Biomarkers of aging

Predictors of Biological Age: The Implications for Wellness and Aging Research

As healthspan and lifespan research breakthroughs have become more commonplace, the need for valid, practical markers of biological age is becoming increasingly paramount. The accessibility and affordability of biological age predictors that can reveal information about mortality and morbidity risk, as well as remaining years of life, has profound clinical and research implications. In this review, we examine 5 groups of aging biomarkers capable of providing accurate biological age estimations. The unique capabilities of these biomarkers have far reaching implications for the testing of both pharmaceutical and non-pharmaceutical interventions designed to slow or reverse biological aging. Additionally, the enhanced validity and availability of these tools may have increasingly relevant clinical value. The authors of this review explore those implications, with an emphasis on lifestyle modification research, and provide an overview of the current evidence regarding 5 biological age predictor categories: Telomere length, composite biomarkers, DNA methylation “epigenetic clocks,” transcriptional predictors of biological age, and functional age predictors.

Hierarchy and control of ageing-related methylation networks

DNA methylation provides one of the most widely studied biomarkers of ageing. Since the methylation of CpG dinucleotides function as switches in cellular mechanisms, it is plausible to assume that by proper adjustment of these switches age may be tuned. Though, adjusting hundreds of CpG methylation levels coherently may never be feasible and changing just a few positions may lead to biologically unstable state.

A prominent example of methylation-based age estimators is provided by Horvath’s clock, based on 353 CpG dinucleotides, showing a high correlation (not necessarily causation) with chronological age across multiple tissue types. On this small subset of CpG dinucleotides we demonstrate how the adjustment of one methylation level leads to a cascade of changes at other sites. Among the studied subset, we locate the most important CpGs (and related genes) that may have a large influence on the rest of the sub-system. According to our analysis, the structure of this network is way more hierarchical compared to what one would expect based on ensembles of uncorrelated connections. Therefore, only a handful of CpGs is enough to modify the system towards a desired state.

When propagation of the change over the network is taken into account, the resulting modification in the predicted age can be significantly larger compared to the effect of isolated CpG perturbations. By adjusting the most influential single CpG site and following the propagation of methylation level changes we can reach up to 5.74 years in virtual age reduction, significantly larger than without taking into account of the network control. Extending our approach to the whole methylation network may identify key nodes that have controller role in the ageing process.

Aging affects all living organisms. In humans, the chronological age correlates with the methylation level of some locations of the DNA. Here we extract an interaction network between these ageing related sites, which shows signs of hierarchical organisation. In addition, modifications in the methylation of single sites of the DNA can impose cascades of changes at other sites over this network. Based on “gedanken-experiments” in a small subset of CpG sites we show that by tuning appropriately selected methylation levels the estimated biological age can be changed. When modifying the most influential locations, the resulting cascades of changes can set back the estimated biological age by more than 5 years. Our study also shows that compared to single site methylation perturbations, the propagation of the change over the interaction network leads to methylation change profiles which are more aligned with the natural direction of ageing in a high dimensional representation of the methylation levels.

Epigenetic age prediction

Advanced age is the main common risk factor for cancer, cardiovascular disease and neurodegeneration. Yet, more is known about the molecular basis of any of these groups of diseases than the changes that accompany ageing itself. Progress in molecular ageing research was slow because the tools predicting whether someone aged slowly or fast (biological age) were unreliable. To understand ageing as a risk factor for disease and to develop interventions, the molecular ageing field needed a quantitative measure; a clock for biological age. Over the past decade, a number of age predictors utilising DNA methylation have been developed, referred to as epigenetic clocks. While they appear to estimate biological age, it remains unclear whether the methylation changes used to train the clocks are a reflection of other underlying cellular or molecular processes, or whether methylation itself is involved in the ageing process. The precise aspects of ageing that the epigenetic clocks capture remain hidden and seem to vary between predictors. Nonetheless, the use of epigenetic clocks has opened the door towards studying biological ageing quantitatively, and new clocks and applications, such as forensics, appear frequently. In this review, we will discuss the range of epigenetic clocks available, their strengths and weaknesses, and their applicability to various scientific queries.

DNA methylation-based age clocks: From age prediction to age reversion

Aging as an irretrievable occurrence throughout the entire life is characterized by a progressive decline in physiological functionality and enhanced disease vulnerability. Numerous studies have demonstrated that epigenetic modifications, particularly DNA methylation (DNAm), correlate with aging and age-related diseases. Several investigations have attempted to predict chronological age using the age-related alterations in the DNAm of certain CpG sites. Here we categorize different studies that tracked the aging process in the DNAm landscape to show how epigenetic age clocks evolved from a chronological age estimator to an indicator of lifespan and healthspan. We also describe the health and disease predictive potential of estimated epigenetic age acceleration regarding different clinical conditions and lifestyle factors. Considering the revealed age-related epigenetic changes, the recent age-reprogramming strategies are discussed which are promising methods for resetting the aging clocks.

In vivo biomarkers of structural and functional brain development and aging in humans

Brain aging is a major determinant of aging. Along with the aging population, prevalence of neurodegenerative diseases is increasing, therewith placing economic and social burden on individuals and society. Individual rates of brain aging are shaped by genetics, epigenetics, and prenatal environmental. Biomarkers of biological brain aging are needed to predict individual trajectories of aging and the risk for age-associated neurological impairments for developing early preventive and interventional measures. We review current advances of in vivo biomarkers predicting individual brain age. Telomere length and epigenetic clock, two important biomarkers that are closely related to the mechanistic aging process, have only poor deterministic and predictive accuracy regarding individual brain aging due to their high intra- and interindividual variability. Phenotype-related biomarkers of global cognitive function and brain structure provide a much closer correlation to age at the individual level. During fetal and perinatal life, autonomic activity is a unique functional marker of brain development. The cognitive and structural biomarkers also boast high diagnostic specificity for determining individual risks for neurodegenerative diseases.

Ranking Biomarkers of Aging by Citation Profiling and Effort Scoring

Aging affects most living organisms and includes the processes that reduce health and survival. The chronological and the biological age of individuals can differ remarkably, and there is a lack of reliable biomarkers to monitor the consequences of aging. In this review we give an overview of commonly mentioned and frequently used potential aging-related biomarkers. We were interested in biomarkers of aging in general and in biomarkers related to cellular senescence in particular. To answer the question whether a biological feature is relevant as a potential biomarker of aging or senescence in the scientific community we used the PICO strategy known from evidence-based medicine. We introduced two scoring systems, aimed at reflecting biomarker relevance and measurement effort, which can be used to support study designs in both clinical and research settings.

