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Extracellular vesicles (EVs) are implicated in inter-organ communication, which becomes particularly relevant during aging and exercise. DNA methylation-based aging clocks reflect lifestyle and environmental factors, while regular exercise is known to induce adaptive responses, including epigenetic adaptations. Twenty individuals with High-fitness (aged 57.7 ± 9.8 years) and twenty Medium-Low-fitness (aged 57.5 ± 9.7 years) subjects provided blood samples. EVs were isolated from the samples using a size exclusion chromatography (SEC)-based method, and their protein content was analyzed by mass spectrometry (MS). Acceleration of the biological age estimator DNAmFitAge (AgeAccelFit) was associated with the protein cargo of EVs, whereas PhenoAge and GrimAge acceleration did not show a significant relationship. This finding suggests that the epigenetic aging-modulating role of exercise may involve inter-organ communication via EVs. Set Enrichment Analysis was performed to identify enriched Gene Ontology (GO) terms for sets of proteins that were either correlated with AgeAccelFit or detected exclusively in individuals with high levels of aerobic fitness. The protein cargo of EVs further suggests that inter-organ communication influences inflammation, the immune system, cellular repair, adhesion, metabolism and coagulation. Our findings help to understand the preventive role of exercise, which could be mediated in part by EVs., Competing Interests: Declarations. Conflict of interest: The authors have no competing interests to declare that are relevant to the content of this article. Ethical approval: Ethical approval was obtained from the Hungarian Ministry of Human Capacities (EMMI) in agreement with the Hungarian Scientific and Research Ethics Committee (ETT TUKEB, 25167–6/2019 EUIG). Consent to participate: Ethical approval was obtained from the Hungarian Ministry of Human Capacities (EMMI) in agreement with the Hungarian Scientific and Research Ethics Committee (ETT TUKEB, 25167–6/2019 EUIG)., (© 2025. The Author(s).)

Usage of the phrase "biological age" has picked up considerably since the advent of aging clocks and it has become commonplace to describe an aging clock's output as biological age. In contrast to this labeling, biological age is also often depicted as a more abstract concept that helps explain how individuals are aging internally, externally, and functionally. Given that the bulk of molecular aging is tissue-specific and aging itself is a remarkably complex, multifarious process, it is unsurprising that most surveyed scientists agree that aging cannot be quantified via a single metric. We share this sentiment and argue that, just like it would not be reasonable to assume that an individual with an ideal grip strength, VO 2 max, or any other aging biomarker is biologically young, we should be careful not to conflate an aging clock with whole-body biological aging. To address this, we recommend that researchers describe the output of an aging clock based on the type of input data used or the name of the clock itself. Epigenetic aging clocks produce epigenetic age, transcriptomic aging clocks produce transcriptomic age, and so forth. If a clock has a unique name, such as our recently developed epigenetic aging clock CheekAge, the name of the clock can double as the output. As a compromise solution, aging biomarkers can be described as indicators of biological age. We feel that these recommendations will help scientists and the public differentiate between aging biomarkers and the much more elusive concept of biological age., (© 2024 Tally Health. Aging Cell published by Anatomical Society and John Wiley & Sons Ltd.)

In a rigorous 40-month study, we evaluated the geroprotective effects of metformin on adult male cynomolgus monkeys, addressing a gap in primate aging research. The study encompassed a comprehensive suite of physiological, imaging, histological, and molecular evaluations, substantiating metformin's influence on delaying age-related phenotypes at the organismal level. Specifically, we leveraged pan-tissue transcriptomics, DNA methylomics, plasma proteomics, and metabolomics to develop innovative monkey aging clocks and applied these to gauge metformin's effects on aging. The results highlighted a significant slowing of aging indicators, notably a roughly 6-year regression in brain aging. Metformin exerts a substantial neuroprotective effect, preserving brain structure and enhancing cognitive ability. The geroprotective effects on primate neurons were partially mediated by the activation of Nrf2, a transcription factor with anti-oxidative capabilities. Our research pioneers the systemic reduction of multi-dimensional biological age in primates through metformin, paving the way for advancing pharmaceutical strategies against human aging., Competing Interests: Declaration of interests J.C.I.B., S.H., C.R.E., P.R., and A.H. are employees of Altos Labs. S.H. is a founder of the non-profit Epigenetic Clock Development Foundation, which has licensed several of his patents from UC Regents and distributes the mammalian methylation array., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The immune system undergoes progressive functional remodeling from neonatal stages to old age. Therefore, understanding how aging shapes immune cell function is vital for precise treatment of patients at different life stages. Here, we constructed the first transcriptomic atlas of immune cells encompassing human lifespan, ranging from newborns to supercentenarians, and comprehensively examined gene expression signatures involving cell signaling, metabolism, differentiation, and functions in all cell types to investigate immune aging changes. By comparing immune cell composition among different age groups, HLA highly expressing NK cells and CD83 positive B cells were identified with high percentages exclusively in the teenager (Tg) group, whereas unknown_T cells were exclusively enriched in the supercentenarian (Sc) group. Notably, we found that the biological age (BA) of pediatric COVID-19 patients with multisystem inflammatory syndrome accelerated aging according to their chronological age (CA). Besides, we proved that inflammatory shift- myeloid abundance and signature correlate with the progression of complications in Kawasaki disease (KD). The shift- myeloid signature was also found to be associated with KD treatment resistance, and effective therapies improve treatment outcomes by reducing this signaling. Finally, based on those age-related immune cell compositions, we developed a novel BA prediction model PHARE ( https://xiazlab.org/phare/ ), which can apply to both scRNA-seq and bulk RNA-seq data. Using this model, we found patients with coronary artery disease (CAD) also exhibit accelerated aging compared to healthy individuals. Overall, our study revealed changes in immune cell proportions and function associated with aging, both in health and disease, and provided a novel tool for successfully capturing features that accelerate or delay aging., (© 2024. The Author(s).)

