Conservation of physiological dysregulation signatures of aging across primates

Two major goals in the current biology of aging are to identify general mechanisms underlying the aging process and to explain species differences in aging. Recent research in humans suggests that one important driver of aging is dysregulation, the progressive loss of homeostasis in complex biological networks. Yet, there is a lack of comparative data for this hypothesis, and we do not know whether dysregulation is widely associated with aging or how well signals of homeostasis are conserved. To address this knowledge gap, we use unusually detailed longitudinal biomarker data from 10 species of nonhuman primates housed in research centers and data from two human populations to test the hypotheses that (a) greater dysregulation is associated with aging across primates and (b) physiological states characterizing homeostasis are conserved across primates to degrees associated with phylogenetic proximity. To evaluate dysregulation, we employed a multivariate distance measure, calculated from sets of biomarkers, that is associated with aging and mortality in human populations. Dysregulation scores positively correlated with age and risk of mortality in most nonhuman primates studied, and signals of homeostatic state were significantly conserved across species, declining with phylogenetic distance. Our study provides the first broad demonstration of physiological dysregulation associated with aging and mortality risk in multiple nonhuman primates. Our results also imply that emergent signals of homeostasis are evolutionarily conserved, although with notable variation among species, and suggest promising directions for future comparative studies on dysregulation and the aging process.     

The metabolome as a biomarker of aging in Drosophila melanogaster

Many biomarkers have been shown to be associated not only with chronological age but also with functional measures of biological age. In human populations, it is difficult to show whether variation in biological age is truly predictive of life expectancy, as such research would require longitudinal studies over many years, or even decades. We followed adult cohorts of 20 Drosophila Genetic Reference Panel (DGRP) strains chosen to represent the breadth of lifespan variation, obtain estimates of lifespan, baseline mortality, and rate of aging, and associate these parameters with age‐specific functional traits including fecundity and climbing activity and with age‐specific targeted metabolomic profiles. We show that activity levels and metabolome‐wide profiles are strongly associated with age, that numerous individual metabolites show a strong association with lifespan, and that the metabolome provides a biological clock that predicts not only sample age but also future mortality rates and lifespan. This study with 20 genotypes and 87 metabolites, while relatively small in scope, establishes strong proof of principle for the fly as a powerful experimental model to test hypotheses about biomarkers and aging and provides further evidence for the potential value of metabolomic profiles as biomarkers of aging.     

Telomere length behaves as biomarker of somatic redundancy rather than biological age

Biomarkers of aging are essential to predict mortality and aging‐related diseases. Paradoxically, age itself imposes a limitation on the use of known biomarkers of aging because their associations with mortality generally diminish with age. How this pattern arises is, however, not understood. With meta‐analysis we show that human leucocyte telomere length (TL) predicts mortality, and that this mortality association diminishes with age, as found for other biomarkers of aging. Subsequently, we demonstrate with simulation models that this observation cannot be reconciled with the popular hypothesis that TL is proportional to biological age. Using the reliability theory of aging, we instead propose that TL is a biomarker of somatic redundancy, the body's capacity to absorb damage, which fits the observed pattern well. We discuss to what extent diminishing redundancy with age may also explain the observed diminishing mortality modulation with age of other biomarkers of aging. Considering diminishing somatic redundancy as the causal agent of aging may critically advance our understanding of the aging process, and improve predictions of life expectancy and vulnerability to aging‐related diseases.     

An association study of cyclase-associated protein 2 and frailty

Frailty is a geriatric syndrome that results from multisystem impairment caused by age-associated accumulation of deficits. The frailty index is used to define the level of frailty. Several studies have searched for molecular biomarkers associated with frailty, to meet the needs for personalized care. Cyclase-associated protein 2 (CAP2) is a multifunctional actin-binding protein involved in various physiological and pathological processes, that might reflect frailty's intrinsic complexity. This study aimed to investigate the association between frailty index and circulating CAP2 concentration in 467 community-dwelling older adults (median age: 79; range: 65–92 years) from Milan, Italy. The selected robust regression model showed that circulating CAP2 concentration was not associated with chronological age, as well as sex and education. However, circulating CAP2 concentration was significantly and inversely associated with the frailty index: a 0.1-unit increase in frailty index leads to ~0.5-point mean decrease in CAP2 concentration. Furthermore, mean CAP2 concentration was significantly lower in frail participants (i.e., frailty index ≥0.25) than in non-frail participants. This study shows the association between serum CAP2 concentration and frailty status for the first time, highlighting the potential of CAP2 as a biomarker for age-associated accumulation of deficits.     

