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

Gut microbiota in centenarians: A potential metabolic and aging regulator in the study of extreme longevity

Centenarians, those aged 100 years or older, are considered the most successful biological aging model in humans. This population is commonly characterized by a low prevalence of chronic diseases, with favorable maintenance of functionality and independence, thus determining a health phenotype of successful aging. There are many factors usually associated with extreme longevity: genetics, lifestyles, diet, among others. However, it is most likely a multifactorial condition where protective factors contribute individually to some extent. The gut microbiota (GM) has emerged as a potential factor associated with the establishment of a favorable health phenotype that allows for extreme longevity, as seen in centenarians. To understand the possible impact generated by the GM, its changes, and the probable causes for successful aging, the aim of this review was to synthesize evidence on the role of the GM as a potential protective factor for achieving extreme longevity, using its relationship with centenarians.

Biomarkers selection and mathematical modeling in biological age estimation

Biological age (BA) is important for clinical monitoring and preventing aging-related disorders and disabilities. Clinical and/or cellular biomarkers are measured and integrated in years using mathematical models to display an individual's BA. To date, there is not yet a single or set of biomarker(s) and technique(s) that is validated as providing the BA that reflects the best real aging status of individuals. Herein, a comprehensive overview of aging biomarkers is provided and the potential of genetic variations as proxy indicators of the aging state is highlighted. A comprehensive overview of BA estimation methods is also provided as well as a discussion of their performances, advantages, limitations, and potential approaches to overcome these limitations.

Progress in biological age research

Biological age (BA) is a common model to evaluate the function of aging individuals as it may provide a more accurate measure of the extent of human aging than chronological age (CA). Biological age is influenced by the used biomarkers and standards in selected aging biomarkers and the statistical method to construct BA. Traditional used BA estimation approaches include multiple linear regression (MLR), principal component analysis (PCA), Klemera and Doubal's method (KDM), and, in recent years, deep learning methods. This review summarizes the markers for each organ/system used to construct biological age and published literature using methods in BA research. Future research needs to explore the new aging markers and the standard in select markers and new methods in building BA models.

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.

High plasma organochlorine pesticide levels are related to increased biological age as calculated by DNA methylation analysis

BACKGROUND

Organochlorine pesticides (OCPs) have been shown in the experimental setting to alter DNA methylation. Since DNA methylation changes during the life-span, formulas have been presented to calculate "DNA methylation age" as a measure of biological age.

OBJECTIVES

We aimed to investigate if circulating levels of three OCPs were related to increased DNA methylation age METHODS: 71CpG DNA methylation age (Hannum formula) was calculated based on data from the Illumina 450 k Bead Methylation chip in 1000 subjects in the Prospective Study of the Vasculature in Uppsala Seniors (PIVUS) study (50% women, all aged 70 years at the examination). The difference between DNA methylation age and chronological age was calculated (DiffAge). 2,2-bis (4-chlorophenyl)-1,1-dichloroethene (p,p'-DDE), hexachlorobenzene (HCB), and transnonachlor (TNC) levels were measured in plasma by high-resolution gas chromatography coupled mass spectrometry (HRGC-HRMS).

RESULTS

Increased p,p'-DDE and TNC, but not HCB, levels were related to increased DiffAge both in sex and BMI-adjusted models, as well as in multiple adjusted models (sex, education level, exercise habits, smoking, energy and alcohol consumption and BMI) (p = 0.0051 and p = 0.011, respectively). No significant interactions between the OCPs and sex or BMI regarding DiffAge were found.

CONCLUSION

In this cross-sectional study, increased levels of two out of three OCPs were related to increased DNA methylation age, further suggesting negative health effects in humans of these widespread environmental contaminants.

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.

