The measurement of biological age

Histopathologic brain age estimation via multiple instance learning

Understanding age acceleration, the discordance between biological and chronological age, in the brain can reveal mechanistic insights into normal physiology as well as elucidate pathological determinants of age-related functional decline and identify early disease changes in the context of Alzheimer's and other disorders. Histopathological whole slide images provide a wealth of pathologic data on the cellular level that can be leveraged to build deep learning models to assess age acceleration. Here, we used a collection of digitized human post-mortem hippocampal sections to develop a histological brain age estimation model. Our model predicted brain age within a mean absolute error of 5.45 ± 0.22 years, with attention weights corresponding to neuroanatomical regions vulnerable to age-related changes. We found that histopathologic brain age acceleration had significant associations with clinical and pathologic outcomes that were not found with epigenetic based measures. Our results indicate that histopathologic brain age is a powerful, independent metric for understanding factors that contribute to brain aging.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

Analysis of transcriptional changes in the immune system associated with pubertal development in a longitudinal cohort of children with asthma

Puberty is an important developmental period marked by hormonal, metabolic and immune changes. Puberty also marks a shift in sex differences in susceptibility to asthma. Yet, little is known about the gene expression changes in immune cells that occur during pubertal development. Here we assess pubertal development and leukocyte gene expression in a longitudinal cohort of 251 children with asthma. We identify substantial gene expression changes associated with age and pubertal development. Gene expression changes between pre- and post-menarcheal females suggest a shift from predominantly innate to adaptive immunity. We show that genetic effects on gene expression change dynamically during pubertal development. Gene expression changes during puberty are correlated with gene expression changes associated with asthma and may explain sex differences in prevalence. Our results show that molecular data used to study the genetics of early onset diseases should consider pubertal development as an important factor that modifies the transcriptome.

Measuring biological age using omics data

Age is the key risk factor for diseases and disabilities of the elderly. Efforts to tackle age-related diseases and increase healthspan have suggested targeting the ageing process itself to 'rejuvenate' physiological functioning. However, achieving this aim requires measures of biological age and rates of ageing at the molecular level. Spurred by recent advances in high-throughput omics technologies, a new generation of tools to measure biological ageing now enables the quantitative characterization of ageing at molecular resolution. Epigenomic, transcriptomic, proteomic and metabolomic data can be harnessed with machine learning to build 'ageing clocks' with demonstrated capacity to identify new biomarkers of biological ageing.

An uncertainty-aware, shareable, and transparent neural network architecture for brain-age modeling

The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk marker of cross-disorder brain changes, growing into a cornerstone of biological age research. However, machine learning models underlying the field do not consider uncertainty, thereby confounding results with training data density and variability. Also, existing models are commonly based on homogeneous training sets, often not independently validated, and cannot be shared because of data protection issues. Here, we introduce an uncertainty-aware, shareable, and transparent Monte Carlo dropout composite quantile regression (MCCQR) Neural Network trained on N = 10,691 datasets from the German National Cohort. The MCCQR model provides robust, distribution-free uncertainty quantification in high-dimensional neuroimaging data, achieving lower error rates compared with existing models. In two examples, we demonstrate that it prevents spurious associations and increases power to detect deviant brain aging. We make the pretrained model and code publicly available.

A Machine Learning-Based Aging Measure Among Middle-Aged and Older Chinese Adults: The China Health and Retirement Longitudinal Study

