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

## TL;DR

The study shows that adding inflammation, glucose metabolism, and nutrition markers improves predicting death risk in patients starting hemodialysis.

## Contribution

The study introduces a new risk model combining traditional and novel biomarkers for better mortality prediction in hemodialysis patients.

## Key findings

- Elevated NLR, PLR, and GLR are linked to higher mortality in hemodialysis patients.
- The full-risk model with biomarkers outperformed traditional models in predicting mortality.
- NLR showed the highest predictive accuracy across multiple time intervals.

## Abstract

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.

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.

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.

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.

Flowchart and table from a study on inflammation, glucose metabolism, and nutritional markers in hemodialysis patients. Methods include following 1,108 patients from 2014 to 2020, resulting in 735 participants analyzed in training and validation sets. Results show hazard ratios for mortality with markers like NLR, PLR, and ALB, offering insights into risk prediction. Conclusion highlights enhancing traditional risk models with these biomarkers.