Epigenetic clocks are accurate predictors of human chronological age based on the analysis of DNA methylation (DNAm) at specific CpG sites. However, a systematic comparison between DNA methylation data and other omics datasets has not yet been performed. Moreover, available DNAm age predictors are based on datasets with limited ethnic representation. To address these knowledge gaps, we generated and analyzed DNA methylation datasets from two independent Chinese cohorts, revealing age-related DNAm changes. Additionally, a DNA methylation aging clock (iCAS-DNAmAge) and a group of DNAm-based multi-modal clocks for Chinese individuals were developed, with most of them demonstrating strong predictive capabilities for chronological age. The clocks were further employed to predict factors influencing aging rates. The DNAm aging clock, derived from multi-modal aging features (compositeAge-DNAmAge), exhibited a close association with multi-omics changes, lifestyles, and disease status, underscoring its robust potential for precise biological age assessment. Our findings offer novel insights into the regulatory mechanism of age-related DNAm changes and extend the application of the DNAm clock for measuring biological age and aging pace, providing the basis for evaluating aging intervention strategies., (© The Author(s) 2024. Published by Oxford University Press on behalf of Higher Education Press.)

Although human core body temperature is known to decrease with age, the age dependency of facial temperature and its potential to indicate aging rate or aging-related diseases remains uncertain. Here, we collected thermal facial images of 2,811 Han Chinese individuals 20-90 years old, developed the ThermoFace method to automatically process and analyze images, and then generated thermal age and disease prediction models. The ThermoFace deep learning model for thermal facial age has a mean absolute deviation of about 5 years in cross-validation and 5.18 years in an independent cohort. The difference between predicted and chronological age is highly associated with metabolic parameters, sleep time, and gene expression pathways like DNA repair, lipolysis, and ATPase in the blood transcriptome, and it is modifiable by exercise. Consistently, ThermoFace disease predictors forecast metabolic diseases like fatty liver with high accuracy (AUC > 0.80), with predicted disease probability correlated with metabolic parameters., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Epigenetic aging clocks are computational models that predict age using DNA methylation information. Initially, first-generation clocks were developed to make predictions using CpGs that change with age. Over time, next-generation clocks were created using CpGs that relate to both age and health. Since existing next-generation clocks were constructed in blood, we sought to develop a next-generation clock optimized for prediction in cheek swabs, which are non-invasive and easy to collect. To do this, we collected MethylationEPIC data as well as lifestyle and health information from 8045 diverse adults. Using a novel simulated annealing approach that allowed us to incorporate lifestyle and health factors into training as well as a combination of CpG filtering, CpG clustering, and clock ensembling, we constructed CheekAge, an epigenetic aging clock that has a strong correlation with age, displays high test-retest reproducibility across replicates, and significantly associates with a plethora of lifestyle and health factors, such as BMI, smoking status, and alcohol intake. We validated CheekAge in an internal dataset and multiple publicly available datasets, including samples from patients with progeria or meningioma. In addition to exploring the underlying biology of the data and clock, we provide a free online tool that allows users to mine our methylomic data and predict epigenetic age., (© 2024. The Author(s).)

Epigenetic clocks can measure aging and predict the incidence of diseases and mortality. Higher levels of physical fitness are associated with a slower aging process and a healthier lifespan. Microbiome alterations occur in various diseases and during the aging process, yet their relation to epigenetic clocks is not explored. To fill this gap, we collected metagenomic (from stool), epigenetic (from blood), and exercise-related data from physically active individuals and, by applying epigenetic clocks, we examined the relationship between gut flora, blood-based epigenetic age acceleration, and physical fitness. We revealed that an increased entropy in the gut microbiome of physically active middle-aged/old individuals is associated with accelerated epigenetic aging, decreased fitness, or impaired health status. We also observed that a slower epigenetic aging and higher fitness level can be linked to altered abundance of some bacterial species often linked to anti-inflammatory effects. Overall our data suggest that alterations in the microbiome can be associated with epigenetic age acceleration and physical fitness., (© 2024 The Authors. Aging Cell published by Anatomical Society and John Wiley & Sons Ltd.)

Computational models that measure biological age and aging rate regardless of chronological age are called aging clocks. The underlying counting mechanisms of the intrinsic timers of these clocks are still unclear. Molecular mediators and determinants of aging rate point to the key roles of DNA damage, epigenetic drift, and inflammation. Persistent DNA damage leads to cellular senescence and the senescence-associated secretory phenotype (SASP), which induces cytotoxic immune cell infiltration; this further induces DNA damage through reactive oxygen and nitrogen species (RONS). I discuss the possibility that DNA damage (or the response to it, including epigenetic changes) is the fundamental counting unit of cell cycles and cellular senescence, that ultimately accounts for cell composition changes and functional decline in tissues, as well as the key intervention points., Competing Interests: Declaration of interests The authors have no interests to declare., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The degenerative processes that occur during aging increase the risk of disease and impaired health. Meanwhile, interventions that target aging to promote healthy longevity are gaining interest, both academically and in the public. While nutritional and physical interventions exist, efficacy is often difficult to determine. It is therefore imperative that an aging score measuring the biological aging process is available to the wider public. However, simple, interpret, and accessible biological aging scores are lacking. Here, we developed PhysiAge, a physiological aging score based on five accessible parameters that have influence on or reflect the aging process: (1) average daily step count, (2) blood glucose, (3) systolic blood pressure, (4) sex, and (5) age. Here, we found that compared to calendar age alone, PhysiAge better predicts mortality, as well as established muscle aging markers such as decrease in NAD + levels, increase in oxidative stress, and decline in physical functioning. In order to demonstrate the usefulness of PhysiAge in identifying relevant factors associated with decelerated aging, we calculated PhysiAges for a cohort of aged individuals and obtained mass spectrometry-based blood plasma metabolomic profiles for each individual. Here, we identified a metabolic signature of decelerated aging, which included components of the TCA cycle, including malate, citrate, and isocitrate. Higher abundance of these metabolites was associated with decelerated aging, in line with supplementation studies in model organisms. PhysiAge represents an accessible way for people to track and intervene in their aging trajectories, and identifies a metabolic signature of decelerated aging in human blood plasma, which can be further studied for its causal involvement in human aging., (© 2023. The Author(s).)

