Body Composition Analysis in Dialysis Patients: Nutrition Screening and Prediction of Outcome
Background and objective: The prevalence of protein energy wasting is very high in dialysis patients and diagnosis is based on biochemical criteria, body composition and dietary intake. The body composition monitor (BCM) is crucial for early recognition of body composition disorders. Nutritional BCM parameters, as lean tissue index (LTI), skeletal muscle index (SMI), fat free mass index (FFMI) and fat tissue index (FTI), have been proved to be a validated markers for protein wasting. The aim of our study was to assess the relationship between BCM measured indices, antropometric measurements, biochemical markers of nutrition, inflammation and mortality in hemodialysis patients.
Materials and methods: Sixty-three patients treated with maintenance hemodialysis for at least 3 months were included into the study in October 2014. Demographic, anthropometric, clinical, dialysis vintage data and comorbidities were recorded at the time of study enrollment. Assessment of body composition was performed using bioimpedance. Nutritional BCM parameters were directly measured and expressed as a percentage of total body mass, or normalized to the body surface area (m2) as indices. The patients were followed until July 2017, the all-cause mortality being the primary outcome.
Results: The median age of the patients was 62 ± 15 years. There were 15 deaths (24% of the patients) during a median follow-up period of 25 months. Among the BCM measured nutritional markers, LTI, SMI and FFMI were significantly lower in the deceased patients group compared with alive patients, while nutritional parameters, expressed in percentages, and FTI did not differ between the groups. Deceased patients also were older, had lower creatinine and higher C-reactive protein (CRP) levels. Correlation analysis between body mass index (BMI) and nutritional BCM parameters showed that BMI had a strong positive correlation with FTI (r = 0.875, P < 0.001). Also, a very strong negative correlation was found between FTM and SM (r = −0.991, P < 0.001). A lower SMI was associated with an increased risk of death: this association was confirmed both in the univariate and the multivariate logistic regression model, when adjusted to age, creatinine and CRP. Kaplan-Meier analysis revealed that a higher SMI was associated with better survival during follow-up: the group of patients with SMI ≥ 10 kg/m2 had better survival than the group of patients with SMI < 10 kg/m2 (P = 0.006).
Conclusions: Our data showed that, of all measured nutritional parameters, the skeletal muscle index was the most important predictor for survival derived. The lower skeletal muscle index was associated with the increased risk of death.
Correspondence to I. Štramaitytė Hospital of Lithuanian University of Health Sciences, Eivenių 2, LT-50161 Kaunas, Lithuania E-mail address: firstname.lastname@example.org