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Multifractal Foundations of Biomarker Discovery for Heart Disease and Stroke Publisher Pubmed



Mangalam M1 ; Sadri A2, 3 ; Hayano J4 ; Watanabe E5 ; Kiyono K6 ; Keltystephen DG7
Authors
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Authors Affiliations
  1. 1. Division of Biomechanics and Research Development, Department of Biomechanics, and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, 68182, NE, United States
  2. 2. Lyceum Scientific Charity, Tehran, Iran
  3. 3. Interdisciplinary Neuroscience Research Program, Students’ Scientific Research Center, Tehran University of Medical Sciences, Tehran, P94V+8MF, Iran
  4. 4. Graduate School of Medicine, Nagoya City University, Aichi, Nagoya, 467-8601, Japan
  5. 5. Division of Cardiology, Department of Internal Medicine, Fujita Health University Bantane Hospital, Aichi, Nagoya, 454-0012, Japan
  6. 6. Graduate School of Engineering Science, Osaka University, Osaka, 560-8531, Japan
  7. 7. Department of Psychology, State University of New York at New Paltz, New Paltz, 12561, NY, United States

Source: Scientific Reports Published:2023


Abstract

Any reliable biomarker has to be specific, generalizable, and reproducible across individuals and contexts. The exact values of such a biomarker must represent similar health states in different individuals and at different times within the same individual to result in the minimum possible false-positive and false-negative rates. The application of standard cut-off points and risk scores across populations hinges upon the assumption of such generalizability. Such generalizability, in turn, hinges upon this condition that the phenomenon investigated by current statistical methods is ergodic, i.e., its statistical measures converge over individuals and time within the finite limit of observations. However, emerging evidence indicates that biological processes abound with nonergodicity, threatening this generalizability. Here, we present a solution for how to make generalizable inferences by deriving ergodic descriptions of nonergodic phenomena. For this aim, we proposed capturing the origin of ergodicity-breaking in many biological processes: cascade dynamics. To assess our hypotheses, we embraced the challenge of identifying reliable biomarkers for heart disease and stroke, which, despite being the leading cause of death worldwide and decades of research, lacks reliable biomarkers and risk stratification tools. We showed that raw R-R interval data and its common descriptors based on mean and variance are nonergodic and non-specific. On the other hand, the cascade-dynamical descriptors, the Hurst exponent encoding linear temporal correlations, and multifractal nonlinearity encoding nonlinear interactions across scales described the nonergodic heart rate variability more ergodically and were specific. This study inaugurates applying the critical concept of ergodicity in discovering and applying digital biomarkers of health and disease. © 2023, Springer Nature Limited.
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