Protein signatures of centenarians and their offspring suggest centenarians age slower than other humans

Using samples from the New England Centenarian Study (NECS), we sought to characterize the serum proteome of 77 centenarians, 82 centenarians'' offspring, and 65 age‐matched controls of the offspring (mean ages: 105, 80, and 79 years). We identified 1312 proteins that significantly differ between centenarians and their offspring and controls (FDR < 1%), and two different protein signatures that predict longer survival in centenarians and in younger people. By comparing the centenarian signature with 2 independent proteomic studies of aging, we replicated the association of 484 proteins of aging and we identified two serum protein signatures that are specific of extreme old age. The data suggest that centenarians acquire similar aging signatures as seen in younger cohorts that have short survival periods, suggesting that they do not escape normal aging markers, but rather acquire them much later than usual. For example, centenarian signatures are significantly enriched for senescence‐associated secretory phenotypes, consistent with those seen with younger aged individuals, and from this finding, we provide a new list of serum proteins that can be used to measure cellular senescence. Protein co‐expression network analysis suggests that a small number of biological drivers may regulate aging and extreme longevity, and that changes in gene regulation may be important to reach extreme old age. This centenarian study thus provides additional signatures that can be used to measure aging and provides specific circulating biomarkers of healthy aging and longevity, suggesting potential mechanisms that could help prolong health and support longevity.     

Telomere length in white blood cells is not associated with morbidity or mortality in the oldest old: a population-based study

Cross-sectional studies have repeatedly suggested peripheral blood monocyte telomere length as a biomarker of aging. To test this suggestion in a large population-based follow-up study of the oldest old, we measured telomere length at baseline in 598 participants of the Leiden 85-plus Study (mean age at baseline 89.8 years). We also obtained second telomere measurements from 81 participants after an average time span of between 3.9 and 12.9 years. Telomere length at baseline was not predictive for mortality (P > 0.40 for all-cause, cardiovascular causes, cancer or infectious diseases, Cox regression for gender-adjusted tertiles of telomere length) or for the incidence of dementia (P = 0.78). Longitudinally, telomere length was highly unstable in a large fraction of participants. We conclude that blood monocyte telomere length is not a predictive indicator for age-related morbidity and mortality at ages over 85 years, possibly because of a high degree of telomere length instability in this group.     

Proteomic architecture of frailty across the spectrum of cardiovascular disease

While frailty is a prominent risk factor in an aging population, the underlying biology of frailty is incompletely described. Here, we integrate 979 circulating proteins across a wide range of physiologies with 12 measures of frailty in a prospective discovery cohort of 809 individuals with severe aortic stenosis (AS) undergoing transcatheter aortic valve implantation. Our aim was to characterize the proteomic architecture of frailty in a highly susceptible population and study its relation to clinical outcome and systems-wide phenotypes to define potential novel, clinically relevant frailty biology. Proteomic signatures (specifically of physical function) were related to post-intervention outcome in AS, specifying pathways of innate immunity, cell growth/senescence, fibrosis/metabolism, and a host of proteins not widely described in human aging. In published cohorts, the “frailty proteome” displayed heterogeneous trajectories across age (20–100 years, age only explaining a small fraction of variance) and were associated with cardiac and non-cardiac phenotypes and outcomes across two broad validation cohorts (N > 35,000) over ≈2–3 decades. These findings suggest the importance of precision biomarkers of underlying multi-organ health status in age-related morbidity and frailty.     

