Studiendatenbank

Adverse childhood experiences (ACEs), such as abuse and neglect, are associated with poor health in adulthood. One proposed biological mechanism linking early adversity to health outcomes is epigenetic age acceleration (EAA), a measure of biological aging derived from DNA methylation. Understanding whether ACEs contribute to EAA might identify pathways linking early life stress to increased risk of morbidity and mortality.This systematic review and meta-analysis examined the relationship between cumulative ACE exposure and EAA in adults across 27 eligible observational studies from 1036 identified by comprehensive screening of the literature. Studies involved more female participants (median 56.6%) and employed a range of epigenetic clocks, most frequently Horvath, GrimAge, and PhenoAge. Risk of bias was assessed using the ROBINS-E tool, with most studies rated as having some concerns, primarily due to a lack of adjustment for key covariates. Meta-analyses of 6 studies using cumulative ACE exposure and standardised regression coefficients revealed no significant associations with EAA for first-generation clocks (Horvath: β =  - 0.03, 95% CI - 0.15 to 0.09; Hannum: β =  - 0.09, 95% CI - 0.41 to 0.23) or second-generation clocks (PhenoAge and GrimAge: both β = 0.21, 95% CIs spanning zero). Narrative synthesis of studies, including those that could not be considered in the meta-analyses, highlighted heterogeneous methodologies and mixed findings, particularly for individual ACEs

Residential greenness has been consistently associated with multiple health benefits; however, the underlying molecular mechanisms remain insufficiently understood. DNA methylation-based epigenetic clocks have emerged as robust biomarkers of biological aging and provide a valuable framework for investigating environmental influences on aging processes. This systematic review synthesizes current human evidence linking greenspace exposure to epigenetic age acceleration and DNA methylation changes. Following PRISMA 2020 guidelines, we searched Scopus and Web of Science databases (inception to May 2026). Studies were eligible if they assessed quantitative indicators of greenspace exposure and DNA methylation-based aging biomarkers in human populations. Out of 97 identified records, 14 studies met the inclusion criteria. Higher levels of greenspace exposure were consistently associated with a deceleration of GrimAge acceleration, with effect sizes ranging from 1.0 to 1.6 years per interquartile range increase in greenness. At the molecular level, greenspace-associated differentially methylated regions (DMRs) were consistently enriched in genes involved in neurodevelopment (HTR2A, BDNF, SLC6A3, SDK1), immune regulation (HLA-DRB5, IL6), stress response (NR3C1), and extracellular matrix remodeling (ADAMTS2). Overall, greenspace exposure is associated with slower epigenetic aging and differential DNA methylation across key biological pathways. These findings support the concept of gre

Age-related decline in intrinsic capacity (IC), defined as the sum of an individual's physical and mental capacities, is a cornerstone for promoting healthy aging by prioritizing maintenance of function over disease treatment. However, assessing IC is resource-intensive, and the molecular and cellular bases of its decline are poorly understood. Here we used the INSPIRE-T cohort (1,014 individuals aged 20-102 years) to construct the IC clock, a DNA methylation-based predictor of IC, trained on the clinical evaluation of cognition, locomotion, psychological well-being, sensory abilities and vitality. In the Framingham Heart Study, DNA methylation IC outperforms first-generation and second-generation epigenetic clocks in predicting all-cause mortality, and it is strongly associated with changes in molecular and cellular immune and inflammatory biomarkers, functional and clinical endpoints, health risk factors and lifestyle choices. These findings establish the IC clock as a validated tool bridging molecular readouts of aging and clinical assessments of IC.

