Department of Cardiovascular Medicine, The Third Xiangya Hospital, Central South University, Changsha, China.
Department of Geriatric Medicine, Center of Coronary Circulation, Xiangya Hospital, Central South University, Changsha, China.
Clin Genet. 2024 Jul;106(1):72-81. doi: 10.1111/cge.14513. Epub 2024 Feb 26.
Considering the high prevalence and poor prognosis of cardiometabolic multimorbidity (CMM), identifying causal factors and actively implementing preventive measures is crucial. However, Mendelian randomization (MR), a key method for identifying the causal factors of CMM, requires knowledge of the effects of SNPs on CMM, which remain unknown. We first analyzed the genetic overlap of single cardiometabolic diseases (CMDs) using the latest genome-wide association study (GWAS) for evidential support and comparison. We observed strong positive genetic correlations and shared loci among all CMDs. Further, GWAS and post-GWAS analyses of CMM were performed in 407 949 European ancestry individuals from the UK Biobank. Eleven loci and 12 lead SNPs were identified. By comparison, we found these SNPs were a subset of SNPs associated with CMDs, including both shared and non-shared SNPs. Then, the polygenic risk score model predicted the risk of CMM (C-index = 0.62) and we identified candidate genes related to lipid metabolism and immune function. Finally, as an example, two-sample MR analysis based on the GWAS revealed potential causal effects of total cholesterol, serum urate, body mass index, and smoking on CMM. These results provide a basis for future MR research and inspire future studies on the mechanism and prevention of CMM.
考虑到心脏代谢性多重疾病（CMM）的高患病率和预后不良，确定因果因素并积极采取预防措施至关重要。然而，孟德尔随机化（MR）是确定 CMM 因果因素的关键方法，它需要了解 SNP 对 CMM 的影响，但这方面的知识尚不清楚。我们首先使用最新的全基因组关联研究（GWAS）来分析单一心血管代谢疾病（CMD）的遗传重叠，以提供证据支持和比较。我们观察到所有 CMD 之间存在强烈的正遗传相关性和共同的遗传位点。此外，在来自英国生物库的 407949 名欧洲血统个体中，对 CMM 进行了 GWAS 和 GWAS 后分析。鉴定出 11 个基因座和 12 个先导 SNP。相比之下，我们发现这些 SNP 是与 CMD 相关的 SNP 的一个子集，包括共享和非共享的 SNP。然后，多基因风险评分模型预测了 CMM 的风险（C 指数=0.62），并鉴定出与脂质代谢和免疫功能相关的候选基因。最后，作为一个例子，基于 GWAS 的两样本 MR 分析揭示了总胆固醇、血清尿酸、体重指数和吸烟对 CMM 的潜在因果影响。这些结果为未来的 MR 研究提供了基础，并激发了对 CMM 机制和预防的进一步研究。
