Biological Age

Biological Age (BA) is a concept used to assess an individual's aging status, offering a more nuanced understanding than Chronological Age (CA). CA refers simply to the amount of time that has elapsed since a person's birth, while BA provides a measure of aging based on various physiological, biochemical, and molecular factors. This distinction is crucial because individuals of the same CA can exhibit significantly different aging processes and health statuses.

Key Aspects of Biological Age

Biomarkers: BA is typically determined by analyzing a range of biomarkers. These can include genetic markers, epigenetic alterations, cellular senescence, telomere length, metabolic markers, and more. The specific biomarkers chosen depend on the method of estimation and the focus of the study.
Health and Functionality: BA reflects the functional state of an individual's organs and systems. A lower BA compared to CA might indicate better health and lower risk for age-related diseases, whereas a higher BA suggests accelerated aging and potentially increased health risks.
Variability: Unlike CA, which is uniform and progresses at a constant rate (one year per year), BA can vary significantly between individuals. Factors such as lifestyle, genetics, environment, and disease can influence the rate at which a person's biological systems age.

Importance in Research and Medicine

Research Tool: In scientific research, BA is valuable for understanding the aging process, identifying aging biomarkers, and evaluating the effectiveness of anti-aging interventions.
Clinical Applications: In a clinical setting, BA can be used to assess an individual's overall health status, predict the risk of age-related diseases, and personalize healthcare and treatment plans.

Estimation Methods

Biological age estimation has emerged as a significant tool in gerontology, aiming to provide a more accurate measure of aging than chronological age.

The accurate estimation of BA has significant implications for clinical practice, including predicting disease onset and prognosis, improving the quality of life for the elderly, and promoting successful aging. Each method offers unique insights, and a comprehensive understanding of these methods can lead to better clinical decision-making and more effective interventions for aging-related conditions.

Various methods have been developed to estimate BA, each with its unique approach and criteria:^[1]

Multiple Linear Regression (MLR) is a statistical technique that estimates BA by relating several independent variables (biomarkers) to a dependent variable (CA). In this method, CA is used as a criterion for selecting biomarkers and is treated as an independent index.
Principal Component Analysis (PCA) is another statistical technique used in BA estimation. PCA reduces the dimensionality of the data by transforming multiple biomarkers into a set of linearly uncorrelated variables, known as principal components.
Hochschild’s Method differs from MLR and PCA by making CA an independent variable. It aims to estimate BA by adjusting CA based on specific biomarkers.
Klemera and Doubal’s Method (KDM) shares a similar concept with Hochschild’s method but uses a more complex statistical approach. It treats CA as an independent variable and incorporates multiple biomarkers to estimate BA.

Comparisons among MLR, PCA, Hochschild’s method, and KDM

Method	Proposer	Year	Core concept	Advantage	Disadvantage	Main researchers
MLR		More than 50 years ago	Aging biomarkers are determined by the correlation with CA using MLR model	MLR is the preliminary method and is easy to operate	(1) The standards of aging biomarkers lead to the paradox of CA (2) MLR also distorts the BA at the regression edge and ignores discontinuity in the aging rate{{pmid\|6873212}}{{pmid\|3226152}}{{pmid\|950448}}	Hollingsworth et al{{pmid\|5841151}} and Kroll and Saxtrup{{pmid\|11708217}}
PCA	Nakamura	1985	PCA uses fewer uncorrelated variables to explain the main variance	(1) Biomarkers are uncorrelated variables{{pmid\|16318865}} (2) PCA avoids the influence of regression edge in MLR{{pmid\|3226152}}	PCA cannot avoid the paradox of CA and some statistical deficiencies of MLR{{pmid\|16318865}}	Nakamura et al,{{pmid\|2737197}}{{pmid\|2282902}} Nakamura and Miyao,{{pmid\|12634284}} Nakamura et al,{{pmid\|8026568}}{{pmid\|9762521}} Nakamura and Miyao,{{pmid\|17921421}} Nakamura et al,{{pmid\|3226152}} Nakamura,<ref>75. Nakamura E. The assessment of physiological age based upon a principal component analysis of various physiological variables. J Kyoto Pref Univ Med. 1985;94:757–769. [Google Scholar]</ref> Nakamura and Miyao,{{pmid\|18840798}} Nakamura et al,{{pmid\|8803500}} Park et al,{{pmid\|18597867}} Bai et al,{{pmid\|19940465}} and Zhang{{pmid\|25470806}}–{{pmid\|24659482}}
Hochschild’s method	Hochschild	1989	Hochschild’s method aims to select aging biomarkers according to their effects on life expectancy{{pmid\|2684676}}	(1) Hochschild’s method solves the paradox of CA (2) Hochschild’s method avoids statistical problems of MLR	(1) Hochschild’s method is nonstandard and relatively complicated (2) Hochschild’s method is not based on the definition of BA (3) A large number of subjects are required when this approach is adopted for another system{{pmid\|20005245}}	Hochschild{{pmid\|2684676}}{{pmid\|2583248}}<ref>76. Hochschild R. Validating Biomarkers of Aging-Mathematical Approaches and Results of a 2462-Person Study. Boca Raton: CRC Press; 1994. [Google Scholar]</ref>
KDM	Klemera and Doubal	2006	KDM is based on minimizing the distance between m regression lines and m biomarker points in an m-dimensional space of all biomarkers{{pmid\|16318865}}	(1) KDM performed better than CA{{pmid\|23213031}} (2) KDM is precise when compared with other methods{{pmid\|23213031}}{{pmid\|20005245}}{{pmid\|28110151}} (3) KDM solves the paradox of CA{{pmid\|23213031}}{{pmid\|20005245}}	The calculation of KDM is complicated{{pmid\|20005245}}	Klemera and Doubal,{{pmid\|16318865}} Levine,{{pmid\|23213031}} Levine and Crimmins,{{pmid\|25088793}} Cho et al{{pmid\|20005245}} and Jee and Park{{pmid\|28110151}}

Comparison and Discussion

While MLR and PCA treat CA as a criterion for biomarker selection, Hochschild’s method and KDM consider CA as an independent variable. The choice of method depends on the specific research goals and the nature of the available data. MLR and PCA are more straightforward and are often used in initial studies, while Hochschild’s method and KDM provide a more nuanced view of the aging process.

Todo

2017, Common methods of biological age estimation ^[1]

References

↑ ^1.0 ^1.1 Jia L et al.: Common methods of biological age estimation. Clin Interv Aging 2017. (PMID 28546743) [PubMed] [DOI] [Full text] 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.

[pmid28546743-1] 1.0 ^1.1 Jia L et al.: Common methods of biological age estimation. Clin Interv Aging 2017. (PMID 28546743) [PubMed] [DOI] [Full text] 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.

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