Life course socioeconomic position and DNA methylation age acceleration in mid-life

Background

Ageing biomarkers can help us better understand how well-established socioeconomic position (SEP) disparities in ageing occur. A promising new set of DNAm methylation (DNAm)-based ageing biomarkers indicate through their age acceleration (AA) measures if biological ageing is slower or faster than chronological ageing. Few studies have investigated the association between SEP and DNAm AA.

Methods

We used linear regression to examine the sex-adjusted relationships between childhood social class, adult social class, intergenerational social class change, education and adult household earnings with first (Horvath AA and Hannum AA) and second generation (PhenoAge AA and GrimAge AA) DNAm AA markers using data from the MRC National Survey of Health and Development.

Results

In the first-generation biomarkers, there was little evidence of any associations with Horvath AA but associations of childhood social class and income with Hannum AA were observed. Strong associations were seen between greater disadvantage in childhood and adult SEP and greater AA in the second generation biomarkers. For example, those with fathers in an unskilled occupational social class in childhood had 3.6 years greater PhenoAge AA (95% CI 1.8 to 5.4) than those with fathers from a professional social class. Individuals without qualifications had higher AA compared with those with higher education (4.1 years greater GrimAge AA (95% CI 3.1 to 5.0)).

Conclusion

Our findings highlight the importance of exposure to social disadvantage in childhood to the biological ageing process. The second generation clocks appear to be more sensitive to the accumulation of social disadvantage across the life course.

Lipids: biomarkers of healthy aging

Life expectancy, and longevity have been increasing in recent years. However, this is, in most cases, accompanied by age-related diseases. Thus, it became essential to better understand the mechanisms inherent to aging, and to establish biomarkers that characterize this physiological process. Among all biomolecules, lipids appear to be a good target for the study of these biomarkers. In fact, some lipids have already been associated with age-related diseases. With the development of analytical techniques such as Mass Spectrometry, and Nuclear Magnetic Resonance, Lipidomics has been increasingly used to study pathological, and physiological states of an organism. Thus, the study of serum, and plasma lipidome in centenarians, and elderly individuals without age-related diseases can be a useful tool for the identification of aging biomarkers, and to understand physiological aging, and longevity. This review focus on the importance of lipids as biomarkers of aging, and summarize the changes in the lipidome that have been associated with aging, and longevity.

Accelerated brain aging predicts impulsivity and symptom severity in depression

Multiple structural and functional neuroimaging measures vary over the course of the lifespan and can be used to predict chronological age. Accelerated brain aging, as quantified by deviations in the MRI-based predicted age with respect to chronological age, is associated with risk for neurodegenerative conditions, bipolar disorder, and mortality. Whether age-related changes in resting-state functional connectivity are accelerated in major depressive disorder (MDD) is unknown, and, if so, it is unclear if these changes contribute to specific cognitive weaknesses that often occur in MDD. Here, we delineated age-related functional connectivity changes in a large sample of normal control subjects and tested whether brain aging is accelerated in MDD. Furthermore, we tested whether accelerated brain aging predicts individual differences in cognitive function. We trained a support vector regression model predicting age using resting-state functional connectivity in 710 healthy adults aged 18-89. We applied this model trained on normal aging subjects to a sample of actively depressed MDD participants (n = 109). The difference between predicted brain age and chronological age was 2.11 years greater (p = 0.015) in MDD patients compared to control participants. An older MDD brain age was significantly associated with increased impulsivity and, in males, increased depressive severity. Unexpectedly, accelerated brain aging was also associated with increased placebo response in a sham-controlled trial of high-frequency repetitive transcranial magnetic stimulation targeting the dorsomedial prefrontal cortex. Our results indicate that MDD is associated with accelerated brain aging, and that accelerated aging is selectively associated with greater impulsivity and depression severity.

Aging biomarkers and the measurement of health and risk

Prevention of age-related disorders is increasingly in focus of health policies, and it is hoped that early intervention on processes of deterioration can promote healthier and longer lives. New opportunities to slow down the aging process are emerging with new fields such as personalized nutrition. Data-intensive research has the potential to improve the precision of existing risk factors, e.g., to replace coarse-grained markers such as blood cholesterol with more detailed multivariate biomarkers. In this paper, we follow an attempt to develop a new aging biomarker. The vision among the project consortium, comprising both research and industrial partners, is that the new biomarker will be predictive of a range of age-related conditions, which may be preventable through personalized nutrition. We combine philosophical analysis and ethnographic fieldwork to explore the possibilities and challenges of managing aging through bodily signs that are not straightforwardly linked to symptomatic disease. We document how the improvement of measurement brings about new conceptual challenges of demarcating healthy and unhealthy states. Moreover, we highlight that the reframing of aging as risk has social and ethical implications, as it is generative of normative notions of what constitutes successful aging and good citizenship.

Epigenetic Aging and Colorectal Cancer: State of the Art and Perspectives for Future Research

Although translational research has identified a large number of potential biomarkers involved in colorectal cancer (CRC) carcinogenesis, a better understanding of the molecular pathways associated with biological aging in colorectal cells and tissues is needed. Here, we aim to summarize the state of the art about the role of age acceleration, defined as the difference between epigenetic age and chronological age, in the development and progression of CRC. Some studies have shown that accelerated biological aging is positively associated with the risk of cancer and death in general. In line with these findings, other studies have shown how the assessment of epigenetic age in people at risk for CRC could be helpful for monitoring the molecular response to preventive interventions. Moreover, it would be interesting to investigate whether aberrant epigenetic aging could help identify CRC patients with a high risk of recurrence and a worst prognosis, as well as those who respond poorly to treatment. Yet, the application of this novel concept is still in its infancy, and further research should be encouraged in anticipation of future applications in clinical practice.

The Resistance of Drosophila melanogaster to Oxidative, Genotoxic, Proteotoxic, Osmotic Stress, Infection, and Starvation Depends on Age According to the Stress Factor

We studied how aging affects the ability of Drosophila melanogaster to tolerate various types of stress factors. Data were obtained on the resistance of D. melanogaster to oxidative and genotoxic (separately paraquat, Fe³⁺, Cu²⁺, and Zn²⁺ ions), proteotoxic (hyperthermia, Cd²⁺ ions), and osmotic (NaCl) stresses, starvation, and infection with the pathological Beauveria bassiana fungus at different ages. In all cases, we observed a strong negative correlation between age and stress tolerance. The largest change in the age-dependent decline in survival occurred under oxidative and osmotic stress. In most experiments, we observed that young Drosophila females have higher stress resistance than males. We checked whether it is possible to accurately assess the biological age of D. melanogaster based on an assessment of stress tolerance. We have proposed a new approach for assessing a biological age of D. melanogaster using a two-parameter survival curve model. For the model, we used an algorithm that evaluated the quality of age prediction for different age and gender groups. The best predictions were obtained for females who were exposed to CdCl₂ and ZnCl₂ with an average error of 0.32 days and 0.36 days, respectively. For males, the best results were observed for paraquat and NaCl with an average error of 0.61 and 0.68 days, respectively. The average accuracy for all stresses in our model was 1.73 days.

