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Biological Age: Difference between revisions
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Biological Age (BA) is a concept used to assess an individual's aging status, offering a more nuanced understanding than Chronological Age (CA). | '''Biological Age (BA)''' is a concept used to assess an individual's aging status, offering a more nuanced understanding than '''Chronological Age (CA)'''. Chronological age refers simply to the amount of time that has elapsed since a person's birth, while biological age provides a measure of aging based on various physiological, biochemical, and molecular factors. This distinction is crucial because individuals of the same chronological age can exhibit significantly different aging processes and health statuses. | ||
=== Key Aspects of Biological Age === | === Key Aspects of Biological Age === | ||
Biological age is estimated as the equivalent age within the same-sex population which corresponds to an individual's mortality risk. | |||
# '''Biomarkers:''' | # '''Biomarkers:''' Biological age is typically determined by analyzing a range of biomarkers. These can include genetic markers, [[Epigenetic Alterations|epigenetic alterations]], [[Cellular Senescence|cellular senescence]], [[Telomere Attrition|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:''' | # '''Health and Functionality:''' Biological age reflects the functional state of an individual's organs and systems. A lower biological age compared to chronological age might indicate better health and lower risk for [[Age-Related Diseases|age-related diseases]], whereas a higher biological age suggests accelerated aging and potentially increased health risks. | ||
# '''Variability:''' Unlike | # '''Variability:''' Unlike chronological age, which is uniform and progresses at a constant rate (one year per year), biological age 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. For example, biological age estimated from blood markers ranged between 20-years younger and 20-years older than individuals' chronological age.{{pmid|37884697}} | ||
=== Importance in Research and Medicine === | === Importance in Research and Medicine === | ||
# '''Research Tool:''' In scientific research, | # '''Research Tool:''' In scientific research, biological age is valuable for understanding the aging process, identifying aging biomarkers, and evaluating the effectiveness of anti-aging interventions. | ||
# '''Clinical Applications:''' In a clinical setting, | # '''Clinical Applications:''' In a clinical setting, biological age can be used to assess an individual's overall health status, predict the risk of age-related diseases, and personalize healthcare and treatment plans. | ||
== Biological Age Estimation Methods == | == Biological Age 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. | 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 | The accurate estimation of biological age 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 | Various methods have been developed to estimate biological age, each with its unique approach and criteria:{{pmid|28546743}} | ||
* '''Multiple Linear Regression (MLR)''' is a statistical technique that estimates | * '''Multiple Linear Regression (MLR)''' is a statistical technique that estimates biological age by relating several independent variables (biomarkers) to a dependent variable (chronological age). In this method, chronological age 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 | * '''Principal Component Analysis (PCA)''' is another statistical technique used in biological age 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 | * '''Hochschild’s Method''' differs from MLR and PCA by making chronological age an independent variable. It aims to estimate biological age by adjusting chronological age 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 | * '''Klemera and Doubal’s Method (KDM)''' shares a similar concept with Hochschild’s method but uses a more complex statistical approach. It treats chronological age as an independent variable and incorporates multiple biomarkers to estimate biological age. | ||
=== Role of Chronological Age === | === Role of Chronological Age === | ||
The role of | The role of chronological age in the estimation of biological age varies significantly between different methods. In MLR and PCA, chronological age is used as a criterion for selecting biomarkers. This means that the biomarkers are chosen based on how well they correlate with chronological age. In this context, chronological age is not an independent variable in the statistical model but rather a reference point against which the predictive power of biomarkers is measured. | ||
In contrast, Hochschild’s method and KDM treat | In contrast, Hochschild’s method and KDM treat chronological age as an independent variable. This means chronological age is directly incorporated into the model as one of the variables that predict biological age. | ||
=== Comparison === | === Comparison === | ||
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| MLR | | MLR | ||
| 1965{{pmid|5841151}} | | 1965{{pmid|5841151}} | ||
| Aging biomarkers are determined by the correlation with | | Aging biomarkers are determined by the correlation with chronological age using MLR model | ||
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* MLR is the preliminary method and is easy to operate | * MLR is the preliminary method and is easy to operate | ||
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* The standards of aging biomarkers lead to the paradox of | * The standards of aging biomarkers lead to the paradox of chronological age | ||
* MLR also distorts the | * MLR also distorts the biological age at the regression edge and ignores discontinuity in the aging rate{{pmid|6873212}}{{pmid|3226152}}{{pmid|950448}} | ||
|- | |- | ||
| PCA | | PCA | ||
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* PCA avoids the influence of regression edge in MLR{{pmid|3226152}} | * PCA avoids the influence of regression edge in MLR{{pmid|3226152}} | ||
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* PCA cannot avoid the paradox of | * PCA cannot avoid the paradox of chronological age and some statistical deficiencies of MLR{{pmid|16318865}} | ||
|- | |- | ||
| Hochschild’s method | | Hochschild’s method | ||
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| Hochschild’s method aims to select aging biomarkers according to their effects on life expectancy{{pmid|2684676}} | | Hochschild’s method aims to select aging biomarkers according to their effects on life expectancy{{pmid|2684676}} | ||
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* Hochschild’s method solves the paradox of | * Hochschild’s method solves the paradox of chronological age | ||
* Hochschild’s method avoids statistical problems of MLR | * Hochschild’s method avoids statistical problems of MLR | ||
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* Hochschild’s method is nonstandard and relatively complicated | * Hochschild’s method is nonstandard and relatively complicated | ||
* Hochschild’s method is not based on the definition of | * Hochschild’s method is not based on the definition of biological age | ||
* A large number of subjects are required when this approach is adopted for another system{{pmid|20005245}} | * A large number of subjects are required when this approach is adopted for another system{{pmid|20005245}} | ||
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| 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}} | | 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}} | ||
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* KDM performed better than | * KDM performed better than chronological age{{pmid|23213031}} | ||
* KDM is precise when compared with other methods{{pmid|23213031}}{{pmid|20005245}}{{pmid|28110151}} | * KDM is precise when compared with other methods{{pmid|23213031}}{{pmid|20005245}}{{pmid|28110151}} | ||
* KDM solves the paradox of | * KDM solves the paradox of chronological age{{pmid|23213031}}{{pmid|20005245}} | ||
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* The calculation of KDM is complicated{{pmid|20005245}} | * The calculation of KDM is complicated{{pmid|20005245}} | ||