Biomarkers of aging

Circadian rhythm analysis using wearable-based accelerometry as a digital biomarker of aging and healthspan

Recognizing the pivotal role of circadian rhythm in the human aging process and its scalability through wearables, we introduce CosinorAge, a digital biomarker of aging developed from wearable-derived circadian rhythmicity from 80,000 midlife and older adults in the UK and US. A one-year increase in CosinorAge corresponded to 8-12% higher all-cause and cause-specific mortality risks and 3-14% increased prospective incidences of age-related diseases. CosinorAge also captured a non-linear decline in resilience and physical functioning, evidenced by an 8-33% reduction in self-rated health and a 3-23% decline in health-related quality of life score, adjusting for covariates and multiple testing. The associations were robust in sensitivity analyses and external validation using an independent cohort from a disparate geographical region using a different wearable device. Moreover, we illustrated a heterogeneous impact of circadian parameters associated with biological aging, with young (<45 years) and fast agers experiencing a substantially delayed acrophase with a 25-minute difference in peak timing compared to slow agers, diminishing to a 7-minute difference in older adults (>65 years). We demonstrated a significant enhancement in the predictive performance when integrating circadian rhythmicity in the estimation of biological aging over physical activity. Our findings underscore CosinorAge's potential as a scalable, economic, and digital solution for promoting healthy longevity, elucidating the critical and multifaceted circadian rhythmicity in aging processes. Consequently, our research contributes to advancing preventive measures in digital medicine.

Ageing as a two-phase process: theoretical framework

Human ageing, along with the ageing of conventional model organisms, is depicted as a continuous and progressive decline of biological capabilities accompanied by an exponentially increasing mortality risk. However, not all organisms experience ageing identically and our understanding of the phenomenon is coloured by human-centric views. Ageing is multifaceted and influences a diverse range of species in varying ways. Some undergo swift declines post-reproduction, while others exhibit insubstantial changes throughout their existence. This vast array renders defining universally applicable "ageing attributes" a daunting task. It is nonetheless essential to recognize that not all ageing features are organism-specific. These common attributes have paved the way for identifying "hallmarks of ageing," processes that are intertwined with age, amplified during accelerated ageing, and manipulations of which can potentially modulate or even reverse the ageing process. Yet, a glaring observation is that individuals within a single population age at varying rates. To address this, demographers have coined the term 'frailty'. Concurrently, scientific advancements have ushered in the era of molecular clocks. These innovations enable a distinction between an individual's chronological age (time since birth) and biological age (physiological status and mortality risk). In 2011, the "Smurf" phenotype was unveiled in Drosophila, delineating an age-linked escalation in intestinal permeability that presages imminent mortality. It not only acts as a predictor of natural death but identifies individuals exhibiting traits normally described as age-related. Subsequent studies have revealed the phenotype in organisms like nematodes, zebrafish, and mice, invariably acting as a death predictor. Collectively, these findings have steered our conception of ageing towards a framework where ageing is not linear and continuous but marked by two distinct, necessary phases, discernible in vivo, courtesy of the Smurf phenotype. This framework includes a mathematical enunciation of longevity trends based on three experimentally measurable parameters. It facilitates a fresh perspective on the evolution of ageing as a function. In this article, we aim to delineate and explore the foundational principles of this innovative framework, emphasising its potential to reshape our understanding of ageing, challenge its conventional definitions, and recalibrate our comprehension of its evolutionary trajectory.

Deep neural network-estimated age using optical coherence tomography predicts mortality

The concept of biological age has emerged as a measurement that reflects physiological and functional decline with ageing. Here we aimed to develop a deep neural network (DNN) model that predicts biological age from optical coherence tomography (OCT). A total of 84,753 high-quality OCT images from 53,159 individuals in the UK Biobank were included, among which 12,631 3D-OCT images from 8,541 participants without any reported medical conditions at baseline were used to develop an age prediction model. For the remaining 44,618 participants, OCT age gap, the difference between the OCT-predicted age and chronological age, was calculated for each participant. Cox regression models assessed the association between OCT age gap and mortality. The DNN model predicted age with a mean absolute error of 3.27 years and showed a strong correlation of 0.85 with chronological age. After a median follow-up of 11.0 years (IQR 10.9-11.1 years), 2,429 deaths (5.44%) were recorded. For each 5-year increase in OCT age gap, there was an 8% increased mortality risk (hazard ratio [HR] = 1.08, CI:1.02-1.13, P = 0.004). Compared with an OCT age gap within ± 4 years, OCT age gap less than minus 4 years was associated with a 16% decreased mortality risk (HR = 0.84, CI: 0.75-0.94, P = 0.002) and OCT age gap more than 4 years showed an 18% increased risk of death incidence (HR = 1.18, CI: 1.02-1.37, P = 0.026). OCT imaging could serve as an ageing biomarker to predict biological age with high accuracy and the OCT age gap, defined as the difference between the OCT-predicted age and chronological age, can be used as a marker of the risk of mortality.

The Pace of Biological Aging Predicts Nonspecific Chronic Low Back Pain Severity

This study aimed to determine if and how the pace of biological aging was associated with nonspecific chronic low back pain (cLBP) and compare what measure of epigenetic age acceleration most strongly predicts cLBP outcomes. We used the Dunedin Pace of Aging from the Epigenome (DunedinPACE), Horvath's, Hannum's, and PhenoAge clocks to determine the pace of biological aging in 69 cLBP, and 49 pain-free controls (PFCs) adults, ages 18 to 85 years. On average, participants with cLBP had higher DunedinPACE (P < .001) but lower Horvath (P = .04) and Hannum (P = .02) accelerated epigenetic age than PFCs. There was no significant difference in PhenoAge acceleration between the cLBP and PFC groups (P = .97). DunedinPACE had the largest effect size (Cohen's d = .78) on group differences. In univariate regressions, a unit increase in DunedinPACE score was associated with 265.98 times higher odds of cLBP than the PFC group (P < .001). After controlling for sex, race, and body mass index (BMI), the odds ratio of cLBP to PFC group was 149.62 (P < .001). Furthermore, among participants with cLBP, DunedinPACE scores positively correlated with pain severity (rs = .385, P = .001) and interference (rs = .338, P = .005). Epigenetic age acceleration from Horvath, Hannum, and PhenoAge clocks were not significant predictors of cLBP. The odds of a faster pace of biological aging are higher among adults with cLBP, and this was associated with greater pain severity and disability. Future interventions to slow the pace of biological aging may improve cLBP outcomes. PERSPECTIVE: Accelerated epigenetic aging is common among adults with nonspecific cLBP. Higher DunedinPACE scores positively correlate with pain severity and interference, and better predict cLBP than other DNA methylation clocks. Interventions to slow the pace of biological aging may be viable targets for improving pain outcomes.