This study introduces EpiAgePublic, a new method to estimate biological age using only three specific sites on the gene <i>ELOVL2,</i> known for its connection to aging. Unlike traditional methods that require complex and extensive data, our model uses a simpler approach that is well-suited for next-generation sequencing technology, which is a more advanced method of analyzing DNA methylation. This new model overcomes some of the common challenges found in older methods, such as errors due to sample quality and processing variations. We tested EpiAgePublic with a large and varied group of over 4,600 people to ensure its accuracy. It performed on par with, and sometimes better than, more complicated models that use much more data for age estimation. We examined its effectiveness in understanding how factors like HIV infection and stress affect aging, confirming its usefulness in real-world clinical settings. Our results prove that our simple yet effective model, EpiAgePublic, can capture the subtle signs of aging with high accuracy. We also used this model in a study involving patients with Alzheimer's Disease, demonstrating the practical benefits of next-generation sequencing in making precise age-related assessments. This study lays the groundwork for future research on aging mechanisms and assessing how different interventions might impact the aging process using this clock.

DNA methylation plays a critical role in gene expression regulation and has emerged as a robust biomarker of biological age. This modification will become heavier or site drift along with aging. Recently, it is termed epigenetic clocks-such as Horvath, Hannum, PhenoAge, and GrimAge-leverage specific methylation patterns to accurately predict age-related decline, disease risk, and mortality. These tools are now widely applied across diverse tissues, populations, and disease contexts. Beyond age-related loss of methylation control, accelerated DNA methylation age has been linked to environmental exposures, lifestyle factors, and chronic diseases, further reinforcing its value as a dynamic and clinically relevant marker of biological aging. DNA methylation is reshaping our understanding of aging and disease risk, with promising implications for preventive medicine and interventions aimed at promoting healthy longevity. However, it must be admitted that some challenges remain, including limited generalizability across populations, an unclear mechanism, and inconsistent longitudinal performance. In this review, we examine the biological foundations of DNA methylation, major advances in epigenetic clock development, and their expanding applications in aging research, disease prediction and health monitoring.

<h4>Objectives</h4>This study aims to test a conceptual mediation model wherein periodontitis is associated with mortality through direct pathways and indirectly via accelerated biological aging.<h4>Material and methods</h4>We analyzed six cycles of National Health and Nutrition Examination Survey data with mortality follow-up of 250 months. Weighted descriptive statistics were used to compare group characteristics, Kaplan-Meier analysis to evaluate periodontitis-mortality associations and generalized linear models to examine the links between periodontitis and biological aging. Cox proportional hazards models integrated with restricted cubic splines were utilized to explore the association between biological age and mortality, and mediation analyses quantified the mediation of biological aging. Additionally, age, gender, and smoking status subgroup analyses were conducted.<h4>Results</h4>Moderate/severe periodontitis was associated with a significantly elevated all‑cause mortality risk (18.31% vs. 10.88% in no/mild periodontitis) and greater biological age advancement (PhenoAge: 1.22 years; KDM: 0.68 years). Biological age acceleration exhibited a non-linear association with mortality, with hazard ratios rising sharply beyond a threshold (PhenoAge: 16.4 years; KDM: 31.8 years). Mediation analysis showed that biological age partially mediated the periodontitis-mortality association, with indirect effect hazard ratios of 1.085 (95% CI: 1.067-1.106) for PhenoAge advance and 1.0

Aging is a complex, multifactorial process, influencing disease risk and overall health. While chronological age (CA) is widely used in clinical practice, it fails to capture individual aging trajectories. Current approaches to estimate biological age (BA) often focus on single organs or predefined clinical biomarkers, limiting comprehensive assessment. We introduce a novel, purely imaging-driven deep learning framework for organ-specific BA estimation across seven organ systems. Our uncertainty-aware ResNet-based models autonomously learned aging-related features from imaging data in 70,000 UK Biobank participants, eliminating manual feature selection biases. Training on a healthy cohort, where CA approximates BA, allows learning normative aging patterns. When applied to a broader cohort, deviations from typical aging indicate older or younger BA. Our findings demonstrate the feasibility of BA estimation, even in organs with subtle aging features. While aging is largely heterogeneous across organs, we also identified correlations in aging patterns. We further showed that accelerated aging is prognostic of mortality and health outcomes, offering insights for personalized assessments.

