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

Resetting the aging clock through epigenetic reprogramming: Insights from natural products

Epigenetic modifications play a critical role in regulating gene expression under various physiological and pathological conditions. Epigenetic modifications reprogramming is a recognized hallmark of aging and a key component of the aging clock used to differentiate between chronological and biological age. The potential for prospective diagnosis and regulatory capabilities position epigenetic modifications as an emerging drug target to extend longevity and alleviate age-related organ dysfunctions. In the past few decades, numerous preclinical studies have demonstrated the therapeutic potential of natural products in various human diseases, including aging, with some advancing to clinical trials and clinical application. This review highlights the discovery and recent advancements in the aging clock, as well as the potential use of natural products as anti-aging therapeutics by correcting disordered epigenetic reprogramming. Specifically, the focus is on the imbalance of histone modifications, alterations in DNA methylation patterns, disrupted ATP-dependent chromatin remodeling, and changes in RNA modifications. By exploring these areas, new insights can be gained into aging prediction and anti-aging interventions.

Association of proBNPage with all-cause and cardiovascular mortality among US adults: an analysis of data from the National Health and Nutrition Examination Survey

OBJECTIVE

Biological age assessed by the Klemera and Doubal method (KDM) and phenotypic age (PhenoAge) was considered as a marker for ageing-related outcomes because it reflects different aspects of biological ageing and health, which are associated with increased risk of death. proBNPage based on N-terminal pro-B-type natriuretic peptide (NT-proBNP) is a novel index for biological age estimation. However, the independence of its relationship with clinical outcomes from established risk factors, KDM or PhenoAge remains uncertain. Their identification could provide valuable information to prognosis.

DESIGN, SETTING AND PARTICIPANTS

This study analysed data from the general population included in the National Health and Nutrition Examination Survey (NHANES). Participants who took part in the cross-sectional survey from 1999 to 2004 were included, and all-cause as well as cardiovascular mortality was recorded (up to 31 December 2019).

OUTCOME MEASURES

All-cause and cardiovascular mortality were considered as outcomes. Clinical risk factors were collected, and biological age was estimated by proBNPage, KDM and PhenoAge. Cox proportional hazards models were used to determine the relationship between proBNPage and outcomes with adjustment for risk factors or other biological age indexes. Restricted cubic spline (RCS) analysis based on multivariate Cox regressions was performed to examine whether there was a non-linear relationship between proBNPage and outcomes.

RESULTS

A total of 9 925 participants were included in this study. The association between proBNPage and outcomes remained significant after adjusting for risk factors, including NT-proBNP (for all-cause mortality, HR 1.14; 95% CI 1.10 to 1.17; for cardiovascular mortality, HR 1.20; 95% CI 1.14 to 1.27). Similar results were obtained after adjusting for KDM plus NT-proBNP (for all-cause mortality, HR 1.31; 95% CI 1.22 to 1.41; for cardiovascular mortality, HR 1.21; 95% CI 1.11 to 1.28) or PhenoAge plus NT-proBNP (for all-cause mortality, HR 1.21; 95% CI 1.16 to 1.28; for cardiovascular mortality, HR 1.35; 95% CI 1.24 to 1.47). These findings were confirmed in most subgroups. A non-linear relationship was observed between proBNPage and all-cause and cardiovascular mortality with an inflection point.

CONCLUSIONS

A non-linear positive relationship was observed between proBNPage and clinical outcomes. After adjusting for established risk factors and other biological age estimation indices (KDM or PhenoAge), proBNPage was significantly associated with mortality. The results remain similar after further adjustment for NT-proBNP. These results suggest that proBNPage is a useful surrogate for biological age estimation.

