Latent Classes of Inflammation-Nutrition-Metabolism Status in Maintenance Hemodialysis: Associations with Arteriovenous Fistula Function and Cardiovascular Prognosis.

Authors

Shanshan Sun, Xiaoyan Yu, Ying Shi, Ruinuo Hu, Shuangyu Li

Published in

Journal of multidisciplinary healthcare. Volume 19. Pages 625937. Epub Sep 07, 2026.

Abstract

To identify heterogeneous subgroups of maintenance hemodialysis (MHD) patients by integrating inflammatory, nutritional, and metabolic indicators using latent class analysis (LCA), and to explore the associations of different subgroups with arteriovenous fistula (AVF) function and cardiovascular prognosis.
This single-center cohort study enrolled 180 MHD patients from January 2022 to December 2025. Baseline data on inflammatory (hs-CRP, NLR, PLR), nutritional (albumin, prealbumin, BMI), and metabolic (calcium, phosphorus, iPTH) indicators were collected. LCA was used to identify patient subgroups. A 1-year prospective follow-up recorded composite endpoint events (AVF dysfunction, major adverse cardiovascular events, all-cause death). Kaplan-Meier curves and Cox regression identified prognostic differences. A nomogram was constructed using LASSO regression.
Three subgroups were identified: high-inflammation and low-nutrition type (26.1%), metabolism-dominant type (40.0%), and relatively stable type (33.9%) (entropy=0.84). The high-inflammation and low-nutrition type showed the lowest AVF blood flow and the highest venous pressure (P<0.001). At 1-year follow-up, this type had the highest incidence of composite endpoint events (46.8%) and all-cause mortality (23.4%) (adjusted HR=3.82, 95% CI: 1.56-9.34, P=0.003). The nomogram showed good discrimination (C-index=0.78, 95% CI: 0.72-0.84) and calibration (Hosmer-Lemeshow P=0.31).
MHD patients exhibit three distinct subgroups based on inflammation-nutrition-metabolism status. The high-inflammation and low-nutrition type is independently associated with worse AVF function and poorer cardiovascular prognosis. The LCA-based nomogram may assist in precision risk stratification.

PMID:
42732379
Bibliographic data and abstract were imported from PubMed on 13 Sep 2026.

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