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

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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.

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Abbreviations

BIC:

Bayesian information criterion

BMI:

Body mass index

CAD:

Coronary artery disease

CV:

Cardiovascular

DBP:

Diastolic blood pressure

CS:

Circumferential strain

LS:

Longitudinal strain

RS:

Radial strain

HyperGEN:

Hypertension Genetic Epidemiology Network

LV:

Left ventricular

SBP:

Systolic blood pressure

References

  1. Oktay, A. A., & Shah, S. J. (2014). Current perspectives on systemic hypertension in heart failure with preserved ejection fraction. Current Cardiology Reports, 16(12), 545.

    Article  PubMed  Google Scholar 

  2. Egan, B. M., Zhao, Y., & Axon, R. N. (2010). US trends in prevalence, awareness, treatment, and control of hypertension, 1988-2008. JAMA, 303(20), 2043–2050.

    Article  CAS  PubMed  Google Scholar 

  3. Citterio, L., Lanzani, C., & Manunta, P. (2011). Polymorphisms, hypertension and thiazide diuretics. Pharmacogenomics, 12(11), 1587–1604.

    Article  CAS  PubMed  Google Scholar 

  4. Cuspidi, C., Rescaldani, M., Sala, C., Negri, F., Grassi, G., & Mancia, G. (2012). Prevalence of electrocardiographic left ventricular hypertrophy in human hypertension: an updated review. Journal of Hypertension, 30(11), 2066–2073.

    Article  CAS  PubMed  Google Scholar 

  5. Cuspidi, C., Sala, C., Negri, F., Mancia, G., Morganti, A., & Italian Society of H. (2012). Prevalence of left-ventricular hypertrophy in hypertension: an updated review of echocardiographic studies. Journal of Human Hypertension, 26(6), 343–349.

  6. Dahlof, B., Devereux, R. B., Kjeldsen, S. E., Julius, S., Beevers, G., de Faire, U., et al. (2002). Cardiovascular morbidity and mortality in the Losartan Intervention For Endpoint reduction in hypertension study (LIFE): a randomised trial against atenolol. Lancet, 359(9311), 995–1003.

    Article  CAS  PubMed  Google Scholar 

  7. Sagie, A., Larson, M. G., & Levy, D. (1993). The natural history of borderline isolated systolic hypertension. The New England Journal of Medicine, 329(26), 1912–1917.

    Article  CAS  PubMed  Google Scholar 

  8. Suonsyrja, T., Hannila-Handelberg, T., Paavonen, K. J., Miettinen, H. E., Donner, K., Strandberg, T., et al. (2008). Laboratory tests as predictors of the antihypertensive effects of amlodipine, bisoprolol, hydrochlorothiazide and losartan in men: results from the randomized, double-blind, crossover GENRES Study. Journal of Hypertension, 26(6), 1250–1256.

    Article  CAS  PubMed  Google Scholar 

  9. Perneger, T. V. (1993). Projections of hypertension-related renal disease in middle-aged residents of the United States. JAMA, 269(10), 1272.

    Article  CAS  PubMed  Google Scholar 

  10. Andreadis, E. A., Tsourous, G. I., Tzavara, C. K., Georgiopoulos, D. X., Katsanou, P. M., Marakomichelakis, G. E., et al. (2007). Metabolic syndrome and incident cardiovascular morbidity and mortality in a Mediterranean hypertensive population. American Journal of Hypertension, 20(5), 558–564.

    Article  PubMed  Google Scholar 

  11. Ceravolo, R., Maio, R., Cuda, G., Scozzafava, A., Sciacqua, A., Vatrano, M., et al. (2003). Relation of fasting insulin related to insertion/deletion polymorphism of angiotensin-converting enzyme-gene and cardiac mass in never-treated patients with systemic hypertension. American Journal of Cardiology, 92(10), 1234–1237.

    Article  CAS  PubMed  Google Scholar 

  12. Conen, D., Zeller, A., Pfisterer, M., & Martina, B. (2006). Usefulness of B-type natriuretic peptide and C-reactive protein in predicting the presence or absence of left ventricular hypertrophy in patients with systemic hypertension. American Journal of Cardiology, 97(2), 249–252.

    Article  CAS  PubMed  Google Scholar 

  13. Greenberg, J. (2006). Are blood pressure predictors of cardiovascular disease mortality different for prehypertensives than for hypertensives? American Journal of Hypertension, 19(5), 454–461.

    Article  PubMed  Google Scholar 

  14. Hiltunen, T. P., & Kontula, K. (2012). Clinical and molecular approaches to individualize antihypertensive drug therapy. Annals of Medicine, 44(Suppl 1), S23–S29.

    Article  CAS  PubMed  Google Scholar 

  15. Hiltunen, T. P., Suonsyrja, T., Hannila-Handelberg, T., Paavonen, K. J., Miettinen, H. E., Strandberg, T., et al. (2007). Predictors of antihypertensive drug responses: initial data from a placebo-controlled, randomized, cross-over study with four antihypertensive drugs (The GENRES Study). American Journal of Hypertension, 20(3), 311–318.

