Low blood glucose levels and high urea nitrogen levels affect patient prognosis, but few studies have investigated whether the blood urea nitrogen to glucose (BGR) ratio predicts the risk of death.This retrospective research examined the connection between the BGR and 365-day mortality in patients with chronic kidney disease (CKD) stages 1-4 admitted to an intensive care unit (ICU). The study utilized data from 6,380 patients in the Medical Information Mart for Intensive Care IV version 2.2 (MIMIC-IV v2.2), taking into account confounding factors such as demographics, vital signs, laboratory indicators, and comorbidities. The study employed both univariate and multivariate Cox regression analyses stratified by BGR quartiles. Additionally, restricted cubic spline regression and inflection point analysis were used to explore the linear relationship between BGR and 365-day mortality, while Kaplan-Meier curve analysis was used to observe mortality changes under different BGR stratifications. Subgroup and mediating effect analyses were performed to evaluate the robustness of BGR's effect on 365-day mortality. The study found a cumulative 365-day mortality rate of 34.2% among CKD stages 1-4 patients, with a 2.43-fold increase in the risk of death associated with BGR and at least a 44% increase in the risk of death for each unit increase in BGR (P = 0.022). A significant nonlinear relationship was identified, showing a stepwise change in the risk of death with a marked increase in the slope of the curve for BGR values below 0.52 and above 0.9 (P < 0.001). Subgroup analyses indicated interactions between BGR and factors such as age, sepsis, first-day antibiotic use, and cerebrovascular disease (P < 0.05). In conclusion, this study confirms that BGR is a significant and stable predictor of 1-year mortality risk in patients with CKD stages 1-4. Interventions aimed at timely adjustment, correction of metabolic imbalances, reduction of inflammation, and management of BGR levels are beneficial for reducing mortality in this patient population.