Hutchinson-Gilford progeria syndrome (HGPS) is a rare genetic disease caused by nuclear envelope alterations that lead to accelerated aging and premature death. Several studies have linked health and longevity to cell-extrinsic mechanisms, highlighting the relevance of circulating factors in the aging process as well as in age-related diseases. We performed a global plasma proteomic analysis in two preclinical progeroid models (Lmna G609G/G609G and Zmpste24 -/- mice) using aptamer-based proteomic technology. Pathways related to the extracellular matrix, growth factor response and calcium ion binding were among the most enriched in the proteomic signature of progeroid samples compared to controls. Despite the global downregulation trend found in the plasma proteome of progeroid mice, several proteins associated with cardiovascular disease, the main cause of death in HGPS, were upregulated. We also developed a chronological age predictor using plasma proteome data from a cohort of healthy mice (aged 1-30 months), that reported an age acceleration when applied to progeroid mice, indicating that these mice exhibit an "old" plasma proteomic signature. Furthermore, when compared to naturally-aged mice, a great proportion of differentially expressed circulating proteins in progeroid mice were specific to premature aging, highlighting secretome-associated differences between physiological and accelerated aging. This is the first large-scale profiling of the plasma proteome in progeroid mice, which provides an extensive list of candidate circulating plasma proteins as potential biomarkers and/or therapeutic targets for further exploration and hypothesis generation in the context of both physiological and premature aging., (© 2023 The Authors. Aging Cell published by Anatomical Society and John Wiley & Sons Ltd.)

Metabolism plays an important role in regulating aging at several levels, and metabolic reprogramming is the main driving force of aging. Due to the different metabolic needs of different tissues, the change trend of metabolites during aging in different organs and the influence of different levels of metabolites on organ function are also different, which makes the relationship between the change of metabolite level and aging more complex. However, not all of these changes lead to aging. The development of metabonomics research has opened a door for people to understand the overall changes in the metabolic level in the aging process of organisms. The omics-based "aging clock" of organisms has been established at the level of gene, protein and epigenetic modifications, but there is still no systematic summary at the level of metabolism. Here, we reviewed the relevant research published in the last decade on aging and organ metabolomic changes, discussed several metabolites with high repetition rate, and explained their role in vivo , hoping to find a group of metabolites that can be used as metabolic markers of aging. This information should provide valuable information for future diagnosis or clinical intervention of aging and age-related diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Fang, Chen, Jin, Liu, Cao and Liu.)

Biological age, distinct from an individual's chronological age, has been studied extensively through predictive aging clocks. However, these clocks have limited accuracy in short time-scales. Here we trained deep learning models on fundus images from the EyePACS dataset to predict individuals' chronological age. Our retinal aging clocking, 'eyeAge', predicted chronological age more accurately than other aging clocks (mean absolute error of 2.86 and 3.30 years on quality-filtered data from EyePACS and UK Biobank, respectively). Additionally, eyeAge was independent of blood marker-based measures of biological age, maintaining an all-cause mortality hazard ratio of 1.026 even when adjusted for phenotypic age. The individual-specific nature of eyeAge was reinforced via multiple GWAS hits in the UK Biobank cohort. The top GWAS locus was further validated via knockdown of the fly homolog, Alk , which slowed age-related decline in vision in flies. This study demonstrates the potential utility of a retinal aging clock for studying aging and age-related diseases and quantitatively measuring aging on very short time-scales, opening avenues for quick and actionable evaluation of gero-protective therapeutics., Competing Interests: SA, AB is not currently affiliated with Google Research, however work for this manuscript was conducted while affiliated with Google Research. The author has no other competing interests to declare, KW, AK, EC, RL, JS No competing interests declared, BB, CM, AV is affiliated with Google Health. The author has no other competing interests to declare, DB, OP, MB is affiliated with Google Research. The author has no other competing interests to declare, PK Reviewing editor, eLife, (© 2023, Ahadi et al.)