Accelerated Aging in Cancer Survivors: Cellular Senescence, Frailty, and Possible Opportunities for Interventions

The population of cancer survivors has markedly increased due to the rapid improvements in cancer treatment. However, cancer survivors experience accelerated aging, which leads to chronic diseases and other age-related conditions, such as frailty. Those conditions may persist years after cancer diagnosis and treatment. Cellular senescence, a hallmark of aging, is one of the mechanisms that contribute to accelerated aging in cancer survivors. Several aging measures, including measures based on clinical markers and biomarkers, have been proposed to estimate the aging process, and some of them have shown associations with mortality and frailty in cancer survivors. Several anti-aging interventions, including lifestyle changes and anti-aging drugs, have been proposed. Future research, particularly in large-scale studies, is needed to determine the efficiency of these aging measures and anti-aging interventions before considering their application in clinics. This review focuses on the mechanisms of cellular senescence and accelerated aging in cancer survivors, assessment of the aging process using clinical markers and biomarkers, and the high prevalence of frailty in that population, as well as possible opportunities for anti-aging interventions. A deeper understanding of aging measures and anti-aging interventions in cancer survivors will contribute to the development of effective strategies to mitigate accelerated aging in cancer survivors and improve their quality of life.

Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data

BACKGROUND

There is divergence in the rate at which people age. The concept of biological age is postulated to capture this variability, and hence to better represent an individual's true global physiological state than chronological age. Biological age predictors are often generated based on cross-sectional data, using biochemical or molecular markers as predictor variables. It is assumed that the difference between chronological and predicted biological age is informative of one's chronological age-independent aging divergence ∆.

METHODS

We investigated the statistical assumptions underlying the most popular cross-sectional biological age predictors, based on multiple linear regression, the Klemera-Doubal method or principal component analysis. We used synthetic and real data to illustrate the consequences if this assumption does not hold.

RESULTS

The most popular cross-sectional biological age predictors all use the same strong underlying assumption, namely that a candidate marker of aging's association with chronological age is directly informative of its association with the aging rate ∆. We called this the identical-association assumption and proved that it is untestable in a cross-sectional setting. If this assumption does not hold, weights assigned to candidate markers of aging are uninformative, and no more signal may be captured than if markers would have been assigned weights at random.

CONCLUSIONS

Cross-sectional methods for predicting biological age commonly use the untestable identical-association assumption, which previous literature in the field had never explicitly acknowledged. These methods have inherent limitations and may provide uninformative results, highlighting the importance of researchers exercising caution in the development and interpretation of cross-sectional biological age predictors.

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.

Biologically informed deep learning for explainable epigenetic clocks

Ageing is often characterised by progressive accumulation of damage, and it is one of the most important risk factors for chronic disease development. Epigenetic mechanisms including DNA methylation could functionally contribute to organismal aging, however the key functions and biological processes may govern ageing are still not understood. Although age predictors called epigenetic clocks can accurately estimate the biological age of an individual based on cellular DNA methylation, their models have limited ability to explain the prediction algorithm behind and underlying key biological processes controlling ageing. Here we present XAI-AGE, a biologically informed, explainable deep neural network model for accurate biological age prediction across multiple tissue types. We show that XAI-AGE outperforms the first-generation age predictors and achieves similar results to deep learning-based models, while opening up the possibility to infer biologically meaningful insights of the activity of pathways and other abstract biological processes directly from the model.

Biological Aging Acceleration Due to Environmental Exposures: An Exciting New Direction in Toxicogenomics Research

Biological clock technologies are designed to assess the acceleration of biological age (B-age) in diverse cell types, offering a distinctive opportunity in toxicogenomic research to explore the impact of environmental stressors, social challenges, and unhealthy lifestyles on health impairment. These clocks also play a role in identifying factors that can hinder aging and promote a healthy lifestyle. Over the past decade, researchers in epigenetics have developed testing methods that predict the chronological and biological age of organisms. These methods rely on assessing DNA methylation (DNAm) levels at specific CpG sites, RNA levels, and various biomolecules across multiple cell types, tissues, and entire organisms. Commonly known as 'biological clocks' (B-clocks), these estimators hold promise for gaining deeper insights into the pathways contributing to the development of age-related disorders. They also provide a foundation for devising biomedical or social interventions to prevent, reverse, or mitigate these disorders. This review article provides a concise overview of various epigenetic clocks and explores their susceptibility to environmental stressors.

Association of Physical Activity and Exercise with Physical Performance and Muscle Mass in Older Adults: Results from the Longevity Check-Up (Lookup) 7+ Project

Regular engagement in physical activity (PA) or physical exercise (PE) is effective at improving physical performance and body composition in older adults. Less is known about the benefits that may be obtained through combining PA with PE and whether the effects of activity habits differ between men and women. This study cross-sectionally investigated the association of PA and/or PE with physical performance and anthropometric measures in a large and relatively unselected sample of older adults enrolled in the Longevity Check-up (Lookup) 7+ project. Participants were individuals 65 years and older living in the community who were recruited in unconventional settings across Italy. Adherence to PA or PE was operationalized as involvement in light walking or various types of exercise, respectively, at least twice weekly for a minimum of 30 min per session throughout the last 12 months. Physical performance measures included handgrip strength and five-time sit-to-stand (5STS) tests. Lower-limb muscle power and appendicular skeletal muscle mass (ASM) were estimated through validated equations. We analyzed data of 4119 participants, of whom 2222 (53.4%) were women. The mean age was 72.8 ± 5.8 years in men and 72.1 ± 5.4 years in women. Regular engagement in PA + PE was reported by 139 (7.3%) men and 100 (4.5%) women. Results indicated that regular walking activity and/or PE were significantly associated with better physical performance and greater ASM with sex-specific patterns. Associations were also influenced by the type of activity, physical performance assessment tool, and anthropometric parameters. Men engaged in PA + PE performed better on the 5STS test and had greater handgrip strength, ASM, and relative and specific muscle power than those practicing either PA or PE. In women, the combination of PA and PE was associated with greater handgrip strength. The findings of this study indicate that older adults regularly practicing PA + PE had better physical performance than those who only engaged in either modality. In men, the combination of PA and PE was also associated with greater ASM.

Hidalgo B, Aroke EN. Neighborhood Environment and Epigenetic Age: A Scoping Review

BACKGROUND

Interest in how the neighborhood environment impacts age-related health conditions has been increasing for decades. Epigenetic changes are environmentally derived modifications to the genome that alter the way genes function-thus altering health status. Epigenetic age, a biomarker for biological age, has been shown to be a useful predictor of several age-related health conditions. Consequently, its relation to the neighborhood environment has been the focus of a growing body of literature.

OBJECTIVE

We aimed to describe the scope of the evidence on the relationship between neighborhood environmental characteristics and epigenetic age.

METHODS

Using scoping review following methods established by Arksey and O'Malley, we first defined our research questions and searched the literature in PubMed, PsycINFO, and EMBASE. Next, we selected the literature to be included, and finally, we analyzed and summarized the information.

RESULTS

Nine articles met the inclusion criteria. Most studies examined deprivation as the neighborhood characteristic of interest. While all studies were observational in design, the articles included diverse participants, including men and women, adults and children, and multiple ethnicities. Results demonstrated a relationship between the neighborhood environment and epigenetic age, whether the characteristic of interest is socioeconomic or physical.

