Evaluating the Effect of Vitamin D Supplementation on Type 2 Diabetes Risk: A Systematic Review
Bibliographic record
Abstract
Although observational studies have linked vitamin D deficiency to diabetes, it is unknown if taking vitamin D supplements can reduce the chance of developing type 2 diabetes mellitus (T2DM). The purpose of this systematic review is to determine whether vitamin D supplementation lowers the risk of type 2 diabetes. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used to search for studies based on pre-established inclusion and exclusion criteria. Five different databases (Scopus, Web of Science, PubMed, Google Scholar, and Cochrane Library) were used to search for relevant studies. These databases contained 1,916 relevant research papers that were examined for duplication using the EndNote software. After a careful text analysis, only 13 of these articles were found to be pertinent. The Newcastle-Ottawa Scale (NOS) was used to assess the risk of bias in each included study. Ten of the 13 trials examined how vitamin D supplementation affected the homeostasis model assessment of insulin resistance (HOMA-IR) of the patients. Patients using vitamin D supplements saw a drop in their HOMA-IR in six of these RCTs but an increase in four of them. The vitamin D group had a lower HOMA-IR than the placebo cohort in seven of the 10 trials that examined HOMA-IR. Despite the impact on insulin resistance, there is not enough data to conclude that vitamin D supplement significantly lowers the incidence of T2DM. To impact clinical guidelines about supplementation with vitamin D among people at risk of type 2 diabetes, more studies in this area might be beneficial.
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.016 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".