Genetic risk scores, sex and dietary factors interact to alter serum uric acid trajectory among African-American urban adults

Abstract

Serum uric acid (SUA), a causative agent for gout among others, is affected by both genetic and dietary factors, perhaps differentially by sex. We evaluated cross-sectional (SUAbase) and longitudinal (SUArate) associations of SUA with a genetic risk score (GRS), diet and sex. We then tested the interactive effect of GRS, diet and sex on SUA. Longitudinal data on 766 African-American urban adults participating in the Healthy Aging in Neighborhood of Diversity across the Lifespan study were used. In all, three GRS for SUA were created from known SUA-associated SNP (GRSbase (n 12 SNP), GRSrate (n 3 SNP) and GRStotal (n 15 SNP)). Dietary factors included added sugar, total alcohol, red meat, total fish, legumes, dairy products, caffeine and vitamin C. Mixed-effects linear regression models were conducted. SUAbase was higher among men compared with that among women, and increased with GRStotal tertiles. SUArate was positively associated with legume intake in women (γ=+0·14; 95 % CI +0·06, +0·22, P=0·001) and inversely related to dairy product intake in both sexes combined (γ=-0·042; 95 % CI -0·075, -0·009), P=0·010). SUAbase was directly linked to alcohol consumption among women (γ=+0·154; 95 % CI +0·046, +0·262, P=0·005). GRSrate was linearly related to SUArate only among men. Legume consumption was also positively associated with SUArate within the GRStotal's lowest tertile. Among women, a synergistic interaction was observed between GRSrate and red meat intake in association with SUArate. Among men, a synergistic interaction between low vitamin C and genetic risk was found. In sum, sex-diet, sex-gene and gene-diet interactions were detected in determining SUA. Further similar studies are needed to replicate our findings.

Keywords: AA African-American; GRS genetic risk score; HANDLS Healthy Aging in Neighborhoods of Diversity Across the Lifespan; SLC2A9 solute carrier family 2 (facilitated GLUT); SUA serum uric acid; UA uric acid; member 9; African-Americans; Diets; Genetic risk scores; Serum uric acid; Urban adults.

Fig. 1

Predictive margins of serum uric acid (SUA) by time and tertiles (T) of genetic risk scores (GRS), (a) GRS_base and (b) GRS_rate, from mixed-effects regression model, total population. Predictive margins obtained from mixed-effects regression model with SUA as the outcome, random effects added to slope and intercept, and both slopes and intercept adjusted for multiple factors including age, sex, poverty status, marital status, education, smoking and drug use, several dietary factors, BMI, ten principal components for population structure and an inverse Mills ratio. The figure simulates the trajectory of a population with comparable characteristics (covariates set at their observed values in the sample) when exposed alternatively to T1, T2 and T3 of GRS_base and GRS_rate, respectively (see Table 3, model 1). (a): –––, GRS_base, T1; ------, GRS_base, T2; …………, GRS_base, T3; (b): –––, GRS_rate, T1; -----, GRS_rate, T2; …………, GRS_rate, T3. Tertiles of GRS_base had the following distribution: T1 (n 258, mean 7.80, sd 1.95, range 2–10); T2 (n 279, mean 11.76, sd 0.94, range 10–13); T3 (n 229, mean 15.18, sd 1.28, range 13–19). Tertiles of GRS_rate had the following distribution: T1 (n 325, mean 0.68, sd 0.46, range 0–1); T2 (n 291, mean 1.85, sd 0.34, range 1–2); T3 (n 150, mean 2.88, sd 0.61, range 2–5).