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Increased genetic risk for obesity in premature coronary artery disease

机译:肥胖导致冠状动脉疾病的遗传风险增加

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There is ongoing controversy as to whether obesity confers risk for CAD independently of associated risk factors including diabetes mellitus. We have carried out a Mendelian randomization study using a genetic risk score (GRS) for body mass index (BMI) based on 35 risk alleles to investigate this question in a population of 5831 early onset CAD cases without diabetes mellitus and 3832 elderly healthy control subjects, all of strictly European ancestry, with adjustment for traditional risk factors (TRFs). We then estimated the genetic correlation between these BMI and CAD (rg) by relating the pairwise genetic similarity matrix to a phenotypic covariance matrix between these two traits. GRSBMI significantly (P=2.12 x 10(-12)) associated with CAD status in a multivariate model adjusted for TRFs, with a per allele odds ratio (OR) of 1.06 (95% CI 1.042-1.076). The addition of GRSBMI to TRFs explained 0.75% of CAD variance and yielded a continuous net recombination index of 16.54% (95% CI=11.82-21.26%, P<0.0001). To test whether GRSBMI explained CAD status when adjusted for measured BMI, separate models were constructed in which the score and BMI were either included as covariates or not. The addition of BMI explained similar to 1.9% of CAD variance and GRSBMI plus BMI explained 2.65% of CAD variance. Finally, using bivariate restricted maximum likelihood analysis, we provide strong evidence of genome-wide pleiotropy between obesity and CAD. This analysis supports the hypothesis that obesity is a causal risk factor for CAD.
机译:关于肥胖是否赋予CAD风险独立于包括糖尿病在内的相关风险因素,一直存在争议。我们进行了孟德尔随机研究,使用基于35个风险等位基因的体重指数(BMI)的遗传风险评分(GRS),对5831例无糖尿病的早发性CAD患者和3832例老年健康对照者进行了调查。 ,严格按照欧洲血统进行,并针对传统风险因素(TRF)进行了调整。然后,我们通过将成对遗传相似性矩阵与这两个性状之间的表型协方差矩阵相关联,来估算这些BMI和CAD(rg)之间的遗传相关性。在校正了TRF的多变量模型中,GRSBMI与CAD状态显着相关(P = 2.12 x 10(-12)),每等位基因比值比(OR)为1.06(95%CI 1.042-1.076)。在TRF中添加GRSBMI可以解释CAD差异的0.75%,并产生16.54%的连续净重组指数(95%CI = 11.82-21.26%,P <0.0001)。为了测试GRSBMI在针对测量的BMI进行调整时是否能够解释CAD状态,构建了单独的模型,其中将得分和BMI包括或不包括为协变量。 BMI的增加解释了CAD差异的1.9%,而GRSBMI加BMI解释了CAD差异的2.65%。最后,使用双变量限制最大似然分析,我们提供了肥胖与CAD之间全基因组多效性的有力证据。该分析支持以下假设:肥胖是CAD的因果风险因素。

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