Objective: Aging is a complicated process that triggers age-related disease susceptibility through intercellular communication in the microenvironment. While the classic secretome of senescence-associated secretory phenotype (SASP) including soluble factors, growth factors, and extracellular matrix remodeling enzymes are known to impact tissue homeostasis during the aging process, the effects of novel SASP components, extracellular small noncoding RNAs (sncRNAs), on human aging are not well established., Methods: Here, by utilizing 446 small RNA-seq samples from plasma and serum of healthy donors found in the Extracellular RNA (exRNA) Atlas data repository, we correlated linear and nonlinear features between circulating sncRNAs expression and age by the maximal information coefficient (MIC) relationship determination. Age predictors were generated by ensemble machine learning methods (Adaptive Boosting, Gradient Boosting, and Random Forest) and core age-related sncRNAs were determined through weighted coefficients in machine learning models. Functional investigation was performed via target prediction of age-related miRNAs., Results: We observed the number of highly expressed transfer RNAs (tRNAs) and microRNAs (miRNAs) showed positive and negative associations with age respectively. Two-variable (sncRNA expression and individual age) relationships were detected by MIC and sncRNAs-based age predictors were established, resulting in a forecast performance where all R 2 values were greater than 0.96 and root-mean-square errors (RMSE) were less than 3.7 years in three ensemble machine learning methods. Furthermore, important age-related sncRNAs were identified based on modeling and the biological pathways of age-related miRNAs were characterized by their predicted targets, including multiple pathways in intercellular communication, cancer and immune regulation., Conclusion: In summary, this study provides valuable insights into circulating sncRNAs expression dynamics during human aging and may lead to advanced understanding of age-related sncRNAs functions with further elucidation., Competing Interests: The authors have no conflicts of interest to declare., (© 2023 The Authors. Aging Medicine published by Beijing Hospital and John Wiley & Sons Australia, Ltd.)

Measuring the abundance of biological molecules and their chemical modifications in blood and tissues has been the cornerstone of research and medical diagnoses for decades. Although the number and variety of molecules that can be measured have expanded exponentially, the blood biomarkers routinely assessed in medical practice remain limited to a few dozen, which have not substantially changed over the last 30-40 years. The discovery of novel biomarkers would allow, for example, risk stratification or monitoring of disease progression or the effectiveness of treatments and interventions, improving clinical practice in myriad ways. In this review, we combine the biomarker discovery concept with geroscience. Geroscience bridges aging research and translation to clinical applications by combining the framework of medical gerontology with high-technology medical research. With the development of geroscience and the rise of blood biomarkers, there has been a paradigm shift from disease prevention and cure to promoting health and healthy aging. New -omic technologies have played a role in the development of blood biomarkers, including epigenetic, proteomic, metabolomic, and lipidomic markers, which have emerged as correlates or predictors of health status, from disease to exceptional health., (© 2023 S. Karger AG, Basel.)

The title usage of Latin Quo vadis 'where are you going' extends the question Unde venisti from where 'did you come?' posed in the accompanying paper and extends consideration of how ancient eukaryotic and eumetazoan functions of progesterone receptor membrane component (PGRMC) proteins (PGRMC1 and PGRMC2 in mammals) could influence modern human health and disease. This paper attempts to extrapolate to modern biology in terms of extensions of hypothetical ancestral functional states from early eukaryotes and the last eumetazoan common ancestor (LEUMCA), to relativize human metabolic physiology and disease. As novel cell types and functional specializations appeared in bilaterian animals, PGRMC functions are hypothesized to have continued to be part of the toolkit used to develop new cell types and manage increasingly complex tasks such as nerve-gut-microbiome neuronal and hormonal communication. A critical role of PGRMC (as one component of a new eumetazoan genetic machinery) is proposed in LEUMCA endocrinology, neurogenesis, and nerve-gut communication with possible involvement in circadian nicotinamide adenine dinucleotide synthesis. This model would explain the contribution of PGRMC to metabolic and differentiation/behavioral changes observed in age-related diseases like diabetes, cancer and perhaps aging itself. Consistent with proposed key regulation of neurogenesis in the LEUMCA, it is argued that Alzheimer's disease is the modern pathology that most closely reflects the suite of functions related to PGRMC biology, with the 'usual suspect' pathologies possibly being downstream of PGRMC1. Hopefully, these thoughts help to signpost directions for future research., Competing Interests: Michael A. Cahill is scientific advisor to and minor shareholder (0.001%) of Cognition Therapeutics (CogRx). This work was prepared without knowledge by or influence from CogRx. There are no other competing financial interests. Given his role as Guest Editor, Michael A. Cahill was not involved in the peer-review of this article and has no access to information regarding its peer-review. Full responsibility for the editorial process for this article was delegated to Giuseppe Ingravallo and Graham Pawelec., (© 2022 The Author(s). Published by IMR Press.)

The title usage of Unde venisti 'from where have you come' is from a now dead language (Latin) that foundationally influenced modern English (not the major influence, but an essential formative one). This is an apt analogy for how both the ancient eukaryotic and eumetazoan functions of PGRMC proteins (PGRMC1 and PGRMC2 in mammals) probably influence modern human biology: via a formative trajectory from an evolutionarily foundational fulcrum. There is an arguable probability, although not a certainty, that PGRMC-like proteins were involved in eukaryogenesis. If so, then the proto-eukaryotic ancestral protein is modelled as having initiated the oxygen-induced and CYP450 (Cytochrome P450)-mediated synthesis of sterols in the endoplasmic reticulum to regulate proto-mitochondrial activity and heme homeostasis, as well as having enabled sterol transport between endoplasmic reticulum (ER) and mitochondria membranes involving the actin cytoskeleton, transport of heme from mitochondria, and possibly the regulation/origins of mitosis/meiosis. Later, during animal evolution, the last eumetazoan common ancestor (LEUMCA) acquired PGRMC phosphorylated tyrosines coincidentally with the gastrulation organizer, Netrin/deleted in colorectal carcinoma (DCC) signaling, muscle fibers, synapsed neurons, and neural recovery via a sleep-like process. Modern PGRMC proteins regulate multiple functions, including CYP450-mediated steroidogenesis, membrane trafficking, heme homeostasis, glycolysis/Warburg effect, fatty acid metabolism, mitochondrial regulation, and genomic CpG epigenetic regulation of gene expression. The latter imposes the system of differentiation status-sensitive cell-type specific proteomic complements in multi-tissued descendants of the LEUMCA. This paper attempts to trace PGRMC functions through time, proposing that key functions were involved in early eukaryotes, and were later added upon in the LEUMCA. An accompanying paper considers the implications of this awareness for human health and disease., Competing Interests: Michael A. Cahill is scientific advisor to and minor shareholder (0.001%) of Cognition Therapeutics (CogRx). This work was prepared without knowledge by or influence from CogRx. There are no other competing financial interests. Given his role as Guest Editor, Michael A. Cahill was not involved in the peer-review of this article and has no access to information regarding its peer-review. Full responsibility for the editorial process for this article was delegated to Graham Pawelec., (© 2022 The Author(s). Published by IMR Press.)