Handgrip strength as a means of monitoring progression of cognitive decline - A scoping review

Cognitive decline in older adults contributes to reduced ability to perform daily tasks and continued disuse leads to muscle weakness and potentiates functional loss. Despite explicit links between the motor and cognitive systems, few health care providers assess motor function when addressing the needs of individuals with cognitive loss. Early and easy measurable biomarkers of cognitive decline have the potential to improve care for individuals with dementia and mild cognitive impairment. The aim of this study was to conduct a systematic search to determine the relationship among handgrip strength, as a measure of global muscle strength, and cognitive decline over time. Fifteen prospective, cohort, longitudinal studies of adults >60years old who were healthy or at risk of cognitive decline at study onset were included in the review. Studies that investigated changes in cognition relative to baseline grip strength and, those that investigated changes in grip strength relative to cognitive function were revealed. Findings here support the use of handgrip strength as a way to monitor cognitive changes and show that reduced handgrip strength over time may serve as a predictor of cognitive loss with advancing age.

Model Construction for Biological Age Based on a Cross-Sectional Study of a Healthy Chinese Han population

OBJECTIVES

Biological age (BA) has been proposed to evaluate the aging status in an objective way instead of chronological age (CA). The purpose of our study is to construct a more precise formula of BA in the cross-sectional study based on a largest-ever sample of our studies. This formula aims at better evaluation of body function and exploring the disciplines of aging in different genders and age stages.

METHODS

A total of 1,373 healthy Chinese Han (age range, 19-93 years) were recruited from five cities in China, including 581 males and 792 females. Physical examination, blood routine, blood chemistry, and other lab tests were performed to obtain a total of 74 clinical variables. Then, the principal component analysis (PCA) was used to select variables and estimate BA. The BA formula was further validated in a population with some diseases (n=266), including cardiovascular diseases, type 2 diabetes, kidney diseases, pulmonary diseases, cancer and disorders in nervous system.

RESULTS

The BA formula was constructed as follows: BA = 0.358 (pulse pressure) + 0.258 (trail making test) - 11.552 (mitral valve E/A peak) + 26.383 (minimum intima-media thickness) + 31.965 (Cystatin C) + 0.163 (CA) - 3.902. In validation of the formula, BAs of patients were older than those of healthy persons. The BA accelerates faster in the middle-aged population than in the elderly population (>75 years old).

CONCLUSION

This BA formula can reflect health condition changes of aging better than CA in a Chinese Han population.

Construction Formula of Biological Age Using the Principal Component Analysis

The biological age (BA) equation is a prediction model that utilizes an algorithm to combine various biological markers of ageing. Different from traditional concepts, the BA equation does not emphasize the importance of a golden index but focuses on using indices of vital organs to represent the senescence of whole body. This model has been used to assess the ageing process in a more precise way and may predict possible diseases better as compared with the chronological age (CA). The principal component analysis (PCA) is applied as one of the common and frequently used methods in the construction of the BA formula. Compared with other methods, PCA has its own study procedures and features. Herein we summarize the up-to-date knowledge about the BA formula construction and discuss the influential factors, so as to give an overview of BA estimate by PCA, including composition of samples, choices of test items, and selection of ageing biomarkers. We also discussed the advantages and disadvantages of PCA with reference to the construction mechanism, accuracy, and practicability of several common methods in the construction of the BA formula.

Sources of variation analysis and derivation of reference intervals for ALP, LDH, and amylase isozymes using sera from the Asian multicenter study on reference values

BACKGROUND

Sources of variation (SV) of ALP, LDH, and amylase isozymes were explored.

METHODS

We analyzed 3511 sera from well-defined healthy individuals recruited during the 2009 Asian project for derivation of common reference intervals (RIs). Up-to-date electrophoresis auto-analyzer and reagents were employed for high resolution and reproducibility. SVs including sex, age, body mass index (BMI), ABO blood groups, and levels of drinking, smoking, and exercise were analyzed by multiple regression analysis. RIs were determined by parametric methods after refining healthy individuals by use of latent reference values exclusion method.

