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Biological Age

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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.

Key Aspects of Biological Age

  1. 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.
  2. 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.
  3. 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

  1. Research Tool: In scientific research, BA is valuable for understanding the aging process, identifying aging biomarkers, and evaluating the effectiveness of anti-aging interventions.
  2. 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[2][3][4]

Hollingsworth et al[5] and Kroll and Saxtrup[6]
PCA Nakamura 1985 PCA uses fewer uncorrelated variables to explain the main variance (1) Biomarkers are uncorrelated variables[7]

(2) PCA avoids the influence of regression edge in MLR[3]

PCA cannot avoid the paradox of CA and some statistical deficiencies of MLR[7] Nakamura et al,[8][9] Nakamura and Miyao,[10] Nakamura et al,[11][12] Nakamura and Miyao,[13] Nakamura et al,[3] Nakamura,[14] Nakamura and Miyao,[15] Nakamura et al,[16] Park et al,[17] Bai et al,[18] and Zhang[19]–[20]
Hochschild’s method Hochschild 1989 Hochschild’s method aims to select aging biomarkers according to their effects on life expectancy[21] (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[22]

Hochschild[21][23][24]
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[7] (1) KDM performed better than CA[25]

(2) KDM is precise when compared with other methods[25][22][26] (3) KDM solves the paradox of CA[25][22]

The calculation of KDM is complicated[22] Klemera and Doubal,[7] Levine,[25] Levine and Crimmins,[27] Cho et al[22] and Jee and Park[26]

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.