Accurate and reliable ageing tools are essential for wildlife conservation and management. While DNA methylation has emerged as a promising tool for age estimation in vertebrates, its application to invertebrates remains contested and has been limited to arthropods. Here, we develop an epigenetic clock for the Pacific crown-of-thorns seastar (CoTS; Acanthaster cf. solaris), a destructive coral predator contributing to habitat degradation across Indo-Pacific reefs. Using Oxford Nanopore Technologies, we generated whole-genome DNA methylation profiles across five age groups and identified 1910 CpG sites with methylation patterns significantly associated with age. We then fitted age prediction models using elastic net regression and evaluated predictive performance with leave-one-out cross-validation (LOOCV), achieving a mean absolute error of 0.31 ± 0.22 years, corresponding to 4-6% of the CoTS lifespan (5-8 years). This accuracy suggests the potential to differentiate annual cohorts, supporting future management-relevant inference. To facilitate practical implementation, we constructed an optimized epigenetic clock from 14 CpG sites consistently selected across LOOCV iterations. Our results demonstrate that DNA methylation-based age estimation is feasible in a deuterostome invertebrate, extending epigenetic ageing approaches beyond arthropods and establishing their potential to advance age determination and management in invertebrates that lack reliable ageing methods.

<h4>Background</h4>The relationship between cognitive function, measured by digital Clock Drawing Test (dCDT), and biological aging is lacking.<h4>Methods</h4>We used linear mixed regression to evaluate the associations between epigenetic aging metrics (Horvath, Hannum, GrimAge, PhenoAge, DunedinPACE) and dCDT scores in the Framingham Heart Study (FHS), adjusting for covariates. Significance was set at a false discovery rate (FDR) <0.05.<h4>Results</h4>Among the 1,789 FHS participants (mean age 65 ± 13, 53% women), higher epigenetic age acceleration metrics at baseline predicted lower dCDT scores approximately seven years later. The magnitude of these associations was greater in older participants (≥65 years, <i>n</i> = 985). The strongest association was observed between the dCDT total score and DunedinPACE in the full sample (beta = -2.1, FDR = 0.0004), the younger (<65 years; beta = -1.9, FDR = 0.02), and older (beta = -2.2, FDR = 0.01) age groups. Additionally, the dCDT total score was associated with age acceleration estimated by Horvath (beta = -1.9, FDR = 0.01) and PhenoAge (beta = -2.5, FDR = 0.01) in older participants, while not in the full sample or younger participants. Furthermore, higher levels of DNAm-based PAI1 (beta = -0.9, FDR = 0.005) and ADM (beta = -2.9, FDR = 0.01), components of GrimAge, were significantly associated with lower dCDT total scores. In analyses of cognitive subdomains, simple motor function was significantly associated with DunedinPACE (FD

Biomarkers of ageing are defined as age-related changes in body function or composition that could serve as a measure of 'biological' age and predict the onset of age-related diseases and/or residual life expectancy. We conducted the MARK-AGE Study, a European population study (3300 subjects aged 35-74) to identify a powerful set of biomarkers of ageing. A total of 362 clinical-chemistry, genetic, cellular or molecular biomarkers were analysed for each subject. Using statistical models as well as machine learning we derived mathematical formulas for females and for males that yield a 'bioage score' of an individual, based on sets of 10 biomarkers for females and 10 for males. Collectively, these biomarkers model chronological age of our study population and, thus yield the 'biological' age of a certain person. 'Age difference' (defined as biological minus chronological age) should then identify biologically older or younger individuals. Using our set of biomarkers, subjects with Down Syndrome and smoking females are biologically older, whereas postmenopausal females taking hormone replacement therapy are biologically younger. Strikingly, our data reveal that age difference of MARK-AGE subjects, but not chronological age, is linearly correlated with levels of HDL, 25-hydroxy-Vitamin D, and CD3+ CD4+/CD45+ ratio in such a way that biologically younger subjects display values that are favourable to good health, whereas other markers such as glucose and HbA1c are correlated with

