Department of Rheumatology, Korea University Anam Hospital, Korea University College of Medicine, 73, Inchon-ro, Seongbuk-gu, Seoul, 02841, South Korea.
Clin Rheumatol. 2018 Nov;37(11):3099-3105. doi: 10.1007/s10067-018-4210-3. Epub 2018 Jul 12.
This study aimed to examine whether smoking behavior is causally related to gout. Summary statistics of publicly available data from genome-wide association studies (GWAS) of smoking behavior (n = 85,997) served as the exposure dataset, while meta-analysis results of 14 studies including 2115 cases and 67,259 controls of European descent served as the outcome dataset. The data were subjected to two-sample Mendelian randomization (MR) analysis using the inverse-variance weighted (IVW), weighted median, and MR-Egger regression methods. Five single-nucleotide polymorphisms (SNPs) from GWAS of smoking behavior were selected as instrumental variables (IVs) to improve inference: CHRNA3 (rs1051730), PDE1C (rs215614), CYP2A6 (rs4105144), CHRNB3 (rs6474412), and CYP2B6 (rs7260329). The IVW data did not support a causal association between smoking behavior and gout (beta = - 0.035, SE = 0.036, p = 0.333). MR-Egger regression indicated that directional pleiotropy did not bias the result (intercept = 0.021; p = 0.560). MR-Egger analysis revealed no causal association between smoking behavior and gout (beta = - 0.074, SE = 0.070, p = 0.366). The weighted median approach did not support a causal association between smoking behavior and gout (beta = - 0.043, SE = 0.040, p = 0.279). Cochran's Q test indicated no evidence of heterogeneity between IV estimates based on individual variants. The results of "leave one out" analysis demonstrated that no single SNP drove the IVW point estimate. MR estimates using IVW, weighted median, and MR-Egger analysis were consistent and did not support a causal inverse association between smoking behavior and gout.
本研究旨在探讨吸烟行为是否与痛风有因果关系。吸烟行为的全基因组关联研究（GWAS）的公开可用数据的汇总统计数据（n=85997）作为暴露数据集，而欧洲血统的 14 项研究（包括 2115 例病例和 67259 例对照）的荟萃分析结果作为结果数据集。使用逆方差加权（IVW）、加权中位数和 MR-Egger 回归方法对两样本 Mendelian 随机化（MR）分析进行了数据处理。从吸烟行为的 GWAS 中选择了五个单核苷酸多态性（SNP）作为工具变量（IVs）以提高推断的可信度：CHRNA3（rs1051730）、PDE1C（rs215614）、CYP2A6（rs4105144）、CHRNB3（rs6474412）和 CYP2B6（rs7260329）。IVW 数据不支持吸烟行为与痛风之间存在因果关系（beta=-0.035，SE=0.036，p=0.333）。MR-Egger 回归表明，方向性偏倚没有影响结果（截距=0.021；p=0.560）。MR-Egger 分析表明，吸烟行为与痛风之间没有因果关系（beta=-0.074，SE=0.070，p=0.366）。加权中位数方法也不支持吸烟行为与痛风之间存在因果关系（beta=-0.043，SE=0.040，p=0.279）。Cochran's Q 检验表明，基于个体变体的 IV 估计值之间没有异质性的证据。“逐一剔除”分析的结果表明，没有单个 SNP 驱动 IVW 点估计。基于 IVW、加权中位数和 MR-Egger 分析的 MR 估计值是一致的，并且不支持吸烟行为与痛风之间存在因果负相关关系。
