Highly accurate skin-specific methylome analysis algorithm as a platform to screen and validate therapeutics for healthy aging

Insights to aging prediction with AI based epigenetic clocks

Over the past century, human lifespan has increased remarkably, yet the inevitability of aging persists. The disparity between biological age, which reflects pathological deterioration and disease, and chronological age, indicative of normal aging, has driven prior research focused on identifying mechanisms that could inform interventions to reverse excessive age-related deterioration and reduce morbidity and mortality. DNA methylation has emerged as an important predictor of age, leading to the development of epigenetic clocks that quantify the extent of pathological deterioration beyond what is typically expected for a given age. Machine learning technologies offer promising avenues to enhance our understanding of the biological mechanisms governing aging by further elucidating the gap between biological and chronological ages. This perspective article examines current algorithmic approaches to epigenetic clocks, explores the use of machine learning for age estimation from DNA methylation, and discusses how refining the interpretation of ML methods and tailoring their inferences for specific patient populations and cell types can amplify the utility of these technologies in age prediction. By harnessing insights from machine learning, we are well-positioned to effectively adapt, customize and personalize interventions aimed at aging.

Identification and discrimination of human keratinized tissues using ATR-FTIR and chemometrics

In forensic investigations, human keratinized tissues like skin and nails are commonly encountered as trace evidence, yet the use of vibrational spectroscopy for their identification and differentiation has been underexplored. This research utilized ATR-FTIR to distinguish between human nails and skin samples collected from a group of 50 participants, employing advanced chemometric analysis techniques. The spectral signatures of human keratinized tissues, such as nails and skin, exhibit similarities consistent with previous studies. Chemometric analysis aimed at distinguishing these tissues showed that the PLS-DA model achieved an overall accuracy of 67 % with an AUC score of 0.65, while the SVM model had an overall accuracy of 56 % with an AUC score of 0.71. For sex identification, the PLS-DA model demonstrated an overall accuracy of 83 % with an AUC value of 1, whereas the SVM model achieved an overall accuracy of 100 % with an AUC score of 1. The study underscores the potential of ATR-FTIR coupled with chemometrics in the precise identification and differentiation of human keratinized tissue, thereby enhancing the capabilities of forensic investigations.

Genetic effects on the skin methylome in healthy older twins

Whole-skin DNA methylation variation has been implicated in several diseases, including melanoma, but its genetic basis has not yet been fully characterized. Using bulk skin tissue samples from 414 healthy female UK twins, we performed twin-based heritability and methylation quantitative trait loci (meQTL) analyses for >400,000 DNA methylation sites. We find that the human skin DNA methylome is on average less heritable than previously estimated in blood and other tissues (mean heritability: 10.02%). meQTL analysis identified local genetic effects influencing DNA methylation at 18.8% (76,442) of tested CpG sites, as well as 1,775 CpG sites associated with at least one distal genetic variant. As a functional follow-up, we performed skin expression QTL (eQTL) analyses in a partially overlapping sample of 604 female twins. Colocalization analysis identified over 3,500 shared genetic effects affecting thousands of CpG sites (10,067) and genes (4,475). Mediation analysis of putative colocalized gene-CpG pairs identified 114 genes with evidence for eQTL effects being mediated by DNA methylation in skin, including in genes implicating skin disease such as ALOX12 and CSPG4. We further explored the relevance of skin meQTLs to skin disease and found that skin meQTLs and CpGs under genetic influence were enriched for multiple skin-related genome-wide and epigenome-wide association signals, including for melanoma and psoriasis. Our findings give insights into the regulatory landscape of epigenomic variation in skin.

