Huang Qiya, Lan Xianmei, Chen Hebing, Li Hao, Sun Yu, Ren Chao, Xing Chao, Bo Xiaochen, Wang Jizheng, Jin Xin, Song Lei
State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
Lancet Reg Health West Pac. 2023 May 22;36:100779. doi: 10.1016/j.lanwpc.2023.100779. eCollection 2023 Jul.
Stroke ranks second worldwide and first in China as a leading cause of death and disability. It has a polygenic architecture and is influenced by environmental and lifestyle factors. However, it remains unknown as to whether and how much the genetic predisposition of stroke is associated with disease burden.
Allele frequency from the whole genome sequencing data in the Chinese Millionome Database of 141,418 individuals and trait-specific polygenic risk score models were applied to estimate the provincial genetic predisposition to stroke, stroke-related risk factors and stroke-related drug response. Disease burden including mortality, disability-adjusted life years (DALYs), years of life lost(YLLs), years lived with disability (YLDs) and prevalence in China was collected from the Global Burden Disease study. The association between stroke genetic predisposition and the epidemiological burden was assessed and then quantified in both regression-based models and machine learning-based models at a provincial resolution.
Among the 30 administrative divisions in China, the genetic predisposition of stroke was characterized by a north-higher-than-south gradient (p < 0.0001). Genetic predisposition to stroke, blood pressure, body mass index, and alcohol use were strongly intercorrelated (rho >0.6; p < 0.05 after Bonferroni correction for each comparison). Genetic risk imposed an independent effect of approximately 1-6% on mortality, DALYs and YLLs.
The distribution pattern of stroke genetic predisposition is different at a macroscopic level, and it subtly but significantly impacts the epidemiological burden. Further research is warranted to identify the detailed aetiology and potential translation into public health measures.
Beijing Municipal Science and Technology Commission (Z191100006619106), CAMS Innovation Fund for Medical Sciences (CAMS-I2M, 2023-I2M-1-001), the National High Level Hospital Clinical Research Funding (2022-GSP-GG-17), National Natural Science Foundation of China (32000398, 32171441 to X.J.), Natural Science Foundation of Guangdong Province, China (2017A030306026 to X.J.), and National Key R&D Program of China (2022YFC2502402).
中风是全球第二大致死和致残原因，在中国则位居首位。它具有多基因结构，受环境和生活方式因素影响。然而，中风的遗传易感性与疾病负担之间是否存在关联以及关联程度如何，目前尚不清楚。
利用中国百万基因组数据库中141418人的全基因组测序数据的等位基因频率和特定性状的多基因风险评分模型，来估计各省中风、中风相关危险因素及中风相关药物反应的遗传易感性。从全球疾病负担研究中收集中国的疾病负担数据，包括死亡率、伤残调整生命年（DALYs）、寿命损失年数（YLLs）、残疾生存年数（YLDs）和患病率。在省级层面，通过基于回归的模型和基于机器学习的模型评估并量化中风遗传易感性与流行病学负担之间的关联。
在中国30个行政区中，中风的遗传易感性呈现北高南低的梯度特征（p < 0.0001）。中风、血压、体重指数和饮酒的遗传易感性之间存在强相关性（rho > 0.6；每次比较经Bonferroni校正后p < 0.05）。遗传风险对死亡率、DALYs和YLLs有大约1%-6%的独立影响。
中风遗传易感性的分布模式在宏观层面存在差异，并且对流行病学负担有微妙但显著的影响。有必要进一步开展研究以确定详细病因，并探讨其转化为公共卫生措施的可能性。
北京市科学技术委员会（Z191100006619106）、中国医学科学院医学与健康科技创新工程（CAMS-I2M，2023-I2M-1-001）、国家高水平医院临床研究专项（2022-GSP-GG-17）、国家自然科学基金（32000398、32171441资助给X.J.）、广东省自然科学基金（2017A030306026资助给X.J.）以及国家重点研发计划（2022YFC2502402）。
