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.

Keywords: aging biomarker; chronological age; statistical method; statistical model.

Figure 1

Flowchart for the basic steps of the BA model constructed using the PCA method. Notes: For the PCA method, correlation analysis is used as the first step to select parameters that vary according to CA. Redundancy analysis is then performed to select uncorrelated parameters. For the PCA step, indicators with eigen values >1 are defined as aging biomarkers. Abbreviations: BA, biological age; PCA, principal component analysis; CA, chronological age.

Figure 2

Diagram of SEM. Notes: In SEM, exogenous variable X and endogenous variable Y are represented by rectangles, exogenous latent variable ξ and endogenous latent variable η are represented by circles, and σ and ε are the errors of exogenous and endogenous variables, respectively. The coefficients between variables and latent variables are expressed by λ; γ describes how exogenous latent variables affect endogenous latent variables. The relationship between endogenous latent variables is described as β, and the residual terms of SEM are represented by ζ. Abbreviation: SEM, structural equation modeling.