BackgroundJuvenile psoriatic arthritis (JPsA) is one of the least common subtypes of JIA. Information on JPsA is mainly clinical, but scarce regarding the impact of musculoskeletal involvement on children’s quality of life.ObjectivesTo describe the clinical and ultrasound (US) characteristics of children with JPsA, and to assess their impact using a composite quality of life index, the PsAID (Psoriasis Arthritis Impact of Disease).MethodsA multicentre cross-sectional observational study recruited consecutive JPsA patients, from January-2020 to May-2022. Inclusion criteria: 1/ age at onset ≤ 16 years, 2/ diagnosis by the prescribing physician based on one of the 2 JPsA diagnostic classification systems (ILAR or Vancouver). All were assessed clinically and US (independently) and completed two questionnaires: PsAID (1) and Child Health Assessment Questionnaire (CHAQ, to measure physical disability). Statistics included correlation Spearman (rho) and PsAID was identified as the dependent variable.ResultsThe 48 children included (mean age at inclusion 11±4 years), 71% girls, 67% had oligoarthritis and 80% psoriasis in a first or second-degree relative. Mostly arthritis started before the psoriasis. The median time lag between the diagnosis and the onset of specific musculoskeletal manifestations was 1 year; interquartile range 0.5-2. ANA and HLAB27 were present in 19 (40%) and 5 (10%) of the children, respectively. A history of axial inflammatory symptoms was present in 3 (5%) patients, and unilateral sacroiliitis was confirmed by MRI in only one patient. Psoriasis and dactylitis were the most frequent manifestations identified (≥50%), while uveitis was less common (12%). Twenty-eight (58%) patients used methotrexate, 21 used anti-TNF agents and 14 needed corticosteroid infiltration. The population showed low clinical activity in the DAPSA composite activity index (md 3.5; range 0-25). Similarly, US showed a low number of affected joints but US was slightly superior to clinical examination, while US was clearly superior to detect enthesitis and tenosynovitis, particularly tendons of the fingers.The PsAID index (median 0.4; range 0-9.2) showed low correlation with CHAQ (r 0.3) and high with DAPSA (r 0.7); however, the correlation between PsAID and total joint count was low for both clinical and US assessment (r 0.4). The correlation between PsAID and total joint count was moderate for clinical (r 0.5) and low for US (r 0.4). Children with the presence of enthesitis and clinical dactylitis had a higher mean PsAID score than those without (dactylitis; p=0.002, and enthesitis; p<0.001).ConclusionThe study supports the existence of an atypical pattern of late-onset JPA characterised by a predominance of girls with peripheral involvement and little uveal involvement. Based on our results, dactylitis and enthesitis have a greater impact on the child’s quality of life than joint involvement per se. Studies with a larger sample size and/or disease activity are needed to confirm these findings.Reference[1]Gossec L, et al.A patient-derived and a patient-reported outcome measure for assessing psoriatic arthritis: elaboration and preliminary validation of the Psoriasis Arthritis Impact of Disease, PsAID questionnaire, a 13-country EULAR initiative. Ann Rheum Dis 2014;73:1012AcknowledgementsSociedad Española de Reumatología Pediátrica (SERPE)Disclosure of InterestsNone Declared.
