Detection of a novel, integrative aging process suggests complex physiological integration

Ratio of Red Blood Cell Distribution Width to Albumin Level and Risk of Mortality

Importance

The ratio of red blood cell distribution width (RDW) to albumin concentration (RAR) has emerged as a reliable prognostic marker for mortality in patients with various diseases. However, whether RAR is associated with mortality in the general population remains unknown.

Objectives

To explore whether RAR is associated with all-cause and cause-specific mortality and to elucidate their dose-response association.

Design, Setting, and Participants

This population-based prospective cohort study used data from participants in the 1998-2018 US National Health and Nutrition Examination Survey (NHANES) and from the UK Biobank with baseline information provided from 2006 to 2010. Included participants had complete data on serum albumin concentration, RDW, and cause of death. The NHANES data were linked to the National Death Index records through December 31, 2019. For the UK Biobank, dates and causes of death were obtained from the National Health Service Information Centre (England and Wales) and the National Health Service Central Register Scotland (Scotland) to November 30, 2022.

Main Outcomes and Measures

Potential associations between RAR and the risk of all-cause and cause-specific mortality were evaluated using Cox proportional hazards regression models. Restricted cubic spline regressions were applied to estimate possible nonlinear associations.

Results

In NHANES, 50 622 participants 18 years of age or older years were included (mean [SD] age, 48.6 [18.7] years; 26 136 [51.6%] female), and their mean (SD) RAR was 3.15 (0.51). In the UK Biobank, 418 950 participants 37 years of age or older (mean [SD], 56.6 [8.1] years; 225 038 [53.7%] female) were included, and their mean RAR (SD) was 2.99 (0.31). The NHANES documented 7590 deaths over a median (IQR) follow-up of 9.4 (5.1-14.2) years, and the UK Biobank documented 36 793 deaths over a median (IQR) follow-up of 13.8 (13.0-14.5) years. According to the multivariate analysis, elevated RAR was significantly associated with greater risk of all-cause mortality (NHANES: hazard ratio [HR], 1.83 [95% CI, 1.76-1.90]; UK Biobank: HR, 2.08 [95% CI, 2.03-2.13]), as well as mortality due to malignant neoplasm (NHANES: HR, 1.89 [95% CI, 1.73-2.07]; UK Biobank: HR, 1.93 [95% CI, 1.86-2.00]), heart disease (NHANES: HR, 1.88 [95% CI, 1.74-2.03]; UK Biobank: HR, 2.42 [95% CI, 2.29-2.57]), cerebrovascular disease (NHANES: HR, 1.35 [95% CI, 1.07-1.69]; UK Biobank: HR, 2.15 [95% CI, 1.91-2.42]), respiratory disease (NHANES: HR, 1.99 [95% CI, 1.68-2.35]; UK Biobank: HR, 2.96 [95% CI, 2.78-3.15]), diabetes (NHANES: HR, 1.55 [95% CI, 1.27-1.90]; UK Biobank: HR, 2.83 [95% CI, 2.35-3.40]), and other causes of mortality (NHANES: HR, 1.97 [95% CI, 1.86-2.08]; UK Biobank: HR, 2.40 [95% CI, 2.30-2.50]) in both cohorts. Additionally, a nonlinear association was observed between RAR levels and all-cause mortality in both cohorts.

Conclusions and Relevance

In this cohort study, a higher baseline RAR was associated with an increased risk of all-cause and cause-specific mortality in the general population. These findings suggest that RAR may be a simple, reliable, and inexpensive indicator for identifying individuals at high risk of mortality in clinical practice.

Validation of biomarkers of aging

The search for biomarkers that quantify biological aging (particularly 'omic'-based biomarkers) has intensified in recent years. Such biomarkers could predict aging-related outcomes and could serve as surrogate endpoints for the evaluation of interventions promoting healthy aging and longevity. However, no consensus exists on how biomarkers of aging should be validated before their translation to the clinic. Here, we review current efforts to evaluate the predictive validity of omic biomarkers of aging in population studies, discuss challenges in comparability and generalizability and provide recommendations to facilitate future validation of biomarkers of aging. Finally, we discuss how systematic validation can accelerate clinical translation of biomarkers of aging and their use in gerotherapeutic clinical trials.

Progress in the study of aging marker criteria in human populations

The use of human aging markers, which are physiological, biochemical and molecular indicators of structural or functional degeneration associated with aging, is the fundamental basis of individualized aging assessments. Identifying methods for selecting markers has become a primary and vital aspect of aging research. However, there is no clear consensus or uniform principle on the criteria for screening aging markers. Therefore, we combine previous research from our center and summarize the criteria for screening aging markers in previous population studies, which are discussed in three aspects: functional perspective, operational implementation perspective and methodological perspective. Finally, an evaluation framework has been established, and the criteria are categorized into three levels based on their importance, which can help assess the extent to which a candidate biomarker may be feasible, valid, and useful for a specific use context.

Discordant Biological and Chronological Age: Implications for Cognitive Decline and Frailty

BACKGROUND

Older adults with discordant biological and chronological ages (BA and CA) may vary in cognitive and physical function from those with concordant BA and CA.

METHODS

To make our approach clinically accessible, we created easy-to-interpret participant groups in the Health, Aging, and Body Composition Study (N = 2 458, 52% female participants, 65% White participants, age: 73.5 ± 2.8) based on medians of CA, and a previously validated BA index comprised of readily available clinical tests. Joint models estimated associations of BA-CA group with cognition (Modified Mini-Mental State Examination [3MS] and Digit Symbol Substitution Test [DSST]) and frailty over 10 years.

RESULTS

The sample included the following: 32%, Young group (BA and CA < median); 21%, Prematurely Aging group (BA ≥ median, CA < median), 27%, Old group (BA and CA ≥ median), and 20%, Resilient group (BA < median, CA ≥ median). In education-adjusted models of cognition, among those with CA < median, the Prematurely Aging group performed worse than the Young at baseline (3MS and DSST p < .0001), but among those with CA ≥ median, the Resilient group did not outperform the Old group (3MS p = .31; DSST p = .25). For frailty, the Prematurely Aging group performed worse than the Young group at baseline (p = .0001), and the Resilient group outperformed the Old group (p = .003). For all outcomes, groups did not differ on change over time based on the same pairwise comparisons (p ≥ .40).

CONCLUSIONS

Discordant BA and CA identify groups who have greater cognitive and physical functional decline or are more protected than their CA would suggest. This information can be used for risk stratification.