Robust Metabolomic Age Prediction Based on a Wide Selection of Metabolites

Chronological age is a major risk factor for numerous diseases. However, chronological age does not capture the complex biological aging process. The difference between chronological age and biologically driven aging could be more informative in reflecting health status. Here, we set out to develop a metabolomic age prediction model by applying ridge regression and bootstrapping with 826 metabolites (678 endogenous and 148 xenobiotics) measured by an untargeted platform in relatively healthy blood donors aged 18-75 years from the INTERVAL study (N = 11 977; 50.2% men). After bootstrapping internal validation, the metabolomic age prediction models demonstrated high performance with an adjusted R2 of 0.83 using all metabolites and 0.82 using only endogenous metabolites. The former was significantly associated with obesity and cardiovascular disease in the Netherlands Epidemiology of Obesity study (N = 599; 47.0% men; age range = 45-65) due to the contribution of medication-derived metabolites-namely salicylate and ibuprofen-and environmental exposures such as cotinine. Additional metabolomic age prediction models using all metabolites were developed for men and women separately. The models had high performance (R² = 0.85 and 0.86) but shared a moderate correlation of 0.72. Furthermore, we observed 163 sex-dimorphic metabolites, including threonine, glycine, cholesterol, and androgenic and progesterone-related metabolites. Our strongest predictors across all models were novel and included hydroxyasparagine (Model Endo + Xeno β = 4.74), vanillylmandelate (β = 4.07), and 5,6-dihydrouridine (β = -4.2). Our study presents a robust metabolomic age model that reveals distinct sex-based age-related metabolic patterns and illustrates the value of including xenobiotic to enhance metabolomic prediction accuracy.

Deep learning and generative artificial intelligence in aging research and healthy longevity medicine

With the global population aging at an unprecedented rate, there is a need to extend healthy productive life span. This review examines how Deep Learning (DL) and Generative Artificial Intelligence (GenAI) are used in biomarker discovery, deep aging clock development, geroprotector identification and generation of dual-purpose therapeutics targeting aging and disease. The paper explores the emergence of multimodal, multitasking research systems highlighting promising future directions for GenAI in human and animal aging research, as well as clinical application in healthy longevity medicine.

Decoding aging clocks: New insights from metabolomics

Chronological age is a crucial risk factor for diseases and disabilities among older adults. However, individuals of the same chronological age often exhibit divergent biological aging states, resulting in distinct individual risk profiles. Chronological age estimators based on omics data and machine learning techniques, known as aging clocks, provide a valuable framework for interpreting molecular-level biological aging. Metabolomics is an intriguing and rapidly growing field of study, involving the comprehensive profiling of small molecules within the body and providing the ultimate genome-environment interaction readout. Consequently, leveraging metabolomics to characterize biological aging holds immense potential. The aim of this review was to provide an overview of metabolomics approaches, highlighting the establishment and interpretation of metabolomic aging clocks while emphasizing their strengths, limitations, and applications, and to discuss their underlying biological significance, which has the potential to drive innovation in longevity research and development.

Metabolomic age (MileAge) predicts health and life span: A comparison of multiple machine learning algorithms

Biological aging clocks produce age estimates that can track with age-related health outcomes. This study aimed to benchmark machine learning algorithms, including regularized regression, kernel-based methods, and ensembles, for developing metabolomic aging clocks from nuclear magnetic resonance spectroscopy data. The UK Biobank data, including 168 plasma metabolites from up to N = 225,212 middle-aged and older adults (mean age, 56.97 years), were used to train and internally validate 17 algorithms. Metabolomic age (MileAge) delta, the difference between metabolite-predicted and chronological age, from a Cubist rule-based regression model showed the strongest associations with health and aging markers. Individuals with an older MileAge were frailer, had shorter telomeres, were more likely to suffer from chronic illness, rated their health worse, and had a higher all-cause mortality hazard (HR = 1.51; 95% CI, 1.43 to 1.59; P < 0.001). This metabolomic aging clock (MileAge) can be applied in research and may find use in health assessments, risk stratification, and proactive health tracking.

The importance of IgG glycosylation-What did we learn after analyzing over 100,000 individuals

All four subclasses of immunoglobulin G (IgG) antibodies have glycan structures attached to the protein part of the IgG molecules. Glycans linked to the Fc portion of IgG are found in all IgG antibodies, while about one-fifth of IgG antibodies in plasma also have glycans attached to the Fab portion of IgG. The IgG3 subclass is characterized by more complex glycosylation compared to other IgG subclasses. In this review, we discuss the significant influence that glycans exert on the structural and functional properties of IgG. We provide a comprehensive overview of how the composition of these glycans can affect IgG's effector functions by modulating its interactions with Fcγ receptors and other molecules such as the C1q component of complement, which in turn influence various immune responses triggered by IgG, including antibody-dependent cell-mediated cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC). In addition, the importance of glycans for the efficacy of therapeutics like monoclonal antibodies and intravenous immunoglobulin (IVIg) therapy is discussed. Moreover, we offer insights into IgG glycosylation characteristics and roles derived from general population, disease-specific, and interventional studies. These studies indicate that IgG glycans are important biomarkers and functional effectors in health and disease.

