Comparison between direct and indirect methods to diagnose malnutrition and cardiometabolic risk in haemodialisys patients

• Published in: Journal of human nutrition and dietetics 2017-10, Vol.30 (5), p.646-654
• Main Authors: Balbino, K. P., Epifânio, A. P. S., Ribeiro, S. M. R., Silva, L. D. M., Gouvea, M. G., Hermsdorff, H. H. M.
• Format: Article
• Language: English
• Subjects: Adipose tissue; Adult; Aged; Aged, 80 and over; Anthropometry; Body Composition; Body fat; Body Mass Index; cardiometabolic risk; Cardiovascular Diseases - blood; Cardiovascular Diseases - diagnosis; Cardiovascular Diseases - etiology; chronic kidney disease; Cross-Sectional Studies; Data processing; Diagnostic systems; Female; Health risk assessment; Health risks; Hemodialysis; Humans; Indicators; Male; Malnutrition; Malnutrition - blood; Malnutrition - diagnosis; Malnutrition - etiology; Metabolic Syndrome - blood; Metabolic Syndrome - diagnosis; Metabolic Syndrome - etiology; Middle Aged; modified global subjective assessment; Nutrition Assessment; Nutritional Status; objective global assessment; Patients; Prevalence; Renal Dialysis - adverse effects; Risk assessment; Risk Factors; ROC curve; Statistical analysis; Subjective assessment; Young Adult
• ISSN: 0952-3871; 1365-277X
• DOI: 10.1111/jhn.12468

Background The present study aimed to evaluate the nutritional status of patients undergoing haemodialysis (HD) by comparing nutritional risk scores with biochemical, anthropometric and body composition variables. Methods Eighty‐five individuals [65.9% male, mean (SD) age 62 (14) years] participated in a cross‐sectional study. Global Objective Assessment (GOA) and Modified Global Subjective Assessment (mGSA) scores, as well as biochemical, anthropometric and body composition data, were collected using standardised procedures. Results The prevalence of malnutrition ranged from 20.0% (% body fat by electrical bioimpedance) to 95.3% (by GOA), depending on the indicator or score used. According to the waist circumference, 61.2% of the individuals presented abdominal obesity and visceral adipose tissue was excessive in 20% of them. Malnutrition diagnosis by GOA showed the relationship between the anthropometric and body composition indicators, as assessed by the extent that the ratings of risk nutritional/mild malnutrition and mainly moderate malnutrition were accompanied by a significant decrease in nutritional status and body composition variables. However, with respect to categories of mGSA, no statistically significant differences were observed for nutritional status and body composition variables. In the receiver operator characteristic curve analyses, mGSA and GOA were good indicators for diagnosing malnutrition because both achieved an AUC > 0.5. Conclusions mGSA and GOA were more sensitive with respect to identifying individuals at nutritional risk compared to the isolated anthropometric indicators, thus indicating their utility in diagnostic malnutrition. However, individuals at high nutritional risk also presented cardiometabolic risk, as diagnosed mainly by central fat indicators, suggesting the application of both malnutrition and cardiometabolic risk markers in HD patients.