Association of Red Blood Cell Distribution Width to Albumin Ratio (RAR) with Cancer Incidence and Prognosis in American Adults: NHANES 2005-2016

Background With the rising incidence and mortality rates of cancer, there is an urgent need for effective biomarkers to predict cancer occurrence and monitor its prognosis. The red blood cell distribution width to albumin ratio (RAR), a novel inflammatory biomarker, has unclear associations with both cancer occurrence and prognosis. This study aims to explore the relationship between RAR and cancer incidence, as well as the prognosis of cancer survivors. Methods This study included 21,452 adult participants from the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2016, of whom 1,910 had cancer. Weighted multivariable logistic regression was used to assess the association between RAR and cancer incidence. To evaluate the relationship between RAR and cancer prognosis, weighted multivariable Cox regression, restricted cubic splines (RCS), and subgroup analysis were employed. Additionally, propensity score matching (PSM) was conducted for sensitivity analysis. Results In the unadjusted model, RAR was significantly positively correlated with cancer incidence; however, this association became non-significant after adjusting for confounding factors. After fully adjusting for potential confounders, RAR was significantly associated with both all-cause and cancer-specific mortality in cancer survivors. Specifically, each additional unit increase in RAR was associated with a 2.42-fold increase in all-cause mortality (HR 2.42, 95% CI: 1.93, 3.03) and a 2.49-fold increase in cancer-specific mortality (HR 2.49, 95% CI: 1.79, 3.47). Subgroup analysis showed that higher RAR was associated with increased mortality risk across all subgroups. The prognostic model based on RAR had a C-index of 0.76, with AUC values of 0.77 for 5 years and 0.83 for 10 years. Conclusion RAR is significantly positively correlated with both all-cause and cancer-specific mortality in cancer survivors. The prognostic model based on RAR effectively predicts cancer survival and provides a basis for early intervention, particularly for populations at higher risk of poor outcomes.