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Abstract 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.

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.

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.

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.

Supplementary files for "DeepMAge: methylation aging clock developed with deep learning" by F.Galkin; P.Mamoshina; K.Kochetov; D.Sidorenko; A.Zhavoronkov (Aging and Disease, 2020) Table S1-S5 are described in the article. DeepMAge_data_partitinon.txt — contains the train-test division used to train and verify DeepMAge; DeepMAge_predictions.txt — contains predicted age values for all samples; original_353_clock_predictions.txt — contains predictions obtained with the 353 CpG clock de_novo_linear_model_verification_predictions.txt — predictions by the elastic network reproduced from Horvath's portocol

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.

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.

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.

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.

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.

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.

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., Using Caenorhabditis elegans RNA seq and lifespan datasets we developed a binarized transcriptomic aging (BiT age) clock with a precision close to the theoretical limit of accuracy that can also be applied to humans. The BiT age clock allows quantification of pro‐ and anti‐aging effects of genetic, nutritional, environmental and pharmacological interventions in the aging process.

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., In order to better understand aging, we comprehensively analyzed 3060 high‐quality human transcriptomic samples. These samples spanned diverse tissues as well as an age range of

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., Machine learning analyses of proteins that change their expression level with age in human plasma discovered an ultra‐predictive proteomic aging clock and also unveiled widely accessible clocks that reflect different aspects of aging. For example, proteins that impact lifespan in animal models when manipulated can accurately predict age in a large human cohort comprised of 3301 individuals (aged 18–76 years).

Summary 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., Graphical Abstract, Highlights • DNNs are the most appropriate model to predict host age from gut microflora profiles • Our DNN models reach MAE of 5.9 years in independent verification • Feature importance analysis gives a starting point for anti-aging intervention design, Microbiology; Microbiome; Bioinformatics; Applied Computing in Medical Science; Artificial Intelligence; Deep Learning; Aging; Biogerontology; Aging Clock

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.

SummaryInduced pluripotent stem cells (IPSCs), with their unlimited regenerative capacity, carry the promise for tissue replacement to counter age-related decline. However, attempts to realise 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 follow different kinetics, suggesting that they can be uncoupled and there could be a safe window where rejuvenation can be achieved with a minimised risk of cancer.