Nutritional status of older patients on hemodialysis: Which nutritional markers can best predict clinical outcomes?

Juliana Rodrigues; Fernanda Santin; Flavia Dos Santos Barbosa Brito; Bengt Lindholm; Peter Stenvinkel; Carla Maria Avesani

doi:10.1016/j.nut.2019.03.002

Nutritional status of older patients on hemodialysis: Which nutritional markers can best predict clinical outcomes?

Nutrition. 2019 Sep:65:113-119. doi: 10.1016/j.nut.2019.03.002. Epub 2019 Mar 20.

Authors

Juliana Rodrigues¹, Fernanda Santin², Flavia Dos Santos Barbosa Brito², Bengt Lindholm³, Peter Stenvinkel³, Carla Maria Avesani⁴

Affiliations

¹ Nutrition Institute, Rio de Janeiro State University, Rio de Janeiro, Brazil. Electronic address: julianacordeiro.nutri@gmail.com.
² Nutrition Institute, Rio de Janeiro State University, Rio de Janeiro, Brazil.
³ Renal Medicine and Baxter Novum, CLINTEC, Karolinska Institutet, Stockholm, Sweden.
⁴ Nutrition Institute, Rio de Janeiro State University, Rio de Janeiro, Brazil; Renal Medicine and Baxter Novum, CLINTEC, Karolinska Institutet, Stockholm, Sweden.

PMID: 31082788
DOI: 10.1016/j.nut.2019.03.002

Abstract

Objective: The aim of this study was to investigate nutritional status in older patients undergoing maintenance hemodialysis (MHD) to determine the prevalence of nutritional markers indicating protein-energy wasting (PEW) as assessed by subjective global assessment (SGA) and other methods, and to explore which nutritional markers can best predict clinical outcomes.

Methods: The study included 173 patients (median age 69 y; 65% men; 38% diabetes) undergoing MHD for >3 mo. Nutritional markers included SGA, malnutrition-inflammation score (MIS), geriatric nutritional risk index (GNRI), handgrip strength (HGS), midarm muscle circumference (MAMC), triceps skinfold thickness (SKF), calf circumference, and albumin. Associations between PEW (diagnosed by different measures and thresholds) and risk for hospitalization (by Poisson regression) and all-cause mortality (by Cox proportional hazards model) were analyzed.

Results: Depending on methods and thresholds used, the prevalence of nutritional markers indicatingPEW varied from 6.9% to 59.5%. In the Poisson models adjusted for age, sex, dialysis length, and diabetes, low SGA, HGS, albumin, and high MIS score were associated with high hospitalization events, whereas in the bivariate Cox regression models adjusted for the same variables, low SGA, GNRI, BMI, calf circumference, and high MIS score were associated with high hazard ratio (HR) for mortality. In addition, in the multivariate models, SGA showed the strongest association with mortality (HR, 2.32; 95% confidence interval [CI], 1.27-4.24) and together with MIS (HR, 2.09; 95% CI, 1.20-3.64), the highest values of C-statistics.

Conclusions: Among older MHD patients, the prevalence of nutritional markers indicating PEW varies substantially depending on methods applied. SGA, MIS, BMI, GNRI, calf circumference, and HGS predicted worse outcomes. SGA and MIS showed the strongest association with hospitalization and mortality risk in the adjusted models.

Keywords: Aging; Chronic kidney disease; End-stage renal disease; Hospitalization; Malnutrition; Mortality; Protein–energy wasting.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Aged
Biomarkers / analysis
Body Mass Index
Female
Geriatric Assessment / methods*
Humans
Inflammation
Male
Middle Aged
Nutrition Assessment*
Nutritional Status
Prevalence
Proportional Hazards Models
Protein-Energy Malnutrition / diagnosis*
Protein-Energy Malnutrition / epidemiology
Protein-Energy Malnutrition / etiology
Renal Dialysis / adverse effects*
Risk Assessment / methods*
Risk Factors
Serum Albumin / analysis
Severity of Illness Index

Substances

Biomarkers
Serum Albumin