Phenomapping for the Identification of Hypertensive Patients with the Myocardial Substrate for Heart Failure with Preserved Ejection Fraction.

Journal Article (Journal Article;Multicenter Study)

We sought to evaluate whether unbiased machine learning of dense phenotypic data ("phenomapping") could identify distinct hypertension subgroups that are associated with the myocardial substrate (i.e., abnormal cardiac mechanics) for heart failure with preserved ejection fraction (HFpEF). In the HyperGEN study, a population- and family-based study of hypertension, we studied 1273 hypertensive patients utilizing clinical, laboratory, and conventional echocardiographic phenotyping of the study participants. We used machine learning analysis of 47 continuous phenotypic variables to identify mutually exclusive groups constituting a novel classification of hypertension. The phenomapping analysis classified study participants into 2 distinct groups that differed markedly in clinical characteristics, cardiac structure/function, and indices of cardiac mechanics (e.g., phenogroup #2 had a decreased absolute longitudinal strain [12.8 ± 4.1 vs. 14.6 ± 3.5%] even after adjustment for traditional comorbidities [p < 0.001]). The 2 hypertension phenogroups may represent distinct subtypes that may benefit from targeted therapies for the prevention of HFpEF.

Full Text

Duke Authors

Cited Authors

  • Katz, DH; Deo, RC; Aguilar, FG; Selvaraj, S; Martinez, EE; Beussink-Nelson, L; Kim, K-YA; Peng, J; Irvin, MR; Tiwari, H; Rao, DC; Arnett, DK; Shah, SJ

Published Date

  • June 2017

Published In

Volume / Issue

  • 10 / 3

Start / End Page

  • 275 - 284

PubMed ID

  • 28258421

Electronic International Standard Serial Number (EISSN)

  • 1937-5395

Digital Object Identifier (DOI)

  • 10.1007/s12265-017-9739-z


  • eng

Conference Location

  • United States