Natural Animal Populations as Model Systems for Understanding Early Life Adversity Effects on Aging

Adverse experiences in early life are associated with aging-related disease risk and mortality across many species. In humans, confounding factors, as well as the difficulty of directly measuring experiences and outcomes from birth till death, make it challenging to identify how early life adversity impacts aging and health. These challenges can be mitigated, in part, through the study of non-human animals, which are exposed to parallel forms of adversity and can age similarly to humans. Furthermore, studying the links between early life adversity and aging in natural populations of non-human animals provides an excellent opportunity to better understand the social and ecological pressures that shaped the evolution of early life sensitivities. Here, we highlight ongoing and future research directions that we believe will most effectively contribute to our understanding of the evolution of early life sensitivities and their repercussions.

Explainable machine learning framework to predict personalized physiological aging

Attaining personalized healthy aging requires accurate monitoring of physiological changes and identifying subclinical markers that predict accelerated or delayed aging. Classic biostatistical methods most rely on supervised variables to estimate physiological aging and do not capture the full complexity of inter-parameter interactions. Machine learning (ML) is promising, but its black box nature eludes direct understanding, substantially limiting physician confidence and clinical usage. Using a broad population dataset from the National Health and Nutrition Examination Survey (NHANES) study including routine biological variables and after selection of XGBoost as the most appropriate algorithm, we created an innovative explainable ML framework to determine a Personalized physiological age (PPA). PPA predicted both chronic disease and mortality independently of chronological age. Twenty-six variables were sufficient to predict PPA. Using SHapley Additive exPlanations (SHAP), we implemented a precise quantitative associated metric for each variable explaining physiological (i.e., accelerated or delayed) deviations from age-specific normative data. Among the variables, glycated hemoglobin (HbA1c) displays a major relative weight in the estimation of PPA. Finally, clustering profiles of identical contextualized explanations reveal different aging trajectories opening opportunities to specific clinical follow-up. These data show that PPA is a robust, quantitative and explainable ML-based metric that monitors personalized health status. Our approach also provides a complete framework applicable to different datasets or variables, allowing precision physiological age estimation.

Efficient representations of binarized health deficit data: the frailty index and beyond

We investigated efficient representations of binarized health deficit data using the 2001-2002 National Health and Nutrition Examination Survey (NHANES). We compared the abilities of features to compress health deficit data and to predict adverse outcomes. We used principal component analysis (PCA) and several other dimensionality reduction techniques, together with several varieties of the frailty index (FI). We observed that the FI approximates the first - primary - component obtained by PCA and other compression techniques. Most adverse outcomes were well predicted using only the FI. While the FI is therefore a useful technique for compressing binary deficits into a single variable, additional dimensions were needed for high-fidelity compression of health deficit data. Moreover, some outcomes - including inflammation and metabolic dysfunction - showed high-dimensional behaviour. We generally found that clinical data were easier to compress than lab data. Our results help to explain the success of the FI as a simple dimensionality reduction technique for binary health data. We demonstrate how PCA extends the FI, providing additional health information, and allows us to explore system dimensionality and complexity. PCA is a promising tool for determining and exploring collective health features from collections of binarized biomarkers.

Exploring Shared Effects of Multisensory Impairment, Physical Dysfunction, and Cognitive Impairment on Physical Activity: An Observational Study in a National Sample

Multisensory, physical, and cognitive dysfunction share age-related physiologic disturbances and may have common health effects. We determined whether the effect of multisensory impairment on physical activity (PA) is explained by physical (timed up and go) or cognitive (Short Portable Mental Status Questionnaire) dysfunction. A National Social Life, Health, and Aging Project participant subset (n = 507) underwent objective sensory testing in 2005-2006 and wrist accelerometry in 2010-2011. We related multisensory impairment to PA using multivariate mixed-effects linear regression and compared the effect magnitude after adjusting for physical then cognitive dysfunction. Worse multisensory impairment predicted lower PA across three scales (Global Sensory Impairment: β = -0.04, 95% confidence interval [-0.07, -0.02]; Total Sensory Burden: β = -0.01, 95% confidence interval [-0.03, -0.003]; and Number of Impaired Senses: β = -0.02, 95% confidence interval [-0.04, -0.004]). Effects were similar after accounting for physical and cognitive dysfunction. Findings suggest that sensory, physical, and cognitive dysfunction have unique mechanisms underlying their PA effects.

A complex systems approach to aging biology

Having made substantial progress understanding molecules, cells, genes and pathways, aging biology research is now moving toward integration of these parts, attempting to understand how their joint dynamics may contribute to aging. Such a shift of perspective requires the adoption of a formal complex systems framework, a transition being facilitated by large-scale data collection and new analytical tools. Here, we provide a theoretical framework to orient researchers around key concepts for this transition, notably emergence, interaction networks and resilience. Drawing on evolutionary theory, network theory and principles of homeostasis, we propose that organismal function is accomplished by the integration of regulatory mechanisms at multiple hierarchical scales, and that the disruption of this ensemble causes the phenotypic and functional manifestations of aging. We present key examples at scales ranging from sub-organismal biology to clinical geriatrics, outlining how this approach can potentially enrich our understanding of aging.

Physiological health indexes predict deterioration and mortality in patients with COVID-19: a comparative study

Old age is a crucial risk factor for severe coronavirus disease 2019 (COVID-19), with serious or fatal outcomes disproportionately affecting older adults compared with the rest of the population. We proposed that the physiological health status and biological age, beyond the chronological age itself, could be the driving trends affecting COVID-19 severity and mortality. A total of 155 participants hospitalized with confirmed COVID-19 aged 26-94 years were recruited for the study. Four different physiological summary indices were calculated: Klemera and Doubal's biological age, PhenoAge, physiological dysregulation (PD; globally and in specific systems), and integrated albunemia. All of these indices significantly predicted the risk of death (p < 0.01) after adjusting for chronological age and sex. In all models, men were 2.4-4.4-times more likely to die than women. The global PD was shown to be a good predictor of deterioration, with the odds of deterioration increasing by 41.7% per 0.5-unit increase in the global PD. As for death, the odds also increased by 68.3% per 0.5-unit increase in the global PD. Our results are partly attributed to common chronic diseases that aggravate COVID-19, but they also suggest that the underlying physiological state could capture vulnerability to severe COVID-19 and serve as a tool for prognosis that would, in turn, help inpatient management.