The need to monitor the aging process as a risk factor for disease and mortality beyond chronological age (CA) has led to numerous investigations into the estimation of the biological age (BA) of individuals. However, the accuracy of BA estimation tools is often judged by their ability to approximate CA, questioning their value in capturing the variance in health status and thus correctly estimating BA. Their biological relevance is often assessed in relation to health outcomes or mortality, underexploiting their potential for real-time monitoring of BA. Furthermore, their complexity may limit their clinical translation to large populations. Here, we describe the gene expression-based age monitoring Clock (GamC), a simple biomarker of aging (BOA), and characterize its biological relevance with synchronous cardiovascular (CV) health-related functional data. GamC is calculated from the expression levels of three genes consistently dysregulated with age in blood (ABLIM1, CCR7, and LEF1). GamC shows moderate but reliable association with CA in three independent cohorts, supported by transcriptome-wide changes. It demonstrates specialized biological meaning, as it specifically describes current physical activity levels, but poorly correlates with autonomic nervous system function, both age-related factors associated with CV health. Finally, it expresses BA monitoring capacity by modestly responding to an effective exercise-based intervention in centenarians. In conclusion, GamC is

<h4>Objective</h4>Aging is characterized by progressive physiological and psychological changes, leading to decreased cellular metabolism and increased vulnerability to age-related diseases.<h4>Methods.</h4>In this study, we examined the relationship between circulating Klotho levels and biological age acceleration (BAA) in a representative cohort of middle-aged and older adults. Data were obtained from the National Health and Nutrition Examination Survey (NHANES, 2007-2010), including 5,654 participants aged 45-85 years. Serum Klotho concentrations were quantified using ELISA, while biological age was estimated with the BioAge R package.<h4>Result</h4>Linear regression analyses demonstrated a robust inverse association between log-transformed Klotho levels and BAA across all statistical models, with reductions of -1.06 (95% CI: -1.77 to -0.36, p = 0.005), -1.44 (95% CI: -2.15 to -0.73, p < 0.001), and -1.30 (95% CI: -2.20 to -0.40, p = 0.01). Consistent results were observed in logistic regression models, where higher Klotho concentrations were linked to lower odds of accelerated aging (OR = 0.72, 95% CI: 0.59-0.88, p = 0.002; OR = 0.63, 95% CI: 0.51-0.77, p < 0.0001; OR = 0.62, 95% CI: 0.46-0.84, p = 0.01). Subgroup analyses revealed significant associations in women, participants over 60 years of age, and individuals without chronic illnesses. Interaction analyses further indicated that age (p-interaction = 0.002), alcohol intake (p-interaction = 0.04), and diabetes status

Biological age (BA) prediction has emerged as a critical research frontier for evaluating individual health status and aging trajectories beyond chronological age (CA). Recent advances in artificial intelligence (AI) have substantially accelerated this field by enabling the integration and interpretation of complex, multimodal biological data. This review provides a systematic overview of AI-driven approaches to BA prediction, covering key components including biomarker selection, feature engineering, model development, bias correction, and performance evaluation. We further highlight the growing recognition of asynchronous aging, a phenomenon in which different organs or physiological systems age at distinct rates, and discuss how AI-particularly deep learning and multimodal fusion-offers powerful tools for capturing such system-specific aging patterns. We summarize current methodologies ranging from traditional machine learning algorithms to advanced neural architectures capable of modeling nonlinear and heterogeneous aging processes. The expanding applications of AI-based BA models in disease risk assessment, geriatric evaluation, and population health monitoring are also examined. Despite rapid methodological progress, significant challenges persist, including data heterogeneity, limited model generalizability, insufficient interpretability, and barriers to clinical translation. Addressing these issues will require standardized methodological practices, robust validation

