Low vitamin D levels are not a contributing factor to higher prevalence of depressive symptoms in people with Type 2 diabetes mellitus: the Hoorn study
Bibliographic record
Abstract
AIM: To test whether a low serum 25-hydroxyvitamin D level explains the greater prevalence of depression among people with Type 2 diabetes. METHODS: We performed a cross-sectional analysis of 527 people, aged 60-87 years, who participated in a population-based cohort study. Type 2 diabetes, impaired glucose tolerance, impaired fasting glucose and normal glucose tolerance were defined according to the 2006 WHO criteria. The Centre for Epidemiologic Studies Depression questionnaire was administered, using a cut-off score of ≥ 16 to determine clinically relevant depressive symptoms. RESULTS: Logistic regression analysis confirmed that women with impaired glucose tolerance/impaired fasting glucose and people with Type 2 diabetes did have a higher risk of depressive symptoms [unadjusted odds ratios 3.66 (95% CI 1.59 to 8.43) and 3.04 (95% CI 1.57 to 5.88), respectively], compared with people with normal glucose tolerance. Serum 25-hydroxyvitamin D level was not a mediating factor in the association between impaired glucose tolerance/impaired fasting glucose or Type 2 diabetes and depressive symptoms [unstandardized indirect effect 0.001 (95% CI -0.063 to 0.079) and 0.004 (95% CI -0.025 to 0.094), respectively]. CONCLUSIONS: The study found no evidence that low vitamin D levels are a contributing factor to higher depression scores in people with Type 2 diabetes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".