Longevity biotechnology: bridging AI, biomarkers, geroscience and clinical applications for healthy longevity

The recent unprecedented progress in ageing research and drug discovery brings together fundamental research and clinical applications to advance the goal of promoting healthy longevity in the human population. We, from the gathering at the Aging Research and Drug Discovery Meeting in 2023, summarised the latest developments in healthspan biotechnology, with a particular emphasis on artificial intelligence (AI), biomarkers and clocks, geroscience, and clinical trials and interventions for healthy longevity. Moreover, we provide an overview of academic research and the biotech industry focused on targeting ageing as the root of age-related diseases to combat multimorbidity and extend healthspan. We propose that the integration of generative AI, cutting-edge biological technology, and longevity medicine is essential for extending the productive and healthy human lifespan.

Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations

Circulating plasma proteins play key roles in human health and can potentially be used to measure biological age, allowing risk prediction for age-related diseases, multimorbidity and mortality. Here we developed a proteomic age clock in the UK Biobank (n = 45,441) using a proteomic platform comprising 2,897 plasma proteins and explored its utility to predict major disease morbidity and mortality in diverse populations. We identified 204 proteins that accurately predict chronological age (Pearson r = 0.94) and found that proteomic aging was associated with the incidence of 18 major chronic diseases (including diseases of the heart, liver, kidney and lung, diabetes, neurodegeneration and cancer), as well as with multimorbidity and all-cause mortality risk. Proteomic aging was also associated with age-related measures of biological, physical and cognitive function, including telomere length, frailty index and reaction time. Proteins contributing most substantially to the proteomic age clock are involved in numerous biological functions, including extracellular matrix interactions, immune response and inflammation, hormone regulation and reproduction, neuronal structure and function and development and differentiation. In a validation study involving biobanks in China (n = 3,977) and Finland (n = 1,990), the proteomic age clock showed similar age prediction accuracy (Pearson r = 0.92 and r = 0.94, respectively) compared to its performance in the UK Biobank. Our results demonstrate that proteomic aging involves proteins spanning multiple functional categories and can be used to predict age-related functional status, multimorbidity and mortality risk across geographically and genetically diverse populations.

Can we suppress chronic systemic inflammation and postpone age-related diseases by targeting the IgG glycome?

INTRODUCTION

Glycans attached to immunoglobulin G are an important regulator of chronic systemic inflammation, one of the key drivers of aging. As people age, glycans that suppress inflammation are being replaced with inflammation-promoting glycans, but the rate of this conversion is highly individual and is affected by genetic, epigenetic, and environmental factors.

AREAS COVERED

This review summarizes key studies of IgG glycosylation changes in aging and disease, effects of lifestyle and pharmacological interventions, and mechanisms that regulate IgG glycosylation.

EXPERT OPINION

IgG glycome is an important contributor to the process of aging that can be modulated by both lifestyle and pharmacological interventions. Small molecule drugs that would suppress chronic systemic inflammation by modulation of the IgG glycome are still not available, but since gene network regulating IgG glycosylation has been identified and a high-throughput in vitro screening system is available, it is likely that this highly innovative approach to manage chronic systemic inflammation will be developed soon.

