Assessment of diagnostic performance of handgrip strength in sarcopenia screening and determining optimal cut-offs in inflammatory bowel disease

Assessment of diagnostic performance of handgrip strength in sarcopenia screening and determining optimal cut-offs in inflammatory bowel disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Assessment of diagnostic performance of handgrip strength in sarcopenia screening and determining optimal cut-offs in inflammatory bowel disease Andreas Vadarlis, Xenophon Theodoridis, Theofanis Maris, Manousos Pramateftakis, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8167171/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objectives Sarcopenia frequently complicates inflammatory bowel disease (IBD), indicating a poor prognosis. Current diagnostic criteria and handgrip strength (HGS) reference values are often not validated for the IBD population. This study aimed to investigate the utility of HGS as a diagnostic tool for sarcopenia in IBD, examining its association with bioelectrical impedance analysis (BIA) and identifying optimal HGS thresholds for screening. Health sciences/Diseases/Gastrointestinal diseases/Inflammatory bowel disease Health sciences/Diseases/Nutrition disorders/Malnutrition Health sciences/Diseases/Gastrointestinal diseases/Nutrition disorders/Malnutrition Handgrip strength Inflammatory Bowel Disease Sarcopenia Crohn’s Disease Ulcerative Colitis Figures Figure 1 Figure 2 Introduction Sarcopenia, characterized by a progressive loss of quantity, function and quality of muscle mass, is considered a common complication in inflammatory bowel disease (IBD) affecting almost a fifth of patients and can lead to adverse outcomes such as a higher risk of surgery, treatment failure and hospitalization[ 1 – 5 ]. Similar to other chronic inflammatory diseases, multiple factors can contribute to sarcopenia in IBD patients, such as inflammation-driven catabolism, disturbed protein synthesis, reduced protein intake, nutrient malabsorption, corticosteroid use and lack of physical exercise[ 5 , 6 ]. Nevertheless, establishing a definitive diagnosis of sarcopenia in routine clinical practice presents considerable challenges due to the absence of a universally accepted definition[ 7 – 10 ]. Guidelines typically advocate for an initial screening phase, often employing instruments such as the Strength, Assistance in walking, Rise of a chair, Climb stairs, Falls (SARC-F) questionnaire[ 11 ] followed by a muscle strength assessment (handgrip strength or chair stand test)[ 12 ]. Subsequent confirmation of diminished muscle quantity is achieved through various body composition analysis techniques, including Dual-energy X-ray absorptiometry (DXA), bioelectric impedance analysis (BIA), Computed Tomography (CT), or magnetic resonance imaging (MRI)[ 13 , 14 ]. Finally, the comprehensive evaluation of sarcopenia severity is conducted using physical performance assessments such as gait speed, the Short Physical Performance Battery (SPPB), the Timed-Up and Go test, or a 400-meter walk[ 15 ]. Despite recommendations for sarcopenia screening, the established reference values for these tools have not been validated within the inflammatory bowel disease population. Furthermore, limited research has explored the utility of handgrip strength in sarcopenia assessment, particularly its correlation with muscle mass quantification in IBD patients. Consequently, the empirical basis for arbitrary HGS cut-off values, such as < 27 kg for men and < 16 kg for women, remains poorly substantiated, and the precise role of HGS in sarcopenia detection among individuals with IBD is yet to be fully elucidated[ 5 , 10 ]. This observational study primarily aims to investigate the utility of handgrip strength in the diagnosis of sarcopenia in inflammatory bowel disease, specifically examining its association with bioelectrical impedance analysis-derived body composition parameters among IBD patients. Secondary objectives include exploring the applicability of age- and gender-standardized HGS z-scores and identifying optimal HGS thresholds for sarcopenia screening in IBD patients. Subjects and Methods This study adhered to the “Strengthening-the-reporting-of-observational-studies-in-epidemiology” (STROBE) reporting guidelines[ 16 ]. Selection of participants and study design This cross-sectional study was conducted in two IBD centers (“G. Papanikolaou and AHEPA University Hospitals, Thessaloniki, Greece). A power analysis (0.05 significance, 0.95 power, 0.3 correlation effect) determined a minimum sample size of 138 participants. Eligible participants were adult patients diagnosed with Crohn’s disease (CD) or Ulcerative colitis (UC) according to clinical, radiologic, endoscopic and histologic criteria[ 17 ], under surveillance in the ambulatory setting. Patients were included in the study regardless of age, gender, disease activity, behavior, location, endoscopic severity, nutritional status or treatment. Patients with high output stoma/fistula, ascites/fluid collections, high corticosteroid dosage/diuretic, parenteral nutrition, or with an implantable defibrillator/pacemaker were excluded. Furthermore, patients unable to complete the dynamometer test due to severe peripheral arthropathy or unwilling to provide consent were also excluded from the study. Data collection Disease activity was evaluated using Harvey-Bradshaw-Index (HBI)[ 18 ] and Simple-Clinical-Colitis-Activity-Index (SCCAI)[ 19 ] for CD and UC respectively. Disease course data included location, behavior, history of abdominal surgery (including intestinal resection), number of corticosteroid courses, extraintestinal manifestations, IBD-related hospitalizations, and ongoing maintenance treatment. Anthropometric data regarding height, weight, waist circumference (WC), hip circumference (HP) were meticulously collected under a strict protocol [Centers for disease control and prevention (CDC) anthropometry procedure manual] by the same investigator using calibrated equipment and standardized techniques (stadiometer, digital scale, non-stretchable tape) to the nearest 0.1cm/kg. Waist circumference (WC) was measured midway between the lowest rib and the iliac crest, and hip circumference over the great trochanters[ 20 ]. Diagnosis of sarcopenia Sarcopenia was defined according to the European Working Group on Sarcopenia in Older People 2 (EWGSOP2), where sarcopenia is probable in the case of reduced muscle strength, definite in the presence of additional low muscle quantity and severe in the case of above and low physical performance[ 21 ]. Handgrip strength measurement Muscle strength was evaluated via handgrip strength, utilizing a digital calibrated isokinetic dynamometer (Jamar dynamometer) measured in kilograms. A standardized protocol was followed with the patient sitting in the same low backed–fixed arm rests chair, in the same sited position (elbow fixed in 90 degrees and wrist in neutral zero degrees position). The highest of three measurements from the dominant hand was recorded, with participants performing maximal isometric contractions for 3–5 seconds and one-minute rest intervals. All assessments were performed by the same trained researcher to ensure consistency and minimize inter-observer variability[ 22 ]. Body composition analysis Bioelectric Impedance Analysis was utilized to assess body composition of the participants following a strict protocol that included 8-hours of fasting. Participants were advised to wear light clothes, avoid carrying metallic objects, and refrain from alcohol or strenuous physical activity for 24-hours. Before testing, participants rested supine for 10-minutes with arms 30 degrees from the body and legs separated. Measurements were performed on a non-conductive exam table with a calibrated analyzer. Electrode sites were cleaned with alcohol, and single-use electrodes were placed according to the manufacturer’s manual. A Bodystat Quadscan 4000 touch impedance analyzer collected data on body fat mass, lean fat mass, total body water, body cell mass, and fat-free mass index, derived from raw measurements at 5, 50, 100, and 200 kHz. All BIA measurements were performed by a single, trained investigator[ 23 , 24 ]. Physical performance evaluation Physical performance was evaluated using the Short Physical Performance Battery (SPPB), assessing balance, lower extremity strength, and functional capacity via balance test (static balance in side-by-side, semi-tandem, and tandem stands, ten seconds each), gait speed (time of a 4-meter walk at a usual pace) and chair stand tests (time for five chair stands). A cumulative score (0–12) indicates performance, with ≤ 8 points indicating poor physical performance[ 25 ]. Statistical Analysis A statistical software package was used for data analysis (IBM,SPSS-Statistics,version29.0.1.0). Normality was assessed using Kolmogorov-Smirnov and Shapiro-Wilk tests, visualized with histograms and Q-Q plots. Pearson correlation was applied for normally distributed continuous variables, while Spearman correlation was used for non-parametric continuous variables. Z-scores were calculated to compare handgrip strength to age- and gender-specific normal values, and percentiles were derived from a standard normal distribution table[ 26 ]. Multiple linear regression analyzed associations and predictive capacities, with assumptions (linearity, normality of residuals, homoscedasticity, independence) rigorously checked, and multicollinearity mitigated. A stepwise approach added variables at p 0.10. Analysis of Variance (ANOVA) confirmed model significance. Statistical significance for all analyses was set at p < 0.05. Receiver operating characteristic (ROC) curves evaluated Handgrip Strength diagnostic accuracy and optimal cut-off values for sarcopenia. Sensitivity and subgroup analyses were also conducted. Results Patient characteristics A total of 144 participants diagnosed with IBD were included in the study (flow chart and reasons for exclusion are depicted in Suppl. Figure 1). The participants’ median age was 46 years with a range of 62 years, while 55% of them were males. Median body mass index (BMI) was 26 kg/m 2 with a range of 35 kg/m 2 and 47% of the patients belonged to the overweight/obese group, whereas only 4% were categorized as underweight. The disease activity was evaluated to be in remission in 43 patients with CD (45%) and in 19 patients with UC (40%), while was mild in 29%, moderate in 22% and severe in 6% of the participants. Surgical intervention, primarily ileum resection, was reported by 31% of CD patients, whereas no UC patients underwent surgery. On average the patients had received 3.8 times corticosteroid courses and experienced 2.6 hospitalizations. The majority of patients received infliximab as a maintenance treatment (55%), followed by ustekinumab (17%) and vedolizumab (10%) (Table 1 and Suppl. table 1). Table 1 Participants’ Characteristics Characteristic CD UC Total (N,%) 144 (100%) N Female (N,%) 46 (32%) Male (N,%) 50 (34%) Female (N,%) 19 (13%) Male (N,%) 29 (20%) Age (years) 47.7 ± 16.2 42.5 ± 13.9 48.0 ± 17.3 47.6 ± 19.4 45.9 ± 16.4 BMI (kg/m 2 ) 26.3 ± 5.1 25.7 ± 5.26 27.0 ± 6.97 27.0 ± 6.47 26.4 ± 5.68 Height (m) 1.64 ± 0.06 1.78 ± 0.07 1.62 ± 0.06 1.76 ± 0.07 1.71 ± 0.10 Weight (kg) 70.8 ± 14.1 81.9 ± 17.2 70.9 ± 19.6 84.8 ± 22.4 77.5 ± 18.7 WC (cm) 90.3 ± 14.4 93.2 ± 20 88.4 ± 17.6 97.1 ± 15.5 92.4 ± 17.5 HC (cm) 104 ± 13.3 102 ± 9.30 103 ± 12.1 105 ± 10.9 103 ± 11.3 WHR 0.86 ± 0.07 0.91 ± 0.16 0.85 ± 0.08 0.92 ± 0.08 0.88 ± 0.11 No. Corticosteroid courses 3.72 ± 3.91 3.76 ± 4.88 3.31 ± 3.55 3.56 ± 4.62 3.80 ± 5.28 No. Hospitalizations 2.64 ± 4.49 3.40 ± 3.73 1.88 ± 2.47 1.76 ± 1.71 2.60 ± 3.58 No. extraintestinal manifestations 1.25 ± 1.41 0.71 ± 0.84 0.41 ± 0.62 0.33 ± 0.56 0.78 ± 1.07 No. Surgical resection 0.24 ± 0.43 0.38 ± 0.49 0 0 0.31 ± 0.46 HGS (kg) 28.0 ± 7.22 51.4 ± 10.6 26.4 ± 5.93 40.8 ± 10.3 38.5 ± 14.0 HGS z-score -0.22 ± 0.98 -1.91 ± 1.64 0.14 ± 2.19 -1.66 ± 1.29 -0.20 ± 1.1 Percentile 0.43 ± 0.28 0.52 ± 0.30 0.39 ± 0.3 0.35 ± 0.32 0.43 ± 0.30 Probable sarcopenia (low HGS) 4 (8.69%) 0 1 (5.26%) 2 (7.00%) 7 (4.86%) Sarcopenia 15 (32.6%) 9 (18.0%) 5 (26.3%) 4 (13.8%) 33 (22.9%) Severe sarcopenia 3 (6.52%) 0 4 (21.0%) 6 (20.7%) 13 (9.03%) Active disease 29 (63.0%) 20 (40.0%) 12 (63.1%) 14 (48.3%) 75 (52.1%) Moderate-severe disease 12 (26.0%) 5 (10.0%) 4 (21%) 9 (31.0%) 30 (20.8%) BMI (Body Mass Index), WC (Waist Circumference), HC (Hip Circumference), WHR (Waist-to-Hip ratio), HGS (Handgrip Strength) Table 1 Participants’ Characteristics BMI (Body Mass Index), WC (Waist Circumference), HC (Hip Circumference), WHR (Waist-to-Hip ratio), HGS (Handgrip Strength) Characteristic CD UC Total (N,%) 144 (100%) N Female (N,%) 46 (32%) Male (N,%) 50 (34%) Female (N,%) 19 (13%) Male (N,%) 29 (20%) Age (years) 47.7 ± 16.2 42.5 ± 13.9 48.0 ± 17.3 47.6 ± 19.4 45.9 ± 16.4 BMI (kg/m 2 ) 26.3 ± 5.1 25.7 ± 5.26 27.0 ± 6.97 27.0 ± 6.47 26.4 ± 5.68 Height (m) 1.64 ± 0.06 1.78 ± 0.07 1.62 ± 0.06 1.76 ± 0.07 1.71 ± 0.10 Weight (kg) 70.8 ± 14.1 81.9 ± 17.2 70.9 ± 19.6 84.8 ± 22.4 77.5 ± 18.7 WC (cm) 90.3 ± 14.4 93.2 ± 20 88.4 ± 17.6 97.1 ± 15.5 92.4 ± 17.5 HC (cm) 104 ± 13.3 102 ± 9.30 103 ± 12.1 105 ± 10.9 103 ± 11.3 WHR 0.86 ± 0.07 0.91 ± 0.16 0.85 ± 0.08 0.92 ± 0.08 0.88 ± 0.11 No. Corticosteroid courses 3.72 ± 3.91 3.76 ± 4.88 3.31 ± 3.55 3.56 ± 4.62 3.80 ± 5.28 No. Hospitalizations 2.64 ± 4.49 3.40 ± 3.73 1.88 ± 2.47 1.76 ± 1.71 2.60 ± 3.58 No. extraintestinal manifestations 1.25 ± 1.41 0.71 ± 0.84 0.41 ± 0.62 0.33 ± 0.56 0.78 ± 1.07 No. Surgical resection 0.24 ± 0.43 0.38 ± 0.49 0 0 0.31 ± 0.46 HGS (kg) 28.0 ± 7.22 51.4 ± 10.6 26.4 ± 5.93 40.8 ± 10.3 38.5 ± 14.0 HGS z-score -0.22 ± 0.98 -1.91 ± 1.64 0.14 ± 2.19 -1.66 ± 1.29 -0.20 ± 1.1 Percentile 0.43 ± 0.28 0.52 ± 0.30 0.39 ± 0.3 0.35 ± 0.32 0.43 ± 0.30 Probable sarcopenia (low HGS) 4 (8.69%) 0 1 (5.26%) 2 (7.00%) 7 (4.86%) Sarcopenia 15 (32.6%) 9 (18.0%) 5 (26.3%) 4 (13.8%) 33 (22.9%) Severe sarcopenia 3 (6.52%) 0 4 (21.0%) 6 (20.7%) 13 (9.03%) Active disease 29 (63.0%) 20 (40.0%) 12 (63.1%) 14 (48.3%) 75 (52.1%) Moderate-severe disease 12 (26.0%) 5 (10.0%) 4 (21%) 9 (31.0%) 30 (20.8%) Prevalence of sarcopenia The prevalence of sarcopenia, was 22.9% overall, 25.0% in patients with CD and 18.7% in patients with UC, while the prevalence of severe sarcopenia was 9.03%. Among the 33 patients diagnosed with sarcopenia, 15 exhibited active disease, comprising 4 participants with severe disease (one female with CD, one female with UC and 2 males with UC), 5 participants with moderately active disease (4 females and one male with CD) and 6 patients with mild disease (2 female CD, 2 male CD and 1 female and 1 male UC). There were no statistically significant differences in the percentages of active disease between sarcopenic and non-sarcopenic patients. Although sarcopenic patients tended to have received more corticosteroid courses in the past (4.47 ± 4.47 vs 3.51 ± 4.43), experienced a higher number of hospitalizations (3.32 ± 4.50 vs 2.32 ± 2.67), and presented with more extraintestinal manifestations (1.21 ± 1.18 vs 0.95 ± 1.24), these observed differences did not reach statistical significance. Conversely, a statistically significant difference was identified in the number of surgical resections the patients undergone (0.53 ± 0.51 vs 0.27 ± 0.45,p < 0.05, Table 2 ). Current HGS cut-offs were able to identify only 21.2% of sarcopenic (82% Specificity) and 23.1% of severely sarcopenic patients (86.9% Specificity). In contrast the application of a HGS z-score below zero enabled the identification of 52.9% sarcopenic (45.5% Specificity) and 46.2% severely sarcopenic patients (4.6% Specificity). Table 2 Differences between sarcopenic and non-sarcopenic patients Characteristic Sarcopenia (22%) Non-sarcopenia (78%) Stat. significance Active disease 45.5% 53.1% p > 0.05 Mild disease 18.2% 22.5% p > 0.05 Moderate disease 15.1% 15.3% p > 0.05 Severe disease 12.1% 15.3% p > 0.05 HGS (kg) 31.9 ± 12.6 40.5 ± 13.8 P < 0.05 HGS z-score -0.36 ± 1.07 -0.15 ± 1.12 P 0.05 N of hospitalizations 3.32 ± 4.50 2.32 ± 2.67 p > 0.05 N. of extraintestinal manifestations 1.21 ± 1.18 0.95 ± 1.24 p > 0.05 N. of surgery (intestinal resection) 0.53 ± 0.51 0.27 ± 0.45 P < 0.05 HGS (Handgrip Strength), N (number) Handgrip strength measurement The mean value of HGS of the total of patients was 38.5 ± 14 kg, 40.2 ± 14.9 kg in CD and 35.1 ± 11.3 kg in UC patients. The mean value of age and gender-standardized HGS-z-score was − 0.20 ± 1.11 in the total of patients, -0.04 ± 1.05 in CD and − 0.50 ± 1.17 in UC patients. According to the calculated percentile groups, 116 participants (80%) belonged in the 5th -95th group, 12 participants (8%) belonged below the 3th percentile group, five participants (3%) in 3th -5th percentile group, five participants (3%) belonged above the 97th percentile group and three participants (2%) in the 95th -97th group (Fig. 1 , Suppl. Figure 2). Sarcopenic patients had statistically significantly lower HGS values (31.9 ± 12.6kg vs 40.5 ± 13.8kg,p < 0.05) and HGS z-scores (-0.36 ± 1.07kg vs -0.15 ± 1.12kg,p < 0.05) compared to non-sarcopenic. This difference was statistically significant and more pronounced by comparing patients with severe sarcopenia (28.9 ± 8.78 vs 39.4 ± 14.1,p < 0.05 and − 0.21 ± 1.13 vs -1.55 ± 0.91,p < 0.05). Investigation of correlations between HGS and BIA Strong statistically significant correlations were found between HGS and patients’ height (r s =0,776, 95%CI = 0.670 to 0.852,p < 0.001), total body water (r = 0,772, 95%CI = 0,664 to 0,849,p < 0.001), lean mass (r s =0.764, 95%CI = 0.652 to 0.843,p < 0.001), body cell mass (r s =0.770, 95%CI = 0.661 to 0.847,p < 0.001), gender (r s =-0,771, 95%CI=-0.848 to -0.663,p < 0.001), FFMI (r s =0.567, 95%CI = 0.395 to 0.701,p < 0.001) and body weight (r = 0.457, 95%CI = 0.262 to 0.616,p < 0.001), (Τable 3). The statistical analysis showed also moderate correlations between HGS and age (r s =-0.288, 95%CI=-0.479 to -0.070,p = 0.008), and weak trends between HGS and waist to hip ratio (WHR, r s =0.210, 95%CI = 0.041 to 0.367,p = 0.012),(Table 3 , Suppl. Figure 3–6). Correlation analysis showed that the values of HGS were not correlated to BMI (r=-0.045,p = 0.597). Table 3 Correlations between handgrip strength (HGS) and bioelectric impedance analysis (BIA) parameters Variable Height Total body water Lean mass Body cell mass Gender FFMI Body weight Age WHR HGS 0,776 0,772 0.764 0.770 -0,771 0.567 0.457 -0.288 0.210 95%CI 0.670 to 0.852 0,664 to 0,849 0.652 to 0.843 0.661 to 0.847 -0.848 to -0.663 0.395 to 0.701 0.262 to 0.616 -0.479 to -0.070 0.041 to 0.367 Stat.significance p < 0.001 p < 0.001 p < 0.001 p < 0.001 p < 0.001 p < 0.001 p < 0.001 p = 0.008 p = 0.012 FFMI (Fat-free Mass Index), WHR (Waist-to-Hip ratio) Multivariate regression analysis A multivariable regression analysis identified height, weight, gender, age, and HGS as significant predictors of lean mass. Confounders (disease activity, endoscopic severity, corticosteroid courses, surgical resections, and hospitalizations) were assessed but had no statistically significant impact, thus no adjustment was required. The independent variables collectively predicted lean mass significantly [F (5,69) = 282.080,p < 0.001)], explaining 95.3% of its variance (R 2 = 0.953). All five hypotheses regarding predictor influence were supported (Table 4 , Suppl. table 2,3). Multicollinearity was assessed through multiple methods: no strong partial correlations between explanatory variables, tolerance values not close to zero, and VIF values under 5. Although the condition index exceeded 15 in two variables, no variance proportion was above 0.9 (Supp. Table 4 ,5). The final regression model equation functions with R = 0.976, R 2 = 0.953, adjusted R 2 = 0.95, standard error of the estimate = 2.9363, R 2 change = 0.003, Durbin-Watson = 1.969: Lean= -27.148 + 38.521(height) + 0.357(weight) -7.093(gender) -0.130(age) + 0.77(HGS), (gender = 1 male,2 female). (Charts illustrating the residuals of the regression analysis including Normal P-P plot, Scatterplot and distribution are depicted in Suppl. Figures 7–9). Table 4 Regression analysis hypothesis results Hypothesis Regression weights B t p-value Results H1 height ◊ lean mass 38.521 6.896 p < 0.001 Supported H 2 weight ◊ lean mass 0.357 19.729 p < 0.001 Supported H 3 gender ◊ lean mass -7.093 -6.205 p < 0.001 Supported H 4 age ◊ lean mass -0.130 -5.650 p < 0.001 Supported H 5 HGS ◊ lean mass 0.77 2.092 p < 0.001 Supported R 2 0.953 F (5, 69) 282.080 H:hypothesis, B: regression coefficient, t: t-statistic, R 2 : coefficient of determination, F: F-statistic Diagnostic accuracy analysis and cut-offs ROC analysis was performed to evaluate the role of HGS in detecting low muscle mass in patients with IBD, utilizing BIA results to define low lean mass values[ 27 ]. The diagnostic accuracy of HGS is characterized as “excellent” with an area under the curve (AUC) of 0.873 (95%CI = 0.814 to 0.932,p = < 0.001, Fig. 2 , Suppl. table 6). A cut-off value of 40.5kg can identify patients with low lean mass with a sensitivity of 94.5% and 60.1% specificity. Furthermore, subgroup analysis was carried to investigate cut-off values out by gender. By including only females in the analysis, HGS functions fairly with an AUC of 0.716 (95%CI = 0.584 to 0.848,p = 0.003) and adopting a sensitivity-maximizing approach, a cut-off value of 28.5kg is able to define low lean mass with a sensitivity of 85% and specificity of 54% (Supp. Figure 10, Suppl. table 7). Including only male in the analysis, HGS also functions fairly with an AUC of 0.714 (65%CI = 0.572 to 0.855,p = 0.009) and utilizing a sensitivity-maximizing approach, a cut-off value of 49.5kg is able to diagnose low lean mass with 87.5% sensitivity and specificity of 55% (Supp. Figure 11, Suppl. table 8). ROC analysis of the diagnostic accuracy of HGS in diagnosing sarcopenia based on FFMI was not statistically significant (AUC 0.650, 95%CI = 0.508 to 0.791,p = 0.071) Subgroup and Sensitivity analysis Subgroup analysis was carried out to investigate the statistical significance in certain groups. Introducing only 96 CD patients in the statistical analysis similar strong correlations were indicated between HGS and patients’ body cell mass (r s =0,727, 95%CI = 0.611 to 0.813,p < 0.001), water (r s =0.726, 95%CI = 0.609 to 0.812,p < 0.001], height [r s =0,721, 