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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). 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.
'''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:''' BA 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.
# '''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:''' 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|age-related diseases]], whereas a higher BA suggests accelerated aging and potentially increased health risks.
# '''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 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.
# '''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, BA is valuable for understanding the aging process, identifying aging biomarkers, and evaluating the effectiveness of anti-aging interventions.
# '''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, 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.
# '''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.


== 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 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 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 biological age, each with its unique approach and criteria:{{pmid|28546743}}
 
* '''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 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 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 chronological age as an independent variable and incorporates multiple biomarkers to estimate biological age.
* '''Deep Learning'''-based approaches represent an emerging and powerful method for biological age estimation. Leveraging complex neural network architectures, these methods can analyze large datasets of biomarkers, integrating diverse types of biological data. Deep learning models can uncover intricate patterns in the data that may be missed by traditional statistical techniques, offering potentially more accurate and personalized biological age estimations. However, they require large, well-curated datasets to be effective.


Various methods have been developed to estimate BA, each with its unique approach and criteria:{{pmid|28546743}}
=== Role of Chronological Age ===
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.


* '''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.
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.
* '''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
=== Comparison ===
Comparisons of various biological age estimation methods
{| class="wikitable"
{| class="wikitable"
! colspan="1" rowspan="1" |Method
! Method
! colspan="1" rowspan="1" |Proposer
! Proposed
! colspan="1" rowspan="1" |Year
! Core concept
! colspan="1" rowspan="1" |Core concept
! Advantage
! colspan="1" rowspan="1" |Advantage
! Disadvantage
! colspan="1" rowspan="1" |Disadvantage
|-
! colspan="1" rowspan="1" |Main researchers
| MLR
| 1965, Hollingsworth{{pmid|5841151}}
| Aging biomarkers are determined by the correlation with chronological age using MLR model
|
* MLR is the preliminary method and is easy to operate
|
* The standards of aging biomarkers lead to the paradox of chronological age
* MLR also distorts the biological age at the regression edge and ignores discontinuity in the aging rate{{pmid|6873212}}{{pmid|3226152}}{{pmid|950448}}
|-
|-
| colspan="1" rowspan="1" |MLR
| PCA
| colspan="1" rowspan="1" |
| 1988, Nakamura{{pmid|2737197}}
| colspan="1" rowspan="1" |More than 50 years ago
| PCA uses fewer uncorrelated variables to explain the main variance
| colspan="1" rowspan="1" |Aging biomarkers are determined by the correlation with CA using MLR model
|
| colspan="1" rowspan="1" |MLR is the preliminary method and is easy to operate
* Biomarkers are uncorrelated variables{{pmid|16318865}}
| colspan="1" rowspan="1" |(1) The standards of aging biomarkers lead to the paradox of CA
* PCA avoids the influence of regression edge in MLR{{pmid|3226152}}
