Interactions between vitamin D deficiency and inflammation on diabetes risk: data from 336,500 UK Biobank adults
Bibliographic record
Abstract
OBJECTIVES: Findings regarding the effects of vitamin D supplementation on diabetes risk are inconclusive. Because inflammation and vitamin D levels are interconnected, we hypothesized that higher inflammation levels moderate the effects of vitamin D deficiency on diabetes risk. DESIGN, SETTING, PARTICIPANTS, AND MEASUREMENTS: UK Biobank participants without pre-existing diabetes at baseline were included (N = 336,500). We first linked vitamin D and C-reactive protein (CRP; inflammation measure) levels with incident diabetes during a mean follow-up of 13.5 years (SD = 1.9). Then, we investigated the moderation effect of CRP on the associations between vitamin D deficiency (<10 ng/mL) and incident diabetes and performed subgroup analyses according to age (<60 vs. ≥60 years) and frailty status (frail; pre-frail; non-frail). Multivariate analyses were conducted using restricted cubic spline Cox proportional hazards regression models. RESULTS: Lower vitamin D and higher CRP levels were significantly associated with an increased risk of diabetes during follow-up. There was a significant interaction between vitamin D deficiency and CRP on incident diabetes (p < 0.001). In participants with higher levels of CRP, the hazard ratio of developing diabetes comparing participants who had vitamin D deficiency to those who did not was lower than that in participants with lower levels of CRP. The moderation effect of CRP was similar between younger and older adults but was stronger in frail or pre-frail older adults than in non-frail older adults. CONCLUSION: Our findings indicate that the effect of vitamin D deficiency on incident diabetes may be affected by inflammation. This finding may explain the inconsistent results from vitamin D supplementation trials. Vitamin D supplementation without considering the potential impact of inflammation might prove unsatisfactory.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".