Division of Biomechanics and Research Development, Department of Biomechanics, and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, NE, 68182, USA.
Lyceum Scientific Charity, Tehran, Iran.
Sci Rep. 2023 Oct 25;13(1):18316. doi: 10.1038/s41598-023-45184-2.
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.
任何可靠的生物标志物都必须具有特异性、可推广性和可重复性，适用于个体和环境。这样的生物标志物的精确值必须代表不同个体和同一个体不同时间的相似健康状态，以最大限度地减少假阳性和假阴性率。标准截断值和风险评分在人群中的应用取决于这种可推广性的假设。这种可推广性又取决于当前统计方法所研究的现象具有遍历性的条件，即其统计度量在个体和时间上的收敛在观测的有限范围内。然而，新出现的证据表明，生物过程中存在大量的非遍历性，这威胁到了这种可推广性。在这里，我们提出了一种如何通过推导出非遍历现象的遍历描述来进行可推广推断的解决方案。为此，我们提出了捕捉许多生物过程中遍历性破坏的起源：级联动力学。为了评估我们的假设，我们接受了为心脏病和中风识别可靠生物标志物的挑战，尽管这些疾病是全世界和几十年研究的主要死因，但仍缺乏可靠的生物标志物和风险分层工具。我们表明，原始 R-R 间隔数据及其基于均值和方差的常见描述符是非遍历的且不具有特异性。另一方面，级联动力学描述符、编码线性时间相关性的赫斯特指数和跨尺度描述非线性相互作用的多重分形非线性，更具遍历性地描述了非遍历心率变异性，并且具有特异性。这项研究开创了在发现和应用健康和疾病的数字生物标志物中应用遍历性这一关键概念的先河。