Objective: Biological age (BA) has been accepted as a more accurate proxy of aging than chronological age (CA). This study aimed to use machine learning (ML) algorithms to estimate BA in the Chinese population. Materials and methods: We used data from 9,771 middle-aged and older Chinese adults (≥45 years) in the 2011/2012 wave of the China Health and Retirement Longitudinal Study and followed until 2018. We used several ML algorithms (e.g., Gradient Boosting Regressor, Random Forest, CatBoost Regressor, and Support Vector Machine) to develop new measures of biological aging (ML-BAs) based on physiological biomarkers. R-squared value and mean absolute error (MAE) were used to determine the optimal performance of these ML-BAs. We used logistic regression models to examine the associations of the best ML-BA and a conventional aging measure-Klemera and Doubal method-BA (KDM-BA) we previously developed-with physical disability and mortality, respectively. Results: The Gradient Boosting Regression model performed the best, resulting in an ML-BA with an R-squared value of 0.270 and an MAE of 6.519. This ML-BA was significantly associated with disability in basic activities of daily living, instrumental activities of daily living, lower extremity mobility, and upper extremity mobility, and mortality, with odds ratios ranging from 1 to 7% (per 1-year increment in ML-BA, all P < 0.001), independent of CA. These associations were generally comparable to that of KDM-BA. Conclusion: This study provides a valid ML-based measure of biological aging for middle-aged and older Chinese adults. These findings support the application of ML in geroscience research and may help facilitate preventive and geroprotector intervention studies.

The effect of Alzheimer's disease risk factors on brain aging in normal Chineses: Cognitive aging and cognitive reserve

Aging has been recognized as a major driving force of the Alzheimer's disease's (AD) progression, however, the relationship between brain aging and AD is still unclear. There is also a lack of studies investigating the influence of AD risk factors on brain aging in cognitively normal people. Here, the "Brain Age Gap Estimation" (BrainAGE) framework was applied to investigate the effects of AD risk factors on individual brain aging. Across a total of 165 cognitively normal elderly subjects, although no significant difference was observed in the BrainAGE scores among the three groups, AD risk dose (i.e., the number of AD risk factors) is tend to associated with an increased BrainAGE scores (high-risk > middle risk > low risk). Female exhibited more advanced brain aging (P = 0.004), and higher education years were associated with preserved brain aging (P < 0.001). APOE-ɛ4 (P = 0.846) and family history (FH) of dementia (P = 0.209) did not increase BrainAGE scores. When comparing 52 aMCI patients with 38 cognitively normal controls from ADNI dataset, aMCI patients showed significantly increased BrainAGE scores. BrainAGE scores were negatively correlated with CSF Aβ42 levels in the aMCI group (r = -0.275, P = 0.048). With an accuracy of 68.9%, BrainAGE outperformed APOE-ɛ4 and hippocampus gray matter volume (GMV) in predicting aMCI. In conclusion, AD is independently associated with structural changes in the brain that reflect advanced aging. Potentially, BrainAGE combined with APOE-ɛ4 and hippocampus GMV could be used as a pre-screening tool in early-stage AD.

Brain-age in midlife is associated with accelerated biological aging and cognitive decline in a longitudinal birth cohort

An individual's brainAGE is the difference between chronological age and age predicted from machine-learning models of brain-imaging data. BrainAGE has been proposed as a biomarker of age-related deterioration of the brain. Having an older brainAGE has been linked to Alzheimer's, dementia, and mortality. However, these findings are largely based on cross-sectional associations which can confuse age differences with cohort differences. To illuminate the validity of brainAGE as a biomarker of accelerated brain aging, a study is needed of a large cohort all born in the same year who nevertheless vary on brainAGE. In the Dunedin Study, a population-representative 1972-73 birth cohort, we measured brainAGE at age 45 years, as well as the pace of biological aging and cognitive decline in longitudinal data from childhood to midlife (N = 869). In this cohort, all chronological age 45 years, brainAGE was measured reliably (ICC = 0.81) and ranged from 24 to 72 years. Those with older midlife brainAGEs tended to have poorer cognitive function in both adulthood and childhood, as well as impaired brain health at age 3. Furthermore, those with older brainAGEs had an accelerated pace of biological aging, older facial appearance, and early signs of cognitive decline from childhood to midlife. These findings help to validate brainAGE as a potential surrogate biomarker for midlife intervention studies that seek to measure dementia-prevention efforts in midlife. However, the findings also caution against the assumption that brainAGE scores represent only age-related deterioration of the brain as they may also index central nervous system variation present since childhood.

