Assessment of biological age by multiple regression analysis

Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data

BACKGROUND

There is divergence in the rate at which people age. The concept of biological age is postulated to capture this variability, and hence to better represent an individual's true global physiological state than chronological age. Biological age predictors are often generated based on cross-sectional data, using biochemical or molecular markers as predictor variables. It is assumed that the difference between chronological and predicted biological age is informative of one's chronological age-independent aging divergence ∆.

METHODS

We investigated the statistical assumptions underlying the most popular cross-sectional biological age predictors, based on multiple linear regression, the Klemera-Doubal method or principal component analysis. We used synthetic and real data to illustrate the consequences if this assumption does not hold.

RESULTS

The most popular cross-sectional biological age predictors all use the same strong underlying assumption, namely that a candidate marker of aging's association with chronological age is directly informative of its association with the aging rate ∆. We called this the identical-association assumption and proved that it is untestable in a cross-sectional setting. If this assumption does not hold, weights assigned to candidate markers of aging are uninformative, and no more signal may be captured than if markers would have been assigned weights at random.

CONCLUSIONS

Cross-sectional methods for predicting biological age commonly use the untestable identical-association assumption, which previous literature in the field had never explicitly acknowledged. These methods have inherent limitations and may provide uninformative results, highlighting the importance of researchers exercising caution in the development and interpretation of cross-sectional biological age predictors.

Sex differences in epigenetic age in Mediterranean high longevity regions

Sex differences in aging manifest in disparities in disease prevalence, physical health, and lifespan, where women tend to have greater longevity relative to men. However, in the Mediterranean Blue Zones of Sardinia (Italy) and Ikaria (Greece) are regions of centenarian abundance, male-female centenarian ratios are approximately one, diverging from the typical trend and making these useful regions in which to study sex differences of the oldest old. Additionally, these regions can be investigated as examples of healthy aging relative to other populations. DNA methylation (DNAm)-based predictors have been developed to assess various health biomarkers, including biological age, Pace of Aging, serum interleukin-6 (IL-6), and telomere length. Epigenetic clocks are biological age predictors whose deviation from chronological age has been indicative of relative health differences between individuals, making these useful tools for interrogating these differences in aging. We assessed sex differences between the Horvath, Hannum, GrimAge, PhenoAge, Skin and Blood, and Pace of Aging predictors from individuals in two Mediterranean Blue Zones and found that men displayed positive epigenetic age acceleration (EAA) compared to women according to all clocks, with significantly greater rates according to GrimAge (β = 3.55; p = 1.22 × 10^-12), Horvath (β = 1.07; p = 0.00378) and the Pace of Aging (β = 0.0344; p = 1.77 × 10^-08). Other DNAm-based biomarkers findings indicated that men had lower DNAm-predicted serum IL-6 scores (β = -0.00301, p = 2.84 × 10^-12), while women displayed higher DNAm-predicted proportions of regulatory T cells than men from the Blue Zone (p = 0.0150, 95% Confidence Interval [0.00131, 0.0117], Cohen's d = 0.517). All clocks showed better correlations with chronological age in women from the Blue Zones than men, but all clocks showed large mean absolute errors (MAE >30 years) in both sexes, except for PhenoAge (MAE <5 years). Thus, despite their equal survival to older ages in these Mediterranean Blue Zones, men in these regions remain biologically older by most measured DNAm-derived metrics than women, with the exception of the IL-6 score and proportion of regulatory T cells.

Improved Human Age Prediction by Using Gene Expression Profiles From Multiple Tissues

Studying transcriptome chronological change from tissues across the whole body can provide valuable information for understanding aging and longevity. Although there has been research on the effect of single-tissue transcriptomes on human aging or aging in mice across multiple tissues, the study of human body-wide multi-tissue transcriptomes on aging is not yet available. In this study, we propose a quantitative model to predict human age by using gene expression data from 46 tissues generated by the Genotype-Tissue Expression (GTEx) project. Specifically, the biological age of a person is first predicted via the gene expression profile of a single tissue. Then, we combine the gene expression profiles from two tissues and compare the predictive accuracy between single and two tissues. The best performance as measured by the root-mean-square error is 3.92 years for single tissue (pituitary), which deceased to 3.6 years when we combined two tissues (pituitary and muscle) together. Different tissues have different potential in predicting chronological age. The prediction accuracy is improved by combining multiple tissues, supporting that aging is a systemic process involving multiple tissues across the human body.

Complications, Additional Surgery, and Joint Survival Analysis After Medial Open-Wedge High Tibial Osteotomy

The reported incidence of complications following medial open-wedge high tibial osteotomy (MOWHTO) varies. The authors sought to assess the complications, additional surgeries, and joint survival following MOWHTO in patients with isolated medial compartment arthrosis during a mean follow-up of 10 years. This retrospective study involved patients implanted with spacer plates, angle adjustable plates, or inverse L-type plates with wedges between 2000 and 2010. A total of 504 knees from 441 patients were examined. Mean age of the study population was 52.6±7.0 years, with 56 (11.1%) knees from men and 448 (88.9%) from women. The 10-year Kaplan-Meier joint survival rate was 94.8%. Overall complication rate for MOWHTO was 63.7%, with complications in 20.3% of treated knees requiring additional surgery. In this population, although the overall complication rate and the need for additional surgery were high, the need for additional surgery resulting from serious complications was low (2.6%). The high joint survival rate and low rate of additional surgery for serious complications indicate that MOWHTO can be safely applied in patients with isolated medial gonarthrosis. [Orthopedics. 2020;43(5):303-314.].