RESULTS

Age-related changes in ALP2-3 were different in females: ALP2, linear increase from 20-64y; ALP3, lowering until 45 y and rising steeply thereafter. ALP2 increased with BMI especially in females. ALP5 was barely detectable except in blood-types O and B. Age-related increases in LDH1-LDH3 were noted in females, whereas BMI-related increases were found only for LDH2-LDH5 in both sexes. Pancreatic amylase showed age-related increase in females and was slightly higher in blood-type O. RIs for absolute and relative activities of each isozyme were derived in consideration of sex and age.

CONCLUSIONS

Investigation of these isozymes revealed various age-, BMI-, and blood-type-related changes that are all relevant in clinical interpretation of enzyme test results.

Evolutionary genetic bases of longevity and senescence

Senescence, as a time-dependent developmental process, affects all organisms at every stage in their development and growth. During this process, genetic, epigenetic and environmental factors are known to introduce a wide range of variation for longevity among individuals. As an important life-history trait, longevity shows ontogenetic relationships with other complex traits, and hence may be viewed as a composite trait. Factors that influence the origin and maintenance of diversity of life are ultimately governed by Darwinian processes. Here we review evolutionary genetic mechanisms underlying longevity and senescence in humans from a life-history and genotype-epigenetic-phenotype (G-E-P) map prospective. We suggest that synergistic and cascading effects of cis-ruptive mechanisms in the genome, and epigenetic disruptive processes in relation to environmental factors may lead to sequential slippage in the G-E-P space. These mechanisms accompany age, stage and individual specific senescent processes, influenced by positive pleiotropy of certain genes, superior genome integrity, negative-frequency dependent selection and other factors that universally regulate rarity in nature. Finally we interpret life span as an inherent property of self-organizing systems that, accordingly, maintain species-specific limits for the entire complex of fitness traits. We conclude that Darwinian approaches provide unique opportunities to discover the biological bases of longevity as well as devise individual specific medical or other interventions toward improving health span.

BioAge: toward a multi-determined, mechanistic account of cognitive aging

The search for reliable early indicators of age-related cognitive decline represents a critical avenue for progress in aging research. Chronological age is a commonly used developmental index; however, it offers little insight into the mechanisms underlying cognitive decline. In contrast, biological age (BioAge), reflecting the vitality of essential biological systems, represents a promising operationalization of developmental time. Current BioAge models have successfully predicted age-related cognitive deficits. Research on aging-related cognitive function indicates that the interaction of multiple risk and protective factors across the human lifespan confers individual risk for late-life cognitive decline, implicating a multi-causal explanation. In this review, we explore current BioAge models, describe three broad yet pathologically relevant biological processes linked to cognitive decline, and propose a novel operationalization of BioAge accounting for both moderating and causal mechanisms of cognitive decline and dementia. We argue that a multivariate and mechanistic BioAge approach will lead to a greater understanding of disease pathology as well as more accurate prediction and early identification of late-life cognitive decline.

Analysis of nonlinear gene expression progression reveals extensive pathway and age-specific transitions in aging human brains

Several recent gene expression studies identified hundreds of genes that are correlated with age in brain and other tissues in human. However, these studies used linear models of age correlation, which are not well equipped to model abrupt changes associated with particular ages. We developed a computational algorithm for age estimation in which the expression of each gene is treated as a dichotomized biomarker for whether the subject is older or younger than a particular age. In addition, for each age-informative gene our algorithm identifies the age threshold with the most drastic change in expression level, which allows us to associate genes with particular age periods. Analysis of human aging brain expression datasets from three frontal cortex regions showed that different pathways undergo transitions at different ages, and the distribution of pathways and age thresholds varies across brain regions. Our study reveals age-correlated expression changes at particular age points and allows one to estimate the age of an individual with better accuracy than previously published methods.