Although aging is an increasingly severe healthy, economic, and social global problem, it is far from well-modeling aging due to the aging process's complexity. To promote the aging modeling, here we did the quantitative measurement based on aging blood transcriptome. Specifically, the aging blood transcriptome landscape was constructed through ensemble modeling in a cohort of 505 people, and 1138 age-related genes were identified. To assess the aging rate in the linear dimension of aging, we constructed a simplified linear aging clock, which distinguished fast-aging and slow-aging populations and showed the differences in the composition of immune cells. Meanwhile, the non-linear dimension of aging revealed the transcriptome fluctuations with a crest around the age of 40 and showed that this crest came earlier and was more vigorous in the fast-aging population. Moreover, the aging clock was applied to evaluate the rejuvenation effect of molecules in vitro , such as Nicotinamide Mononucleotide (NMN) and Metformin. In sum, this study developed a de novo aging clock to evaluate age-dependent precise medicine by revealing its fluctuation nature based on comprehensively mining the aging blood transcriptome, promoting the development of personal aging monitoring and anti-aging therapies., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

Although chronological age correlates with various age-related diseases and conditions, it does not adequately reflect an individual's functional capacity, well-being, or mortality risk. In contrast, biological age provides information about overall health and indicates how rapidly or slowly a person is aging. Estimates of biological age are thought to be provided by aging clocks, which are computational models (e.g., elastic net) that use a set of inputs (e.g., DNA methylation sites) to make a prediction. In the past decade, aging clock studies have shown that several age-related diseases, social variables, and mental health conditions associate with an increase in predicted biological age relative to chronological age. This phenomenon of age acceleration is linked to a higher risk of premature mortality. More recent research has demonstrated that predicted biological age is sensitive to specific interventions. Human trials have reported that caloric restriction, a plant-based diet, lifestyle changes involving exercise, a drug regime including metformin, and vitamin D3 supplementation are all capable of slowing down or reversing an aging clock. Non-interventional studies have connected high-quality sleep, physical activity, a healthy diet, and other factors to age deceleration. Specific molecules have been associated with the reduction or reversal of predicted biological age, such as the antihypertensive drug doxazosin or the metabolite alpha-ketoglutarate. Although rigorous clinical trials are needed to validate these initial findings, existing data suggest that aging clocks are malleable in humans. Additional research is warranted to better understand these computational models and the clinical significance of lowering or reversing their outputs., (© 2022 Tally Health. Aging Cell published by Anatomical Society and John Wiley & Sons Ltd.)

Complexity is a fundamental feature of biological systems. Omics techniques like lipidomics can simultaneously quantify many thousands of molecules, thereby directly capturing the underlying biological complexity. However, this approach transfers the original biological complexity to the resulting datasets, posing challenges in data reduction and analysis. Aging is a prime example of a process that exhibits complex behaviour across multiple scales of biological organisation. The aging process is characterised by slow, cumulative and detrimental changes that are driven by intrinsic biological stochasticity and mediated through non-linear interactions and feedback within and between these levels of organization (ranging from metabolites, macromolecules, organelles and cells to tissue and organs). Only collectively and over long timeframes do these changes manifest as the exponential increases in morbidity and mortality that define biological aging, making aging a problem more difficult to study than the aetiologies of specific diseases. But aging's time dependence can also be exploited to extract key insights into its underlying biology. Here we explore this idea by using data on changes in lipid composition across the lifespan of an organism to construct and test a LipidClock to predict biological age in the nematode Caenorhabdits elegans . The LipidClock consist of a feature transformation via Principal Component Analysis followed by Elastic Net regression and yields and Mean Absolute Error of 1.45 days for wild type animals and 4.13 days when applied to mutant strains with lifespans that are substantially different from that of wild type. Gompertz aging rates predicted by the LipidClock can be used to simulate survival curves that are in agreement with those from lifespan experiments., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Unfried, Ng, Cazenave-Gassiot, Batchu, Kennedy, Wenk, Tolwinski and Gruber.)