Blood-based epigenetic estimators of chronological age in human adults using DNA methylation data from the Illumina MethylationEPIC array

BACKGROUND

Epigenetic clocks have been recognized for their precise prediction of chronological age, age-related diseases, and all-cause mortality. Existing epigenetic clocks are based on CpGs from the Illumina HumanMethylation450 BeadChip (450 K) which has now been replaced by the latest platform, Illumina MethylationEPIC BeadChip (EPIC). Thus, it remains unclear to what extent EPIC contributes to increased precision and accuracy in the prediction of chronological age.

RESULTS

We developed three blood-based epigenetic clocks for human adults using EPIC-based DNA methylation (DNAm) data from the Norwegian Mother, Father and Child Cohort Study (MoBa) and the Gene Expression Omnibus (GEO) public repository: 1) an Adult Blood-based EPIC Clock (ABEC) trained on DNAm data from MoBa (n = 1592, age-span: 19 to 59 years), 2) an extended ABEC (eABEC) trained on DNAm data from MoBa and GEO (n = 2227, age-span: 18 to 88 years), and 3) a common ABEC (cABEC) trained on the same training set as eABEC but restricted to CpGs common to 450 K and EPIC. Our clocks showed high precision (Pearson correlation between chronological and epigenetic age (r) > 0.94) in independent cohorts, including GSE111165 (n = 15), GSE115278 (n = 108), GSE132203 (n = 795), and the Epigenetics in Pregnancy (EPIPREG) study of the STORK Groruddalen Cohort (n = 470). This high precision is unlikely due to the use of EPIC, but rather due to the large sample size of the training set.

CONCLUSIONS

Our ABECs predicted adults' chronological age precisely in independent cohorts. As EPIC is now the dominant platform for measuring DNAm, these clocks will be useful in further predictions of chronological age, age-related diseases, and mortality.

A Biological Age Model Designed for Health Promotion Interventions: Protocol for an Interdisciplinary Study for Model Development

BACKGROUND

Actions to improve healthy aging and delay morbidity are crucial, given the global aging population. We believe that biological age estimation can help promote the health of the general population. Biological age reflects the heterogeneity in functional status and vulnerability to disease that chronological age cannot. Thus, biological age assessment is a tool that provides an intuitively meaningful outcome for the general population, and as such, facilitates our understanding of the extent to which lifestyle can increase health span.

OBJECTIVE

This interdisciplinary study intends to develop a biological age model and explore its usefulness.

METHODS

The model development comprised three consecutive phases: (1) conducting a cross-sectional study to gather candidate biomarkers from 100 individuals representing normal healthy aging people (the derivation cohort); (2) estimating the biological age using principal component analysis; and (3) testing the clinical use of the model in a validation cohort of overweight adults attending a lifestyle intervention course.

RESULTS

We completed the data collection and analysis of the cross-sectional study, and the initial results of the principal component analysis are ready. Interpretation and refinement of the model is ongoing. Recruitment to the validation cohort is forthcoming. We expect the results to be published by December 2021.

CONCLUSIONS

We expect the biological age model to be a useful indicator of disease risk and metabolic risk, and further research should focus on validating the model on a larger scale.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03680768, https://clinicaltrials.gov/ct2/show/NCT03680768 (Phase 1 study); NCT04279366 https://clinicaltrials.gov/ct2/show/NCT04279366 (Phase 3 study).

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/19209.

Systemic overexpression of C-C motif chemokine ligand 2 promotes metabolic dysregulation and premature death in mice with accelerated aging

Injection of tissues with senescent cells induces changes that mimic aging, and this process is delayed in mice engineered to eliminate senescent cells, which secrete proinflammatory cytokines, including C-C motif chemokine ligand 2 (Ccl2). Circulating levels of Ccl2 correlate with age, but the impact of Ccl2 on tissue homeostasis has not been established. We generated an experimental model by crossbreeding mice overexpressing Ccl2 with progeroid mice bearing a mutation in the lamin A (Lmna) gene. Wild-type animals and progeroid mice that do not overexpress Ccl2 were used as controls. Ccl2 overexpression decreased the lifespan of the progeroid mice and induced the dysregulation of glycolysis, the citric acid cycle and one-carbon metabolism in skeletal muscle, driving dynamic changes in energy metabolism and DNA methylation. This impact on cellular bioenergetics was associated with mitochondrial alterations and affected cellular metabolism, autophagy and protein synthesis through AMPK/mTOR pathways. The data revealed the ability of Ccl2 to promote death in mice with accelerated aging, which supports its putative use as a biomarker of an increased senescent cell burden and for the assessment of the efficacy of interventions aimed at extending healthy aging.

Improved Human Age Prediction by Using Gene Expression Profiles From Multiple Tissues

Studying transcriptome chronological change from tissues across the whole body can provide valuable information for understanding aging and longevity. Although there has been research on the effect of single-tissue transcriptomes on human aging or aging in mice across multiple tissues, the study of human body-wide multi-tissue transcriptomes on aging is not yet available. In this study, we propose a quantitative model to predict human age by using gene expression data from 46 tissues generated by the Genotype-Tissue Expression (GTEx) project. Specifically, the biological age of a person is first predicted via the gene expression profile of a single tissue. Then, we combine the gene expression profiles from two tissues and compare the predictive accuracy between single and two tissues. The best performance as measured by the root-mean-square error is 3.92 years for single tissue (pituitary), which deceased to 3.6 years when we combined two tissues (pituitary and muscle) together. Different tissues have different potential in predicting chronological age. The prediction accuracy is improved by combining multiple tissues, supporting that aging is a systemic process involving multiple tissues across the human body.

DNA Methylation Clocks and Their Predictive Capacity for Aging Phenotypes and Healthspan

The number of age predictors based on DNA methylation (DNAm) profile is rising due to their potential in predicting healthspan and application in age-related illnesses, such as neurodegenerative diseases. The cumulative assessment of DNAm levels at age-related CpGs (DNAm clock) may reflect biological aging. Such DNAm clocks have been developed using various training models and could mirror different aspects of disease/aging mechanisms. Hence, evaluating several DNAm clocks together may be the most effective strategy in capturing the complexity of the aging process. However, various confounders may influence the outcome of these age predictors, including genetic and environmental factors, as well as technical differences in the selected DNAm arrays. These factors should be taken into consideration when interpreting DNAm clock predictions. In the current review, we discuss 15 reported DNAm clocks with consideration for their utility in investigating neurodegenerative diseases and suggest research directions towards developing a more optimal measure for biological aging.

