Proinflammatory Dietary Pattern and the Risk of Female Gout: Sex‐Specific Findings From Three Prospective Cohort Studies
Bibliographic record
Abstract
OBJECTIVE: We aimed to determine whether a proinflammatory dietary pattern (mechanism-based diet) is associated with incident female gout in two large cohorts of US women. METHODS: We prospectively observed 79,104 women from the Nurses' Health Study (1984-2016) and 94,382 women from the Nurses' Health Study II (1991-2017); 45,445 men from the Health Professionals Follow-up Study (1986-2016) served as a comparison cohort. Validated food frequency questionnaires were used to calculate Empirical Dietary Inflammatory Pattern (EDIP) (food-based index predictive of circulating inflammatory biomarkers) scores every four years. Cox proportional hazards models were used to evaluate multivariable associations between EDIP and incident, physician-diagnosed gout. We further tested whether these associations were independent of key guideline-based healthy eating patterns that previously showed beneficial associations with gout (ie, Dietary Approaches to Stop Hypertension [DASH], Alternative Healthy Eating Index [AHEI]). RESULTS: We documented 5,425 incident female gout cases over 4,372,243 person-years. EDIP was positively associated with female gout risk; hazard ratio (HR) (95% confidence interval [CI]) for the most versus least proinflammatory quintile was 2.02 (1.83-2.22). Additional adjustment for body mass index attenuated this association (HR 1.71 [95% CI 1.55-1.88]). Reversing the EDIP score, women in the most anti-inflammatory EDIP quintile had the largest magnitude of protective association for gout (HR 0.58 [95% CI 0.53-0.65]) compared with HRs for healthiest DASH (HR 0.80 [95% CI 0.73-0.87]) and AHEI quintiles (HR 0.81 [95% CI 0.74-0.89]). The magnitude of association between EDIP and male gout was substantially smaller (HR 1.24 [95% CI 1.13-1.37]; P-heterogeneity <0.0001). CONCLUSION: These findings support a pivotal role of inflammation as a potential modulator between diet and gout onset, particularly among women.
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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) | 0.000 | 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 | 0.001 |
| 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".