Quantifying the physiology of aging is essential for improving our understanding of age-related disease and the heterogeneity of healthy aging. Recent studies have shown that, in regression models using "-omic" platforms to predict chronological age, residual variation in predicted age is correlated with health outcomes, and suggest that these "omic clocks" provide measures of biological age. This paper presents predictive models for age using metabolomic profiles of cerebrospinal fluid (CSF) from healthy human subjects and finds that metabolite and lipid data are generally able to predict chronological age within 10 years. We use these models to predict the age of a cohort of subjects with Alzheimer's and Parkinson's disease and find an increase in prediction error, potentially indicating that the relationship between the metabolome and chronological age differs with these diseases. However, evidence is not found to support the hypothesis that our models will consistently overpredict the age of these subjects. In our analysis of control subjects, we find the carnitine shuttle, sucrose, biopterin, vitamin E metabolism, tryptophan, and tyrosine to be the most associated with age. We showcase the potential usefulness of age prediction models in a small data set (n = 85) and discuss techniques for drift correction, missing data imputation, and regularized regression, which can be used to help mitigate the statistical challenges that commonly arise in this setting. To our knowledge, this work presents the first multivariate predictive metabolomic and lipidomic models for age using mass spectrometry analysis of CSF., (© The Author(s) 2021. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Aging is closely related to the occurrence of human diseases; however, its exact biological mechanism is unclear. Advancements in high-throughput technology provide new opportunities for omics research to understand the pathological process of various complex human diseases. However, single-omics technologies only provide limited insights into the biological mechanisms of diseases. DNA, RNA, protein, metabolites, and microorganisms usually play complementary roles and perform certain biological functions together. In this review, we summarize multi-omics methods based on the most relevant biomarkers in single-omics to better understand molecular functions and disease causes. The integration of multi-omics technologies can systematically reveal the interactions among aging molecules from a multidimensional perspective. Our review provides new insights regarding the discovery of aging biomarkers, mechanism of aging, and identification of novel antiaging targets. Overall, data from genomics, transcriptomics, proteomics, metabolomics, integromics, microbiomics, and systems biology contribute to the identification of new candidate biomarkers for aging and novel targets for antiaging interventions.

The earth displays daily, seasonal and annual environmental cycles that have led to evolutionarily adapted ultradian, circadian and infradian rhythmicities in the entire biosphere. All biological organisms must adapt to these cycles that synchronize the function of their circadiome. The objective of this review is to discuss the latest knowledge regarding the role of circadiomics in health and aging. The biological timekeepers are responsive to the environmental cues at microsecond to seasonal time-scales and act with precision of a clock machinery. The robustness of these rhythms is essential to normal daily function of cells, tissues and organs. Mis-alignment of circadian rhythms makes the individual prone to aging, sleep disorders, cancer, diabetes, and neuro-degenerative diseases. Circadian and CircadiOmic medicine are emerging fields that leverage our in-depth understanding of health issues, that arise as a result of disturbances in circadian rhythms, towards establishing better therapeutic approaches in personalized medicine and for geroprotection., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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.

In our recent transcriptomic meta-analysis, we used random forest machine learning to accurately predict age in human blood, bone, brain, heart, and retina tissues given gene inputs. Although each tissue-specific model utilized a unique number of genes for age prediction, we found that the following six genes were prioritized in all five tissues: CHI3L2 , CIDEC , FCGR3A , RPS4Y1 , SLC11A1 , and VTCN1 . Since being selected for age prediction in multiple tissues is unique, we decided to explore these pan-tissue clock genes in greater detail. In the present study, we began by performing over-representation and network topology-based enrichment analyses in the Gene Ontology Biological Process database. These analyses revealed that the immunological terms "response to protozoan," "immune response," and "positive regulation of immune system process" were significantly enriched by these clock inputs. Expression analyses in mouse and human tissues identified that these inputs are frequently upregulated or downregulated with age. A detailed literature search showed that all six genes had noteworthy connections to age-related disease. For example, mice deficient in Cidec are protected against various metabolic defects, while suppressing VTCN1 inhibits age-related cancers in mouse models. Using a large multitissue transcriptomic dataset, we additionally generate a novel, minimalistic aging clock that can predict human age using just these six genes as inputs. Taken all together, these six genes are connected to diverse aspects of aging.

Machine learning models capable of predicting age given a set of inputs are referred to as aging clocks. We recently developed an aging clock that utilizes 491 plasma protein inputs, has an exceptional accuracy, and is capable of measuring biological age. Here, we demonstrate that this clock is extremely predictive (r = 0.95) when used to measure age in a novel plasma proteomic dataset derived from 370 human subjects aged 18-69 years. Over-representation analyses of the proteins that make up this clock in the Gene Ontology and Reactome databases predominantly implicated innate and adaptive immune system processes. Immunological drugs and various age-related diseases were enriched in the DrugBank and GLAD4U databases. By performing an extensive literature review, we find that at least 269 (54.8 %) of these inputs regulate lifespan and/or induce changes relevant to age-related disease when manipulated in an animal model. We also show that, in a large plasma proteomic dataset, the majority (57.2 %) of measurable clock proteins significantly change their expression level with human age. Different subsets of proteins were overlapped with distinct epigenetic, transcriptomic, and proteomic aging clocks. These findings indicate that the inputs of this age predictor likely represent a rich source of anti-aging drug targets., (Copyright © 2021 Elsevier B.V. All rights reserved.)

DeepMAge is a deep-learning DNA methylation aging clock that measures the organismal pace of aging with the information from human epigenetic profiles. In blood samples, DeepMAge can predict chronological age within a 2.8 years error margin, but in saliva samples, its performance is drastically reduced since aging clocks are restricted by the training set domain. However, saliva is an attractive fluid for genomic studies due to its availability, compared to other tissues, including blood. In this article, we display how cell type deconvolution and elastic net can be used to expand the domain of deep aging clocks to other tissues. Using our approach, DeepMAge's error in saliva samples was reduced from 20.9 to 4.7 years with no retraining., Competing Interests: FG, KK, AZ, PM were employed by Deep Longevity Limited - part of Endurance RP Limited (SEHK: 0575.HK). AZ was employed by Insilico Medicine Hong Kong Limited. DeepMAge is a patent-pending aging clock., (Copyright © 2021 Galkin, Kochetov, Mamoshina and Zhavoronkov.)