Further Reading

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

See Also

References

  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.
  2. ↑ Dubina TL et al.: Biological age and its estimation. II. Assessment of biological age of albino rats by multiple regression analysis. Exp Gerontol 1983. (PMID 6873212) [PubMed] [DOI] Multiple regression model of biological age (BA) theoretically gives agreement with the main concept of BA. When assessment of BA is based on the model, the age being in regression center, the method provides satisfactory results, whereas BA estimates of individuals in extreme age groups are erroneous. Investigation of male and female Wistar rats of age 5-29 months showed the BA estimates calculated from 4-10 physiological indices in young (5-7 mo) animals are overestimated, and in old (24-28 mo) animals are underestimated. Coincidence of average BA in one-age group of animals with its chronological age served as a criterion for the correspondence of the estimate to "real" BA. The paper also examines the following questions: the necessary and sufficient number of physiological indices; the sample size from the intact animal population to establish normal aging standard; the relationship between BA and animal weight.
  3. ↑ 3.0 3.1 3.2 Nakamura E et al.: Assessment of biological age by principal component analysis. Mech Ageing Dev 1988. (PMID 3226152) [PubMed] [DOI] A method of assessing biological age by the application of principal component analysis is reported. Healthy individuals (462) randomly selected from about 6000 men who had taken a 2-day health examination were studied. Out of the 30 physiological variables examined in routine check-ups, 11 variables were selected as suitable for the assessment of biological age based on the results of factor analysis and the physiological meaning of each test. This variable set was then submitted to principal component analysis, and the 1st principal component obtained from this analysis was used as an equation for assessing one's biological age. However, the biological age calculated from this equation is expressed as a score, so the estimated score was transformed to years (biological age) using the T-score idea. The biological age estimated by this method is practically useful and theoretically valid in contrast with the multiple regression model, because this approach eliminates and overcomes the following 2 big problems of the multiple regression model: (1) the distortion of the individual biological age at the regression edges; and (2) a theoretical contradiction in that a perfect model will merely be predicting the subject's chronological age, not his biological age.
  4. ↑ Webster IW & Logie AR: A relationship between functional age and health status in female subjects. J Gerontol 1976. (PMID 950448) [PubMed] [DOI] A multiple regression equation was used to predict age from seven clinical variables in 1080 apparently well female subjects. A multiple correlation coefficient of R = 0.77 was achieved by five of the variables: timed forced expiratory volume, systolic blood pressure, plasma urea nitrogen, cholesterol, and alkaline phosphatase. On the basis of selection by medical questionnaire responses and other objective criteria, 9% of the subjects were nonsmokers and healthier than the rest. These selected subjects showed a significant reduction in preducted age. Within this group, subjective perception of health was associated with differences in predicted age: poor health with an increase and good health with a decrease in functional age. This study of functional age was based on the healthiest segment of the population in order to minimize the effect of overt pathological processes on the aging rate. An association has been demonstrated between health impairment and predicted age as a measure of the aging rate.
  5. ↑ Hollingsworth JW et al.: Correlations between tests of aging in Hiroshima subjects--an attempt to define "physiologic age". Yale J Biol Med 1965. (PMID 5841151) [PubMed] [Full text]
  6. ↑ Krøll J & Saxtrup O: On the use of regression analysis for the estimation of human biological age. Biogerontology 2000. (PMID 11708217) [PubMed] [DOI] The present investigation compares three linear regression procedures for the definition of human biological age (bioage). As a model system for bioage definition is used the variations with age of blood hemoglobin (B-hemoglobin) in males in the age range 50-95 years. The bioage measures compared are: 1: P-bioage; defined from regression of chronological age on B-hemoglobin results. 2: AC-bioage; obtained by indirect regression, using in reverse the equation describing the regression of B-hemoglobin on age in a reference population. 3: BC-bioage; defined by orthogonal regression on the reference regression line of B-hemoglobin on age. It is demonstrated that the P-bioage measure gives an overestimation of the bioage in the younger and an underestimation in the older individuals. This 'regression to the mean' is avoided using the indirect regression procedures. Here the relatively low SD of the BC-bioage measure results from the inclusion of individual chronological age in the orthogonal regression procedure. Observations on male blood donors illustrates the variation of the AC- and BC-bioage measures in the individual.
