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

Journal: Journal of cardiovascular translational research
Published Date: Jun 1, 2017

Abstract

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.

Authors

  • Daniel H Katz
    Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
  • Rahul C Deo
    From the Division of Cardiology, Department of Medicine; Cardiovascular Research Institute; Institute for Human Genetics; and Institute for Computational Health Sciences, University of California San Francisco, and California Institute for Quantitative Biosciences (R.C.D.); and VA Health Services Research and Development Center for Clinical Management Research, VA Ann Arbor Healthcare System, MI; Michigan Center for Health Analytics and Medical Prediction (M-CHAMP), Department of Internal Medicine, University of Michigan Medical School, Ann Arbor (B.K.N.). rahul.deo@ucsf.edu.
  • Frank G Aguilar
    Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, 676 N. St. Clair St., Suite 600, Chicago, IL, 60611, USA.
  • Senthil Selvaraj
    Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, 676 N. St. Clair St., Suite 600, Chicago, IL, 60611, USA.
  • Eva E Martinez
    Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, 676 N. St. Clair St., Suite 600, Chicago, IL, 60611, USA.
  • Lauren Beussink-Nelson
    Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
  • Kwang-Youn A Kim
    Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
  • Jie Peng
    School of Physical Education, Liupanshui Normal University, Liupanshui, China.
  • Marguerite R Irvin
    Departments of Epidemiology and Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA.
  • Hemant Tiwari
    Departments of Epidemiology and Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA.
  • D C Rao
    Division of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
  • Donna K Arnett
    School of Public Health, University of Kentucky, Lexington, KY, USA.
  • Sanjiv J Shah
    Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

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