Comparative Dynamics of Individual Ageing among the Investigative Type of Professionals Living in Russia and Russian Migrants to the EU Countries

The goal of this study was to uncover the influence of professional activity, migration, and gender on dynamics of subjective age and ageing biomarkers. We examined the representatives of investigative types of professions (ITP), 30–75 years old in Russia, (101/62 women), and Russian migrants to the European Union, (101/56 women). ITPs appeared to be ageing slower than statistical standards; men age faster than women; the pre-retirement group (51–65 years old) showed acceleration of relative biological ageing in the Russian sample (women +4.5 years, men +10.7 years) against the EU sample, suggesting a boost of pre-retirement stress in Russia; subjectively, Russian people (51–65 years old) feel close to their chronological age, while EU people perceive themselves far below their calendar age (men—lower by 20.4, women—lower by 10.9 years). The subjective ageing depends on the country of residence, while biological ageing depends on occupation, gender, and negative expectations of retirement.

Difference between the biologic and chronologic age as an individualized indicator for the skincare intensity selection: skin cell profile and age difference studies

Background

The present research addresses the issue of skin aging and corresponding skin treatment individualization. Particular research question was on the development of a simplified criterion supporting patient-specific decisions about the necessity and intensity of skin treatment. Basing on published results and a wide pool of our own experimental data, a hypothesis is formulated that a difference between biologic and chronologic age can be used as a powerful indicator of skin aging.

Methods

In the present paper, we report the results of studies with 80 volunteers between 15 and 65 years of age linking skin cell profile parameters to biologic and chronologic age. Biologic age was calculated using the empirical expressions based on the forced vital lung capacity, systolic blood pressure, urea concentration, and blood cholesterol level. Epidermis and derma cellular structures were studied using skin biopsy samples taken from the gluteal region.

Results

The present study supports the conclusion that biologic and chronologic age difference is changing in the progress of life. Our studies are showing that time point when calculated biologic age becomes equal to the chronologic one reflecting the onset of specific changes in the age dependencies of experimentally measured skin cell profile parameters. Thus, it is feasible that a difference between chronologic and individually assessed biologic age indeed reflects the process of skin aging.

Conclusions

With all reservations to the relatively small number of study participants, it seems feasible that a difference between biologic and chronologic age can be used as an indicator of skin aging. Additional research linking blood immune profile and skin topography to the difference of biologic and chronologic age (reported in the following paper) provides further support for the formulated hypotheses. So, a difference between calculated biologic age and chronologic age can be used as an individualized criterion supporting decisions on skin treatment strategies. Further research involving larger numbers of participants aimed at optimizing the expressions for calculating biologic age could lead to reliable and easily available express criterion supporting the decision for the individualized skin treatment.

Estimating Biological Age in the Singapore Longitudinal Aging Study

Background

Biological age (BA) is a more accurate measure of the rate of human aging than chronological age (CA). However, there is limited consensus regarding measures of BA in life span and healthspan.

Methods

This study investigated measurement sets of 68 physiological biomarkers using data from 2,844 Chinese Singaporeans in two age subgroups (55–70 and 71–94 years) in the Singapore Longitudinal Aging Study (SLAS-2) with 8-year follow-up frailty and mortality data. We computed BA estimate using three commonly used algorithms: Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and Klemera and Doubal (KD) method, and additionally, explored the use of machine learning methods for prediction of mortality and frailty. The most optimal algorithmic estimate of BA compared to CA was evaluated for their associations with risk factors and health outcome.

Results

Stepwise selection procedures resulted in the final selection of 8 biomarkers in males and 10 biomarkers in females. The highest-ranking biomarkers were estimated glomerular filtration rate for both genders, and the forced expiratory volume in 1 second in males and females. The BA estimates robustly predicted frailty and mortality and outperformed CA. The best performing KD measure of BA was notably predictive in the younger group (aged 55–70 years). BA estimates obtained using a machine learning train-test method were not more accurate than conventional BA estimates in predicting mortality and frailty in most situations. Biologically older people with the same CA as biologically younger individuals had higher prevalence of frailty and 8-year mortality, and worse health, behavioral, and functional characteristics.

Conclusions

BA is better than CA for measuring life span (mortality) and healthspan (frailty). This measurement set of physiological markers of biological aging among Chinese robustly differentiate biologically old from younger individuals with the same CA.

What's my age again? The influence of subjective age on consumer health-related attitudes

We examine how shifts in subjective age influence consumer health-related attitudes. In Study 1, participants made to feel subjectively young (vs. old) exhibited more positive attitudes after viewing a health-related marketing message. Underlying this effect, subjectively young (vs. old) participants experienced higher levels of self-esteem. Study 2 tested our process by manipulating subjective physical, as opposed to mental age. Our research is the first to investigate the impact of temporary changes in subjective age on consumer attitudes towards healthy behaviors, and the first to compare the effects of mental (vs. physical) subjective age on these attitudes.

