Modifiable Factors and Incident Gout Across Ethnicity Within a Large Multiethnic Cohort of Older Adults
Bibliographic record
Abstract
Objective Gout disproportionately affects older Pacific Islander and Black populations relative to White populations. However, the ethnic-specific determinants remain understudied within these groups, as well as within other ethnicities. We examined gout incidence and associations with behavioral factors, including diet, alcohol, and smoking, within a large multiethnic population of older adults from the Multiethnic Cohort Study, which linked prospective cohort data to Medicare gout claims between 1999–2016. Methods Using samples of Black (n = 12,370), Native Hawaiian (n = 6459), Japanese (n = 29,830), Latino (n = 17,538), and White (n = 26,067) participants, we conducted multiple Cox regressions, producing hazard ratios (HRs) and 95% CIs. Results Relative to White individuals, Native Hawaiians had the highest risk of gout (HR 2.21, 95% CI 2.06–2.38), followed successively by Black and Japanese participants, whereas Latino individuals had a lower risk of gout (HR 0.78, 95% CI 0.73–0.83). Alcohol use was associated with an increased risk, with significantly greater effects observed among Japanese participants drinking ≥ 3 drinks per day (HR 1.46, 95% CI 1.27–1.66), or > 5 beers per week (HR 1.29, 95% CI 1.17–1.43), compared to White individuals (Pinteraction < 0.001). Former smokers with ≥ 20 pack-years had an increased risk (HR 1.14, 95% CI 1.06–1.22). Higher dietary quality was associated with a decreased gout risk, with the largest effect observed among White participants (HRQ5vsQ1 0.84, 95% CI 0.79–0.90), whereas vitamin C was weakly associated with a decreased risk of gout only among Japanese individuals (HR 0.91, 95% CI 0.85–0.98). Conclusion Overall, notable ethnic differences were observed in both gout risk and associations with modifiable behavioral factors. Our findings offer crucial insights that may improve precision in preventing and managing gout.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".