OP0235 PRO-INFLAMMATORY DIET AND RISK OF INCIDENT GOUT: 3 PROSPECTIVE COHORT STUDIES OF US MEN AND WOMEN

Background: Emerging evidence suggests inflammation may drive progression from hyperuricemia to clinical gout, but the role of extrinsic, modifiable sources of chronic inflammation, such as diet, on gout risk is unknown. Notably, greater dietary inflammatory potential has been independently associated with increased risk of incident cardiovascular disease (CVD) 1 and type 2 diabetes (T2D). 2 Objectives: Prospectively examine the relation between dietary inflammatory potential and risk of gout in three large cohorts of US women and men over 30 years. Methods: Ascertaining the ACR survey criteria for gout for several decades, 3 we studied gout risk among 164,090 women from Nurses Health Study I (1986-2016) and II (1989-2017) and 40,598 men from Health Professionals Follow-up Study (1986-2016), free of gout at baseline. Dietary intake and covariates were assessed by validated questionnaires every 4 years. Inflammatory potential of diet was evaluated using a food-based empirical dietary index of inflammatory potential score (EDIP) pre-defined based on circulating levels of IL-6, C-reactive protein, adiponectin, and TNFαR2. 4 We assigned an EDIP score for each participant, adjusted for total energy take, and prospectively examined the association between quintiles of EDIP score and incident gout, adjusting for potential confounders. We also stratified by alcohol intake, as alcohol has anti-inflammatory properties, 4 but is associated with a higher gout risk, particularly beer. 5 Results: We documented 2,874 incident gout cases over 5,124,940 person-years of follow-up. In pooled multivariable-adjusted analyses, those in the highest EDIP quintile had 59% higher gout risk (multivariable RR 1.59; 95% CI 1.41–1.79), compared with the lowest (Table 1 ). This remained positive with further adjustment for BMI, a likely causal intermediate (RR 1.27, 1.12 to 1.42), and was stronger among non-drinkers (RR 2.37, 1.58 to 2.56) than drinkers (RR 1.57, 1.38 to 1.78) (Table 1). Table 1. Risk Ratio (95% CI) of Gout According to Quintiles of Inflammatory Diet Score, Overall and by Alcohol Use Q1: lowest Q2 Q3 Q4 Q5: highest P for trend Overall N cases 473 493 530 623 755 Person-years 1,024,571 1,025,618 1,025,284 1,024,779 1,024,688 Age-adjusted RR 1.00 (Ref) 1.05 (0.92, 1.19) 1.13 (0.99, 1.27) 1.33 (1.18, 1.50) 1.64 (1.46, 1.84) <0.001 MV-Adjusted* RR 1.00 (Ref) 1.04 (0.92, 1.18) 1.12 (0.98, 1.26) 1.31 (1.16, 1.48) 1.59 (1.41, 1.79) <0.001 MV-Adjusted** RR (+ BMI) 1.00 (Ref) 1.00 (0.88, 1.13) 1.03 (0.91, 1.17) 1.16 (1.02, 1.31) 1.27 (1.12, 1.42) <0.001 No Alcohol Use N cases 26 58 84 143 251 Person-years 118,301 189,938 249,389 313,511 396,080 MV-Adjusted* RR 1.00 (Ref) 1.31 (0.82, 2.08) 1.37 (0.88, 2.13) 1.80 (1.18, 2.74) 2.37 (1.58, 2.56) <0.001 MV-Adjusted** RR (+ BMI) 1.00 (Ref) 1.28 (0.80, 2.03) 1.32 (0.85, 2.05) 1.61 (1.06, 2.45) 1.85 (1.23, 2.79) <0.001 Alcohol Use N cases 447 435 446 480 504 Person-years 906,271 835,680 775,895 711,267 628,609 MV-Adjusted* RR 1.00 (Ref) 1.04 (0.91, 1.19) 1.13 (0.99, 1.29) 1.31 (1.15, 1.50) 1.57 (1.38, 1.78) <0.001 MV-Adjusted** RR (+ BMI) 1.00 (Ref) 1.00 (0.88, 1.14) 1.05 (0.92, 1.20) 1.17 (1.03, 1.33) 1.28 (1.12, 1.46) <0.001 *Multivariable (MV) models adjusted for age (month), White race, smoking, menopause (women only), hormone use (women only), physical activity, history of hypertension, and diuretic use. **MV + BMI models additionally adjusted for BMI (a likely causal intermediate) Conclusion: Habitual pro-inflammatory dietary pattern was independently associated with higher risk of incident gout in these prospective cohorts, even beyond the pathway through adiposity. Our findings support a role for chronic inflammation in development of gout, similar to CVD 1 and T2D. 2 Adhering to a diet with lower inflammatory potential may modulate systemic inflammation, potentially reducing gout risk and these life-threatening comorbidities. References: [1]Li et al. J Amer Coll Cardiology (2020) PMID 33153576 [2]Lee et al. Diabetes Care (2020) PMID 32873589 [3]Wallace et al. PMID 856219 [4]Tabung et al. PMID 27358416 [5]Choi et al. PMID 15094272 Disclosure of Interests: Natalie McCormick: None declared, Chio Yokose: None declared, Leo Lu: None declared, Amit Joshi: None declared, Hyon Choi Consultant of: Ironwood, Selecta, Horizon, Takeda, Kowa, Vaxart, Grant/research support from: Ironwood, Horizon