Determination of functional fitness age in women aged 50 and older

BACKGROUND

The construction of useful and attainable indicators of fitness assessment deserves special attention in clinical practice. We aimed to construct an indicator of the functional fitness age (FFA) of women aged 50 and older by an equation using fitness outcomes and its correlation with chronological age (CA) and to analyze the external validity of our results by comparing our sample to others.

METHODS

Participants (n = 459, age: 70.3 ± 7.9 years, mean ± SD) were evaluated using the Senior Fitness Test battery. We applied a multiple regression and a subsequent Holt's exponential smoothing to analyze the outcomes.

RESULTS

We obtained a statistically significant expression of F(6, 452) = 328.384; p < 0.0005 in which the coefficients of the equation explain 81% of variability (R ² _corrected = 0.813). The equation correlates fitness assessment in women aged 50 and over with regards to CA: FFA = 40.146 + 0.350 × CS (stand) - 0.714 × AC (rep) - 0.110 × ST (step) - 0.177 × CSR (cm) - 0.101 × BS (cm) + 8.835 × FUG (s) where CS means chair stand test, AC means arm curl test, ST means 2-min step test, CSR means chair sit-and-reach test, BS means back scratch test, FUG means 8-foot up-and-go test. We compared this index with percentiles distribution from our sample and from other studies.

CONCLUSION

We suggest the use of FFA as a valid indicator of fitness in adult and senior women as well as a useful motivational tool to undertake exercise programs.

Biological Age Is Associated with the Active Use of Nutrition Data

Purpose Biological age (BA) has recently emerged as a substitute for chronological age (CA), and many subjects seek to optimally control their BA. However, in South Korea, no study has adequately explored factors that affect BA, although individual health management is essential to preventing chronic diseases. In the present study, we focus on the use of health information, in particular nutrition facts, to control BA. Methods We used data from the Korea National Health and Nutrition Examination Surveys (2010–2015; 26,914 eligible participants) using BA and age differences as outcome variables. We used multiple linear regression to explore the relationship between the use of nutrition data and differences in BA after adjusting for covariates. In addition, we used multiple linear regression to examine subgroup differences in such relationships. Results 12.8% of males and 27.5% of females used nutrition facts when deciding which foods to purchase. The more attention paid to such facts, the lower the BA and BA differences in both males and females (males: β = −2.646, females: β = −2.787, p < 0.05, for BA; males: β = −1.181, females, β = −2.161, p < 0.05, for BA differences). However, BA differences were more significant in subjects with chronic disease, obesity, and/or a family history of chronic disease. Conclusion High-level awareness of and active use of nutrition facts permitted effective self-management in preventing chronic disease and improving BA, particularly in subjects at higher risk for chronic disease. Thus, considering nutrition facts when deciding what to purchase is important.

Development of a Physiological Frailty Index for the World Trade Center General Responder Cohort

Responders to the 9/11/2001 WTC attacks were exposed to multiple toxic pollutants. Since 2002, the health of the responder cohort has been continuously tracked by the WTC Health Monitoring Program. However, no assessments have been made of frailty, an important health metric given the current average age of the WTC responder cohort (55 years). In this study, we use laboratory test results and other physiological parameters to construct a physiological frailty index (FI-Lab) for this cohort. The study sample comprised responders aged 40 years or older who completed a health monitoring visit at Mount Sinai Center within the past 5 years. For each subject, FI-Lab was computed as the proportion of 20 physiological parameters (lab tests, pulmonary function, and blood pressure) on which the subject had abnormal values. Using negative binomial regression models, we tested FI-Lab's association with the SF-12 wellbeing score and various demographic characteristics. FI-Lab showed strong associations with the physical and mental components of the SF-12 as well as age, race, and smoking status. Using a cutoff of 0.25 to define presence of physiological/preclinical frailty, we found frailty prevalence in the study sample to be approximately 12%. This study demonstrates the feasibility of assessing preclinical frailty in the WTC responder cohort.

Common methods of biological age estimation

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.

Electrophoretic mobility of cell nuclei (EMN index) as a biomarker of the biological aging process: Considering the association between EMN index and age

The present study examined whether a specific property of cell microstructures may be useful as a biomarker of aging. Specifically, the association between age and changes of cellular structures reflected in electrophoretic mobility of cell nuclei index (EMN index) values across the adult lifespan was examined. This report considers findings from cross sections of females (n=1273) aged 18-98 years, and males (n=506) aged 19-93 years. A Biotest apparatus was used to perform intracellular microelectrophoresis on buccal epithelial cells collected from each individual. EMN index was calculated on the basis of the number of epithelial cells with mobile nuclei in reference to the cells with immobile nuclei per 100cells. Regression analyses indicated a significant negative association between EMN index value and age for men (r=-0.71, p<0.001) and women (r=-0.60, p<0.001); demonstrating a key requirement that must be met by a biomarker of aging. The strength of association observed between EMN index and age for both men and women was encouraging and supports the potential use of EMN index for determining a biological age of an individual (or a group). In this study, a new attempt of complex explanation of cellular mechanisms contributing to age related changes of the EMN index was made. In this study, a new attempt of complex explanation of cellular mechanisms contributing to age related changes of the EMN index was made. EMN index has demonstrated potential to meet criteria proposed for biomarkers of aging and further investigations are necessary.