    Article  CAS  PubMed  Google Scholar 

  16. Matsui, Y., Eguchi, K., Shibasaki, S., Ishikawa, J., Shimada, K., & Kario, K. (2010). Morning hypertension assessed by home monitoring is a strong predictor of concentric left ventricular hypertrophy in patients with untreated hypertension. Journal of Clinical Hypertension (Greenwich, Conn.), 12(10), 776–783.

    Article  Google Scholar 

  17. Nakamura, M., Tanaka, F., Yonezawa, S., Satou, K., Nagano, M., & Hiramori, K. (2003). The limited value of plasma B-type natriuretic peptide for screening for left ventricular hypertrophy among hypertensive patients. American Journal of Hypertension, 16(12), 1025–1029.

    Article  CAS  PubMed  Google Scholar 

  18. Perez-Lloret, S., Toblli, J. E., Cardinali, D. P., Malateste, J. C., & Milei, J. (2008). Nocturnal hypertension defined by fixed cut-off limits is a better predictor of left ventricular hypertrophy than non-dipping. International Journal of Cardiology, 127(3), 387–389.

    Article  PubMed  Google Scholar 

  19. Phillips, R. A. (1998). Relation among left ventricular mass, insulin resistance, and blood pressure in nonobese subjects. Journal of Clinical Endocrinology & Metabolism, 83(12), 4284–4288.

    CAS  Google Scholar 

  20. Schillaci, G., Pirro, M., Vaudo, G., Gemelli, F., Marchesi, S., Porcellati, C., et al. (2004). Prognostic value of the metabolic syndrome in essential hypertension. Journal of the American College of Cardiology, 43(10), 1817–1822.

    Article  PubMed  Google Scholar 

  21. Vasan, R. S., Benjamin, E. J., Larson, M. G., Leip, E. P., Wang, T. J., Wilson, P. W., et al. (2002). Plasma natriuretic peptides for community screening for left ventricular hypertrophy and systolic dysfunction: the Framingham Heart Study. JAMA, 288(10), 1252–1259.

    Article  CAS  PubMed  Google Scholar 

  22. International Consortium for Blood Pressure Genome-Wide Association, S, Ehret, G. B., Munroe, P. B., Rice, K. M., Bochud, M., Johnson, A. D., et al. (2011). Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature, 478(7367), 103–109.

    Article  Google Scholar 

  23. James, P. A., Oparil, S., Carter, B. L., Cushman, W. C., Dennison-Himmelfarb, C., Handler, J., et al. (2014). 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). JAMA, 311(5), 507–520.

    Article  CAS  PubMed  Google Scholar 

  24. Shah, S. J., Katz, D. H., Selvaraj, S., Burke, M. A., Yancy, C. W., Georghiade, M., et al. (2015). Phenomapping for Novel classification of heart failure with preserved ejection fraction. Circulation, 131(3), 269–279.

    Article  PubMed  Google Scholar 

  25. Kao, D. P., Stevens, L. M., Hinterberg, M. A., & Gorg, C. (2017). Phenotype-specific association of single-nucleotide polymorphisms with heart failure and preserved ejection fraction: a genome-wide association analysis of the cardiovascular health study. Journal of Cardiovascular Translational Research. Epub ahead of print.

  26. Luo, Y., Ahmad, F. S., & Shah, S. J. (2017). Tensor factorization for precision medicine in heart failure with preserved ejection fraction. Journal of Cardiovascular Translational Research. Epub ahead of print.

  27. Williams, R. R., Rao, D. C., Ellison, R. C., Arnett, D. K., Heiss, G., Oberman, A., et al. (2000). NHLBI family blood pressure program: methodology and recruitment in the HyperGEN network. Hypertension genetic epidemiology network. Annals of Epidemiology, 10(6), 389–400.

    Article  CAS  PubMed  Google Scholar 

  28. Devereux, R. B., Roman, M. J., de Simone, G., O’Grady, M. J., Paranicas, M., Yeh, J. L., et al. (1997). Relations of left ventricular mass to demographic and hemodynamic variables in American Indians: the Strong Heart Study. Circulation, 96(5), 1416–1423.

    Article  CAS  PubMed  Google Scholar 

  29. Palmieri, V., Dahlof, B., DeQuattro, V., Sharpe, N., Bella, J. N., de Simone, G., et al. (1999). Reliability of echocardiographic assessment of left ventricular structure and function: the PRESERVE study. Prospective Randomized Study Evaluating Regression of Ventricular Enlargement. Journal of the American College of Cardiology, 34(5), 1625–1632.

    Article  CAS  PubMed  Google Scholar 

  30. Sahn, D. J., DeMaria, A., Kisslo, J., & Weyman, A. (1978). Recommendations regarding quantitation in M-mode echocardiography: results of a survey of echocardiographic measurements. Circulation, 58(6), 1072–1083.

