Biomarkers of aging

SOX2 expression diminishes with ageing in several tissues in mice and humans

SOX2 (Sex-determining region Y box 2) is a transcription factor expressed in several foetal and adult tissues and its deregulated activity has been linked to chronic diseases associated with ageing. Nevertheless, the level of SOX2 expression in aged individuals at the tissue level has not previously been examined. In this work, we show that SOX2 expression decreases significantly in the brain with ageing, in both humans and rodents. The administration of resveratrol for 6 months in mice partly attenuated this reduction. We also identified an age-related decline in SOX2 mRNA and protein expression in several other organs, namely, the lung, heart, kidney, spleen and liver. Moreover, peripheral blood mononuclear cells (PBMCs) from elderly expressed lower levels of SOX2 than those from young individuals. Mechanistically, SOX2 expression inversely correlates with p16^Ink4a levels. Together, these data show a widespread decrease in SOX2 with age, suggesting that the decline in SOX2 expression might be used as a biomarker of ageing.

Inactivation of RAD52 and HDF1 DNA repair genes leads to premature chronological aging and cellular instability

The present study aims to investigate the role of radiation sensitive 52 (RAD52) and high-affinity DNA binding factor 1 (HDF1) DNA repair genes on the life span of budding yeasts during chronological aging. Wild type (wt) and rad52, hdf1, and rad52 hdf1 mutant Saccharomyces cerevisiae strains were used. Chronological aging and survival assays were studied by clonogenic assay and drop test. DNA damage was analyzed by electrophoresis after phenol extraction. Mutant analysis, colony forming units and the index of respiratory competence were studied by growing on dextrose and glycerol plates as a carbon source. Rad52 and rad52 hdf1 mutants showed a gradual decrease in surviving fraction in relation to wt and hdf1 mutant during aging. Genomic DNA was spontaneously more degraded during aging, mainly in rad52 mutants. This strain showed an increased percentage of revertant colonies. Moreover, all mutants showed a decrease in the index of respiratory competence during aging. The inactivation of RAD52 leads to premature chronological aging with an increase in DNA degradation and mutation frequency. In addition, RAD52 and HDF1 contribute to maintain the metabolic state, in a different way, during chronological aging. The results obtained could have important implications in the chronobiology of aging.

Aging Kidney and Aging-Related Disease

With the development of society and improvement of health care, the life span is much longer than before, which brings serious aging problems. Among all the aging problems, renal aging grows to be nonnegligible issue. The aging process of kidney is always accompanied with structural and functional changes. Molecular changes, including Klotho and Sirtuins, are the basic causes of phenotypical changes. Cell senescence and cell autophagy play fundamental roles in the process of renal aging. To effectively intervene in the process of renal aging, different methods have been tried separately, which could produce different effects. Effective intervention of renal aging could be meaningful for healthy state of the whole body.

Predicting age by mining electronic medical records with deep learning characterizes differences between chronological and physiological age

Determining the discrepancy between chronological and physiological age of patients is central to preventative and personalized care. Electronic medical records (EMR) provide rich information about the patient physiological state, but it is unclear whether such information can be predictive of chronological age. Here we present a deep learning model that uses vital signs and lab tests contained within the EMR of Mount Sinai Health System (MSHS) to predict chronological age. The model is trained on 377,686 EMR from patients of ages 18-85 years old. The discrepancy between the predicted and real chronological age is then used as a proxy to estimate physiological age. Overall, the model can predict the chronological age of patients with a standard deviation error of ∼7 years. The ages of the youngest and oldest patients were more accurately predicted, while patients of ages ranging between 40 and 60 years were the least accurately predicted. Patients with the largest discrepancy between their physiological and chronological age were further inspected. The patients predicted to be significantly older than their chronological age have higher systolic blood pressure, higher cholesterol, damaged liver, and anemia. In contrast, patients predicted to be younger than their chronological age have lower blood pressure and shorter stature among other indicators; both groups display lower weight than the population average. Using information from ∼10,000 patients from the entire cohort who have been also profiled with SNP arrays, genome-wide association study (GWAS) uncovers several novel genetic variants associated with aging. In particular, significant variants were mapped to genes known to be associated with inflammation, hypertension, lipid metabolism, height, and increased lifespan in mice. Several genes with missense mutations were identified as novel candidate aging genes. In conclusion, we demonstrate how EMR data can be used to assess overall health via a scale that is based on deviation from the patient's predicted chronological age.

The Effect of Aging on Physical Performance Among Elderly Manual Workers: Protocol of a Cross-Sectional Study

BACKGROUND

In 2012, the Danish Parliament decided to increase retirement age. Unfortunately, elderly people working in a physically demanding environment may be rendered unable to retain the ability to adequately perform the physical requirements of their jobs, due to age-related decreases in physical performance. Therefore, increasing the retirement age may not necessarily lead to the goal of keeping everybody in the labor market for a longer time. To date, our knowledge about the variations in physical performance of the elderly workforce is limited.

OBJECTIVE

In this cross-sectional study we seek to investigate the effects of aging on physical performance among elderly manual workers.

METHODS

Approximately 100 Danish manual workers between 50 and 70 years of age will be recruited. The main measurement outcomes include: (1) inflammatory status from blood samples; (2) body composition; (3) lung function; (4) static and dynamic balance; (5) reaction time, precision, and movement variability during a hammering task; (6) handgrip strength, rate of force development, and force tracking; (7) estimated maximal rate of oxygen consumption; and (8) back mobility. Additionally, information regarding working conditions, physical activity levels, and health status will be assessed with a questionnaire.

RESULTS

Data collection is expected to take place between autumn 2017 and spring 2018.

CONCLUSIONS

This study will increase the knowledge regarding variations in physical performance in the elderly workforce and may identify potential workplace hazards. Moreover, this study might shed light on the potentially problematic decision to increase retirement age for all Danish citizens.