The Gut and Skin Microbiome and Its Association with Aging Clocks

Aging clocks are predictive models of biological age derived from age-related changes, such as epigenetic changes, blood biomarkers, and, more recently, the microbiome. Gut and skin microbiota regulate more than barrier and immune function. Recent studies have shown that human microbiomes may predict aging. In this narrative review, we aim to discuss how the gut and skin microbiomes influence aging clocks as well as clarify the distinction between chronological and biological age. A literature search was performed on PubMed/MEDLINE databases with the following keywords: "skin microbiome" OR "gut microbiome" AND "aging clock" OR "epigenetic". Gut and skin microbiomes may be utilized to create aging clocks based on taxonomy, biodiversity, and functionality. The top contributing microbiota or metabolic pathways in these aging clocks may influence aging clock predictions and biological age. Furthermore, gut and skin microbiota may directly and indirectly influence aging clocks through the regulation of clock genes and the production of metabolites that serve as substrates or enzymatic regulators. Microbiome-based aging clock models may have therapeutic potential. However, more research is needed to advance our understanding of the role of microbiota in aging clocks.

CheekAge: a next-generation buccal epigenetic aging clock associated with lifestyle and health

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.

A miRNA-based epigenetic molecular clock for biological skin-age prediction

Skin aging is one of the visible characteristics of the aging process in humans. In recent years, different biological clocks have been generated based on protein or epigenetic markers, but few have focused on biological age in the skin. Arrest the aging process or even being able to restore an organism from an older to a younger stage is one of the main challenges in the last 20 years in biomedical research. We have implemented several machine learning models, including regression and classification algorithms, in order to create an epigenetic molecular clock based on miRNA expression profiles of healthy subjects to predict biological age-related to skin. Our best models are capable of classifying skin samples according to age groups (18-28; 29-39; 40-50; 51-60 or 61-83 years old) with an accuracy of 80% or predict age with a mean absolute error of 10.89 years using the expression levels of 1856 unique miRNAs. Our results suggest that this kind of epigenetic clocks arises as a promising tool with several applications in the pharmaco-cosmetic industry.

Building Minimized Epigenetic Clock by iPlex MassARRAY Platform

Epigenetic clocks are valuable tools for estimating both chronological and biological age by assessing DNA methylation levels at specific CpG dinucleotides. While conventional epigenetic clocks rely on genome-wide methylation data, targeted approaches offer a more efficient alternative. In this study, we explored the feasibility of constructing a minimized epigenetic clock utilizing data acquired through the iPlex MassARRAY technology. The study enrolled a cohort of relatively healthy individuals, and their methylation levels of eight specific CpG dinucleotides in genes SLC12A5, LDB2, FIGN, ACSS3, FHL2, and EPHX3 were evaluated using the iPlex MassARRAY system and the Illumina EPIC array. The methylation level of five studied CpG sites demonstrated significant correlations with chronological age and an acceptable convergence of data obtained by the iPlex MassARRAY and Illumina EPIC array. At the same time, the methylation level of three CpG sites showed a weak relationship with age and exhibited a low concordance between the data obtained from the two technologies. The construction of the epigenetic clock involved the utilization of different machine-learning models, including linear models, deep neural networks (DNN), and gradient-boosted decision trees (GBDT). The results obtained from these models were compared with each other and with the outcomes generated by other well-established epigenetic clocks. In our study, the TabNet architecture (deep tabular data learning architecture) exhibited the best performance (best MAE = 5.99). Although our minimized epigenetic clock yielded slightly higher age prediction errors compared to other epigenetic clocks, it still represents a viable alternative to the genome-wide epigenotyping array.

Extracellular vesicles derived from mesenchymal stem cells: the wine in Hebe's hands to treat skin aging

Owing to its constant exposure to the external environment and various stimuli, skin ranks among the organs most vulnerable to manifestations of aging. Preventing and delaying skin aging has become one of the prominent research subjects in recent years. Mesenchymal stem cells (MSCs) are multipotent stem cells derived from mesoderm with high self-renewal ability and multilineage differentiation potential. MSC-derived extracellular vesicles (MSC-EVs) are nanoscale biological vesicles that facilitate intercellular communication and regulate biological behavior. Recent studies have shown that MSC-EVs have potential applications in anti-aging therapy due to their anti-inflammatory, anti-oxidative stress, and wound healing promoting abilities. This review presents the latest progress of MSC-EVs in delaying skin aging. It mainly includes the MSC-EVs promoting the proliferation and migration of keratinocytes and fibroblasts, reducing the expression of matrix metalloproteinases, resisting oxidative stress, and regulating inflammation. We then briefly discuss the recently discovered treatment methods of MSC-EVs in the field of skin anti-aging. Moreover, the advantages and limitations of EV-based treatments are also presented.