(2) MLR also distorts the BA at the regression edge and ignores discontinuity in the aging rate{{pmid|6873212}}{{pmid|3226152}}{{pmid|950448}}
|
| colspan="1" rowspan="1" |Hollingsworth et al{{pmid|5841151}} and Kroll and Saxtrup{{pmid|11708217}}
* PCA cannot avoid the paradox of chronological age and some statistical deficiencies of MLR{{pmid|16318865}}
|-
|-
| colspan="1" rowspan="1" |PCA
| Hochschild’s method
| colspan="1" rowspan="1" |Nakamura
| 1989, Hochschild{{pmid|2684676}}
| colspan="1" rowspan="1" |1985
| Hochschild’s method aims to select aging biomarkers according to their effects on life expectancy{{pmid|2684676}}
| colspan="1" rowspan="1" |PCA uses fewer uncorrelated variables to explain the main variance
|
| colspan="1" rowspan="1" |
* Hochschild’s method solves the paradox of chronological age
# Biomarkers are uncorrelated variables{{pmid|16318865}}
* Hochschild’s method avoids statistical problems of MLR
# PCA avoids the influence of regression edge in MLR{{pmid|3226152}}
|
| colspan="1" rowspan="1" |PCA cannot avoid the paradox of CA and some statistical deficiencies of MLR{{pmid|16318865}}
* Hochschild’s method is nonstandard and relatively complicated
| colspan="1" rowspan="1" |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 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}}
|-
|-
| colspan="1" rowspan="1" |Hochschild’s method
| KDM
| colspan="1" rowspan="1" |Hochschild
| 2006, Klemera and Doubal{{pmid|16318865}}
| colspan="1" rowspan="1" |1989
| 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}}
| colspan="1" rowspan="1" |Hochschild’s method aims to select aging biomarkers according to their effects on life expectancy{{pmid|2684676}}
|
| colspan="1" rowspan="1" |(1) Hochschild’s method solves the paradox of CA
* KDM performed better than chronological age{{pmid|23213031}}
(2) Hochschild’s method avoids statistical problems of MLR
* KDM is precise when compared with other methods{{pmid|23213031}}{{pmid|20005245}}{{pmid|28110151}}
| colspan="1" rowspan="1" |(1) Hochschild’s method is nonstandard and relatively complicated
* KDM solves the paradox of chronological age{{pmid|23213031}}{{pmid|20005245}}
(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}}
* The calculation of KDM is complicated{{pmid|20005245}}
| colspan="1" rowspan="1" |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>
|-
|-
| colspan="1" rowspan="1" |KDM
|Deep learning
| colspan="1" rowspan="1" |Klemera and Doubal
|2015{{pmid|26017442}}
| colspan="1" rowspan="1" |2006
|Deep learning is a subfield of machine learning, where good features can be learned automatically using a general-purpose learning procedure{{pmid|26017442}}. Deep neural networks (DNNs){{pmid|27191382}}{{pmid|29340580}}{{pmid|30644411}}{{pmid|34453631}}, convolutional neural networks (CNNs){{pmid|29581467}}{{pmid|33744131}}, and recurrent neural networks (RNNs){{pmid|31388024}} have been employed to build BA models in recent years.
| colspan="1" rowspan="1" |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}}
|
| colspan="1" rowspan="1" |(1) KDM performed better than CA{{pmid|23213031}}
* Good at handling high-dimensional dataset{{pmid|26017442}}
(2) KDM is precise when compared with other methods{{pmid|23213031}}{{pmid|20005245}}{{pmid|28110151}}
* The machine extracts features autonomously by learning{{pmid|26017442}}
(3) KDM solves the paradox of CA{{pmid|23213031}}{{pmid|20005245}}
|
| colspan="1" rowspan="1" |The calculation of KDM is complicated{{pmid|20005245}}
* Difficulty in building large data{{pmid|27191382}}
| colspan="1" rowspan="1" |Klemera and Doubal,{{pmid|16318865}} Levine,{{pmid|23213031}} Levine and Crimmins,{{pmid|25088793}} Cho et al{{pmid|20005245}} and Jee and Park{{pmid|28110151}}
* The existence of a “black box” and uncontrollable results
* Excellent programming skills and computer hardware and software support required
|}
|}


====Comparison and Discussion====
== Biomarkers for Biological Age Estimation ==
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.
