AUTHOR=Kurban Mrigul , Zeng Na , Wang Meng , Liu Hong , Wu Jin-Ru , Feng Guo , Liu Min , Guo Qin 

TITLE=Role of Human Body Composition Analysis and Malnutrition Risk Questionnaire in the Assessment of Nutritional Status of Patients With Initially Diagnosed Crohn's Disease

JOURNAL=Frontiers in Medicine

VOLUME=Volume 7 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2020.00106

DOI=10.3389/fmed.2020.00106

ISSN=2296-858X

ABSTRACT=Objective: This study was to investigate the role and necessity of human body composition analysis in assessing the nutritional status of initially diagnosed Crohn's disease (CD) patients. Methods: 47 initially diagnosed CD patients were recruited. The skeletal muscle mass index (SMI), fat-free mass index (FFMI), body fat mass (BFM), body fat percent (BFP), visceral fat area (VFA) and body cell mass (BCM) were determined with Biospace Inbody S10 composition analyzer. Results: In 47 patients with initially diagnosed CD, SMI could determine the muscular mass reduction that could not be determined by the body mass index (BMI) (35.3%), albumin (ALB) (65.6%), nutrition risk screening (NRS)2002 (25.0%) and Patient-Generated Subjective Global Assessment (PG-SGA) (55.6%). FFMI could determine the malnutrition that could not be determined by the BMI (58.8%), albumin (90.6%), NRS2002 (50.0%) and PG-SGA (55.6%). VFA in the fistulizing CD patients was significantly higher than in the stricturing and non-fistulizing, non-stricturing patients (P<0.05). SMI and BMI had the same performance (P=1.000) and general consistence (P=0.001) in the assessment of malnutrition; SMI and ALB had different performance (P<0.001) and inconsistence was noted (P=0.489) in the assessment of malnutrition; the results of nutrition assessment were different between SMI and NRS2002 (P=0.002), and inconsistence was observed (P=0.071). SMI and PG-SGA had the same performance in the assessment of nutrition (P=0.143), but there was inconsistence (P=0.464). FFMI and BMI had general consistence in the assessment of malnutrition (P<0.001), but the positive rate determined by FFMI (85.1%) was markedly higher than that by BMI (63.8%) (P=0.002). FFMI and ALB had different performance in the assessment of malnutrition (P<0.001) and there was inconsistence (P=0.877). FFMI and NRS2002 had the same performance in the assessment of malnutrition (P=0.453), but the consistence was poor (P=0.039). The results determined by SMI and PG-SGA were consistent (P=0.727), but the consistence was poor (P=0.006). Conclusion: Human body composition analysis can identify the patients with muscular mass reduction that can’t be identified by commonly used nutrition assessment scale / parameters. Thus, it is helpful for the assessment of disease severity and also important for the nutrition assessment in CD patients.