Insights into ageing rates comparison across tissues from recalibrating cerebellum DNA methylation clock

DNA methylation (DNAm)-based age clocks have been studied extensively as a biomarker of human ageing and a risk factor for age-related diseases. Despite different tissues having vastly different rates of proliferation, it is still largely unknown whether they age at different rates. It was previously reported that the cerebellum ages slowly; however, this claim was drawn from a single clock using a relatively small sample size and so warrants further investigation. We collected the largest cerebellum DNAm dataset (N = 752) to date. We found the respective epigenetic ages are all severely underestimated by six representative DNAm age clocks, with the underestimation effects more pronounced in the four clocks whose training datasets do not include brain-related tissues. We identified 613 age-associated CpGs in the cerebellum, which accounts for only 14.5% of the number found in the middle temporal gyrus from the same population (N = 404). From the 613 cerebellum age-associated CpGs, we built a highly accurate age prediction model for the cerebellum named CerebellumClockspecific (Pearson correlation=0.941, MAD=3.18 years). Ageing rate comparisons based on the two tissue-specific clocks constructed on the 201 overlapping age-associated CpGs support the cerebellum has younger DNAm age. Nevertheless, we built BrainCortexClock to prove a single DNAm clock is able to unbiasedly estimate DNAm ages of both cerebellum and cerebral cortex, when they are adequately and equally represented in the training dataset. Comparing ageing rates across tissues using DNA methylation multi-tissue clocks is flawed. The large underestimation of age prediction for cerebellums by previous clocks mainly reflects the improper usage of these age clocks. There exist strong and consistent ageing effects on the cerebellar methylome, and we suggest the smaller number of age-associated CpG sites in cerebellum is largely attributed to its extremely low average cell replication rates.

Initiation phase cellular reprogramming ameliorates DNA damage in the ERCC1 mouse model of premature aging

Unlike aged somatic cells, which exhibit a decline in molecular fidelity and eventually reach a state of replicative senescence, pluripotent stem cells can indefinitely replenish themselves while retaining full homeostatic capacity. The conferment of beneficial-pluripotency related traits via in vivo partial cellular reprogramming in vivo partial reprogramming significantly extends lifespan and restores aging phenotypes in mouse models. Although the phases of cellular reprogramming are well characterized, details of the rejuvenation processes are poorly defined. To understand whether cellular reprogramming can ameliorate DNA damage, we created a reprogrammable accelerated aging mouse model with an ERCC1 mutation. Importantly, using enhanced partial reprogramming by combining small molecules with the Yamanaka factors, we observed potent reversion of DNA damage, significant upregulation of multiple DNA damage repair processes, and restoration of the epigenetic clock. In addition, we present evidence that pharmacological inhibition of ALK5 and ALK2 receptors in the TGFb pathway are able to phenocopy some benefits including epigenetic clock restoration suggesting a role in the mechanism of rejuvenation by partial reprogramming.

Epigenetics insights from perceived facial aging

Facial aging is the most visible manifestation of aging. People desire to look younger than others of the same chronological age. Hence, perceived age is often used as a visible marker of aging, while biological age, often estimated by methylation markers, is used as an objective measure of age. Multiple epigenetics-based clocks have been developed for accurate estimation of general biological age and the age of specific organs, including the skin. However, it is not clear whether the epigenetic biomarkers (CpGs) used in these clocks are drivers of aging processes or consequences of aging. In this proof-of-concept study, we integrate data from GWAS on perceived facial aging and EWAS on CpGs measured in blood. By running EW Mendelian randomization, we identify hundreds of putative CpGs that are potentially causal to perceived facial aging with similar numbers of damaging markers that causally drive or accelerate facial aging and protective methylation markers that causally slow down or protect from aging. We further demonstrate that while candidate causal CpGs have little overlap with known epigenetics-based clocks, they affect genes or proteins with known functions in skin aging, such as skin pigmentation, elastin, and collagen levels. Overall, our results suggest that blood methylation markers reflect facial aging processes, and thus can be used to quantify skin aging and develop anti-aging solutions that target the root causes of aging.