Quantitative measures of healthy aging and biological age

Numerous genetic and non-genetic factors contribute to aging. To facilitate the study of these factors, various descriptors of biological aging, including 'successful aging' and 'frailty', have been put forth as integrative functional measures of aging. A separate but related quantitative approach is the 'frailty index', which has been operationalized and frequently used. Various frailty indices have been constructed. Although based on different numbers and types of health variables, frailty indices possess several common properties that make them useful across different studies. We have been using a frailty index termed FI34 based on 34 health variables. Like other frailty indices, FI34 increases non-linearly with advancing age and is a better indicator of biological aging than chronological age. FI34 has a substantial genetic basis. Using FI34, we found elevated levels of resting metabolic rate linked to declining health in nonagenarians. Using FI34 as a quantitative phenotype, we have also found a genomic region on chromosome 12 that is associated with healthy aging and longevity.

Models for estimating the biological age of five organs using clinical biomarkers that are commonly measured in clinical practice settings

OBJECTIVES

To date, no worldwide studies have been conducted to estimate the biological age of five organs using clinical biomarkers that are associated with the aging status. Therefore, we conducted this study to develop the models for estimating the biological age of five organs (heart, lung, liver, pancreas, and kidney) using clinical biomarkers which are commonly measured in clinical practice.

DESIGN

A cross sectional study.

METHODS

Subjects were recruited from the routine health check-up centers in Korea from 2004 through 2010. Data obtained from 121,189 subjects (66,168 men and 55,021 women) were used for clinical evaluation and statistical analysis. We examined the relations between clinical biomarkers associated with five organs and the chronological age and proposed a model for estimating the biological age of five organs.

RESULTS

In the models for predicting the biological ages of the heart, lung, liver, pancreas and kidney in men, 12, 2, 8, 3, and 5 parameters were respectively included (R(2)=0.652, 0.427, 0.107, 0.245, and 0.651). In contrast to men, 10, 2, 8, 3, and 5 parameters in women were respectively included (R(2)=0.780, 0.435, 0.140, 0.384, and 0.501).

CONCLUSION

We first proposed the models for predicting the biological age of five organs in the current study. We developed those using clinical parameters that can be easily obtained in clinical practice settings. Our biological age prediction models may be used as supplementary tools to assess the aging status of five organs in clinical practice settings.

Constructing an index of physical fitness age for Japanese elderly based on 7-year longitudinal data: sex differences in estimated physical fitness age

A standardized method for assessing the physical fitness of elderly adults has not yet been established. In this study, we developed an index of physical fitness age (fitness age score, FAS) for older Japanese adults and investigated sex differences based on the estimated FAS. Healthy elderly adults (52 men, 70 women) who underwent physical fitness tests once yearly for 7 years between 2002 and 2008 were included in this study. The age of the participants at the beginning of this study ranged from 60.0 to 83.0 years. The physical fitness tests consisted of 13 items to measure balance, agility, flexibility, muscle strength, and endurance. Three criteria were used to evaluate fitness markers of aging: (1) significant cross-sectional correlation with age; (2) significant longitudinal change with age consistent with the cross-sectional correlation; and (3) significant stability of individual differences. We developed an equation to assess individual FAS values using the first principal component derived from principal component analysis. Five candidate fitness markers of aging (10-m walking time, functional reach, one leg stand with eyes open, vertical jump and grip strength) were selected from the 13 physical fitness tests. Individual FAS was predicted from these five fitness markers using a principal component model. Individual FAS showed high longitudinal stability for age-related changes. This investigation of the longitudinal changes of individual FAS revealed that women had relatively lower physical fitness compared with men, but their rate of physical fitness aging was slower than that of men.

Development of models for predicting biological age (BA) with physical, biochemical, and hormonal parameters

Individual differences are the hallmark of aging. Chronological age (CHA) is known that fails to provide an accurate indicator of the aging but biological age (BA) estimates the functional status of an individual in reference to his or her chronological peers on the basis of how well he or she functions in comparison with others of the same CHA. Therefore, we developed models for predicting BA that can be applicable in clinical practice settings. This was a community-based cross-sectional study. Subjects were recruited from the health promotion center in Korea from 2001 to 2005. Among these, data obtained from the 3575 participants (1302 men and 2273 women) was used for clinical evaluation and statistical analysis. For our test battery we selected 25 parameters among the routine tests. For males, the best models were developed using 15, 7, 5, and 4 of the 25 chosen parameters for total, physical, biochemical and hormonal characteristics, respectively (R(2)=0.62, 0.38, 0.33, and 0.36, respectively). Similar to males, for the females, 14, 6, 8, and 3 parameters were developed as the models (R(2)=0.66, 0.40, 0.42, and 0.37, respectively). Our BA prediction models may be used as supplementary tools adding knowledge in the evaluation of aging status.

Developing a biological age assessment equation using principal component analysis and clinical biomarkers of aging in Korean men

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.

A method for identifying biomarkers of aging and constructing an index of biological age in humans

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.