Homeostatic dysregulation proceeds in parallel in multiple physiological systems

An increasing number of aging researchers believes that multi‐system physiological dysregulation may be a key biological mechanism of aging, but evidence of this has been sparse. Here, we used biomarker data on nearly 33 000 individuals from four large datasets to test for the presence of multi‐system dysregulation. We grouped 37 biomarkers into six a priori groupings representing physiological systems (lipids, immune, oxygen transport, liver function, vitamins, and electrolytes), then calculated dysregulation scores for each system in each individual using statistical distance. Correlations among dysregulation levels across systems were generally weak but significant. Comparison of these results to dysregulation in arbitrary ‘systems’ generated by random grouping of biomarkers showed that a priori knowledge effectively distinguished the true systems in which dysregulation proceeds most independently. In other words, correlations among dysregulation levels were higher using arbitrary systems, indicating that only a priori systems identified distinct dysregulation processes. Additionally, dysregulation of most systems increased with age and significantly predicted multiple health outcomes including mortality, frailty, diabetes, heart disease, and number of chronic diseases. The six systems differed in how well their dysregulation scores predicted health outcomes and age. These findings present the first unequivocal demonstration of integrated multi‐system physiological dysregulation during aging, demonstrating that physiological dysregulation proceeds neither as a single global process nor as a completely independent process in different systems, but rather as a set of system‐specific processes likely linked through weak feedback effects. These processes – probably many more than the six measured here – are implicated in aging.     

Detection of a Novel,Integrative Aging Process Suggests Complex Physiological Integration

Many studies of aging examine biomarkers one at a time, but complex systems theory and network theory suggest that interpretations of individual markers may be context-dependent. Here, we attempted to detect underlying processes governing the levels of many biomarkers simultaneously by applying principal components analysis to 43 common clinical biomarkers measured longitudinally in 3694 humans from three longitudinal cohort studies on two continents (Women’s Health and Aging I & II, InCHIANTI, and the Baltimore Longitudinal Study on Aging). The first axis was associated with anemia, inflammation, and low levels of calcium and albumin. The axis structure was precisely reproduced in all three populations and in all demographic sub-populations (by sex, race, etc.); we call the process represented by the axis “integrated albunemia.” Integrated albunemia increases and accelerates with age in all populations, and predicts mortality and frailty – but not chronic disease – even after controlling for age. This suggests a role in the aging process, though causality is not yet clear. Integrated albunemia behaves more stably across populations than its component biomarkers, and thus appears to represent a higher-order physiological process emerging from the structure of underlying regulatory networks. If this is correct, detection of this process has substantial implications for physiological organization more generally.     

The companion dog as a model for human aging and mortality

Around the world, human populations have experienced large increases in average lifespan over the last 150 years, and while individuals are living longer, they are spending more years of life with multiple chronic morbidities. Researchers have used numerous laboratory animal models to understand the biological and environmental factors that influence aging, morbidity, and longevity. However, the most commonly studied animal species, laboratory mice and rats, do not experience environmental conditions similar to those to which humans are exposed, nor do we often diagnose them with many of the naturally occurring pathologies seen in humans. Recently, the companion dog has been proposed as a powerful model to better understand the genetic and environmental determinants of morbidity and mortality in humans. However, it is not known to what extent the age‐related dynamics of morbidity, comorbidity, and mortality are shared between humans and dogs. Here, we present the first large‐scale comparison of human and canine patterns of age‐specific morbidity and mortality. We find that many chronic conditions that commonly occur in human populations (obesity, arthritis, hypothyroidism, and diabetes), and which are associated with comorbidities, are also associated with similarly high levels of comorbidity in companion dogs. We also find significant similarities in the effect of age on disease risk in humans and dogs, with neoplastic, congenital, and metabolic causes of death showing similar age trajectories between the two species. Overall, our study suggests that the companion dog may be an ideal translational model to study the many complex facets of human morbidity and mortality.     

