Vitamin D levels, prediabetes risk and hemoglobin A1c levels in young non‐diabetic Saudi women
Bibliographic record
Abstract
AIMS/INTRODUCTION: Vitamin D levels are negatively correlated with prediabetes risk and hemoglobin A1c levels in individuals with prediabetes. The data are, however, scarce and inconsistent among different populations. We aimed to assess the association of vitamin D with prediabetes risk and hemoglobin A1c levels in young Saudi women with normoglycemia and prediabetes. MATERIALS AND METHODS: We analyzed the data of individuals without diabetes (without diabetes history and hemoglobin A1c <6.4%) from the Princess Nourah bint Abdulrahman University's non-communicable diseases student registry. Demographic data, anthropometric and blood pressure measurements, and hemoglobin A1c and vitamin D results were retrieved and analyzed. RESULTS: In total, 345 participants were included in the analysis. The prediabetes status showed no association with vitamin D levels, but it was significantly associated with the participants' weight and body mass index. Additionally, there was no correlation between the levels of vitamin D and hemoglobin A1c across the whole population, even after correction for body mass index. However, in the body mass index subgroups, when individuals with potentially harmful levels of vitamin D (>125 nmol/L) were excluded, a positive association was detected between vitamin D and hemoglobin A1c levels in the underweight individuals. Hemoglobin A1c values showed a positive correlation only with bodyweight and body mass index. CONCLUSIONS: Vitamin D levels did not predict prediabetes status and showed no correlation with hemoglobin A1c levels in this population. Vitamin D levels' effect on the risk of prediabetes might be small compared with other well-established risk factors, such as obesity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".