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== Estimation Methods ==
== 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.{{pmid|28546743}}
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.
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) ===
Various methods have been developed to estimate BA, each with its unique approach and criteria:{{pmid|28546743}}
'''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====
* '''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.
#'''Selection of Biomarkers:''' Biomarkers are chosen based on their correlation with CA.
* '''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.
#'''Model Construction:''' A linear regression model is built with CA as the dependent variable and the selected biomarkers as independent variables.
* '''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.
#'''Estimation of BA:''' The model predicts BA based on the levels of 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.
===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.
Comparisons among MLR, PCA, Hochschild’s method, and KDM
====Key Steps in PCA for BA Estimation====
{| class="wikitable"
#'''Data Standardization:''' Biomarkers are standardized for a fair comparison.
! colspan="1" rowspan="1" |Method
#'''Extraction of Principal Components:''' Principal components are derived, which capture the maximum variance in the data.
! colspan="1" rowspan="1" |Proposer
#'''BA Estimation:''' The first few principal components, which explain the most variance, are used to estimate BA.
! colspan="1" rowspan="1" |Year
===Hochschild’s Method===
! colspan="1" rowspan="1" |Core concept
'''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.
! colspan="1" rowspan="1" |Advantage
====Key Steps in Hochschild’s Method====
! colspan="1" rowspan="1" |Disadvantage
#'''Identification of Biomarkers:''' Biomarkers are selected based on their relationship with aging.
! colspan="1" rowspan="1" |Main researchers
#'''Adjustment of CA:''' CA is adjusted according to the levels of these biomarkers to estimate BA.
|-
===Klemera and Doubal’s Method (KDM)===
| colspan="1" rowspan="1" |MLR
'''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.
| colspan="1" rowspan="1" |
====Key Steps in KDM====
| colspan="1" rowspan="1" |More than 50 years ago
#'''Selection of Biomarkers:''' Biomarkers are chosen for their relevance to aging.
| colspan="1" rowspan="1" |Aging biomarkers are determined by the correlation with CA using MLR model
#'''Construction of a Complex Model:''' A sophisticated statistical model is developed, considering CA and biomarkers.
| colspan="1" rowspan="1" |MLR is the preliminary method and is easy to operate
#'''Estimation of BA:''' The model provides an estimate of BA, adjusting for CA.
| colspan="1" rowspan="1" |(1) The standards of aging biomarkers lead to the paradox of CA
===Comparison and Discussion===
(2) MLR also distorts the BA at the regression edge and ignores discontinuity in the aging rate<nowiki>{{pmid|6873212}}</nowiki><nowiki>{{pmid|3226152}}</nowiki><nowiki>{{pmid|950448}}</nowiki>
| colspan="1" rowspan="1" |<nowiki>Hollingsworth et al{{pmid|5841151}} and Kroll and Saxtrup{{pmid|11708217}}</nowiki>
|-
| colspan="1" rowspan="1" |PCA
| colspan="1" rowspan="1" |Nakamura
| colspan="1" rowspan="1" |1985
| colspan="1" rowspan="1" |PCA uses fewer uncorrelated variables to explain the main variance
| colspan="1" rowspan="1" |<nowiki>(1) Biomarkers are uncorrelated variables{{pmid|16318865}}</nowiki>
(2) PCA avoids the influence of regression edge in MLR<nowiki>{{pmid|3226152}}</nowiki>
| colspan="1" rowspan="1" |<nowiki>PCA cannot avoid the paradox of CA and some statistical deficiencies of MLR{{pmid|16318865}}</nowiki>
| colspan="1" rowspan="1" |<nowiki>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}}</nowiki>
|-
| colspan="1" rowspan="1" |Hochschild’s method
| colspan="1" rowspan="1" |Hochschild
| colspan="1" rowspan="1" |1989
| colspan="1" rowspan="1" |<nowiki>Hochschild’s method aims to select aging biomarkers according to their effects on life expectancy{{pmid|2684676}}</nowiki>
| colspan="1" rowspan="1" |(1) Hochschild’s method solves the paradox of CA
(2) Hochschild’s method avoids statistical problems of MLR
| colspan="1" rowspan="1" |(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<nowiki>{{pmid|20005245}}</nowiki>
| colspan="1" rowspan="1" |<nowiki>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></nowiki>
|-
| colspan="1" rowspan="1" |KDM
| colspan="1" rowspan="1" |Klemera and Doubal
| colspan="1" rowspan="1" |2006
| colspan="1" rowspan="1" |KDM is based on minimizing the distance between ''m'' regression lines and ''m'' biomarker points in an ''m''<nowiki>-dimensional space of all biomarkers{{pmid|16318865}}</nowiki>
| colspan="1" rowspan="1" |<nowiki>(1) KDM performed better than CA{{pmid|23213031}}</nowiki>
(2) KDM is precise when compared with other methods<nowiki>{{pmid|23213031}}</nowiki><nowiki>{{pmid|20005245}}</nowiki><nowiki>{{pmid|28110151}}</nowiki>
(3) KDM solves the paradox of CA<nowiki>{{pmid|23213031}}</nowiki><nowiki>{{pmid|20005245}}</nowiki>
| colspan="1" rowspan="1" |<nowiki>The calculation of KDM is complicated{{pmid|20005245}}</nowiki>
| colspan="1" rowspan="1" |<nowiki>Klemera and Doubal,{{pmid|16318865}} Levine,{{pmid|23213031}} Levine and Crimmins,{{pmid|25088793}} Cho et al{{pmid|20005245}} and Jee and Park{{pmid|28110151}}</nowiki>
|}
 
====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 ==
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