Biostatistics and Bioinformatics Monthly Seminar with Weihua Guan, PhD
Advancing Biological Aging Insights: Developing Proteomic Aging Clocks and Longitudinal Aging Indices
Meeting ID: 856 7331 4592
Passcode: kn3Zwj2i
Abstract:
Biological age reflects an individual’s physiological and functional state based on biomarkers, offering a more accurate measure of aging than chronological age. Among cancer survivors, physiological dysregulation often occurs at younger chronological ages compared to those without cancer, suggesting that their biological age exceeds their chronological age—indicative of accelerated aging. In this talk, Biological age reflects an individual’s physiological and functional state based on biomarkers, offering a more accurate measure of aging than chronological age. Among cancer survivors, physiological dysregulation often occurs at younger chronological ages compared to those without cancer, suggesting that their biological age exceeds their chronological age—indicative of accelerated aging. The construction of LPAI involves a two-step approach that employs functional principal component analysis (FPCA) followed by elastic net penalized Cox regression. We trained and tested LPAI in a large cohort study with more than 4000 participants who had proteomic measures cross three visits and in another independent cohort. LPAI demonstrated significant association with cancer incidence and mortality. The PACs have the potential to provide comprehensive and accurate insights into biological aging and its trends over time, offering valuable tools for understanding aging-related health outcomes.
This group meets monthly from Noon – 1 p.m.