Relationship between adiponectin and intramuscular fat content determined by ultrasonography in older adults

Age-associated intramuscular adipose tissue (IntraMAT) deposition induces the development of insulin resistance and metabolic syndrome. However, the relationship between IntraMAT and biochemical parameters in older adults remains unclear. The purpose of this study, therefore, was to elucidate the relationship between adiponectin and echo intensity–estimated IntraMAT using ultrasonography in normal-weight older adults (men 9, women 13) and examine biochemical parameters. Blood tests were performed to determine fasting levels of glucose, insulin, hemoglobin A1c, total cholesterol (Total-C), high-density-lipoprotein cholesterol, low-density-lipoprotein cholesterol (LDL-C), free fatty acid, triglycerides (TGs), adiponectin, leptin, high-sensitivity C-reactive protein, and high-sensitivity tumor necrosis factor, and homoeostasis model assessment index of insulin resistance (HOMA-IR). Mean gray-scale echo intensity was calculated as the IntraMAT index of the vastus lateralis. Waist circumference was measured at the level of the navel as the visceral adipose tissue (VAT) index. Echo intensity was significantly inversely correlated with adiponectin or LDL-C, and that was significantly positively correlated with TG. Adiponectin level was inversely correlated with waist circumference. Partial correlation analysis with waist circumference as the control variable revealed that adiponectin was inversely correlated with echo intensity, independent of waist circumference, whereas no such correlation was observed after controlling for LDL-C and TG levels. When biochemical parameters were grouped in the principal component analysis, among men, Total-C, insulin, and HOMA-IR or hemoglobin A1c, and high-sensitivity tumor necrosis factor–alpha were grouped with the same distribution for factors 1 and 2. Among women, glucose, insulin, HOMA-IR, and Total-C or TGs were grouped with the same distribution for factors 1 and 2. These data suggest that adiponectin level is related to IntraMAT content, independent of VAT in normal-weight older adults. The dynamics of adiponectin might not be similar to those of other circulating biochemical parameters in older men and women.

Feature Selection Algorithms Enhance the Accuracy of Frailty Indexes as Measures of Biological Age

Biological age captures some of the variance in life expectancy for which chronological age is not accountable, and it quantifies the heterogeneity in the presentation of the aging phenotype in various individuals. Among the many quantitative measures of biological age, the mathematically uncomplicated frailty/deficit index is simply the proportion of the total health deficits in various health items surveyed in different individuals. We used 3 different statistical methods that are popular in machine learning to select 17-28 health items that together are highly predictive of survival/mortality, from independent study cohorts. From the selected sets, we calculated frailty indexes and Klemera-Doubal's biological age estimates, and then compared their mortality prediction performance using Cox proportional hazards regression models. Our results indicate that the frailty index outperforms age and Klemera-Doubal's biological age estimates, especially among the oldest old who are most prone to biological aging-caused mortality. We also showed that a DNA methylation index, which was generated by applying the frailty/deficit index calculation method to 38 CpG sites that were selected using the same machine learning algorithms, can predict mortality even better than the best performing frailty index constructed from health, function, and blood chemistry.

A Biomarker-based Biological Age in UK Biobank: Composition and Prediction of Mortality and Hospital Admissions

Background

Chronological age is the strongest risk factor for most chronic diseases. Developing a biomarker-based age and understanding its most important contributing biomarkers may shed light on the effects of age on later-life health and inform opportunities for disease prevention.

Methods

A subpopulation of 141 254 individuals healthy at baseline were studied, from among 480 019 UK Biobank participants aged 40–70 recruited in 2006–2010, and followed up for 6–12 years via linked death and secondary care records. Principal components of 72 biomarkers measured at baseline were characterized and used to construct sex-specific composite biomarker ages using the Klemera Doubal method, which derived a weighted sum of biomarker principal components based on their linear associations with chronological age. Biomarker importance in the biomarker ages was assessed by the proportion of the variation in the biomarker ages that each explained. The proportions of the overall biomarker and chronological age effects on mortality and age-related hospital admissions explained by the biomarker ages were compared using likelihoods in Cox proportional hazard models.

Results

Reduced lung function, kidney function, reaction time, insulin-like growth factor 1, hand grip strength, and higher blood pressure were key contributors to the derived biomarker age in both men and women. The biomarker ages accounted for >65% and >84% of the apparent effect of age on mortality and hospital admissions for the healthy and whole populations, respectively, and significantly improved prediction of mortality (p < .001) and hospital admissions (p < 1 × 10⁻¹⁰) over chronological age alone.

Conclusions

This study suggests that a broader, multisystem approach to research and prevention of diseases of aging warrants consideration.

Robust Physiological Metrics From Sparsely Sampled Networks

Physiological and biochemical networks are highly complex, involving thousands of nodes as well as a hierarchical structure. True network structure is also rarely known. This presents major challenges for applying classical network theory to these networks. However, complex systems generally share the property of having a diffuse or distributed signal. Accordingly, we should predict that system state can be robustly estimated with sparse sampling, and with limited knowledge of true network structure. In this review, we summarize recent findings from several methodologies to estimate system state via a limited sample of biomarkers, notably Mahalanobis distance, principal components analysis, and cluster analysis. While statistically simple, these methods allow novel characterizations of system state when applied judiciously. Broadly, system state can often be estimated even from random samples of biomarkers. Furthermore, appropriate methods can detect emergent underlying physiological structure from this sparse data. We propose that approaches such as these are a powerful tool to understand physiology, and could lead to a new understanding and mapping of the functional implications of biological variation.

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.

Multi-system physiological dysregulation and ageing in a subsistence population

Humans have the longest post-reproductive lifespans and lowest rates of actuarial ageing among primates. Understanding the links between slow actuarial ageing and physiological change is critical for improving the human 'healthspan'. Physiological dysregulation may be a key feature of ageing in industrialized populations with high burdens of chronic 'diseases of civilization', but little is known about age trajectories of physiological condition in subsistence populations with limited access to public health infrastructure. To better characterize human physiological dysregulation, we examined age trajectories of 40 biomarkers spanning the immune (n = 13 biomarkers), cardiometabolic (n = 14), musculoskeletal (n = 6) and other (n = 7) systems among Tsimane forager-horticulturalists of the Bolivian Amazon using mixed cross-sectional and longitudinal data (n = 22 115 observations). We characterized age-related changes using a multi-system statistical index of physiological dysregulation (Mahalanobis distance; Dm) that increases with age in both humans and other primates. Although individual biomarkers showed varied age profiles, we found a robust increase in age-related dysregulation for Tsimane (β = 0.17-0.18) that was marginally faster than that reported for an industrialized Western sample (β = 0.14-0.16), but slower than that of other non-human primates. We found minimal sex differences in the pace or average level of dysregulation for Tsimane. Our findings highlight some conserved patterns of physiological dysregulation in humans, consistent with the notion that somatic ageing exhibits species-typical patterns, despite cross-cultural variation in environmental exposures, lifestyles and mortality. This article is part of the theme issue 'Evolution of the primate ageing process'.