95%CI = 0.603 to 0.809,p < 0.001), lean mass (r s =0,708, 95%CI = 0.586 to 0.799,p < 0.001], FFMI [r s =0,535, 95%CI = 0.366 to 0.669,p < 0.001), weight (r s =0.342, 95%CI = 0.143 to 0.515,p < 0.001), and age (r s =-0.274, 95%CI=-0.457 to -0.70,p = 0.007). Similarly, there were not found any associations in the correlation between HGS and BMI (Supp. Table 9). To increase the sensitivity of the model, patients belonging below the HGS 25th percentile were included in the analysis. An analysis of 47 patients found similar levels of correlation between HGS and total body water (r s =0.780, 95%CI = 0.631 to 0.874,p < 0.001), lean mass (r s =0.766, 95%CI = 0.609 to 0.866,p < 0.001), body cell mass (r s =0.675, 95%CI = 0.474 to 0.809,p < 0.001), and height (r s =0.644, 95%CI = 0.430 to 0.789,p < 0.001). Similar results were obtained by including in the statistical analysis only participants below the 50th percentile with significant associations between HGS and total body water (r s =0.735, 95%CI = 0.608 to 0.825,p < 0.001), height (r s =0.716, 95%CI = 0.583 to 0.812,p < 0.001), lean mass (r s =0.709, 95%CI = 0.573 to 0.807,p < 0.001), body cell mass (r s =0.678, 95%CI = 0.532 to 0.785,p < 0.001), FFMI (r s =0.563, 95%CI = 0.384 to 0.701,p < 0.001), and age (r s =-0.348, 95%CI=-0.534 to -0.129,p = 0.002). Discussion This study aimed to explore the role of HGS as a non-invasive screening tool for reduced muscle mass within the IBD population, where current diagnostic guidelines and established HGS reference values are not validated or suboptimal [ 28 ]. Sarcopenic patients in the present study exhibited lower HGS values, especially those with severe sarcopenia, while a significant association was demonstrated between HGS and BIA, a noninvasive, repeatable and reliable method of body composition assessment, used to monitor changes in body composition or hydration in time[ 29 ]. Based on these results, HGS could be used as a readily implementable and robust sarcopenia screening tool in patients with IBD. Despite most patients being overweight or obese, nearly a quarter of them presented with sarcopenia and a tenth exhibited severe sarcopenia. This confirms the high prevalence of sarcopenic obesity and body composition alterations in IBD, which are associated with adverse clinical outcomes and poorer prognosis[ 30 – 33 ]. Adipose tissue can contribute in inflammation in IBD by release of adipokines, adipocyte stress response and macrophage stimulation[ 34 ] and obese patients manifest more often anoperineal complications, a more marked year-by-year disease activity[ 35 ] and a potentially poor response to anti-TNF-α treatment[ 36 ]. In the present study, a significant association was demonstrated between lean mass and HGS which alongside height, weight, gender, and age, collectively explained 95.3% of the variance in lean mass. These observations support that HGS is a valid, easily obtainable integral component in muscle mass assessment, that can be used not only as an initial method to unmask sarcopenia, presenting an advantage over the exclusive reliance on BMI, but also to monitor nutritional support and to guide muscle mass growth in every-day clinical practice. A primary objective of the present study was to establish IBD specific HGS cut-off values for sarcopenia screening, as current generalized low HGS cut-offs were derived from healthy population to determine physical performance and are poorly sensitive (21%) [ 37 ]. Conversely, the application of gender and age standardized HGS z-scores with a cut-off below zero proved to be more sensitive in diagnosing sarcopenic (53%) and severely sarcopenic patients (46%). ROC analysis indicated "excellent" diagnostic accuracy for HGS in detecting low lean mass, with an optimal cut-off value of 40.5 kg, which yielded a high sensitivity (94.5%) and moderate specificity (60.1%) and gender-specific cut-offs were derived to enhance precision: 28.5 kg for females and 49.5 kg for males. Although these thresholds are more sensitive, lack specificity and require further validation in larger populations. Subgroup analysis in CD patients reported similar results confirming associations and reducing sample heterogeneity. Likewise, sensitivity analysis was conducted only in patients with low muscle strength belonging below the 25th and 50th percentile with the intention to increase the validity of the results and showed similar results. It has to be acknowledged though, that this study has several limitations. Firstly, BIA rather than the gold standard DXA was utilized for sarcopenia diagnosis[ 10 ]. BIA relies on predictive, population-specific equations and indirectly estimates body composition[ 29 ]. Its accuracy is susceptible to various factors, including hydration status, body position, and prior physical activity, leading to questions about its overall reliability[ 38 ]. Compared to DXA, BIA can underestimate fat mass and overestimate lean mass, with more pronounced discrepancies in overweight and obese individuals[ 39 ]. Secondly, the observational design of the study prevents the establishment of causal relationships, necessitating future prospective studies to validate the HGS’s prognostic role. Lastly, although gender-specific, the cut-offs are not age-standardized due to limitations in statistical power, indicating that larger patient samples are needed to establish more robust and generalizable conclusions. HGS is a fundamental, simple and reproducible screening tool for sarcopenia, strongly correlating with muscle mass in IBD. Higher cut off values and age/gender-standardized z-scores are more sensitive compared to the non-standardized recommended cut offs. Although the equation model uses easily obtainable and can predict with great accuracy the quantity of lean mass in IBD patients, further validation is required. Future research should integrate HGS with other non-invasive diagnostic tools to develop comprehensive sarcopenia screening protocols in IBD Word count: 2999 Abbreviations ANOVA Analysis of Variance AUC Area Under the Curve BIA Bioelectric Impedance Analysis BMI Body Mass Index CD Crohn’s Disease CT Computed Tomography DXA Dual-Energy X-ray Absorptiometry EWGSOP2 European Working Group on Sarcopenia in Older People 2 FFMI Fat-Free Mass Index HBI Harvey-Bradshaw Index HGS Handgrip Strength HP Hip Circumference IBD Inflammatory Bowel Disease MRI Magnetic Resonance Imaging ROC Receiver Operating Characteristics Curve SARC-F Strength, Ambulation, Rising from a chair, Stair climbing and history of Falling SCCAI Simple Clinical Colitis Activity Index SPPB Short Physical Performance Battery STROBE Strengthening the Reporting of Observational studies in Epidemiology TUG Τimed-Up and Go test UC Ulcerative Colitis VIF Variance Inflation Factor WC Waist Circumference WHR Waist to Hip Ratio Declarations Acknowledgments Author Contribution statement: AV conceived research, collected data, analyzed data, drafted and edited the manuscript, XT: collected data, reviewed and edited the manuscript, TM: collected data, reviewed the manuscript, MGP reviewed the manuscript, GG conceived research, reviewed the manuscript, MC conceived research, reviewed the manuscript Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Ethical approval: The present study was conducted in accordance with the ethical principles of the Declaration of Helsinki[40]. Approval was secured from the Ethics Committee of the Aristotle University of Thessaloniki, as well as from the ethics review boards of the two participating institutions (protocol number 2/7.12.2021) [40]. All patients provided the consent prior the commencement of the study. Competing interest: All authors declare that they have no conflict of interest or financial conflicts to disclose, and manuscript is approved by all authors for publication. The authors would like to acknowledge the assistance of “Jenni.ai”, a large language model developed by “Google”, for its support in refining the vocabulary and performing grammar checks during the preparation of this manuscript. Jenni.ai was not used to generate the core text, provide scientific ideas, or draw conclusions. References Campbell JP, Teigen L, Manski S, et al. Sarcopenia Is More Prevalent Among Inflammatory Bowel Disease Patients Undergoing Surgery and Predicts Progression to Surgery Among Medically Treated Patients. Inflamm Bowel Dis 2022; 28: 1844–1850. Bamba S, Sasaki M, Takaoka A, et al. Sarcopenia is a predictive factor for intestinal resection in admitted patients with Crohn’s disease. PLoS One 2017; 12: e0180036. Liu S, Ding X, Maggiore G, et al. Sarcopenia is associated with poor clinical outcomes in patients with inflammatory bowel disease: a prospective cohort study. Ann Transl Med 2022; 10: 367–367. Fatani H, Olaru A, Stevenson R, et al. Systematic review of sarcopenia in inflammatory bowel disease. Clinical Nutrition 2023; 42: 1276–1291. Dhaliwal A, Quinlan JI, Overthrow K, et al. Sarcopenia in Inflammatory Bowel Disease: A Narrative Overview. Nutrients 2021; 13: 1–17. Nishikawa H, Nakamura S, Miyazaki T, et al. Inflammatory Bowel Disease and Sarcopenia: Its Mechanism and Clinical Importance. Journal of Clinical Medicine 2021, Vol 10, Page 4214 2021; 10: 4214. Chen LK, Woo J, Assantachai P, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc 2020; 21: 300–307.e2. Studenski SA, Peters KW, Alley DE, et al. The FNIH Sarcopenia Project: Rationale, Study Description, Conference Recommendations, and Final Estimates. J Gerontol A Biol Sci Med Sci 2014; 69: 547. Dent E, Morley JE, Cruz-Jentoft AJ, et al. International Clinical Practice Guidelines for Sarcopenia (ICFSR): Screening, Diagnosis and Management. J Nutr Health Aging 2018; 22: 1148–1161. Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing 2019; 48: 16–31. Malmstrom TK, Miller DK, Simonsick EM, et al. SARC-F: a symptom score to predict persons with sarcopenia at risk for poor functional outcomes. J Cachexia Sarcopenia Muscle 2015; 7: 28. Bruyère O, Beaudart C, Reginster JY, et al. Assessment of muscle mass, muscle strength and physical performance in clinical practice: An international survey. Eur Geriatr Med 2016; 7: 243–246. Cederholm T, Barazzoni R, Austin P, et al. ESPEN Guideline ESPEN guidelines on definitions and terminology of clinical nutrition. Epub ahead of print 2017. DOI: 10.1016/j.clnu.2016.09.004 . Malmstrom TK, Miller DK, Simonsick EM, et al. SARC-F: a symptom score to predict persons with sarcopenia at risk for poor functional outcomes. J Cachexia Sarcopenia Muscle 2016; 7: 28. Cederholm T, Bosaeus I, Barazzoni R, et al. Diagnostic criteria for malnutrition - An ESPEN Consensus Statement. Clinical Nutrition 2015; 34: 335–340. Cuschieri S. The STROBE guidelines. Saudi J Anaesth 2019; 13: S31. Maaser C, Sturm A, Vavricka SR, et al. ECCO-ESGAR Guideline for Diagnostic Assessment in IBD Part 1: Initial diagnosis, monitoring of known IBD, detection of complications. J Crohns Colitis 2019; 13: 144-164K. Harvey RF, Jane Bradshaw M. Measuring Crohn’s disease activity. Lancet 1980; 1: 1134–1135. Walmsley RS, Ayres RCS, Pounder RE, et al. A simple clinical colitis activity index. Gut 1998; 43: 29–32. National Health and Nutrition Examination Survey: 2021 anthropometry procedures manual, http://stacks.cdc.gov/view/cdc/127207 (2021, accessed 3 October 2025). Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: Revised European consensus on definition and diagnosis. Age Ageing 2019; 48: 16–31. Hamilton GF, McDonald C, Chenier TC. Measurement of grip strength: validity and reliability of the sphygmomanometer and jamar grip dynamometer. J Orthop Sports Phys Ther 1992; 16: 215–219. Brantlov S, Jødal L, Lange A, et al. Standardisation of bioelectrical impedance analysis for the estimation of body composition in healthy paediatric populations: a systematic review. J Med Eng Technol 2017; 41: 460–479. Bioelectrical impedance analysis in body composition measurement: National Institutes of Health Technology Assessment Conference statement. American Journal of Clinical Nutrition ; 64. Epub ahead of print September 1996. DOI: 10.1093/ajcn/64.3.524s . Welch SA, Ward RE, Beauchamp MK, et al. The Short Physical Performance Battery (SPPB): A Quick and Useful Tool for Fall Risk Stratification Among Older Primary Care Patients. J Am Med Dir Assoc 2020; 22: 1646. Andrade C. Z Scores, Standard Scores, and Composite Test Scores Explained. Indian J Psychol Med 2021; 43: 555. Ofenheimer A, Breyer-Kohansal R, Hartl S, et al. Reference values of body composition parameters and visceral adipose tissue (VAT) by DXA in adults aged 18–81 years-results from the LEAD cohort. Eur J Clin Nutr 2020; 74: 1181–1191. Potcovaru CG, Filip PV, Neagu OM, et al. Diagnostic Criteria and Prognostic Relevance of Sarcopenia in Patients with Inflammatory Bowel Disease—A Systematic Review. Journal of Clinical Medicine 2023, Vol 12, Page 4713 2023; 12: 4713. Marra M, Sammarco R, De Lorenzo A, et al. Assessment of Body Composition in Health and Disease Using Bioelectrical Impedance Analysis (BIA) and Dual Energy X-Ray Absorptiometry (DXA): A Critical Overview. Contrast Media Mol Imaging ; 2019. Epub ahead of print 2019. DOI: 10.1155/2019/3548284 . Ding NS, Tassone D, Al Bakir I, et al. Systematic Review: The Impact and Importance of Body Composition in Inflammatory Bowel Disease. J Crohns Colitis 2022; 16: 1475–1492. Connelly TM, Juza RM, Sangster W, et al. Volumetric fat ratio and not body mass index is predictive of ileocolectomy outcomes in Crohn’s disease patients. Dig Surg 2014; 31: 219–224. Ding NS, Malietzis G, Lung PFC, et al. The body composition profile is associated with response to anti-TNF therapy in Crohn’s disease and may offer an alternative dosing paradigm. Aliment Pharmacol Ther 2017; 46: 883–891. Lim Z, Welman CJ, Raymond W, et al. The Effect of Adiposity on Anti–Tumor Necrosis Factor-Alpha Levels and Loss of Response in Crohn’s Disease Patients. Clin Transl Gastroenterol 2020; 11: e00233. Bryant R V., Ooi S, Schultz CG, et al. Low muscle mass and sarcopenia: common and predictive of osteopenia in inflammatory bowel disease. Aliment Pharmacol Ther 2015; 41: 895–906. Braga M, Gianotti L, Gentilini O, et al. Crohn’s disease clinical course and severity in obese patients. Clinical Nutrition 2002; 21: 51–57. Bhalme M, Sharma A, Keld R, et al. Does weight-adjusted anti-tumour necrosis factor treatment favour obese patients with Crohn’s disease? Eur J Gastroenterol Hepatol 2013; 25: 543–549. Chen LK, Liu LK, Woo J, et al. Sarcopenia in Asia: consensus report of the Asian Working Group for Sarcopenia. J Am Med Dir Assoc 2014; 15: 95–101. Ward LC. Bioelectrical impedance analysis for body composition assessment: reflections on accuracy, clinical utility, and standardisation. Eur J Clin Nutr 2019; 73: 194–199. Day K, Kwok A, Evans A, et al. Comparison of a Bioelectrical Impedance Device against the Reference Method Dual Energy X-Ray Absorptiometry and Anthropometry for the Evaluation of Body Composition in Adults. Nutrients ; 10. Epub ahead of print 10 October 2018. DOI: 10.3390/NU10101469 . World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 2013; 310: 2191–2194. Additional Declarations There is NO conflict of interest to disclose. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: revise 13 Apr, 2026 Review # 1 received at journal 09 Mar, 2026 Reviewer # 1 agreed at journal 23 Feb, 2026 Reviewers invited by journal 30 Jan, 2026 Editor assigned by journal 21 Nov, 2025 Submission checks completed at journal 21 Nov, 2025 First submitted to journal 20 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8167171","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":583234274,"identity":"016a45a8-3c54-456c-8778-7810cbba6a8f","order_by":0,"name":"Andreas Vadarlis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYHACAwaGAgkog8EGiBkbDxDWYgDXkgbS0kCMFhiD4TCYhVeLwfHDGz9XGFjY889u3vbhY9t5u7Xth4G21NhE49RyJq1Y8oyBROKMO8eKZ85su5287UwiUMuxtNwGHFokG3IMJBsMJBIYbuQYM/Nuu51sdgCohbHhMG4t/W+MfwK12MuDtPzddi7Z7PxD/Fr4JXLMQLYwbgBpYdx2wM7sBgFb+CWelVkCtSRuvJFWzNj7LznB7AbQlgQ8fmHjT958s6Gizl7uRvJmhh9n7OzNzqc/fPChxganFgyQCFaZQKxyELAnRfEoGAWjYBSMDAAA4JZibSJNTRQAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-6130-7127","institution":"Laboratory of Hygiene, Social \u0026 Preventive Medicine and Medical Statistics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece","correspondingAuthor":true,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Vadarlis","suffix":""},{"id":583234275,"identity":"86d78d83-7f26-41bf-ad95-54a25a3e9954","order_by":1,"name":"Xenophon 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Germanidis","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Georgios","middleName":"","lastName":"Germanidis","suffix":""},{"id":583234279,"identity":"27a606c8-6676-4488-89ef-7e4586870494","order_by":5,"name":"Michael Chourdakis","email":"","orcid":"","institution":"Faculty of Health Sciences, Aristotle University of Thessaloniki","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Chourdakis","suffix":""}],"badges":[],"createdAt":"2025-11-20 18:10:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8167171/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8167171/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101790357,"identity":"0b8edf9c-61ce-49c6-9dc8-b24f7f8b0010","added_by":"auto","created_at":"2026-02-03 16:04:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31345,"visible":true,"origin":"","legend":"\u003cp\u003ePatient distribution in Handgrip strength percentiles\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8167171/v1/a3c9b0b5cc2324002d048ae3.png"},{"id":101881159,"identity":"fe95471e-c13e-40d5-a227-bb989110a920","added_by":"auto","created_at":"2026-02-04 15:10:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20486,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristics (ROC) curve of the diagnostic accuracy of HGS in detecting low lean mass in patients with IBD (Area under the curve, AUC: 0.873, Asymptotic 95% Confidence Interval: 0.814 to 0.932, p\u0026lt;0.001)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8167171/v1/702c2538f06cf4d552a46f96.png"},{"id":101943178,"identity":"e59cdaf8-1dc2-4b33-8165-5b1097b660c3","added_by":"auto","created_at":"2026-02-05 09:40:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1060397,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8167171/v1/6adbbf6b-10c4-47f3-90a0-48d5fdc9c44b.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Assessment of diagnostic performance of handgrip strength in sarcopenia screening and determining optimal cut-offs in inflammatory bowel disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSarcopenia, characterized by a progressive loss of quantity, function and quality of muscle mass, is considered a common complication in inflammatory bowel disease (IBD) affecting almost a fifth of patients and can lead to adverse outcomes such as a higher risk of surgery, treatment failure and hospitalization[\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Similar to other chronic inflammatory diseases, multiple factors can contribute to sarcopenia in IBD patients, such as inflammation-driven catabolism, disturbed protein synthesis, reduced protein intake, nutrient malabsorption, corticosteroid use and lack of physical exercise[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNevertheless, establishing a definitive diagnosis of sarcopenia in routine clinical practice presents considerable challenges due to the absence of a universally accepted definition[\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e–\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Guidelines typically advocate for an initial screening phase, often employing instruments such as the Strength, Assistance in walking, Rise of a chair, Climb stairs, Falls (SARC-F) questionnaire[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] followed by a muscle strength assessment (handgrip strength or chair stand test)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Subsequent confirmation of diminished muscle quantity is achieved through various body composition analysis techniques, including Dual-energy X-ray absorptiometry (DXA), bioelectric impedance analysis (BIA), Computed Tomography (CT), or magnetic resonance imaging (MRI)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Finally, the comprehensive evaluation of sarcopenia severity is conducted using physical performance assessments such as gait speed, the Short Physical Performance Battery (SPPB), the Timed-Up and Go test, or a 400-meter walk[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite recommendations for sarcopenia screening, the established reference values for these tools have not been validated within the inflammatory bowel disease population. Furthermore, limited research has explored the utility of handgrip strength in sarcopenia assessment, particularly its correlation with muscle mass quantification in IBD patients. Consequently, the empirical basis for arbitrary HGS cut-off values, such as \u0026lt; 27 kg for men and \u0026lt; 16 kg for women, remains poorly substantiated, and the precise role of HGS in sarcopenia detection among individuals with IBD is yet to be fully elucidated[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis observational study primarily aims to investigate the utility of handgrip strength in the diagnosis of sarcopenia in inflammatory bowel disease, specifically examining its association with bioelectrical impedance analysis-derived body composition parameters among IBD patients. Secondary objectives include exploring the applicability of age- and gender-standardized HGS z-scores and identifying optimal HGS thresholds for sarcopenia screening in IBD patients.\u003c/p\u003e "},{"header":"Subjects and Methods","content":"\u003cp\u003eThis study adhered to the “Strengthening-the-reporting-of-observational-studies-in-epidemiology” (STROBE) reporting guidelines[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSelection of participants and study design\u003c/p\u003e\u003cp\u003eThis cross-sectional study was conducted in two IBD centers (“G. Papanikolaou and AHEPA University Hospitals, Thessaloniki, Greece). A power analysis (0.05 significance, 0.95 power, 0.3 correlation effect) determined a minimum sample size of 138 participants. Eligible participants were adult patients diagnosed with Crohn’s disease (CD) or Ulcerative colitis (UC) according to clinical, radiologic, endoscopic and histologic criteria[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], under surveillance in the ambulatory setting. Patients were included in the study regardless of age, gender, disease activity, behavior, location, endoscopic severity, nutritional status or treatment. Patients with high output stoma/fistula, ascites/fluid collections, high corticosteroid dosage/diuretic, parenteral nutrition, or with an implantable defibrillator/pacemaker were excluded. Furthermore, patients unable to complete the dynamometer test due to severe peripheral arthropathy or unwilling to provide consent were also excluded from the study.