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

Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality

Biological aging of human organ systems reflects the interplay of age, chronic disease, lifestyle and genetic risk. Using longitudinal brain imaging and physiological phenotypes from the UK Biobank, we establish normative models of biological age for three brain and seven body systems. Here we find that an organ's biological age selectively influences the aging of other organ systems, revealing a multiorgan aging network. We report organ age profiles for 16 chronic diseases, where advanced biological aging extends from the organ of primary disease to multiple systems. Advanced body age associates with several lifestyle and environmental factors, leukocyte telomere lengths and mortality risk, and predicts survival time (area under the curve of 0.77) and premature death (area under the curve of 0.86). Our work reveals the multisystem nature of human aging in health and chronic disease. It may enable early identification of individuals at increased risk of aging-related morbidity and inform new strategies to potentially limit organ-specific aging in such individuals.

The plasma metabolome as a predictor of biological aging in humans

Chronological age is an important predictor of morbidity and mortality; however, it is unable to account for heterogeneity in the decline of physiological function and health with advancing age. Several attempts have been made to instead define a "biological age" using multiple physiological parameters in order to account for variation in the trajectory of human aging; however, these methods require technical expertise and are likely too time-intensive and costly to be implemented into clinical practice. Accordingly, we sought to develop a metabolomic signature of biological aging that could predict changes in physiological function with the convenience of a blood sample. A weighted model of biological age was generated based on multiple clinical and physiological measures in a cohort of healthy adults and was then applied to a group of healthy older adults who were tracked longitudinally over a 5-10-year timeframe. Plasma metabolomic signatures were identified that were associated with biological age, including some that could predict whether individuals would age at a faster or slower rate. Metabolites most associated with the rate of biological aging included amino acid, fatty acid, acylcarnitine, sphingolipid, and nucleotide metabolites. These results not only have clinical implications by providing a simple blood-based assay of biological aging, but also provide insight into the molecular mechanisms underlying human healthspan.

An empirical comparative study on biological age estimation algorithms with an application of Work Ability Index (WAI)

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.

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.

Quantitative genetic analysis of biobehavioral markers of aging in Swedish studies of adult twins

OBJECTIVES

This article will examine the generalizability of markers of aging across samples and cultures and the genetic and environmental influences on them.

METHODS

As part of two studies, 18 demographic, cognitive, physiological, personality, and behavioral variables were available from 459 twin pairs ranging in age from 40 to 90 years. Twelve markers of aging were identified using stepwise regression. Factor analysis of the markers produced four factors: general knowledge, fluid abilities, cardiovascular functioning, and well-being.

RESULTS

Analysis of twin similarity for the factors suggested that genetic and environmental influences varied greatly. Significant age differences in heritability were found for three of the four factors.

DISCUSSION

Results indicate one aging theory cannot account for changes in all markers of aging. Aging of various systems occurs as a result of different combinations of genetic and environmental influences.

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.

Cancer chemotherapy in the elderly

As the geriatric population is growing, it is increasingly important to be familiar with chemotherapy for the elderly. Age-related changes in pharmacokinetics are documented for doxorubicin, etoposide, ifosfamide, daunorubicin, mitomycin, cisplatin and methotrexate. The hematological toxicity of most standard-dose chemotherapy is not affected by age in patients with normal organic functions and good performance status, although increased toxicity with aging is suggested in the use of actinomycin-D, etoposide, vinblastin, methotrexate, methyl-CCNU, doxorubicin and mitomycin, and in dose-intensive chemotherapy. Among non-hematological toxicities, only doxorubicin-induced cardiomyopathy and bleomycin-induced pulmonary toxicity are demonstrated to be accelerated in the elderly. There is no evidence that advanced age decreases the efficacy of chemotherapy for tumors, except for Hodgkin's disease and acute leukemia. These results suggest that advanced chronological age alone is not always associated with severe toxicity and poor prognosis, and that many elderly patients with cancer will benefit from chemotherapy. To answer questions regarding the optimal chemotherapy regimen, dose and intensity in this population, the influence of age should be analyzed in a multivariate approach in future studies.

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.

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.