Evidence from two cohorts for the frailty syndrome as an emergent state of parallel dysregulation in multiple physiological systems

Frailty is a clinical syndrome often present in older adults and characterized by a heightened vulnerability to stressors. The biological antecedents and etiology of frailty are unclear despite decades of research: frailty is associated with dysregulation in a wide range of physiological systems, but no specific cause has been identified. Here, we test predictions stemming from the hypothesis that there is no specific cause: that frailty is an emergent property arising from the complex systems dynamics of the broad loss of organismal homeostasis. Specifically, we use dysregulation of six physiological systems using the Mahalanobis distance approach in two cohorts of older adults to test the breadth, diffuseness, and nonlinearity of associations between frailty and system-specific dysregulation. We find clear support for the breadth of associations between frailty and physiological dysregulation: positive associations of all systems with frailty in at least some analyses. We find partial support for diffuseness: the number of systems or total amount of dysregulation is more important than the identity of the systems dysregulated, but results only partially replicate across cohorts. We find partial support for nonlinearity: trends are exponential but not always significantly so, and power is limited for groups with very high levels of dysregulation. Overall, results are consistent with-but not definitive proof of-frailty as an emergent property of complex systems dynamics. Substantial work remains to understand how frailty relates to underlying physiological dynamics across systems.

Association of Blood Chemistry Quantifications of Biological Aging With Disability and Mortality in Older Adults

Quantification of biological aging has been proposed for population surveillance of age-related decline in system integrity and evaluation of geroprotective therapies. However, methods of quantifying biological aging have been little studied in geriatric populations. We analyzed three clinical-biomarker-algorithm methods to quantify biological aging. Klemera-Doubal method Biological Age and homeostatic dysregulation algorithms were parameterized from analysis of U.S. National Health and Nutrition Examination Surveys (NHANES) data (N = 36,207) based on published methods. Levine method Biological Age was adapted from published analysis of NHANES data. Algorithms were applied to biomarker data from the Duke Established Populations for Epidemiologic Studies of the Elderly (Duke-EPESE) cohort of older adults (N = 1,374, aged 71-102 years, 35% male, 52% African American). We tested associations of biological aging measures with participant reported Activities of daily living (ADL), instrumental activities of daily living (IADL) dependencies, and mortality. We evaluated the sensitivity of results to the demographic composition of reference samples and biomarker sets used to develop biological aging algorithms. African American and white Duke-EPESE participants with more advanced biological aging reported dependence in more ADLs and IADLs and were at increased risk of death over follow-up through 2017. Effect sizes were similar across algorithms, but were strongest for Levine method Biological Age (per-quintile increase in ADL incidence rate ratio = 1.25, 95% confidence interval [1.17-1.37], IADL incidence rate ratio = 1.23 [1.15-1.32], mortality hazard ratio = 1.12 [1.08-1.16]). Results were insensitive to demographic composition of reference samples, but modestly sensitive to the biomarker sets used to develop biological aging algorithms. Blood-chemistry-based quantifications of biological aging show promise for evaluating the effectiveness of interventions to extend healthy life span in older adults.

An Emergent Integrated Aging Process Conserved Across Primates

Aging is a complex process emerging from integrated physiological networks. Recent work using principal component analysis (PCA) of multisystem biomarkers proposed a novel fundamental physiological process, "integrated albunemia," which was consistent across human populations and more strongly associated with age and mortality risk than individual biomarkers. Here we tested for integrated albunemia and associations with age and mortality across six diverse nonhuman primate species and humans. PCA of 13 physiological biomarkers recovered in all species a primary axis of variation (PC1) resembling integrated albunemia, which increased with age in all but one species but was less predictive of mortality risk. Within species, PC1 scores were often reliably recovered with a minimal biomarker subset and usually stable between sexes. Even among species, correlations in PC1 structure were often strong, but the effect of phylogeny was inconclusive. Thus, integrated albunemia likely reflects an evolutionarily conserved process across primates and appears to be generally associated with aging but not necessarily with negative impacts on survival. Integrated albunemia is unlikely to be the only conserved emergent physiological process; our findings hence have implications both for the evolution of the aging process and of physiological networks more generally.

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.

Difference between the biologic and chronologic age as an individualized indicator for the skincare intensity selection: skin cell profile and age difference studies

Background

The present research addresses the issue of skin aging and corresponding skin treatment individualization. Particular research question was on the development of a simplified criterion supporting patient-specific decisions about the necessity and intensity of skin treatment. Basing on published results and a wide pool of our own experimental data, a hypothesis is formulated that a difference between biologic and chronologic age can be used as a powerful indicator of skin aging.

Methods

In the present paper, we report the results of studies with 80 volunteers between 15 and 65 years of age linking skin cell profile parameters to biologic and chronologic age. Biologic age was calculated using the empirical expressions based on the forced vital lung capacity, systolic blood pressure, urea concentration, and blood cholesterol level. Epidermis and derma cellular structures were studied using skin biopsy samples taken from the gluteal region.

Results

The present study supports the conclusion that biologic and chronologic age difference is changing in the progress of life. Our studies are showing that time point when calculated biologic age becomes equal to the chronologic one reflecting the onset of specific changes in the age dependencies of experimentally measured skin cell profile parameters. Thus, it is feasible that a difference between chronologic and individually assessed biologic age indeed reflects the process of skin aging.

Conclusions

With all reservations to the relatively small number of study participants, it seems feasible that a difference between biologic and chronologic age can be used as an indicator of skin aging. Additional research linking blood immune profile and skin topography to the difference of biologic and chronologic age (reported in the following paper) provides further support for the formulated hypotheses. So, a difference between calculated biologic age and chronologic age can be used as an individualized criterion supporting decisions on skin treatment strategies. Further research involving larger numbers of participants aimed at optimizing the expressions for calculating biologic age could lead to reliable and easily available express criterion supporting the decision for the individualized skin treatment.

Effectiveness of a muticomponent workout program integrated in an evidence based multimodal program in hyperfrail elderly patients: POWERAGING randomized clinical trial protocol

BACKGROUND

Short-term and mid-term comparison of the efficacy of a multimodal program that incorporates a therapeutic workout program, medication review, diet adjustment and health education, in comparison to the standard medical practice in the improvement of the neuromuscular and physiological condition. Furthermore, it is intended to analyse the maintenance of these effects in a long-term follow-up (12 months) from the onset of the intervention.