A longitudinal study of DNA methylation as a potential mediator of age-related diabetes risk

DNA methylation (DNAm) has been found to show robust and widespread age-related changes across the genome. DNAm profiles from whole blood can be used to predict human aging rates with great accuracy. We sought to test whether DNAm-based predictions of age are related to phenotypes associated with type 2 diabetes (T2D), with the goal of identifying risk factors potentially mediated by DNAm. Our participants were 43 women enrolled in the Women's Health Initiative. We obtained methylation data via the Illumina 450K Methylation array on whole blood samples from participants at three timepoints, covering on average 16 years per participant. We employed the method and software of Horvath, which uses DNAm at 353 CpGs to form a DNAm-based estimate of chronological age. We then calculated the epigenetic age acceleration, or Δ_age, at each timepoint. We fit linear mixed models to characterize how Δ_age contributed to a longitudinal model of aging and diabetes-related phenotypes and risk factors. For most participants, Δ_age remained constant, indicating that age acceleration is generally stable over time. We found that Δ_age associated with body mass index (p = 0.0012), waist circumference (p = 0.033), and fasting glucose (p = 0.0073), with the relationship with BMI maintaining significance after correction for multiple testing. Replication in a larger cohort of 157 WHI participants spanning 3 years was unsuccessful, possibly due to the shorter time frame covered. Our results suggest that DNAm has the potential to act as a mediator between aging and diabetes-related phenotypes, or alternatively, may serve as a biomarker of these phenotypes.

DNA methylation-based measures of biological age: meta-analysis predicting time to death

Estimates of biological age based on DNA methylation patterns, often referred to as "epigenetic age", "DNAm age", have been shown to be robust biomarkers of age in humans. We previously demonstrated that independent of chronological age, epigenetic age assessed in blood predicted all-cause mortality in four human cohorts. Here, we expanded our original observation to 13 different cohorts for a total sample size of 13,089 individuals, including three racial/ethnic groups. In addition, we examined whether incorporating information on blood cell composition into the epigenetic age metrics improves their predictive power for mortality. All considered measures of epigenetic age acceleration were predictive of mortality (p≤8.2x10^-9), independent of chronological age, even after adjusting for additional risk factors (p<5.4x10^-4), and within the racial/ethnic groups that we examined (non-Hispanic whites, Hispanics, African Americans). Epigenetic age estimates that incorporated information on blood cell composition led to the smallest p-values for time to death (p=7.5x10^-43). Overall, this study a) strengthens the evidence that epigenetic age predicts all-cause mortality above and beyond chronological age and traditional risk factors, and b) demonstrates that epigenetic age estimates that incorporate information on blood cell counts lead to highly significant associations with all-cause mortality.

A Modified Healthy Aging Index and Its Association with Mortality: The National Health and Nutrition Examination Survey, 1999-2002

BACKGROUND

Comorbidity indices that are based on clinically recognized disease do not capture the full spectrum of health. The Healthy Aging Index (HAI) was recently developed to describe a wider range of health and disease across multiple organ systems. We characterized the distribution of a modified HAI (mHAI) by sociodemographics in a representative sample of the U.S. population. We also examined the association of the mHAI with mortality across individuals with different levels of clinically recognizable comorbidities.

METHODS

Data are from the National Health and Nutrition Examination Survey (1999-2000, 2001-2002) on 2,451 adults aged 60 years or older. Five mHAI components (systolic blood pressure, Digit Symbol Substitution Test, cystatin C, glucose, and respiratory problems) were scored 0 (healthiest), 1, or 2 (unhealthiest) by sex-specific tertiles or clinically relevant cutoffs and summed to construct the mHAI.

RESULTS

The mean mHAI score was 4.3; 20.6% had a score of 0-2. 33.2% had a score of 3-4, 31.0% had a score of 5-6, and 15.2% had a score of 7-10. Mean mHAI scores were lower in adults who were younger, non-Hispanic whites, more educated, and married/living with partner. After multivariate adjustment, per unit higher of the mHAI was associated with higher all-cause mortality (HR = 1.19, 95% CI = 1.11-1.27) and higher cardiovascular mortality (HR = 1.23, 95% CI = 1.11-1.35). Within each comorbidity category (0, 1, 2, 3, 4+), the mHAI was still widely distributed and further stratified mortality.

CONCLUSIONS

Substantial variation exists in the mHAI across sociodemographic subgroups. The mHAI could provide incremental value for mortality risk prediction beyond clinically diagnosed chronic diseases among elders.

Evaluating minimally invasive sample collection methods for telomere length measurement

OBJECTIVES

Telomere length (TL) is a biomarker of aging and age-related decline. Although venous blood is considered the "gold standard" for TL measurement, its collection is often not feasible or desired in nonclinical settings. Saliva and dried blood spots (DBS) have been used as alternatives when venipuncture cannot be performed. However, it is not known whether these sample types yield TL measurements comparable to those obtained from venous blood. We sought to determine whether different samples from the same individual yield comparable TL measurements.

METHODS

We extracted DNA from matched buffy coat, saliva (Oragene and Oasis), and DBS (venous and capillary) samples from 40 women aged 18-77 years. We used the monochrome multiplex qPCR (MMQPCR) assay to measure TL in all sample types for each participant and applied quality control measures to retain only high-quality samples for analysis. We then compared TL from buffy coat and saliva to examine how these measurements differ and to test if TL is correlated across sample types.

RESULTS

TL differed significantly across buffy coat, Oragene saliva, and Oasis saliva samples. TL from buffy coat and Oragene saliva was moderately correlated (ρ = 0.48, P = .002) and the most similar in size. Oasis saliva TL was not correlated with buffy coat or Oragene saliva TL, and was the shortest. DBS DNA yields were inadequate for TL measurement using the MMQPCR assay.

CONCLUSIONS

Using a matched dataset we demonstrate that sample type significantly influences the TL measurement obtained using the MMQPCR assay.