Identifying prognostic biomarkers and risk stratification for COVID-19 patients is a challenging necessity. One of the core survival factors is patient age. However, chronological age is often severely biased due to dormant conditions and existing comorbidities. In this retrospective cohort study, we analyzed the data from 5315 COVID-19 patients (1689 lethal cases) admitted to 11 public hospitals in New York City from 1 March 2020 to 1 December. We calculated patients' pace of aging with BloodAge-a deep learning aging clock trained on clinical blood tests. We further constructed survival models to explore the prognostic value of biological age compared to that of chronological age. A COVID-19 score was developed to support a practical patient stratification in a clinical setting. Lethal COVID-19 cases had higher predicted age, compared to non-lethal cases (Δ = 0.8-1.6 years). Increased pace of aging was a significant risk factor of COVID-related mortality (hazard ratio = 1.026 per year, 95% CI = 1.001-1.052). According to our logistic regression model, the pace of aging had a greater impact (adjusted odds ratio = 1.09 ± 0.00, per year) than chronological age (1.04 ± 0.00, per year) on the lethal infection outcome. Our results show that a biological age measure, derived from routine clinical blood tests, adds predictive power to COVID-19 survival models.

Aging clocks dissociate biological from chronological age. The estimation of biological age is important for identifying gerontogenes and assessing environmental, nutritional, or therapeutic impacts on the aging process. Recently, methylation markers were shown to allow estimation of biological age based on age-dependent somatic epigenetic alterations. However, DNA methylation is absent in some species such as Caenorhabditis elegans and it remains unclear whether and how the epigenetic clocks affect gene expression. Aging clocks based on transcriptomes have suffered from considerable variation in the data and relatively low accuracy. Here, we devised an approach that uses temporal scaling and binarization of C. elegans transcriptomes to define a gene set that predicts biological age with an accuracy that is close to the theoretical limit. Our model accurately predicts the longevity effects of diverse strains, treatments, and conditions. The involved genes support a role of specific transcription factors as well as innate immunity and neuronal signaling in the regulation of the aging process. We show that this binarized transcriptomic aging (BiT age) clock can also be applied to human age prediction with high accuracy. The BiT age clock could therefore find wide application in genetic, nutritional, environmental, and therapeutic interventions in the aging process., (© 2021 The Authors. Aging Cell published by Anatomical Society and John Wiley & Sons Ltd.)

Aging in humans is an incredibly complex biological process that leads to increased susceptibility to various diseases. Understanding which genes are associated with healthy aging can provide valuable insights into aging mechanisms and possible avenues for therapeutics to prolong healthy life. However, modeling this complex biological process requires an enormous collection of high-quality data along with cutting-edge computational methods. Here, we have compiled a large meta-analysis of gene expression data from RNA-Seq experiments available from the Sequence Read Archive. We began by reprocessing more than 6000 raw samples-including mapping, filtering, normalization, and batch correction-to generate 3060 high-quality samples spanning a large age range and multiple different tissues. We then used standard differential expression analyses and machine learning approaches to model and predict aging across the dataset, achieving an R 2 value of 0.96 and a root-mean-square error of 3.22 years. These models allow us to explore aging across health status, sex, and tissue and provide novel insights into possible aging processes. We also explore how preprocessing parameters affect predictions and highlight the reproducibility limits of these machine learning models. Finally, we develop an online tool for predicting the ages of human transcriptomic samples given raw gene expression counts. Together, this study provides valuable resources and insights into the transcriptomics of human aging., (© 2020 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.)

Aging clocks that accurately predict human age based on various biodata types are among the most important recent advances in biogerontology. Since 2016 multiple deep learning solutions have been created to interpret facial photos, omics data, and clinical blood parameters in the context of aging. Some of them have been patented to be used in commercial settings. However, psychological changes occurring throughout the human lifespan have been overlooked in the field of "deep aging clocks". In this paper, we present two deep learning predictors trained on social and behavioral data from Midlife in the United States (MIDUS) study: (a) PsychoAge, which predicts chronological age, and (b) SubjAge, which describes personal aging rate perception. Using 50 distinct features from the MIDUS dataset these models have achieved a mean absolute error of 6.7 years for chronological age and 7.3 years for subjective age. We also show that both PsychoAge and SubjAge are predictive of all-cause mortality risk, with SubjAge being a more significant risk factor. Both clocks contain actionable features that can be modified using social and behavioral interventions, which enables a variety of aging-related psychology experiment designs. The features used in these clocks are interpretable by human experts and may prove to be useful in shifting personal perception of aging towards a mindset that promotes productive and healthy behaviors.

We previously identified 529 proteins that had been reported by multiple different studies to change their expression level with age in human plasma. In the present study, we measured the q-value and age coefficient of these proteins in a plasma proteomic dataset derived from 4263 individuals. A bioinformatics enrichment analysis of proteins that significantly trend toward increased expression with age strongly implicated diverse inflammatory processes. A literature search revealed that at least 64 of these 529 proteins are capable of regulating life span in an animal model. Nine of these proteins (AKT2, GDF11, GDF15, GHR, NAMPT, PAPPA, PLAU, PTEN, and SHC1) significantly extend life span when manipulated in mice or fish. By performing machine-learning modeling in a plasma proteomic dataset derived from 3301 individuals, we discover an ultra-predictive aging clock comprised of 491 protein entries. The Pearson correlation for this clock was 0.98 in the learning set and 0.96 in the test set while the median absolute error was 1.84 years in the learning set and 2.44 years in the test set. Using this clock, we demonstrate that aerobic-exercised trained individuals have a younger predicted age than physically sedentary subjects. By testing clocks associated with 1565 different Reactome pathways, we also show that proteins associated with signal transduction or the immune system are especially capable of predicting human age. We additionally generate a multitude of age predictors that reflect different aspects of aging. For example, a clock comprised of proteins that regulate life span in animal models accurately predicts age., (© 2020 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.)