CONCLUSIONS

Overall, studies concluded there was a relationship between neighborhood characteristics and epigenetic age, whether the characteristic of interest was socioeconomic or physical. However, findings varied based on how the neighborhood characteristic and/or epigenetic age was measured. Furthermore, a paucity of investigations on physical characteristics was noticeable and warrants increased attention.

How is Big Data reshaping preclinical aging research?

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

Biological age estimation using circulating blood biomarkers

Biological age captures physiological deterioration better than chronological age and is amenable to interventions. Blood-based biomarkers have been identified as suitable candidates for biological age estimation. This study aims to improve biological age estimation using machine learning models and a feature-set of 60 circulating biomarkers available from the UK Biobank (n = 306,116). We implement an Elastic-Net derived Cox model with 25 selected biomarkers to predict mortality risk (C-Index = 0.778; 95% CI [0.767-0.788]), which outperforms the well-known blood-biomarker based PhenoAge model (C-Index = 0.750; 95% CI [0.739-0.761]), providing a C-Index lift of 0.028 representing an 11% relative increase in predictive value. Importantly, we then show that using common clinical assay panels, with few biomarkers, alongside imputation and the model derived on the full set of biomarkers, does not substantially degrade predictive accuracy from the theoretical maximum achievable for the available biomarkers. Biological age is estimated as the equivalent age within the same-sex population which corresponds to an individual's mortality risk. Values ranged between 20-years younger and 20-years older than individuals' chronological age, exposing the magnitude of ageing signals contained in blood markers. Thus, we demonstrate a practical and cost-efficient method of estimating an improved measure of Biological Age, available to the general population.

Accelerated Aging and the Life Course of Individuals Born Preterm

Individuals born preterm have shorter lifespans and elevated rates of chronic illness that contribute to mortality risk when compared to individuals born at term. Emerging evidence suggests that individuals born preterm or of low birthweight also exhibit physiologic and cellular biomarkers of accelerated aging. It is unclear whether, and to what extent, accelerated aging contributes to a higher risk of chronic illness and mortality among individuals born preterm. Here, we review accelerated aging phenotypes in adults born preterm and biological pathways that appear to contribute to accelerated aging. We highlight biomarkers of accelerated aging and various resiliency factors, including both pharmacologic and non-pharmacologic factors, that might buffer the propensity for accelerated aging among individuals born preterm.

Development of a Machine Learning-Based Model to Predict Timed-Up-and-Go Test in Older Adults

INTRODUCTION

The measurement of physical frailty in elderly patients with orthopedic impairments remains a challenge due to its subjectivity, unreliability, time-consuming nature, and limited applicability to uninjured individuals. Our study aims to address this gap by developing objective, multifactorial machine models that do not rely on mobility data and subsequently validating their predictive capacity concerning the Timed-up-and-Go test (TUG test) in orthogeriatric patients.

METHODS

We utilized 67 multifactorial non-mobility parameters in a pre-processing phase, employing six feature selection algorithms. Subsequently, these parameters were used to train four distinct machine learning algorithms, including a generalized linear model, a support vector machine, a random forest algorithm, and an extreme gradient boost algorithm. The primary goal was to predict the time required for the TUG test without relying on mobility data.

RESULTS

The random forest algorithm yielded the most accurate estimations of the TUG test time. The best-performing algorithm demonstrated a mean absolute error of 2.7 s, while the worst-performing algorithm exhibited an error of 7.8 s. The methodology used for variable selection appeared to exert minimal influence on the overall performance. It is essential to highlight that all the employed algorithms tended to overestimate the time for quick patients and underestimate it for slower patients.

CONCLUSION

Our findings demonstrate the feasibility of predicting the TUG test time using a machine learning model that does not depend on mobility data. This establishes a basis for identifying patients at risk automatically and objectively assessing the physical capacity of currently immobilized patients. Such advancements could significantly contribute to enhancing patient care and treatment planning in orthogeriatric settings.

Development and Characterization of Proteomic Aging Clocks in the Atherosclerosis Risk in Communities (ARIC) Study

Biological age may be estimated by proteomic aging clocks (PACs). Previous published PACs were constructed either in smaller studies or mainly in White individuals, and they used proteomic measures from only one-time point. In the Atherosclerosis Risk in Communities (ARIC) study of about 12,000 persons followed for 30 years (around 75% White, 25% Black), we created de novo PACs and compared their performance to published PACs at two different time points. We measured 4,712 plasma proteins by SomaScan in 11,761 midlife participants, aged 46-70 years (1990-92), and 5,183 late-life pariticpants, aged 66-90 years (2011-13). All proteins were log2-transformed to correct for skewness. We created de novo PACs by training them against chronological age using elastic net regression in two-thirds of healthy participants in midlife and late life and compared their performance to three published PACs. We estimated age acceleration (by regressing each PAC on chronological age) and its change from midlife to late life. We examined their associations with mortality from all-cause, cardiovascular disease (CVD), cancer, and lower respiratory disease (LRD) using Cox proportional hazards regression in all remaining participants irrespective of health. The model was adjusted for chronological age, smoking, body mass index (BMI), and other confounders. The ARIC PACs had a slightly stronger correlation with chronological age than published PACs in healthy participants at each time point. Associations with mortality were similar for the ARIC and published PACs. For late-life and midlife age acceleration for the ARIC PACs, respectively, hazard ratios (HRs) per one standard deviation were 1.65 and 1.38 (both p<0.001) for all-cause mortality, 1.37 and 1.20 (both p<0.001) for CVD mortality, 1.21 (p=0.03) and 1.04 (p=0.19) for cancer mortality, and 1.46 and 1.68 (both p<0.001) for LRD mortality. For the change in age acceleration, HRs for all-cause, CVD, and LRD mortality were comparable to those observed for late-life age acceleration. The association between the change in age acceleration and cancer mortality was insignificant. In this prospective study, the ARIC and published PACs were similarly associated with an increased risk of mortality and advanced testing in relation to various age-related conditions in future studies is suggested.

Re-wilding model organisms: Opportunities to test causal mechanisms in social determinants of health and aging

Social experiences are strongly associated with individuals' health, aging, and survival in many mammalian taxa, including humans. Despite their role as models of many other physiological and developmental bases of health and aging, biomedical model organisms (particularly lab mice) remain an underutilized tool in resolving outstanding questions regarding social determinants of health and aging, including causality, context-dependence, reversibility, and effective interventions. This status is largely due to the constraints of standard laboratory conditions on animals' social lives. Even when kept in social housing, lab animals rarely experience social and physical environments that approach the richness, variability, and complexity they have evolved to navigate and benefit from. Here we argue that studying biomedical model organisms outside under complex, semi-natural social environments ("re-wilding") allows researchers to capture the methodological benefits of both field studies of wild animals and laboratory studies of model organisms. We review recent efforts to re-wild mice and highlight discoveries that have only been made possible by researchers studying mice under complex, manipulable social environments.

Biomarkers of aging for the identification and evaluation of longevity interventions

With the rapid expansion of aging biology research, the identification and evaluation of longevity interventions in humans have become key goals of this field. Biomarkers of aging are critically important tools in achieving these objectives over realistic time frames. However, the current lack of standards and consensus on the properties of a reliable aging biomarker hinders their further development and validation for clinical applications. Here, we advance a framework for the terminology and characterization of biomarkers of aging, including classification and potential clinical use cases. We discuss validation steps and highlight ongoing challenges as potential areas in need of future research. This framework sets the stage for the development of valid biomarkers of aging and their ultimate utilization in clinical trials and practice.