Systemic Age-Associated DNA Hypermethylation of ELOVL2 Gene: In Vivo and In Vitro Evidences of a Cell Replication Process

Epigenetic remodeling is one of the major features of the aging process. We recently demonstrated that DNA methylation of ELOVL2 and FHL2 CpG islands is highly correlated with age in whole blood. Here we investigated several aspects of age-associated hypermethylation of ELOVL2 and FHL2. We showed that ELOVL2 methylation is significantly different in primary dermal fibroblast cultures from donors of different ages. Using epigenomic data from public resources, we demonstrated that most of the tissues show ELOVL2 and FHL2 hypermethylation with age. Interestingly, ELOVL2 hypermethylation was not found in tissues with very low replication rate. We demonstrated that ELOVL2 hypermethylation is associated with in vitro cell replication rather than with senescence. We confirmed intra-individual hypermethylation of ELOVL2 and FHL2 in longitudinally assessed participants from the Doetinchem Cohort Study. Finally we showed that, although the methylation of the two loci is not associated with longevity/mortality in the Leiden Longevity Study, ELOVL2 methylation is associated with cytomegalovirus status in nonagenarians, which could be informative of a higher number of replication events in a fraction of whole-blood cells. Collectively, these results indicate that ELOVL2 methylation is a marker of cell divisions occurring during human aging.

Testing Two Evolutionary Theories of Human Aging with DNA Methylation Data

The evolutionary theories of mutation accumulation (MA) and disposable soma (DS) provide possible explanations for the existence of human aging. To better understand the relative importance of these theories, we devised a test to identify MA- and DS-consistent sites across the genome using familial DNA methylation data. Two key characteristics of DNA methylation allowed us to do so. First, DNA methylation exhibits distinct and widespread changes with age, with numerous age-differentially-methylated sites observed across the genome. Second, many sites show heritable DNA methylation patterns within families. We extended heritability predictions of MA and DS to DNA methylation, predicting that MA-consistent age-differentially-methylated sites will show increasing heritability with age, while DS-consistent sites will show the opposite. Variance components models were used to test for changing heritability of methylation with age at 48,601 age-differentially-methylated sites across the genome in 610 individuals from 176 families. Of these, 102 sites showed significant MA-consistent increases in heritability with age, while 2266 showed significant DS-consistent decreases in heritability. These results suggest that both MA and DS play a role in explaining aging and aging-related changes, and that while the majority of DNA methylation changes observed in aging are consistent with epigenetic drift, targeted changes exist and may mediate effects of aging-related genes.

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.

Environmental Canalization of Life Span and Gene Expression in Caenorhabditis elegans

Animals, particularly poikilotherms, exhibit distinct physiologies at different environmental temperatures. Here, we hypothesized that temperature-based differences in physiology could affect the amount of variation in complex quantitative traits. Specifically, we examined, in Caenorhabditis elegans, how different temperatures (15°C, 20°C, and 25°C) affected the amount of interindividual variation in life span and also expression of three reporter genes-transcriptional reporters for vit-2, gpd-2, and hsp-16.2 (a life-span biomarker). We found the expected inverse relationship between temperature and average life span. Surprisingly, we found that at the highest temperature, there were fewer differences between individuals in life span and less interindividual variation in expression of all three reporters. We suggest that growth at 25°C might canalize (reduce interindividual differences in) life span and expression of some genes by eliciting a small constitutive heat shock response. Growth at 25°C requires wild-type hsf-1, which encodes the main heat shock response transcriptional activator. We speculate that increased chaperone activity at 25°C may reduce interindividual variation in gene expression by increasing protein folding efficiency. We hypothesize that reduced variation in gene expression may ultimately cause reduced variation in life span.

Proteomics and metabolomics in ageing research: from biomarkers to systems biology

Age is the single greatest risk factor for a wide range of diseases, and as the mean age of human populations grows steadily older, the impact of this risk factor grows as well. Laboratory studies on the basic biology of ageing have shed light on numerous genetic pathways that have strong effects on lifespan. However, we still do not know the degree to which the pathways that affect ageing in the lab also influence variation in rates of ageing and age-related disease in human populations. Similarly, despite considerable effort, we have yet to identify reliable and reproducible 'biomarkers', which are predictors of one's biological as opposed to chronological age. One challenge lies in the enormous mechanistic distance between genotype and downstream ageing phenotypes. Here, we consider the power of studying 'endophenotypes' in the context of ageing. Endophenotypes are the various molecular domains that exist at intermediate levels of organization between the genotype and phenotype. We focus our attention specifically on proteins and metabolites. Proteomic and metabolomic profiling has the potential to help identify the underlying causal mechanisms that link genotype to phenotype. We present a brief review of proteomics and metabolomics in ageing research with a focus on the potential of a systems biology and network-centric perspective in geroscience. While network analyses to study ageing utilizing proteomics and metabolomics are in their infancy, they may be the powerful model needed to discover underlying biological processes that influence natural variation in ageing, age-related disease, and longevity.

The role of epigenetics in renal ageing

An ability to separate natural ageing processes from processes specific to morbidities is required to understand the heterogeneity of age-related organ dysfunction. Mechanistic insight into how epigenetic factors regulate ageing throughout the life course, linked to a decline in renal function with ageing, is already proving to be of value in the analyses of clinical and epidemiological cohorts. Noncoding RNAs provide epigenetic regulatory circuits within the kidney, which reciprocally interact with DNA methylation processes, histone modification and chromatin. These interactions have been demonstrated to reflect the biological age and function of renal allografts. Epigenetic factors control gene expression and activity in response to environmental perturbations. They also have roles in highly conserved signalling pathways that modulate ageing, including the mTOR and insulin/insulin-like growth factor signalling pathways, and regulation of sirtuin activity. Nutrition, the gut microbiota, inflammation and environmental factors, including psychosocial and lifestyle stresses, provide potential mechanistic links between the epigenetic landscape of ageing and renal dysfunction. Approaches to modify the renal epigenome via nutritional intervention, targeting the methylome or targeting chromatin seem eminently feasible, although caution is merited owing to the potential for intergenerational and transgenerational effects.

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.