Premature Aging in Chronic Kidney Disease: The Outcome of Persistent Inflammation beyond the Bounds

Over the last hundred years, life expectancy in developed countries has increased because of healthier living habits and the treatment of chronic pathologies causing premature aging. Aging is an inexorable, time-dependent, multifactorial process characterized by a series of progressive and irreversible physiological changes associated with loss of functional, psychological, and social capabilities. Numerous factors, such as oxidative stress, inflammation, and cellular senescence, and an irreversible geriatric syndrome known as frailty, contribute to human body deterioration in aging. The speed of aging may differ between individuals depending on the presence or absence of multiple factors (genetic and/or environment) and the subsequent misbalance of homeostasis, together with the increase of frailty, which also plays a key role in developing chronic diseases. In addition, pathological circumstances have been reported to precipitate or accelerate the aging process. This review investigated the mechanisms involved in the developing pathologies, particularly chronic kidney disease, associated with aging.

Multimodality neuroimaging brain-age in UK biobank: relationship to biomedical, lifestyle, and cognitive factors

The brain-age paradigm is proving increasingly useful for exploring aging-related disease and can predict important future health outcomes. Most brain-age research uses structural neuroimaging to index brain volume. However, aging affects multiple aspects of brain structure and function, which can be examined using multimodality neuroimaging. Using UK Biobank, brain-age was modeled in n = 2205 healthy people with T1-weighted MRI, T2-FLAIR, T2∗, diffusion-MRI, task fMRI, and resting-state fMRI. In a held-out healthy validation set (n = 520), chronological age was accurately predicted (r = 0.78, mean absolute error = 3.55 years) using LASSO regression, higher than using any modality separately. Thirty-four neuroimaging phenotypes were deemed informative by the regression (after bootstrapping); predominantly gray-matter volume and white-matter microstructure measures. When applied to new individuals from UK Biobank (n = 14,701), significant associations with multimodality brain-predicted age difference (brain-PAD) were found for stroke history, diabetes diagnosis, smoking, alcohol intake and some, but not all, cognitive measures (corrected p < 0.05). Multimodality neuroimaging can improve brain-age prediction, and derived brain-PAD values are sensitive to biomedical and lifestyle factors that negatively impact brain and cognitive health.

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Brain-age was predicted from 6 different neuroimaging modalities.

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Combined multi-modality brain-age was more accurate than any single modality.

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Thirty-four neuroimaging measures were informative for brain-age prediction.

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Informative measures generally reflect brain volume and white-matter microstructure.

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Brain-age was associated with stroke, diabetes, smoking, alcohol and cognition.

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.

Anticipatory measure: Alex Comfort, experimental gerontology and the measurement of senescence

Ageing is routinely measured by counting the number of years lived since the birth of an individual but at least since at least the 1930s, the validity, precision and sensitivity of chronological age as a measure has been criticised across the biological and behavioural sciences of ageing. This quest that has been reinforced by the contemporary investment in the possibility of technologically manipulating the rate of ageing to delay the onset the age-associated diseases. This paper explores the epistemic, institutional and political conditions that led to the formulation, at the turhn of the 1970s, of Alex Comfort's (1920-2000) seminal proposal to measure human biological ageing rate. Drawing on published and archival sources, I argue that Comfort's suggested measure of ageing can be understood as a form of 'anticipation work', and should be understood as an effort to evidence, and to make present, the technological and social promises that Comfort linked to experimental gerontology.

Brain age and other bodily 'ages': implications for neuropsychiatry

As our brains age, we tend to experience cognitive decline and are at greater risk of neurodegenerative disease and dementia. Symptoms of chronic neuropsychiatric diseases are also exacerbated during ageing. However, the ageing process does not affect people uniformly; nor, in fact, does the ageing process appear to be uniform even within an individual. Here, we outline recent neuroimaging research into brain ageing and the use of other bodily ageing biomarkers, including telomere length, the epigenetic clock, and grip strength. Some of these techniques, using statistical approaches, have the ability to predict chronological age in healthy people. Moreover, they are now being applied to neurological and psychiatric disease groups to provide insights into how these diseases interact with the ageing process and to deliver individualised predictions about future brain and body health. We discuss the importance of integrating different types of biological measurements, from both the brain and the rest of the body, to build more comprehensive models of the biological ageing process. Finally, we propose seven steps for the field of brain-ageing research to take in coming years. This will help us reach the long-term goal of developing clinically applicable statistical models of biological processes to measure, track and predict brain and body health in ageing and disease.