Remodelling of biological parameters during human ageing: evidence for complex regulation in longevity and in type 2 diabetes

Factor structure analyses have revealed the presence of specific biological system markers in healthy humans and diseases. However, this type of approach in very old persons and in type 2 diabetes (T2DM) is lacking. A total sample of 2,137 Italians consisted of two groups: 1,604 healthy and 533 with T2DM. Age (years) was categorized as adults (≤65), old (66-85), oldest old (>85-98) and centenarians (≥99). Specific biomarkers of routine haematological and biochemical testing were tested across each age group. Exploratory factorial analysis (EFA) by principal component method with Varimax rotation was used to identify factors including related variables. Structural equation modelling (SEM) was applied to confirm factor solutions for each age group. EFA and SEM identified specific factor structures according to age in both groups. An age-associated reduction of factor structure was observed from adults to oldest old in the healthy group (explained variance 60.4% vs 50.3%) and from adults to old in the T2DM group (explained variance 57.4% vs 44.2%). Centenarians showed three-factor structure similar to those of adults (explained variance 58.4%). The inflammatory component became the major factor in old group and was the first one in T2DM. SEM analysis in healthy subjects suggested that the glucose levels had an important role in the oldest old. Factorial structure change during healthy ageing was associated with a decrease in complexity but showed an increase in variability and inflammation. Structural relationship changes observed in healthy subjects appeared earlier in diabetic patients and later in centenarians.

Linking biological and cognitive aging: toward improving characterizations of developmental time

OBJECTIVES

Chronological age is the most frequently employed predictor in life-span developmental research, despite repeated assertions that it is best conceived as a proxy for true mechanistic changes that influence cognition across time. The present investigation explores the potential that selected functional biomarkers may contribute to the more effective conceptual and operational definitions of developmental time.

METHODS

We used data from the Victoria Longitudinal Study to explore both static and dynamic biological or physiological markers that arguably influence process-specific mechanisms underlying cognitive changes in late life. Multilevel models were fit to test the dynamic coupling between change in theoretically relevant biomarkers (e.g., grip strength, pulmonary function) and change in select cognitive measures (e.g., executive function, episodic and semantic memory).

RESULTS

Results showed that, independent of the passage of developmental time (indexed as years in study), significant time-varying covariation was observed linking corresponding declines for select cognitive outcomes and biological markers.

DISCUSSION

Our findings support the interpretation that cognitive decline is not due to chronological aging per se but rather reflects multiple causal factors from a broad range of biological and physical health domains that operate along the age continuum.

How pleiotropic genetics of the musculoskeletal system can inform genomics and phenomics of aging

Genetic study can provide insight into the biologic mechanisms underlying inter-individual differences in susceptibility to (or resistance to) organisms' aging. Recent advances in molecular genetics and genetic epidemiology provide the necessary tools to perform a study of the genetic sources of biological aging. However, to be successful, the genetic study of a complex condition requires a heritable phenotype to be developed and validated. Genome-wide association studies offer an unbiased approach to identify new candidate genes for human diseases. It is hypothesized that convergent results from multiple aging-related traits will point out the genes responsible for the general aging of the organism. This perspective focuses on the musculoskeletal aging as an example of an approach to identify a downstream common pathway that summarizes aging processes. Since the musculoskeletal traits are linked to the state of many vital functions, disability, and ultimately survival rates, we postulate that there is significance in studying musculoskeletal aging. Construction of an integrated phenotype of aging can be achieved based on shared genetics among multiple musculoskeletal biomarkers. Valid biomarkers from other systems of the organism should be similarly explored. The new composite aging score needs to be validated by determining whether it predicts all-cause mortality, incidences of major chronic diseases, and disability late in life. Comprehensive databases on biomarkers of musculoskeletal aging in multiple large cohort studies, along with information on various health outcomes, are needed to validate the proposed measure of biological aging.

Is telomere length a biomarker of aging? A review

Telomeres, the DNA-protein structures located at the ends of chromosomes, have been proposed to act as a biomarker of aging. In this review, the human evidence that telomere length is a biomarker of aging is evaluated. Although telomere length is implicated in cellular aging, the evidence suggesting telomere length is a biomarker of aging in humans is equivocal. More studies examining the relationships between telomere length and mortality and with measures that decline with "normal" aging in community samples are required. These studies would benefit from longitudinal measures of both telomere length and aging-related parameters.

Does senescence give rise to disease?