Biological Age Predictors

The search for reliable indicators of biological age, rather than chronological age, has been ongoing for over three decades, and until recently, largely without success. Advances in the fields of molecular biology have increased the variety of potential candidate biomarkers that may be considered as biological age predictors. In this review, we summarize current state-of-the-art findings considering six potential types of biological age predictors: epigenetic clocks, telomere length, transcriptomic predictors, proteomic predictors, metabolomics-based predictors, and composite biomarker predictors. Promising developments consider multiple combinations of these various types of predictors, which may shed light on the aging process and provide further understanding of what contributes to healthy aging. Thus far, the most promising, new biological age predictor is the epigenetic clock; however its true value as a biomarker of aging requires longitudinal confirmation.

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Telomere length is the most well studied biological age predictor, but many new predictors are emerging.

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The epigenetic clock is currently the best biological age predictor, as it correlates well with age and predicts mortality.

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The various biological age predictors tend to reflect different aspects of the aging process.

Individual CpG sites that are associated with age and life expectancy become hypomethylated upon aging

BACKGROUND

There is a growing interest in simple molecular biomarkers for biological aging. Age-associated DNA methylation (DNAm) changes at specific CG dinucleotides can be combined into epigenetic age predictors to estimate chronological age-and the deviation of chronological and predicted age (∆_age) seems to be associated with all-cause mortality. In this study, we have further validated this association and analyzed whether or not individual age-associated CG-dinucleotides (CpGs) are related to life expectancy.

FINDINGS

In the German ESTHER cohort, we used 864 DNAm profiles of blood samples as the discovery set and 1000 DNAm profiles as the validation set to predict chronological age with three previously reported age predictors-based on 99, 71, or 353 age-associated CpGs. Several of these individual CpGs were significantly associated with life expectancy, and for some of these CpGs, this was even reproducible in the independent datasets. Notably, those CpGs that revealed significant association with life expectancy were overall rather hypomethylated upon aging.

CONCLUSION

Individual age-associated CpGs may provide biomarkers for all-cause mortality-but confounding factors need to be critically taken into consideration, and alternative methods, which facilitate more quantitative measurements at individual CpGs, might be advantageous. Our data suggest that particularly specific CpGs that become hypomethylated upon aging are indicative of biological aging.

Genetic Influences on Functional Capacities in Aging

PURPOSE

Older populations are characterized by great heterogeneity in functional capacities and understanding the factors underlying these differences has been a major area of research for some decades. Genetic differences arguably play an important role in the heterogeneity observed for many outcomes among older individuals. However, the role of genes in the variation and trajectories of functional capacities in older age is poorly understood. This review was conducted to explore the evidence for genetic influences on physical functional capacities in aging.

DESIGN AND METHODS

This rapid review was conducted using the following criteria: journal articles retrieved from the PubMed, Embase, AgeLine, Scopus, and Web of Science electronic databases including the key words: genetics, genotype, polymorphism, physical or functional performance, functional capacity, activities of daily living, older, and elderly. In total, 118 articles were included for initial review.

RESULTS

The heritability of objective measures of physical function ranges from 30% to 60% in studies of older twins. There is a paucity of evidence about genetic influences on functional capacities, but some candidate genes related to functional capacity have been identified.

IMPLICATIONS

No strong candidate genes exist for functional capacities. Current methodologies are beginning to generate new evidence about genetic influences on overall physical function at older ages, but the variety of measures of functional capacity makes evidence difficult to compare.

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.

Therapeutic Manipulation of Ageing: Repurposing Old Dogs and Discovering New Tricks

Ageing is a leading risk factor for many debilitating diseases. While age-related diseases have been the subject of over a century of intense investigation, until recently, physiological ageing was considered unavoidable. Pharmacological and genetic studies have since shown that ageing is a malleable process and that its abrogation can prevent its associated diseases. This review summarises a sample of the most promising efforts to deliver the products of ageing research to the clinic. Current efforts include the use of clinically approved drugs that have since been repurposed, as well as the development of novel therapeutics, to target ageing. Furthermore, ongoing research has sought reliable biomarkers of ageing that will accelerate the development of such therapeutics. Development of these technologies will improve quality of late-life and help relieve the enormous stress placed on state healthcare systems by a rapidly ageing global population. Thus, for both medical and socioeconomic reasons, it is imperative that ageing is made to yield to intervention.

Extended Twilight among Isogenic C. elegans Causes a Disproportionate Scaling between Lifespan and Health

Although many genetic factors and lifestyle interventions are known to affect the mean lifespan of animal populations, the physiological variation displayed by individuals across their lifespans remains largely uncharacterized. Here, we use a custom culture apparatus to continuously monitor five aspects of aging physiology across hundreds of isolated Caenorhabditis elegans individuals kept in a constant environment from hatching until death. Aggregating these measurements into an overall estimate of senescence, we find two chief differences between longer- and shorter-lived individuals. First, though long- and short-lived individuals are physiologically equivalent in early adulthood, longer-lived individuals experience a lower rate of physiological decline throughout life. Second, and counter-intuitively, long-lived individuals have a disproportionately extended "twilight" period of low physiological function. While longer-lived individuals experience more overall days of good health, their proportion of good to bad health, and thus their average quality of life, is systematically lower than that of shorter-lived individuals. We conclude that, within a homogeneous population reared under constant conditions, the period of early-life good health is comparatively uniform, and the most plastic period in the aging process is end-of-life senescence.

A cross-sectional study of male and female C57BL/6Nia mice suggests lifespan and healthspan are not necessarily correlated

Lifespan provides a discrete metric that is intuitively appealing and the assumption has been that healthspan is extended concomitant with lifespan. Medicine has been more successful at extending life than preserving health during aging. Interventions that extend lifespan in model organisms do not always result in a corresponding increase in healthspan, suggesting that lifespan and healthspan may be uncoupled. To understand how interventions that extend life affect healthspan, we need measures that distinguish between young and old animals. Here we measured age-related changes in healthspan in male and female C57BL/6JNia mice assessed at 4 distinct ages (4 months, 20 months, 28 months and 32 months). Correlations between health parameters and age varied. Some parameters show consistent patterns with age across studies and in both sexes, others changed in one sex only and others showed no significant differences in mice of different ages. Few correlations existed among health assays, suggesting that physiological function in domains we assessed change independently in aging mice. With one exception, health parameters were not significantly associated with an increased probability of premature death. Our results show the need for more robust measures of murine health and suggest a potential disconnect between health and lifespan in mice.

