Common methods of biological age estimation

Abstract

At present, no single indicator could be used as a golden index to estimate aging process. The biological age (BA), which combines several important biomarkers with mathematical modeling, has been proposed for >50 years as an aging estimation method to replace chronological age (CA). The common methods used for BA estimation include the multiple linear regression (MLR), the principal component analysis (PCA), the Hochschild’s method, and the Klemera and Doubal’s method (KDM). The fundamental differences in these four methods are the roles of CA and the selection criteria of aging biomarkers. In MLR and PCA, CA is treated as the selection criterion and an independent index. The Hochschild’s method and KDM share a similar concept, making CA an independent variable. Previous studies have either simply constructed the BA model by one or compared the four methods together. However, reviews have yet to illustrate and compare the four methods systematically. Since the BA model is a potential estimation of aging for clinical use, such as predicting onset and prognosis of diseases, improving the elderly’s living qualities, and realizing successful aging, here we summarize previous BA studies, illustrate the basic statistical steps, and thoroughly discuss the comparisons among the four common BA estimation methods.

Advanced Age is not a Risk Factor for Mortality in Patients with Bacteremia Caused by Extended-Spectrum β-Lactamase-Producing Organisms: a Multicenter Cohort Study

“…Moreover, there may be a difference between biological age and chronological age. The influence of biological age on the likelihood of developing cardiovascular, respiratory, nervous, renal, liver, or hematological disease and on life expectancy is stronger than that of chronological age (21), and estimated biological age is higher in patients with multiple comorbidities. This suggests that underlying disease affects mortality more than chronological age, and our findings support this concept.…”

“…However, the present study included many patients who were not admitted to an ICU and adjusted for underlying disease by propensity score analysis. Furthermore, life expectancy varies, even in individuals of a similar age, depending on genetic, lifestyle, and environmental factors (21). Therefore, age≥85 years is not necessarily an indication for empirical carbapenem therapy.…”

Structure–function multi‐scale connectomics reveals a major role of the fronto‐striato‐thalamic circuit in brain aging

“…gained major scientific interest in the past years due to its potential implication for health and disease of age-related molecular, genetic, cellular and organ-specific dynamics and their genetic, epigenetic, and environmental modulators (Jia, Zhang, & Chen, 2017). Indeed, it is well established that aging is a major risk factor for most of the late-onset diseases such as cancer, cardiovascular disease, diabetes, and neurodegenerative diseases (Fulop et al, 2010).…”

Aging Biomarkers: From Functional Tests to Multi‐Omics Approaches

“…Several mathematical algorithms have been proposed for calculating biological age from multiparameter data, including the multiple linear regression (MLR), the principal component analysis (PCA), and the Klemera and Doubal method. [85] Recently created "blood aging clocks" based on various machine learning methods have a mean average error ranging between 5-7 years (Table S1, Supporting Information); these clocks also show high heterogeneity of predictions between human populations. [86,87] However, some indicators of human aging demonstrate nonlinear age-related changes and therefore cannot be interpreted using standard linear regression, which is commonly applied to composite panels of biomarkers.…”