    Article  CAS  PubMed  Google Scholar 

  31. Lang, R. M., Bierig, M., Devereux, R. B., Flachskampf, F. A., Foster, E., Pellikka, P. A., et al. (2005). Recommendations for chamber quantification: a report from the American Society of Echocardiography’s Guidelines and Standards Committee and the Chamber Quantification Writing Group, developed in conjunction with the European Association of Echocardiography, a branch of the European Society of Cardiology. Journal of the American Society of Echocardiography, 18(12), 1440–1463.

    Article  PubMed  Google Scholar 

  32. Galema, T. W., Geleijnse, M. L., Yap, S. C., van Domburg, R. T., Biagini, E., Vletter, W. B., et al. (2008). Assessment of left ventricular ejection fraction after myocardial infarction using contrast echocardiography. European Journal of Echocardiography, 9(2), 250–254.

    PubMed  Google Scholar 

  33. Peteiro, J., Pinon, P., Perez, R., Monserrat, L., Perez, D., & Castro-Beiras, A. (2007). Comparison of 2- and 3-dimensional exercise echocardiography for the detection of coronary artery disease. Journal of the American Society of Echocardiography, 20(8), 959–967.

    Article  PubMed  Google Scholar 

  34. Aguilar, F. G., Selvaraj, S., Martinez, E. E., Katz, D. H., Beussink, L., Kim, K. Y., et al. (2016). Archeological echocardiography: digitization and speckle tracking analysis of archival echocardiograms in the HyperGEN study. Echocardiography, 33(3), 386–397.

    Article  PubMed  Google Scholar 

  35. Hastie, T., Tibshirani, R., & Friedman, J. (2009). Unsupervised learning: hierarchical clustering. In T. Hastie, R. Tibshirani, & J. Friedman (Eds.), The elements of statistical learning (2nd ed., pp. 520–528). New York: Springer.

    Chapter  Google Scholar 

  36. Hahsler, M., Hornik, K., & Buchta, C. (2008). Getting things in order: an introduction to the R package seriation. Journal of Statistical Software, 25(3), 1–34.

    Article  Google Scholar 

  37. Fraley, C., & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97, 611–631.

    Article  Google Scholar 

  38. Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507.

    Article  CAS  PubMed  Google Scholar 

  39. Coffman, T. M. (2011). Under pressure: the search for the essential mechanisms of hypertension. Nature Medicine, 17(11), 1402–1409.

    Article  CAS  PubMed  Google Scholar 

  40. Zile, M. R., & Baicu, C. F. (2013). Biomarkers of diastolic dysfunction and myocardial fibrosis: application to heart failure with a preserved ejection fraction. Journal of Cardiovascular Translational Research, 6(4), 501–515.

    Article  PubMed  Google Scholar 

  41. Kriegel, A. J., Gartz, M., Afzal, M. Z., de Lange, W. J., Ralphe, J. C., & Strande, J. L. (2016). Molecular approaches in HFpEF: MicroRNAs and iPSC-derived cardiomyocytes. Journal of Cardiovascular Translational Research. Epub ahead of print.

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Authors and Affiliations

  1. Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    Daniel H. Katz

  2. Division of Cardiology, Department of Medicine, Institute for Human Genetics, California Institute for Quantitative Biosciences, and Cardiovascular Research Institute, University of California, San Francisco, CA, USA

    Rahul C. Deo

  3. Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, 676 N. St. Clair St., Suite 600, Chicago, IL, 60611, USA

    Frank G. Aguilar, Senthil Selvaraj, Eva E. Martinez, Lauren Beussink-Nelson & Sanjiv J. Shah

  4. Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Kwang-Youn A. Kim & Jie Peng

  5. Departments of Epidemiology and Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA

    Marguerite R. Irvin & Hemant Tiwari

  6. Division of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO, USA

    D. C. Rao

  7. School of Public Health, University of Kentucky, Lexington, KY, USA

    Donna K. Arnett

Authors
  1. Daniel H. Katz
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  13. Sanjiv J. Shah
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Corresponding author

Correspondence to Sanjiv J. Shah.

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Conflicts of Interest

The authors declare that they have no conflict of interest.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Funding Sources

The HyperGEN cardiac mechanics ancillary study was funded by the National Institutes of Health (NIH; R01 HL107577 to S.J.S.). The HyperGEN echocardiography ancillary study was funded by the National Institutes of Health (R01 HL55673 to D.K.A.). The HyperGEN parent study was funded by cooperative agreements (U10) with the National Heart, Lung, and Blood Institute: HL54471, HL54472, HL54473, HL54495, HL54496, HL54497, HL54509, HL54515. Dr. Shah was also supported by NIH HL127028 and American Heart Association grants #16SFRN28780016 and 15CVGPSD27260148). Dr. Katz was supported by an Alpha Omega Alpha Carolyn L. Kuckein Research Fellowship.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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None.

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Associate Editor Paul J. R. Barton oversaw the review of this article

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Katz, D.H., Deo, R.C., Aguilar, F.G. et al. Phenomapping for the Identification of Hypertensive Patients with the Myocardial Substrate for Heart Failure with Preserved Ejection Fraction. J. of Cardiovasc. Trans. Res. 10, 275–284 (2017). https://doi.org/10.1007/s12265-017-9739-z

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