Biomarkers of skin aging

Aging is a complex process not only influenced by inherited but also by several environmental factors. It is characterized by a progressive loss of function in multiple tissues, which leads to an increased probability of death. On the other hand, several morphological and histological changes are registered in aged skin that is mostly dependent on the cumulative exposure in environmental aging promoters, such as ultraviolet radiation. Understanding of individual pathogenesis and introduction of preventive measurements require objective assessment, i.e., the administration of biomarkers. Because of the complexity of skin aging, the exact definition of biomarkers is a major research challenge. In this article, we summarize the basic knowledge involving skin aging and its biomarkers.

Trajectories of function and biomarkers with age: the CHS All Stars Study

BACKGROUND

Multimorbidity is a major driver of physical and cognitive impairment, but rates of decline are also related to ageing. We sought to determine trajectories of decline in a large cohort by disease status, and examined their correspondence with biomarkers of ageing processes including growth hormone, sex steroid, inflammation, visceral adiposity and kidney function pathways.

METHODS

We have followed the 5888 participants in the Cardiovascular Health Study (CHS) for healthy ageing and longevity since 1989-90. Gait speed, grip strength, modified mini-mental status examination (3MSE) and the digit symbol substitution test (DSST) were assessed annually to 1998-99 and again in 2005-06. Insulin-like growth hormone (IGF-1), dehydroepiandrosterone sulphate (DHEAS), interleukin-6 (IL-6), adiponectin and cystatin-C were assessed 3-5 times from stored samples. Health status was updated annually and dichotomized as healthy vs not healthy. Trajectories for each function measure and biomarker were estimated using generalized estimating equations as a function of age and health status using standardized values.

RESULTS

Trajectories of functional decline showed strong age acceleration late in life in healthy older men and women as well as in chronically ill older adults. Adiponectin, IL-6 and cystatin-C tracked with functional decline in all domains; cystatin-C was consistently associated with functional declines independent of other biomarkers. DHEAS was independently associated with grip strength and IL-6 with grip strength and gait speed trajectories.

CONCLUSIONS

Functional decline in late life appears to mark a fundamental ageing process in that it occurred and was accelerated in late life regardless of health status. Cystatin C was most consistently associated with these functional declines.

Geroprotectors.org: a new, structured and curated database of current therapeutic interventions in aging and age-related disease

As the level of interest in aging research increases, there is a growing number of geroprotectors, or therapeutic interventions that aim to extend the healthy lifespan and repair or reduce aging-related damage in model organisms and, eventually, in humans. There is a clear need for a manually-curated database of geroprotectors to compile and index their effects on aging and age-related diseases and link these effects to relevant studies and multiple biochemical and drug databases. Here, we introduce the first such resource, Geroprotectors (http://geroprotectors.org). Geroprotectors is a public, rapidly explorable database that catalogs over 250 experiments involving over 200 known or candidate geroprotectors that extend lifespan in model organisms. Each compound has a comprehensive profile complete with biochemistry, mechanisms, and lifespan effects in various model organisms, along with information ranging from chemical structure, side effects, and toxicity to FDA drug status. These are presented in a visually intuitive, efficient framework fit for casual browsing or in-depth research alike. Data are linked to the source studies or databases, providing quick and convenient access to original data. The Geroprotectors database facilitates cross-study, cross-organism, and cross-discipline analysis and saves countless hours of inefficient literature and web searching. Geroprotectors is a one-stop, knowledge-sharing, time-saving resource for researchers seeking healthy aging solutions.

The Compression of Comorbidity

Epidemiologic evidence on the compression of morbidity issue suggests that despite the recent increase in longevity of older Americans, the age-specific prevalence rates for most chronic illnesses have not concomitantly declined. However, current morbidity survey methods may not be fully able to characterize the scope of illness, function, physiologic status, and homeostatic capacity of a population to the extent necessary to determine whether morbidity is actually being "compressed." This article considers some additional ways to assess long-term trends in population health status, emphasizing patterns of comorbidity and summary health measures, that may give insights into the overall biologic robustness of older persons.

Increased levels of hyper-stable protein aggregates in plasma of older adults

Proteins that misfold into hyper-stable/degradation-resistant species during aging may accumulate and disrupt protein homeostasis (i.e., proteostasis), thereby posing a survival risk to any organism. Using the method diagonal two-dimensional (D2D) SDS-PAGE, which separates hyper-stable SDS-resistant proteins at a proteomics level, we analyzed the plasma of healthy young (<30 years) and older (60-80 years) adults. We discovered the presence of soluble SDS-resistant protein aggregates in the plasma of older adults, but found significantly lower levels in the plasma of young adults. We identified the inflammation-related chaperone protein haptoglobin as the main component of the hyper-stable aggregates. This observation is consistent with the growing link between accumulations of protein aggregates and aging across many organisms. It is plausible higher amounts of SDS-resistant protein aggregates in the plasma of older adults may reflect a compromise in proteostasis that may potentially indicate cellular aging and/or disease risk. The results of this study have implications for further understanding the link between aging and the accumulation of protein aggregates, as well as potential for the development of aging-related biomarkers. More broadly, this novel application of D2D SDS-PAGE may be used to identify, quantify, and characterize the degradation-resistant protein aggregates in human plasma or any biological system.