The aging process results in multiple traceable footprints, which can be quantified and used to estimate an organism's age. Examples of such aging biomarkers include epigenetic changes, telomere attrition, and alterations in gene expression and metabolite concentrations. More than a dozen aging clocks use molecular features to predict an organism's age, each of them utilizing different data types and training procedures. Here, we offer a detailed comparison of existing mouse and human aging clocks, discuss their technological limitations and the underlying machine learning algorithms. We also discuss promising future directions of research in biohorology - the science of measuring the passage of time in living systems. Overall, we expect deep learning, deep neural networks and generative approaches to be the next power tools in this timely and actively developing field., Competing Interests: Declaration of Competing Interest FG, PM, AA, AZ work for Insilico Medicine, a for-profit biotechnology company developing the end-to-end target identification and drug discovery pipeline for a broad spectrum of age-related diseases. The company may have commercial interests in this publication. Products of InSilico Medicine include “Young.AI” system mentioned in this article. PM works for Deep Longevity, a for-profit longevity company. JPM is an advisor for Centaura, Longevity Vision Fund and is the founder of Magellan Science Ltd, a company providing consulting services in longevity science., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

The human gut microbiome is a complex ecosystem that both affects and is affected by its host status. Previous metagenomic analyses of gut microflora revealed associations between specific microbes and host age. Nonetheless there was no reliable way to tell a host's age based on the gut community composition. Here we developed a method of predicting hosts' age based on microflora taxonomic profiles using a cross-study dataset and deep learning. Our best model has an architecture of a deep neural network that achieves the mean absolute error of 5.91 years when tested on external data. We further advance a procedure for inferring the role of particular microbes during human aging and defining them as potential aging biomarkers. The described intestinal clock represents a unique quantitative model of gut microflora aging and provides a starting point for building host aging and gut community succession into a single narrative., Competing Interests: Declaration of Interests F.G., P.M., and A.Z. are affiliated with Deep Longevity, Inc, a for-profit company developing biomarkers of aging and longevity commonly referred to as the “aging clocks. A.A., E.P., V.M., and A.Z. work for Insilico Medicine, a for-profit longevity biotechnology company developing the end-to-end target identification and drug discovery pipeline for a broad spectrum of age-related diseases. The microbiome aging model described in this article is a patent pending technology (application number 20200075127). The microbiomic aging clock is integrated in the Aging.AI system operated by the company. The company may have commercial interests in this publication., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Accumulation of various damages is considered the primary cause of aging throughout the history of gerontology. No progress has been made in extending animal lifespan under the guidance of this concept. This concept denies the existence of longevity genes, but it has been experimentally shown that manipulating genes that affect cell division rates can increase the maximum lifespan of animals. These methods of prolonging life are unsuitable for humans because of dangerous side effects, but they undoubtedly indicate the programmed nature of aging., Objective: The objective was to understand the mechanism of programmed aging to determine how to solve the problem of longevity., Methods: Fundamental research has already explored key details relating to the mechanism of programmed aging, but they are scattered across different fields of knowledge. The way was to recognize and combine them into a uniform mechanism., Results: Only a decrease in bioenergetics is under direct genetic control. This causes many different harmful processes that serve as the execution mechanism of the aging program. The aging rate and, therefore, lifespan are determined by the rate of cell proliferation and the magnitude of the decrease in bioenergetics per cell division in critical tissues., Conclusion: The mechanism of programmed aging points the way to achieving an unlimited healthy life; it is necessary to develop a means for managing bioenergetics. It has already been substantially studied by molecular biologists and is now waiting for researchers from gerontology., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Multiple recent advances in machine learning enabled computer systems to exceed human performance in many tasks including voice, text, and speech recognition and complex strategy games. Aging is a complex multifactorial process driven by and resulting in the many minute changes transpiring at every level of the human organism. Deep learning systems trained on the many measurable features changing in time can generalize and learn the many biological processes on the population and individual levels. The deep age predictors can help advance aging research by establishing causal relationships in non-linear systems. Deep aging clocks can be used for identification of novel therapeutic targets, evaluating the efficacy of the various interventions, data quality control, data economics, prediction of health trajectories, mortality, and many other applications. Here we present the current state of development of the deep aging clocks in the context of the pharmaceutical research and development and clinical applications.

Induced pluripotent stem cells (IPSCs), with their unlimited regenerative capacity, carry the promise for tissue replacement to counter age-related decline. However, attempts to realize in vivo iPSC have invariably resulted in the formation of teratomas. Partial reprogramming in prematurely aged mice has shown promising results in alleviating age-related symptoms without teratoma formation. Does partial reprogramming lead to rejuvenation (i.e., "younger" cells), rather than dedifferentiation, which bears the risk of cancer? Here, we analyse the dynamics of cellular age during human iPSC reprogramming and find that partial reprogramming leads to a reduction in the epigenetic age of cells. We also find that the loss of somatic gene expression and epigenetic age follows different kinetics, suggesting that they can be uncoupled and there could be a safe window where rejuvenation can be achieved with a minimized risk of cancer., (© 2018 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.)