Long‐term lignan intake, whole grain foods, and the risk of gout: results from two prospective cohort studies
Bibliographic record
Abstract
OBJECTIVE: Multiple plant-based dietary patterns are inversely associated with gout, although the individual constituents driving this association remain unclear. Dietary lignans, a major group of phytoestrogens abundant in plant foods, are metabolized by the gut microflora and may modulate gout risk. We examined the associations between dietary lignan intake, certain whole grain foods rich in lignans, and incident gout. METHODS: We analyzed data from 122,680 individuals in the Health Professionals Follow-up Study and Nurses' Health Study. We administered a food frequency questionnaire every two to four years. We used Cox models to evaluate associations between dietary lignans, whole grain foods, and confirmed gout. RESULTS: Higher intakes of matairesinol (hazard ratio [HR] comparing extreme quintiles, 0.78; 95% confidence interval [CI], 0.69-0.90; P trend = 0.002) and secoisolariciresinol (HR, 0.78; 95% CI, 0.68-0.89; P trend = 0.002) were both associated with lower gout risk, whereas pinoresinol and lariciresinol were not associated with gout. We found inverse associations of whole grain cold breakfast cereals (HR for those consuming ≥1 serving per day, 0.62; 95% CI, 0.53-0.73), cooked oatmeal/oat bran (HR for those consuming ≥2 servings per week, 0.78; 95% CI, 0.70-0.86), and bran added to food (HR for those consuming ≥2 servings per week, 0.84; 95% CI, 0.74-0.95), but not dark breads or other cooked breakfast cereals, with gout. CONCLUSION: Higher intakes of matairesinol and secoisolariciresinol, as well as whole grain cold breakfast cereals, oatmeal, and added bran, were each significantly associated with lower gout risk. These findings support adherence to healthful plant-based diets for gout and support a potential role of the gut microbiome in gout pathogenesis.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".