Using measures of single-cell physiology and physiological state to understand organismic aging

Genetically identical organisms in homogeneous environments have different lifespans and healthspans. These differences are often attributed to stochastic events, such as mutations and 'epimutations', changes in DNA methylation and chromatin that change gene function and expression. But work in the last 10 years has revealed differences in lifespan- and health-related phenotypes that are not caused by lasting changes in DNA or identified by modifications to DNA or chromatin. This work has demonstrated persistent differences in single-cell and whole-organism physiological states operationally defined by values of reporter gene signals in living cells. While some single-cell states, for example, responses to oxygen deprivation, were defined previously, others, such as a generally heightened ability to make proteins, were, revealed by direct experiment only recently, and are not well understood. Here, we review technical progress that promises to greatly increase the number of these measurable single-cell physiological variables and measureable states. We discuss concepts that facilitate use of single-cell measurements to provide insight into physiological states and state transitions. We assert that researchers will use this information to relate cell level physiological readouts to whole-organism outcomes, to stratify aging populations into groups based on different physiologies, to define biomarkers predictive of outcomes, and to shed light on the molecular processes that bring about different individual physiologies. For these reasons, quantitative study of single-cell physiological variables and state transitions should provide a valuable complement to genetic and molecular explanations of how organisms age.

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.

Living Long and Well: Prospects for a Personalized Approach to the Medicine of Ageing

Research into ageing and its underlying molecular basis enables us to develop and implement targeted interventions to ameliorate or cure its consequences. However, the efficacy of interventions often differs widely between individuals, suggesting that populations should be stratified or even individualized. Large-scale cohort studies in humans, similar systematic studies in model organisms as well as detailed investigations into the biology of ageing can provide individual validated biomarkers and mechanisms, leading to recommendations for targeted interventions. Human cohort studies are already ongoing, and they can be supplemented by in silico simulations. Systematic studies in animal models are made possible by the use of inbred strains or genetic reference populations of mice. Combining the two, a comprehensive picture of the various determinants of ageing and 'health span' can be studied in detail, and an appreciation of the relevance of results from model organisms to humans is emerging. The interactions between genotype and environment, particularly the psychosocial environment, are poorly studied in both humans and model organisms, presenting serious challenges to any approach to a personalized medicine of ageing. To increase the success of preventive interventions, we argue that there is a pressing need for an individualized evaluation of interventions such as physical exercise, nutrition, nutraceuticals and calorie restriction mimetics as well as psychosocial and environmental factors, separately and in combination. The expected extension of the health span enables us to refocus health care spending on individual prevention, starting in late adulthood, and on the brief period of morbidity at very old age.

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.

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

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

A proposed panel of biomarkers of healthy ageing

BACKGROUND

There is no criterion reference for assessing healthy ageing and this creates difficulties when conducting and comparing research on ageing across studies. A cardinal feature of ageing is loss of function which translates into wide-ranging consequences for the individual and for family, carers and society. We undertook comprehensive reviews of the literature searching for biomarkers of ageing on five ageing-related domains including physical capability and cognitive, physiological and musculoskeletal, endocrine and immune functions. Where available, we used existing systematic reviews, meta-analyses and other authoritative reports such as the recently launched NIH Toolbox for assessment of neurological and behavioural function, which includes test batteries for cognitive and motor function (the latter described here as physical capability). We invited international experts to comment on our draft recommendations. In addition, we hosted an experts workshop in Newcastle, UK, on 22-23 October 2012, aiming to help capture the state-of-the-art in this complex area and to provide an opportunity for the wider ageing research community to critique the proposed panel of biomarkers.

DISCUSSION

Here we have identified important biomarkers of healthy ageing classified as subdomains of the main areas proposed. Cardiovascular and lung function, glucose metabolism and musculoskeletal function are key subdomains of physiological function. Strength, locomotion, balance and dexterity are key physical capability subdomains. Memory, processing speed and executive function emerged as key subdomains of cognitive function. Markers of the HPA-axis, sex hormones and growth hormones were important biomarkers of endocrine function. Finally, inflammatory factors were identified as important biomarkers of immune function. We present recommendations for a panel of biomarkers that address these major areas of function which decline during ageing. This biomarker panel may have utility in epidemiological studies of human ageing, in health surveys of older people and as outcomes in intervention studies that aim to promote healthy ageing. Further, the inclusion of the same common panel of measures of healthy ageing in diverse study designs and populations may enhance the value of those studies by allowing the harmonisation of surrogate endpoints or outcome measures, thus facilitating less equivocal comparisons between studies and the pooling of data across studies.

A proposed panel of biomarkers of healthy ageing

BACKGROUND

There is no criterion reference for assessing healthy ageing and this creates difficulties when conducting and comparing research on ageing across studies. A cardinal feature of ageing is loss of function which translates into wide-ranging consequences for the individual and for family, carers and society. We undertook comprehensive reviews of the literature searching for biomarkers of ageing on five ageing-related domains including physical capability and cognitive, physiological and musculoskeletal, endocrine and immune functions. Where available, we used existing systematic reviews, meta-analyses and other authoritative reports such as the recently launched NIH Toolbox for assessment of neurological and behavioural function, which includes test batteries for cognitive and motor function (the latter described here as physical capability). We invited international experts to comment on our draft recommendations. In addition, we hosted an experts workshop in Newcastle, UK, on 22-23 October 2012, aiming to help capture the state-of-the-art in this complex area and to provide an opportunity for the wider ageing research community to critique the proposed panel of biomarkers.

DISCUSSION

Here we have identified important biomarkers of healthy ageing classified as subdomains of the main areas proposed. Cardiovascular and lung function, glucose metabolism and musculoskeletal function are key subdomains of physiological function. Strength, locomotion, balance and dexterity are key physical capability subdomains. Memory, processing speed and executive function emerged as key subdomains of cognitive function. Markers of the HPA-axis, sex hormones and growth hormones were important biomarkers of endocrine function. Finally, inflammatory factors were identified as important biomarkers of immune function. We present recommendations for a panel of biomarkers that address these major areas of function which decline during ageing. This biomarker panel may have utility in epidemiological studies of human ageing, in health surveys of older people and as outcomes in intervention studies that aim to promote healthy ageing. Further, the inclusion of the same common panel of measures of healthy ageing in diverse study designs and populations may enhance the value of those studies by allowing the harmonisation of surrogate endpoints or outcome measures, thus facilitating less equivocal comparisons between studies and the pooling of data across studies.