The distinctions between senescence and disease are blurred in the literature of evolutionary biology, biodemography, biogerontology and medicine. Theories of senescence that have emerged over the past several decades are based on the concepts that organisms are a byproduct of imperfect structural designs built with imperfect materials and maintained by imperfect processes. Senescence is a complex mixture of processes rather than a monolithic process. Senescence and disease have overlapping biological consequences. Senescence gives rise to disease, but disease does not give rise to senescence. Current data indicate that treatment of disease can delay the age of death but there are no convincing data that these interventions alter senescence. An understanding of these basic tenets suggests that there are biological limits to duration of life and the life expectancy of populations and reveal biological domains where the development of interventions and/or treatments may modulate senescence.

How long will my mouse live? Machine learning approaches for prediction of mouse life span

Prediction of individual life span based on characteristics evaluated at middle-age represents a challenging objective for aging research. In this study, we used machine learning algorithms to construct models that predict life span in a stock of genetically heterogeneous mice. Life-span prediction accuracy of 22 algorithms was evaluated using a cross-validation approach, in which models were trained and tested with distinct subsets of data. Using a combination of body weight and T-cell subset measures evaluated before 2 years of age, we show that the life-span quartile to which an individual mouse belongs can be predicted with an accuracy of 35.3% (±0.10%). This result provides a new benchmark for the development of life-span–predictive models, but improvement can be expected through identification of new predictor variables and development of computational approaches. Future work in this direction can provide tools for aging research and will shed light on associations between phenotypic traits and longevity.

Sex differences in human biological aging

This study aims to clarify sex differences in human biological aging and to explore the gender gaps in health and longevity. Eighty-six men and 93 women who received a 2-day routine health checkup for 6-7 years beginning in 1992 at the Kyoto Red Cross Hospital were selected. Five candidate biomarkers of aging (forced expiratory volume in 1.0 second per square of height [FEV(1)/Ht(2)], systolic blood pressure [SBP], red blood cells [RBC], albumin [ALBU], and blood urea nitrogen [BUN]) were selected from 29 physiological variables. Individual biological ages (BAS) were estimated from these five biomarkers by a principal component model. From the investigation of the longitudinal changes of individual BAS, it was suggested that (i) beyond 65 years, the rate of aging showed a rapid increase, and (ii) women had relatively lower functional capabilities compared with men, but the rate of aging was slower than that of men, suggesting that these differences might present both disadvantages and advantages for women with regard to health and longevity.

Biological age--a concept whose time has come: a preliminary study

OBJECTIVE

Chronology poorly predicts biological age (BA) or physiologic reserve (PR). An objective approach to the heterogeneity of aging would greatly help clinical decision making in the elderly.

MATERIALS AND METHODS

The first pilot study evaluated 130 "healthy" volunteers, ages 70 to 95 years. A summary BA/PR index was developed, using measures of endurance, strength, flexibility, balance, cognition, depression, comorbidity, and exercise. The second study applied the BA/PR concept to prediction of death after a first elective coronary artery bypass graft, using a Veterans Administration database.

RESULTS

The BA/PR index was a better predictor of 3-year functional outcomes and death than was chronological age. In the coronary artery bypass graft study, the inclusion of BA/PR variables significantly improved prediction of 6-month and long-term death for Veterans Administration patients.

CONCLUSIONS

The usefulness of a biological age (BA/PR) approach in predicting outcomes in the elderly was supported. Needed research should develop tools for routine "tracking" of the aging process.

Disentangling the genetic determinants of human aging: biological age as an alternative to the use of survival measures

The choice of a phenotype is critical for the study of a complex genetically regulated process, such as aging. To date, most of the twin and family studies have focused on broad survival measures, primarily age at death or exceptional longevity. However, on the basis of recent studies of twins and families, biological age has also been shown to have a strong genetic component, with heritability estimates ranging from 27% to 57%. The aim of this review is twofold: first, to summarize growing consensus on reliable methods of biological age assessment, and second, to demonstrate validity of this phenotype for research in the genetics of aging in humans.