Region-based epigenetic clock design improves RRBS-based age prediction

Recent studies suggest that epigenetic rejuvenation can be achieved using drugs that mimic calorie restriction and techniques such as reprogramming-induced rejuvenation. To effectively test rejuvenation in vivo, mouse models are the safest alternative. However, we have found that the recent epigenetic clocks developed for mouse reduced-representation bisulphite sequencing (RRBS) data have significantly poor performance when applied to external datasets. We show that the sites captured and the coverage of key CpGs required for age prediction vary greatly between datasets, which likely contributes to the lack of transferability in RRBS clocks. To mitigate these coverage issues in RRBS-based age prediction, we present two novel design strategies that use average methylation over large regions rather than individual CpGs, whereby regions are defined by sliding windows (e.g. 5 kb), or density-based clustering of CpGs. We observe improved correlation and error in our regional blood clocks (RegBCs) compared to published individual-CpG-based techniques when applied to external datasets. The RegBCs are also more robust when applied to low coverage data and detect a negative age acceleration in mice undergoing calorie restriction. Our RegBCs offer a proof of principle that age prediction of RRBS datasets can be improved by accounting for multiple CpGs over a region, which negates the lack of read depth currently hindering individual-CpG-based approaches.

Biomarkers selection and mathematical modeling in biological age estimation

Biological age (BA) is important for clinical monitoring and preventing aging-related disorders and disabilities. Clinical and/or cellular biomarkers are measured and integrated in years using mathematical models to display an individual's BA. To date, there is not yet a single or set of biomarker(s) and technique(s) that is validated as providing the BA that reflects the best real aging status of individuals. Herein, a comprehensive overview of aging biomarkers is provided and the potential of genetic variations as proxy indicators of the aging state is highlighted. A comprehensive overview of BA estimation methods is also provided as well as a discussion of their performances, advantages, limitations, and potential approaches to overcome these limitations.

Wearable-based accelerometer activity profile as digital biomarker of inflammation, biological age, and mortality using hierarchical clustering analysis in NHANES 2011-2014

Repeated disruptions in circadian rhythms are associated with implications for health outcomes and longevity. The utilization of wearable devices in quantifying circadian rhythm to elucidate its connection to longevity, through continuously collected data remains largely unstudied. In this work, we investigate a data-driven segmentation of the 24-h accelerometer activity profiles from wearables as a novel digital biomarker for longevity in 7,297 U.S. adults from the 2011-2014 National Health and Nutrition Examination Survey. Using hierarchical clustering, we identified five clusters and described them as follows: "High activity", "Low activity", "Mild circadian rhythm (CR) disruption", "Severe CR disruption", and "Very low activity". Young adults with extreme CR disturbance are seemingly healthy with few comorbid conditions, but in fact associated with higher white blood cell, neutrophils, and lymphocyte counts (0.05-0.07 log-unit, all p < 0.05) and accelerated biological aging (1.42 years, p < 0.001). Older adults with CR disruption are significantly associated with increased systemic inflammation indexes (0.09-0.12 log-unit, all p < 0.05), biological aging advance (1.28 years, p = 0.021), and all-cause mortality risk (HR = 1.58, p = 0.042). Our findings highlight the importance of circadian alignment on longevity across all ages and suggest that data from wearable accelerometers can help in identifying at-risk populations and personalize treatments for healthier aging.

Phenome-wide analyses identify an association between the parent-of-origin effects dependent methylome and the rate of aging in humans

BACKGROUND

The variation in the rate at which humans age may be rooted in early events acting through the genomic regions that are influenced by such events and subsequently are related to health phenotypes in later life. The parent-of-origin-effect (POE)-regulated methylome includes regions enriched for genetically controlled imprinting effects (the typical type of POE) and regions influenced by environmental effects associated with parents (the atypical POE). This part of the methylome is heavily influenced by early events, making it a potential route connecting early exposures, the epigenome, and aging. We aim to test the association of POE-CpGs with early and later exposures and subsequently with health-related phenotypes and adult aging.

RESULTS

We perform a phenome-wide association analysis for the POE-influenced methylome using GS:SFHS (Ndiscovery = 5087, Nreplication = 4450). We identify and replicate 92 POE-CpG-phenotype associations. Most of the associations are contributed by the POE-CpGs belonging to the atypical class where the most strongly enriched associations are with aging (DNAmTL acceleration), intelligence, and parental (maternal) smoking exposure phenotypes. A proportion of the atypical POE-CpGs form co-methylation networks (modules) which are associated with these phenotypes, with one of the aging-associated modules displaying increased within-module methylation connectivity with age. The atypical POE-CpGs also display high levels of methylation heterogeneity, fast information loss with age, and a strong correlation with CpGs contained within epigenetic clocks.

CONCLUSIONS

These results identify the association between the atypical POE-influenced methylome and aging and provide new evidence for the "early development of origin" hypothesis for aging in humans.

An NIMH Workshop on Non-Affective Psychosis in Midlife and Beyond: Research Agenda on Phenomenology, Clinical Trajectories, Underlying Mechanisms, and Intervention Targets

We present a review of the state of the research in the phenomenology, clinical trajectories, biological mechanisms, aging biomarkers, and treatments for middle-aged and older people with schizophrenia (PwS) discussed at the NIMH sponsored workshop "Non-affective Psychosis in Midlife and Beyond." The growing population of PwS has specific clinical needs that require tailored and mechanistically derived interventions. Differentiating between the effects of aging and disease progression is a key challenge of studying older PwS. This review of the workshop highlights the recent findings in this understudied clinical population and the critical gaps in knowledge and consensus for research priorities. This review showcases the major challenges and opportunities for research to advance clinical care for this growing and understudied population.

Interleukin 6 and Interleukin 1β hypomethylation and overexpression are common features of apical periodontitis: a case-control study with gingival tissue as control

OBJECTIVES

Apical periodontitis is a periradicular tissue disorder that usually arises from infection by microorganisms in the root canal system resulting in local bone resorption. This usually involves the dysregulation of inflammatory mediators, which can be mediated by epigenetic mechanisms. Thus, the objective of this study was to evaluate Interleukin 6 (IL6) and Interleukin 1β (IL1β) and DNA methylation and gene expression levels in apical periodontitis.

METHODS

Gene expression was analyzed in 60 participants using quantitative polymerase chain reaction, while the methylation levels of IL6 and IL1β promoters were analyzed in 72 patients using pyrosequencing. All statistical analyzes were performed using the GraphPad Prism software version 8.0. The p value was considered statistically significant when < 0.05.