Molecular and physiological manifestations and measurement of aging in humans

Biological aging is associated with a reduction in the reparative and regenerative potential in tissues and organs. This reduction manifests as a decreased physiological reserve in response to stress (termed homeostenosis) and a time-dependent failure of complex molecular mechanisms that cumulatively create disorder. Aging inevitably occurs with time in all organisms and emerges on a molecular, cellular, organ, and organismal level with genetic, epigenetic, and environmental modulators. Individuals with the same chronological age exhibit differential trajectories of age-related decline, and it follows that we should assess biological age distinctly from chronological age. In this review, we outline mechanisms of aging with attention to well-described molecular and cellular hallmarks and discuss physiological changes of aging at the organ-system level. We suggest methods to measure aging with attention to both molecular biology (e.g., telomere length and epigenetic marks) and physiological function (e.g., lung function and echocardiographic measurements). Finally, we propose a framework to integrate these molecular and physiological data into a composite score that measures biological aging in humans. Understanding the molecular and physiological phenomena that drive the complex and multifactorial processes underlying the variable pace of biological aging in humans will inform how researchers assess and investigate health and disease over the life course. This composite biological age score could be of use to researchers seeking to characterize normal, accelerated, and exceptionally successful aging as well as to assess the effect of interventions aimed at modulating human aging.

Predicting all-cause mortality from basic physiology in the Framingham Heart Study

Using longitudinal data from a cohort of 1349 participants in the Framingham Heart Study, we show that as early as 28-38 years of age, almost 10% of variation in future lifespan can be predicted from simple clinical parameters. Specifically, we found diastolic and systolic blood pressure, blood glucose, weight, and body mass index (BMI) to be relevant to lifespan. These and similar parameters have been well-characterized as risk factors in the relatively narrow context of cardiovascular disease and mortality in middle to old age. In contrast, we demonstrate here that such measures can be used to predict all-cause mortality from mid-adulthood onward. Further, we find that different clinical measurements are predictive of lifespan in different age regimes. Specifically, blood pressure and BMI are predictive of all-cause mortality from ages 35 to 60, while blood glucose is predictive from ages 57 to 73. Moreover, we find that several of these parameters are best considered as measures of a rate of 'damage accrual', such that total historical exposure, rather than current measurement values, is the most relevant risk factor (as with pack-years of cigarette smoking). In short, we show that simple physiological measurements have broader lifespan-predictive value than indicated by previous work and that incorporating information from multiple time points can significantly increase that predictive capacity. In general, our results apply equally to both men and women, although some differences exist.

Has the Rate of Human Aging Already Been Modified?

In recent years, three hypotheses have been set forth positing variations on a common question-Has the rate of human aging already been modified? There is no disputing that people now live longer than ever before in history, and considerable variation in duration of life persists as a fundamental attribute of human longevity, but are these events caused by a measurable and verifiable difference in the rate at which people age, or are there other reasons why they occur? In this article, I explore the historical record involving changes in survival and life expectancy at older ages dating back to 1900, and examine what factors will likely contribute to changes in longevity in the United States through 2040. Evidence suggests that despite the absence of verifiable metrics of biological age, delayed aging is unlikely to be a cause of secular increases in life expectancy, but it could explain variation in survival among population subgroups, and it is the most likely explanation for why exceptionally long-lived people experience less disease and live longer than the rest of the population. If genetic heterogeneity explains any significant part of current variation in longevity, this opens the door to the development of therapeutic interventions that confer these advantages to the rest of the population.