Cell Tree Rings: the structure of somatic evolution as a human aging timer

Biological age is typically estimated using biomarkers whose states have been observed to correlate with chronological age. A persistent limitation of such aging clocks is that it is difficult to establish how the biomarker states are related to the mechanisms of aging. Somatic mutations could potentially form the basis for a more fundamental aging clock since the mutations are both markers and drivers of aging and have a natural timescale. Cell lineage trees inferred from these mutations reflect the somatic evolutionary process, and thus, it has been conjectured, the aging status of the body. Such a timer has been impractical thus far, however, because detection of somatic variants in single cells presents a significant technological challenge. Here, we show that somatic mutations detected using single-cell RNA sequencing (scRNA-seq) from thousands of cells can be used to construct a cell lineage tree whose structure correlates with chronological age. De novo single-nucleotide variants (SNVs) are detected in human peripheral blood mononuclear cells using a modified protocol. A default model based on penalized multiple regression of chronological age on 31 metrics characterizing the phylogenetic tree gives a Pearson correlation of 0.81 and a median absolute error of ~4 years between predicted and chronological ages. Testing of the model on a public scRNA-seq dataset yields a Pearson correlation of 0.85. In addition, cell tree age predictions are found to be better predictors of certain clinical biomarkers than chronological age alone, for instance glucose, albumin levels, and leukocyte count. The geometry of the cell lineage tree records the structure of somatic evolution in the individual and represents a new modality of aging timer. In addition to providing a numerical estimate of "cell tree age," it unveils a temporal history of the aging process, revealing how clonal structure evolves over life span. Cell Tree Rings complements existing aging clocks and may help reduce the current uncertainty in the assessment of geroprotective trials.

Application of a mathematical model to clarify the statistical characteristics of a pan-tissue DNA methylation clock

DNA methylation clocks estimate biological age based on DNA methylation profiles. This study developed a mathematical model to describe DNA methylation aging and the establishment of a pan-tissue DNA methylation clock. The model simulates the aging dynamics of DNA methylation profiles based on passive demethylation as well as the process of cross-sectional bulk data acquisition. As a result, this study identified two conditions under which the pan-tissue DNA methylation clock can successfully predict biological age: one condition is that the target tissues are sufficiently well represented in the training dataset, and the other condition is that the target sample contains cell subsets that are common among different tissues. When either of these conditions is met, the clock performs well. It is also suggested that the epigenetic age of all samples in the target tissue tends to be either over or underestimated when biological age prediction fails. The model can reveal the statistical characteristics of DNA methylation clocks.

Glutathione amino acid precursors protect skin from UVB-induced damage and improve skin tone

BACKGROUND

UV radiation exposure causes skin irritation, erythema, darkening and barrier disruption by inducing oxidative stress and inflammation. Glutathione, a master antioxidant, plays an important role in the antioxidant defence network of the skin.

OBJECTIVE

This study aimed to assess the in vitro protective effects of the glutathione amino acid precursors blend (GAP) on transcriptomic and phenotypic endpoints against UVB-induced challenges.

METHODS

Normal human epidermal melanocytes (NHEMs) were exposed to GAP, ascorbic acid (AA) and its derivatives. Viability was assessed using the CCK8 method. Melakutis®, a pigmented living skin equivalent (pLSE) model, underwent repeated 50 mJ/cm2 UVB irradiation with or without GAP treatment. Images of the model were captured with consistent camera parameters, and the model's light intensity was measured using a spectrophotometer. Melanin content was determined by measuring absorbance at 405 nm. Confirmation of melanin deposition and distribution was achieved through Fontana-Masson staining. Transcriptomic analysis was conducted using RNA sequencing (RNA-Seq), and a machine learning approach was employed for transcriptomic aging clock analysis.

RESULTS

In NHEMs, all tested compounds exhibited over 85% viability compared to the vehicle control, indicating no heightened risk of cytotoxicity. Notably, GAP demonstrated greater efficacy in inhibiting melanin production than AA derivatives at equivalent concentrations. In pLSE models, GAP notably enhanced model lightness, and reduced melanin content and deposition following the UVB challenge, whereas AA showed minimal impact. GAP effectively counteracted UVB-induced alterations in gene expression linked to pigmentation, inflammation and aging. Moreover, recurrent UVB exposure substantially elevated the biological age of pLSE models, a phenomenon mitigated by GAP treatment.

CONCLUSIONS

In NHEMs, GAP exhibited enhanced effectiveness in inhibiting melanin production at identical tested doses in comparison to AA derivatives. Noteworthy protective effects of GAP against UVB irradiation were observed in the pLSE models, as evidenced by skin pigmentation measurements and transcriptomic changes.

