Biological Age: Difference between revisions
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===Comparison and Discussion=== | ===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. | 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 == | == Todo == | ||
Revision as of 02:28, 30 January 2024
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. Various methods have been developed to estimate BA, each with its unique approach and criteria.[1]
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.
Multiple Linear Regression (MLR)
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.
Key Steps in MLR for BA Estimation
- Selection of Biomarkers: Biomarkers are chosen based on their correlation with CA.
- Model Construction: A linear regression model is built with CA as the dependent variable and the selected biomarkers as independent variables.
- Estimation of BA: The model predicts BA based on the levels of biomarkers.
Principal Component Analysis (PCA)
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.
Key Steps in PCA for BA Estimation
- Data Standardization: Biomarkers are standardized for a fair comparison.
- Extraction of Principal Components: Principal components are derived, which capture the maximum variance in the data.
- BA Estimation: The first few principal components, which explain the most variance, are used to estimate BA.
Hochschild’s Method
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.
Key Steps in Hochschild’s Method
- Identification of Biomarkers: Biomarkers are selected based on their relationship with aging.
- Adjustment of CA: CA is adjusted according to the levels of these biomarkers to estimate BA.
Klemera and Doubal’s Method (KDM)
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.
Key Steps in KDM
- Selection of Biomarkers: Biomarkers are chosen for their relevance to aging.
- Construction of a Complex Model: A sophisticated statistical model is developed, considering CA and biomarkers.
- Estimation of BA: The model provides an estimate of BA, adjusting for CA.
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]
See Also
- Epigenetic Clocks
- Wikipedia - Biomarkers of aging
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.