Assessment of biological age by multiple regression analysis

Improving the precision of biological age determinations. Part 1: A new approach to calculating biological age

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.

Biological age versus physical fitness age

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".

Assessment of biological age by principal component analysis

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.

In defense of the concept of biological aging measurement--current status

Biological age is the objective assessment of a person's health status. Theoretically, a 'normal' person's biological age--in terms of appearance, performance, and functional capacity--should be the same as his chronological age. Many scientists have attempted to develop systems to accurately determine individuals' biological age. Typically, the approach is to select a battery of test parameters comprised of tests which correlate closely with chronological age. This approach assumes that those traits which vary most closely with age are the best indicators of the aging process. The goal has been to compare an individual to his chronological age peers to determine his relative aging status. Two papers (Costa and McCrae, 1980 and 1985) that criticize this concept and approach have heretofore gone unanswered. Lack of published dissent has caused many gerontologists to assume that Costa and McCrae are correct in their assertions that biological age cannot be measured and is not a valid concept. Consequently, some scientists have been reluctant to pursue research in this area. The purposes of this paper are: to critically evaluate the questions raised by Costa and McCrae; to reaffirm the validity of the concept of biological age; and to urge continued research in this most important subject.

Physiological system markers of aging

Superficially physiological measurements and processes would appear to be excellent candidates to serve as biomarkers of aging. A considered analysis reveals that the validation of them as biomarkers of aging is difficult. The available evidence indicates that the concept of physiological age does not appear to be of much value. Although physiological measurements can serve as predictors of the future occurrence of age-associated diseases and as predictors of impending mortality, such predictors may be of little value as markers of the aging processes. It seems likely that physiological markers of aging can be validated only through the appropriate use of maximum life span and the knowledge base that must be obtained to do so is discussed.

Use of motor performance tests to differentiate aging effects from disease effects

The present study has demonstrated that behavioral testing can provide non-invasive methods for monitoring the time-courses of aging and disease processes. Performance on two of our tests changed linearly with increasing age in both cardiomyopathic and healthy hamsters, but these changes occurred at an earlier age in the CM hamsters than in healthy control hamsters. In addition, healthy hamsters showed age-related changes in performance on some tests, while sick CM hamsters did not show parallel changes. These results indicate that disease can modify the rate of change in some markers of aging, and thus they suggest that disease might be able to modify the aging process. However, such a preliminary hypothesis requires much further work. Performance on another behavioral test was shown to be stable throughout much of the lifespan of healthy hamsters, and yet disease in CM hamsters was clearly able to affect it. Thus it was possible to find some tests which discriminated between aging and disease effects, but only because the time-course of the disease we were studying was well defined. Most importantly, these results have suggested a way of thinking about the problem of disease in aging studies that may be more fruitful than others previously used. Examining the changes over time in both healthy subjects (putatively disease-free) and those with disease should allow one to determine which age-related changes are dependent on the presence of organic disease and to separate these from the changes which might inevitably occur solely from increasing age.

Alternatives to age for assessing occupational performance capacity

For the past four decades, the assessment of performance of older workers has commanded considerable discussion but limited systematic investigation from industrial gerontologists. Progress has not been substantial; only recently have they concerned themselves with the legal implications of various personnel assessment strategies. This report is thus a critical examination of the concept of functional age in both psychological and legal arenas. Criticisms of this approach as well as litigation that has arisen from the difficulty of measuring older worker performance via functional age strategies receive special attention. It is suggested that intrinsic attributes serve as the basis for determination of the competence of both older and younger workers.

Biological age and its estimation. III. Introduction of a correction to the multiple regression model of biological age in cross-sectional and longitudinal studies

In order to eliminate systematic error inherent in assessment of an individual biological age (BA), a correction is suggested for the multiple regression model of BA. The larger the difference between an individual's chronological age and middle age of the sample, the greater the correction value. BA estimates of apparently healthy people of 60 to 100 years were assessed using measurements of three physiological indices: hand grip strength, short-time memory, and vibrotactile sensitivity. Introduction of the correction allowed comparison of BA estimates of the same individual obtained from repeated observations. Preliminary results show there are at least four types of the BA changes with advancing age.

Physiological assays for biological age in mice: relationship of collagen, renal function, and longevity

Tests of physiological changes with age are illustrated by collagen denaturation times of tail tendon fibers, and urine concentrating abilities; the tests are evaluated using the following four criteria: change with age, repeatability, relationship to other assays, and relationship to longevity. These tests usually showed highly significant changes with age when mice of different ages were compared for nine mouse genotypes, however neither appeared to be related to subsequent longevities of individual mice. When average values for eleven mouse genotypes were compared, the mean longevities of the genotypes were not significantly correlated with their mean collagen denaturation times or mean renal concentrating abilities, testes at two different ages. The relationships between all three factors--collagen denaturation times, urine concentrating abilities, and longevities--were tested in the same individuals for mice of six different genotypes at 600-700 days of age. Only one marginally significant correlation appeared out of 21 tested; this probably occurred by chance. We conclude that tail tendon collagen denaturation times and urine concentrating abilities change with age independently of each other; furthermore, these changes are unrelated to subsequent longevities, at least when linear relationships are tested. These data suggest that aging is timed by more than one mechanism and demonstrate that strong correlations with chronological age do not necessarily indicate that independent tests will be correlated with longevity or with each other.