{| class="wikitable"
! Organ system
! PCA
! MLR
! Hochschild’s
! KDM
|-
| rowspan="10" | Cardiovascular system
| Pulse pressure{{pmid|19940465}}{{pmid|24659482}}{{pmid|29188884}}
|
|
|
|-
| Systolic blood pressure{{pmid|2737197}}{{pmid|2282902}}{{pmid|17921421}}{{pmid|18597867}}{{pmid|12672981}}{{pmid|18840798}}{{pmid|8803500}}{{pmid|28110151}}
| Systolic blood pressure{{pmid|23213031}}{{pmid|6967883}}{{pmid|5841151}}{{pmid|950448}}{{pmid|28110151}}
|
| Systolic blood pressure{{pmid|28110151}}
|-
| Heart rate{{pmid|2737197}}{{pmid|2282902}}
|
|
|
|-
| Intima-media thickness{{pmid|19940465}}{{pmid|24522464}}{{pmid|24659482}}
|
|
|
|-
| Maximum internal diameter of carotid artery{{pmid|24522464}}{{pmid|24659482}}
|
|
|
|-
| End diastolic velocity{{pmid|19940465}}
|
|
|
|-
| Mitral valve annulus ventricular septum of the peak velocity of early filling{{pmid|24522464}}
|
|
|
|-
| Mitral valve annulus lateral wall of peak velocity of early filling{{pmid|19940465}}
|
|
|
|-
| Mitral annulus peak E anterior wall{{pmid|24659482}}
|
|
|
|-
| Ratio of peak velocity of early filling to atrial filling{{pmid|19940465}}
|
|
|
|-
| rowspan="4" | Respiratory system
| VO2 max{{pmid|22433233}}{{pmid|18597867}}
|
|
|
|-
| Forced expiratory volume in 1 second{{pmid|22433233}}{{pmid|17921421}}{{pmid|18597867}}{{pmid|12672981}}{{pmid|18840798}}
| Forced expiratory volume in 1 second{{pmid|23213031}}{{pmid|6967883}}{{pmid|28110151}}
| Forced expiratory volume in 1 second{{pmid|2583248}}
| Forced expiratory volume in 1 second{{pmid|28110151}}
|-
| Forced vital capacity{{pmid|2737197}}{{pmid|2282902}}{{pmid|8803500}}
| Forced vital capacity{{pmid|20005245}}
| Forced vital capacity{{pmid|2583248}}
|
|-
| Maximal mid expiratory flow rate 75/25{{pmid|24522464}}
| Vital capacity{{pmid|5841151}}
|
|
|-
| rowspan="9" | Nervous system
| Trail making test{{pmid|24522464}}{{pmid|29188884}}
|
|
|
|-
| Digital symbol test{{pmid|20005245}}{{pmid|24659482}}
| Digital symbol test{{pmid|20005245}}
|
|
|-
| Memory test linking names with faces{{pmid|20005245}}
| Memory test linking names with faces{{pmid|20005245}}
|
|
|-
| Memory test: which picture is at what place{{pmid|20005245}}
| Memory test: which picture is at what place{{pmid|20005245}}
|
|
|-
| Speed test: pointing icons from 1 to 15 sequentially, mixed in random positions{{pmid|20005245}}
|
|
|
|-
| Visual reaction time{{pmid|20005245}}{{pmid|22433233}}
|
| Visual reaction time{{pmid|2583248}}
|
|-
|
|
| Sequence of lamps{{pmid|2583248}}
|
|-
|
|
| Alternate button tapping time with/without decision{{pmid|2583248}}
|
|-
|
|
| Movement time with/without decision{{pmid|2583248}}
|
|-
| rowspan="3" | Renal system
| Blood urea nitrogen{{pmid|2737197}}{{pmid|2282902}}{{pmid|17921421}}{{pmid|18597867}}{{pmid|29380856}}{{pmid|18840798}}{{pmid|8803500}}
| Blood urea nitrogen{{pmid|23213031}}{{pmid|6967883}}{{pmid|28110151}}
|
| Blood urea nitrogen{{pmid|28110151}}
