Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature.
10.1007/s11684-023-0982-1
- Author:
Jiaqi DAI
1
;
Tao WANG
2
;
Ke XU
1
;
Yang SUN
1
;
Zongzhe LI
1
;
Peng CHEN
1
;
Hong WANG
3
;
Dongyang WU
3
;
Yanghui CHEN
3
;
Lei XIAO
3
;
Hao LIU
3
;
Haoran WEI
3
;
Rui LI
1
;
Liyuan PENG
1
;
Ting YU
1
;
Yan WANG
1
;
Zhongsheng SUN
4
;
Dao Wen WANG
5
Author Information
1. Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
2. Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
3. Hubei Key Laboratory of Genetics and Molecular Mechanism of Cardiologic Disorders, Huazhong University of Science and Technology, Wuhan, 430030, China.
4. Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China. sunzsbiols@126.com.
5. Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China. dwwang@tjh.tjmu.edu.cn.
- Publication Type:Journal Article
- Keywords:
genetic risk;
hypertrophic cardiomyopathy;
machine learning methods
- From:
Frontiers of Medicine
2023;17(4):768-780
- CountryChina
- Language:English
-
Abstract:
Previous studies have revealed that patients with hypertrophic cardiomyopathy (HCM) exhibit differences in symptom severity and prognosis, indicating potential HCM subtypes among these patients. Here, 793 patients with HCM were recruited at an average follow-up of 32.78 ± 27.58 months to identify potential HCM subtypes by performing consensus clustering on the basis of their echocardiography features. Furthermore, we proposed a systematic method for illustrating the relationship between the phenotype and genotype of each HCM subtype by using machine learning modeling and interactome network detection techniques based on whole-exome sequencing data. Another independent cohort that consisted of 414 patients with HCM was recruited to replicate the findings. Consequently, two subtypes characterized by different clinical outcomes were identified in HCM. Patients with subtype 2 presented asymmetric septal hypertrophy associated with a stable course, while those with subtype 1 displayed left ventricular systolic dysfunction and aggressive progression. Machine learning modeling based on personal whole-exome data identified 46 genes with mutation burden that could accurately predict subtype propensities. Furthermore, the patients in another cohort predicted as subtype 1 by the 46-gene model presented increased left ventricular end-diastolic diameter and reduced left ventricular ejection fraction. By employing echocardiography and genetic screening for the 46 genes, HCM can be classified into two subtypes with distinct clinical outcomes.
