THU0610-HPR PREDICTION EQUATION FOR MUSCLE MASS OVERESTIMATES MUSCLE MASS IN PATIENTS WITH RHEUMATOID ARTHRITIS
Authors: R. Cavalheiro Do Espírito Santo, L. Santos, L. Filippin, P. Lora, R. Xavier
Journal: Annals of the Rheumatic Diseases (2020)

Background: Rheumatoid Arthritis (RA) is a chronic, progressive, inflammatory autoimmune disease characterized by systemic manifestations. Often is observed in RA patients changes in body composition, such as reduced muscle mass (sarcopenia) with stable or increased fat mass (FM) [1]. Total-body skeletal muscle mass (SMM), specifically appendicular skeletal muscle, is a key diagnostic feature for the assessment of geriatric syndromes associated with skeletal muscle wasting, such as sarcopenia [2]. Estimation of SMM can be accomplished by a variety of methods, but the majority that considered the gold standard for this purpose are high cost. Due high cost, this methods are unfeasible in population studies and increases the difficulty of use in different clinical contexts. Predictive equations have been developed for estimation of whole-body skeletal muscle mass on the basis of anthropometric data, which can be collected in a more affordable manner, in an attempt to make SMM calculation easier and enable its use in epidemiological research and in clinical settings [3]. However, these equations were not developed for RA populations. Objectives: To compare the anthropometric equation that estimate SMM with body composition measurements derived from DXA in RA patients. Methods: Ninety patients diagnosed with RA according to ACR/EULAR criteria were recruited. Body composition was assessed by total body dual-energy x-ray absorptiometry (DXA) for measurement of appendicular lean mass index (ALMI, kg/m2). The prediction equation for muscle mass proposed by Lee et al (variables included: body weight, height, age, sex and race) was used to generate estimates of SMM, stratified by BMI. Frequency analysis, independent student’s t test and intraclass correlation coefficients (ICC) were performed. Statistical significance was considered as p&lt;0.05 Results: Of the 90 patients analyzed, most were women (86.7%; 78/91), with mean age of 56.5±7.3 and median disease duration time of 8.5 (3-18) years. The mean of BMI was 27.39±5.14. Thirty (33.3%) RA patients had normal weight, forty patients (44.4%) were overweight and twenty patients (22.2%) were obese. In normal weight patients, just like overweight and obese patients, the estimates of SMM obtained by Lee equation were higher than those obtained by DXA measurements(Obese: Lee 10.66±1.19 vs DXA 7.10±0.73; Overweight: Lee 8.63±0.99 vs DXA 6.57±0.82; Normal weight: Lee 7.14±0.85vs DXA 6.03±0.71; p&lt;0.05). The Lee equation estimates showed ICC of 0.78 (0.66 - 0.85) with DXA measurements. When stratified by BMI, Lee equation showed ICC of 0.87 (0.72 - 0.94) for normal weight, 0.83 (0.68 - 0.91) for overweight and 0.77 (0.42 - 0.90) for obese with DXA. Conclusion: The muscle mass index by Lee equation overestimates the muscle mass in overweight or obese RA patients compared to DXA. Thus, sarcopenic RA patients may be wrongly classified as normal by the equation. This is probably related to the obese cachexia that these patients often present. More studies are necessary to analysis to better prediction equations for muscle mass in RA patients. References: [1]Smolen JS et al. Nat Rev Dis Prim. 2018;4:18001; [2] Kim J et al. Am J Clin Nutr 2002; 76: 378–83.; [3] Lee RC et al. Am J Clin Nutr 2000;72:796-803. Acknowledgments: We thank the Coordination for the Improvement of Higher Level Personnel (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—CAPES) institution, the Foundation for Research Support of the Rio Grande do Sul State (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul—FAPERGS), the Research and Events Incentive Fund (Fundo de Incentivo à Pesquisa e Eventos—FIPE) of HCPA and Technological Development (Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq). Disclosure of Interests: Rafaela Cavalheiro do Espírito Santo: None declared, Leonardo Santos: None declared, Lidiane Filippin: None declared, Priscila Lora: None declared, Ricardo Xavier Consultant of: AbbVie, Pfizer, Novartis, Janssen, Eli Lilly, Roche