\u003c/p\u003e\u003cp\u003eData collection\u003c/p\u003e\u003cp\u003eDisease activity was evaluated using Harvey-Bradshaw-Index (HBI)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and Simple-Clinical-Colitis-Activity-Index (SCCAI)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] for CD and UC respectively. Disease course data included location, behavior, history of abdominal surgery (including intestinal resection), number of corticosteroid courses, extraintestinal manifestations, IBD-related hospitalizations, and ongoing maintenance treatment. Anthropometric data regarding height, weight, waist circumference (WC), hip circumference (HP) were meticulously collected under a strict protocol [Centers for disease control and prevention (CDC) anthropometry procedure manual] by the same investigator using calibrated equipment and standardized techniques (stadiometer, digital scale, non-stretchable tape) to the nearest 0.1cm/kg. Waist circumference (WC) was measured midway between the lowest rib and the iliac crest, and hip circumference over the great trochanters[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDiagnosis of sarcopenia\u003c/p\u003e\u003cp\u003eSarcopenia was defined according to the European Working Group on Sarcopenia in Older People 2 (EWGSOP2), where sarcopenia is probable in the case of reduced muscle strength, definite in the presence of additional low muscle quantity and severe in the case of above and low physical performance[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHandgrip strength measurement\u003c/p\u003e\u003cp\u003eMuscle strength was evaluated via handgrip strength, utilizing a digital calibrated isokinetic dynamometer (Jamar dynamometer) measured in kilograms. A standardized protocol was followed with the patient sitting in the same low backed–fixed arm rests chair, in the same sited position (elbow fixed in 90 degrees and wrist in neutral zero degrees position). The highest of three measurements from the dominant hand was recorded, with participants performing maximal isometric contractions for 3–5 seconds and one-minute rest intervals. All assessments were performed by the same trained researcher to ensure consistency and minimize inter-observer variability[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBody composition analysis\u003c/p\u003e\u003cp\u003eBioelectric Impedance Analysis was utilized to assess body composition of the participants following a strict protocol that included 8-hours of fasting.\u003c/p\u003e\u003cp\u003eParticipants were advised to wear light clothes, avoid carrying metallic objects, and refrain from alcohol or strenuous physical activity for 24-hours. Before testing, participants rested supine for 10-minutes with arms 30 degrees from the body and legs separated. Measurements were performed on a non-conductive exam table with a calibrated analyzer. Electrode sites were cleaned with alcohol, and single-use electrodes were placed according to the manufacturer’s manual. A Bodystat Quadscan 4000 touch impedance analyzer collected data on body fat mass, lean fat mass, total body water, body cell mass, and fat-free mass index, derived from raw measurements at 5, 50, 100, and 200 kHz. All BIA measurements were performed by a single, trained investigator[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePhysical performance evaluation\u003c/p\u003e\u003cp\u003ePhysical performance was evaluated using the Short Physical Performance Battery (SPPB), assessing balance, lower extremity strength, and functional capacity via balance test (static balance in side-by-side, semi-tandem, and tandem stands, ten seconds each), gait speed (time of a 4-meter walk at a usual pace) and chair stand tests (time for five chair stands). A cumulative score (0–12) indicates performance, with ≤ 8 points indicating poor physical performance[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eA statistical software package was used for data analysis (IBM,SPSS-Statistics,version29.0.1.0). Normality was assessed using Kolmogorov-Smirnov and Shapiro-Wilk tests, visualized with histograms and Q-Q plots. Pearson correlation was applied for normally distributed continuous variables, while Spearman correlation was used for non-parametric continuous variables. Z-scores were calculated to compare handgrip strength to age- and gender-specific normal values, and percentiles were derived from a standard normal distribution table[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Multiple linear regression analyzed associations and predictive capacities, with assumptions (linearity, normality of residuals, homoscedasticity, independence) rigorously checked, and multicollinearity mitigated. A stepwise approach added variables at p \u0026lt; 0.05 and removed at p \u0026gt; 0.10. Analysis of Variance (ANOVA) confirmed model significance. Statistical significance for all analyses was set at p \u0026lt; 0.05. Receiver operating characteristic (ROC) curves evaluated Handgrip Strength diagnostic accuracy and optimal cut-off values for sarcopenia. Sensitivity and subgroup analyses were also conducted.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003ePatient characteristics\u003c/p\u003e \u003cp\u003eA total of 144 participants diagnosed with IBD were included in the study (flow chart and reasons for exclusion are depicted in Suppl. Figure\u0026nbsp;1). The participants\u0026rsquo; median age was 46 years with a range of 62 years, while 55% of them were males. Median body mass index (BMI) was 26 kg/m\u003csup\u003e2\u003c/sup\u003e with a range of 35 kg/m\u003csup\u003e2\u003c/sup\u003e and 47% of the patients belonged to the overweight/obese group, whereas only 4% were categorized as underweight. The disease activity was evaluated to be in remission in 43 patients with CD (45%) and in 19 patients with UC (40%), while was mild in 29%, moderate in 22% and severe in 6% of the participants. Surgical intervention, primarily ileum resection, was reported by 31% of CD patients, whereas no UC patients underwent surgery. On average the patients had received 3.8 times corticosteroid courses and experienced 2.6 hospitalizations. The majority of patients received infliximab as a maintenance treatment (55%), followed by ustekinumab (17%) and vedolizumab (10%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Suppl. table 1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipants\u0026rsquo; Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal (N,%)\u003c/p\u003e \u003cp\u003e144 (100%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale (N,%)\u003c/p\u003e \u003cp\u003e46 (32%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale (N,%)\u003c/p\u003e \u003cp\u003e50 (34%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale (N,%)\u003c/p\u003e \u003cp\u003e19 (13%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMale (N,%)\u003c/p\u003e \u003cp\u003e29 (20%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.5\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.6\u0026thinsp;\u0026plusmn;\u0026thinsp;19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.9\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.9\u0026thinsp;\u0026plusmn;\u0026thinsp;19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.8\u0026thinsp;\u0026plusmn;\u0026thinsp;22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.5\u0026thinsp;\u0026plusmn;\u0026thinsp;18.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.2\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.4\u0026thinsp;\u0026plusmn;\u0026thinsp;17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.1\u0026thinsp;\u0026plusmn;\u0026thinsp;15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92.4\u0026thinsp;\u0026plusmn;\u0026thinsp;17.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHC (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104\u0026thinsp;\u0026plusmn;\u0026thinsp;13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102\u0026thinsp;\u0026plusmn;\u0026thinsp;9.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e103\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. Corticosteroid courses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.72\u0026thinsp;\u0026plusmn;\u0026thinsp;3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.76\u0026thinsp;\u0026plusmn;\u0026thinsp;4.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.31\u0026thinsp;\u0026plusmn;\u0026thinsp;3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.56\u0026thinsp;\u0026plusmn;\u0026thinsp;4.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.80\u0026thinsp;\u0026plusmn;\u0026thinsp;5.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. Hospitalizations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.64\u0026thinsp;\u0026plusmn;\u0026thinsp;4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.40\u0026thinsp;\u0026plusmn;\u0026thinsp;3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.60\u0026thinsp;\u0026plusmn;\u0026thinsp;3.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. extraintestinal manifestations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. Surgical resection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGS (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.5\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGS z-score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProbable sarcopenia\u003c/p\u003e \u003cp\u003e(low HGS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (8.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (5.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (7.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (4.86%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (32.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (18.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33 (22.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere sarcopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (6.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (21.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (20.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 (9.03%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (63.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (63.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (48.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75 (52.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate-severe disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (26.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (31.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30 (20.