METHODS

A randomized clinical trial of elderly frail patients drawn from the Clinical Management Unit "Tiro de Pichón", Health District of Malaga, will be included in the study (after meeting the inclusion / exclusion criteria) will be randomized in two groups: a control group that will undergo an intervention consistent of medication review + diet adjustment + health education (regular workout recommendations within a complete advice on healthy lifestyles) and an experimental group whose intervention will consist of a multimodal treatment: therapeutic workout program+ medication review+ diet adjustment + health education. The sociodemographic, clinical and tracing variables will be reflected at the beginning of the study. In addition, the follow-up variables will be gathered at the second and sixth months after the beginning of the treatment and at the third and sixth months after the treatment (follow-up). The follow-up variables that will be measured are: body mass index, general health condition, fatigue, frailty, motor control, attention- concentration- memory, motor memory, spatial orientation, grip strength, balance (static, semi-dynamic), gait speed and metabolomics. A descriptive analysis of the sociodemographic variables of the participants will be conducted. One-Factor ANOVA will be used for the Within-Subject analysis and as for the Between-Subject analysis, the outcome variables between both the groups in each moment of the data collection will be compared.

DISCUSSION

A multimodal program that incorporates a therapeutic workout program, medication review, diet adjustment and health education may be effective treatment to reduce the functional decline in elderly. The results of the study will provide information on the possible strengths and benefits in multimodal program in elderly.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02772952 registered May 2017.

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.

Physiological predictors of reproductive performance in the European Starling (Sturnus vulgaris)

Background

It is widely assumed that variation in fitness components has a physiological basis that might underlie selection on trade-offs, but the mechanisms driving decreased survival and future fecundity remain elusive. Here, we assessed whether physiological variables are related to workload ability or immediate fitness consequences and if they mediate future survival or reproductive success. We used data on 13 physiological variables measured in 93 female European starlings (Sturnus vulgaris) at two breeding stages (incubation, chick-rearing), for first-and second-broods over two years (152 observations).

Results

There was little co-variation among the physiological variables, either in incubating or chick-rearing birds, but some systematic physiological differences between the two stages. Chick-rearing birds had lower hematocrit and plasma creatine kinase but higher hemoglobin, triglyceride and uric acid levels. Only plasma corticosterone was repeatable between incubation and chick-rearing. We assessed relationships between incubation or chick-rearing physiology and measures of workload, current productivity, future fecundity or survival in a univariate manner, and found very few significant relationships. Thus, we next explored the utility of multivariate analysis (principal components analysis, Mahalanobis distance) to account for potentially complex physiological integration, but still found no clear associations.

Conclusions

This implies either that a) birds maintained physiological variables within a homeostatic range that did not affect their performance, b) there are relatively few links between physiology and performance, or, more likely, c) that the complexity of these relationships exceeds our ability to measure it. Variability in ecological context may complicate the relationship between physiology and behavior. We thus urge caution regarding the over-interpretation of isolated significant findings, based on single traits in single years, in the literature.

Electronic supplementary material

The online version of this article (10.1186/s12983-018-0288-3) contains supplementary material, which is available to authorized users.

Biological Age, Not Chronological Age, Is Associated with Late-Life Depression

Background

The pathophysiology of late-life depression (LLD) is complex and heterogeneous, with age-related processes implicated in its pathogenesis. This study examined the cross-sectional and longitudinal association between depressive symptoms and a baseline multibiomarker algorithm of biological age (BA) that aggregates indicators of inflammatory, metabolic, cardiovascular, lung, liver, and kidney functioning.

Method

Data were analyzed from 2,776 men and women from the prospective observational Health Aging and Body Composition Study, who had both evaluable chronological age (CA) and BA. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression (CES-D) scale.

Results

A covariate-adjusted regression model showed that BA (B = 0.03, p = .0471) but not CA (B = -0.01, p = .7185) is associated with baseline CES-D scores. The mean baseline BA for individuals with a CES-D ≥ 10 was 1.28 years greater than in those with a CES-D < 10. Comparatively, there is only a 0.05-year difference in mean CA between the two depression groups. A covariate-adjusted longitudinal model found that baseline BA predicts CES-D score at follow-up (B = 0.04, p = .0058), whereas CA does not (B = 0.03, p = .4125). Additionally, an older BA significantly predicted a CES-D ≥ 10 (B = 0.02, p = .032) over a 10-year period.

Conclusions

A multibiomarker index of an older adult's BA outperformed their CA in predicting subsequent increased and clinically significant depressive symptoms. This result supports the evolving view of LLD as a brain disorder resulting from deleterious age-associated changes across numerous physiological systems.

Frailty and anesthesia

PURPOSE OF REVIEW

This review will deliberate on contemporary concepts regarding the frailty syndrome and its association with the perioperative period. Frailty syndrome and its relevance to organ systems, scoring tools and intervention measures will be discussed in detail.

RECENT FINDINGS

Frail patients have a reduced ability to respond to physical stress, similar to a decreased physiological reserve in the perioperative period. Frailty assessment is gaining popularity as a tool to guide medical interventions in the elderly population. Various measurement tools for preoperative frailty assessment were developed and show promising ability to predict perioperative morbidity, mortality and possibly to guide patient selection and intervention. Preoperative optimization for the frail patient shows mixed results.

SUMMARY

Preoperative frailty is associated with significant morbidity and mortality. Recently, frailty assessment tools have been developed and show good ability to predict postoperative adverse events. These tools might become a preoperative routine, as they set the ground for patient's selection, guide perioperative interventions for the frail elderly population and thus may influence patient's outcome.

Redefining Aging in HIV Infection Using Phenotypes

Purpose of review

This article critically reviews the utility of “phenotypes” as behavioral descriptors in aging/HIV research that inform biological underpinnings and treatment development. We adopt a phenotypic redefinition of aging conceptualized within a broader context of HIV infection and of aging. Phenotypes are defined as dimensions of behavior, closely related to fundamental mechanisms, and, thus, may be more informative than chronological age. Primary emphasis in this review is given to comorbid aging and cognitive aging, though other phenotypes (i.e., disability, frailty, accelerated aging, successful aging) are also discussed in relation to comorbid aging and cognitive aging.

Recent findings

The main findings that emerged from this review are as follows: (1) the phenotypes, comorbid aging and cognitive aging, are distinct from each other, yet overlapping; (2) associative relationships are the rule in HIV for comorbid and cognitive aging phenotypes; and (3) HIV behavioral interventions for both comorbid aging and cognitive aging have been limited.

Summary

Three paths for research progress are identified for phenotype-defined aging/HIV research (i.e., clinical and behavioral specification, biological mechanisms, intervention targets), and some important research questions are suggested within each of these research paths.

Immunosenescence and Inflamm-Aging As Two Sides of the Same Coin: Friends or Foes?