Multi-Omic Biological Age Estimation and Its Correlation With Wellness and Disease Phenotypes: A Longitudinal Study of 3,558 Individuals

Biological age (BA), derived from molecular and physiological measurements, has been proposed to better predict mortality and disease than chronological age (CA). In the present study, a computed estimate of BA was investigated longitudinally in 3,558 individuals using deep phenotyping, which encompassed a broad range of biological processes. The Klemera-Doubal algorithm was applied to longitudinal data consisting of genetic, clinical laboratory, metabolomic, and proteomic assays from individuals undergoing a wellness program. BA was elevated relative to CA in the presence of chronic diseases. We observed a significantly lower rate of change than the expected ~1 year/year (to which the estimation algorithm was constrained) in BA for individuals participating in a wellness program. This observation suggests that BA is modifiable and suggests that a lower BA relative to CA may be a sign of healthy aging. Measures of metabolic health, inflammation, and toxin bioaccumulation were strong predictors of BA. BA estimation from deep phenotyping was seen to change in the direction expected for both positive and negative health conditions. We believe BA represents a general and interpretable "metric for wellness" that may aid in monitoring aging over time.

Measuring Aging and Identifying Aging Phenotypes in Cancer Survivors

Observational data have shown that some cancer survivors develop chronic conditions like frailty, sarcopenia, cardiac dysfunction, and mild cognitive impairment earlier and/or at a greater burden than similarly aged individuals never diagnosed with cancer or exposed to systemic or targeted cancer therapies. In aggregate, cancer- and treatment-related physical, cognitive, and psychosocial late- and long-term morbidities experienced by cancer survivors are hypothesized to represent accelerated or accentuated aging trajectories. However, conceptual, measurement, and methodological challenges have constrained efforts to identify, predict, and mitigate aging-related consequences of cancer and cancer treatment. In July 2018, the National Cancer Institute convened basic, clinical, and translational science experts for a think tank titled "Measuring Aging and Identifying Aging Phenotypes in Cancer Survivors." Through the resulting deliberations, several research and resource needs were identified, including longitudinal studies to examine aging trajectories that include detailed data from before, during, and after cancer treatment; mechanistic studies to elucidate the pathways that lead to the emergence of aging phenotypes in cancer survivors; long-term clinical surveillance to monitor survivors for late-emerging effects; and tools to integrate multiple data sources to inform understanding of how cancer and its therapies contribute to the aging process. Addressing these needs will help expand the evidence base and inform strategies to optimize healthy aging of cancer survivors.

Electrophysiological Measures of Aging Pharynx Function in C. elegans Reveal Enhanced Organ Functionality in Older, Long-lived Mutants

The function of the pharynx, an organ in the model system Caenorhabditis elegans, has been correlated with life span and motility (another measure of health) since 1980. In this study, in order to further understand the relationship between organ function and life span, we measured the age-related decline of the pharynx using an electrophysiological approach. We measured and analyzed electropharyngeograms (EPG) of wild type animals, short-lived hsf-1 mutants, and long-lived animals with genetically decreased insulin signaling or increased heat shock pathway signaling; we recorded a total of 2,478 EPGs from 1,374 individuals. As expected, the long-lived daf-2(e1370) and hsf-1OE(uthIs235) animals maintained pharynx function relatively closer to the youthful state during aging, whereas the hsf-1(sy441) and wild type animals' pharynx function deviated significantly further from the youthful state at advanced age. Measures of the amount of variation in organ function can act as biomarkers of youthful physiology as well. Intriguingly, the long-lived animals had greater variation in the duration of pharynx contraction at older ages.

The Role of Nutri(epi)genomics in Achieving the Body's Full Potential in Physical Activity

Physical activity represents a powerful tool to achieve optimal health. The overall activation of several molecular pathways is associated with many beneficial effects, mainly converging towards a reduced systemic inflammation. Not surprisingly, regular activity can contribute to lowering the “epigenetic age”, acting as a modulator of risk toward several diseases and enhancing longevity. Behind this, there are complex molecular mechanisms induced by exercise, which modulate gene expression, also through epigenetic modifications. The exercise-induced epigenetic imprint can be transient or permanent and contributes to the muscle memory, which allows the skeletal muscle adaptation to environmental stimuli previously encountered. Nutrition, through key macro- and micronutrients with antioxidant properties, can play an important role in supporting skeletal muscle trophism and those molecular pathways triggering the beneficial effects of physical activity. Nutrients and antioxidant food components, reversibly altering the epigenetic imprint, have a big impact on the phenotype. This assigns a role of primary importance to nutri(epi)genomics, not only in optimizing physical performance, but also in promoting long term health. The crosstalk between physical activity and nutrition represents a major environmental pressure able to shape human genotypes and phenotypes, thus, choosing the right combination of lifestyle factors ensures health and longevity.

Is inflammatory signaling involved in disease-related muscle wasting? Evidence from osteoarthritis, chronic obstructive pulmonary disease and type II diabetes

Muscle loss is an important feature that occurs in multiple pathologies including osteoarthritis (OA), chronic obstructive pulmonary disease (COPD) and type II diabetes (T2D). Despite differences in pathogenesis and disease-related complications, there are reasons to believe that some fundamental underlying mechanisms are inherent to the muscle wasting process, irrespective of the pathology. Recent evidence shows that inflammation, either local or systemic, contributes to the modulation of muscle mass and/or muscle strength, via an altered molecular profile in muscle tissue. However, it remains ambiguous to which extent and via which mechanisms inflammatory signaling affects muscle mass in disease. Therefore, the objective of the present review is to discuss the role of inflammation on skeletal muscle anabolism, catabolism and functionality in three pathologies that are characterized by an eventual loss in muscle mass (and muscle strength), i.e. OA, COPD and T2D. In OA and COPD, most rodent models confirmed that systemic (COPD) or muscle (OA) inflammation directly induces muscle loss or muscle dysfunctionality. However, in a patient population, the association between inflammation and muscular maladaptations are more ambiguous. For example, in T2D patients, systemic inflammation is associated with muscle loss whereas in OA patients this link has not consistently been established. T2D rodent models revealed that increased levels of advanced glycation end-products (AGEs) and a decreased mTORC1 activation play a key role in muscle atrophy, but it remains to be elucidated whether AGEs and mTORC1 are interconnected and contribute to muscle loss in T2D patients. Generally, if any, associations between inflammation and muscle are mainly based on observational and cross-sectional data. There is definitely a need for longitudinal evidence through well-powered randomized control trials that take into account confounders such as age, disease-phenotypes, comorbidities, physical (in) activity etc. This will allow to improve our understanding of the complex interaction between inflammatory signaling and muscle mass loss and hence contribute to the development of therapeutic strategies to combat muscle wasting in these diseases.