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBMI (Body Mass Index), WC (Waist Circumference), HC (Hip Circumference), WHR (Waist-to-Hip ratio), HGS (Handgrip Strength)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipants\u0026rsquo; Characteristics BMI (Body Mass Index), WC (Waist Circumference), HC (Hip Circumference), WHR (Waist-to-Hip ratio), HGS (Handgrip Strength)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal (N,%)\u003c/p\u003e \u003cp\u003e144 (100%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale (N,%)\u003c/p\u003e \u003cp\u003e46 (32%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale (N,%)\u003c/p\u003e \u003cp\u003e50 (34%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale (N,%)\u003c/p\u003e \u003cp\u003e19 (13%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMale (N,%)\u003c/p\u003e \u003cp\u003e29 (20%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.5\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.6\u0026thinsp;\u0026plusmn;\u0026thinsp;19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.9\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.9\u0026thinsp;\u0026plusmn;\u0026thinsp;19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.8\u0026thinsp;\u0026plusmn;\u0026thinsp;22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.5\u0026thinsp;\u0026plusmn;\u0026thinsp;18.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.2\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.4\u0026thinsp;\u0026plusmn;\u0026thinsp;17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.1\u0026thinsp;\u0026plusmn;\u0026thinsp;15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92.4\u0026thinsp;\u0026plusmn;\u0026thinsp;17.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHC (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104\u0026thinsp;\u0026plusmn;\u0026thinsp;13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102\u0026thinsp;\u0026plusmn;\u0026thinsp;9.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e103\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. Corticosteroid courses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.72\u0026thinsp;\u0026plusmn;\u0026thinsp;3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.76\u0026thinsp;\u0026plusmn;\u0026thinsp;4.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.31\u0026thinsp;\u0026plusmn;\u0026thinsp;3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.56\u0026thinsp;\u0026plusmn;\u0026thinsp;4.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.80\u0026thinsp;\u0026plusmn;\u0026thinsp;5.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. Hospitalizations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.64\u0026thinsp;\u0026plusmn;\u0026thinsp;4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.40\u0026thinsp;\u0026plusmn;\u0026thinsp;3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.60\u0026thinsp;\u0026plusmn;\u0026thinsp;3.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. extraintestinal manifestations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. Surgical resection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGS (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.5\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGS z-score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProbable sarcopenia\u003c/p\u003e \u003cp\u003e(low HGS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (8.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (5.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (7.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (4.86%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (32.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (18.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33 (22.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere sarcopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (6.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (21.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (20.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 (9.03%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (63.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (63.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (48.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75 (52.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate-severe disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (26.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (31.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30 (20.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePrevalence of sarcopenia\u003c/p\u003e \u003cp\u003eThe prevalence of sarcopenia, was 22.9% overall, 25.0% in patients with CD and 18.7% in patients with UC, while the prevalence of severe sarcopenia was 9.03%. Among the 33 patients diagnosed with sarcopenia, 15 exhibited active disease, comprising 4 participants with severe disease (one female with CD, one female with UC and 2 males with UC), 5 participants with moderately active disease (4 females and one male with CD) and 6 patients with mild disease (2 female CD, 2 male CD and 1 female and 1 male UC). There were no statistically significant differences in the percentages of active disease between sarcopenic and non-sarcopenic patients. Although sarcopenic patients tended to have received more corticosteroid courses in the past (4.47\u0026thinsp;\u0026plusmn;\u0026thinsp;4.47 vs 3.51\u0026thinsp;\u0026plusmn;\u0026thinsp;4.43), experienced a higher number of hospitalizations (3.32\u0026thinsp;\u0026plusmn;\u0026thinsp;4.50 vs 2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67), and presented with more extraintestinal manifestations (1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18 vs 0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24), these observed differences did not reach statistical significance. Conversely, a statistically significant difference was identified in the number of surgical resections the patients undergone (0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51 vs 0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Current HGS cut-offs were able to identify only 21.2% of sarcopenic (82% Specificity) and 23.1% of severely sarcopenic patients (86.9% Specificity). In contrast the application of a HGS z-score below zero enabled the identification of 52.9% sarcopenic (45.5% Specificity) and 46.2% severely sarcopenic patients (4.6% Specificity).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferences between sarcopenic and non-sarcopenic patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSarcopenia (22%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-sarcopenia (78%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStat. significance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGS (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.5\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGS z-score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN of corticosteroid courses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.47\u0026thinsp;\u0026plusmn;\u0026thinsp;4.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.51\u0026thinsp;\u0026plusmn;\u0026thinsp;4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN of hospitalizations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.32\u0026thinsp;\u0026plusmn;\u0026thinsp;4.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN. of extraintestinal manifestations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN. of surgery (intestinal resection)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eHGS (Handgrip Strength), N (number)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHandgrip strength measurement\u003c/p\u003e \u003cp\u003eThe mean value of HGS of the total of patients was 38.5\u0026thinsp;\u0026plusmn;\u0026thinsp;14 kg, 40.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.9 kg in CD and 35.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3 kg in UC patients. The mean value of age and gender-standardized HGS-z-score was \u0026minus;\u0026thinsp;0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11 in the total of patients, -0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05 in CD and \u0026minus;\u0026thinsp;0.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17 in UC patients. According to the calculated percentile groups, 116 participants (80%) belonged in the 5th -95th group, 12 participants (8%) belonged below the 3th percentile group, five participants (3%) in 3th -5th percentile group, five participants (3%) belonged above the 97th percentile group and three participants (2%) in the 95th -97th group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Suppl. Figure\u0026nbsp;2). Sarcopenic patients had statistically significantly lower HGS values (31.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6kg vs 40.5\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8kg,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and HGS z-scores (-0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07kg vs -0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12kg,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to non-sarcopenic. This difference was statistically significant and more pronounced by comparing patients with severe sarcopenia (28.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.78 vs 39.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and \u0026minus;\u0026thinsp;0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13 vs -1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInvestigation of correlations between HGS and BIA\u003c/p\u003e \u003cp\u003eStrong statistically significant correlations were found between HGS and patients\u0026rsquo; height (r\u003csub\u003es\u003c/sub\u003e=0,776, 95%CI\u0026thinsp;=\u0026thinsp;0.670 to 0.852,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), total body water (r\u0026thinsp;=\u0026thinsp;0,772, 95%CI\u0026thinsp;=\u0026thinsp;0,664 to 0,849,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lean mass (r\u003csub\u003es\u003c/sub\u003e=0.764, 95%CI\u0026thinsp;=\u0026thinsp;0.652 to 0.843,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), body cell mass (r\u003csub\u003es\u003c/sub\u003e=0.770, 95%CI\u0026thinsp;=\u0026thinsp;0.661 to 0.847,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), gender (r\u003csub\u003es\u003c/sub\u003e=-0,771, 95%CI=-0.848 to -0.663,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), FFMI (r\u003csub\u003es\u003c/sub\u003e=0.567, 95%CI\u0026thinsp;=\u0026thinsp;0.395 to 0.701,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and body weight (r\u0026thinsp;=\u0026thinsp;0.457, 95%CI\u0026thinsp;=\u0026thinsp;0.262 to 0.616,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), (Τable 3). The statistical analysis showed also moderate correlations between HGS and age (r\u003csub\u003es\u003c/sub\u003e=-0.288, 95%CI=-0.479 to -0.070,p\u0026thinsp;=\u0026thinsp;0.008), and weak trends between HGS and waist to hip ratio (WHR, r\u003csub\u003es\u003c/sub\u003e=0.210, 95%CI\u0026thinsp;=\u0026thinsp;0.041 to 0.367,p\u0026thinsp;=\u0026thinsp;0.012),(Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Suppl. Figure\u0026nbsp;3\u0026ndash;6). Correlation analysis showed that the values of HGS were not correlated to BMI (r=-0.045,p\u0026thinsp;=\u0026thinsp;0.597).