P523 - CORRELATION AND MULTIVARIATE REGRESSION ANALYSIS BETWEEN HANDGRIP STRENGTH AND BIOELECTRIC IMPEDANCE ANALYSIS PARAMETERS IN PATIENTS WITH INFLAMMATORY BOWEL DISEASE

P523

CORRELATION AND MULTIVARIATE REGRESSION ANALYSIS BETWEEN HANDGRIP STRENGTH AND BIOELECTRIC IMPEDANCE ANALYSIS PARAMETERS IN PATIENTS WITH INFLAMMATORY BOWEL DISEASE

A. Vadarlis^1,*, X. Theodoridis¹, T. Maris², M. G. Pramateftakis³, G. Germanidis⁴, M. Chourdakis¹

¹Laboratory of Hygiene, Social & Preventive Medicine and Medical Statistics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, ²Gastroenterology, General Hospital of Thessaloniki "G.Papanikolaou", ³Fourth Surgical Department, General Hospital of Thessaloniki “G. Papanikolaou“, School of Medicine, ⁴Division of Gastroenterology, First Department of Internal Medicine, “AHEPA” University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece

Rationale: Sarcopenia can frequently complicate the disease course in inflammatory bowel disease (IBD). However, there is paucity of data regarding the utilization of handgrip strength (HGS) measurement in early detection of sarcopenia. The scope of this study was to investigate the association between HGS and bioelectric impedance analysis (BIA) parameters in patients with IBD

Methods: This study included patients diagnosed with Crohn’s disease (CD) or ulcerative colitis (UC) from two IBD centers. Patients with high output stoma, fluid collections, under corticosteroid or diuretic treatment were excluded. HGS was measured with a digital isokinetic dynamometer and the body composition was analyzed with a BIA, under a strict protocol. HGS gender- and age-standardized z-scores were calculated using reference values

Results: A total of 144 patients, 96 with CD and 48 with UC included in the study. The prevalence of sarcopenia was 18% and HGS z-scores identified 1.6 more sarcopenic patients compared to HGS. Statistically significant correlations were found between HGS and patients’ total body water (r=0,772, p<0.001), lean mass (r_s=0.764, p<0.001), body cell mass (r_s=0.770, p<0.001) and fat-free mass index (FFMI, r_s=0.567, p<0.001]. The multivariate regression analysis provided an equation that can accurately predict the lean mass using five variables (Lean=-27.148+38.521height+0.357weight-7.093gender-0.130age+0.77HGS, R²=0.953, where gender=1 for male and 2 for female)

Conclusion: HGS strongly correlates with BIA-derived body composition analysis parameters. The calculation of age and gender standardized -z-scores detects more sarcopenic patients compared to cut-off values. Although the regression model using five simple variables can predict  the quantity of lean mass with great accuracy, further validation is required to be used in clinical practice

Disclosure of Interest: None declared