  7. ↑ 7.0 7.1 7.2 7.3 Klemera P & Doubal S: A new approach to the concept and computation of biological age. Mech Ageing Dev 2006. (PMID 16318865) [PubMed] [DOI] The lack of exact definition of the concept of biological age (BA) is a typical feature of works concerning BA. That is why comparison of results of various published methods makes little sense and eventual proof of their optimality is impossible. Based on natural and simple presumptions, an attempt to express mathematically the supposed relation between chronological age (CA) and BA has proven to be unexpectedly fruitful. In the present paper, an optimum method of estimation of BA, which is easily applicable even in nonlinear cases, is derived. Moreover, the method allows evaluating the precision of the estimates and also offers tools for validation of presumptions of the method. A special feature of the method is that CA should be used as a standard biomarker, leading to essential improving the precision of BA-estimate and illuminating relativity of the known "paradox of biomarkers". All theoretical results of the method were fully approved by means of a special simulation program. Further, the theory and the results of the simulation have proven that many published results of BA-estimates using multiple linear regression (MLR) are very probably disserviceable because CA is typically more precise estimate of BA than estimates computed by MLR. This unpleasant conclusion also concerns methods, which use MLR as the final step after transformation of the battery of biomarkers by factor analysis or by principal component analysis.
  8. ↑ Nakamura E et al.: Biological age versus physical fitness age. Eur J Appl Physiol Occup Physiol 1989. (PMID 2737197) [PubMed] [DOI] A population of healthy middle-aged (n = 69) and elderly men (n = 12), who participated in a health promotion program, was studied to determine whether really physically fit individuals are in good biological condition, and also whether improvement of physical fitness in the middle-aged and the elderly reduces their "rate of aging". Biological and physical fitness ages of the individuals studied were estimated from the data for 18 physiological function tests and 5 physical fitness tests, respectively, by a principal component model. The correlation coefficient between the estimated biological and physical fitness ages was 0.72 (p less than 0.01). Detailed analyses of the relationship between the estimated biological and physical fitness ages revealed that those who manifested a higher ("older") physical fitness age did not necessarily have a higher biological age, but those who manifested a lower ("younger") physical fitness age were also found to have a lower biological age. These results suggested that there were considerable individual variations in the relationship between biological condition and physical fitness among individuals with an old physical fitness age, but those who were in a state of high physical fitness maintained a relatively good biological condition. The data regarding the elderly men who had maintained a regular exercise program indicated that their estimated biological ages were considerably younger than the expected values. This might suggest that in older individuals regular physical activity may provide physiological improvements which in turn might reduce "the rate of aging".
  9. ↑ Nakamura E et al.: Biological age versus physical fitness age in women. Eur J Appl Physiol Occup Physiol 1990. (PMID 2282902) [PubMed] [DOI] The purpose of this study was to determine whether adult women who are in a state of high physical fitness are in a good state biologically, in terms of biological and physical fitness ages as estimated by statistical means. The subjects were 65 healthy Japanese women (aged 20-64 years). Biological and physical fitness ages were estimated from the data for 18 physiological function tests and 5 physical fitness tests, respectively, by a principal component model. The correlation coefficient between biological and physical fitness ages was 0.70 (P less than 0.01), which was generally regarded as a high correlation. Therefore, those who were in a state of high physical fitness were considered to be in good biological condition. This result is in good agreement with the results (r = 0.72) from adult men, on whom we reported previously. A statistical analysis to ascertain the relative importance of each contributory variable associated with the variance in biological age suggested that routine clinical evaluation of blood pressure and lipid metabolism might play an important role in determining not only the presence and severity of vascular disease but also the rate of biological aging in women.
  10. ↑ Nakamura E & Miyao K: Further evaluation of the basic nature of the human biological aging process based on a factor analysis of age-related physiological variables. J Gerontol A Biol Sci Med Sci 2003. (PMID 12634284) [PubMed] [DOI] This study aimed to reexamine whether there exists a primary aging process that controls the rate of aging in a number of different functions. Eighty-six adult males who successively received a 2-day routine health checkup test for 7 years from 1992 to 1998 at the Kyoto Red Cross Hospital were selected as subjects. Nine candidate biomarkers of aging were selected from the 25 physiological variables based on the investigation of age-related changes. A principal factor analysis was applied to the partial correlation matrix for 9 selected biomarkers calculated by controlling for age. Furthermore, a confirmatory factor analysis in testing first- and second-order factor models was applied to the covariance matrix for 9 biomarkers. The results of these factor analyses revealed that there existed one general factor and three system-specific factors. Therefore, biological age changes can be viewed as a time-dependent complex integration of the primary and secondary aging processes.