Blood-borne biomarkers of mortality risk: systematic review of cohort studies

BACKGROUND

Lifespan and the proportion of older people in the population are increasing, with far reaching consequences for the social, political and economic landscape. Unless accompanied by an increase in health span, increases in age-related diseases will increase the burden on health care resources. Intervention studies to enhance healthy ageing need appropriate outcome measures, such as blood-borne biomarkers, which are easily obtainable, cost-effective, and widely accepted. To date there have been no systematic reviews of blood-borne biomarkers of mortality.

AIM

To conduct a systematic review to identify available blood-borne biomarkers of mortality that can be used to predict healthy ageing post-retirement.

METHODS

Four databases (Medline, Embase, Scopus, Web of Science) were searched. We included prospective cohort studies with a minimum of two years follow up and data available for participants with a mean age of 50 to 75 years at baseline.

RESULTS

From a total of 11,555 studies identified in initial searches, 23 fulfilled the inclusion criteria. Fifty-one blood borne biomarkers potentially predictive of mortality risk were identified. In total, 20 biomarkers were associated with mortality risk. Meta-analyses of mortality risk showed significant associations with C-reactive protein (Hazard ratios for all-cause mortality 1.42, p<0.001; Cancer-mortality 1.62, p<0.009; CVD-mortality 1.31, p = 0.033), N Terminal-pro brain natriuretic peptide (Hazard ratios for all-cause mortality 1.43, p<0.001; CHD-mortality 1.58, p<0.001; CVD-mortality 1.67, p<0.001) and white blood cell count (Hazard ratios for all-cause mortality 1.36, p = 0.001). There was also evidence that brain natriuretic peptide, cholesterol fractions, erythrocyte sedimentation rate, fibrinogen, granulocytes, homocysteine, intercellular adhesion molecule-1, neutrophils, osteoprotegerin, procollagen type III aminoterminal peptide, serum uric acid, soluble urokinase plasminogen activator receptor, tissue inhibitor of metalloproteinases 1 and tumour necrosis factor receptor II may predict mortality risk. There was equivocal evidence for the utility of 14 biomarkers and no association with mortality risk for CD40 ligand, cortisol, dehydroepiandrosterone, ferritin, haemoglobin, interleukin-12, monocyte chemoattractant protein 1, matrix metalloproteinase 9, myelopereoxidase, P-selectin, receptor activator of nuclear factor KappaB ligand, sex hormone binding globulin, testosterone, transferrin, and thyroid stimulating hormone and thyroxine.

CONCLUSIONS

Twenty biomarkers should be prioritised as potential predictors of mortality in future studies. More studies using standardised protocols and reporting methods, and which focus on mortality rather than risk of disease or health status as an outcome, are needed.

The Search for the Genetic Basis of Aging: The Identification of Gerontogenes in the NematodeCaenorhabditis elegans

Study of C. elegans has provided much information for gerontologists. The influence of the genome on life span is clearly observable, and at least one gerontogene, age-1, has been defined. Data relating to important evolutionary questions has emerged and will continue to be used in testing current hypotheses. We are using an approach unbiased by theoretical constraints to delineate aging processes simultaneously at the molecular and organismal levels. Much remains to be discovered before fundamental questions posed in this article are answered to a satisfactory degree. The immediate agenda is the identification and isolation of gerontogenes which influence life span in invertebrate models. This work is well in hand and will lead to the unraveling of specific life-span-determining processes. At this point we may be able to predict whether analogous processes also limit life in mammals. If we are fortunate and aging processes exhibit evolutionary conservation, many exciting possibilities await. Molecular tools provided by the invertebrate system can then be used to isolate homologous mammalian gerontogenes that could be subsequently utilized in highly targeted attempts to intervene in mammalian aging. This offers the most direct strategy for identifying life-span prolongation genes in humans.

MARK-AGE biomarkers of ageing

Many candidate biomarkers of human ageing have been proposed in the scientific literature but in all cases their variability in cross-sectional studies is considerable, and therefore no single measurement has proven to serve a useful marker to determine, on its own, biological age. A plausible reason for this is the intrinsic multi-causal and multi-system nature of the ageing process. The recently completed MARK-AGE study was a large-scale integrated project supported by the European Commission. The major aim of this project was to conduct a population study comprising about 3200 subjects in order to identify a set of biomarkers of ageing which, as a combination of parameters with appropriate weighting, would measure biological age better than any marker in isolation.

Chronic kidney disease and premature ageing

Chronic kidney disease (CKD) shares many phenotypic similarities with other chronic diseases, including heart failure, chronic obstructive pulmonary disease, HIV infection and rheumatoid arthritis. The most apparent similarity is premature ageing, involving accelerated vascular disease and muscle wasting. We propose that in addition to a sedentary lifestyle and psychosocial and socioeconomic determinants, four major disease-induced mechanisms underlie premature ageing in CKD: an increase in allostatic load, activation of the 'stress resistance response', activation of age-promoting mechanisms and impairment of anti-ageing pathways. The most effective current interventions to modulate premature ageing-treatment of the underlying disease, optimal nutrition, correction of the internal environment and exercise training-reduce systemic inflammation and oxidative stress and induce muscle anabolism. Deeper mechanistic insight into the phenomena of premature ageing as well as early diagnosis of CKD might improve the application and efficacy of these interventions and provide novel leads to combat muscle wasting and vascular impairment in chronic diseases.

Inbred mouse strains reveal biomarkers that are pro-longevity, antilongevity or role switching

Traditionally, biomarkers of aging are classified as either pro-longevity or antilongevity. Using longitudinal data sets from the large-scale inbred mouse strain study at the Jackson Laboratory Nathan Shock Center, we describe a protocol to identify two kinds of biomarkers: those with prognostic implication for lifespan and those with longitudinal evidence. Our protocol also identifies biomarkers for which, at first sight, there is conflicting evidence. Conflict resolution is possible by postulating a role switch. In these cases, high biomarker values are, for example, antilongevity in early life and pro-longevity in later life. Role-switching biomarkers correspond to features that must, for example, be minimized early, but maximized later, for optimal longevity. The clear-cut pro-longevity biomarkers we found reflect anti-inflammatory, anti-immunosenescent or anti-anaemic mechanisms, whereas clear-cut antilongevity biomarkers reflect inflammatory mechanisms. Many highly significant blood biomarkers relate to immune system features, indicating a shift from adaptive to innate processes, whereas most role-switching biomarkers relate to blood serum features and whole-body phenotypes. Our biomarker classification approach is applicable to any combination of longitudinal studies with life expectancy data, and it provides insights beyond a simplified scheme of biomarkers for long or short lifespan.