Molecular Aging Markers in Patients with Klinefelter Syndrome

Molecular aging markers provide the opportunity for biological age determination in humans and to study factors, such as genetic determinants, affecting the ageing process. In males with Klinefelter syndrome (KS, non-mosaic karyotype 47, XXY), which is the most common sex chromosome aneuploidy, age-related morbidity and mortality are increased, and a significantly reduced life span has been observed. The aim of this study was to investigate whether Klinefelter patients exhibit molecular signs of premature ageing. We studied, specifically, age-associated DNA methylation patterns (by pyrosequencing) and relative telomere length (TL; by quantitative polymerase chain reaction) in blood in a cohort of Klinefelter patients (n=178 and 266 for DNA methylation and TL, respectively) aged 18-71 years and compared them to the data of age-matched healthy male (n = 184 and 196 for DNA methylation and TL, respectively) and female controls (n = 50). Age-associated DNA methylation patterns were not indicative of accelerated ageing in Klinefelter men. Significantly longer telomeres were found in the young Klinefelter subjects aged 18-24 years (mean=1.51 vs. 1.09 and 1.26 in female and male controls, respectively). However, telomere length in subsequent age groups showed no difference to controls. Gonosomal aneuploidy in Klinefelter syndrome is associated with higher baseline TL at adolescent age, but comparable TL with progressive age in other age groups.

DNA Methylation Age and Physical and Cognitive Aging

BACKGROUND

DNA methylation (DNAm) age acceleration (AgeAccel) has been shown to be predictive of all-cause mortality but it is unclear what functional aspect(s) of aging it captures. We examine associations between four measures of AgeAccel in adults aged 45-87 years and physical and cognitive performance and their age-related decline.

METHODS

AgeAccelHannum, AgeAccelHorvath, AgeAccelPheno, and AgeAccelGrim were calculated in the Medical Research Council National Survey of Health and Development (NSHD), National Child Development Study (NCDS) and TwinsUK. Three measures of physical (grip strength, chair rise speed, and forced expiratory volume in one second [FEV1]) and two measures of cognitive (episodic memory and mental speed) performance were assessed.

RESULTS

AgeAccelPheno and AgeAccelGrim, but not AgeAccelHannum and AgeAccelHorvath were related to performance in random effects meta-analyses (n = 1,388-1,685). For example, a 1-year increase in AgeAccelPheno or AgeAccelGrim was associated with a 0.01 mL (95% confidence interval [CI]: 0.01, 0.02) or 0.03 mL (95% CI: 0.01, 0.05) lower mean FEV1 respectively. In NSHD, AgeAccelPheno and AgeAccelGrim at 53 years were associated with age-related decline in performance between 53 and 69 years as tested by linear mixed models (p < .05). In a subset of NSHD participants (n = 482), there was little evidence that change in any AgeAccel measure was associated with change in performance conditional on baseline performance.

CONCLUSIONS

We found little evidence to support associations between the first generation of DNAm-based biomarkers of aging and age-related physical or cognitive performance in midlife to early old age. However, there was evidence that the second generation biomarkers, AgeAccelPheno and AgeAccelGrim, could act as makers of an individual's healthspan as proposed.

Parallel Progress in Perceived Age and Life Expectancy

BACKGROUND

Human life expectancy continues to rise in most populations. This rise not only leads to longer lives but also is accompanied by improved health at a given age, that is, recent cohorts show a reduction of biological age for a given chronological age. Despite or even because of the diversity of biomarkers of aging, an accurate quantification of a general shift in biological age across time has been challenging.

METHODS

Here, we compared age perception of facial images taken in 2001 over a decade and related these changes in age perception to changes in life expectancy.

RESULTS

We show that age perception changes substantially across time and parallels the progress in life expectancy. In 2012, people aged more than 70 years needed to look 2.3 years younger to be rated the same age as in 2002.

CONCLUSIONS

Our results suggest that age perception reflects the past life events better than predicts future length of life, that is, it is written in your face how much you have aged so far. We draw this conclusion as age perception among elderly individuals paralleled changes in life expectancy at birth but not changes in remaining life expectancies. We suggest that changes in age perception should be explored for younger age classes to inform on aging processes, including whether aging is delayed or slowed with increasing life expectancy.

Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge

We ranked third in the Predictive Analytics Competition (PAC) 2019 challenge by achieving a mean absolute error (MAE) of 3.33 years in predicting age from T1-weighted MRI brain images. Our approach combined seven algorithms that allow generating predictions when the number of features exceeds the number of observations, in particular, two versions of best linear unbiased predictor (BLUP), support vector machine (SVM), two shallow convolutional neural networks (CNNs), and the famous ResNet and Inception V1. Ensemble learning was derived from estimating weights via linear regression in a hold-out subset of the training sample. We further evaluated and identified factors that could influence prediction accuracy: choice of algorithm, ensemble learning, and features used as input/MRI image processing. Our prediction error was correlated with age, and absolute error was greater for older participants, suggesting to increase the training sample for this subgroup. Our results may be used to guide researchers to build age predictors on healthy individuals, which can be used in research and in the clinics as non-specific predictors of disease status.

Monitoring plasma protein aggregation during aging using conformation-specific antibodies and FTIR spectroscopy

The loss of proteostasis during aging has been well described using different models, however little is known with respect to protein aggregation levels in biofluids with aging. Therefore, the aim of this study was to assess the pattern of age-related protein aggregation in human plasma using two distinct approaches: analysis with conformation-specific antibodies and FTIR spectroscopy. The latter has been widely used in biomedical research to study protein conformational changes in health and disease. Samples from a primary care based-cohort from the Aveiro region, Portugal, were used for slot-blot analyses followed by immunodetection with conformation-specific antibodies and for the acquisition of FTIR spectra. Immunoblot analyses revealed an age-dependent evolution of the protein conformational profile in human plasma, towards a decrease in prefibrillar oligomers and an increase in fibrillar structures. This finding was also supported by PLS-R multivariate analysis of FTIR data, where a positive correlation between the age of the donors and secondary structure of plasma proteins could be observed. Samples from younger donors are characterized by antiparallel β-sheet-containing structures while intermolecular β-sheets characterized older samples. Exclusion of age-associated co-morbidities improved the correlation between protein conformational profiles and aging. The results reveal structural changes in human plasma proteins from middle to old age, confirming the age-associated changes in protein aggregation, and support the applicability of FTIR as a reliable approach to study proteostasis during aging.