  11. ↑ Nakamura E et al.: Evaluating measures of hematology and blood chemistry in male rhesus monkeys as biomarkers of aging. Exp Gerontol 1994. (PMID 8026568) [PubMed] [DOI] Reliable and valid biomarkers of aging can provide valuable tools for examining the effectiveness of interventions that may influence the rate of aging processes. However, a standardized method for identifying biomarkers of aging has yet to be developed. The current analysis focused on hematology and blood chemistry variables obtained from a 5-year longitudinal study of male rhesus monkeys (N = 29) on a diet restriction regime known to retard aging processes and extend lifespan in laboratory rodents (70% of the diet intake of controls). For the current analysis, the major screening criteria for identifying candidate biomarkers of aging were cross-sectional and longitudinal correlation with chronological age (CA) and stability of individual differences. Six potential variables from the battery of blood chemistry tests were identified: 1) serum glutamic oxalacetic transaminase; 2) alkaline phosphatase; 3) total protein; 4) globulin; 5) blood urea nitrogen to creatinine ratio; and 6) phosphates. When submitted to principle component analysis, these variables loaded onto a single component that accounted for over 50% of the total variance to indicate marked covariance among them. By applying the factor score coefficients from the first principle component, an equation was derived for estimating a biological age score (BAS) for each individual monkey. A comparison of BAS between control and diet-restricted monkeys revealed no statistically significant difference at present; however, the slope of the regression of BAS onto CA appeared steeper for the control group compared to the experimental group. Thus, while demonstration of the validity of the candidate biomarkers awaits further evidence, a strategy by which additional biomarkers of aging can be identified is proposed as an improvement over past approaches.
  12. ↑ Nakamura E et al.: A strategy for identifying biomarkers of aging: further evaluation of hematology and blood chemistry data from a calorie restriction study in rhesus monkeys. Exp Gerontol 1998. (PMID 9762521) [PubMed] [DOI] We examined a dataset derived from a battery of hematology and blood chemistry tests to identify candidate biomarkers of aging in a sample of 33 male rhesus monkeys (Macaca mulatta) ranging in age from 4-27 years. About half this sample comprised an experimental group subjected to 30% calorie restriction for six to seven years compared to the control group fed the same nutritionally fortified diet to approximate ad lib levels. Variables that met the following criteria were selected: (1) longitudinal change within the cohorts of control monkeys; (2) cross-sectional correlation with age across the adult lifespan in the control group; (3) stability of individual differences within all groups; and (4) no obvious redundancy with other selected variables. Five variables emerged from this step-wise selection, including the percentage lymphocytes, and serum levels of alkaline phosphatase, albumin, creatinine, and calcium. These variables were then submitted to a principal component analysis, which yielded a single component accounting for about 58% of the total variance. Based on this marked degree of covariance, these candidate biomarkers of aging could be combined into a biological age score (BAS) for the control and experimental groups. When chronological age was regressed onto BAS, the slopes of the control and experimental groups could be compared. Although a trend toward a slower aging rate in calorie-restricted monkeys was apparent, this analysis did not detect a statistically significant difference in the rate of aging between these groups estimated by this index. Despite this result, a logical strategy was confirmed for expanding the search for candidate biomarkers of aging to apply to this and to other studies assessing interventions that purport to affect the rate of aging in long-lived species.
  13. ↑ Nakamura E & Miyao K: A method for identifying biomarkers of aging and constructing an index of biological age in humans. J Gerontol A Biol Sci Med Sci 2007. (PMID 17921421) [PubMed] [DOI] This study was conducted to identify biomarkers of aging and to construct an index of biological age in humans. Healthy adult men (n = 86) who had received an annual health examination from 1992 through 1998 were studied. From 29 physiological variables, five variables (forced expiratory volume in 1 second, systolic blood pressure, hematocrit, albumin, blood urea nitrogen) were selected as candidate biomarkers of aging. Five candidate biomarkers expressed substantial covariance along one principal component. The first principal component obtained from a principal component analysis was used to calculate biological age scores (BAS). Individual BAS showed high longitudinal stability of age-related changes. Age-related changes of BAS are characterized by three components: age, peak functional capacity, and aging rate. A logistic regression analysis suggested that aging rate was influenced by environmental factors, but peak functional capacity was almost independent of environmental factors.
  14. ↑ 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]
  15. ↑ Nakamura E & Miyao K: Sex differences in human biological aging. J Gerontol A Biol Sci Med Sci 2008. (PMID 18840798) [PubMed] [DOI] This study aims to clarify sex differences in human biological aging and to explore the gender gaps in health and longevity. Eighty-six men and 93 women who received a 2-day routine health checkup for 6-7 years beginning in 1992 at the Kyoto Red Cross Hospital were selected. Five candidate biomarkers of aging (forced expiratory volume in 1.0 second per square of height [FEV(1)/Ht(2)], systolic blood pressure [SBP], red blood cells [RBC], albumin [ALBU], and blood urea nitrogen [BUN]) were selected from 29 physiological variables. Individual biological ages (BAS) were estimated from these five biomarkers by a principal component model. From the investigation of the longitudinal changes of individual BAS, it was suggested that (i) beyond 65 years, the rate of aging showed a rapid increase, and (ii) women had relatively lower functional capabilities compared with men, but the rate of aging was slower than that of men, suggesting that these differences might present both disadvantages and advantages for women with regard to health and longevity.
