 38 articles have been included in this study. The lower level of vitamin D was found in people with schizophrenia compared with controls (WMD =-4.50, 95%CI [-6.69, -2.32]). Subgroup analyses based on study design, the patient’s hospitalization status, quality of the study, outcomes, and the country explained part of between-study heterogeneity. No significant differences in publication bias were observed. The overall prevalence of vitamin D deficiency and insufficiency in schizophrenic patients were 66%, 95%CI [57%–76%] and 76%, 95%CI [69%, 83%], respectively. Subjects with schizophrenia were more likely to have vitamin D deficiency or vitamin D insufficiency compared to controls (2.03, 95%CI [1.49, 2.77]; 2.43, 95%CI [1.40, 4.23]).Conclusion: The results of this systematic review show that low vitamin D levels might contribute to the development of schizophrenia. The prevalence of vitamin D deficient or insufficient in schizophrenia was higher than that in healthy controls or another psychotic disease .
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.061 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".