Jiaqi Dai, Tao Wang, Ke Xu, Yang Sun, Zongzhe Li, Peng Chen, Hong Wang, Dongyang Wu, Yanghui Chen, Lei Xiao, Hao Liu, Haoran Wei, Rui Li, Liyuan Peng, Ting Yu, Yan Wang, Zhongsheng Sun, Dao Wen Wang
{"title":"机器学习建模识别具有遗传特征的肥厚型心肌病亚型。","authors":"Jiaqi Dai, Tao Wang, Ke Xu, Yang Sun, Zongzhe Li, Peng Chen, Hong Wang, Dongyang Wu, Yanghui Chen, Lei Xiao, Hao Liu, Haoran Wei, Rui Li, Liyuan Peng, Ting Yu, Yan Wang, Zhongsheng Sun, Dao Wen Wang","doi":"10.1007/s11684-023-0982-1","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12558,"journal":{"name":"Frontiers of Medicine","volume":null,"pages":null},"PeriodicalIF":3.9000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature.\",\"authors\":\"Jiaqi Dai, Tao Wang, Ke Xu, Yang Sun, Zongzhe Li, Peng Chen, Hong Wang, Dongyang Wu, Yanghui Chen, Lei Xiao, Hao Liu, Haoran Wei, Rui Li, Liyuan Peng, Ting Yu, Yan Wang, Zhongsheng Sun, Dao Wen Wang\",\"doi\":\"10.1007/s11684-023-0982-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":12558,\"journal\":{\"name\":\"Frontiers of Medicine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2023-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers of Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s11684-023-0982-1\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/5/1 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers of Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s11684-023-0982-1","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/5/1 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature.
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.
Frontiers of MedicineONCOLOGYMEDICINE, RESEARCH & EXPERIMENTAL&-MEDICINE, RESEARCH & EXPERIMENTAL
CiteScore
18.30
自引率
0.00%
发文量
800
期刊介绍:
Frontiers of Medicine is an international general medical journal sponsored by the Ministry of Education of China. The journal is jointly published by the Higher Education Press and Springer. Since the first issue of 2010, this journal has been indexed in PubMed/MEDLINE.
Frontiers of Medicine is dedicated to publishing original research and review articles on the latest advances in clinical and basic medicine with a focus on epidemiology, traditional Chinese medicine, translational research, healthcare, public health and health policies.