  16. ↑ Nakamura E et al.: Effects of habitual physical exercise on physiological age in men aged 20-85 years as estimated using principal component analysis. Eur J Appl Physiol Occup Physiol 1996. (PMID 8803500) [PubMed] [DOI] A population of 221 healthy adult men (aged 20-85 years) was studied to determine whether those who exercised regularly were in good biological condition, and also whether those who were in a state of high physical fitness were in a good state biologically, in terms of physiological age (PA) and physical fitness age (FA) as estimated by principal component analysis. A group of 17 physiological function tests and 5 physical fitness tests were employed to estimate PA and FA, respectively. The results of this study indicated that those who maintained high physical fitness at all age decade groups from 20 to 79 years had a trend towards maintaining a relatively lower PA (physiologically younger). Mean PA and FA of the trained group were younger by 4.7 and 7.3 years, respectively than those of the untrained group. In addition, the slope of regression line of PA on chronological age was more gentle in the trained group than that in the untrained group. These results would suggest that those who are in a state of high physical fitness maintain a relatively good physiological condition, and that regular physical exercise may delay physiological changes normally seen with aging, and consequently may increase the life span.
  17. ↑ Park J et al.: Developing a biological age assessment equation using principal component analysis and clinical biomarkers of aging in Korean men. Arch Gerontol Geriatr 2009. (PMID 18597867) [PubMed] [DOI] The purpose of the present study is to find clinically useful candidate biomarkers of aging, and using these to develop an equation measuring biological age (BA) in Korean men, then to validate the clinical usefulness of it. Among 4288 men who received medical health examinations, we selected 1588 men who met the normality criteria of each variable. We assumed that chronological ages (CA) of healthy persons represent the BA of them. Variables showing significant correlations with CA were selected. Redundant variables were excluded. We selected 11 variables: VO(2)max, percent body fat (%BF), waist circumference (WC), forced expiratory volume in 1 s (FEV1), systolic blood pressure (SBP), low density cholesterol (LDLCH), blood urea nitrogen (BUN), serum albumin (SA), erythrocyte sedimentation rate(ESR) hearing threshold (HT), and glycosylated hemoglobin (HBA1C). These 11 variables were then submitted into principal component analysis (PCA) and standardized BA scores were obtained. Using them and T-scale idea, the following equation to assess BA was developed: BA=-28.7+0.83(%BF)+0.48(WC)+0.13(SBP)-0.27(VO(2)max)+0.19(HT)-3.1(FEV1)+0.32(BUN)+0.06(LDLCH)-3.0(SA)+0.34(ESR)+4.6(HBA1C). We compared the BA of 3122 men by their fasting glucose and age level. The BA of the higher glucose level group was significantly higher than that of others at all CA levels. The selected 11 biomarkers encompassed known clinically important factors of adult diseases and functional disabilities. This BA assessment equation can be used in the general Korean male population and it proved to be clinically useful.
  18. ↑ Bai X et al.: Evaluation of biological aging process - a population-based study of healthy people in China. Gerontology 2010. (PMID 19940465) [PubMed] [DOI] BACKGROUND: Although there have been cross-sectional and longitudinal studies examining biological age (BA) with chronological age (CA)-related changes in physical, physiological, biochemical, and hormonal variables, few studies have performed echocardiographic evaluation of the cardiovascular system and inflammatory biomarkers. Furthermore, little is known about biomarkers of aging and BA score (BAS) for healthy people in China. OBJECTIVES: The purpose of this study was to identify the biomarkers of healthy aging and to establish BAS for healthy people in China. METHODS: We examined 2,876 men and women aged 30-98 years old in three Chinese cities, and 852 healthy subjects were assessed with 108 physical, morphological, physiological and biochemical variables. After excluding binary variables, variables that had a correlation coefficient with CA of < or =0.25 and redundant variables, eight variables including CA, arterial pulse pressure (PP), intima-media thickness (IMT), end diastolic velocity (EDV), ratio of peak velocity of early filling to atrial filling (E/A), mitral valve annulus lateral wall of peak velocity of early filling (MVEL), cystatin C (CYSC), and fibrinogen (FIB) were selected as candidate biomarkers of aging based on a factor-weighted BAS composite for predicting BA. RESULTS: The BAS equation was 0.248 (CA) + 0.195 (IMT) - 0.196 (EDV) - 0.167 (E/A) - 0.166 (MVEL) + 0.188 (PP) + 0.182 (FIB) + 0.193 (CYSC). Individual BAS were significantly correlated with CA (r = 0.893, p < 0.001). Biological aging rate predicted by BAS was accelerated with increases in CA, and peaked when healthy men and women reached > or =75 years of age. CONCLUSIONS: Our data suggest that BAS is superior to CA in assessing the rate of aging in healthy Chinese people. The cardiovascular variables play a crucial role in the evaluation of biological aging. Biological aging rate appears to be age specific.