Utility of DNA Methylation as a Biomarker in Aging and Alzheimer's Disease

Epigenetic mechanisms such as DNA methylation have been implicated in a number of diseases including cancer, heart disease, autoimmune disorders, and neurodegenerative diseases. While it is recognized that DNA methylation is tissue-specific, a limitation for many studies is the ability to sample the tissue of interest, which is why there is a need for a proxy tissue such as blood, that is reflective of the methylation state of the target tissue. In the last decade, DNA methylation has been utilized in the design of epigenetic clocks, which aim to predict an individual's biological age based on an algorithmically defined set of CpGs. A number of studies have found associations between disease and/or disease risk with increased biological age, adding weight to the theory of increased biological age being linked with disease processes. Hence, this review takes a closer look at the utility of DNA methylation as a biomarker in aging and disease, with a particular focus on Alzheimer's disease.

Senotherapeutic peptide treatment reduces biological age and senescence burden in human skin models

Cellular senescence is known to play a role in age-related skin function deterioration which potentially influences longevity. Here, a two-step phenotypic screening was performed to identify senotherapeutic peptides, leading to the identification of Peptide (Pep) 14. Pep 14 effectively decreased human dermal fibroblast senescence burden induced by Hutchinson-Gilford Progeria Syndrome (HGPS), chronological aging, ultraviolet-B radiation (UVB), and etoposide treatment, without inducing significant toxicity. Pep 14 functions via modulation of PP2A, an understudied holoenzyme that promotes genomic stability and is involved in DNA repair and senescence pathways. At the single-cell level, Pep 14 modulates genes that prevent senescence progression by arresting the cell cycle and enhancing DNA repair, which consequently reduce the number of cells progressing to late senescence. When applied on aged ex vivo skin, Pep 14 promoted a healthy skin phenotype with structural and molecular resemblance to young ex vivo skin, decreased the expression of senescence markers, including SASP, and reduced the DNA methylation age. In summary, this work shows the safe reduction of the biological age of ex vivo human skins by a senomorphic peptide.

Biomarkers of aging

Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.

A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator

BACKGROUND

The field of epigenomics holds great promise in understanding and treating disease with advances in machine learning (ML) and artificial intelligence being vitally important in this pursuit. Increasingly, research now utilises DNA methylation measures at cytosine-guanine dinucleotides (CpG) to detect disease and estimate biological traits such as aging. Given the challenge of high dimensionality of DNA methylation data, feature-selection techniques are commonly employed to reduce dimensionality and identify the most important subset of features. In this study, our aim was to test and compare a range of feature-selection methods and ML algorithms in the development of a novel DNA methylation-based telomere length (TL) estimator. We utilised both nested cross-validation and two independent test sets for the comparisons.

RESULTS

We found that principal component analysis in advance of elastic net regression led to the overall best performing estimator when evaluated using a nested cross-validation analysis and two independent test cohorts. This approach achieved a correlation between estimated and actual TL of 0.295 (83.4% CI [0.201, 0.384]) on the EXTEND test data set. Contrastingly, the baseline model of elastic net regression with no prior feature reduction stage performed less well in general-suggesting a prior feature-selection stage may have important utility. A previously developed TL estimator, DNAmTL, achieved a correlation of 0.216 (83.4% CI [0.118, 0.310]) on the EXTEND data. Additionally, we observed that different DNA methylation-based TL estimators, which have few common CpGs, are associated with many of the same biological entities.

