Inflammatory, Oxidative Stress, and Cardiac Damage Biomarkers and Radiation-Induced Fatigue in Breast Cancer Survivors
Alexi Vasbinder, Hilaire J. Thompson, Oleg Zaslavksy, Susan R. Heckbert, Nazmus Saquib, Aladdin H. Shadyab, Rowan T. Chlebowski, Lisa Martin, Electra D. Paskett, Kerryn W. Reding
Abstract
Purpose Studies examining biomarkers associated with fatigue in breast cancer survivors treated with radiation are limited. Therefore, we examined the longitudinal association between serum biomarkers and post-breast cancer fatigue in survivors treated with radiation: [oxidative stress] 8-hydroxyguanosine, myeloperoxidase; [inflammation] interleukin-6 (IL-6), c-reactive protein, growth differentiation factor-15 (GDF-15), placental growth factor, transforming growth factor-beta, [cardiac damage] cystatin-C, troponin-I. Methods In a secondary analysis, we included participants from the Women’s Health Initiative if they had: a previous breast cancer diagnosis (stages I-III), no prior cardiovascular diseases, pre-and post-breast cancer serum samples drawn approximately 3 years apart, and fatigue measured using the Short-Form 36 vitality subscale at both serum collections. Biomarkers were measured using ELISA or RT-qPCR and modeled as the log 2 post-to pre-breast cancer ratio. Results Overall, 180 women with a mean (SD) age of 67.0 (5.5) years were included. The mean (SD) vitality scores were 66.2 (17.2) and 59.7 (19.7) pre- and post-breast cancer, respectively. Using multivariable weighted linear regression, higher biomarker ratios of cystatin-C, IL-6, and GDF-15 were associated with a lower vitality score (i.e., higher fatigue). For example, for each 2-fold difference in cystatin-C biomarker ratio, the vitality score was lower by 7.31 points (95% CI: −14.2, −0.45). Conclusion Inflammatory and cardiac damage biomarkers are associated with fatigue in breast cancer survivors treated with radiation; however, these findings should be replicated in a larger sample. Biomarkers could be measured in clinical practice or assessed in risk prediction models to help identify patients at high risk for fatigue.