  19. ↑ Zhang WG et al.: Association of Klotho and interleukin 6 gene polymorphisms with aging in Han Chinese population. J Nutr Health Aging 2014. (PMID 25470806) [PubMed] [DOI] Certain gene polymorphisms are associated with human aging. This study investigated polymorphisms of a metabolism-related gene, Klotho, and an inflammatory gene, IL6, for association with the aging process in a healthy Han Chinese population. A total of 482 healthy subjects were recruited and divided into aging and young groups according to chronological age and biological age. Snapshots were used to detect a Klotho gene tag SNP (rs571118) and the F-SNPs rs9536314 (F352V) and rs9527025 (C370S), and an interleukin 6 (IL-6) gene tag SNP (rs1524107) and the F-SNPs rs1800795 (-174G/C) and rs1800796 (-572G/C). Klotho F352V and IL-6-174G/C was G homozygous, C370S was T homozygous while IL-6-572G/C MAF less than 5%. There was a statistically significant difference in the Klotho rs571118 SNP between chronological age groups, but not biological age groups. However, other SNPs, including IL-6 gene SNPs, didn't correlate with age in the Han Chinese population. Human aging is a complex process that includes chronological and biological aging. Our current data showed that Klotho gene rs571118 SNP was associated with chronological aging, but not biological aging, in a Han Chinese population. Further study will investigate genetic build up for the difference between chronological and biological aging.
  20. ↑ Zhang WG et al.: Select aging biomarkers based on telomere length and chronological age to build a biological age equation. Age (Dordr) 2014. (PMID 24659482) [PubMed] [DOI] [Full text] The purpose of this study is to build a biological age (BA) equation combining telomere length with chronological age (CA) and associated aging biomarkers. In total, 139 healthy volunteers were recruited from a Chinese Han cohort in Beijing. A genetic index, renal function indices, cardiovascular function indices, brain function indices, and oxidative stress and inflammation indices (C-reactive protein [CRP]) were measured and analyzed. A BA equation was proposed based on selected parameters, with terminal telomere restriction fragment (TRF) and CA as the two principal components. The selected aging markers included mitral annulus peak E anterior wall (MVEA), intima-media thickness (IMT), cystatin C (CYSC), D-dimer (DD), and digital symbol test (DST). The BA equation was: BA = −2.281TRF + 26.321CYSC + 0.025DD − 104.419MVEA + 34.863IMT − 0.265DST + 0.305CA + 26.346. To conclude, telomere length and CA as double benchmarks may be a new method to build a BA.
  21. ↑ 21.0 21.1 Hochschild R: Improving the precision of biological age determinations. Part 1: A new approach to calculating biological age. Exp Gerontol 1989. (PMID 2684676) [PubMed] [DOI] In calculating biological age, almost all prior studies used multiple regression of chronological age on scores of biomarkers of aging. Multiple regression is invalid for this purpose for three, and in some circumstances four, reasons. These are: a) weighting of the contribution of each biomarker's scores according to strength of association with chronological age; b) regression of calculated ages to sample mean age and the inadequacy of proposed corrections; c) frequent occurrence of regression coefficients whose sign equates poorer adult performance on a test to younger biological ages; and d) multicollinearity when lung function scores and height are on the same side of the regression equation. An alternative method for calculating biological age is outlined. Regression to sample mean age and its solution are illustrated on data for highest audible pitch, one of 12 biomarkers measured in a study of 2462 office workers. Prior published studies employing multiple regression to calculate biological age appear to have been in error.
  22. ↑ 22.0 22.1 22.2 22.3 22.4 Cho IH et al.: An empirical comparative study on biological age estimation algorithms with an application of Work Ability Index (WAI). Mech Ageing Dev 2010. (PMID 20005245) [PubMed] [DOI] In this study, we described the characteristics of five different biological age (BA) estimation algorithms, including (i) multiple linear regression, (ii) principal component analysis, and somewhat unique methods developed by (iii) Hochschild, (iv) Klemera and Doubal, and (v) a variant of Klemera and Doubal's method. The objective of this study is to find the most appropriate method of BA estimation by examining the association between Work Ability Index (WAI) and the differences of each algorithm's estimates from chronological age (CA). The WAI was found to be a measure that reflects an individual's current health status rather than the deterioration caused by a serious dependency with the age. Experiments were conducted on 200 Korean male participants using a BA estimation system developed principally under the concept of non-invasive, simple to operate and human function-based. Using the empirical data, BA estimation as well as various analyses including correlation analysis and discriminant function analysis was performed. As a result, it had been confirmed by the empirical data that Klemera and Doubal's method with uncorrelated variables from principal component analysis produces relatively reliable and acceptable BA estimates.
  23. ↑ Hochschild R: Improving the precision of biological age determinations. Part 2: Automatic human tests, age norms and variability. Exp Gerontol 1989. (PMID 2583248) [PubMed] [DOI] In order to eliminate variability due to test operators, procedures for measuring 12 physiological functions that are candidate biomarkers of aging have been automated. Data was collected from a norm group of 2462 male and female office workers using an instrument which requires no test operators, administers all 12 tests in about 45 min. per subject, computes biological age, prints out results, and stores data on floppy disks for transfer to other computers for analysis. This report a) describes the instrumentation and test procedures, b) presents normal age/sex standards for each of the 12 biomarkers, c) reports the variance of the data for each biomarker by sex, d) lists sources of biomarker variance, e) discusses criteria for biomarker selection and f) examines implications for information loss when biomarker data is combined to calculate biological age. After eliminating chronological age as a variable, the standard deviations of the frequency distributions of predicted age for individual biomarkers were found to vary from .226 to 1.075, a range of more than 4 to 1. Procedures are discussed for improving the ratio of useful-to-useless variance in calculating biological age.