Fourier Analysis of the Envelope of the Ophthalmic Artery Blood Flow Velocity

The harmonic content of the envelope waveform of the blood flow velocity in the ophthalmic artery was analyzed in aging and arterial hypertension. The case-control study enrolled 98 healthy men (age: 44.0±15.6 years), 100 healthy women (age: 44.5±19.1 years), which is group 1, and overall 199 hypertensive patients with increased systolic and diastolic blood pressure (in millimeters of mercury) before registration of the blood flow velocity using pulsed Doppler sonography. Group 2 was sufficiently treated (≤140/≤90), group 3 (>140/≤90) and group 5 (>140/>90) were insufficiently treated, and group 4 (>140/≤90) and group 6 (>140/>90) were untreated. Cronbach-α reliability of the calculated spectral coefficient and SI (SI) was 0.88 and 0.93, respectively. In control subjects, the SI was influenced by age (men: 45.1%; women: 50.2%; P <0.001 each) and in women additionally by mean arterial pressure (13.1%; P <0.001). The SI differed in subjects >43 years of age (men: 0.37±0.11; women: 0.26±0.08; P <0.001 each) as compared with subjects ≤43 years of age (men: 0.60±0.18; women: 0.48±0.13). All of these changes were lacking for the resistance index. In both men and women with hypertension, the SI decreases, whereas the mean arterial pressure increases, but the resistance index did not change. The SI of the ophthalmic artery allows an assessment of the ocular circulation in consideration of age and arterial blood pressure in contrast to the resistance index.

A Note on Age-Related Biomarkers

Forty-eight randomly selected age-related human biological functions were analyzed in order to establish whether or not their (linear) regressions were modified by age-related standard errors. Possible reasons for this are advanced. Statistically significant multiple regressions were obtained in 25%, and 21% of the functions yielded statistically significant changes in the correlations between data and age when partial correlation coefficients were calculated. The conclusion is that age-related data need to be subjected to the above tests in order to minimize confounding factors.

Further evaluation of the basic nature of the human biological aging process based on a factor analysis of age-related physiological variables

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.

Biological age--what is it and can it be measured?

Biological age is a concept used loosely and with little objectivity to describe a shortfall between a population cohort average life expectancy and the perceived life expectancy of an individual of the same age. Many biomarkers decline roughly linearly with age with a slope of <1% per annum. The use of a battery of 16 biomarkers is described as a method of calculating an individual biological age. They include: the concentration of prostacyclin in fibroblasts, cell membrane viscosity, the electroretinogram, baroreflex regulation of the heart rate, the concentration of lymphocytes, leucocyte density and velocity, grip strength, cells of the corneal endothelium and the buccal epithelium, neck muscle mobility, and vital capacity. Although not subjected to a prospective validation, the method might provide an objective approach to this widely used concept.

Biomarkers of aging in women and the rate of longitudinal changes

The purposes of this study were (1) to estimate biological age score (BAS) in Japanese healthy women based on the 4-7 years longitudinal data for physiological, hematological and biochemical examinations and (2) to examine the rate of aging changes in adult women based on the estimated BAS. The samples consisted of cross-sectional (n=981) and longitudinal (n=110) groups. Out of 31 variables examined, five variables (forced expiratory volume in 1.0 s, systolic blood pressure, mean corpuscular hemoglobin, glucose, albumin/globulin ratio) that met the following criteria: 1) significant cross-sectional correlation with age; 2) significant longitudinal change in the same direction as the cross-sectional correlation; and (3) assessment of redundancy, were selected as candidate biomarkers of aging. This variable set was then submitted into a principal component analysis, and the first principal component obtained from this analysis was used as an equation for assessing one's BAS. Individual BAS showed a high longitudinal stability of age-related changes, suggesting high predictive validity of our newly developed aging measurement equation. However, changes in the aging rate based on the estimated BAS were not constant. The mean slopes of the regression lines of BAS for the three age groups (age<45, 45</=age<65 yrs, 65</=age) were 0.095, 0.065, 0.138, respectively. One-way analysis of variance detected a significant difference (F=5.14, p<0.01) among the three age groups. These results suggest that the rate of aging in adult women is relatively slower until 65 years of age, but after 65, the rate of aging shows a rapid increase. We concluded that the longitudinal method used for selection of variables to compute the BAS was useful and theoretically valid compared to those obtained from cross-sectional data analysis.

Genetic determination of biological age in the Mennonites of the Midwestern United States

Numerous studies have shown that longevity is moderately heritable in human populations. Longevity, however, contains limited information on functional status, since individuals may exhibit differential survival patterns. In this study, we employed a stepwise multiple regression approach to estimate biological aging in a Mennonite population, using chronological age as a dependent variable and various predictors of chronological age including subphenotypes related to diabetes, coronary heart disease, hypertension, renal function, and markers of functional ability. The residual (the difference between chronological and predicted ages) is considered a marker of biological age. In fact, two different data sets were used to obtain residuals due to the availability of data. In each analysis, chronological age was regressed on predictor variables in a stepwise manner, retaining the variables significant at the 5% level. The first analysis (N=729) included 6 significant predictors (R(2)=44.3%): glucose, blood urea nitrogen (BUN), cholesterol, albumin, systolic blood pressure (SBP), and ln potassium, and the second analysis (N=232) included 9 significant predictors (R(2)=71.5%): BUN, albumin, SBP, low-density lipoprotein cholesterol, forced expiratory volume in 1 sec (FEV1), grip strength, trunk flexibility, reaction time, and FEV1xsex. Using a variance components approach, we found that the data set-specific residuals were significantly heritable (h(2)+/-SE): first analysis=0.265+/-0.106, and second analysis=0.469+/-0.180. The residuals from the second data set appear to be more informative for biological aging, perhaps due to the inclusion of functional ability-related phenotypes in addition to the blood chemistry variables. In summary, we have shown that markers of biological aging in Mennonites are under substantial additive genetic influences.

