Causal determinants of gout in 614,000 adults

Gout affects over 55 million people worldwide, with prevalence projected to rise by 70% by 2050. Although observational studies have implicated several dietary, lifestyle, and metabolic risk factors, causal relationships remain uncertain because of confounding and reverse causation. Univariable and multivariable two-sample Mendelian randomization (MR) analyses were performed using genome-wide association data from 2 European cohorts: UK Biobank (6543 self-reported gout cases and 456,390 controls) and FinnGen (3576 cases and 147,221 controls). Genetic instruments for 12 exposures, including dried fruit, salad, and cheese intake; smoking initiation; physical activity; body mass index (BMI); and lipid traits, were derived from established genome-wide association study datasets. Inverse-variance weighted regression with multiplicative random effects was the primary analysis, complemented by weighted median, weighted mode, MR-Egger, MR-Pleiotropy RESidual Sum and Outlier, and 3 predefined multivariable models. Benjamini-Hochberg correction was applied across all 24 tests. BMI showed the strongest and most consistent causal effect on gout (FinnGen: odds ratio [OR] = 1.97, 95% confidence interval [CI] = 1.51–2.57; UK Biobank: OR = 1.006, 95% CI = 1.003–1.009; both P < .001) and remained significant in 5 of 6 multivariable models. Triglycerides increased risk in both cohorts (FinnGen: OR = 1.34, 95% CI = 1.08–1.66; UK Biobank: OR = 1.008, 95% CI = 1.004–1.012). These associations survived Benjamini-Hochberg correction, as did high-density lipoprotein cholesterol in UK Biobank (OR = 0.997, 95% CI = 0.994–0.999). Dried fruit intake was inversely associated with gout in FinnGen (OR = 0.34, 95% CI = 0.13–0.92, P = .034) but not in UK Biobank, and did not survive correction for multiple testing. No other exposure reached significance in either cohort. This study provides genetic evidence that BMI is the dominant modifiable causal determinant of gout, with triglycerides contributing independently. Dietary associations were weaker and did not withstand correction for multiple testing, and should be regarded as hypothesis-generating. These findings support prioritizing weight management and metabolic health in gout prevention.