Impact of early personal‐history characteristics on the Pace of Aging: implications for clinical trials of therapies to slow aging and extend healthspan

Therapies to extend healthspan are poised to move from laboratory animal models to human clinical trials. Translation from mouse to human will entail challenges, among them the multifactorial heterogeneity of human aging. To inform clinical trials about this heterogeneity, we report how humans’ pace of biological aging relates to personal‐history characteristics. Because geroprotective therapies must be delivered by midlife to prevent age‐related disease onset, we studied young‐adult members of the Dunedin Study 1972–73 birth cohort (n = 954). Cohort members’ Pace of Aging was measured as coordinated decline in the integrity of multiple organ systems, by quantifying rate of decline across repeated measurements of 18 biomarkers assayed when cohort members were ages 26, 32, and 38 years. The childhood personal‐history characteristics studied were known predictors of age‐related disease and mortality, and were measured prospectively during childhood. Personal‐history characteristics of familial longevity, childhood social class, adverse childhood experiences, and childhood health, intelligence, and self‐control all predicted differences in cohort members’ adulthood Pace of Aging. Accumulation of more personal‐history risks predicted faster Pace of Aging. Because trials of anti‐aging therapies will need to ascertain personal histories retrospectively, we replicated results using cohort members’ retrospective personal‐history reports made in adulthood. Because many trials recruit participants from clinical settings, we replicated results in the cohort subset who had recent health system contact according to electronic medical records. Quick, inexpensive measures of trial participants’ early personal histories can enable clinical trials to study who volunteers for trials, who adheres to treatment, and who responds to anti‐aging therapies.     

Stochastic model for analysis of longitudinal data on aging and mortality

Aging-related changes in a human organism follow dynamic regularities, which contribute to the observed age patterns of incidence and mortality curves. An organism's 'optimal' (normal) physiological state changes with age, affecting the values of risks of disease and death. The resistance to stresses, as well as adaptive capacity, declines with age. An exposure to improper environment results in persisting deviation of individuals' physiological (and biological) indices from their normal state (due to allostatic adaptation), which, in turn, increases chances of disease and death. Despite numerous studies investigating these effects, there is no conceptual framework, which would allow for putting all these findings together, and analyze longitudinal data taking all these dynamic connections into account. In this paper we suggest such a framework, using a new version of stochastic process model of aging and mortality. Using this model, we elaborated a statistical method for analyses of longitudinal data on aging, health and longevity and tested it using different simulated data sets. The results show that the model may characterize complicated interplay among different components of aging-related changes in humans and that the model parameters are identifiable from the data.     

Circulating levels of monocyte chemoattractant protein‐1 as a potential measure of biological age in mice and frailty in humans

A serum biomarker of biological versus chronological age would have significant impact on clinical care. It could be used to identify individuals at risk of early‐onset frailty or the multimorbidities associated with old age. It may also serve as a surrogate endpoint in clinical trials targeting mechanisms of aging. Here, we identified MCP‐1/CCL2, a chemokine responsible for recruiting monocytes, as a potential biomarker of biological age. Circulating monocyte chemoattractant protein‐1 (MCP‐1) levels increased in an age‐dependent manner in wild‐type (WT) mice. That age‐dependent increase was accelerated in Ercc1^?/Δ and Bubr1^H/H mouse models of progeria. Genetic and pharmacologic interventions that slow aging of Ercc1^?/Δ and WT mice lowered serum MCP‐1 levels significantly. Finally, in elderly humans with aortic stenosis, MCP‐1 levels were significantly higher in frail individuals compared to nonfrail. These data support the conclusion that MCP‐1 can be used as a measure of mammalian biological age that is responsive to interventions that extend healthy aging.     

Sexually divergent DNA methylation patterns with hippocampal aging

DNA methylation is a central regulator of genome function, and altered methylation patterns are indicative of biological aging and mortality. Age‐related cellular, biochemical, and molecular changes in the hippocampus lead to cognitive impairments and greater vulnerability to neurodegenerative disease that varies between the sexes. The role of hippocampal epigenomic changes with aging in these processes is unknown as no genome‐wide analyses of age‐related methylation changes have considered the factor of sex in a controlled animal model. High‐depth, genome‐wide bisulfite sequencing of young (3 month) and old (24 month) male and female mouse hippocampus revealed that while total genomic methylation amounts did not change with aging, specific sites in CG and non‐CG (CH) contexts demonstrated age‐related increases or decreases in methylation that were predominantly sexually divergent. Differential methylation with age for both CG and CH sites was enriched in intergenic and intronic regions and under‐represented in promoters, CG islands, and specific enhancer regions in both sexes, suggesting that certain genomic elements are especially labile with aging, even if the exact genomic loci altered are predominantly sex‐specific. Lifelong sex differences in autosomal methylation at CG and CH sites were also observed. The lack of genome‐wide hypomethylation, sexually divergent aging response, and autosomal sex differences at CG sites was confirmed in human data. These data reveal sex as a previously unappreciated central factor of hippocampal epigenomic changes with aging. In total, these data demonstrate an intricate regulation of DNA methylation with aging by sex, cytosine context, genomic location, and methylation level.     

