Methyl Donor Nutrient Intake and Incidence of Type 2 Diabetes: Results From Three Large U.S. Cohorts
Bibliographic record
Abstract
OBJECTIVE: We examined whether intake of methyl donor nutrients, including vitamins B2, B6, and B12 and folate, from foods and/or supplements is associated with type 2 diabetes risk. RESEARCH DESIGN AND METHODS: We included 203,644 women and men from the Nurses' Health Study (1984-2016), Nurses' Health Study 2 (1991-2017), and Health Professionals Follow-Up Study (1986-2016). Dietary data were collected every 2-4 years with use of semiquantitative food-frequency questionnaires. Cox proportional hazards models with time-varying covariates were used to evaluate associations between each nutrient and type 2 diabetes risk. We combined cohort-specific hazard ratios (HRs) using inverse variance-weighted fixed-effects meta-analyses. RESULTS: During 4,900,181 person-years of follow-up, we documented 19,475 incident type 2 diabetes cases. In multivariable-adjusted meta-analyses, participants in the highest quintiles of total vitamin B2 and B6 intakes had lower risk of diabetes compared with those in the lowest quintiles (HR 0.93 [95% CI 0.89, 0.98] for B2 and 0.93 [0.89, 0.97] for B6). With stratification by source, significant associations remained for B2 from food but not from supplements. Neither association for B6 from food nor association for B6 from supplements attained significance. No association was observed between total B12 intake and diabetes. However, B12 from food was marginally associated with higher diabetes risk (1.05 [1.00-1.11]) but not after additional adjustment for red meat intake (1.04 [0.99-1.10]). No evidence of association was observed between intakes of folate and diabetes. CONCLUSIONS: The results of our study suggest that higher intake of vitamin B2 and B6, especially B2 from food sources, may be associated with a modestly lower type 2 diabetes risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".