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Published on: September 27, 2024
Explaining the Obesity Paradox: The Association between Body Composition and Colorectal Cancer Survival (C-SCANS
Bette J Caan1, Jeffrey A Meyerhardt2, Candyce H Kroenke3
1Division of Research, Kaiser Permanente, Oakland, California. Bette.caan@kp.org.
Abstract:
Background: Body composition may partially explain the U-shaped association between body mass index (BMI) and colorectal cancer survival.Methods: Muscle and adiposity at colorectal cancer diagnosis and survival were examined in a retrospective cohort using Kaplan-Meier curves, multivariable Cox regression, and restricted cubic splines in 3,262 early-stage (I-III) male (50%) and female (50%) patients. Sarcopenia was defined using optimal stratification and sex- and BMI-specific cut points. High adiposity was defined as the highest tertile of sex-specific total adipose tissue (TAT). Primary outcomes were overall mortality and colorectal cancer-specific mortality (CRCsM).Results: Slightly over 42% patients were sarcopenic. During 5.8 years of follow-up, 788 deaths occurred, including 433 from colorectal cancer. Sarcopenic patients had a 27% [HR, 1.27; 95% confidence interval (CI), 1.09-1.48] higher risk of overall mortality than those who were not sarcopenic. Females with both low muscle and high adiposity had a 64% higher risk of overall mortality (HR, 1.64; 95% CI, 1.05-2.57) than females with adequate muscle and lower adiposity. The lowest risk of overall mortality was seen in patients with a BMI between 25 and <30 kg/m2, a range associated with the greatest number of patients (58.6%) who were not at increased risk of overall mortality due to either low muscle or high adiposity.Conclusions: Sarcopenia is prevalent among patients with non-metastatic colorectal cancer, and should, along with adiposity be a standard oncological marker.Impact: Our findings suggest a biologic explanation for the obesity paradox in colorectal cancer and refute the notion that the association between overweight and lower mortality is due solely to methodologic biases. Cancer Epidemiol Biomarkers Prev; 26(7); 1008-15. ©2017 AACR.
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