Skip to main content
SpringerLink
Log in

Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

  • Research Article
  • Published:
Frontiers of Medicine Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Maron BJ, Mathenge R, Casey SA, Poliac LC, Longe TF. Clinical profile of hypertrophic cardiomyopathy identified de novo in rural communities. J Am Coll Cardiol 1999; 33(6): 1590–1595

    Article  CAS  PubMed  Google Scholar 

  2. Zou Y, Song L, Wang Z, Ma A, Liu T, Gu H, Lu S, Wu P, Zhangdagger Y, Shendagger L, Cai Y, Zhendoubledagger Y, Liu Y, Hui R. Prevalence of idiopathic hypertrophic cardiomyopathy in China: a population-based echocardiographic analysis of 8080 adults. Am J Med 2004; 116(1): 14–18

    Article  PubMed  Google Scholar 

  3. Eriksson MJ, Sonnenberg B, Woo A, Rakowski P, Parker TG, Wigle ED, Rakowski H. Long-term outcome in patients with apical hypertrophic cardiomyopathy. J Am Coll Cardiol 2002; 39(4): 638–645

    Article  PubMed  Google Scholar 

  4. Maron BJ, Rowin EJ, Casey SA, Link MS, Lesser JR, Chan RH, Garberich RF, Udelson JE, Maron MS. Hypertrophic cardiomyopathy in adulthood associated with low cardiovascular mortality with contemporary management strategies. J Am Coll Cardiol 2015; 65(18): 1915–1928

    Article  PubMed  Google Scholar 

  5. Olivotto I, Cecchi F, Poggesi C, Yacoub MH. Patterns of disease progression in hypertrophic cardiomyopathy: an individualized approach to clinical staging. Circ Heart Fail 2012; 5(4): 535–546

    Article  PubMed  Google Scholar 

  6. Harris KM, Spirito P, Maron MS, Zenovich AG, Formisano F, Lesser JR, Mackey-Bojack S, Manning WJ, Udelson JE, Maron BJ. Prevalence, clinical profile, and significance of left ventricular remodeling in the end-stage phase of hypertrophic cardiomyopathy. Circulation 2006; 114(3): 216–225

    Article  PubMed  Google Scholar 

  7. Melacini P, Basso C, Angelini A, Calore C, Bobbo F, Tokajuk B, Bellini N, Smaniotto G, Zucchetto M, Iliceto S, Thiene G, Maron BJ. Clinicopathological profiles of progressive heart failure in hypertrophic cardiomyopathy. Eur Heart J 2010; 31(17): 2111–2123

    Article  PubMed  PubMed Central  Google Scholar 

  8. Watkins H, McKenna WJ, Thierfelder L, Suk HJ, Anan R, O’ Donoghue A, Spirito P, Matsumori A, Moravec CS, Seidman JG, Seidman CE. Mutations in the genes for cardiac troponin T and α-tropomyosin in hypertrophic cardiomyopathy. N Engl J Med 1995; 332(16): 1058–1065

    Article  CAS  PubMed  Google Scholar 

  9. Coppini R, Ho CY, Ashley E, Day S, Ferrantini C, Girolami F, Tomberli B, Bardi S, Torricelli F, Cecchi F, Mugelli A, Poggesi C, Tardiff J, Olivotto I. Clinical phenotype and outcome of hypertrophic cardiomyopathy associated with thin-filament gene mutations. J Am Coll Cardiol 2014; 64(24): 2589–2600

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Harper AR, Goel A, Grace C, Thomson KL, Petersen SE, Xu X, Waring A, Ormondroyd E, Kramer CM, Ho CY, Neubauer S; HCMR Investigators; Tadros R, Ware JS, Bezzina CR, Farrall M, Watkins H. Common genetic variants and modifiable risk factors underpin hypertrophic cardiomyopathy susceptibility and expressivity. Nat Genet 2021; 53(2): 135–142