Pharmacotherapy in the ageing patient: The impact of age per se (A review)

A literature search was carried out to review the influence of 'ageing' on pharmacotherapeutic decision-making, specifically how 'age' is defined and considered in the utilisation of medication. Embase, Medline, International Pharmaceutical Abstracts, and Google scholar were canvassed in a three-tiered search according to pre-established inclusion criteria. In tier 1, a total of 22 studies were identified highlighting the underutilisation of medication in elderly patients, with a particular focus on warfarin. Four studies highlighted an age-bias in medication-prescribing for elderly patients, specifically in relation to medicines for rheumatoid arthritis, angina, and hypertension. Tier 2 identified diverse definitions for 'elderly', including biological age, chronological age, physiological age, as well as various descriptions of 'elderly' in clinical trials and guidelines. Finally, medication optimisation tools were identified through the third tier, emphasising the use of chronological age to describe the 'elderly'. Old age influences pharmacotherapeutic decision-making at various levels, however, what complicates the situation is the absence of a comprehensive definition of 'elderly'. Clinical recommendations need to be based more on objective factors known to affect medication effectiveness and safety.

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.

Development and application of biological age prediction models with physical fitness and physiological components in Korean adults

BACKGROUND

Several biological age (BA) prediction models have been suggested with a variety of biomarkers. Valid models should be able to measure BA in a relatively short time period and predict subsequent physiological capability. Physiological and physical fitness variables have been shown to be distinctive markers for predicting BA and morbidity. The practical and noninvasive nature of such variables makes them useful as clinical assessment tools in estimating BA for in-depth diagnosis and corresponding intervention.

OBJECTIVE

To identify, develop and evaluate biomarkers and BA prediction models and validate their clinical usefulness for the practical diagnosis of functional aging.

METHODS

Fourteen variables were measured in 3,112 male and 1,233 female participants aged 30 and older between the years 2004 and 2007. Through a series of parsimonious stepwise elimination processes, two sets of 8 gender-specific variables were selected as candidate biomarkers for 1,604 men and 760 women. Principal component analysis, linear regression analysis and adjustment methods were further applied to obtain two sets of true BA (TBA) prediction models. The TBA models were examined for validity by comparing TBA to the corresponding chronological age (CA) with clinical risk factors.

RESULTS

TBA prediction models with r(2) values of 0.638 and 0.672 were developed, each unique to men and women, respectively. The overall mean TBA and CA of the participants were 53.9 and 51.8 years, respectively, with a marginal difference of -2.1 and -1.3 years. The regression slopes or rates of TBA as a function of CA were 1.00 and 1.28 for men and women with r values of 0.799 and 0.820 (p < 0.001), respectively. In comparing TBA to CA rates between healthy and clinical risk groups, both sarcopenic and obese groups showed significant increases in TBA.

CONCLUSIONS

The selected biomarkers encompass various complex physiopathological factors related to intrinsic and extrinsic physiological and functional aging. The BA prediction models based on the selected biomarkers could be practical in assessing BA for Korean adults.

A pilot study of neurocognitive function in older and younger cocaine abusers and controls

This pilot study compared basic neurocognitive functioning among older and younger cocaine abusers and control participants, as a preliminary assessment of whether specific cognitive deficits exist in an aged cocaine-abusing population. We hypothesized an interaction between aging and cocaine abuse, such that older cocaine abusers would exhibit decreased neuropsychological test performance relative to both younger cocaine abusers and older control participants. Four groups (n = 20 each) were examined: older cocaine abusers (ages 51-70), younger cocaine abusers (ages 21-39), and two non-illicit substance-using control groups. Basic neuropsychological and psychiatric measures were administered to all participants. Older participants performed more poorly than younger participants on the Mini-Mental State Examination (MMSE, p < .01), Digit Span Backward (p < .01), and Trail Making Test (TMT) Parts A and B (p < .01). Cocaine abusers performed more poorly than controls on TMT A (p < .01). Older and younger cocaine abusers used similar amounts of cocaine (p > .05). Older cocaine abusers performed more poorly than older control participants and younger cocaine abusers on the Digit Span Forward (p < .0125). Older cocaine abusers also performed more poorly than younger cocaine abusers on TMT A (p < .0125). This study provides preliminary evidence that older cocaine abusers use a significant amount of cocaine and that there is an interaction between aging and cocaine abuse on psychomotor speed, attention, and short-term memory. Future examination of neurocognitive function in older cocaine abusers is clearly warranted. 

