目的: 基于国际疾病分类(International Classification of Diseases,ICD)第10版(ICD-10),通过分子流行病学的方法探索心血管疾病(cardiovascular diseases,CVD)主要亚型分类的分子生物学依据。方法: 研究所采用的表型数据和基因型数据均来源于英国生物样本库(UK Biobank,UKB)。共纳入年龄在40~69岁的380 083个样本,其中对照组是没有任何心血管疾病(不具有以字母I开头的ICD-10代码)的246 437个样本,心血管疾病五大亚型分别为:(1)缺血性心脏病(ischaemic heart diseases,IHD);(2)肺源性心脏病和肺循环疾病(pulmonary heart disease and diseases of pulmonary circulation,PHD);(3)脑血管疾病(cerebrovascular diseases,CRB);(4)动脉、小动脉和毛细血管疾病(diseases of arteries, arterioles and capillaries,AAC);(5)静脉、淋巴管和淋巴结疾病,不可归类在他处者(diseases of veins, lymphatic vessels and lymph nodes, not elsewhere classified,VLL)。本研究首先对五大亚型分别进行全基因组关联分析(genome-wide association study, GWAS);然后,基于GWAS分析结果对五大亚型两两之间进行连锁不平衡回归分析(linkage disequilibrium score regression,LDSC)来计算亚型之间的遗传相关性;最后,对每一对亚型进行孟德尔随机化分析(Mendelian randomizatoin,MR)来评估亚型之间的因果联系。结果: GWAS研究新发现28个显著性基因位点。LDSC分析显示IHD分别和VLL (P=2.52×10^-7)、PHD (P=3.77×10^-3)、AAC (P=4.90×10^-3) 这3个亚型具有显著的遗传相关性。MR分析显示IHD对VLL (P=7.40×10^-5) 和AAC (P=1.50×10^-3) 这两个疾病的风险增加有正向因果关系,而反向因果皆不成立。结论: 通过分子流行病学的方法,本研究发现基于ICD-10分类的一部分心血管疾病亚型之间存在遗传相关性和因果关联,对新版ICD标准的制定提供依据。

Objective: To study the molecular connection among cardiovascular diseases (CVD) subtypes defined by the International Classification of Diseases (ICD) version 10 (ICD-10). Methods: Both phenotypic data and genotypic data used in this study were obtained from the UK Biobank. A total of 380 083 participants aged between 40 and 69 years were included. Those without any cardiovascular disease (either no ICD-10 code at all or no ICD-10 code containing letter I) were assigned to the control group. The five CVD subtypes were: ischaemic heart diseases (IHD), pulmonary heart disease and diseases of pulmonary circulation (PHD), cerebrovascular diseases (CRB), diseases of arteries, arterioles and capillaries (AAC), diseases of veins, lymphatic vessels and lymph nodes, and diseases not elsewhere classified (VLL). We first performed a genome-wide association study (GWAS) for each of the five subtypes. We summarized novel loci using genome-wide significance threshold P=5×10^-8. Next, we used linkage disequilibrium score regression (LDSC) method to assess genetic correlation among the five subtypes. Lastly, we applied mendelian randomization (MR) approach to assess the causal relationship among the subtypes. The particular software that we used was generalised summary-data-based mendelian randomisation (GSMR). Results: Through GWAS, we identified hundreds of genome-wide significant SNPs: 672 for IHD, 241 for PHD, 31 for CRB, 48 for AAC, and 193 for VLL. By comparing with published literature, we found 28 novel loci, for PHD (n=14), CRB (n =7) and AAC (n =7). Eight of these 28 loci were rare, where the lead SNP had minor allele frequency (MAF) less than 1%. LDSC analyses indicated IHD had significant genetic correlation with VLL (P=2.52×10^-7), PHD (P=3.77×10^-3) and AAC (P=4.90×10^-3), respectively. Bidrectional GSMR analyses showed that IHD had a positive causal relationship with VLL (P=7.40×10^-5) and AAC (P=1.50×10^-3), while reverse causality was not supported. Conclusion: This study adopted an innovative approach to study the molecular connection among CVD subtypes that are defined by ICD. We identified potentially positive genetic correlation and causal effects among some of these subtypes. Research along this line will provide scientific insights and serve as a guidance for future ICD standards.