CONCLUSIONS

The variance in performance across tested approaches shows that estimators are sensitive to data set heterogeneity and the development of an optimal DNA methylation-based estimator should benefit from the robust methodological approach used in this study. Moreover, our methodology which utilises a range of feature-selection approaches and ML algorithms could be applied to other biological markers and disease phenotypes, to examine their relationship with DNA methylation and predictive value.

Refining epigenetic prediction of chronological and biological age

BACKGROUND

Epigenetic clocks can track both chronological age (cAge) and biological age (bAge). The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. As cohort sample sizes increase, estimates of cAge and bAge become more precise. Here, we aim to develop accurate epigenetic predictors of cAge and bAge, whilst improving our understanding of their epigenomic architecture.

METHODS

First, we perform large-scale (N = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Next, to create a cAge predictor, we use methylation data from 24,674 participants from the Generation Scotland study, the Lothian Birth Cohorts (LBC) of 1921 and 1936, and 8 other cohorts with publicly available data. In addition, we train a predictor of time to all-cause mortality as a proxy for bAge using the Generation Scotland cohort (1214 observed deaths). For this purpose, we use epigenetic surrogates (EpiScores) for 109 plasma proteins and the 8 component parts of GrimAge, one of the current best epigenetic predictors of survival. We test this bAge predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Women's Health Initiative study).

RESULTS

Through the inclusion of linear and non-linear age-CpG associations from the EWAS, feature pre-selection in advance of elastic net regression, and a leave-one-cohort-out (LOCO) cross-validation framework, we obtain cAge prediction with a median absolute error equal to 2.3 years. Our bAge predictor was found to slightly outperform GrimAge in terms of the strength of its association to survival (HRGrimAge = 1.47 [1.40, 1.54] with p = 1.08 × 10-52, and HRbAge = 1.52 [1.44, 1.59] with p = 2.20 × 10-60). Finally, we introduce MethylBrowsR, an online tool to visualise epigenome-wide CpG-age associations.

CONCLUSIONS

The integration of multiple large datasets, EpiScores, non-linear DNAm effects, and new approaches to feature selection has facilitated improvements to the blood-based epigenetic prediction of biological and chronological age.

Epigenetic clocks provide clues to the mystery of uterine ageing

BACKGROUND

Rising maternal ages and age-related fertility decline are a global challenge for modern reproductive medicine. Clinicians and researchers pay specific attention to ovarian ageing and hormonal insufficiency in this regard. However, uterine ageing is often left out of the picture, with the majority of reproductive clinicians being close to unanimous on the absence of age-related functional decline in the uterine tissues. Therefore, most existing techniques to treat an age-related decline in implantation rates are based primarily on hormonal supplementation and oocyte donation. Solving the issue of uterine ageing might lead to an adjustment to these methods.

OBJECTIVE AND RATIONALE

A focus on uterine ageing and the possibility of slowing it emerged with the development of the information theory of ageing, which identifies genomic instability and erosion of the epigenetic landscape as important drivers of age-related decline in the functionality of most cells and tissues. Age-related smoothing of this landscape and a decline in tissue function can be assessed by measuring the ticking of epigenetic clocks. Within this review, we explore whether the uterus experiences age-related alterations using this elegant approach. We analyse existing data on epigenetic clocks in the endometrium, highlight approaches to improve the accuracy of the clocks in this cycling tissue, speculate on the endometrial pathologies whose progression might be predicted by the altered speed of epigenetic clocks and discuss the possibilities of slowing down the ticking of these clocks.

SEARCH METHODS

Data for this review were identified by searches of Medline, PubMed and Google Scholar. References from relevant articles using the search terms 'ageing', 'maternal age', 'female reproduction', 'uterus', 'endometrium', 'implantation', 'decidualization', 'epigenetic clock', 'biological age', 'DNA methylation', 'fertility' and 'infertility' were selected. A total of 95 articles published in English between 1985 and 2022 were included, six of which describe the use of the epigenetic clock to evaluate uterine/endometrium ageing.