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Ommen SR, Mital S, Burke MA, Day SM, Deswal A, Elliott P, Evanovich LL, Hung J, Joglar JA, Kantor P, Kimmelstiel C, Kittleson M, Link MS, Maron MS, Martinez MW, Miyake CY, Schaff HV, Semsarian C, Sorajja P. 2020 AHA/ACC guideline for the diagnosis and treatment of patients with hypertrophic cardiomyopathy: a report of the American College of Cardiology/American Heart Association Joint Committee on clinical practice guidelines. Circulation 2020; 142(25): e558–e631

    PubMed  Google Scholar 

  12. Dai J, Li Z, Huang W, Chen P, Sun Y, Wang H, Wu D, Chen Y, Li C, Xiao L, Liu H, Wei H, Li R, Duan Q, Peng L, Song X, Yu T, Wang Y, Wang DW. Rbm20 is a candidate gene for hypertrophic cardiomyopathy. Can J Cardiol 2021; 37(11): 1751–1759

    Article  PubMed  Google Scholar 

  13. Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 2010; 26(12): 1572–1573

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ingles J, Goldstein J, Thaxton C, Caleshu C, Corty EW, Crowley SB, Dougherty K, Harrison SM, McGlaughon J, Milko LV, Morales A, Seifert BA, Strande N, Thomson K, Peter van Tintelen J, Wallace K, Walsh R, Wells Q, Whiffin N, Witkowski L, Semsarian C, Ware JS, Hershberger RE, Funke B. Evaluating the clinical validity of hypertrophic cardiomyopathy genes. Circ Genom Precis Med 2019; 12(2): e002460

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW, Hegde M, Lyon E, Spector E, Voelkerding K, Rehm HL; ACMG Laboratory Quality Assurance Committee. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 2015; 17(5): 405–424

    Article  PubMed  PubMed Central  Google Scholar 

  16. Li J, Zhao T, Zhang Y, Zhang K, Shi L, Chen Y, Wang X, Sun Z. Performance evaluation of pathogenicity-computation methods for missense variants. Nucleic Acids Res 2018; 46(15): 7793–7804

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Zhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res 2019; 47(W1): W234–W241

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, Koplev S, Jenkins SL, Jagodnik KM, Lachmann A, McDermott MG, Monteiro CD, Gundersen GW, Ma’ayan A. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res 2016; 44(W1): W90–W97

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Maron BA, Wang RS, Shevtsov S, Drakos SG, Arons E, Wever-Pinzon O, Huggins GS, Samokhin AO, Oldham WM, Aguib Y, Yacoub MH, Rowin EJ, Maron BJ, Maron MS, Loscalzo J. Individualized interactomes for network-based precision medicine in hypertrophic cardiomyopathy with implications for other clinical pathophenotypes. Nat Commun 2021; 12(1): 873

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Geske JB, Ong KC, Siontis KC, Hebl VB, Ackerman MJ, Hodge DO, Miller VM, Nishimura RA, Oh JK, Schaff HV, Gersh BJ, Ommen SR. Women with hypertrophic cardiomyopathy have worse survival. Eur Heart J 2017; 38(46): 3434–3440

    Article  PubMed  PubMed Central  Google Scholar 

  21. Rhee EP, Yang Q, Yu B, Liu X, Cheng S, Deik A, Pierce KA, Bullock K, Ho JE, Levy D, Florez JC, Kathiresan S, Larson MG, Vasan RS, Clish CB, Wang TJ, Boerwinkle E, O’Donnell CJ, Gerszten RE. An exome array study of the plasma metabolome. Nat Commun 2016; 7(1): 12360