|-
|
| Serum creatinine{{pmid|28110151}}
|
| Serum creatinine{{pmid|28110151}}
|-
| Cystatin C{{pmid|19940465}}{{pmid|24522464}}{{pmid|24659482}}{{pmid|29188884}}
|
|
|
|-
| rowspan="7" | Liver
| Serum albumin{{pmid|17921421}}{{pmid|18597867}}{{pmid|29380856}}{{pmid|18840798}}
| Serum albumin{{pmid|950448}}
|
|
|-
| Glutamic oxaloacetic transaminase{{pmid|2737197}}{{pmid|2282902}}
| Glutamic oxaloacetic transaminase{{pmid|28110151}}
|
| Glutamic oxaloacetic transaminase{{pmid|28110151}}
|-
| Glutamic pyruvic transaminase{{pmid|8803500}}
|
|
|
|-
| Ratio of albumin to globulin{{pmid|12672981}}
|
|
|
|-
| Lactate dehydrogenase{{pmid|2737197}}{{pmid|2282902}}{{pmid|8803500}}
|
|
|
|-
|
| Serum globulin{{pmid|950448}}
|
|
|-
|
| Alkaline phosphatase{{pmid|950448}}
|
|
|-
| rowspan="7" | Hematologic system
| Erythrocyte sedimentation rate{{pmid|18597867}}
| Erythrocyte sedimentation rate{{pmid|950448}}
|
|
|-
| Mean corpuscular hemoglobin{{pmid|12672981}}
|
|
|
|-
| Red blood cell count{{pmid|18840798}}
|
|
|
|-
| Hematocrit{{pmid|17921421}}
|
|
|
|-
| Hemoglobin concentration{{pmid|2737197}}{{pmid|2282902}}
|
|
|
|-
| Fibrinogen{{pmid|19940465}}
|
|
|
|-
|
| Ferratin{{pmid|28110151}}
|
| Ferratin{{pmid|28110151}}
|-
| rowspan="6" | Metabolism
| Glycosylated hemoglobin{{pmid|18597867}}
|
|
|
|-
| Glucose{{pmid|12672981}}{{pmid|8803500}}
| Glucose{{pmid|950448}}
|
|
|-
| Low-density cholesterol{{pmid|18597867}}
|
|
|
|-
| Atherogenic index{{pmid|2737197}}{{pmid|2282902}}
|
|
|
|-
| Triglyceride{{pmid|2282902}}
| Triglycerides{{pmid|6967883}}
|
|
|-
| Total cholesterol{{pmid|8803500}}
| Total cholesterol{{pmid|23213031}}{{pmid|6967883}}{{pmid|5841151}}{{pmid|28110151}}
|
| Total cholesterol{{pmid|28110151}}
|-
| rowspan="4" | Muscle and fat
| Grip strength{{pmid|20005245}}{{pmid|22433233}}
| Grip strength{{pmid|20005245}}{{pmid|5841151}}
|
|
|-
| Soft lean mass{{pmid|22433233}}
|
|
|
|-
| Waist circumference{{pmid|22433233}}{{pmid|18597867}}
| Waist circumference{{pmid|28110151}}
|
| Waist circumference{{pmid|28110151}}
|-
| Percent body fat{{pmid|18597867}}
|
|
|
|-
| rowspan="8" | Sensory system
| Hearing threshold{{pmid|18597867}}
|
|
|
|-
| Highest audible pitch{{pmid|20005245}}
| Highest audible pitch{{pmid|20005245}}
| Highest audible pitch{{pmid|2583248}}
|
|-
|
| Light extinction test{{pmid|5841151}}
|
|
|-
|
| Visual acuity{{pmid|5841151}}
|
|
|-
|
| Auditory function{{pmid|5841151}}
|
|
|-
|
| Vibrotactile sensitivity{{pmid|5841151}}
| Vibrotactile sensitivity{{pmid|2583248}}
|
|-
|
| Auditory reaction time{{pmid|20005245}}
| Auditory reaction time{{pmid|2583248}}
|
|-
|
| Focal range test using a Landolt ring{{pmid|20005245}}
| Visual accommodation{{pmid|2583248}}
|
|-
| Genetic index
| Telomere restriction fragment{{pmid|24659482}}
|
|
|
|}
 
== Biomarkers 2 ==
The common aging biomarkers of four methods in different systems.
{| class="wikitable sortable"
! System
!
