ArticleFrontiers in nutrition2026
Longitudinal trajectories of nutrition-related biomarkers and mortality risk in maintenance hemodialysis patients: a joint modeling analysis.
Article in Frontiers in nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Malnutrition and nutrition-related abnormalities are common in people with kidney failure, particularly among patients receiving maintenance hemodialysis, and are associated with adverse clinical outcomes. Nutritional status and related biological processes are dynamic; however, the prognostic relevance of their longitudinal changes remains incompletely understood. Objective: To investigate the associations between longitudinal trajectories of nutrition-related biomarkers and all-cause mortality in patients receiving maintenance hemodialysis using joint modeling. Methods: This retrospective longitudinal cohort study included 345 maintenance hemodialysis patients, among whom 99 deaths occurred during follow-up. Longitudinal changes in nutrition-related biomarkers were analyzed in relation to all-cause mortality using joint models that integrate repeated biomarker measurements with time-to-event outcomes. Dynamic prediction was evaluated using a fixed 12-month prediction horizon. Results: In adjusted univariable joint models, several longitudinal biomarkers were associated with all-cause mortality. In the primary multivariable joint model incorporating longitudinal C-reactive protein (CRP) and serum iron, the current value of CRP was positively associated with mortality risk (HR, 1.030; 95% credible interval [CrI], 1.001-1.058), as was the current value of serum iron (HR, 1.073; 95% CrI, 1.015-1.132). Across landmark times from 6 to 36 months, time-dependent AUC values ranged from 0.756 to 0.807 for 12-month dynamic prediction. Conclusion: The current values of CRP and serum iron were positively associated with all-cause mortality in maintenance hemodialysis patients. Joint modeling of repeated biomarker measurements may help characterize dynamic nutrition-related inflammatory and metabolic risk in kidney failure.
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