  24. ↑ 76. Hochschild R. Validating Biomarkers of Aging-Mathematical Approaches and Results of a 2462-Person Study. Boca Raton: CRC Press; 1994. [Google Scholar]
  25. ↑ 25.0 25.1 25.2 25.3 Levine ME: Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age?. J Gerontol A Biol Sci Med Sci 2013. (PMID 23213031) [PubMed] [DOI] [Full text] Biological age (BA) is useful for examining differences in aging rates. Nevertheless, little consensus exists regarding optimal methods for calculating BA. The aim of this study is to compare the predictive ability of five BA algorithms. The sample included 9,389 persons, aged 30-75 years, from National Health and Nutrition Examination Survey III. During the 18-year follow-up, 1,843 deaths were counted. Each BA algorithm was compared with chronological age on the basis of predictive sensitivity and strength of association with mortality. Results found that the Klemera and Doubal method was the most reliable predictor of mortality and performed significantly better than chronological age. Furthermore, when included with chronological age in a model, Klemera and Doubal method had more robust predictive ability and caused chronological age to no longer be significantly associated with mortality. Given the potential of BA to highlight heterogeneity, the Klemera and Doubal method algorithm may be useful for studying a number of questions regarding the biology of aging.
  26. ↑ 26.0 26.1 Jee H & Park J: Selection of an optimal set of biomarkers and comparative analyses of biological age estimation models in Korean females. Arch Gerontol Geriatr 2017. (PMID 28110151) [PubMed] [DOI] To date, an optimal working model which predicts biological age (BA) with a set of working biomarkers has not been devised to represent the Korean female population. Accuracy of prediction and applicability are required of an optimal set of commonly assessed biomarkers to provide information on the health status. The goal of this study was to identify a set of biomarkers that represent the aging process and to develop and compare different BA prediction models to elucidate the most fitting and applicable model for providing information on health status in the Korean female population. Using a series of selection processes, eight clinically assessable variables were selected by analyzing relations between 31 clinical variables and chronologic age in 912 normal, healthy individuals among 3642 female participants with ages ranging from 30 to 80 years. The multiple linear regression (MLR), principal component analysis (PCA), and the Klemera-Doubal (KDM) statistical methods were applied to obtain three different sets of BA prediction models. These three models were assessed by calculating and performing the coefficient determinations (r2), regression slopes, effect sizes, pairwise t-tests, and Bland-Altman plots. The BA models were further compared for the applicability by calculating the BAs of clinical risk groups. MLR showed the narrowing effects at the either ends of the age spectrum with greatest effect sizes. PCA showed the greatest degree of dispersion and deviation from the regression center. These MLR and PCA trends were also exhibited by clinically risk groups. In conclusion, the KDM BA prediction model based on the selected biomarkers was found to provide the most reliable and stable results for the practical assessment of BA.
  27. ↑ Levine ME & Crimmins EM: A comparison of methods for assessing mortality risk. Am J Hum Biol 2014. (PMID 25088793) [PubMed] [DOI] [Full text] OBJECTIVES: Concepts such as Allostatic Load, Framingham Risk Score, and Biological Age were developed to combine information from multiple measures into a single latent variable that can be used to quantify a person's biological state. Given these varying approaches, the goal of this article is to compare how well these three measures predict subsequent all-cause and disease-specific mortality within a large nationally representative U.S. sample. METHODS: Our study population consisted of 9,942 adults, ages 30 and above from National Health and Nutrition Examination Survey III. Receiver Operating Characteristic curves and Cox Proportional Hazard models for the whole sample and for stratified age groups were used to compare how well Allostatic Load, Framingham Risk Score, and Biological Age predict ten-year all-cause and disease-specific mortality in the sample, for whom there were 1,076 deaths over 96,420 person years of exposure. RESULTS: Overall, Biological Age predicted 10-year mortality more accurately than other measures for the full age range, as well as for participants ages 50 to 69 and 70+. Additionally, out of the three measures, Biological Age had the strongest association with all-cause and cancer mortality, while the Framingham Risk Score had the strongest association with CVD mortality. CONCLUSIONS: Methods for quantifying biological risk provide important approaches to improving our understanding of the causes and consequences of changes in physiological function and dysregulation. Biological Age offers an alternative and, in some cases a more accurate summary approach to the traditionally used methods, such as Allostatic Load and Framingham Risk Score.
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