Vitamin D and inflammation in the prevention of type 2 diabetes: public health relevance
Bibliographic record
Abstract
The incidence of type 2 diabetes mellitus (T2DM) is increasing worldwide. To reduce the disease risk and burden at the population level, preventative strategies should be developed with minimal cost and effort and with no side-effects. Low-grade inflammation resulting from imbalances in the innate immune system has been associated with an array of chronic disorders that predispose to the later development of T2DM (e.g., obesity, metabolic syndrome, and insulin resistance). As a result, inflammation may contribute to the pathogenesis of T2DM. Therefore, attenuation of this inflammatory response via modulating the innate immune system could lead to improved insulin sensitivity and delayed disease onset. Dietary supplementation with vitamin D may represent a novel strategy toward the prevention and control of T2DM at the population level due to its anti-inflammatory and antioxidant properties. This review examines current knowledge linking T2DM to chronic low-grade inflammation and the role of vitamin D in modulating this relationship. The concept that vitamin D, via attenuating inflammation, could be employed as a novel preventive measure for T2DM is evaluated in the context of its relevance to health care and public health practices.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".