Association Between Serum Vitamin D Levels and Parkinson's Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Vitamin D is an important secosteroid which is involved the development and regulation of brain activity. Several studies have focused on exploring the relationship between serum vitamin D levels and Parkinson’s disease (PD), but the conclusion remains ambiguous. Methods: We searched observational studies that explored the association between serum vitamin D levels and PD based on PubMed, EMBASE and Cochrane library from inception through to December 2017. The quality of included studies was evaluated by using Newcastle-Ottawa Scale（NOS）. Statistical analysis of this meta-analysis was performed by Stata version 12.0 and R software. Results: Twenty-one studies with a total of 3052 PD patients and 3536 controls were included. Compared with controls, PD patients had lower serum vitamin D levels (WMD -5.21, 95%CI -6.48, -3.94), especially in higher latitude regions (WMD -5.53, 95%CI -7.49, -3.57). Assay methods contributed significantly to high heterogeneity. Furthermore, PD patients with deficient vitamin D levels had advanced risk (OR 2.08, 95%CI 1.35, 3.19) than those patients with insufficient ones (OR=1.73, 95%CI 1.48, 2.03). In addition, serum vitamin D levels were also related to the severity of PD (WMD-6.31, 95%CI -9.13, -3.50) and the summary correlation coefficient showed strongly negative correlation (r=-0.61, 95%CI -0.76, -0.39). Moreover, the pooled correlation coefficient revealed that serum vitamin D levels were also negatively correlated to the Unified Parkinson’s Disease Rating Scale III (UPDRS III) (r=-0.36, 95%CI -0.53, -0.16), but did not correlate with the duration of PD (P=0.37) and age of patients (P=0.49). Conclusion: Serum vitamin D levels are inversely associated with the risk and severity of PD. Our results provided an updated evidence of association between low vitamin D levels and PD and prompt the adjunctive therapeutic decisions about vitamin D replacement in PD.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".