BackgroundSystemic lupus erythematosus (SLE) is a chronic disease of autoimmune origin mediated by autoantibodies, affecting various organs12. 95% of patients with SLE develop musculoskeletal involvement1, most of the time as arthralgia or non-erosive arthritis, mainly affecting the hands and knees. A subgroup of patients with lupus that is seen with increasing frequency, late develops a deforming arthropathy as a result of the laxity of ligaments and peritendinous apparatus that produces a joint subluxation. The foot is a highly affected structure that may initially go unnoticed, but it leads to significant disability.ObjectivesTo know the prevalence of foot problems in a sample of patients with SLE.MethodsA cross-sectional study design. Forty-seven subjects with a diagnosis of SLE were consecutively recruited in a Rheumatology Unit between March and May 2021. The inclusion criteria were: patients with a diagnosis of SLE according to the EULAR / ACR 2019 criteria, with at least one year of evolution and age equal to or greater than 18 years. A Rheumatology nurse collected information on socio-demographic data and the characteristics of the feet regarding the musculoskeletal system, skin, appendages, circulatory system and nervous system using a pre-designed questionnaire. The study was approved by the Ethics Committee, following the recommendations of the Declaration of Helsinki and the legal regulations in force in our country regarding clinical research and the current Good Clinical Practice standards. All participants were informed of the objectives and methods of the study and signed the informed consent. Descriptive statistical analysis.Results47 subjects participated (93.6% women) with a mean age (SD) of 49.2 (10.8) years (range 23-66 years). Thirty-five (74.5%) patients presented a low internal longitudinal arch in one or both feet, 19 (40%) Hallux Abductus Valgus (HAV) and 30 (63.8%) presented alterations in the other toes. Seventeen (36.2%) patients had pain in the hindfoot, 16 (34.0%) in the midfoot, 17 (36.2%) in the forefoot, and 14 (29.8%) in the toes. Stiffness in the feet appeared in 8 (17%) while joint swelling appeared in only one (2.1%). On the skin and appendages: 10 (21.3%) showed nail lesions and 39 (80.3%) presented some dermal lesion of the hyperkeratosis type due to friction or poor support. In the circulatory system there were no findings. Two (4.3%) patients had less bilateral sensitivity in the examination of the nervous system in the feet.ConclusionThe most prevalent foot problems affect the musculoskeletal system and are related to Jaccoud arthropathy, especially the sinking of the internal longitudinal arch. Hyperkeratosis associated with poor foot support were also very prevalent.References[1]Cherry L, Alcacer-Pitarch B, Hopkinson N, Teh LS, Vital EM, Edwards CJ, et al. The prevalence of self-reported lower limb and foot health problems experienced by participants with systemic lupus erythematosus: Results of a UK national survey. Lupus. 2017 Apr;26(4):410-416.[2]Durcan L, O’Dwyer T, Petri M. Management strategies and future directions for systemic lupus erythematosus in adults. Lancet. 2019 Jun 8;393(10188):2332-2343.Disclosure of InterestsNone declared
BackgroundSarcopenia is a muscle disease that presents as a loss of skeletal muscle mass and function. It is a condition associated with chronic diseases and aging that predicts disability, hospitalization and death.ObjectivesTo describe the prevalence of sarcopenia and identify risk factors associated with sarcopenia in patients with spondyloarthritis (SpA) older than 65 years.MethodsDesign: Case-control study. Participants: Cases: They were recruited by simple random sampling among patients over 65 years of age with SpA (ACR/EULAR 2010 criteria) treated at 2 university hospitals. Controls: They were recruited for convenience by asking the cases to attend a consultation with a person of the same age (+/- 5 years) and sex.Variables: The main variable: sarcopenia, defined according to the European Working Group on Sarcopenia in Older People (EWGSOPII) 2019 criteria. The risk factors for sarcopenia evaluated were: economic level, malnutrition, measured with the Mini Nutritional Assessment (MNA), toxic habits, comorbidities and Charlson index, physical activity measured with the Global Physical Activity Questionnaire (GPAQ) and Short Physical Performance Battery (SPPB), muscle assessment measured by ultrasound. Other variables were: hemoglobin, calcium, vitamins D and B12, albumin, C-reactive protein, BMI (body mass index), polypharmacy (≥5), quality of life (EQ-5D) and factors related to SpA: activity of disease measured with BASDAI and ASDAS, physical function measured with BASFI, and treatments. Statistical analysis: descriptive and multivariate analysis was performed to identify factors associated with sarcopenia in SpA.Results36 patients and 36 controls were recruited, of whom 54 (75%) were men, with a mean (± SD) age of 70 years (±4.37). Of the 36 patients with SpA, 20 (55.6%) had axial SpA and 15 (44.5) had SpA with axial and peripheral involvement with a mean of 32 years (±10.9) of disease. The prevalence of sarcopenia in patients with SpA is 8.3%. No differences were found in sarcopenia between patients [3(8.3%) and controls [1(2.8%)], p=0.614.Patients with SpA who had sarcopenia, compared with those who did not, had a mean years of evolution of their major disease [45.6 (±3.1) Vs 31.06 (± 10.5)], p=0.24; worse performance tests in the Short Physical Performance Battery (p=0.26), in relation to ultrasound parameters, a lower thickness was observed in the right forearm 75% radial and left forearm 66% radial [11.1(0.2)Vs 14, 7(2.4)], p=0.17 and left rectus femoris area [11.5(1.2)Vs15.4(2.8)], p=0.26, greater thigh fat right to 50% [25.6(4.7) Vs 15.2(8.3)], p=0.041 and lower albumin levels [7.17(21.5) Vs 19.02(608, 5)] p=0.053. On the other hand, no significant differences were found in the rest of the parameters studied for disease activity, disability, quality of life (EQ-5D), malnutrition, toxic habits, comorbidities or physical activity.In the multivariate model, the years of disease evolution (p=0.041) Table 1 were identified as an independent predictor of sarcopenia in patients with SpA. This model would explain 33% of sarcopenia in RA (R2=0.37).ConclusionIn our study we found no differences in sarcopenia in patients older than 65 years with SpA compared to controls. The longer evolution time of their disease in patients with SpA is associated with a greater risk of sarcopenia.Table 1.Multivariate analysis (VD: Sarcopenia) in patients with SpAOR(IC)p-valorEvolution of SpA (years)1,172 (1,002-1,363)0,047R2=0,33REFERENCES:NIL.Acknowledgements:NIL.Disclosure of InterestsNone Declared.