Development of Tissue-Specific Age Predictors Using DNA Methylation Data

DNA methylation patterns have been shown to change throughout the normal aging process. Several studies have found epigenetic aging markers using age predictors, but these studies only focused on blood-specific or tissue-common methylation patterns. Here, we constructed nine tissue-specific age prediction models using methylation array data from normal samples. The constructed models predict the chronological age with good performance (mean absolute error of 5.11 years on average) and show better performance in the independent test than previous multi-tissue age predictors. We also compared tissue-common and tissue-specific aging markers and found that they had different characteristics. Firstly, the tissue-common group tended to contain more positive aging markers with methylation values that increased during the aging process, whereas the tissue-specific group tended to contain more negative aging markers. Secondly, many of the tissue-common markers were located in Cytosine-phosphate-Guanine (CpG) island regions, whereas the tissue-specific markers were located in CpG shore regions. Lastly, the tissue-common CpG markers tended to be located in more evolutionarily conserved regions. In conclusion, our prediction models identified CpG markers that capture both tissue-common and tissue-specific characteristics during the aging process.

The AgeGuess database, an open online resource on chronological and perceived ages of people aged 5-100

In many developed countries, human life expectancy has doubled over the last 180 years. Underlying this higher life expectancy is a change in how we age. Biomarkers of ageing are used to quantify changes in the aging process and to determine biological age. Perceived age is such a biomarker that correlates with biological age. Here we present a unique database rich with possibilities to study the human ageing process. Using perceived age enables us to collect large amounts of data on biological age through a citizen science project, where people upload facial pictures and guess the ages of other people at www.ageguess.org . The data on perceived age we present here span birth cohorts from the years 1877 to 2012. The database currently contains around 220,000 perceived age guesses. Almost 4500 citizen scientists from over 120 countries of origin have uploaded ~4700 facial photographs. Beyond studying the ageing process, the data present a wealth of possibilities to study how humans guess ages and who is better at guessing ages.

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.

If my muscle could talk: Myokines as a biomarker of frailty

Frailty is a potentially reversible state of increased vulnerability to negative health-related outcomes that occurs as a result of multisystem biological impairment and environmental aspects. Given the relevance of this condition in both clinics and research, biomarkers of frailty have been actively sought after. Although several candidate biomarkers of frailty have been identified, none of them has yet been incorporated in the assessment or monitoring of the condition. Over the last years, increasing research interest has been focused on myokines, a set of cytokines, small proteins and proteoglycan peptides that are synthetized, expressed and released by skeletal myocytes in response to muscular contractions. Myokines may act in autocrine, paracrine, and endocrine manner and regulate several processes associated with physical frailty, including muscle wasting, dynapenia, and slowness. This review discusses the rationale to support the use of myokines as biomarkers of frailty in older adults.

Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing

BACKGROUND

DNA methylation changes with age. Chronological age predictors built from DNA methylation are termed 'epigenetic clocks'. The deviation of predicted age from the actual age ('age acceleration residual', AAR) has been reported to be associated with death. However, it is currently unclear how a better prediction of chronological age affects such association.

METHODS

In this study, we build multiple predictors based on training DNA methylation samples selected from 13,661 samples (13,402 from blood and 259 from saliva). We use the Lothian Birth Cohorts of 1921 (LBC1921) and 1936 (LBC1936) to examine whether the association between AAR (from these predictors) and death is affected by (1) improving prediction accuracy of an age predictor as its training sample size increases (from 335 to 12,710) and (2) additionally correcting for confounders (i.e., cellular compositions). In addition, we investigated the performance of our predictor in non-blood tissues.

RESULTS

We found that in principle, a near-perfect age predictor could be developed when the training sample size is sufficiently large. The association between AAR and mortality attenuates as prediction accuracy increases. AAR from our best predictor (based on Elastic Net, https://github.com/qzhang314/DNAm-based-age-predictor ) exhibits no association with mortality in both LBC1921 (hazard ratio = 1.08, 95% CI 0.91-1.27) and LBC1936 (hazard ratio = 1.00, 95% CI 0.79-1.28). Predictors based on small sample size are prone to confounding by cellular compositions relative to those from large sample size. We observed comparable performance of our predictor in non-blood tissues with a multi-tissue-based predictor.

CONCLUSIONS

This study indicates that the epigenetic clock can be improved by increasing the training sample size and that its association with mortality attenuates with increased prediction of chronological age.

Behavioral and molecular markers of death in Drosophila melanogaster

Fly movement was tracked through 3-dimensional (3D) space as the fly died, using either reflected visible light, reflected infrared (IR) light, or fly GFP fluorescence. Behaviors measured included centrophobism, negative geotaxis, velocity, and total activity. In addition, frequency of directional heading changes (FDHC) was calculated as a measure of erratic movement. Nine middle-aged flies were tracked as they died during normal aging, and fifteen young flies were tracked as they died from dehydration/starvation stress. Episodes of increased FDHC were observed 0-8 h prior to death for the majority of the flies. FDHC was also increased with age in flies with neuronal expression of a human Abeta42 protein fragment associated with Alzheimer's disease. Finally, green autofluorescence appeared in the eye and body immediately prior to and coincident with death, and fluorescence of GFP targeted to the retina increased immediately prior to and coincident with death. The results suggest the potential utility of FDHC, green autofluorescence, and retinal GFP as markers of neuronal malfunction and imminent death.

Health and Aging: Unifying Concepts, Scores, Biomarkers and Pathways

Despite increasing research efforts, there is a lack of consensus on defining aging or health. To understand the underlying processes, and to foster the development of targeted interventions towards increasing one's health, there is an urgent need to find a broadly acceptable and useful definition of health, based on a list of (molecular) features; to operationalize features of health so that it can be measured; to identify predictive biomarkers and (molecular) pathways of health; and to suggest interventions, such as nutrition and exercise, targeted at putative causal pathways and processes. Based on a survey of the literature, we propose to define health as a state of an individual characterized by the core features of physiological, cognitive, physical and reproductive function, and a lack of disease. We further define aging as the aggregate of all processes in an individual that reduce its wellbeing, that is, its health or survival or both. We define biomarkers of health by their attribute of predicting future health better than chronological age. We define healthspan pathways as molecular features of health that relate to each other by belonging to the same molecular pathway. Our conceptual framework may integrate diverse operationalizations of health and guide precision prevention efforts.

DNA methylation-based biomarkers and the epigenetic clock theory of ageing

Identifying and validating molecular targets of interventions that extend the human health span and lifespan has been difficult, as most clinical biomarkers are not sufficiently representative of the fundamental mechanisms of ageing to serve as their indicators. In a recent breakthrough, biomarkers of ageing based on DNA methylation data have enabled accurate age estimates for any tissue across the entire life course. These 'epigenetic clocks' link developmental and maintenance processes to biological ageing, giving rise to a unified theory of life course. Epigenetic biomarkers may help to address long-standing questions in many fields, including the central question: why do we age?

