Biologisches Alter testen lassen? Sechs Fragen vor dem Kauf

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.

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.

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

<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.