#AI reads Urine# A urinary microRNA aging clock accurately predicts biological age
Published 08 February, 2026
To address the lack of reliable non-invasive biomarkers for biological age, this study developed and validated an aging clock based on urinary extracellular vesicle microRNAs (uEV-miRNAs) using machine learning, with 6,331 Japanese adults as research subjects. In independent validation, the clock achieved a mean absolute error (MAE) of approximately 4.4 years and an R² of ~0.79. While slightly less accurate than DNA methylation clocks, it outperformed blood-based miRNA and mRNA clocks. The key biomarkers include classic "geromiRs," and these biomarkers are involved in aging-related pathways such as bone remodeling and apoptosis. The study also found that patients with type 2 diabetes exhibit accelerated biological age, and miRNAs associated with dementia can be detected in urine. Additionally, the clock’s accuracy is limited for individuals under 25 or over 80 years old and is affected by factors like urine concentration. However, its non-invasive and scalable advantages provide a new approach for biological age assessment and the discovery of biomarkers for age-related diseases, holding promise for applications in geroscience-driven personalized preventive medicine.
NPJ Aging. 2025 Dec 15. doi: 10.1038/s41514-025-00311-3.
Youhe Gao
Statement: During the preparation of this work the author(s) used Doubao / AI reading for summarizing the content. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.
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