RESULTS

A significantly higher IL6 and IL1β expression levels were observed in cases relative to controls (fold-changes of 27.4 and 11.43, respectively, and p < 0.0001). By comparing the same groups, lower promoter methylation levels were observed for both genes in cases (methylation percentage delta relative to controls of -24.57% and -16.02%, respectively, and p < 0.0001). A significant inverse correlation between gene expression and promoter methylation was observed for both IL6 (p = 0.0002) and IL1β (p = 0.001). Neither IL6 expression nor promoter methylation were significantly associated with cases' age, smoking history, alcohol consumption history or sex. For IL1β, alcoholic cases showed lower methylation level relative to non-alcoholic cases (p = 0.01), while females showed higher methylation levels relative to males (p = 0.03).

CONCLUSIONS

Our data suggest a role for DNA methylation in IL6 and IL1β upregulation in apical periodontitis.

Hallmarks and Biomarkers of Skin Senescence: An Updated Review of Skin Senotherapeutics

Aging is a complex process characterized by an ongoing decline in physiological functions, leading to degenerative diseases and an increased probability of death. Cellular senescence has been typically considered as an anti-proliferative process; however, the chronic accumulation of senescent cells contributes to tissue dysfunction and aging. In this review, we discuss some of the most important hallmarks and biomarkers of cellular senescence with a special focus on skin biomarkers, reactive oxygen species (ROS), and senotherapeutic strategies to eliminate or prevent senescence. Although most of them are not exclusive to senescence, the expression of the senescence-associated beta-galactosidase (SA-β-gal) enzyme seems to be the most reliable biomarker for distinguishing senescent cells from those arrested in the cell cycle. The presence of a stable DNA damage response (DDR) and the accumulation of senescence-associated secretory phenotype (SASP) mediators and ROS are the most representative hallmarks for senescence. Senotherapeutics based on natural compounds such as quercetin, naringenin, and apigenin have shown promising results regarding SASP reduction. These compounds seem to prevent the accumulation of senescent cells, most likely through the inhibition of pro-survival signaling pathways. Although studies are still required to verify their short- and long-term effects, these therapies may be an effective strategy for skin aging.

Machine Learning Approximations to Predict Epigenetic Age Acceleration in Stroke Patients

Age acceleration (Age-A) is a useful tool that is able to predict a broad range of health outcomes. It is necessary to determine DNA methylation levels to estimate it, and it is known that Age-A is influenced by environmental, lifestyle, and vascular risk factors (VRF). The aim of this study is to estimate the contribution of these easily measurable factors to Age-A in patients with cerebrovascular disease (CVD), using different machine learning (ML) approximations, and try to find a more accessible model able to predict Age-A. We studied a CVD cohort of 952 patients with information about VRF, lifestyle habits, and target organ damage. We estimated Age-A using Hannum's epigenetic clock, and trained six different models to predict Age-A: a conventional linear regression model, four ML models (elastic net regression (EN), K-Nearest neighbors, random forest, and support vector machine models), and one deep learning approximation (multilayer perceptron (MLP) model). The best-performing models were EN and MLP; although, the predictive capability was modest (R² 0.358 and 0.378, respectively). In conclusion, our results support the influence of these factors on Age-A; although, they were not enough to explain most of its variability.

Phenome-wide analysis identifies parent-of-origin effects on the human methylome associated with changes in the rate of aging

Variation in the rate at which humans age may be rooted in early life events acting through genomic regions that are influenced by such events and subsequently are related to health phenotypes in later life. The parent-of-origin-effect (POE)-regulated methylome includes regions either enriched for genetically controlled imprinting effects (the typical type of POE) or atypical POE introduced by environmental effects associated with parents. This part of the methylome is heavily influenced by early life events, making it a potential route connecting early environmental exposures, the epigenome and the rate of aging. Here, we aim to test the association of POE-influenced methylation of CpG dinucleotides (POE-CpG sites) with early and later environmental exposures and subsequently with health-related phenotypes and adult aging phenotypes. We do this by performing phenome-wide association analyses of the POE-influenced methylome using a large family-based population cohort (GS:SFHS, Ndiscovery=5,087, Nreplication=4,450). At the single CpG level, 92 associations of POE-CpGs with phenotypic variation were identified and replicated. Most of the associations were contributed by POE-CpGs belonging to the atypical class and the most strongly enriched associations were with aging (DNAmTL acceleration), intelligence and parental (maternal) smoking exposure phenotypes. We further found that a proportion of the atypical-POE-CpGs formed co-methylation networks (modules) which are associated with these phenotypes, with one of the aging-associated modules displaying increased internal module connectivity (strength of methylation correlation across constituent CpGs) with age. Atypical POE-CpGs also displayed high levels of methylation heterogeneity and epigenetic drift (i.e. information loss with age) and a strong correlation with CpGs contained within epigenetic clocks. These results identified associations between the atypical-POE-influenced methylome and aging and provided new evidence for the "early development of origin" hypothesis for aging in humans.

Machine learning approaches to predict age from accelerometer records of physical activity at biobank scale

Physical activity improves quality of life and protects against age-related diseases. With age, physical activity tends to decrease, increasing vulnerability to disease in the elderly. In the following, we trained a neural network to predict age from 115,456 one week-long 100Hz wrist accelerometer recordings from the UK Biobank (mean absolute error = 3.7±0.2 years), using a variety of data structures to capture the complexity of real-world activity. We achieved this performance by preprocessing the raw frequency data as 2,271 scalar features, 113 time series, and four images. We defined accelerated aging for a participant as being predicted older than one's actual age and identified both genetic and environmental exposure factors associated with the new phenotype. We performed a genome wide association on the accelerated aging phenotypes to estimate its heritability (h_g2 = 12.3±0.9%) and identified ten single nucleotide polymorphisms in close proximity to genes in a histone and olfactory cluster on chromosome six (e.g HIST1H1C, OR5V1). Similarly, we identified biomarkers (e.g blood pressure), clinical phenotypes (e.g chest pain), diseases (e.g hypertension), environmental (e.g smoking), and socioeconomic (e.g income and education) variables associated with accelerated aging. Physical activity-derived biological age is a complex phenotype associated with both genetic and non-genetic factors.

Introducing an adolescent cognitive maturity index

Children show substantial variation in the rate of physical, cognitive, and social maturation as they traverse adolescence and enter adulthood. Differences in developmental paths are thought to underlie individual differences in later life outcomes, however, there remains a lack of consensus on the normative trajectory of cognitive maturation in adolescence. To address this problem, we derive a Cognitive Maturity Index (CMI), to estimate the difference between chronological and cognitive age predicted with latent factor estimates of inhibitory control, risky decision-making and emotional processing measured with standard neuropsychological instruments. One hundred and forty-one children from the Adolescent Development Study (ADS) were followed longitudinally across three time points from ages 11-14, 13-16, and 14-18. Age prediction with latent factor estimates of cognitive skills approximated age within ±10 months (r = 0.71). Males in advanced puberty displayed lower cognitive maturity relative to peers of the same age; manifesting as weaker inhibitory control, greater risk-taking, desensitization to negative affect, and poor recognition of positive affect.