! MLR
! PCA
! KDM
! Deep learning
|-
| Cardiovascular system
|Systolic Blood Pressure (SBP)
| 23213031  5841151  3226152  950448  6667707  17889950  23642770  28110151  30899733
| 23213031  3226152  2737197  2282902  8803500  12634284  12672981  17921421  18840798  18597867  22433233  28110151  30899733
| 23213031  26150497  28110151  28958059  28977464  30999227  30899733  31566204  32946548  34038024
|
|-
| Cardiovascular system
|Diastolic Blood Pressure (DBP)
| 17889950  23642770  31179487
| 31179487
| 27216811 31179487
|
|-
| Cardiovascular system
|Pulse Pressure
| 23642770
| 29188884  24522464  19940465
|
|
|-
| Cardiovascular system
|Mean Arterial Pressure
|
| 28203066
|
|
|-
| Cardiovascular system
|Pulse
| 3226152
| 3226152  2737197
|
|
|-
| Cardiovascular system
|Pulse Wave Velocity
| 6667707
|
|
|
|-
| Cardiovascular system
|Heart Rate
|
| 2282902
|
|
|-
| Cardiovascular system
|Intima-Media Thickness
|
| 24659482 19940465
|
|
|-
| Cardiovascular system
|Minimum Intima-Media Thickness
|
| 29188884 24522464
|
|
|-
| Cardiovascular system
|End Diastolic Velocity
|
| 19940465
|
|
|-
| Cardiovascular system
|Mitral Valve E/A Peak
|
| 29188884 19940465
|
|
|-
| Cardiovascular system
|MVEL, MVES, MVEA
|
| MVEL 19940465 , MVES 24522464 , MVEA 24659482
|
|
|-
| Cardiovascular system
|Atherosclerosis Index
|
| 2737197  2282902
|
|
|-
| Cardiovascular system
|NT-proBNP
|
|
|
| 34453631
|-
| Cardiovascular system
|Cardiac Troponin I
|
|
|
| 34453631
|-
| Cardiovascular system
|Creatine Phosphokinase
| 23642770
|
|
|
|-
| Cardiovascular system
|Homocysteine
| 23642770
|
|
|
|-
| Respiratory system
|Forced Vital Capacity (FVC)
| 3226152  17889950  23642770  20005245
| 3226152  2737197  2282902  8803500  12634284
| 20005245
|
|-
|Respiratory system
|Forced Expiratory Volume in 1 Second (FEV1)
| 23213031  950448  7162237  17889950  23642770  28110151  30899733  31179487
| 23213031  12634284  12672981  17921421  18840798  18597867  22433233  28110151  30899733  31179487
| 23213031  26150497  28110151  28958059  28977464  30899733  31179487
|
|-
|Respiratory system
|Vital Capacity
| 5841151  6667707
|
|
|
|-
|Respiratory system
|Maximal Midexpiratory Flow Rate 75/25
|
| 24522464
|
|
|-
|Respiratory system
|VO2 Max
|
| 18597867  22433233
|
|
|-
|Respiratory system
|Chest Radiography
|
|
|
| 33744131
|-
| Nervous system
|Mini-Mental State Examination (MMSE)
| 31179487
| 31179487
| 31179487
|
|-
| Nervous system
|Digital Symbol Test
| 6667707
| 24659482
|
|
|-
| Nervous system
|Numeric Memory
| 20005245
|
| 20005245
|
|-
| Nervous system
|Associated Memory
| 20005245
|
| 20005245
|
|-
| Nervous system
|Topological Memory
| 20005245
|
| 20005245
|
|-
| Nervous system
|Short-Time Memory
| 6610563
|
|
|
|-
| Nervous system
|Concentration
| 20005245
|
| 20005245
|
|-
| Nervous system
|Intellectuality - Mental Defect
| 6667707
|
|
|
|-
| Nervous system
|Trail Making Test
|
| 29188884  24522464
|
|
|-
| Endocrine metabolic system
|Glucose
| 17889950  23642770
| 8803500  12672981  28203066
| 28977464
| 27191382  29340580  30644411  34453631
|-
| Endocrine metabolic system
|HBA1C
| 23213031  23642770
| 23213031  18597867
| 23213031  26150497  28958059  30999227  31566204  32946548  31693736  34038024
| 30644411
|-
| Endocrine metabolic system
|C-peptide
|
|
|
| 34453631
|-