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Wessel J, Chu AY, Willems SM, Wang S, Yaghootkar H, Brody JA, Dauriz M, Hivert MF, Raghavan S, Lipovich L, Hidalgo B, Fox K, Huffman JE, An P, Lu Y, Rasmussen-Torvik LJ, Grarup N, Ehm MG, Li L, Baldridge AS, Stančáková A, Abrol R, Besse C, Boland A, Bork-Jensen J, Fornage M, Freitag DF, Garcia ME, Guo X, Hara K, Isaacs A, Jakobsdottir J, Lange LA, Layton JC, Li M, Hua Zhao J, Meidtner K, Morrison AC, Nalls MA, Peters MJ, Sabater-Lleal M, Schurmann C, Silveira A, Smith AV, Southam L, Stoiber MH, Strawbridge RJ, Taylor KD, Varga TV, Allin KH, Amin N, Aponte JL, Aung T, Barbieri C, Bihlmeyer NA, Boehnke M, Bombieri C, Bowden DW, Burns SM, Chen Y, Chen YD, Cheng CY, Correa A, Czajkowski J, Dehghan A, Ehret GB, Eiriksdottir G, Escher SA, Farmaki AE, Frånberg M, Gambaro G, Giulianini F, Goddard WA 3rd, Goel A, Gottesman O, Grove ML, Gustafsson S, Hai Y, Hallmans G, Heo J, Hoffmann P, Ikram MK, Jensen RA, Jørgensen ME, Jørgensen T, Karaleftheri M, Khor CC, Kirkpatrick A, Kraja AT, Kuusisto J, Lange EM, Lee IT, Lee WJ, Leong A, Liao J, Liu C, Liu Y, Lindgren CM, Linneberg A, Malerba G, Mamakou V, Marouli E, Maruthur NM, Matchan A, McKean-Cowdin R, McLeod O, Metcalf GA, Mohlke KL, Muzny DM, Ntalla I, Palmer ND, Pasko D, Peter A, Rayner NW, Renström F, Rice K, Sala CF, Sennblad B, Serafetinidis I, Smith JA, Soranzo N, Speliotes EK, Stahl EA, Stirrups K, Tentolouris N, Thanopoulou A, Torres M, Traglia M, Tsafantakis E, Javad S, Yanek LR, Zengini E, Becker DM, Bis JC, Brown JB, Cupples LA, Hansen T, Ingelsson E, Karter AJ, Lorenzo C, Mathias RA, Norris JM, Peloso GM, Sheu WH, Toniolo D, Vaidya D, Varma R, Wagenknecht LE, Boeing H, Bottinger EP, Dedoussis G, Deloukas P, Ferrannini E, Franco OH, Franks PW, Gibbs RA, Gudnason V, Hamsten A, Harris TB, Hattersley AT, Hayward C, Hofman A, Jansson JH, Langenberg C, Launer LJ, Levy D, Oostra BA, O’Donnell CJ, O’Rahilly S, Padmanabhan S, Pankow JS, Polasek O, Province MA, Rich SS, Ridker PM, Rudan I, Schulze MB, Smith BH, Uitterlinden AG, Walker M, Watkins H, Wong TY, Zeggini E; EPIC-InterAct Consortium; Laakso M, Borecki IB, Chasman DI, Pedersen O, Psaty BM, Tai ES, van Duijn CM, Wareham NJ, Waterworth DM, Boerwinkle E, Kao WH, Florez JC, Loos RJ, Wilson JG, Frayling TM, Siscovick DS, Dupuis J, Rotter JI, Meigs JB, Scott RA, Goodarzi MO. Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility. Nat Commun 2015; 6(1): 5897

    Article  CAS  PubMed  Google Scholar 

  23. Zuk O, Schaffner SF, Samocha K, Do R, Hechter E, Kathiresan S, Daly MJ, Neale BM, Sunyaev SR, Lander ES. Searching for missing heritability: designing rare variant association studies. Proc Natl Acad Sci USA 2014; 111(4): E455–E464

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Maron BJ, Spirito P. Implications of left ventricular remodeling in hypertrophic cardiomyopathy. Am J Cardiol 1998; 81(11): 1339–1344

    CAS  PubMed  Google Scholar 

  25. Klues HG, Schiffers A, Maron BJ. Phenotypic spectrum and patterns of left ventricular hypertrophy in hypertrophic cardiomyopathy: morphologic observations and significance as assessed by two-dimensional echocardiography in 600 patients. J Am Coll Cardiol 1995; 26(7): 1699–1708

    Article  CAS  PubMed  Google Scholar 

  26. Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X. Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet 2011; 89(1): 82–93