Age-Related Changes in Biomarkers: Longitudinal Data from a Population-Based Sample

Identifying how biological parameters change with age can provide insights into the physiological determinants of disease, and ultimately, death. Most prior studies of age-related change in biomarkers are based on cross-sectional data, small or selective samples, or a limited number of biomarkers. We use data from a nationally-representative longitudinal sample of 639 Taiwanese aged 54 and older in 2000 to assess changes over a six-year period in a wide range of biomarkers. Markers that increased most with age were glycoslyated hemoglobin, interleukin-6, and norepinephrine. Markers that decreased most with age were diastolic blood pressure and creatinine clearance. For example, glycoslyated hemoglobin increased by 8-13%, on average, over this six-year period. Several standard clinical risk factors exhibited little evidence of age-related change. Further research is needed to determine whether the observed variation between individuals in biomarker changes represents differences in underlying physiological function that are predictive of future health and survival.     

Biomarkers of aging in Drosophila

Low environmental temperature and dietary restriction (DR) extend lifespan in diverse organisms. In the fruit fly Drosophila, switching flies between temperatures alters the rate at which mortality subsequently increases with age but does not reverse mortality rate. In contrast, DR acts acutely to lower mortality risk; flies switched between control feeding and DR show a rapid reversal of mortality rate. Dietary restriction thus does not slow accumulation of aging‐related damage. Molecular species that track the effects of temperatures on mortality but are unaltered with switches in diet are therefore potential biomarkers of aging‐related damage. However, molecular species that switch upon instigation or withdrawal of DR are thus potential biomarkers of mechanisms underlying risk of mortality, but not of aging‐related damage. Using this approach, we assessed several commonly used biomarkers of aging‐related damage. Accumulation of fluorescent advanced glycation end products (AGEs) correlated strongly with mortality rate of flies at different temperatures but was independent of diet. Hence, fluorescent AGEs are biomarkers of aging‐related damage in flies. In contrast, five oxidized and glycated protein adducts accumulated with age, but were reversible with both temperature and diet, and are therefore not markers either of acute risk of dying or of aging‐related damage. Our approach provides a powerful method for identification of biomarkers of aging.     

High‐throughput serum proteomics for the identification of protein biomarkers of mortality in older men

The biological perturbations associated with incident mortality are not well elucidated, and there are limited biomarkers for the prediction of mortality. We used a novel high‐throughput proteomics approach to identify serum peptides and proteins associated with 5‐year mortality in community‐dwelling men age ≥65 years who participated in a longitudinal observational study of musculoskeletal aging (Osteoporotic Fractures in Men: MrOS). In a discovery phase, serum specimens collected at baseline in 2473 men were analyzed using liquid chromatography–ion mobility–mass spectrometry, and incident mortality in the subsequent 5 years was ascertained by tri‐annual questionnaire. Rigorous statistical methods were utilized to identify 56 peptides (31 proteins) that were associated with 5‐year mortality. In an independent replication phase, selected reaction monitoring was used to examine 21 of those peptides in baseline serum from 750 additional men; 81% of those peptides remained significantly associated with mortality. Mortality‐associated proteins included a variety involved in inflammation or complement activation; several have been previously linked to mortality (e.g., C‐reactive protein, alpha 1‐antichymotrypsin) and others are not previously known to be associated with mortality. Other novel proteins of interest included pregnancy‐associated plasma protein, VE‐cadherin, leucine‐rich α‐2 glycoprotein 1, vinculin, vitronectin, mast/stem cell growth factor receptor, and Saa4. A panel of peptides improved the predictive value of a commonly used clinical predictor of mortality. Overall, these results suggest that complex inflammatory pathways, and proteins in other pathways, are linked to 5‐year mortality risk. This work may serve to identify novel biomarkers for near‐term mortality.