| Endocrine metabolic system
|Insulin
|
|
|
| 34453631
|-
| Endocrine metabolic system
|Triglyceride
| 950448  17889950  23642770
| 2282902  28203066
| 32946548
| 29340580  34453631
|-
| Endocrine metabolic system
|Total Cholesterol (TC)
| 23213031  5841151  3226152  950448  7162237  17889950  23642770  28110151
| 23213031  3226152  8803500  28110151
| 23213031  26150497  28110151  28958059  28977464  31566204  32946548  34038024
| 27191382  29340580
|-
| Endocrine metabolic system
|High-Density Lipoprotein (HDL)
| 17889950 ,
| 28203066
|
| 29340580  34453631
|-
| Endocrine metabolic system
|Low-Density Lipoprotein (LDL)
|23642770
|18597867
|
|29340580 34453631
|-
| Endocrine metabolic system
|Apolipoprotein A1 and B
|
|
|
| 34453631
|-
| Endocrine metabolic system
|Thyroid-Stimulating Hormone (TSH)
| 17889950
|
| 27216811
|
|-
| Endocrine metabolic system
|Testosterone
| 17889950
|
|
| Testosterone 34453631
|-
| Endocrine metabolic system
|Vitamin D
| 30899733
| 30899733
| 30899733
| 34453631
|-
| Endocrine metabolic system
|Calcium
|
|
| 27216811
| 29340580
|-
| Endocrine metabolic system
|Potassium
|
|
|
| 29340580
|-
| Endocrine metabolic system
|Sodium
|
|
|
| 29340580
|-
| Endocrine metabolic system
|Inorganic Phosphorus
|
|
| 27216811
|
|-
| Endocrine metabolic system
|Urea
| 23213031  3226152  950448  23642770  28110151
| 23213031  3226152  2737197  2282902  8803500  12634284  17921421  18840798  18597867  28110151
| 23213031  26150497  27216811  28110151  30999227  31566204  32946548  31693736
| 27191382  29340580  30644411
|-
| Endocrine metabolic system
|Creatinine
| 23213031  17889950  23642770  28110151  30899733
| 28110151  30899733
| 23213031  26150497  27216811  28958059  30999227  31566204  32946548  31693736  34038024
| 29340580  34453631
|-
| Endocrine metabolic system
|Estimated Glomerular Filtration Rate (eGFR)
| 31179487
| 31179487
| 31179487
|
|-
| Endocrine metabolic system
|Uric Acid
|
|
| 30999227  31693736
| 34453631
|-
| Endocrine metabolic system
|Cystatin C
|
| 29188884  24659482  24522464  19940465
|
| 34453631
|-
| Endocrine metabolic system
|Creatinine Clearance
| 23642770
|
|
|
|-
| Endocrine metabolic system
|Urine Specific Gravity
| 23642770
|
|
|
|-
| Endocrine metabolic system
|Urine pH
| 23642770
|
|
|
|-
| Digestive system
|Alanine Aminotransferase (ALT)
| 23642770
| 8803500
|
| 34453631
|-
| Digestive system
|Aspartate Aminotransferase (AST)
| 3226152  23642770  28110151
| 3226152  2737197  2282902  28110151
| 28110151
| 34453631
|-
| Digestive system
|Alkaline Phosphatase (ALP)
| 23213031  950448  23642770
| 23213031
| ALP 23213031  26150497  27216811  28958059  30999227  31693736  34038024
| ALP 27191382
|-
| Digestive system
|Total Protein
| Total protein 23642770
|
| Total protein 27216811
| Total protein 29340580
|-
| Digestive system
|Albumin
| Albumin 23213031  3226152  23642770
| Albumin 23213031  3226152  12634284  17921421  18840798  18597867
| Albumin 23213031  26150497  27216811  28958059  30999227  31566204  32946548  31693736  34038024
| Albumin 27191382  29340580  34453631
|-
| Digestive system
|Albumin/Globulin Ratio (A/G)
| A/G 3226152  23642770
| A/G 3226152  12634284  12672981
|
|
|-
| Digestive system
|Total Bilirubin
| Total bilirubin 23642770
|
|
| Total bilirubin 29340580
|-
| Digestive system