Proposition of an aging indicator from general health examination in France

We present an indicator of aging based on eight quantitative variables, measured during the periodical health examination offered by French social security to the general population in France. The used sample derived from the population examined each year. We selected a reference group of 24,510 adults older than 25 years (non-smokers, drinking less than 44 g alcohol per day, with body mass index less than 35, and not using medication). A multiple regression analysis by gender was used to select significant variables to predict age from all quantitative variables measured during the health examination. The resulting biological age could be calculated from the measured variables and the aging indicator was defined as the difference between the calculated age and the chronological age. By definition, this indicator is independent of age in the reference population and gives an indication of the difference between an individual's status in the process of aging and the mean status of people of the same chronological age. Associations between this indicator and life style habits such as tobacco and alcohol use, and occupational status were observed. A geographical analysis also exhibited significant variation over France. Such an indicator can be used at the individual level as a pedagogical tool to explain the observed individual health status. It is also of interest for the epidemiological studies, where it could contribute to a better understanding of the aging process and associated factors.

Biological ages of adult men and women with Down's syndrome and its changes with aging

In order to examine to what extent a gene dysregulation such as Down's syndrome (DS) causes the advance of global biological aging as well as segmental progeroid syndrome, 8 years of longitudinal data were gathered on 14 hematology and blood chemistry characteristics of five adult men and six adult women with DS and four adult men with cerebral palsy (CP). Biological age (BA) was established through the application of principal component analysis based on the data for the same 14 variables of 436 healthy adult men. The BAs of five adult men and six adult women with DS, and four adult men with CP were estimated by using the equation calculated from healthy adult men data, and the BAs were compared. The result of this study indicated that: (1) a genetic condition such as Down's syndrome causes not only segmental progeroid syndrome but also premature aging accompanying global senescence in various organ levels; (2) premature aging exhibited by adult men and women with DS justifies the evidence of primary aging; and (3) the rate of aging for BA in DS patients is nearly a twofold increase as compared to healthy subjects.

A strategy for identifying biomarkers of aging: further evaluation of hematology and blood chemistry data from a calorie restriction study in rhesus monkeys

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.

A review of anthropological approaches to ageing

It is evident that the pattern of ageing among humans has a unique character. Therefore, when undertaking any research on human ageing one has to specify a proper methodology and methods which are available in the anthropological perspective. The paper is aimed at providing a review of anthropological approaches to the study of ageing. On the basis of the meaning and scope of the concept of ageing, its sources and causal factors are discussed. Further, functional, physiological and morphological indicators of ageing are briefly described. Much attention is focused on the concept of biological age which is the key notion for assessment of variation in the rates of human ageing.

Significance of health fitness appraisal in an aging society

There is no doubt that the older population in Japan is rapidly increasing. The over-65 age group is the fastest growing age group in Japanese society. The quality of life for this rapidly growing segment of the population can no longer be ignored without disastrous consequences. The advent of an increased life expectancy has focused attention on the issue of functionality versus disability. We are all faced with the inevitable consequences of aging, yet each of us has the capacity to modify the aging process physiologically through appropriate physical activity and other preventive health measures. Therefore, with the aid of a physically healthy lifestyle, an exercise participant can be physically capable, energetic, and live actively beyond the ages of 50, 60, or even 70 years. Consequently, a key issue for successful or healthy aging would appear to be the improvement in perception of physical ability through education, as well as improvement in health-related physical fitness through a change in lifestyle involving regular exercise. In addition, it is a major responsibility of the physical education profession and related health fields to clarify and publicize the benefits, risks, and specific parameters of physical activity, and to develop an effective prescription for physical activity in programs that are age adjusted. This review discusses from this perspective the significance of health fitness appraisal in the aged society. Much more research is needed to clarify these issues in Japanese society.

Human biological decline and mortality rates

Intercepts on the x (age)-axis of 107 normalized declining human biological functions were determined and assembled in 3 histograms, being placed in increasing order within each decade (10 year period). The histograms were classed accordingly as they contained properties associated with dividing cells, sensory properties and non-dividing cells respectively. Their cumulants were determined, multiple regressions calculated and compared with current death-rates for women and men respectively, for 10 amongst the longest living populations in the World. An alternative verification based on risk theory led to an estimate of an optimal life expectancy of 96 years. The survival curve turns out to be of the form (See text: Formula) where the inner integral represents the cumulant dimension (t') and the outer one age (t"). The premises underlying this study are compatible with the notion of a probable life-span, rather than a fixed one.

Measuring human functional age: a review of empirical findings

A review of empirical functional age studies published in English was conducted. Types of biomarkers used in functional age studies included sensorimotor, cognitive, psychosocial, behavioral, anthropometric, biomedical, physiological, and dental variables. Previous criticisms of the validity and utility of functional age research were evaluated with reference to empirical studies. While some of these criticisms remain valid, areas of research currently using established biomarkers to predict functional outcomes were identified, including driving, falls, and cognitive functioning. It was concluded that the success of functional age research is dependent on the relevance of biomarkers to specific functional outcomes.

Effects of habitual physical exercise on physiological age in men aged 20-85 years as estimated using principal component analysis

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.