Accelerated bio-cognitive aging in Down syndrome: State of the art and possible deceleration strategies

Down syndrome (DS) has been proposed by George Martin as a segmental progeroid syndrome since 1978. In fact, DS persons suffer from several age‐associated disorders much earlier than euploid persons. Furthermore, a series of recent studies have found that DS persons display elevated levels of age biomarkers, thus supporting the notion that DS is a progeroid trait. Nowadays, due to the progressive advancements in social inclusion processes and medical assistance, DS persons live much longer than in the past; therefore, the early‐onset health problems of these persons are becoming an urgent and largely unmet social and medical burden. In particular, the most important ailment of DS persons is the accelerated cognitive decline that starts when they reach about 40 years of age. This decline can be at least in part counteracted by multi‐systemic approaches including early‐onset cognitive training, physical activity, and psychosocial assistance. However, no pharmacological treatment is approved to counteract this decline. According to the most advanced conceptualization of Geroscience, tackling the molecular mechanisms underpinning the aging process should be a smart/feasible strategy to combat and/or delay the great majority of age‐related diseases, including cognitive decline. We think that a debate is needed urgently on if (and how) this strategy could be integrated in protocols to face DS‐associated dementia and overall unhealthy aging. In particular we propose that, on the basis of data obtained in different clinical settings, metformin is a promising candidate that could be exploited to counteract cognitive decline in DS.

DNA Methylation Clocks in Aging: Categories, Causes, and Consequences

Age-associated changes to the mammalian DNA methylome are well documented and thought to promote diseases of aging, such as cancer. Recent studies have identified collections of individual methylation sites whose aggregate methylation status measures chronological age, referred to as the DNA methylation clock. DNA methylation may also have value as a biomarker of healthy versus unhealthy aging and disease risk; in other words, a biological clock. Here we consider the relationship between the chronological and biological clocks, their underlying mechanisms, potential consequences, and their utility as biomarkers and as targets for intervention to promote healthy aging and longevity.

Vaccination in the elderly: The challenge of immune changes with aging

The unprecedented increase of life expectancy challenges society to protect the elderly from morbidity and mortality making vaccination a crucial mean to safeguard this population. Indeed, infectious diseases, such as influenza and pneumonia, are among the top killers of elderly people in the world. Elderly individuals are more prone to severe infections and less responsive to vaccination prevention, due to immunosenescence combined with the progressive increase of a proinflammatory status characteristic of the aging process (inflammaging). These factors are responsible for most age-related diseases and correlate with poor response to vaccination. Therefore, it is of utmost interest to deepen the knowledge regarding the role of inflammaging in vaccination responsiveness to support the development of effective vaccination strategies designed for elderly. In this review we analyse the impact of age-associated factors such as inflammaging, immunosenescence and immunobiography on immune response to vaccination in the elderly, and we consider systems biology approaches as a mean for integrating a multitude of data in order to rationally design vaccination approaches specifically tailored for the elderly.

Age-dependent changes and biomarkers of aging in Caenorhabditis elegans

Caenorhabditis elegans is an exceptionally valuable model for aging research because of many advantages, including its genetic tractability, short lifespan, and clear age‐dependent physiological changes. Aged C. elegans display a decline in their anatomical and functional features, including tissue integrity, motility, learning and memory, and immunity. Caenorhabditis elegans also exhibit many age‐associated changes in the expression of microRNAs and stress‐responsive genes and in RNA and protein quality control systems. Many of these age‐associated changes provide information on the health of the animals and serve as valuable biomarkers for aging research. Here, we review the age‐dependent changes in C. elegans and their utility as aging biomarkers indicative of the physiological status of aging.

Chronic Obstructive Pulmonary Disease as a Main Factor of Premature Aging

(1) Background: Chronic obstructive pulmonary disease (COPD) is defined as an inflammatory disorder that presents an increasingly prevalent health problem. Accelerated aging has been examined as a pathologic mechanism of many chronic diseases like COPD. We examined whether COPD is combined with accelerated aging, studying two hormones, dehydroepiandrosterone (DHEA) and growth hormone (GH), known to be characteristic biological markers of aging. (2) Methods: Data were collected from 119 participants, 70 (58.8%) COPD patients and 49 (41.2%) from a health control group over the period of 2014–2016 in a spirometry program. Information about their medical history, tobacco use, and blood tests was obtained. (3) Results: The average age of the health control patients was 73.5 years (SD = 5.5), and that of the COPD patients was 75.4 years (SD = 6.9). Both groups were similar in age and sex. A greater proportion of smokers were found in the COPD group (87.1%) versus the control group (36.7%). The majority of COPD patients were classified as STAGE II (51.4%) and STAGE III (37.1%) according to GOLD (Global Initiative for Chronic Obstructive Pulmonary Disease). Levels of DHEA (SD = 17.1) and GH (SD = 0.37) were significantly lower in the COPD group (p < 0.001) compared to those in the controls (SD = 26.3, SD = 0.79). DHEA and GH were more significant and negatively correlated with age. The regression equation of DHEA with age produced a coefficient equal to 1.26. In this study, the difference in DHEA between COPD patients and controls was, on average, 30.2 μg/dL, indicating that the biological age of a COPD patient is on average about 24 years older than that of a control subject of the same age. Similarly, the difference in GH between COPD patients and controls was, on average, 0.42 ng/mL, indicating that the biological age of a COPD patient is on average about 13.1 years older than that of a control subject of the same age. (4) Conclusions: The findings of our study strongly suggest the presence of premature biological aging in COPD patients. Their biological age could actually vary from 13 to 23 years older than non-COPD controls according to DHEA and GH variation.

Do people living with HIV experience greater age advancement than their HIV-negative counterparts?

OBJECTIVES

Despite successful antiretroviral therapy, people living with HIV (PLWH) may show signs of premature/accentuated aging. We compared established biomarkers of aging in PLWH, appropriately chosen HIV-negative individuals, and blood donors, and explored factors associated with biological age advancement.

DESIGN

Cross-sectional analysis of 134 PLWH on suppressive antiretroviral therapy, 79 lifestyle-comparable HIV-negative controls aged 45 years or older from the Co-morBidity in Relation to AIDS (COBRA) cohort, and 35 age-matched blood donors.

METHODS

Biological age was estimated using a validated algorithm based on 10 biomarkers. Associations between 'age advancement' (biological minus chronological age) and HIV status/parameters, lifestyle, cytomegalovirus (CMV), hepatitis B (HBV) and hepatitis C virus (HCV) infections were investigated using linear regression.

