Comparar métodos

	Deficit-Accumulation Frailty Index	Biological Age Estimation
Área	Social Gerontology	Social Gerontology
Família	Process / pipeline	Regression model
Ano de origem	2001	2006
Autor original	Arnold Mitnitski & Kenneth Rockwood	Petr Klemera and Stanislav Doubal
Tipo	Continuous index of frailty as accumulated health deficits	Estimator of biological age from a panel of age-related biomarkers
Fonte seminal	Mitnitski, A. B., Mogilner, A. J., & Rockwood, K. (2001). Accumulation of deficits as a proxy measure of aging. The Scientific World Journal, 1, 323-336.	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.
Outros nomes	Frailty Index, Rockwood Frailty Index, Deficit Accumulation Index, FI-CGA	KDM Biological Age, Klemera-Doubal Method, Biomarker-Based Biological Age, Physiological Age Estimation
Relacionados	4	4
Resumo	The deficit-accumulation frailty index measures frailty as the proportion of a long list of age-related health deficits that a person has accumulated. Introduced by Arnold Mitnitski and Kenneth Rockwood in 2001, it treats frailty not as a fixed syndrome but as a quantitative state: the more things have gone wrong across many body systems, the frailer the person. Counting 30 or more deficits — symptoms, signs, diseases, disabilities, and laboratory abnormalities — and dividing by the number considered yields a continuous score between 0 and 1 that rises with age, predicts mortality and adverse outcomes, and behaves remarkably consistently regardless of exactly which deficits are used. A standardized procedure by Searle and colleagues made the index easy to construct from existing data.	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.