Porovnat metody

	Healthy Aging Index Construction	Biological Age Estimation
Obor	Social Gerontology	Social Gerontology
Rodina	Process / pipeline	Regression model
Rok vzniku	2014	2006
Tvůrce	Jason L. Sanders, Anne B. Newman, and colleagues (Cardiovascular Health Study; Long Life Family Study)	Petr Klemera and Stanislav Doubal
Typ	Composite physiologic index of multisystem biological aging	Estimator of biological age from a panel of age-related biomarkers
Původní zdroj	Sanders, J. L., Minster, R. L., Barmada, M. M., Matteini, A. M., Boudreau, R. M., Christensen, K., Walston, J. D., Newman, A. B. (2014). Heritability of and mortality prediction with a longevity phenotype: the healthy aging index. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 69(4), 479-485.	Klemera, P., & Doubal, S. (2006). A new approach to the concept and computation of biological age. Mechanisms of Ageing and Development, 127(3), 240-248.
Další názvy	HAI, Healthy Ageing Index, Multisystem Healthy Aging Index, Physiologic Aging Index	KDM Biological Age, Klemera-Doubal Method, Biomarker-Based Biological Age, Physiological Age Estimation
Příbuzné	4	4
Shrnutí	The Healthy Aging Index (HAI) is a simple composite that summarizes the burden of subclinical physiologic decline across several organ systems into a single score. Introduced by Jason Sanders, Anne Newman, and colleagues in 2014 using the Cardiovascular Health Study, it captures the idea that biological aging is a multisystem process rather than the failure of any one organ. The index combines five readily measured markers, one from each of five physiologic systems: systolic blood pressure (vascular), fasting glucose (metabolic), Mini-Mental State Examination score (cognitive), serum creatinine (renal), and forced vital capacity (pulmonary). Each marker is scored 0, 1, or 2 according to which tertile of risk an individual falls into, and the five scores are summed to give a total from 0 to 10, with higher values indicating worse aging. The HAI predicts mortality and was shown to be heritable, supporting its interpretation as a phenotype of biological aging. Its appeal lies in being inexpensive, transparent, and built from routine clinical measurements rather than specialized assays.	Biological age estimation seeks to measure how old a person's body actually is, as distinct from the number of years since their birth. The most influential statistical approach is the Klemera-Doubal method (KDM), introduced in 2006, which derives a single biological-age value from a panel of age-related biomarkers. The central idea is that many physiological measures change predictably with age, so by regressing each biomarker on chronological age in a reference sample one can learn how each one tracks aging and then combine them to infer an individual's underlying biological age. Klemera and Doubal showed mathematically that treating biological age as a latent quantity estimated from all biomarkers jointly, weighted by how strongly and how cleanly each tracks age, yields a more accurate estimate than simply regressing chronological age on the biomarkers. The gap between estimated biological age and chronological age, often called biological age acceleration, indicates whether a person is aging faster or slower than average. This deviation predicts mortality and morbidity beyond chronological age, which is what makes the estimate useful.