The relative utility of health-related fitness tests and skilled motor performance tests as measures of biological age in Japanese men

In the present paper we report the results of a study in which we compared 2 different approaches to the computation of biological age (BA) in a sample of 322 Japanese men (age range 20 to 79 years). In the first approach, 4 commonly used measures of health-related fitness (VO2peak, trunk flexion from a standing position, body fat, and grip strength) were reduced to a single BA score (HRF Age) using principal component analysis. In contrast, in the second approach, 3 commonly used measures of skilled motor performance and agility (vertical jump, stepping side-to-side, and balancing on one leg with eyes closed) were reduced to a single BA score (SMP Age) using similar multivariate procedures. The criterion-related validity of both of the BA measures was examined by assessing each measure's ability to discriminate between healthy and active groups of subjects. This was achieved by classifying the original subject pool into regularly active (ACT; n = 108) and healthy (HLTH; n = 169) subgroups on the basis of self-reported activity levels. Analyses revealed that HRF Age was a more powerful discriminator between the two activity groups than SMP Age. While HRF Age of HLTH subjects was very close to their chronological age (CA), in the ACT group, HRF Age was on average 15 years less than their CA (P < 0.05). In a separate analysis, we assessed the HRF Age of patients with ischemic heart disease, hypertension, obesity, or diabetes (PAT; n = 45). The HRF Age of these subjects averaged 10 years above their CA. Our data suggest that commonly used measures of health-related fitness can be usefully employed as indices of BA which differentiate between individuals of similar ages but differing health and physical activity status. In contrast, measures of skilled motor performance were found to be less valuable measures of BA. The implication of our findings for future experimental design in exercise and aging research is discussed.

Presbyopia — a maverick of human aging

The aim of this study was to examine the extent to which the age-related variations of properties of the human lens may be able to account for presbyopia. Dimensionless linear regressions were calculated for age-related biological functions with special reference to ocular and lenticular ones. Their intercepts on the x-(age-)axis are compared, and their distribution is analyzed. An analysis was made of the effect of the growth of the lens on the relation between its shape and the proximal zonular anchorages on the one hand and the age-related variation of the angle between the zonule and the equatorial plane of the lens. The lens is not unusual in seeming to have evolved in support of a life-span of about 120 years. Presbyopia, however, fails to fit into the general picture and this is hypothesized to result from lenticular growth and a combination of factors which are not all governed by senescence. The potential involvement of the root of the iris throws an interesting light on the apparently worldwide variation of the condition.

Why does the human visual system age in the way it does?

The tentative observation that the decrease of lenticular glutathione in man and in cattle may be under genetic control is extended to other biological functions which show a systematic reduction with age. Ocular and visual parameters are shown to decline consistently with the view that the human eye has evolved in keeping with other biological functions sustaining a life-span of approximately 120 years. Analysis of the data suggests that presbyopia represents an outlier in the distribution of ocular attributes, and should not be used as a biomarker for ageing.

The Assessment of Functional Age Using “Activities of Daily Living” Performance Tests: A Study of Korean Women

The purpose of this study was to assess the extent to which a battery of 24 activities of daily living (ADL) performance tasks could be used to determine functional age in a sample of older women. The subjects were 253 older adult Korean women, aged 60 to 91 years. All subjects completed a comprehensive battery of 24 performance tests related to common activities of daily living. Correlations between the measures were computed, and principal component analysis was applied to the 24 × 24 correlation matrix. A principal component score was computed for each subject and was found to decrease significantly with advancing age. Multiple regression analysis revealed that out of the initial 24 variables, 5 variables accounted for 81% of the variability. An equation was developed to determine ADL age; the equation was considered useful for the assessment of daily living activities of older adult Korean women.

The assessment of biological age: a report from the Department of Environment Study

Indicators of ageing were measured in 397 male and 130 female London Civil Servants aged from 37 to 58 years of age. Grey hair, skin inelasticity, and arcus senilis were strongly and independently related to chronological age. Also independently related were serum albumin (negatively related in both sexes), baldness, serum creatinine, systolic blood pressure, serum calcium (negatively) and ESR in men, and serum cholesterol in women. Chronological age was regressed on the above variables for men and women separately to provide regression equations. Biological age for an individual was calculated by entering his or her results and calculating the residuals. Biological age in those who stopped smoking tended to be younger than chronological age by an average of 12-13 months in men and 1-4 months in women. Men and women who had never smoked had higher biological than actual ages but not after adjusting for regression dilution bias. Men who currently smoked had higher biological ages of 2-3 months but not women smokers (4-6 months lower). Similarly, men of lower employment grades had an average biological age 13 months older and women 8 months, compared with higher employment grades. These differences between employment grade appeared to be due to ESR, arcus senilis, systolic blood pressure and serum cholesterol. These four measurements may be markers of biological rather than chronological age, and the value of attempting to measure biological age is discussed.

Human sensory functions and longevity

Age-related variations of human sensory properties have been expressed in a manner which permits them to be validly compared with other biological functions presented on a similar basis. They are found to be representative of much larger data-base, and it is tentatively suggested that they may have evolved in support of a life-span of approximately 120 years. Some theoretical arguments are advanced to explain this.

Evaluating measures of hematology and blood chemistry in male rhesus monkeys as biomarkers of aging

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.

Have human biological functions evolved in support of a life-span?