Causes and consequences of telomere lengthening in a wild vertebrate population

Telomeres have been advocated to be important markers of biological age in evolutionary and ecological studies. Telomeres usually shorten with age and shortening is frequently associated with environmental stressors and increased subsequent mortality. Telomere lengthening - an apparent increase in telomere length between repeated samples from the same individual - also occurs. However, the exact circumstances, and consequences, of telomere lengthening are poorly understood. Using longitudinal data from the Seychelles warbler (Acrocephalus sechellensis), we tested whether telomere lengthening - which occurs in adults of this species - is associated with specific stressors (reproductive effort, food availability, malarial infection and cooperative breeding) and predicts subsequent survival. In females, telomere shortening was observed under greater stress (i.e., low food availability, malaria infection), while telomere lengthening was observed in females experiencing lower stress (i.e., high food availability, assisted by helpers, without malaria). The telomere dynamics of males were not associated with the key stressors tested. These results indicate that, at least for females, telomere lengthening occurs in circumstances more conducive to self-maintenance. Importantly, both females and males with lengthened telomeres had improved subsequent survival relative to individuals that displayed unchanged, or shortened, telomeres - indicating that telomere lengthening is associated with individual fitness. These results demonstrate that telomere dynamics are bidirectionally responsive to the level of stress that an individual faces, but may poorly reflect the accumulation of stress over an individuals lifetime.

Measuring biological age using omics data

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

An evaluation of aging measures: from biomarkers to clocks

With the increasing number of aged population and growing burden of healthy aging demands, a rational standard for evaluation aging is in urgent need. The advancement of medical testing technology and the prospering of artificial intelligence make it possible to evaluate the biological status of aging from a more comprehensive view. In this review, we introduced common aging biomarkers and concluded several famous aging clocks. Aging biomarkers reflect changes in the organism at a molecular or cellular level over time while aging clocks tend to be more of a generalization of the overall state of the organism. We expect to construct a framework for aging evaluation measurement from both micro and macro perspectives. Especially, population-specific aging clocks and multi-omics aging clocks may better fit the demands to evaluate aging in a comprehensive and multidimensional manner and make a detailed classification to represent different aging rates at tissue/organ levels. This framework will promisingly provide a crucial basis for disease diagnosis and intervention assessment in geroscience.

Familial aggregation of the aging process: biological age measured in young adult offspring as a predictor of parental mortality

Measures of biological age (BA) integrate information across organ systems to quantify "biological aging," i.e., inter-individual differences in aging-related health decline. While longevity and lifespan aggregate in families, reflecting transmission of genes and environments across generations, little is known about intergenerational continuity of biological aging or the extent to which this continuity may be modified by environmental factors. Using data from the Jerusalem Perinatal Study (JPS), we tested if differences in offspring BA were related to mortality in their parents. We measured BA using biomarker data collected from 1473 offspring during clinical exams in 2007-2009, at age 32 ± 1.1. Parental mortality was obtained from population registry data for the years 2004-2016. We fitted parametric survival models to investigate the associations between offspring BA and parental all-cause and cause-specific mortality. We explored potential differences in these relationships by socioeconomic position (SEP) and offspring sex. Participants' BAs widely varied (SD = 6.95). Among those measured to be biologically older, parents had increased all-cause mortality (HR = 1.10, 95% CI: 1.08, 1.13), diabetes mortality (HR = 1.19, 95% CI: 1.08, 1.30), and cancer mortality (HR = 1.07, 95% CI: 1.02, 1.13). The association with all-cause mortality was stronger for families with low compared with high SEP (P_interaction = 0.04) and for daughters as compared to sons (P_interaction < 0.001). Using a clinical-biomarker-based BA estimate, observable by young adulthood prior to the onset of aging-related diseases, we demonstrate intergenerational continuity of the aging process. Furthermore, variation in this familial aggregation according to household socioeconomic position (SEP) at offspring birth and between families of sons and daughters proposes that the environment alters individuals' aging trajectory set by their parents.

Protein intake and physical function in older adults: A systematic review and meta-analysis

BACKGROUND

The present study explored cross-sectional and longitudinal associations between protein intake and physical function in older adults.

METHODS

We conducted a systematic review and meta-analysis of cross-sectional and longitudinal studies that investigated the association between protein intake and measures of physical function in older adults. Cross-sectional, case-control, and longitudinal cohort studies that investigated the association between protein intake and physical function as a primary or secondary outcome in people aged 60 + years were included. Studies published in languages other than English, Italian, Portuguese, or Spanish were excluded. Studies were retrieved from MEDLINE, SCOPUS, EMBASE, CINAHL, AgeLine, and Food Science Source databases through January 31, 2022. A pooled effect size was calculated based on standard mean differences (SMD), MD, log odds ratio (OR) and Z-score..

RESULTS

Twenty-two cross-sectional studies examined a total of 11,332 community-dwellers, hospitalized older adults, and elite senior athletes with a mean age of approximately 75 years. The pooled analysis indicated that a protein intake higher than the recommended dietary allowance (RDA) was significantly associated with higher Short Physical Performance Battery (SPPB) scores (SMD: 0.63, 95% CI: 0.27, 0.99, P-value: 0.0006), faster walking speed, greater lower-limb (SMD: 0.22, 95% CI: 0.04, 0.40, P-value: 0.02) and isometric handgrip strength (Z-score: 0.087, 95% CI: 0.046-0.128, P-value: 0.0001), and better balance (SMD: 0.33, 95% CI: 0.05, 0.62, P-value: 0.02). Nine longitudinal studies investigated 12,424 community-dwelling and native older adults with a mean age of approximately 85 years. A protein intake higher than the current RDA was not associated with lower decline in either isometric handgrip strength (logOR: 0.99, 95% CI: 0.97-1.02, P-value= 0.67) or walking speed (logOR: 0.92, 95% CI: 0.77-1.10, P-value= 0.35).

CONCLUSIONS

A protein intake higher than the RDA is cross-sectionally associated with better physical performance and greater muscle strength in older adults. However, a high consumption of proteins does not seem to prevent physical function decline over time.

Evaluation of different computational methods for DNA methylation-based biological age

In recent years there has been a widespread interest in researching biomarkers of aging that could predict physiological vulnerability better than chronological age. Aging, in fact, is one of the most relevant risk factors for a wide range of maladies, and molecular surrogates of this phenotype could enable better patients stratification. Among the most promising of such biomarkers is DNA methylation-based biological age. Given the potential and variety of computational implementations (epigenetic clocks), we here present a systematic review of such clocks. Furthermore, we provide a large-scale performance comparison across different tissues and diseases in terms of age prediction accuracy and age acceleration, a measure of deviance from physiology. Our analysis offers both a state-of-the-art overview of the computational techniques developed so far and a heterogeneous picture of performances, which can be helpful in orienting future research.

