A urinary microRNA aging clock accurately predicts biological age

Area of Science:

• Biogerontology
• Molecular Biology
• Biomarker Discovery

Background:

• Aging is a complex process with significant health implications.
• Non-invasive biomarkers for aging are highly sought after.
• Urine offers a promising source for scalable biomarker development.

Purpose of the Study:

• To develop and validate a urinary microRNA (miRNA) based aging clock.
• To establish urine as a viable, non-invasive source for aging biomarkers.
• To identify key urinary miRNAs associated with biological age.

Main Methods:

• Machine learning algorithms were applied to urinary miRNA expression data from 6331 adults.
• A predictive model for biological age was trained and independently validated.
• Key miRNA biomarkers, including known geromiRs, were identified.

Main Results:

• The urinary miRNA aging clock achieved a Mean Absolute Error (MAE) of approximately 4.4 years and an R² of approximately 0.79 upon validation.
• The clock identified specific miRNAs such as miR-34a-5p, miR-31-5p, miR-146a-5p, and miR-155-5p as key aging biomarkers.
• While slightly less accurate than DNA methylation clocks, the urinary miRNA clock surpassed blood-based miRNA and mRNA clocks in performance.

Conclusions:

• Urinary miRNAs represent a promising and truly non-invasive biomarker for assessing biological age.
• This novel aging clock has potential applications in predicting age-related disease risk.
• Urine analysis offers a scalable and accessible method for aging biomarker monitoring.