|Direct Bilirubin
| Direct bilirubin 23642770
|
|
|
|-
| Digestive system
|Amylase
| Amylase 23642770
|
|
|
|-
| Digestive system
|Lactate Dehydrogenase
| Lactate dehydrogenase 7162237  23642770
| Lactate dehydrogenase 2282902  8803500
|
|
|-
| Digestive system
|Alpha 2 Globulin
|
|
|
| Alpha 2 globulin 27191382
|-
| Digestive system
|Gamma Glutamyl Transpeptidase
| Gamma glutamyl
transpeptidase 23642770
|
|
|
|-
| Hematologic System
|Red Blood Cell
| Red blood cell 30899733
| Red blood cell 12634284  18840798  30899733
| Red blood cell 30899733  32946548
| Red blood cell 27191382  29340580  34453631
|-
| Hematologic System
|Red Blood Cell Volume Distribution Width
|
|
| Red blood cell volume
distribution width 30999227
| Red blood cell volume
distribution width 27191382  34453631
|-
| Hematologic System
|Hematocrit
|
| Hematocrit 12634284  17921421
|
| Hematocrit 27191382  29340580
|-
| Hematologic System
|Mean Corpuscular Volume
|
|
| Mean corpuscular
volume 30999227  31566204  31693736
| Mean corpuscular
volume 29340580  34453631
|-
| Hematologic System
|Mean Corpuscular Hemoglobin
|
| Mean corpuscular
hemoglobin 12672981
|
|
|-
| Hematologic System
|Mean Corpuscular Hemoglobin Concentration
|
|
|
| Mean corpuscular hemoglobin concentration 29340580  34453631
|-
| Hematologic System
|Hemoglobin
| Hemoglobin 3226152  31179487
| Hemoglobin 3226152  2737197  2282902  8803500  12634284  31179487
| Hemoglobin 27216811  31179487
| Hemoglobin 29340580  34453631
|-
| Hematologic System
|White Blood Cell
|
|
| White blood cell 30999227  31566204  31693736
| White blood cell 34453631
|-
| Hematologic System
|Granulocytes
|
|
|
| Granulocytes 34453631
|-
| Hematologic System
|Neutrophils
|
|
|
| Neutrophils 34453631
|-
| Hematologic System
|Basophils, Eosinophils
|
|
|
| Basophils 34453631 , Eosinophils 34453631
|-
| Hematologic System
|Lymphocytes
|
|
| Lymphocytes 30999227  31566204  31693736
| Lymphocytes 27191382  34453631
|-
| Hematologic System
|Monocytes
| Monocytes 31179487
| Monocytes 31179487
| Monocytes 31179487
| Monocytes 34453631
|-
| Hematologic System
|Platelet
|
|
| Platelet 32946548
| Platelet 29340580  34453631
|-
| Hematologic System
|Mean Platelet Volume
|
|
|
| Mean platelet volume 34453631
|-
| Hematologic System
|Platelet Distribution Width
|
|
|
| Platelet distribution width 34453631
|-
| Hematologic System
|Erythrocyte Sedimentation Rate
| 950448  17889950
| 18597867
|
|
|-
| Hematologic System
|D-dimer, Fibrinogen
|
| D-dimer 24659482
Fibrinogen 19940465
|
| D-dimer 34453631
|-
| Hematologic System
|Ferritin
| Ferritin 28110151
| Ferritin 28110151
| Ferritin 28110151  32946548
| Ferritin 30644411
|-
| Hematologic System
|Transferrin
|
|
| Fransferrin 32946548
|
|-
| Sensory system
|Visual Accommodation
| Visual accommodation 6667707  20005245
|
| Visual accommodation 20005245
|
|-
| Sensory system
|Visual Reaction Time
| Visual reaction time 20005245
|
| Visual reaction time 20005245
|
|-
| Sensory system
|Visual Acuity
| Visual acuity 5841151  3226152
| Visual acuity 3226152
|
|
|-
| Sensory system
|Hearing
| Hearing 5841151  7162237  6667707  20005245
| Hearing 18597867
| Hearing 20005245
|
|-
| Sensory system
|Vibrotactile
| Vibrotactile 5841151  6610563  20005245
|
| Vibrotactile 20005245
|
|-
| Sensory system
|Retinal Photos
|
|
|
| Retinal photos