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations between handgrip strength (HGS) and bioelectric impedance analysis (BIA) parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal body water\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLean mass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBody cell mass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFFMI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBody weight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.670 to 0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,664 to 0,849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.652 to 0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.661 to 0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.848 to -0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.395 to 0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.262 to 0.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.479 to -0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.041 to 0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStat.significance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eFFMI (Fat-free Mass Index), WHR (Waist-to-Hip ratio)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMultivariate regression analysis\u003c/p\u003e \u003cp\u003eA multivariable regression analysis identified height, weight, gender, age, and HGS as significant predictors of lean mass. Confounders (disease activity, endoscopic severity, corticosteroid courses, surgical resections, and hospitalizations) were assessed but had no statistically significant impact, thus no adjustment was required. The independent variables collectively predicted lean mass significantly [F (5,69)\u0026thinsp;=\u0026thinsp;282.080,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)], explaining 95.3% of its variance (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.953). All five hypotheses regarding predictor influence were supported (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Suppl. table 2,3). Multicollinearity was assessed through multiple methods: no strong partial correlations between explanatory variables, tolerance values not close to zero, and VIF values under 5. Although the condition index exceeded 15 in two variables, no variance proportion was above 0.9 (Supp. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e,5). The final regression model equation functions with R\u0026thinsp;=\u0026thinsp;0.976, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.953, adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.95, standard error of the estimate\u0026thinsp;=\u0026thinsp;2.9363, R\u003csup\u003e2\u003c/sup\u003e change\u0026thinsp;=\u0026thinsp;0.003, Durbin-Watson\u0026thinsp;=\u0026thinsp;1.969: Lean= -27.148\u0026thinsp;+\u0026thinsp;38.521(height)\u0026thinsp;+\u0026thinsp;0.357(weight) -7.093(gender) -0.130(age)\u0026thinsp;+\u0026thinsp;0.77(HGS), (gender\u0026thinsp;=\u0026thinsp;1 male,2 female). (Charts illustrating the residuals of the regression analysis including Normal P-P plot, Scatterplot and distribution are depicted in Suppl. Figures\u0026nbsp;7\u0026ndash;9).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression analysis hypothesis results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRegression weights\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eResults\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003eH1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eheight \u0026loz;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elean mass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eweight \u0026loz;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elean mass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egender \u0026loz;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elean mass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-6.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eage \u0026loz;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elean mass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHGS \u0026loz;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elean mass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF (5, 69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e282.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eH:hypothesis, B: regression coefficient, t: t-statistic, R\u003csup\u003e2\u003c/sup\u003e: coefficient of determination, F: F-statistic\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDiagnostic accuracy analysis and cut-offs\u003c/p\u003e \u003cp\u003eROC analysis was performed to evaluate the role of HGS in detecting low muscle mass in patients with IBD, utilizing BIA results to define low lean mass values[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The diagnostic accuracy of HGS is characterized as \u0026ldquo;excellent\u0026rdquo; with an area under the curve (AUC) of 0.873 (95%CI\u0026thinsp;=\u0026thinsp;0.814 to 0.932,p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Suppl. table 6). A cut-off value of 40.5kg can identify patients with low lean mass with a sensitivity of 94.5% and 60.1% specificity. Furthermore, subgroup analysis was carried to investigate cut-off values out by gender. By including only females in the analysis, HGS functions fairly with an AUC of 0.716 (95%CI\u0026thinsp;=\u0026thinsp;0.584 to 0.848,p\u0026thinsp;=\u0026thinsp;0.003) and adopting a sensitivity-maximizing approach, a cut-off value of 28.5kg is able to define low lean mass with a sensitivity of 85% and specificity of 54% (Supp. Figure\u0026nbsp;10, Suppl. table 7). Including only male in the analysis, HGS also functions fairly with an AUC of 0.714 (65%CI\u0026thinsp;=\u0026thinsp;0.572 to 0.855,p\u0026thinsp;=\u0026thinsp;0.009) and utilizing a sensitivity-maximizing approach, a cut-off value of 49.5kg is able to diagnose low lean mass with 87.5% sensitivity and specificity of 55% (Supp. Figure\u0026nbsp;11, Suppl. table 8). ROC analysis of the diagnostic accuracy of HGS in diagnosing sarcopenia based on FFMI was not statistically significant (AUC 0.650, 95%CI\u0026thinsp;=\u0026thinsp;0.508 to 0.791,p\u0026thinsp;=\u0026thinsp;0.071)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubgroup and Sensitivity analysis\u003c/p\u003e \u003cp\u003eSubgroup analysis was carried out to investigate the statistical significance in certain groups. Introducing only 96 CD patients in the statistical analysis similar strong correlations were indicated between HGS and patients\u0026rsquo; body cell mass (r\u003csub\u003es\u003c/sub\u003e=0,727, 95%CI\u0026thinsp;=\u0026thinsp;0.611 to 0.813,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), water (r\u003csub\u003es\u003c/sub\u003e=0.726, 95%CI\u0026thinsp;=\u0026thinsp;0.609 to 0.812,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001], height [r\u003csub\u003es\u003c/sub\u003e=0,721, 95%CI\u0026thinsp;=\u0026thinsp;0.603 to 0.809,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lean mass (r\u003csub\u003es\u003c/sub\u003e=0,708, 95%CI\u0026thinsp;=\u0026thinsp;0.586 to 0.799,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001], FFMI [r\u003csub\u003es\u003c/sub\u003e=0,535, 95%CI\u0026thinsp;=\u0026thinsp;0.366 to 0.669,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), weight (r\u003csub\u003es\u003c/sub\u003e=0.342, 95%CI\u0026thinsp;=\u0026thinsp;0.143 to 0.515,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and age (r\u003csub\u003es\u003c/sub\u003e=-0.274, 95%CI=-0.457 to -0.70,p\u0026thinsp;=\u0026thinsp;0.007). Similarly, there were not found any associations in the correlation between HGS and BMI (Supp. Table\u0026nbsp;9).\u003c/p\u003e \u003cp\u003eTo increase the sensitivity of the model, patients belonging below the HGS 25th percentile were included in the analysis. An analysis of 47 patients found similar levels of correlation between HGS and total body water (r\u003csub\u003es\u003c/sub\u003e=0.780, 95%CI\u0026thinsp;=\u0026thinsp;0.631 to 0.874,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lean mass (r\u003csub\u003es\u003c/sub\u003e=0.766, 95%CI\u0026thinsp;=\u0026thinsp;0.609 to 0.866,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), body cell mass (r\u003csub\u003es\u003c/sub\u003e=0.675, 95%CI\u0026thinsp;=\u0026thinsp;0.474 to 0.809,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and height (r\u003csub\u003es\u003c/sub\u003e=0.644, 95%CI\u0026thinsp;=\u0026thinsp;0.430 to 0.789,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similar results were obtained by including in the statistical analysis only participants below the 50th percentile with significant associations between HGS and total body water (r\u003csub\u003es\u003c/sub\u003e=0.735, 95%CI\u0026thinsp;=\u0026thinsp;0.608 to 0.825,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), height (r\u003csub\u003es\u003c/sub\u003e=0.716, 95%CI\u0026thinsp;=\u0026thinsp;0.583 to 0.812,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lean mass (r\u003csub\u003es\u003c/sub\u003e=0.709, 95%CI\u0026thinsp;=\u0026thinsp;0.573 to 0.807,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), body cell mass (r\u003csub\u003es\u003c/sub\u003e=0.678, 95%CI\u0026thinsp;=\u0026thinsp;0.532 to 0.785,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), FFMI (r\u003csub\u003es\u003c/sub\u003e=0.563, 95%CI\u0026thinsp;=\u0026thinsp;0.384 to 0.701,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and age (r\u003csub\u003es\u003c/sub\u003e=-0.348, 95%CI=-0.534 to -0.129,p\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to explore the role of HGS as a non-invasive screening tool for reduced muscle mass within the IBD population, where current diagnostic guidelines and established HGS reference values are not validated or suboptimal [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Sarcopenic patients in the present study exhibited lower HGS values, especially those with severe sarcopenia, while a significant association was demonstrated between HGS and BIA, a noninvasive, repeatable and reliable method of body composition assessment, used to monitor changes in body composition or hydration in time[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Based on these results, HGS could be used as a readily implementable and robust sarcopenia screening tool in patients with IBD.\u003c/p\u003e \u003cp\u003eDespite most patients being overweight or obese, nearly a quarter of them presented with sarcopenia and a tenth exhibited severe sarcopenia. This confirms the high prevalence of sarcopenic obesity and body composition alterations in IBD, which are associated with adverse clinical outcomes and poorer prognosis[\u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Adipose tissue can contribute in inflammation in IBD by release of adipokines, adipocyte stress response and macrophage stimulation[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and obese patients manifest more often anoperineal complications, a more marked year-by-year disease activity[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and a potentially poor response to anti-TNF-α treatment[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In the present study, a significant association was demonstrated between lean mass and HGS which alongside height, weight, gender, and age, collectively explained 95.3% of the variance in lean mass. These observations support that HGS is a valid, easily obtainable integral component in muscle mass assessment, that can be used not only as an initial method to unmask sarcopenia, presenting an advantage over the exclusive reliance on BMI, but also to monitor nutritional support and to guide muscle mass growth in every-day clinical practice.