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Marian AJ, Braunwald E. Hypertrophic cardiomyopathy: genetics, pathogenesis, clinical manifestations, diagnosis, and therapy. Circ Res 2017; 121(7): 749–770

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Maron BJ, Maron MS, Maron BA, Loscalzo J. Moving beyond the sarcomere to explain heterogeneity in hypertrophic cardiomyopathy: Jacc review topic of the week. J Am Coll Cardiol 2019; 73(15): 1978–1986

    Article  PubMed  PubMed Central  Google Scholar 

  29. Friederich MW, Timal S, Powell CA, Dallabona C, Kurolap A, Palacios-Zambrano S, Bratkovic D, Derks TGJ, Bick D, Bouman K, Chatfield KC, Damouny-Naoum N, Dishop MK, Falik-Zaccai TC, Fares F, Fedida A, Ferrero I, Gallagher RC, Garesse R, Gilberti M, González C, Gowan K, Habib C, Halligan RK, Kalfon L, Knight K, Lefeber D, Mamblona L, Mandel H, Mory A, Ottoson J, Paperna T, Pruijn GJM, Rebelo-Guiomar PF, Saada A, Sainz B Jr, Salvemini H, Schoots MH, Smeitink JA, Szukszto MJ, Ter Horst HJ, van den Brandt F, van Spronsen FJ, Veltman JA, Wartchow E, Wintjes LT, Zohar Y, Fernández-Moreno MA, Baris HN, Donnini C, Minczuk M, Rodenburg RJ, Van Hove JLK. Pathogenic variants in glutamyl-tRNAGln amidotransferase subunits cause a lethal mitochondrial cardiomyopathy disorder. Nat Commun 2018; 9(1): 4065

    Article  PubMed  PubMed Central  Google Scholar 

  30. Marin TM, Keith K, Davies B, Conner DA, Guha P, Kalaitzidis D, Wu X, Lauriol J, Wang B, Bauer M, Bronson R, Franchini KG, Neel BG, Kontaridis MI. Rapamycin reverses hypertrophic cardiomyopathy in a mouse model of LEOPARD syndrome-associated PTPN11 mutation. J Clin Invest 2011; 121(3): 1026–1043

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Lee LY, Loscalzo J. Network medicine in pathobiology. Am J Pathol 2019; 189(7): 1311–1326

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We gratefully acknowledge all the participants and funding sources. This study was funded by the National Key R&D Program of China (No. 2017YFC0909400), the National Natural Science Foundation of China (Nos. 91439203, 91839302, and 81700413), Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01), and the Fundamental Research Funds for the Central Universities, HUST (No. 2016JCTD117).

Author information

Author notes

  1. Jiaqi Dai, Tao Wang, and Ke Xu contributed equally to this work.

Authors and Affiliations

  1. Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China

    Jiaqi Dai, Ke Xu, Yang Sun, Zongzhe Li, Peng Chen, Rui Li, Liyuan Peng, Ting Yu, Yan Wang & Dao Wen Wang

  2. Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China

    Tao Wang & Zhongsheng Sun

  3. Hubei Key Laboratory of Genetics and Molecular Mechanism of Cardiologic Disorders, Huazhong University of Science and Technology, Wuhan, 430030, China

    Jiaqi Dai, Ke Xu, Yang Sun, Zongzhe Li, Peng Chen, Hong Wang, Dongyang Wu, Yanghui Chen, Lei Xiao, Hao Liu, Haoran Wei, Rui Li, Yan Wang & Dao Wen Wang

Authors
  1. Jiaqi Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ke Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yang Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zongzhe Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Peng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Dongyang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yanghui Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Lei Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Hao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Haoran Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Rui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Liyuan Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Ting Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Yan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Zhongsheng Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Dao Wen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Zhongsheng Sun or Dao Wen Wang.

Ethics declarations

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, and Dao Wen Wang declare no conflict of interest. All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all the patients for being included in the study.

Supplemental Material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dai, J., Wang, T., Xu, K. et al. Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature. Front. Med. 17, 768–780 (2023). https://doi.org/10.1007/s11684-023-0982-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11684-023-0982-1

Keywords

Search

Navigation