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Updated: Sep 8, 2025

Deficient Pms2, ERCC1, Ku86, CcOI in Field Defects During Progression to Colon Cancer
Published on: July 28, 2010
Biological Aging and Chemotoxicity in Patients with Colorectal Cancer: A Secondary Data Analysis Using EHR Data
Claire J Han1, Ashley E Rosko2, Jesse J Plascak3
1Center for Healthy Aging, Self-Management and Complex Care, The Ohio State University College of Nursing, The Ohio State University-James: Cancer Treatment, and Research Center, Columbus, OH 43210, USA.
Abstract:
Background: Biological aging influences cancer outcomes, but its changes during chemotherapy and impact on chemotoxicity in colorectal cancer (CRC) remain underinvestigated. We examined (1) trajectories of biological aging (using Levine Phenotypic Age) during six months of chemotherapy, (2) sociodemographic and clinical risk factors for biological aging, and (3) links between biological aging and chemotoxicity. Methods: Using data from electronic health records (2013-2019) from 1129 adult CRC patients, we computed biological aging (raw Levine Phenotypic Age and its age acceleration [Levine Phenotypic Age-chronological age]) from routine blood tests (e.g., complete blood counts, hepatorenal/inflammatory markers). Chemotoxicity was identified primarily via International Classification of Diseases (ICD-9 and -10) codes. Results: Chemotherapy accelerated biological aging over time. Biological aging at baseline and changes over time predicted chemotoxicity. However, changes in biological aging over time showed stronger associations than baseline biological aging. Advanced cancer stages, higher comorbidity burden, and socioeconomic disadvantage (especially area-level deprivation) were associated with accelerated biological aging at baseline and over time. Biological aging occurred across both young and older adults. Conclusions: Levine Phenotypic Age, computed from routine blood tests in EHRs, offers a feasible clinical tool for aging-related chemotoxicity risk stratification. Validation in diverse cohorts and the development of predictive models are needed.

