Inflammation, glucose metabolism, and nutritional markers in relation to all-cause and cardiac mortality among initial hemodialysis patients: a multicenter cohort study

Shi-Mei Hou¹Yuting Gao¹Menghuan Wu¹Yu-Xin Ren¹Jing Zheng²Yao Wang³Jing-yuan Cao²Xiao-Xu Wang³Yan Yang¹Bin Wang^2*Min Yang^1*Min Li^1*

• ¹First People's Hospital of Changzhou, Changzhou, China
• ²Southeast University Zhongda Hospital, Nanjing, China
• ³Affiliated Hospital of Yangzhou University, Yangzhou, China

Objective: To investigate the prognostic value of inflammatory biomarkers including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR), glucose metabolism (glucose-to-lymphocyte ratio, GLR), and nutritional (albumin, ALB) biomarkers for predicting all-cause and cardiac mortality in patients initiating hemodialysis (HD), and evaluates their incremental value when integrated into traditional risk models. Methods: A retrospective cohort of 795 initial HD patients (2014–2020) was analyzed, with follow-up through 2022. Cox proportional hazards models were used to assess associations between biomarkers and mortality. Predictive performance was evaluated using time-dependent ROC curves, C-index, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Patients were randomly assigned to training (n=557) and validation (n=238) sets, and a survival nomogram was developed based on a full-risk model incorporating both traditional and biomarker variables. Results: Elevated NLR, PLR, and GLR were independently associated with increased all-cause and cardiac mortality, whereas lower LMR and ALB were protective (all P < 0.05). NLR exhibited the highest predictive accuracy across 1-, 3-, and 5-year intervals, followed by GLR and PLR. The full-risk model significantly outperformed the baseline model, with AUCs up to 0.980 and 0.966 for all-cause mortality and 0.947 and 0.978 for cardiac mortality in training and validation sets, respectively (all P < 0.001). Improvements in C-index, NRI, and IDI supported its enhanced predictive utility. Conclusion: Incorporating inflammatory, glucose metabolism and nutritional biomarkers into traditional risk models substantially improves long-term mortality risk stratification in initial HD patients, offering a robust, clinically applicable tool to support individualized prognostic assessment and intervention planning.