Vitamin D Is Associated With Susceptibility and Disease Severity in Systemic Lupus Erythematosus: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVES: Vitamin D, recognized for its immunomodulatory properties, is potentially associated with autoimmune diseases like systemic lupus erythematosus (SLE). This systematic review and meta-analysis assessed the relationship between Vitamin D levels and SLE, highlighting its role in modulating disease activity and immunological markers like anti-dsDNA, C3, and C4. METHODS: Case-control studies reporting Vitamin D, disease activity indices, C3, C4, and anti-dsDNA antibody levels in SLE patients and healthy controls were evaluated. Systematic searches in PubMed, Scopus, ScienceDirect, Web of Science, and Embase were last updated on April 6, 2024. Inclusion criteria targeted studies on SLE patients and healthy controls reporting these specific markers. Study quality was assessed with the Newcastle-Ottawa Scale (NOS), and publication bias was checked using Egger's test and Begg's funnel plot. The meta-analyses were conducted with CMAv4. RESULTS: Analysis of 43 studies with 2940 SLE patients and 2458 healthy controls showed significantly lower Vitamin D levels in SLE patients (mean difference: -10.070 ng/mL; 95% CI: -12.85 to -7.28; p < 0.001). Vitamin D levels negatively correlated with SLEDAI scores (correlation: -0.427; 95% CI: -0.541 to -0.298; p < 0.001) and anti-dsDNA antibodies (correlation: -0.397; 95% CI: -0.611 to -0.130; p = 0.004), and positively correlated with complement components C3 (correlation: 0.268; 95% CI: 0.077-0.440; p = 0.006) and C4 (correlation: 0.299; 95% CI: 0.192-0.400; p < 0.001). Sensitivity analyses confirmed these findings. CONCLUSIONS: The findings revealed a strong association between low Vitamin D levels and increased SLE severity. However, limitations like some inconsistency, small-study biases, and possible language restrictions in study inclusion necessitate cautious interpretation.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".