Prescription use disorders in older adults

The number of older adults needing substance abuse treatment is projected to rise significantly in the next few decades. This paper will focus on the epidemic of prescription use disorders in older adults. Particular vulnerabilities of older adults to addiction will be considered. Specifically, the prevalence and patterns of use of opioids, stimulants, and benzodiazepines will be explored, including the effects of these substances on morbidity and mortality. Treatment intervention strategies will be briefly discussed, and areas for future research are suggested.

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.

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.

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.

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.

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.

Postoperative delirium in the elderly

Postoperative delirium is common in the elderly in the postoperative period. It can result in increased morbidity, delayed functional recovery, and prolonged hospital stay. In surgical patients, factors such as age, alcohol abuse, low baseline cognition, severe metabolic derangement, hypoxia, hypotension, and type of surgery appear to contribute to postoperative delirium. Anesthetics, notably anticholinergic drugs and benzodiazepines, increase the risk for delirium. Despite the above recommendations, postoperative delirium in the elderly is poorly understood. Clearly, further studies are needed to determine the risk and long-term outcome of delirium in the elderly population. Research is also needed to define the effects of hypoxemia on cerebral function and whether oxygen therapy has any benefits. The geriatric-anesthesiologic intervention program of pre- and postoperative geriatric assessment, early surgery, thrombosis prophylaxis, oxygen therapy, prevention and treatment of perioperative decrease in blood pressure, and vigorous treatment of any postoperative complications showed some promise, but further definitive studies are needed.

Strain, stress, neurodegeneration and longevity

Laboratory studies indicate that the life-span of inbred rodent strains is inversely related to the intensity of their behavioral and neuroendocrine responses to stressful stimuli. In the brain, a shorter life-span is associated with accelerated age-dependent degenerative changes in specific stress-responsive neuronal systems. The evidence suggests a possible genetic linkage between the intensity of the stress response, the rate of age-dependent neurodegeneration and the individual's life expectancy. It is proposed that inherent hyper-reactivity to stressors is genetically linked to a shorter life-span and to accelerated age-dependent neurodegeneration. Several experimental approaches to test 'this stress-longevity-neurodegeneration linkage hypothesis' are outlined.

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.

Divergence of biological and chronological aging: evidence from rodent studies

Literature on aging populations of rodents supports the intuitive view that significant functional variation exists among like-aged, elderly individuals: chronological age as a solitary measure is a poor indicator of biological age. In this report, we review a variety of studies which classify aged rodents based on genetic and/or behavioral similarities, in addition to chronological age, and have provided valuable neurobiological and physiological information on age-related changes which accompany functional impairments, or the lack of them. Beyond their descriptive value for gerontological research, these findings suggest ways in which biological aging can be manipulated to promote good function in aged individuals.

Anaesthesia for patients over 90 years of age. Outcomes after regional and general anaesthetic techniques for two common surgical procedures

Peri-operative morbidity and mortality and long term outcome of patients over 90 years of age who underwent either total hip arthroplasty or transurethral prostate resection were studied retrospectively. The outcomes of patients who received regional or general anaesthesia were compared. One hundred and forty-one patients underwent total hip arthroplasty and 44 patients underwent transurethral prostate resection during the study period (1975-1985). Overall in-hospital mortality was 4.9% Mortality at 30 days was 5.3% in patients who underwent hip arthroplasty during regional anaesthesia, compared with 6.8% in those who received general anaesthesia. Long term survival was similar for these two groups and was longer than projected for age and gender-matched general population cohorts. The 30-day mortality rate was 3.2% for patients who underwent prostatic resection under regional anaesthesia; no deaths occurred in the general anaesthesia group. This difference was not statistically significant. Long term survival was similar for patients in both groups and was better than predicted. Anaesthetic technique did not influence short term morbidity and mortality or long term outcome for these procedures.