The Achilles' heel of cancer survivors: fundamentals of accelerated cellular senescence

Recent improvements in cancer treatment have increased the lifespan of pediatric and adult cancer survivors. However, cancer treatments accelerate aging in survivors, which manifests clinically as the premature onset of chronic diseases, such as endocrinopathies, osteoporosis, cardiac dysfunction, subsequent cancers, and geriatric syndromes of frailty, among others. Therefore, cancer treatment-induced early aging accounts for significant morbidity, mortality, and health expenditures among cancer survivors. One major mechanism driving this accelerated aging is cellular senescence; cancer treatments induce cellular senescence in tumor cells and in normal, nontumor tissue, thereby helping mediate the onset of several chronic diseases. Studies on clinical monitoring and therapeutic targeting of cellular senescence have made considerable progress in recent years. Large-scale clinical trials are currently evaluating senotherapeutic drugs, which inhibit or eliminate senescent cells to ameliorate cancer treatment-related aging. In this article, we survey the recent literature on phenotypes and mechanisms of aging in cancer survivors and provide an up-to-date review of the major preclinical and translational evidence on cellular senescence as a mechanism of accelerated aging in cancer survivors, as well as insight into the potential of senotherapeutic drugs. However, only with time will the clinical effect of senotherapies on cancer survivors be visible.

A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study

BACKGROUND

Individual differences in the rate of aging and susceptibility to disease are not accounted for by chronological age alone. These individual differences are better explained by biological age, which may be estimated by biomarker prediction models. In the light of the aging demographics of the global population and the increase in lifestyle-related morbidities, it is interesting to invent a new biological age model to be used for health promotion.

OBJECTIVE

This study aims to develop a model that estimates biological age based on physiological biomarkers of healthy aging.

METHODS

Carefully selected physiological variables from a healthy study population of 100 women and men were used as biomarkers to establish an estimate of biological age. Principal component analysis was applied to the biomarkers and the first principal component was used to define the algorithm estimating biological age.

RESULTS

The first principal component accounted for 31% in women and 25% in men of the total variance in the biological age model combining mean arterial pressure, glycated hemoglobin, waist circumference, forced expiratory volume in 1 second, maximal oxygen consumption, adiponectin, high-density lipoprotein, total cholesterol, and soluble urokinase-type plasminogen activator receptor. The correlation between the corrected biological age and chronological age was r=0.86 (P<.001) and r=0.81 (P<.001) for women and men, respectively, and the agreement was high and unbiased. No difference was found between mean chronological age and mean biological age, and the slope of the regression line was near 1 for both sexes.

CONCLUSIONS

Estimating biological age from these 9 biomarkers of aging can be used to assess general health compared with the healthy aging trajectory. This may be useful to evaluate health interventions and as an aid to enhance awareness of individual health risks and behavior when deviating from this trajectory.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03680768; https://clinicaltrials.gov/ct2/show/NCT03680768.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/19209.

Pan-tissue methylation aging clock: Recalibrated and a method to analyze and interpret the selected features

The abundance of the biological data and the rapid evolution of the newer machine learning technologies have increased the epigenetics research in the last decade. This has enhanced the ability to measure the biological age of humans and different organisms via their omics data. DNA methylation array data are commonly used in the prediction of methylation age. Horvath clock has been adopted in various aging studies as a DNA methylation age predicting clock due to its higher accuracy and multi tissue prediction potential. In the current study, we have developed a pan tissue methylation-aging clock by using the publicly available illumina 450k and EPIC array methylation datasets. In doing that, we developed a highly accurate epigenetic clock, which predicts the age of multiple tissues with higher accuracy. We have also analyzed the selected probes for their biological relevance. Upon analyzing the selected features further, we found out evidences, which support the Antagonistic pleiotropy theory of aging.

Prospective Study of Ageing Trajectories in the European DO-HEALTH Study

INTRODUCTION

Ageing trajectories range from delayed ageing with extended health to accelerated ageing, with an increased risk of frailty. We evaluated the prevalence and prospective change between health states among community-dwelling European older adults.

METHODS

This prospective study is a secondary analysis of DO-HEALTH, a randomized trial that included adults aged 70 years and older across 5 European countries. Healthy agers (HA) fulfilled the Nurses' Health Study healthy ageing criteria and accelerated agers were non-HA being at least pre-frail according to the Fried frailty criteria. We assessed the proportion of participants changing between health states over 4 assessments and evaluated the odds of changing to a more favourable category. To increase reliability and avoid regression to the mean, we averaged the first 2 years and compared them to the average of the last 2 years.

RESULTS

Of 2,157 participants, 12.4% were excluded for meeting both healthy ageing and pre-frailty criteria simultaneously. Among the remaining 1,889 participants (mean age 75.1 years, 60.9% female), 23.1% were initially HA, 44.4% were non-HA but not pre-frail, and 32.6% were pre-frail or frail. Subsequently, 65.3% remained in the same health state, 12.0% improved to a healthier state, and 22.8% progressed to a less advantageous state. After adjusting for sex, study centre, treatment, and body mass index, each year of age was associated with 6% lower odds of improving health states. Women had 35% higher odds than men of following a disadvantageous trajectory.

CONCLUSION

We observed dynamic trajectories of ageing where transitioning to a healthier state became less likely with advancing age and among women.

Aging Challenges. Perceived Age – a New Predictor of Longevity?

The ageing process is accompanied by the manifestation of many characteristics, so-called biomarkers, which can be quantified and used to assess a patient's health status. One of these signs is the progressive decline of a human's facial look, which is described by the concept of 'perceived age'. Facial aging is the most important parameter of perceived age. However, over the years, researchers have identified risk factors that affect the facial skin, including smoking, systematic consumption of alcoholic beverages, overweight or underweight, environmental conditions, and psychosocial determinants. The influence of psychological state on the appearance and life prognosis is shown. The authors presented data from the international literature on the study of perceived age. The frontiers of using perceived age as a biomarker of aging were Danish scientists who developed the main methodological approaches to determine this indicator. One such methodology used in population studies has been the clinical technique of assessing perceived age through photography. The review presents this methodology in detail, with its advantages and modifications. The authors conclude that the measurement of an individual's perceived age can serve not only as a prognostic indicator,  but also over time can become a   useful marker of the effectiveness of various treatments. Until now perceived age has hardly been studied in population studies, the authors presented data from the works of V.A.  Labunskaya, G.V.  Serikov,  T.A.  Shkurko who develop the direction related to psychology of perceived age  and in their studies use social-psychological approaches of appearance assessment.

Implications of Sphingolipids on Aging and Age-Related Diseases

Aging is a process leading to a progressive loss of physiological integrity and homeostasis, and a primary risk factor for many late-onset chronic diseases. The mechanisms underlying aging have long piqued the curiosity of scientists. However, the idea that aging is a biological process susceptible to genetic manipulation was not well established until the discovery that the inhibition of insulin/IGF-1 signaling extended the lifespan of C. elegans. Although aging is a complex multisystem process, López-Otín et al. described aging in reference to nine hallmarks of aging. These nine hallmarks include: genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and altered intercellular communication. Due to recent advances in lipidomic, investigation into the role of lipids in biological aging has intensified, particularly the role of sphingolipids (SL). SLs are a diverse group of lipids originating from the Endoplasmic Reticulum (ER) and can be modified to create a vastly diverse group of bioactive metabolites that regulate almost every major cellular process, including cell cycle regulation, senescence, proliferation, and apoptosis. Although SL biology reaches all nine hallmarks of aging, its contribution to each hallmark is disproportionate. In this review, we will discuss in detail the major contributions of SLs to the hallmarks of aging and age-related diseases while also summarizing the importance of their other minor but integral contributions.

