Herz jung, Leber alt? Warum Ihre Organe unterschiedlich schnell altern

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.

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

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