OUTCOMES

Application of the Horvath and DNAm PhenoAge epigenetic clocks demonstrated a poor correlation with chronological age in the endometrium. Several approaches were suggested to enhance the predictive power of epigenetic clocks for the endometrium. The first was to increase the number of samples in the training dataset, as for the Zang clock, or to use more sophisticated clock-building algorithms, as for the AltumAge clock. The second method is to adjust the clocks according to the dynamic nature of the endometrium. Using either approach revealed a strong correlation with chronological age in the endometrium, providing solid evidence for age-related functional decline in this tissue. Furthermore, age acceleration/deceleration, as estimated by epigenetic clocks, might be a promising tool to predict or to gain insights into the origin of various endometrial pathologies, including recurrent implantation failure, cancer and endometriosis. Finally, there are several strategies to slow down or even reverse epigenetic clocks that might be applied to reduce the risk of age-related uterine impairments.

WIDER IMPLICATIONS

The uterine factor should be considered, along with ovarian issues, to correct for the decline in female fertility with age. Epigenetic clocks can be tested to gain a deeper understanding of various endometrial disorders.

On the Potential Role of the Antioxidant Couple Vitamin E/Selenium Taken by the Oral Route in Skin and Hair Health

The relationship between oxidative stress and skin aging/disorders is well established. Many topical and oral antioxidants (vitamins C and E, carotenoids, polyphenols) have been proposed to protect the skin against the deleterious effect induced by increased reactive oxygen species production, particularly in the context of sun exposure. In this review, we focused on the combination of vitamin E and selenium taken in supplements since both molecules act in synergy either by non-enzymatic and enzymatic pathways to eliminate skin lipids peroxides, which are strongly implicated in skin and hair disorders.

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

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

How good is the evidence that cellular senescence causes skin ageing?

Skin is the largest organ of the body with important protective functions, which become compromised with time due to both intrinsic and extrinsic ageing processes. Cellular senescence is the primary ageing process at cell level, associated with loss of proliferative capacity, mitochondrial dysfunction and significantly altered patterns of expression and secretion of bioactive molecules. Intervention experiments have proven cell senescence as a relevant cause of ageing in many organs. In case of skin, accumulation of senescence in all major compartments with ageing is well documented and might be responsible for most, if not all, the molecular changes observed during ageing. Incorporation of senescent cells into in-vitro skin models (specifically 3D full thickness models) recapitulates changes typically associated with skin ageing. However, crucial evidence is still missing. A beneficial effect of senescent cell ablation on skin ageing has so far only been shown following rather unspecific interventions or in transgenic mouse models. We conclude that evidence for cellular senescence as a relevant cause of intrinsic skin ageing is highly suggestive but not yet completely conclusive.

The skin redoxome

Redoxome is the network of redox reactions and redox active species (ReAS) that affect the homeostasis of cells and tissues. Due to the intense and constant interaction with external agents, the human skin has a robust redox signalling framework with specific pathways and magnitudes. The establishment of the skin redoxome concept is key to expanding knowledge of skin disorders and establishing better strategies for their prevention and treatment. This review starts with its definition and progress to propose how the master redox regulators are maintained and activated in the different conditions experienced by the skin and how the lack of redox regulation is involved in the accumulation of several oxidation end products that are correlated with various skin disorders.

Cellular reprogramming and epigenetic rejuvenation

Ageing is an inevitable condition that afflicts all humans. Recent achievements, such as the generation of induced pluripotent stem cells, have delivered preliminary evidence that slowing down and reversing the ageing process might be possible. However, these techniques usually involve complete dedifferentiation, i.e. somatic cell identity is lost as cells are converted to a pluripotent state. Separating the rejuvenative properties of reprogramming from dedifferentiation is a promising prospect, termed epigenetic rejuvenation. Reprogramming-induced rejuvenation strategies currently involve using Yamanaka factors (typically transiently expressed to prevent full dedifferentiation) and are promising candidates to safely reduce biological age. Here, we review the development and potential of reprogramming-induced rejuvenation as an anti-ageing strategy.