Quantifying self-rated age

Chronological age, in conjunction with population life tables, is widely used for estimating future life expectancy. The aims of this study are to estimate a subjective ageing indicator, namely self-rated age, and to evaluate its concurrent validity in comparison with other age indicators: subjective survival probabilities, subjective age, and biological age. We use data from the Wave 6 of the Survey of Health, Ageing and Retirement in Europe, Wave 12 of the Health and Retirement Study in the United States, and life tables from the Human Mortality Database. For the statistical analysis we use multinomial regression models. Our results indicate that health status and frequency of physical activities imply similar patterns of self- rated age, subjective survival probabilities, subjective age, and biological age. However, the impact of cognitive function differs by geographical region. Self-rated age can be interpreted as a subjective adjustment that better reflects the ageing 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.

A catalogue of omics biological ageing clocks reveals substantial commonality and associations with disease risk

Biological age (BA), a measure of functional capacity and prognostic of health outcomes that discriminates between individuals of the same chronological age (chronAge), has been estimated using a variety of biomarkers. Previous comparative studies have mainly used epigenetic models (clocks), we use ~1000 participants to compare fifteen omics ageing clocks, with correlations of 0.21-0.97 with chronAge, even with substantial sub-setting of biomarkers. These clocks track common aspects of ageing with 95% of the variance in chronAge being shared among clocks. The difference between BA and chronAge - omics clock age acceleration (OCAA) - often associates with health measures. One year’s OCAA typically has the same effect on risk factors/10-year disease incidence as 0.09/0.25 years of chronAge. Epigenetic and IgG glycomics clocks appeared to track generalised ageing while others capture specific risks. We conclude BA is measurable and prognostic and that future work should prioritise health outcomes over chronAge.

Telomere length as a biomarker of aging and diseases

As research related to healthspan and lifespan has become a hot topic, the necessity for a reliable and practical biomarker of aging (BoA), which can provide information about mortality and morbidity risk, along with remaining life expectancy, has increased. The chromosome terminus non-coding protective structure that prevents genomic instability is called a telomere. The continual shortening of telomeres, which affects their structure as well as function, is a hallmark of agedness. The aforementioned process is a potential cause of age-related diseases (ARDs), leading to a bad prognosis and a low survival rate, which compromise health and longevity. Hence, studies scrutinizing the BoAs often include telomere length (TL) as a prospective candidate. The results of these studies suggest that TL measurement can only provide an approximate appraisal of the aging rate, and its implementation into clinical practice and routine use as a BoA has many limitations and challenges. Nevertheless, measuring TL while determining other biomarkers can be used to assess biological age. This review focuses on the importance of telomeres in health, senescence, and diseases, as well as on summarizing the results and conclusions of previous studies evaluating TL as a potential BoA.

Predicting physiological aging rates from a range of quantitative traits using machine learning

It is widely thought that individuals age at different rates. A method that measures “physiological age” or physiological aging rate independent of chronological age could therefore help elucidate mechanisms of aging and inform an individual’s risk of morbidity and mortality. Here we present machine learning frameworks for inferring individual physiological age from a broad range of biochemical and physiological traits including blood phenotypes (e.g., high-density lipoprotein), cardiovascular functions (e.g., pulse wave velocity) and psychological traits (e.g., neuroticism) as main groups in two population cohorts SardiNIA (~6,100 participants) and InCHIANTI (~1,400 participants). The inferred physiological age was highly correlated with chronological age (R2 > 0.8). We further defined an individual’s physiological aging rate (PAR) as the ratio of the predicted physiological age to the chronological age. Notably, PAR was a significant predictor of survival, indicating an effect of aging rate on mortality. Our trait-based PAR was correlated with DNA methylation-based epigenetic aging score (r = 0.6), suggesting that both scores capture a common aging process. PAR was also substantially heritable (h2~0.3), and a subsequent genome-wide association study of PAR identified significant associations with two genetic loci, one of which is implicated in telomerase activity. Our findings support PAR as a proxy for an underlying whole-body aging mechanism. PAR may thus be useful to evaluate the efficacy of treatments that target aging-related deficits and controllable epidemiological factors.

Biomarkers of geroprotection and cardiovascular health: An overview of omics studies and established clinical biomarkers in the context of diet

The slowdown, inhibition, or reversal of age-related decline (as a composite of disease, dysfunction, and, ultimately, death) by diet or natural compounds can be defined as dietary geroprotection. While there is no single reliable biomarker to judge the effects of dietary geroprotection, biomarker signatures based on omics (epigenetics, gene expression, microbiome composition) are promising candidates. Recently, omic biomarkers started to supplement established clinical ones such as lipid profiles and inflammatory cytokines. In this review, we focus on human data. We first summarize the current take on genetic biomarkers based on epidemiological studies. However, most of the remaining biomarkers that we describe, whether omics-based or clinical, are related to intervention studies. Then, because of their promising potential in the context of dietary geroprotection, we focus on the effects of berry-based interventions, which up to now have been mostly described employing clinical markers. We provide an aggregation and tabulation of all the recent systematic reviews and meta-analyses that we could find related to this topic. Finally, we present evidence for the importance of the "nutribiography," that is, the influence that an individual's history of diet and natural compound consumption can have on the effects of dietary geroprotection.

Periodontitis and Accelerated Biological Aging: A Geroscience Approach

As the whole world is epidemically aging, the burden of periodontitis and tooth loss is becoming a major health concern. Growing meta-epidemiological data implicate chronic systemic inflammation/infection due to periodontitis as an independent risk factor for aging-related diseases and mortality. However, because people age differently, chronological age is not a reliable marker of an individual's functional status. Recent advances in geroscience have shown that various biomarker signatures of biological aging are longitudinally associated with declined physical function, morbidity, and mortality due to major age-related diseases, including periodontitis. Here, we emphasize novel research developments bidirectionally linking periodontitis to accelerated biological aging. Using a composite biomarker age estimator, a striking increase in periodontitis and tooth loss was observed in subjects whose biological age at baseline was higher than their chronological age. Moreover, significantly shortened telomeres were encountered in populations affected by severe periodontitis. Second, we elucidate the cellular and molecular pillars of the aging process at the periodontal level. Accumulating evidence suggests that cellular senescence, stem cell exhaustion, and immunoaging are hallmarks of biological aging implicated in the impairment of periodontal homeostasis and the pathophysiology of periodontitis. Indeed, persistent bacteria-derived lipopolysaccharide stimulation influences cellular senescence in osteocytes, driving alveolar bone resorption. Moreover, inflammaging status induced by chronic hyperglycemia elevates the burden of senescent cells in gingival tissues, impairing their barrier function. Lastly, we reviewed a recent breakthrough in senotherapy to directly target the mechanisms of aging at the periodontal level. Physical exercise and intermittent fasting, together with natural compounds, senolytic drugs, and cell therapy, are increasingly being evaluated to rejuvenate the oral cavity. Following these innovations in geroscience, further advancements could provide oral clinicians the chance to intercept biological aging when still "subclinical" and set interventions for halting or delaying the trajectory toward aging-related diseases while patients are still chronologically young.

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

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