Measures and markers of biological aging: 'a great clamoring ... of fleeting significance'

In response to Dean and Morgan (1988), we review our position on approaches to functional or biological aging. Researchers have attempted to assess an hypothesized underlying 'rate of aging' by combining information from the functioning of several different physical or psychological systems. None of these attempts has yet demonstrated success; because many different processes contribute to what we call 'aging', the concept of a single biological age is itself probably fundamentally flawed. We advocate more sophisticated interdisciplinary and longitudinal research as the best hope for understanding and ameliorating the effects of aging processes.

Key questions in developing biomarkers of aging

A series of questions is presented regarding a logical strategy for developing biomarkers of aging. The questions pertain to the conceptualization process in determining how to define aging and what extraneous and possibly confounding variables must be controlled in measuring this epiphenomenon. In addition, the investigator must consider the degree of generalization that is intended to apply to a candidate biomarker of aging. Empirical questions are also to be considered. Specifically, how will reliability and validity of the candidate biomarker be quantified and assessed? What statistical methods will be applied in this process? The need for biomarkers of aging as research tools in gerontology is argued, but the greater need for agreement on how to direct the conceptualization of this effort is also emphasized.

Biomarkers of aging

This article presents a conceptual discussion of some aspects involved in biomarkers of aging. A biomarker of aging is a biological parameter of an organism that either alone or in some multivariate composite will, in the absence of disease, better predict functional capability at some late age than will chronological age. The reasons for undertaking biomarker research, criteria for putative biomarkers, measurement and assessment of putative biomarkers, and the new initiative by the National Institute on Aging in biomarker research are discussed.

Physiological fitness measures and sensory and motor performance in aging

This study investigated relationships between a multivariate index of physiological fitness and the degree of age-related sensory and motor performance deterioration observed in a group of 70 male subjects (age 63 +/- 12 years). Physiological fitness was determined by reducing a battery of resting pulmonary, hemodynamic and biochemical variables to a single score, the Index of Physiological Status (IPS). In addition, where possible, maximal oxygen uptake (VO2max) was predicted from submaximal values using a standard treadmill procedure. Both IPS and VO2 scores were significantly correlated with age (p less than .01). High scores on the IPS were associated with faster reaction times, improved hearing at high frequencies, greater phonatory control and improved lens accommodation. In contrast, VO2 measures were found to be insensitive to changes in sensory and motor performance. While all subjects were able to complete the tests required for the generation of the IPS, 33% of the participants were unable to complete the treadmill protocol required for estimation of VO2max. These data suggest that a simple noninvasive instrument for the evaluation of physiological fitness, which is applicable to almost all elderly subjects, may be more sensitive to age-related sensory and performance changes than more traditional exercise based measures which require the exclusion of less healthy subjects.

Assessing the predictive validity of psychomotor tests as measures of biological age in mice

Two experiments assessed the predictive validity of a psychomotor test battery in male C57BL/6J mice. First, performance was recorded for 66 mice in rotorod, tightrope, grip strength, exploratory activity, and runwheel activity tasks at 24 mo of age. Except in the rotorod task, performance was positively and significantly correlated to lifespan, i.e. better performance indicated longer lifespan. Body weight and body temperature were also significantly correlated with lifespan, while water consumption was negatively related. Using the five behavioral scores in a multiple regression analysis, about 40% of the variance in lifespan was explainable. When measures of body weight, body temperature, and water consumption were added to the regression equation, about 54% of the variance in lifespan could be explained. As revealed by factor analysis, a high degree of interrelationship existed among variables. In a second experiment, 54 mice were tested in the psychomotor battery every 8 weeks from 24 mo. Scores in the tightrope and both locomotor activity tasks revealed age-related declines, whether considering all individuals or only those surviving to 28 mo. Significant correlations between first and subsequent scores indicated stability of individual differences for tightrope and exploratory activity at most ages and for runwheel activity at 26 mo but not later. Rotorod and grip strength scores were not stable and suggested confounding by learning. Significant correlations with lifespan were obtained at some ages for all tests. In contrast to the first experiment, however, there were no significant correlations between lifespan and scores at 24-mo for any test and little correlation among scores.(ABSTRACT TRUNCATED AT 250 WORDS)