The immune system is the most important protective physiological system of the organism. It has many connections with other systems and is, in fact, often considered as part of the larger neuro-endocrine-immune axis. Most experimental data on immune changes with aging show a decline in many immune parameters when compared to young healthy subjects. The bulk of these changes is termed immunosenescence. Immunosenescence has been considered for some time as detrimental because it often leads to subclinical accumulation of pro-inflammatory factors and inflamm-aging. Together, immunosenescence and inflamm-aging are suggested to stand at the origin of most of the diseases of the elderly, such as infections, cancer, autoimmune disorders, and chronic inflammatory diseases. However, an increasing number of immune-gerontologists have challenged this negative interpretation of immunosenescence with respect to its significance in aging-related alterations of the immune system. If one considers these changes from an evolutionary perspective, they can be viewed preferably as adaptive or remodeling rather than solely detrimental. Whereas it is conceivable that global immune changes may lead to various diseases, it is also obvious that these changes may be needed for extended survival/longevity. Recent cumulative data suggest that, without the existence of the immunosenescence/inflamm-aging duo (representing two sides of the same phenomenon), human longevity would be greatly shortened. This review summarizes recent data on the dynamic reassessment of immune changes with aging. Accordingly, attempts to intervene on the aging immune system by targeting its rejuvenation, it may be more suitable to aim to maintain general homeostasis and function by appropriately improving immune-inflammatory-functions.

Deep biomarkers of human aging: Application of deep neural networks to biomarker development

One of the major impediments in human aging research is the absence of a comprehensive and actionable set of biomarkers that may be targeted and measured to track the effectiveness of therapeutic interventions. In this study, we designed a modular ensemble of 21 deep neural networks (DNNs) of varying depth, structure and optimization to predict human chronological age using a basic blood test. To train the DNNs, we used over 60,000 samples from common blood biochemistry and cell count tests from routine health exams performed by a single laboratory and linked to chronological age and sex. The best performing DNN in the ensemble demonstrated 81.5 % epsilon-accuracy r = 0.90 with R² = 0.80 and MAE = 6.07 years in predicting chronological age within a 10 year frame, while the entire ensemble achieved 83.5% epsilon-accuracy r = 0.91 with R² = 0.82 and MAE = 5.55 years. The ensemble also identified the 5 most important markers for predicting human chronological age: albumin, glucose, alkaline phosphatase, urea and erythrocytes. To allow for public testing and evaluate real-life performance of the predictor, we developed an online system available at http://www.aging.ai. The ensemble approach may facilitate integration of multi-modal data linked to chronological age and sex that may lead to simple, minimally invasive, and affordable methods of tracking integrated biomarkers of aging in humans and performing cross-species feature importance analysis.

Grip strength and inflammatory biomarker profiles in very old adults

Background

weak grip strength (GS) and chronic inflammation have been implicated in the aetiology of sarcopenia in older adults. Given the interrelationships between inflammatory biomarkers, a summary variable may provide better insight into the relationship between inflammation and muscle strength. This approach has not been investigated in very old adults (aged ≥85) who are at highest risk of muscle weakness.

Methods

we used mixed models to explore the prospective association between GS over 5 years in 845 participants in the Newcastle 85+ Study, and inflammatory components identified by principal component analysis (PCA). Cut-offs of ≤27 kg (men) and ≤16 (women) were used to define sub-cohorts with weak and normal GS at each assessment.

Results

PCA identified three components, which explained 70% of the total variance in seven baseline biomarkers. Basal interleukin-6 (IL-6) and tumour necrosis factor (TNF-α) had the highest loadings on Component 1; stimulated IL-6 and TNF-α and homocysteine the highest on Component 2; high-sensitivity C-reactive protein (hsCRP) loaded positively and albumin negatively to Component 3. In adjusted mixed models, only Component 3 was associated with GS. One SD increase of Component 3 was associated with a 0.41 kg lower GS initially (P = 0.03) in all participants, but not with GS decline over time. Similar conclusions held for those in the weak and normal GS sub-cohorts.

Conclusion

an inflammatory profile including hsCRP and albumin was independently associated with baseline GS. Future studies linking inflammatory profiles and muscle strength are needed to corroborate these findings in older adults.

Molecular and physiological manifestations and measurement of aging in humans

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

Human T cell immunosenescence and inflammation in aging

The aging process is driven by a finite number of inter-related mechanisms that ultimately lead to the emergence of characteristic phenotypes, including increased susceptibility to multiple chronic diseases, disability, and death. New assays and analytical tools have become available that start to unravel some of these mechanisms. A prevailing view is that aging leads to an imbalance between stressors and stress-buffering mechanisms that causes loss of compensatory reserve and accumulation of unrepaired damage. Central to this paradigm are changes in the immune system and the chronic low-grade proinflammatory state that affect many older individuals, even when they are apparently healthy and free of risk factors. Independent of chronological age, high circulating levels of proinflammatory markers are associated with a high risk of multiple adverse health outcomes in older persons. In this review, we discuss current theories about causes and consequences of the proinflammatory state of aging, with a focus on changes in T cell function. We examine the role of NF-κB activation and its dysregulation and how NF-κB activity differs among subgroups of T cells. We explore emerging hypotheses about immunosenescence and changes in T cell behavior with age, including consideration of the T cell antigen receptor and regulatory T cells (T_regs). We conclude by illustrating how research using advanced technology is uncovering clues at the core of inflammation and aging. Some of the preliminary work in this field is already improving our understanding of the complex mechanisms by which immunosenescence of T cells is intertwined during human aging.

The risks of biomarker-based epidemiology: Associations of circulating calcium levels with age, mortality, and frailty vary substantially across populations

Recent studies have shown contradictory associations between calcium levels and health outcomes. We suspected these conflicting results were the consequence of more general issues with how biomarkers are analyzed in epidemiological studies, particularly in the context of aging. To demonstrate the risks of typical analyses, we used three longitudinal aging cohort studies and their demographic subsets to analyze how calcium levels change with age and predict risk of mortality and frailty. We show that calcium levels either increase or decrease with age depending on the population, and positively or negatively predict frailty depending on the population and analysis; both age and frailty results showed substantial heterogeneity. Mortality analyses revealed few significant associations but were likely underpowered. Variation in population composition (demographics, diseases, diet, etc.) leads to contradictory findings in the literature for calcium and likely for other biomarkers. Epidemiological studies of biomarkers are particularly sensitive to population composition both because biomarkers generally have non-linear and often non-monotonic relationships with other key variables, notably age and health outcomes, and because there is strong interdependence among biomarkers, which are integrated into complex regulatory networks. Consequently, most biomarkers have multiple physiological roles and are implicated in multiple pathologies. We argue that epidemiological studies of aging using biomarkers must account for these factors, and suggest methods to do this.