Epigenetic age prediction

Advanced age is the main common risk factor for cancer, cardiovascular disease and neurodegeneration. Yet, more is known about the molecular basis of any of these groups of diseases than the changes that accompany ageing itself. Progress in molecular ageing research was slow because the tools predicting whether someone aged slowly or fast (biological age) were unreliable. To understand ageing as a risk factor for disease and to develop interventions, the molecular ageing field needed a quantitative measure; a clock for biological age. Over the past decade, a number of age predictors utilising DNA methylation have been developed, referred to as epigenetic clocks. While they appear to estimate biological age, it remains unclear whether the methylation changes used to train the clocks are a reflection of other underlying cellular or molecular processes, or whether methylation itself is involved in the ageing process. The precise aspects of ageing that the epigenetic clocks capture remain hidden and seem to vary between predictors. Nonetheless, the use of epigenetic clocks has opened the door towards studying biological ageing quantitatively, and new clocks and applications, such as forensics, appear frequently. In this review, we will discuss the range of epigenetic clocks available, their strengths and weaknesses, and their applicability to various scientific queries.

DNA methylation-based age clocks: From age prediction to age reversion

Aging as an irretrievable occurrence throughout the entire life is characterized by a progressive decline in physiological functionality and enhanced disease vulnerability. Numerous studies have demonstrated that epigenetic modifications, particularly DNA methylation (DNAm), correlate with aging and age-related diseases. Several investigations have attempted to predict chronological age using the age-related alterations in the DNAm of certain CpG sites. Here we categorize different studies that tracked the aging process in the DNAm landscape to show how epigenetic age clocks evolved from a chronological age estimator to an indicator of lifespan and healthspan. We also describe the health and disease predictive potential of estimated epigenetic age acceleration regarding different clinical conditions and lifestyle factors. Considering the revealed age-related epigenetic changes, the recent age-reprogramming strategies are discussed which are promising methods for resetting the aging clocks.

Hallmarks of Aging in Macrophages: Consequences to Skin Inflammaging

The skin is our largest organ and the outermost protective barrier. Its aging reflects both intrinsic and extrinsic processes resulting from the constant insults it is exposed to. Aging in the skin is accompanied by specific epigenetic modifications, accumulation of senescent cells, reduced cellular proliferation/tissue renewal, altered extracellular matrix, and a proinflammatory environment favoring undesirable conditions, including disease onset. Macrophages (Mφ) are the most abundant immune cell type in the skin and comprise a group of heterogeneous and plastic cells that are key for skin homeostasis and host defense. However, they have also been implicated in orchestrating chronic inflammation during aging. Since Mφ are related to innate and adaptive immunity, it is possible that age-modified skin Mφ promote adaptive immunity exacerbation and exhaustion, favoring the emergence of proinflammatory pathologies, such as skin cancer. In this review, we will highlight recent findings pertaining to the effects of aging hallmarks over Mφ, supporting the recognition of such cell types as a driving force in skin inflammaging and age-related diseases. We will also present recent research targeting Mφ as potential therapeutic interventions in inflammatory skin disorders and cancer.

ARDD 2020: from aging mechanisms to interventions

Aging is emerging as a druggable target with growing interest from academia, industry and investors. New technologies such as artificial intelligence and advanced screening techniques, as well as a strong influence from the industry sector may lead to novel discoveries to treat age-related diseases. The present review summarizes presentations from the 7th Annual Aging Research and Drug Discovery (ARDD) meeting, held online on the 1st to 4th of September 2020. The meeting covered topics related to new methodologies to study aging, knowledge about basic mechanisms of longevity, latest interventional strategies to target the aging process as well as discussions about the impact of aging research on society and economy. More than 2000 participants and 65 speakers joined the meeting and we already look forward to an even larger meeting next year. Please mark your calendars for the 8th ARDD meeting that is scheduled for the 31st of August to 3rd of September, 2021, at Columbia University, USA.