Select aging biomarkers based on telomere length and chronological age to build a biological age equation

The purpose of this study is to build a biological age (BA) equation combining telomere length with chronological age (CA) and associated aging biomarkers. In total, 139 healthy volunteers were recruited from a Chinese Han cohort in Beijing. A genetic index, renal function indices, cardiovascular function indices, brain function indices, and oxidative stress and inflammation indices (C-reactive protein [CRP]) were measured and analyzed. A BA equation was proposed based on selected parameters, with terminal telomere restriction fragment (TRF) and CA as the two principal components. The selected aging markers included mitral annulus peak E anterior wall (MVEA), intima-media thickness (IMT), cystatin C (CYSC), D-dimer (DD), and digital symbol test (DST). The BA equation was: BA = −2.281TRF + 26.321CYSC + 0.025DD − 104.419MVEA + 34.863IMT − 0.265DST + 0.305CA + 26.346. To conclude, telomere length and CA as double benchmarks may be a new method to build a BA.

Biomarkers of healthy ageing: expectations and validation

The challenge of devising a set of biomarkers capable of measuring the ageing rate in human subjects was articulated long ago. In recent years, progress in the basic biology of ageing suggests the realistic possibility of preventive or restaurative interventions that may extend healthy lifespan in mammals including human subjects. Specifically, frailty is being increasingly recognised as a clinically relevant syndrome that may be therapeutically addressed. This greatly enhances the need for sensitive and specific biomarkers of healthy ageing that are validated in both experimental animals and, importantly, in human subjects over the whole age range. Here, we will discuss the present challenges and requirements for biomarker validation in human subjects. We propose the central requirements for a validated biomarker of healthy ageing as: (i) better predictive power than chronological age for multiple dimensions of ageing; (ii) identification of the age range in which the marker is informative; (iii) establishment of sensitivity/specificity as indicators of its predictive power at the level of the individual; (iv) minimisation of methodological variation between laboratories.

Seasonal variation of peripheral blood leukocyte telomere length in Costa Rica: A population-based observational study

OBJECTIVES

Peripheral blood leukocyte telomere length (LTL) is increasingly being used as a biomarker of aging, but its natural variation in human populations is not well understood. Several other biomarkers show seasonal variation, as do several determinants of LTL. We examined whether there was monthly variation in LTL in Costa Rica, a country with strong seasonal differences in precipitation and infection.

METHODS

We examined a longitudinal population-based cohort of 581 Costa Rican adults age 60 and above, from which blood samples were drawn between October 2006 and July 2008. LTL was assayed from these samples using the quantitative PCR method. Multivariate regression models were used to examine correlations between month of blood draw and LTL.

RESULTS

Telomere length from peripheral blood leukocytes varied by as much as 200 base pairs depending on month of blood draw, and this difference is not likely to be due to random variation. A moderate proportion of this association is statistically accounted for by month and region specific average rainfall. We found shorter telomere length associated with greater rainfall.

CONCLUSIONS

There are two possible explanations of our findings. First, there could be relatively rapid month-to-month changes in LTL. This conclusion would have implications for understanding the natural population dynamics of telomere length. Second, there could be seasonal differences in constituent cell populations. This conclusion would suggest that future studies of LTL use methods to account for the potential impact of constituent cell type.

Do changes in circulating biomarkers track with each other and with functional changes in older adults?

BACKGROUND

It is unclear if changes in proposed circulating biomarkers of aging are strongly correlated to each other or functional change. We tested if biomarker changes track with each other and with functional measures over 9 years in older adults.

METHODS

Dehydroepiandrosterone sulfate (DHEAS), adiponectin, insulin-like growth factor 1 (IGF-1), IGF binding proteins 1 (IGFBP-1) and 3 (IGFBP-3), interleukin-6 (IL-6), cholesterol, and function (gait speed, grip strength, Modified Mini Mental Status Exam [3MSE] and Digit Symbol Substitution Test [DSST] scores) were measured in 1996-1997 and 2005-2006 in the Cardiovascular Health Study All Stars study (N = 901, mean [standard deviation, SD] age 85.3 [3.6] years in 2005-2006). Adjusted Pearson correlations illustrated if biomarkers tracked together. Multivariable linear regression demonstrated if biomarker changes tracked with functional changes.

RESULTS

Correlations among biomarker changes were mostly <0.2. In models with each biomarker entered separately, a 1-SD increase biomarker change was associated with change in function as follows: grip strength (DHEAS β = 0.61kg, p = .001; IL-6 β = -0.46kg, p = .012; cholesterol men β = 0.79kg, p = .016); gait speed (DHEAS β = 0.02 meters per second, p = .039; IL-6 β = -0.018 meters per second, p = .049); and DSST score (DHEAS women β = 1.46, p = .004; IL-6 β = -0.83, p = .027). When biomarkers were entered in the same model, significant associations remaining were as follows: grip strength (DHEAS β = 0.54kg, p = .005; IL-6 β = -0.43kg, p = .022); 3MSE score (IGF-1 β = 0.96, p = .04; IGFBP-3 β = -1.07, p = .024); and DSST score (DHEAS women β = 1.27, p = .012; IL-6 β = -0.80, p = .04).

CONCLUSION

Changes in biomarkers were poorly correlated, supporting a model of stochastic, independent change across systems. DHEAS and IL-6 tracked most closely with function, illustrating that changes in inflammation and sex steroids may play dominant roles in changes of these functional outcomes.