Mortality is associated with inflammation, anemia, specific diseases and treatments, and molecular markers

Lifespan is a complex trait, and longitudinal data for humans are naturally scarce. We report the results of Cox regression and Pearson correlation analyses using data of the Study of Health in Pomerania (SHIP), with mortality data of 1518 participants (113 of which died), over a time span of more than 10 years. We found that in the Cox regression model based on the Bayesian information criterion, apart from chronological age of the participant, six baseline variables were considerably associated with higher mortality rates: smoking, mean attachment loss (i.e. loss of tooth supporting tissue), fibrinogen concentration, albumin/creatinine ratio, treated gastritis, and medication during the last 7 days. Except for smoking, the causative contribution of these variables to mortality was deemed inconclusive. In turn, four variables were found to be associated with decreased mortality rates: treatment of benign prostatic hypertrophy, treatment of dyslipidemia, IGF-1 and being female. Here, being female was an undisputed causative variable, the causal role of IFG-1 was deemed inconclusive, and the treatment effects were deemed protective to the degree that treated subjects feature better survival than respective controls. Using Cox modeling based on the Akaike information criterion, diabetes, mean corpuscular hemoglobin concentration, red blood cell count and serum calcium were also associated with mortality. The latter two, together with albumin and fibrinogen, aligned with an”integrated albunemia” model of aging proposed recently.

Biomarker signatures of aging

Because people age differently, age is not a sufficient marker of susceptibility to disabilities, morbidities, and mortality. We measured nineteen blood biomarkers that include constituents of standard hematological measures, lipid biomarkers, and markers of inflammation and frailty in 4704 participants of the Long Life Family Study (LLFS), age range 30–110 years, and used an agglomerative algorithm to group LLFS participants into clusters thus yielding 26 different biomarker signatures. To test whether these signatures were associated with differences in biological aging, we correlated them with longitudinal changes in physiological functions and incident risk of cancer, cardiovascular disease, type 2 diabetes, and mortality using longitudinal data collected in the LLFS. Signature 2 was associated with significantly lower mortality, morbidity, and better physical function relative to the most common biomarker signature in LLFS, while nine other signatures were associated with less successful aging, characterized by higher risks for frailty, morbidity, and mortality. The predictive values of seven signatures were replicated in an independent data set from the Framingham Heart Study with comparable significant effects, and an additional three signatures showed consistent effects. This analysis shows that various biomarker signatures exist, and their significant associations with physical function, morbidity, and mortality suggest that these patterns represent differences in biological aging. The signatures show that dysregulation of a single biomarker can change with patterns of other biomarkers, and age‐related changes of individual biomarkers alone do not necessarily indicate disease or functional decline.

Advanced analytical methodologies for measuring healthy ageing and its determinants, using factor analysis and machine learning techniques: the ATHLOS project

A most challenging task for scientists that are involved in the study of ageing is the development of a measure to quantify health status across populations and over time. In the present study, a Bayesian multilevel Item Response Theory approach is used to create a health score that can be compared across different waves in a longitudinal study, using anchor items and items that vary across waves. The same approach can be applied to compare health scores across different longitudinal studies, using items that vary across studies. Data from the English Longitudinal Study of Ageing (ELSA) are employed. Mixed-effects multilevel regression and Machine Learning methods were used to identify relationships between socio-demographics and the health score created. The metric of health was created for 17,886 subjects (54.6% of women) participating in at least one of the first six ELSA waves and correlated well with already known conditions that affect health. Future efforts will implement this approach in a harmonised data set comprising several longitudinal studies of ageing. This will enable valid comparisons between clinical and community dwelling populations and help to generate norms that could be useful in day-to-day clinical practice.

Management of Cancer in the Older Age Person: An Approach to Complex Medical Decisions

The management of cancer in older aged people is becoming a common problem due to the aging of the population. There are many variables determining the complex situation that are interconnected. Some of them can be assessed, such as risk of mortality and risk of treatment complications, but many others are still unknown, such as the course of disease, the host-related factors that influence cancer aggressiveness, and the phenotype heralding risk of permanent treatment-related damage.This article presents a dynamic and personalized approach to older people with cancer based on our experience on aging, cancer, and their biological interactions. Also, novel treatments and management approaches to older individuals, based on their functional age and their social and emotional needs, are thoughtfully explored here. The Oncologist 2017;22:335-342 IMPLICATIONS FOR PRACTICE: The goal of this article is to suggest a practical approach to complexity, a clinical situation becoming increasingly common with the aging of the population. Beginning with the analysis of two clinical cases, the authors offer an algorithm for approaching cancer in the older person that involves the assessment of life expectancy without cancer, the risk that cancer might compromise a patient's survival, function, or quality of life, and the potential benefits and risks of the treatments based on a clinical evaluation. The authors then review possible laboratory assessment of functional age and the importance of rapid-learning databases in the study of cancer and age.

The Longitudinal Study of Aging in Human Young Adults: Knowledge Gaps and Research Agenda

BACKGROUND

To prevent onset of age-related diseases and physical and cognitive decline, interventions to slow human aging and extend health span must eventually be applied to people while they are still young and healthy. Yet most human aging research examines older adults, many with chronic disease, and little is known about aging in healthy young humans.

METHOD

This article explains how this knowledge gap is a barrier to extending health span and puts forward the case that geroscience should invest in researching the pace of aging in young adults. As one illustrative example, we describe an initial effort to study the pace of aging in a young-adult birth cohort by using repeated waves of biomarkers collected across the third and fourth decades to quantify the pace of coordinated physiological deterioration across multiple organ systems (eg, pulmonary, periodontal, cardiovascular, renal, hepatic, metabolic, and immune function).

RESULTS

Findings provided proof of principle that it is possible to quantify individual variation in the pace of aging in young adults still free of age-related diseases.

CONCLUSIONS

This article articulates research needs to improve longitudinal measurement of the pace of aging in young people, to pinpoint factors that slow or speed the pace of aging, to compare pace of aging against genomic clocks, to explain slow-aging young adults, and to apply pace of aging in preventive clinical trials of antiaging therapies. This article puts forward a research agenda to fill the knowledge gap concerning lifelong causes of aging.

Classification for Longevity Potential: The Use of Novel Biomarkers

Background

In older people, chronological age may not be the best predictor of residual lifespan and mortality, because with age the heterogeneity in health is increasing. Biomarkers for biological age and residual lifespan are being developed to predict disease and mortality better at an individual level than chronological age. In the current paper, we aim to classify a group of older people into those with longevity potential or controls.

Methods

In the Leiden Longevity Study participated 1671 offspring of nonagenarian siblings, as the group with longevity potential, and 744 similarly aged controls. Using known risk factors for cardiovascular disease, previously reported markers for human longevity and other physiological measures as predictors, classification models for longevity potential were constructed with multiple logistic regression of the offspring-control status.