Toward the behavioral assessment of biological aging in the laboratory mouse: concepts, terminology, and objectives

The thesis is presented that much of the controversy and confusion concerning the measurement of biological aging stems from the lack of agreement on clearly defined objectives, constructs, and methods to validate measures. To alleviate some of the confusion, basic terminology borrowed from psychometrics is offered to provide a conceptual framework within which such issues can be discussed. This psychometric nomenclature is subsequently applied to the development and evaluation of a test battery designed to assess biological aging in laboratory mice at a behavioral level of analysis. Reference is made to the assessment of reliability, content validity, construct validity, and predictive validity of tests of biological aging.

The assessment of biological age and sex differences of human aging

Biological age can be assessed by means of clinical parameters. Some parameters are more suitable than others; criteria for a rational selection of such parameters are discussed in detail. The main tool used in this essay is multiple linear regression. The data reveal a characteristic sex difference in human and, presumably, mammalian aging.

Biological age and its estimation. II. Assessment of biological age of albino rats by multiple regression analysis

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.

Physiological and behavioral correlates of lifespan in aged C57BL/6J mice

Physiological and behavioral measurements were made in a cohort of 29-month-old male C57BL/6J mice to determine whether any correlated significantly with lifespan. Significant linear relationships with lifespan were found among the physiological measures, including hematocrit and hemoglobin levels and collagen denaturation rate; however, body weight failed to be a significant predictor of survival. Among the behavioral variables studied, significant quadratic relationships with lifespan were found for exploratory activity and passive avoidance learning, while performance on a rotorod and a tightwire showed no significant relationships with lifespan. Through the use of multiple regression techniques, about one-third of the variance in lifespan could be explained by a combination of physiological variables, and about two-fifths could be explained by a combination of behavioral variables.

Comparison of visually estimated age with physiologically predicted age as indicators of rates of aging

The commonly held view that people age at different rates derives largely from visual estimates of age. Although most people "look their age' everyone can cite examples of individuals in middle and late adulthood who appear to be aging very slowly or very rapidly. Efforts to quantify aging rates scientifically require measurement of a large number of physiological parameters in a large population sample. This paper compares visual estimates of age with physiologically predicted measures to determine their value as indicators of the rate of aging. This study used data from 1086 male participants in the Baltimore Longitudinal Study of the Gerontology Research Center, NIA. These men have provided comprehensive biomedical and psychosocial data at one and one-half years intervals for as long as 20 years. The visual estimate of age was made by the examining physician at the first study visit of each participant, without knowledge of the man's actual age. The error of this estimate was determined by subtracting actual age from estimated age. Correlation analysis of error in estimated age with an objective assessment of biological age based on physiological variables indicated a significant association between the two approaches. When men who have died since their study participation were compared with survivors, the former were found to have been significantly 'older for their age' than the latter using both visual and physiological estimate approaches. To determine whether certain lifestyle traits were associated with variation in these two indicators, multiple regression analyses were performed. These showed that men who smoked, who were fatter, or who were in poor health were predicted as older than their chronological age peers using both approaches. Results of this study suggest that the easily determined visual estimate of age may be a useful indicator of aging rate within a population.

The relationship between in vitro cellular aging and in vivo human age

Differences between early and late passage cell cultures on the organelle and macromolecular levels have been attributed to cellular "aging". However, concern has been expressed over whether changes in diploid cell populations after serial passage in vitro accurately reflect human cellular aging in vivo. Studies were therefore undertaken to determine if significant differences would be observed in the in vitro lifespans of skin fibroblast cultures from old and young normal, non-hospitalized volunteers and to examine if parameters that change with in vitro "aging" are altered as a function of age in vivo. Statistically signigificant (P less than 0.05) decreases were found in the rate of fibroblast migration, onset of cell culture senescence, in vitro lifespan, cell population replication rate, and cell number at confluency of fibroblast cultures derived from the old donor group when compared to parallel cultures from young donors. No significant differences were observed in modal cell volumes and cellular macromolecular contents. The differences observed in cell cultures from old and young donors were quantitatively and qualitatively distinct from those cellular alterations observed in early and late passage WI-38 cells (in vitro "aging"). Therefore, although early and late passage cultures of human diploid cells may provide an important cell system for examining loss of replicative potential, fibroblast cultures derived from old and young human donors may be a more appropriate model system for studying human cellular aging.