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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. [Display omitted] • Metformin prevents brain atrophy, elevating cognitive function in aged male primates • Metformin slows the pace of aging across diverse male primate tissues • Metformin counterparts neuronal aging, delivering geroprotection via Nrf2 in male primates Prolonged metformin treatment diminishes the multidimensional biological age and alleviates neuronal aging through the Nrf2 pathway in male primates. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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, VO2 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. [ABSTRACT FROM AUTHOR]

Abstract 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.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

Purpose: We develop blood test-based aging clocks and examine how these clocks reflect high-volume sports activity. Methods: We use blood tests and body metrics data of 421 Hungarian athletes and 283 age-matched controls (mean age, 24.1 and 23.9 yr, respectively), the latter selected from a group of healthy Caucasians of the National Health and Nutrition Examination Survey (NHANES) to represent the general population (n = 11,412). We train two age prediction models (i.e., aging clocks) using the NHANES dataset: the first model relies on blood test parameters only, whereas the second one additionally incorporates body measurements and sex. Results: We find lower age acceleration among athletes compared with the age-matched controls with a median value of-1.7 and 1.4 yr, P< 0.0001. BMI is positively associated with age acceleration among the age-matched controls (r = 0.17, P< 0.01) and the unrestricted NHANES population (r = 0.11, P< 0.001). We find no association between BMI and age acceleration within the athlete dataset. Instead, age acceleration is positively associated with body fat percentage (r = 0.21, P < 0.05) and negatively associated with skeletal muscle mass (Pearson r = -0.18, P<0.05) among athletes. The most important blood test features in age predictions were serum ferritin, mean cell volume, blood urea nitrogen, and albumin levels. Conclusions: We develop and apply blood test-based aging clocks to adult athletes and healthy controls. The data suggest that high-volume sports activity is associated with slowed biological aging. Here, we propose an alternative, promising application of routine blood tests. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aging clocks determine aging rate by the difference between predicted and chronological age. Clock-determined aging rate is associated with health, morbidity, and mortality. The DNA damage response and its associated epigenetic changes may be the fundamental counting unit of aging clocks. Cellular senescence could be the next upper-level unit of aging clocks. Oxidative stress and inflammation contribute to DNA damage and accelerate aging clocks. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

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 (LmnaG609G/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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract 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 R2 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.

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 R2 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. [ABSTRACT FROM AUTHOR]

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.

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.

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.

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. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • Collected >2,800 thermal facial images and developed ThermoFace aging clock models • ThermoFace is related to metabolic indicators, DNA repair, ATPase, and lipolysis • ThermoFace aging rate increases with metabolic diseases and decreases with adequate sleep • ThermoFace can rapidly assess aging and metabolic diseases and intervention By collecting and analyzing thermal facial images, Yu et al. find that human facial temperature distribution contains information on aging and metabolic states and has potential for rapid screening of aging and metabolic disorders and for revealing intervention strategies. [ABSTRACT FROM AUTHOR]

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.

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 multi-tissue transcriptomic dataset (n = 1,125), we additionally generate a novel, minimalistic aging clock that can predict human age (R = 0.76, root-mean square error = 9.77 years) using just these six genes as inputs. Taken all together, these six genes are connected to diverse aspects of aging. [ABSTRACT FROM AUTHOR]

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.

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.

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. [ABSTRACT FROM AUTHOR]

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 R2 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. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

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.

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.)

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).)

In attempts to develop an instrument for prolonging life, mankind has created more than 300 theories of aging, each of which offers its own original cause of the age-dependent degradation. Among them, there are many logically perfect theories based on actual, repeatedly checked facts, but none of them have given a practically significant result. The theory presented here is based on the conception that life is a phenomenon that represents many interrelated physicochemical processes propelled by the energy of the mitochondrial bioenergetic machine. The gradual age-dependent degradation of all vital processes is caused by a programmed decrease in the bioenergetics level. This theory unites all existing aging theories that are built on authentic facts; the data accumulated in different fields of biology have served the basis to show that the fundamental phenomena that accompany the aging process, such as an increase in the level of reactive oxygen species (ROS), a decrease in the general level of protein synthesis, the limitation of cellular proliferation (Hayflick limit), a decrease in the efficiency of reparation mechanisms are caused by the attenuation of bioenergetics. Each of these phenomena in turn generates a number of harmful secondary processes. Almost all of the current theories are based on one of these destructive phenomena or their combination. Hence, each theory describes one side of the aging process that is initially caused by a programmed decrease in the bioenergetics level. The united theory makes it possible to understand the nature of the aging clock and explains the phenomenon of lifespan extension under the conditions of food restriction. Failures of attempts to develop a remedy for senescence are explained by the fact that the currently used manipulations with the separate secondary phenomena of bioenergetics attenuation are not capable of advancing longevity beyond the bounds of its natural duration, although they can improve the quality of life in old age. The only way to achieve unlimited healthy life is to find a way of managing bioenergetics. [ABSTRACT FROM AUTHOR]