Results

The Framingham Risk Score (FRS) is predictive for longevity potential [area under the receiver operating characteristic curve (AUC) = 64.7]. Physiological parameters involved in immune responses and glucose, lipid and energy metabolism further improve the prediction performance for longevity potential (AUCmale = 71.4, AUCfemale = 68.7).

Conclusion

Using the FRS, the classification of older people in groups with longevity potential and controls is moderate, but can be improved to a reasonably good classification in combination with markers of immune response, glucose, lipid, and energy metabolism. We show that individual classification of older people for longevity potential may be feasible using biomarkers from a wide variety of different biological processes.

Biomarkers of Aging: From Function to Molecular Biology

Aging is a major risk factor for most chronic diseases and functional impairments. Within a homogeneous age sample there is a considerable variation in the extent of disease and functional impairment risk, revealing a need for valid biomarkers to aid in characterizing the complex aging processes. The identification of biomarkers is further complicated by the diversity of biological living situations, lifestyle activities and medical treatments. Thus, there has been no identification of a single biomarker or gold standard tool that can monitor successful or healthy aging. Within this short review the current knowledge of putative biomarkers is presented, focusing on their application to the major physiological mechanisms affected by the aging process including physical capability, nutritional status, body composition, endocrine and immune function. This review emphasizes molecular and DNA-based biomarkers, as well as recent advances in other biomarkers such as microRNAs, bilirubin or advanced glycation end products.

Interventions for Human Frailty: Physical Activity as a Model

In the last 100 years, populations in developed countries have experienced an unprecedented addition of 30 years to life expectancy. Developing countries are now experiencing this same phenomenon, but over a shorter time frame. With this success comes the challenge of maximizing health and vitality across these added years. The compression of morbidity to the latest point in the human life span could unleash a sustained third demographic dividend that benefits all of society. To accomplish this, society needs to invest in the prevention and treatment of frailty, as well as in the prevention of chronic diseases at every age and stage of life. A model intervention, physical activity, may offer a road map.

Optimal Versus Realized Trajectories of Physiological Dysregulation in Aging and Their Relation to Sex-Specific Mortality Risk

While longitudinal changes in biomarker levels and their impact on health have been characterized for individual markers, little is known about how overall marker profiles may change during aging and affect mortality risk. We implemented the recently developed measure of physiological dysregulation based on the statistical distance of biomarker profiles in the framework of the stochastic process model of aging, using data on blood pressure, heart rate, cholesterol, glucose, hematocrit, body mass index, and mortality in the Framingham original cohort. This allowed us to evaluate how physiological dysregulation is related to different aging-related characteristics such as decline in stress resistance and adaptive capacity (which typically are not observed in the data and thus can be analyzed only indirectly), and, ultimately, to estimate how such dynamic relationships increase mortality risk with age. We found that physiological dysregulation increases with age; that increased dysregulation is associated with increased mortality, and increasingly so with age; and that, in most but not all cases, there is a decreasing ability to return quickly to baseline physiological state with age. We also revealed substantial sex differences in these processes, with women becoming dysregulated more quickly but with men showing a much greater sensitivity to dysregulation in terms of mortality risk.

Association of Aging-Related Endophenotypes With Mortality in 2 Cohort Studies: the Long Life Family Study and the Health, Aging and Body Composition Study

One method by which to identify fundamental biological processes that may contribute to age-related disease and disability, instead of disease-specific processes, is to construct endophenotypes comprising linear combinations of physiological measures. Applying factor analyses methods to phenotypic data (2006-2009) on 28 traits representing 5 domains (cognitive, cardiovascular, metabolic, physical, and pulmonary) from 4,472 US and Danish individuals in 574 pedigrees from the Long Life Family Study (United States and Denmark), we constructed endophenotypes and assessed their relationship with mortality. The most dominant endophenotype primarily reflected the physical activity and pulmonary domains, was heritable, was significantly associated with mortality, and attenuated the association of age with mortality by 24.1%. Using data (1997-1998) on 1,794 Health, Aging and Body Composition Study participants from Memphis, Tennessee, and Pittsburgh, Pennsylvania, we obtained strikingly similar endophenotypes and relationships to mortality. We also reproduced the endophenotype constructs, especially the dominant physical activity and pulmonary endophenotype, within demographic subpopulations of these 2 cohorts. Thus, this endophenotype construct may represent an underlying phenotype related to aging. Additional genetic studies of this endophenotype may help identify genetic variants or networks that contribute to the aging process.

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.

Quantification of biological aging in young adults

Antiaging therapies show promise in model organism research. Translation to humans is needed to address the challenges of an aging global population. Interventions to slow human aging will need to be applied to still-young individuals. However, most human aging research examines older adults, many with chronic disease. As a result, little is known about aging in young humans. We studied aging in 954 young humans, the Dunedin Study birth cohort, tracking multiple biomarkers across three time points spanning their third and fourth decades of life. We developed and validated two methods by which aging can be measured in young adults, one cross-sectional and one longitudinal. Our longitudinal measure allows quantification of the pace of coordinated physiological deterioration across multiple organ systems (e.g., pulmonary, periodontal, cardiovascular, renal, hepatic, and immune function). We applied these methods to assess biological aging in young humans who had not yet developed age-related diseases. Young individuals of the same chronological age varied in their "biological aging" (declining integrity of multiple organ systems). Already, before midlife, individuals who were aging more rapidly were less physically able, showed cognitive decline and brain aging, self-reported worse health, and looked older. Measured biological aging in young adults can be used to identify causes of aging and evaluate rejuvenation therapies.

Complex systems dynamics in aging: new evidence, continuing questions

There have long been suggestions that aging is tightly linked to the complex dynamics of the physiological systems that maintain homeostasis, and in particular to dysregulation of regulatory networks of molecules. This review synthesizes recent work that is starting to provide evidence for the importance of such complex systems dynamics in aging. There is now clear evidence that physiological dysregulation—the gradual breakdown in the capacity of complex regulatory networks to maintain homeostasis—is an emergent property of these regulatory networks, and that it plays an important role in aging. It can be measured simply using small numbers of biomarkers. Additionally, there are indications of the importance during aging of emergent physiological processes, functional processes that cannot be easily understood through clear metabolic pathways, but can nonetheless be precisely quantified and studied. The overall role of such complex systems dynamics in aging remains an important open question, and to understand it future studies will need to distinguish and integrate related aspects of aging research, including multi-factorial theories of aging, systems biology, bioinformatics, network approaches, robustness, and loss of complexity.