RESULTS

The average (95% CI) age advancement was greater in both HIV-positive [13.2 (11.6-14.9) years] and HIV-negative [5.5 (3.8-7.2) years] COBRA participants compared with blood donors [-7.0 (-4.1 to -9.9) years, both P's < 0.001)], but also in HIV-positive compared with HIV-negative participants (P < 0.001). Chronic HBV, higher anti-CMV IgG titer and CD8 T-cell count were each associated with increased age advancement, independently of HIV-status/group. Among HIV-positive participants, age advancement was increased by 3.5 (0.1-6.8) years among those with nadir CD4+ T-cell count less than 200 cells/μl and by 0.1 (0.06-0.2) years for each additional month of exposure to saquinavir.

CONCLUSION

Both treated PLWH and lifestyle-comparable HIV-negative individuals show signs of age advancement compared with blood donors, to which persistent CMV, HBV co-infection and CD8+ T-cell activation may have contributed. Age advancement remained greatest in PLWH and was related to prior immunodeficiency and cumulative saquinavir exposure.

Senescent Cells in Early Vascular Ageing and Bone Disease of Chronic Kidney Disease-A Novel Target for Treatment

Together with bone-mineral disorders, premature vascular ageing is a common feature of the uremic phenotype. A detailed understanding of mechanisms involved remains unclear and warrants further research. Available treatment options for end stage renal disease are principally dialysis and organ transplantation, as other treatment alternatives have proven insufficient. Chronic kidney disease (CKD) has been proposed as a model of early vascular and bone ageing, with accumulating evidence supporting the contribution of cellular senescence and the senescence-associated secretory phenotype (SASP) to cardiovascular pathology in CKD. Correspondingly, novel therapies based around the use of senolytic compounds and nuclear factor-erythroid-2-related factor 2 (Nrf2) agonists, have been suggested as attractive novel treatment options. In this review, we detail the contribution of the uremic environment to these processes underpinning ageing and how these relate to vascular health.

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.

A framework for selection of blood-based biomarkers for geroscience-guided clinical trials: report from the TAME Biomarkers Workgroup

Recent advances indicate that biological aging is a potentially modifiable driver of late-life function and chronic disease and have led to the development of geroscience-guided therapeutic trials such as TAME (Targeting Aging with MEtformin). TAME is a proposed randomized clinical trial using metformin to affect molecular aging pathways to slow the incidence of age-related multi-morbidity and functional decline. In trials focusing on clinical end-points (e.g., disease diagnosis or death), biomarkers help show that the intervention is affecting the underlying aging biology before sufficient clinical events have accumulated to test the study hypothesis. Since there is no standard set of biomarkers of aging for clinical trials, an expert panel was convened and comprehensive literature reviews conducted to identify 258 initial candidate biomarkers of aging and age-related disease. Next selection criteria were derived and applied to refine this set emphasizing: (1) measurement reliability and feasibility; (2) relevance to aging; (3) robust and consistent ability to predict all-cause mortality, clinical and functional outcomes; and (4) responsiveness to intervention. Application of these selection criteria to the current literature resulted in a short list of blood-based biomarkers proposed for TAME: IL-6, TNFα-receptor I or II, CRP, GDF15, insulin, IGF1, cystatin C, NT-proBNP, and hemoglobin A1c. The present report provides a conceptual framework for the selection of blood-based biomarkers for use in geroscience-guided clinical trials. This work also revealed the scarcity of well-vetted biomarkers for human studies that reflect underlying biologic aging hallmarks, and the need to leverage proposed trials for future biomarker discovery and validation.

Recent Advances in the Systems Biology of Aging

SIGNIFICANCE

Reductionist studies have contributed greatly to our understanding of the basic biology of aging in recent years but we still do not understand fundamental mechanisms for many identified drugs and pathways. Use of systems approaches will help us move forward in our understanding of aging. Recent Advances: Recent work described here has illustrated the power of systems biology to inform our understanding of aging through the study of (i) diet restriction, (ii) neurodegenerative disease, and (iii) biomarkers of aging.

CRITICAL ISSUES

Although we do not understand all of the individual genes and pathways that affect aging, as we continue to uncover more of them, we have now also begun to synthesize existing data using systems-level approaches, often to great effect. The three examples noted here all benefit from computational approaches that were unknown a few years ago, and from biological insights gleaned from multiple model systems, from aging laboratories as well as many other areas of biology.

FUTURE DIRECTIONS

Many new technologies, such as single-cell sequencing, advances in epigenetics beyond the methylome (specifically, assay for transposase-accessible chromatin with high throughput sequencing ), and multiomic network studies, will increase the reach of systems biologists. This suggests that approaches similar to those described here will continue to lead to striking findings, and to interventions that may allow us to delay some of the many age-associated diseases in humans; perhaps sooner that we expect. Antioxid. Redox Signal. 29, 973-984.

Index of Healthy Aging in Chinese Older Adults: China Health and Retirement Longitudinal Study

OBJECTIVES

To characterize the distribution of an index of healthy aging-the Chinese Healthy Aging Index (CHAI)-in Chinese adults aged 60 and older according to sociodemographic characteristics and geographic region and to examine the association between the CHAI and mortality, disability, and functional limitation over 4 years.

DESIGN

Nationally representative cohort study.

SETTING

China Health and Retirement Longitudinal Study.

PARTICIPANTS

Chinese adults aged 60 and older (N=3,740).

MEASUREMENTS

Six CHAI components (systolic blood pressure, peak expiratory flow, Telephone Interview for Cognitive Status, estimated glomerular filtration rate, fasting glucose, C-reactive protein) were scored 0 (healthiest), 1, and 2 (unhealthiest) according to sex-specific tertiles or clinically relevant cut-points and summed to construct the CHAI (range 0-12).

RESULTS

Mean CHAI score was 5.6; 5.7% had a score of 0 to 2 (healthiest), 23.0% a score of 3 or 4, 37.5% a score of 5 or 6, and 33.8% a score of 7 to 12 (unhealthiest). Participants who were younger, more educated, and married were much more likely to have an ideal CHAI profile (score 0-2). Age-adjusted prevalence of an ideal CHAI profile ranged from 1.7% in the south to 8.1% in the north. After multivariable adjustment, persons with a CHAI score of 3 to 12 had substantially higher odds of mortality, disability, and functional limitation than those with a score of 0 to 2. The CHAI further stratified outcomes for persons with no clinically recognizable comorbidities.

CONCLUSION

Substantial variation exists in the CHAI according to sociodemographic characteristics and geographic regions. The CHAI could identify Chinese elderly adults with low risk of adverse outcomes and provide incremental value for risk prediction beyond clinically diagnosed comorbidities.

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.