A phenotypic taxonomy of hypertrophic cardiomyopathy

Background Hypertrophic cardiomyopathy (HCM) is an important cause of sudden cardiac death associated with heterogeneous structural phenotypes but there is no systematic framework for classifying morphology or assessing associated risks. In this study we quantitatively survey genotype-phenotype associations in HCM to derive a data-driven taxonomy of disease expression for automated patient stratification. Methods An observational, single-centre study enrolled 436 HCM patients (median age 60 years; 28.8% women) with clinical, genetic and imaging data. An independent cohort of 60 HCM patients from Singapore (median age 59 years; 11% women) and a normative reference population from UK Biobank (n = 16,691, mean age 55 years; 52.5% women) with equivalent data were also recruited. We used machine learning to analyse the three dimensional structure of the left ventricle from cardiac magnetic resonance imaging and build a tree-based classification of HCM phenotypes. Genotype and mortality risk distributions were projected on the tree. Results The prevalence of pathogenic or likely pathogenic variants for HCM (P/LP) was 24.6%, while 66% were genotype negative. Carriers of P/LP variants had lower left ventricular mass, but greater basal septal hypertrophy, with reduced lifespan (mean follow-up 9.9 years) compared to genotype negative individuals (hazard ratio: 2.66; 95% confidence interval [CI]: 1.42-4.96; P < 0.002). Four main phenotypic branches were identified using unsupervised learning of three dimensional shape: 1) non-sarcomeric hypertrophy with co-existing hypertension; 2) diffuse and basal asymmetric hypertrophy associated with outflow tract obstruction; 3) isolated basal hypertrophy; 4) milder non-obstructive hypertrophy enriched for familial sarcomeric HCM (odds ratio for P/LP variants: 2.18 [95% CI: 1.93-2.28, P = 0.0001]). Phenotypic variation and associated risks could be visualised as a continuous distribution across the taxonomic tree. The model was generalisable to an independent cohort (trustworthiness M1: 0.86-0.88). Conclusions We report a data-driven taxonomy of HCM for identifying groups of patients with similar morphology while preserving a continuum of disease severity, genetic risk and outcomes. This approach will be of value for developing personalized clinical profiles to guide diagnosis, surveillance and intervention in patients with HCM, and improve understanding of the drivers of heterogeneity.

J.S.W. has consulted for MyoKardia, Inc., Foresite Labs, and Pfizer, and receives research support from Bristol Myers Squibb outside the submitted work. D.P.OR. has consulted for Bayer AG and Bristol Myers Squibb, and also receives research support from Bayer AG outside the submitted work. The remaining authors have nothing to disclose.

The study was supported by the National Institute for Health Research (NIHR) Royal Brompton Cardiovascular Biomedical Research Unit; Medical Research Council (MC_UP_1605/13); National Institute for Health Research (NIHR) Imperial College Biomedical Research Centre; the British Heart Foundation (RG/19/6/34387, RE/18/4/34215, FS/IPBSRF/22/27059, FS/ICRF/21/26019); Engineering and Physical Sciences Research Council (EP/W01842X/1); Academy of Medical Sciences (SGL015/1006); Mason Medical Research Trust; Sir Jules Thorn Charitable Trust [21JTA]; the NHLI Foundation Royston Centre for Cardiomyopathy Research; and the Rosetrees Trust.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

All participants provided written informed consent and the study was approved by the National Research Ethics Service, UK (19/SC/0257, 11/NW/0382); and Singhealth Centralised Institutional Review Board (2020/2353) and the Singhealth Biobank Research Scientific Advisory Executive Committee (SBRSA 2019/001v1), Singapore.

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The code used in the study is available on GitHub. Population data is available from UK Biobank.

https://github.com/ImperialCollegeLondon/HCM-taxonomy