Published data on age-related human biological functions are surveyed. For the many showing a linear decrease, regressions were calculated, or existing functions extrapolated, to yield an intercept on the abscissa. The values of chi(0), the age at which a function ceases, provide a tentative common means of comparison. The distribution of chi(0) is skewed because of the apparent longevity of nervous and cerebral functions. The database is sufficiently large to enable one to distinguish those biological functions which appear to have immediate survival value from those which do not. Such an approach may link biomarkers to estimates of the life-span of species.

Age variation in the upper limit of hearing

The upper limit of hearing was measured in 6105 otologically normal ears of subjects ranging in age from 5 to 89 years. The results are as follows: in each age group from 5 to 59 years in both sexes, the upper limit of hearing showed an approximately normal distribution if a logarithmic scale was used for the upper limit of hearing axis. The mode of the distribution shifted to a lower frequency with increasing age. Over age 60 years, the distribution became much wider. Standard upper limit age curves were established by calculating 10th, 25th, 50th, 75th and 90th percentiles for each age group. From early childhood where no age variation was recognized in conventional audiometry, deterioration of the upper limit of hearing was already in progress. This deterioration was slight between ages 25 and 39 but at ages over 40 it was accelerated and led to so-called presbycousis. The upper limit of hearing was found to be one of the best parameters for showing the quantitative age-related changes in hearing.

Biological age in Italian adults: influence of social and behavioural factors

This study investigated changes with age in biological characters in a sample of 571 Italian males aged between 25 and 64 years. The influence of environmental factors on inter-individual variation in physical efficiency was examined by biological age determination. Data on somatometric, physiometric, haematological variables, personal background and lifestyle, were collected. Principal-component analysis was used to study the patterns of relationship in these characters. Over 45% of the total variance is explained by the ageing process, but the secular trend also affects the variation of height and of height-related characters. Changes with age were investigated clustering the sample in eight 5-year classes: results are in agreement with previous findings in Western populations. Ten selected variables were transformed into biological age scores reflecting a man's status relative to his chronological age peers (Borkan and Norris 1980a). Biological age scores of subsamples characterized by different social and behavioural situations were compared. Occupation, educational level and physical activity seem to have the greatest influence on biological age status. The intensity or duration of some habits can induce a clear trend in biological age scores. Results suggest that the environmental influence could be related to the interindividual differences in physical efficiency and to the increase in variability with age observed for some characters.

A study on the basic nature of human biological aging processes based upon a hierarchical factor solution of the age-related physiological variables

The purpose of this study is to determine the laws governing the aging processes based on a hierarchical factor solution of the age-related physiological variables. Healthy individuals (462) who had taken a 2-day health examination were studied. Twenty-seven physiological variables were submitted to hierarchical factor solution. The analysis revealed that there was a fixed pattern in the variations of these 27 physiological variables. In this hierarchical factor solution, three group factors as functioning at different levels of complexity composed of the 16 common factors were extracted. Of these group factors, the first and third group factors can be referred to the aging process. The first group factor, the so-called factor 1 loaded with most of physiological variables, was interpreted as a primary aging factor which presented the primary process of human aging. The third group factor, the so-called factor 6 to 16 loaded only with a few variables related to a single physiological system or organ, were interpreted as system-specific secondary aging factor. These results obtained by the hierarchical factor solution thus seem to support the notion that biological age changes can be viewed as time dependent complex integration of the primary and secondary aging processes.

Functional interrelationships in aging processes: alterations and reversibility

The problem of assessing the relevance of the reversibility of age-related functional alterations for aging studies has been presented. Transduction mechanisms of adrenergic stimulation have been chosen as the target of age-related changes and thymus as the effector of some corrective interventions performed at advanced age. Both alterations of adrenoceptor characteristics and their reversibility have been reviewed. beta-adrenoceptors have been studied in organs bearing only one subtype of receptors or both, revealing an age-related decrease in density only in the beta1-subtype. It has been shown that a similar age-related decrease is present in alpha1-adrenoceptor density. Such alterations are corrected by grafting a neonatal thymus into old mice. On the contrary, thymus fails to correct the alteration of T4-induced upregulation of beta-adrenoceptors indicating some limits to its corrective effect when the net of functional interrelationships becomes relatively complex. Both failures and successes of thymic grafts and thymic extracts in reversing age-related changes are discussed taking into account the effects induced on the life span of the animals. Different unsolved problems stemming from the previous considerations are also presented. Among them the controversial question about linearity and non-linearity of biological parameters presumed to be good indices of aging is discussed, with the aid of a simple model as a schematic example.

Assessment of modifications of the rate of aging in the rat

Multivariate models of BA are, at present, the only way of measuring the main subject of gerontological research, namely, aging of the organism as a whole. Aging is obviously not a completely random process without any regularity, but shows a pattern which is determined by the organism's principles of order and its answer to the stochastic events of primary aging. Based on this theoretical concept, which is supported by results of factor analysis, multicellular aging may be described by a single complex variable, BA. However, the non-linearity of age changes and the different extent to which primary and secondary aging are involved, as well as factors other than aging, restrict the sensitivity of multiple regression models. Sensitivity may be increased by selecting parameters according to their discriminating power revealed by discriminant analysis. Wherever influences on aging are to be tested, a combination of the three types of multivariate methods will allow for an objectivation of effects and provide clues to a possible mode of action.