Biological age estimation using circulating blood biomarkers

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

OMICmAge: An integrative multi-omics approach to quantify biological age with electronic medical records

Biological aging is a multifactorial process involving complex interactions of cellular and biochemical processes that is reflected in omic profiles. Using common clinical laboratory measures in ~30,000 individuals from the MGB-Biobank, we developed a robust, predictive biological aging phenotype, EMRAge, that balances clinical biomarkers with overall mortality risk and can be broadly recapitulated across EMRs. We then applied elastic-net regression to model EMRAge with DNA-methylation (DNAm) and multiple omics, generating DNAmEMRAge and OMICmAge, respectively. Both biomarkers demonstrated strong associations with chronic diseases and mortality that outperform current biomarkers across our discovery (MGB-ABC, n=3,451) and validation (TruDiagnostic, n=12,666) cohorts. Through the use of epigenetic biomarker proxies, OMICmAge has the unique advantage of expanding the predictive search space to include epigenomic, proteomic, metabolomic, and clinical data while distilling this in a measure with DNAm alone, providing opportunities to identify clinically-relevant interconnections central to the aging process.

How do metabolic processes age: Evidence from human metabolomic studies

Metabolomics, the global profiling of small molecules in the body, has emerged as a promising analytical approach for assessing molecular changes associated with ageing at the population level. Understanding root metabolic ageing pathways may have important implications for managing age-related disease risk. In this short review, relevant studies published in the last few years that have made valuable contributions to this field will be discussed. These include large-scale studies investigating metabolic changes with age, metabolomic clocks, and metabolic pathways associated with ageing phenotypes. Recent significant advances include the use of longitudinal study designs, populations spanning the whole life course, standardised analytical platforms of enhanced metabolome coverage and development of multivariate analyses. While many challenges remain, recent studies have demonstrated the considerable promise of this field.

Biomarkers of aging for the identification and evaluation of longevity interventions

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

Age prediction from human blood plasma using proteomic and small RNA data: a comparative analysis

Aging clocks, built from comprehensive molecular data, have emerged as promising tools in medicine, forensics, and ecological research. However, few studies have compared the suitability of different molecular data types to predict age in the same cohort and whether combining them would improve predictions. Here, we explored this at the level of proteins and small RNAs in 103 human blood plasma samples. First, we used a two-step mass spectrometry approach measuring 612 proteins to select and quantify 21 proteins that changed in abundance with age. Notably, proteins increasing with age were enriched for components of the complement system. Next, we used small RNA sequencing to select and quantify a set of 315 small RNAs that changed in abundance with age. Most of these were microRNAs (miRNAs), downregulated with age, and predicted to target genes related to growth, cancer, and senescence. Finally, we used the collected data to build age-predictive models. Among the different types of molecules, proteins yielded the most accurate model (R² = 0.59 ± 0.02), followed by miRNAs as the best-performing class of small RNAs (R² = 0.54 ± 0.02). Interestingly, the use of protein and miRNA data together improved predictions (R2 = 0.70 ± 0.01). Future work using larger sample sizes and a validation dataset will be necessary to confirm these results. Nevertheless, our study suggests that combining proteomic and miRNA data yields superior age predictions, possibly by capturing a broader range of age-related physiological changes. It will be interesting to determine if combining different molecular data types works as a general strategy to improve future aging clocks.

Drugs, clocks and exercise in ageing: hype and hope, fact and fiction

Ageing is a biological process that is linked to a functional decline, ultimately resulting in death. Large interindividual differences exist in terms of life- and healthspan, representing life expectancy and the number of years spent in the absence of major diseases, respectively. The genetic and molecular mechanisms that are involved in the regulation of the ageing process, and those that render age the main risk factor for many diseases are still poorly understood. Nevertheless, a growing number of compounds have been put forward to affect this process. However, for scientists and laypeople alike, it is difficult to separate fact from fiction, and hype from hope. In this review, we discuss the currently pursued pharmacological anti-ageing approaches. These are compared to non-pharmacological interventions, some of which confer powerful effects on health and well-being, in particular an active lifestyle and exercise. Moreover, functional parameters and biological clocks as well as other molecular marks are compared in terms of predictive power of morbidity and mortality. Then, conceptual aspects and roadblocks in the development of anti-ageing drugs are outlined. Finally, an overview on current and future strategies to mitigate age-related pathologies and the extension of life- and healthspan is provided.

