Association between genetic predisposition and disease burden of stroke in China: a genetic epidemiological study.

China Disease burden Genetic risk Geographical variation Stroke

Journal

The Lancet regional health. Western Pacific
ISSN: 2666-6065
Titre abrégé: Lancet Reg Health West Pac
Pays: England
ID NLM: 101774968

Informations de publication

Date de publication:
Jul 2023
Historique:
received: 08 02 2023
revised: 31 03 2023
accepted: 17 04 2023
medline: 7 8 2023
pubmed: 7 8 2023
entrez: 7 8 2023
Statut: epublish

Résumé

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).

Sections du résumé

Background UNASSIGNED
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.
Methods UNASSIGNED
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.
Findings UNASSIGNED
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.
Interpretation UNASSIGNED
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.
Funding UNASSIGNED
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).

Identifiants

pubmed: 37547044
doi: 10.1016/j.lanwpc.2023.100779
pii: S2666-6065(23)00097-4
pmc: PMC10398595
doi:

Types de publication

Journal Article

Langues

eng

Pagination

100779

Informations de copyright

© 2023 The Author(s).

Déclaration de conflit d'intérêts

We declare no competing interests.

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Auteurs

Qiya Huang (Q)

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.

Xianmei Lan (X)

College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
BGI-Shenzhen, Shenzhen, China.

Hebing Chen (H)

Institute of Health Service and Transfusion Medicine, Beijing, China.

Hao Li (H)

Institute of Health Service and Transfusion Medicine, Beijing, China.

Yu Sun (Y)

Institute of Health Service and Transfusion Medicine, Beijing, China.

Chao Ren (C)

Institute of Health Service and Transfusion Medicine, Beijing, China.

Chao Xing (C)

Eugene McDermott Center for Human Growth and Development, Department of Bioinformatics, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.

Xiaochen Bo (X)

Institute of Health Service and Transfusion Medicine, Beijing, China.

Jizheng Wang (J)

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.

Xin Jin (X)

School of Medicine, South China University of Technology, Guangzhou, Guangdong, China.
BGI-Shenzhen, Shenzhen, China.

Lei Song (L)

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.
National Clinical Research Center of Cardiovascular Diseases, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Classifications MeSH