Integrating aging biomarkers and immune function to predict kidney health: insights from the future of families and child wellbeing study

Abstract

Biomarkers of biological aging predict health outcomes more accurately than chronological age. This study examines the relationship between aging biomarkers, immune function, and kidney health using the Future of Families and Child Wellbeing Study Biomarker Dataset. Using Wave 5 (year 9) and Wave 6 (year 15), we examined biomarker data from a total of 4898 individuals. The panel of aging biomarkers, comprised of epigenetic clocks (GrimAge, Horvath), immune function markers (CD8 + T cells, plasmablasts), and metabolic indicators (GDF-15, leptin), was evaluated in depth. We used principal component analysis (PCA) and K-means clustering for subtype identification. A random forest regressor was employed to predict kidney function using Cystatin C levels, and the importance of features was assessed. Three clusters with unique biological age and immune function profiles were identified. Cluster 1 had younger biological age markers. In Cluster 2, both GrimAge and GDF-15 levels were significantly increased, indicating an elevated risk for age-related diseases. According to predictive modeling, GrimAge, Pack Years, and immune function markers had the strongest influence on Cystatin C levels (R² = 0.856). The incorporation of immune aging markers enhanced the predictive power, emphasizing the importance of immunosenescence in renal health. Aging biomarkers and immune function significantly impact kidney health prediction. The study results call for the utilization of extensive biomarker tests for individualized elderly care and early recognition of kidney deterioration. Clinical practice can be improved by incorporating biological age assessments for the prevention and management of age-related diseases.