Slowing down decay: biological clocks in personalized medicine

This article discusses so-called biological clocks. These technologies, based on aging biomarkers, trace and measure molecular changes in order to monitor individuals' "true" biological age against their chronological age. Drawing on the concept of decay, and building on ethnographic fieldwork in an academic laboratory and a commercial firm, we analyze the implications of the development and commercialization of biological clocks that can identify when decay is "out of tempo." We show how the building of biological clocks rests on particular forms of knowing decay: In the academic laboratory, researchers focus on endo-processes of decay that are internal to the person, but when the technology moves to the market, the focus shifts as staff bracket decay as exo-processes, which are seen as resulting from a person's lifestyle. As the technology of biological clocks travels from the laboratory to the market of online testing of the consumer's biological age, we observe shifting visions of aging: from an inevitable trajectory of decline to a malleable and plastic one. While decay is an inevitable trajectory starting at birth and ending with death, the commercialization of biological clocks points to ways of stretching time between birth and death as individuals "optimize" their biological age through lifestyle changes. Regardless of admitted uncertainties about what is measured and the connection between maintenance and future health outcomes, the aging person is made responsible for their decaying body and for enacting maintenance to slow down decay. We show how the biological clock's way of "knowing" decay turns aging and its maintenance into a life-long concern and highlight the normative implications of framing decay as malleable and in need of intervention.

Heritability of the glycan clock of biological age

Immunoglobulin G is posttranslationally modified by the addition of complex N-glycans affecting its function and mediating inflammation at multiple levels. IgG glycome composition changes with age and health in a predictive pattern, presumably due to inflammaging. As a result, a novel biological aging biomarker, glycan clock of age, was developed. Glycan clock of age is the first of biological aging clocks for which multiple studies showed a possibility of clock reversal even with simple lifestyle interventions. However, none of the previous studies determined to which extent the glycan clock can be turned, and how much is fixed by genetic predisposition. To determine the contribution of genetic and environmental factors to phenotypic variation of the glycan clock, we performed heritability analysis on two TwinsUK female cohorts. IgG glycans from monozygotic and dizygotic twin pairs were analyzed by UHPLC and glycan age was calculated using the glycan clock. In order to determine additive genetic, shared, and unique environmental contributions, a classical twin design was applied. Heritability of the glycan clock was calculated for participants of one cross-sectional and one longitudinal cohort with three time points to assess the reliability of measurements. Heritability estimate for the glycan clock was 39% on average, suggesting a moderate contribution of additive genetic factors (A) to glycan clock variation. Remarkably, heritability estimates remained approximately the same in all time points of the longitudinal study, even though IgG glycome composition changed substantially. Most environmental contributions came from shared environmental factors (C), with unique environmental factors (E) having a minor role. Interestingly, heritability estimates nearly doubled, to an average of 71%, when we included age as a covariant. This intervention also inflated the estimates of unique environmental factors contributing to glycan clock variation. A complex interplay between genetic and environmental factors defines alternative IgG glycosylation during aging and, consequently, dictates the glycan clock's ticking. Apparently, environmental factors (including lifestyle choices) have a strong impact on the biological age measured with the glycan clock, which additionally clarifies why this aging clock is one of the most potent biomarkers of biological aging.

Novel feature selection methods for construction of accurate epigenetic clocks

Epigenetic clocks allow us to accurately predict the age and future health of individuals based on the methylation status of specific CpG sites in the genome and are a powerful tool to measure the effectiveness of longevity interventions. There is a growing need for methods to efficiently construct epigenetic clocks. The most common approach is to create clocks using elastic net regression modelling of all measured CpG sites, without first identifying specific features or CpGs of interest. The addition of feature selection approaches provides the opportunity to optimise the identification of predictive CpG sites. Here, we apply novel feature selection methods and combinatorial approaches including newly adapted neural networks, genetic algorithms, and 'chained' combinations. Human whole blood methylation data of ~470,000 CpGs was used to develop clocks that predict age with R2 correlation scores of greater than 0.73, the most predictive of which uses 35 CpG sites for a R2 correlation score of 0.87. The five most frequent sites across all clocks were modelled to build a clock with a R2 correlation score of 0.83. These two clocks are validated on two external datasets where they maintain excellent predictive accuracy. When compared with three published epigenetic clocks (Hannum, Horvath, Weidner) also applied to these validation datasets, our clocks outperformed all three models. We identified gene regulatory regions associated with selected CpGs as possible targets for future aging studies. Thus, our feature selection algorithms build accurate, generalizable clocks with a low number of CpG sites, providing important tools for the field.

Human age reversal: Fact or fiction?

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.