Reynolds Richard J, Vazquez Ana I, Srinivasasainagendra Vinodh, Klimentidis Yann C, Bridges S Louis, Allison David B, Singh Jasvinder A
Division of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at Birmingham, Faculty Office Tower 805B, 510 20th Street S, Birmingham, AL, 35294, USA.
Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA.
Rheumatol Int. 2016 Feb;36(2):263-70. doi: 10.1007/s00296-015-3364-4. Epub 2015 Oct 1.
We hypothesized that serum urate-associated SNPs, individually or collectively, interact with BMI and renal disease to contribute to risk of incident gout. We measured the incidence of gout and associated comorbidities using the original and offspring cohorts of the Framingham Heart Study. We used direct and imputed genotypes for eight validated serum urate loci. We fit binomial regression models of gout incidence as a function of the covariates, age, type 2 diabetes, sex, and all main and interaction effects of the eight serum urate SNPs with BMI and renal disease. Models were also fit with a genetic risk score for serum urate levels which corresponds to the sum of risk alleles at the eight SNPs. Model covariates, age (P = 5.95E-06), sex (P = 2.46E-39), diabetes (P = 2.34E-07), BMI (P = 1.14E-11) and the SNPs, rs1967017 (P = 9.54E-03), rs13129697 (P = 4.34E-07), rs2199936 (P = 7.28E-03) and rs675209 (P = 4.84E-02) were all associated with incident gout. No BMI by SNP or BMI by serum urate genetic risk score interactions were statistically significant, but renal disease by rs1106766 was statistically significant (P = 6.12E-03). We demonstrated that minor alleles of rs1106766 (intergenic, INHBC) were negatively associated with the risk of incident gout in subjects without renal disease, but not for individuals with renal disease. These analyses demonstrate that a significant component of the risk of gout may involve complex interplay between genes and environment.
我们假设,与血清尿酸相关的单核苷酸多态性(SNPs),无论单独还是共同作用,都会与体重指数(BMI)和肾脏疾病相互作用,从而增加痛风发病风险。我们利用弗雷明汉心脏研究的原始队列和子代队列,测量了痛风及相关合并症的发病率。我们使用了8个经过验证的血清尿酸位点的直接基因型和推算基因型。我们构建了二项式回归模型,将痛风发病率作为协变量、年龄、2型糖尿病、性别以及8个血清尿酸SNPs与BMI和肾脏疾病的所有主要及交互作用的函数。模型还采用了血清尿酸水平的遗传风险评分进行拟合,该评分对应于8个SNPs处风险等位基因的总和。模型协变量、年龄(P = 5.95E - 06)、性别(P = 2.46E - 39)、糖尿病(P = 2.34E - 07)、BMI(P = 1.14E - 11)以及SNPs,即rs1967017(P = 9.54E - 03)、rs13129697(P = 4.34E - 07)、rs2199936(P = 7.28E - 03)和rs675209(P = 4.84E - 02)均与痛风发病相关。SNPs与BMI之间或血清尿酸遗传风险评分与BMI之间的交互作用均无统计学意义,但rs1106766与肾脏疾病之间的交互作用具有统计学意义(P = 6.12E - 03)我们证明,rs1106766(基因间,INHBC)的次要等位基因与无肾脏疾病受试者的痛风发病风险呈负相关,但在有肾脏疾病的个体中并非如此。这些分析表明,痛风风险的一个重要组成部分可能涉及基因与环境之间的复杂相互作用。