Protein-energy wasting, which involves loss of fat and muscle mass, is prevalent and is associated with mortality in hemodialysis (HD) patients. We investigated the associations of fat tissue and muscle mass indices with all-cause mortality in HD patients. The study included 162 patients undergoing HD. The fat tissue index (FTI) and skeletal muscle mass index (SMI), which represent respective tissue masses normalized to height squared, were measured by bioimpedance analysis after dialysis. Patients were divided into the following four groups according to the medians of FTI and SMI values: group 1 (G1), lower FTI and lower SMI; G2, higher FTI and lower SMI; G3, lower FTI and higher SMI; and G4, higher FTI and higher SMI. The associations of the FTI, SMI, and body mass index (BMI) with all-cause mortality were evaluated. During a median follow-up of 2.5 years, 29 patients died. The 5-year survival rates were 48.6%, 76.1%, 95.7%, and 87.4% in the G1, G2, G3, and G4 groups, respectively (P = 0.0002). The adjusted hazard ratio values were 0.34 (95% confidence interval [CI] 0.10–0.95, P = 0.040) for G2 vs. G1, 0.13 (95%CI 0.01–0.69, P = 0.013) for G3 vs. G1, and 0.25 (95%CI 0.07–0.72, P = 0.0092) for G4 vs. G1, respectively. With regard to model discrimination, on adding both FTI and SMI to a model with established risk factors, the C-index increased significantly when compared with the value for a model with BMI (0.763 vs. 0.740, P = 0.016). Higher FTI and/or higher SMI values were independently associated with reduced risks of all-cause mortality in HD patients. Moreover, the combination of the FTI and SMI may more accurately predict all-cause mortality when compared with BMI. Therefore, these body composition indicators should be evaluated simultaneously in this population.

The ratio of extracellular fluid (ECF) to intracellular fluid (ICF) may be associated with mortality in patients undergoing hemodialysis, possibly associated with protein-energy wasting. We therefore investigated the relationship of the ECF/ICF ratio and the geriatric nutritional risk index (GNRI) with the all-cause and cardiovascular-specific mortality in 234 patients undergoing hemodialysis. Bioimpedance analysis of the ECF and ICF was performed and the ECF/ICF ratio was independently associated with GNRI (β = −0.247, p < 0.0001). During a median follow-up of 2.8 years, 72 patients died, of which 29 were cardiovascular. All-cause mortality was independently associated with a lower GNRI (adjusted hazard ratio [aHR] 3.48, 95% confidence interval [CI] 2.01–6.25) and a higher ECF/ICF ratio (aHR 11.38, 95%CI 5.29–27.89). Next, we divided patients into four groups: group 1 (G1), higher GNRI and lower ECF/ICF ratio; G2, lower GNRI and lower ECF/ICF ratio; G3, higher GNRI and higher ECF/ICF ratio; and G4, lower GNRI and higher ECF/ICF ratio. Analysis of these groups revealed 10-year survival rates of 91.2%, 67.2%, 0%, and 0% in G1, G2, G3, and G4, respectively. The aHR for G4 versus G1 was 43.4 (95%CI 12.2–279.8). Adding the GNRI alone, the ECF/ICF ratio alone, or both to the established risk model improved the net reclassification improvement by 0.444, 0.793 and 0.920, respectively. Similar results were obtained for cardiovascular mortality. In conclusion, the ECF/ICF ratio was independently associated with GNRI and could predict mortality in patients undergoing hemodialysis. Combining the GNRI and ECF/ICF ratio could improve mortality predictions.

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Although an increased body mass index is associated with lower mortality in patients undergoing hemodialysis (HD), known as the “obesity paradox,” the relationship of abdominal fat levels with all-cause mortality has rarely been studied. We investigated the impact of computed-tomography-measured abdominal fat levels (visceral fat area (VFA) and subcutaneous fat area (SFA)) on all-cause mortality in this population. A total of 201 patients undergoing HD were enrolled and cross-classified by VFA and SFA levels according to each cutoff point, VFA of 78.7 cm2 and SFA of 93.2 cm2, based on the receiver operator characteristic (ROC) curve as following; group 1 (G1): lower VFA and lower SFA, G2: higher VFA and lower SFA, G3: lower VFA and higher SFA, G4: higher VFA and higher SFA. During a median follow-up of 4.3 years, 67 patients died. Kaplan–Meier analysis revealed 10-year survival rates of 29.0%, 50.0%, 62.6%, and 72.4% in G1, G2, G3, and G4 (p < 0.0001), respectively. The adjusted hazard ratio was 0.30 (95% confidence interval [CI] 0.05–1.09, p = 0.070) for G2 vs. G1, 0.37 (95% CI 0.18–0.76, p = 0.0065) for G3 vs. G1, and 0.21 (95% CI 0.07–0.62, p = 0.0035) for G4 vs. G1, respectively. In conclusion, combined SFA and VFA levels were negatively associated with risks for all-cause mortality in patients undergoing HD. These results are a manifestation of the “obesity paradox.”

Regular nutritional assessment may decrease the mortality rate in patients undergoing hemodialysis. This study aimed to evaluate whether annual change in geriatric nutritional risk index (ΔGNRI) can precisely predict mortality. We retrospectively examined 229 patients undergoing hemodialysis who measured geriatric nutritional risk index (GNRI). Patients were divided into four groups according to the baseline GNRI of 91.2, previously reported cutoff value, and declined or maintained GNRI during the first year (ΔGNRI < 0% vs. ΔGNRI ≥ 0%): Group 1 (G1), GNRI ≥ 91.2 and ΔGNRI ≥ 0%; G2, GNRI ≥ 91.2 and ΔGNRI < 0%; G3, GNRI < 91.2 and ΔGNRI ≥ 0%; and G4, GNRI < 91.2 and ΔGNRI < 0%. They were followed for mortality. During a median follow-up of 3.7 (1.9–6.9) years, 74 patients died, of which 35 had cardiovascular-specific causes. The GNRI significantly decreased from 94.8 ± 6.3 to 94.1 ± 6.7 in the first year (p = 0.035). ΔGNRI was negatively associated with baseline GNRI (ρ = −0.199, p = 0.0051). The baseline GNRI < 91.2 and ΔGNRI < 0% were independently associated with all-cause mortality (adjusted hazard ratio (aHR) 2.59, 95%, confidence interval (CI) 1.54–4.33, and aHR 2.33, 95% CI 1.32–4.32, respectively). The 10-year survival rates were 69.8%, 43.2%, 39.9%, and 19.2% in G1, G2, G3, and G4, respectively (p < 0.0001). The aHR value for G4 vs. G1 was 3.88 (95% CI 1.62–9.48). With regards to model discrimination, adding ΔGNRI to the baseline risk model including the baseline GNRI significantly improved the net reclassification improvement by 0.525 (p = 0.0005). With similar results obtained for cardiovascular mortality. We concluded that the ΔGNRI could not only predict all-cause and cardiovascular mortality but also improve predictability for mortality; therefore, GNRI might be proposed to be serially evaluated.