\u003c/p\u003e \u003cp\u003eA primary objective of the present study was to establish IBD specific HGS cut-off values for sarcopenia screening, as current generalized low HGS cut-offs were derived from healthy population to determine physical performance and are poorly sensitive (21%) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Conversely, the application of gender and age standardized HGS z-scores with a cut-off below zero proved to be more sensitive in diagnosing sarcopenic (53%) and severely sarcopenic patients (46%). ROC analysis indicated \"excellent\" diagnostic accuracy for HGS in detecting low lean mass, with an optimal cut-off value of 40.5 kg, which yielded a high sensitivity (94.5%) and moderate specificity (60.1%) and gender-specific cut-offs were derived to enhance precision: 28.5 kg for females and 49.5 kg for males. Although these thresholds are more sensitive, lack specificity and require further validation in larger populations.\u003c/p\u003e \u003cp\u003eSubgroup analysis in CD patients reported similar results confirming associations and reducing sample heterogeneity. Likewise, sensitivity analysis was conducted only in patients with low muscle strength belonging below the 25th and 50th percentile with the intention to increase the validity of the results and showed similar results.\u003c/p\u003e \u003cp\u003eIt has to be acknowledged though, that this study has several limitations. Firstly, BIA rather than the gold standard DXA was utilized for sarcopenia diagnosis[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. BIA relies on predictive, population-specific equations and indirectly estimates body composition[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Its accuracy is susceptible to various factors, including hydration status, body position, and prior physical activity, leading to questions about its overall reliability[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Compared to DXA, BIA can underestimate fat mass and overestimate lean mass, with more pronounced discrepancies in overweight and obese individuals[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Secondly, the observational design of the study prevents the establishment of causal relationships, necessitating future prospective studies to validate the HGS\u0026rsquo;s prognostic role. Lastly, although gender-specific, the cut-offs are not age-standardized due to limitations in statistical power, indicating that larger patient samples are needed to establish more robust and generalizable conclusions.\u003c/p\u003e \u003cp\u003eHGS is a fundamental, simple and reproducible screening tool for sarcopenia, strongly correlating with muscle mass in IBD. Higher cut off values and age/gender-standardized z-scores are more sensitive compared to the non-standardized recommended cut offs. Although the equation model uses easily obtainable and can predict with great accuracy the quantity of lean mass in IBD patients, further validation is required. Future research should integrate HGS with other non-invasive diagnostic tools to develop comprehensive sarcopenia screening protocols in IBD\u003c/p\u003e \u003cp\u003eWord count: 2999\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANOVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalysis of Variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea Under the Curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBIA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBioelectric Impedance Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCrohn\u0026rsquo;s Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComputed Tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDXA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDual-Energy X-ray Absorptiometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEWGSOP2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEuropean Working Group on Sarcopenia in Older People 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFFMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFat-Free Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHBI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHarvey-Bradshaw Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHGS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHandgrip Strength\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHip Circumference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIBD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInflammatory Bowel Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMagnetic Resonance Imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver Operating Characteristics Curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSARC-F\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStrength, Ambulation, Rising from a chair, Stair climbing and history of Falling\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSCCAI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSimple Clinical Colitis Activity Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPPB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eShort Physical Performance Battery\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTROBE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStrengthening the Reporting of Observational studies in Epidemiology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTUG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eΤimed-Up and Go test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUlcerative Colitis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVIF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVariance Inflation Factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWaist Circumference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWaist to Hip Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eAuthor Contribution statement: AV conceived research, collected data, analyzed data, drafted and edited the manuscript, XT: collected data, reviewed and edited the manuscript, TM: collected data, reviewed the manuscript, MGP reviewed the manuscript, GG conceived research, reviewed the manuscript, MC conceived research, reviewed the manuscript\u003c/p\u003e\n\u003cp\u003eFunding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eEthical approval: The present study was conducted in accordance with the ethical principles of the Declaration of Helsinki[40]. \u0026nbsp; Approval was secured from the Ethics Committee of the Aristotle University of Thessaloniki, as well as from the ethics review boards of the two participating institutions (protocol number 2/7.12.2021) [40]. All patients provided the consent prior the commencement of the study.\u003c/p\u003e\n\u003cp\u003eCompeting interest: All authors declare that they have no conflict of interest or financial conflicts to disclose, and manuscript is approved by all authors for publication.\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the assistance of “Jenni.ai”, a large language model developed by “Google”, for its support in refining the vocabulary and performing grammar checks during the preparation of this manuscript. Jenni.ai was not used to generate the core text, provide scientific ideas, or draw conclusions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCampbell JP, Teigen L, Manski S, et al. 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DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/NU10101469\u003c/span\u003e\u003cspan address=\"10.3390/NU10101469\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. \u003cem\u003eJAMA\u003c/em\u003e 2013; 310: 2191\u0026ndash;2194.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-clinical-nutrition","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejcn","sideBox":"Learn more about [European Journal of Clinical Nutrition](http://www.nature.com/ejcn/)","snPcode":"41430","submissionUrl":"https://mts-ejcn.nature.com/cgi-bin/main.plex","title":"European Journal of Clinical Nutrition","twitterHandle":"@ejcneditor","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Handgrip strength, Inflammatory Bowel Disease, Sarcopenia, Crohn’s Disease, Ulcerative Colitis","lastPublishedDoi":"10.21203/rs.3.rs-8167171/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8167171/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eSarcopenia frequently complicates inflammatory bowel disease (IBD), indicating a poor prognosis. Current diagnostic criteria and handgrip strength (HGS) reference values are often not validated for the IBD population. This study aimed to investigate the utility of HGS as a diagnostic tool for sarcopenia in IBD, examining its association with bioelectrical impedance analysis (BIA) and identifying optimal HGS thresholds for screening.\u003c/p\u003e","manuscriptTitle":"Assessment of diagnostic performance of handgrip strength in sarcopenia screening and determining optimal cut-offs in inflammatory bowel disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 16:04:42","doi":"10.21203/rs.3.rs-8167171/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2026-04-13T15:07:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-03-09T10:15:25+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-02-23T10:57:36+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2026-01-30T18:18:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-21T11:06:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-21T10:58:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Clinical Nutrition","date":"2025-11-20T18:09:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-clinical-nutrition","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejcn","sideBox":"Learn more about [European Journal of Clinical Nutrition](http://www.nature.com/ejcn/)","snPcode":"41430","submissionUrl":"https://mts-ejcn.nature.com/cgi-bin/main.plex","title":"European Journal of Clinical Nutrition","twitterHandle":"@ejcneditor","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"77fd95ff-18a7-443e-9121-1d9122f62756","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62056095,"name":"Health sciences/Diseases/Gastrointestinal diseases/Inflammatory bowel disease"},{"id":62056096,"name":"Health sciences/Diseases/Nutrition disorders/Malnutrition"},{"id":62056097,"name":"Health sciences/Diseases/Gastrointestinal diseases/Nutrition disorders/Malnutrition"}],"tags":[],"updatedAt":"2026-04-18T08:05:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 16:04:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8167171","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8167171","identity":"rs-8167171","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}