|-
| Inflammatory index
|C-Reactive Protein (CRP)
| CRP 23213031
| CRP 23213031
| CRP 23213031  26150497  28958059  28977464  30999227  31566204  32946548  31693736  34038024
| CRP 34453631
|-
| Inflammatory index
|Cytomegalovirus Optical Density
| Cytomegalovirus optical density 23213031
| Cytomegalovirus
optical density 23213031
| Cytomegalovirus
optical density 23213031  26150497  31566204
|
|-
| Inflammatory index
|Interleukin-6
| colspan="1" rowspan="2" |
| colspan="1" rowspan="2" |
| Interleukin-6 28977464
| colspan="1" rowspan="2" |
|-
| Inflammatory index
|P-selectin
| P-selectin 28977464
|-
| Motion index
|Grip Strength
| Grip strength 5841151  6610563  31179487  20005245
| Grip strength 22433233  31179487
| Grip strength 31179487  20005245
|
|-
| Motion index
|Vertical Jump
|
| Vertical jump 22433233
|
|
|-
| Motion index
|Timed Up and Go Test
| Timed up and go test 31179487
| Timed up and go test 31179487
| Timed up and go test 31179487
|
|-
| Motion index
|Chair Rise Time
| Chair rise time 31179487
| Chair rise time 31179487
| Chair rise time 31179487
|
|-
| Motion index
|1-Week Physical Activity
|
|
|
| 1-week physical activity 29581467  31388024
|-
| Body morphology index
|Waist Circumference (WC)
| WC 23642770  28110151
| WC 18597867  22433233  28110151  28203066
| WC 28110151
|
|-
| Body morphology index
|Waist-to-Hip Ratio
| Waist-to-hip ratio 17889950  23642770
|
|
|
|-
| Body morphology index
|Waist-to-Height Ratio
| Waist-to-height ratio 30899733
| Waist-to-height ratio 30899733
| Waist-to-height ratio 30899733
|
|-
| Body morphology index
|Body Mass Index (BMI)
| Body mass index 17889950  23642770
|
|
|
|-
| Body morphology index
|Weight
| Weight 6667707
|
|
|
|-
| Body morphology index
|Height
| Height 31179487
| Height 31179487
| Height 31179487
|
|-
| Body morphology index
|Body Fat
| Body fat 17889950  23642770
| Body fat 18597867
|
|
|-
| Body morphology index
|Lean Body Mass
| Lean body mass 17889950  23642770
|
|
|
|-
| Body morphology index
|Soft Lean Mass
|
| Soft lean mass 22433233
|
|
|-
| Genetic index
|Terminal Telomere Restriction Fragment
|
| Terminal telomere restriction
fragment 24659482
|
|
|}
SBP, systolic blood pressure; DBP, diastolic blood pressure; NT-proBNP, N-terminal pro brain natriuretic peptide; MVEA, mitral annulus peak E anterior wall; MVEL, mitral valve annulus lateral wall of peak velocity of early filling; MVES, mitral valve annulus ventricular septum of the peak velocity of early filling; FEV1, forced expiratory volume in 1.0 s; FVC, forced vital capacity; MMSE, mini-mental state examination; eGFR, estimated glomerular filtration rat; HBA1C, glycosylated hemoglobin; HDL, high density lipoprotein; LDL, low density lipoprotein; TSH, thyroid stimulating hormone; ALT, Alanine aminotransferase; AST, Aspartate aminotransferase; ALP, alkaline ''phosphatase; A/G, ratio of albumin to globulin; CRP, c-reactive protein; WC, waist circumference''.


== Further Reading ==
== Further Reading ==


* {{pmid text|28546743}}
* {{pmid text|28546743}}
* {{pmid text|37124811}}
== Todo ==
* {{pmid text|28396265}}
* {{pmid text|36499430}}
* {{pmid text|34595331}}
* {{pmid text|37884697}}
* {{pmid text|32363395}}


== See Also ==
== See Also ==
Retrieved from "https://www.longewiki.com/wiki/Special:MobileDiff/2626...2661"