Role of Vitamin D in the Pathophysiology and Treatment of Type 2 Diabetes
Bibliographic record
Abstract
The secosteroid vitamin D is best known for its role in calcium regulation and bone metabolism. Recently, however, an emerging body of evidence has suggested that vitamin D may have previously-unrecognized effects on a variety of physiologic processes, including those relating to glucose homeostasis. Indeed, vitamin D insufficiency has been linked with type 2 diabetes (T2DM). In this review, the potential association between vitamin D and T2DM will be evaluated from both a pathophysiologic and clinical perspective. We consider the biologic evidence in support of a mechanistic contribution of vitamin D insufficiency to insulin resistance and beta-cell dysfunction, the two main pathophysiologic defects underlying T2DM. We also evaluate the clinical data linking vitamin D with these metabolic defects and dysglycemia. Finally, interventional studies addressing the effect of vitamin D supplementation on glucose homeostasis are considered. At present, this evolving literature is marked by many conflicting results and methodologic limitations, such that definitive conclusion on the role of vitamin D in T2DM remains elusive. Nevertheless, in light of the widespread prevalence of both vitamin D insufficiency and T2DM, this potential relationship could hold enormous public health implications and hence demands further study to address its unresolved questions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".