<h4>Background</h4>Age-related DNA methylation changes are promising biomarkers to track the individual aging process. Particularly second-generation epigenetic clocks capture aspects of biological age more accurately, but the requirement of genome wide profiles hampers implementation into practice. We therefore aimed to develop a simplified, targeted approach based on individual age- and mortality-associated CpG sites measurable by digital PCR.<h4>Results</h4>We selected three CpG sites strongly associated with all-cause mortality in the Lothian Birth Cohorts and with chronological age in multiple publicly available repositories to establish the targeted age- and mortality-associated epigenetic clock (TaM clock). For comparison, we applied a previously published three-CpG signature selected solely for correlation with chronological age (TaC clock). These signatures were initially benchmarked using DNA methylation profiles from 20 frail and 20 non-frail participants of the ActiFE cohort. In fact, in this subset the TaM clock revealed significant association between the delta age and the frailty status based on a 32-item frailty index. Furthermore, the TaM clock outperformed the TaC clock at capturing a significant increase in epigenetic age in Down syndrome, Werner syndrome and HIV. We subsequently developed digital PCR assays to analyse 446 samples from the ActiFE cohort. One TaM clock site (cg20595453) showed significant association with both mortality and frailty. However,

The musculoskeletal system is key to aging. Nonetheless, existing protein-based musculoskeletal aging clocks have limited predictive performance and fail to characterize genetic or environmental determinants. We first constructed a musculoskeletal protein pool by integrating transcriptomic enrichment, protein functional annotation, and literature expertise. Two musculoskeletal aging clocks-MSKAge and MSKAgeMort-were then developed using proteomic data of 21,070 UK Biobank participants. Acceleration metric (MSKAgeAccel/MSKAgeMortAccel) quantified deviations of biological age from chronological age, with positive values indicating accelerated musculoskeletal aging. Associations of MSKAgeAccel/MSKAgeMortAccel with mortality, musculoskeletal disorders, and age-related diseases were assessed using Cox proportional hazards models. Environmental and genetic determinants were evaluated by linear regression and genome-wide association analyses, respectively. Drug repurposing was used to identify potential targets of musculoskeletal diseases. A total of 39 musculoskeletal-related proteins constituted the pool. Leveraging these proteins, MSKAge (r_female = 0.62; r_male = 0.56) and MSKAgeMort (r_female = 0.93; r_male = 0.88) were constructed. Both MSKAgeAccel and (in particular) MSKAgeMortAccel predicted mortality, musculoskeletal diseases, and age-related diseases effectively. MSKAgeMortAccel showed significant associations with musculoskeletal di

The "unexplained failure" of rotator cuff repair is multifactorial, but its structural endpoint is anatomically consistent, i.e., the failure of the tendon-to-bone interface (enthesis) to heal. The native enthesis is a four-zone fibrocartilaginous gradient that does not regenerate but heals as a mechanically inferior fibrovascular scar, so the outcome of repair hinges on the interface's healing capacity-which chronological age predicts poorly. This review organizes the aging biology governing bone-to-tendon healing capacity into eight domains: progenitor competence, cellular senescence and the SASP, immune aging, extracellular-matrix and collagen aging via advanced glycation end-product cross-linking, footprint angiogenesis, morphogen signaling, mechanotransduction, and bone quality. We then precisely define the DNA-methylation epigenetic clock-a continuous value produced by weighted CpG methylation, with defined units, reproducibility, and effect sizes-and propose it as a candidate quantitative readout of these domains; whether or not it truly integrates them into a single biologically meaningful measure at the enthesis is a hypothesis of this review, not an established mechanism. In 1087 twins, epigenetic age acceleration predicted fracture and osteoporosis risk, with hazard ratios of 1.29-3.17 per standard deviation; moreover, aging is tissue-specific, so the enthesis may run ahead of blood. Critically, the clock provides a single axis on which current regenerative-medicin