Werner's syndrome and aging: a reappraisal

Although a clear distinction between the Werner syndrome and premature aging was made in our review published in 1966, repeated allusions in the literature indicate either an ambivalence about this relationship or a belief that the two conditions are fundamentally related to one another. Therefore, the criteria for premature aging originally used have been reexamined and found to be still acceptable. Evaluation of the features of the Werner syndrome in the light of these criteria once again indicates that there is no substantial evidence to support and considerable evidence to oppose equating the Werner syndrome with aging. This conclusion applies not only to the Werner syndrome in toto, but also to those aspects of the syndrome considered to qualify it as a segmental progeroid syndrome and to its characteristic cellular abnormalities (severely limited in vitro proliferation and variegated translocation mosaicism). The Werner syndrome should not, therefore, be forced into the mold of premature aging but should be studied on its own merits as a condition which may provide us with clues to the pathogenesis of many important problems.

Lack of correlation between serum cholesterol levels, lymphocyte plasma membrane fluidity and mitogen responsiveness in young and aged chickens

Chickens were studied in an attempt to demonstrate correlations between serum lipid levels and peripheral blood lymphocyte (PBL) plasma membrane fluidity and mitogen responsiveness: (a) in the laying hen; (b) during aging; and (c) following dietary manipulation of serum cholesterol of young and aged chickens. The membrane fluidity of PBL from laying hens was significantly greater than that of immature birds. However, no direct correlation was found between serum lipid levels, nor the serum free cholesterol/phospholipid (FC/Pl) mole composition and PBL membrane fluidity in any of the age-groups tested. Likewise, no correlation was found either between serum FC/Pl mole ratio or membrane fluidity and mitogen responsiveness of PBL from birds up to 5 years of age nor was there any evidence for a decline in mitogen responsiveness up to this age. Supplementation of diets with 1% cholesterol induced hypercholesterolemia, mainly in the very low density lipoprotein (VLDL) fraction, but membrane fluidity and mitogen responsiveness remained unaffected.

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.

Chemical composition and solubility of human glomerular and tubular basement membranes of adult and senescent men

The effect of aging on the composition of human renal basement membranes was studied in persons aged 22-90 years. The relative proportion of cortex in kidney appears to decrease with aging. Twenty pairs of GBM and TBM preparations were isolated using the detergent method. Protein content of the basement membrane preparations amounts to about 66% and is independent of type of membrane or age. Amino acid and carbohydrate analyses of both GBM and TBM revealed that the extents of hydroxylation of proline at the C4 position and of lysine decrease with aging. In the case of lysine this occurred from the seventh of life onwards. The decreases are probably not caused by a change of the collagen content. The extent of glycosylation of hydroxylysine is similar for all basement membrane preparations. An adapted protein assay is presented for solutions containing SDS and dithiothreitol. In the presence of these two compounds, solubility of GBM and TBM from adult and aged persons is similar and amounts to about 55 and 85% after 10 and 60 min, respectively, of heating at 95 degrees C. In SDS-polyacrylamide gels, no differences were observed for the major peptide bands between the preparations, irrespective of basement membrane type or 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.

The thickness of the glomerular basement membrane in irradiated and unirradiated Wistar rats and C-57 BL mice of different age groups

In both Wistar rats and C-57 BL mice, the thickness of the glomerular basement membrane increases progressively with age. This change, which can be measured in cohorts of manageable size, produces a measurement of biological age--if one accepts the assumption that it correlates with the viability of the organism that exhibit it. Whole-body exposure to x-rays has no observable influence on this age-related change.