BackgroundIgA vasculitis (IgAV) and IgA nephropathy (IgAN) are inflammatory conditions that share pathophysiological mechanisms, being B-cells crucial players in both diseases [1]. In this regard, some authors have suggested that IgAV and IgAN may represent different outcomes of a continuous spectrum of a disease [2]. In addition,CD40, BLKandBANK1are relevant genes involved in the development and signalling of B-cells and are also identified as susceptibilitylocifor several immune-mediated diseases [3-6].ObjectivesTo determine whether IgAV and IgAN may be different outcomes of a single disease, by assessing theCD40, BLKandBANK1genetic pattern.MethodsThree genetic variants withinCD40(rs1883832, rs1535045, rs4813003), three genetic polymorphisms withinBLK(rs2254546, rs2736340, rs2618476) as well as twoBANK1genetic variants (rs10516487, rs3733197) were genotyped in 380 Caucasian patients diagnosed with IgAV, 90 patients diagnosed with IgAN and 1,012 ethnically matched healthy controls. The eight polymorphisms selected were previously associated with several inflammatory diseases [3-6].ResultsSimilar genotype and allele frequencies were observed in IgAV patients when compared to those with IgAN, whenCD40, BLKandBANK1variants were analyzed independently (Table 1). In addition, no statistically significant differences were observed between patients with IgAV and healthy controls as well as between patients with IgAN and healthy controls, whenCD40, BLKandBANK1genetic variants were analyzed independently (Table 1). Similar results were disclosed when haplotype frequencies ofCD40, BLKandBANK1were compared between patients with IgAV and those with IgAN, as well as between patients with IgAV and healthy controls and between IgAN and healthy controls.ConclusionOur results reveal a similarCD40, BLKandBANK1genetic distribution in IgAV and IgAN, supporting that IgAV and IgAN may represent different outcomes of a single disease.References[1] N Engl J Med 2013;368:2402-14;[2] Am J Kidney Dis 1988;12:373-7;[3] Nat Genet 2009;41:824-8;[4] Ann Rheum Dis 2012;71:136-42;[5] Nat Genet 2012;44:517-21;[6] Nat Genet 2012;44:522-5.Table 1.Genotype and allele frequencies ofCD40, BLKandBANK1in patients with IgAV, patients with IgAN and healthy controls.ChangeGenotypes, % (n)Alleles, % (n)Polymorphism1/2Data set1/11/22/212CD40rs1883832C/TIgAV54.0 (204)37.0 (140)9.0 (34)72.5 (548)27.5 (208)IgAN56.5 (48)36.5 (31)7.0 (6)74.7 (127)25.3 (43)Healthy controls52.9 (532)40.4 (409)6.7 (68)73.1 (1,479)26.9 (545)CD40rs1535045C/TIgAV53.6 (200)39.4 (147)7.0 (26)73.3 (547)26.7 (199)IgAN51.8 (44)41.2 (35)7.1 (6)72.4 (123)27.6 (47)Healthy controls56.7 (574)36.2 (366)7.1 (72)74.8 (1,514)25.2 (510)CD40rs4813003C/TIgAV78.0 (291)20.1 (75)1.9 (7)88.1 (657)11.9 (89)IgAN76.6 (59)20.8 (16)2.6 (2)87.0 (134)13.0 (20)Healthy controls74.9 (758)22.7 (230)2.4 (24)86.3 (1,746)13.7 (278)BLKrs2254546G/AIgAV74.5 (278)22.3 (83)3.2 (12)85.7 (639)14.3 (107)IgAN64.7 (55)31.8 (27)3.5 (3)80.6 (137)19.4 (33)Healthy controls71.3 (722)26.8 (271)1.9 (19)84.7 (1,715)15.3 (309)BLKrs2736340C/TIgAV62.8 (236)31.9 (120)5.3 (20)78.7 (592)21.3 (160)IgAN67.5 (52)31.2 (24)1.3 (1)83.1 (128)16.9 (26)Healthy controls59.9 (606)35.8 (362)4.3 (44)77.8 (1,574)22.2 (450)BLKrs2618476T/CIgAV60.2 (227)34.2 (129)5.6 (21)77.3 (583)22.7 (171)IgAN68.2 (58)24.7 (21)7.1 (6)80.6 (137)19.4 (33)Healthy controls57.9 (586)37.3 (377)4.8 (49)76.5 (1,549)23.5 (475)BANK1rs10516487G/AIgAV52.8 (200)39.8 (151)7.4 (28)72.7 (551)27.3 (207)IgAN50.6 (42)38.6 (32)10.8 (9)69.9 (116)30.1 (50)Healthy controls50.8 (514)41.8 (423)7.4 (75)71.7 (1,451)28.3 (573)BANK1rs3733197G/AIgAV50.0 (187)39.0 (146)11.0 (41)69.5 (520)30.5 (228)IgAN47.1 (40)37.6 (32)15.3 (13)65.9 (112)34.1 (58)Healthy controls49.5 (501)41.6 (421)8.9 (90)70.3 (1,423)29.7 (601)AcknowledgementsThis study has been funded by Instituto de Salud Carlos III (ISCIII) through the project PI18/00042 and PI21/00042, co-funded by European Regional Development Fund (ERDF), `Investing in your future´; VP-C: PI18/00042 from ISCIII, co-funded by ERDF; MSM-G is supported by funds of TRANSVAL22/01 from IDIVAL; RL-M: Miguel Servet type II programme fellowship from the ISCIII, co-funded by the European Social Fund (`Investing in your future´) [CPII21/00004].Disclosure of InterestsVerónica Pulito-Cueto: None declared, Fernanda Genre Romero: None declared, Sara Remuzgo Martinez: None declared, Belén Sevilla: None declared, Norberto Ortego: None declared, Maite Leonardo: None declared, Ana Peñalba: None declared, J. Narváez: None declared, Luis Martín-Penagos: None declared, Lara Belmar-Vega: None declared, Cristina Gomez-Fernandez: None declared, María Sebastián Mora-Gil: None declared, LUIS CAMINAL MONTERO: None declared, PAZ COLLADO: None declared, Antonio Fernandez-Nebro: None declared, Gisela Diaz-Cordobes: None declared, Secundino Cigarrán: None declared, Jesús Calviño: None declared, Carmen Cobelo: None declared, Diego de Argila: None declared, Javier Sanchez Perez: None declared, Miren Uriarte-Ecenarro: None declared, Esteban Rubio-Romero: None declared, MANUEL LEON LUQUE: None declared, Juan María Blanco-Madrigal: None declared, E. Galíndez-Agirregoikoa: None declared, Javier Martin Ibanez: None declared, Santos Castañeda: None declared, Miguel A González-Gay Speakers bureau: Abbvie, Pfizer, Roche, Sanofi, Lilly, Celgene, MSD and GSK, Grant/research support from: Abbvie, MSD, Jansen and Roche, Ricardo Blanco Speakers bureau: Abbvie, Pfizer, Roche, Bristol-Myers, Janssen and MSD, Consultant of: Abbvie, Pfizer, Roche, Bristol-Myers, Janssen and MSD, Grant/research support from: Abbvie, MSD and Roche, Raquel López-Mejías: None declared.
BackgroundInterstitial lung disease (ILD) is the most frequent non-pleural pulmonary manifestation in rheumatoid arthritis (RA) and causes high morbidity and mortality [1-3]. Currently, there are no clinically useful serum markers for the diagnosis and prognosis of RA associated ILD (RA-ILD) [4].ObjectivesTo identify soluble cytokines that work as biomarkers for diagnosis and prognosis in RA-ILD and explore whether there is an association between those and pulmonary progression.MethodsObservational case-control study nested in a prospective cohort of cases of patients with RA (ACR/EULAR 2010) [5] with and without ILD, paired by sex, age, and time of RA evolution. All subjects underwent pulmonary function tests (PFTs) and high-resolution computed tomography (HRCT) on the inclusion date (protocol date) and, in cases of RA-ILD, also on diagnosis of ILD. The primary variable ILD was defined according to lung biopsy or HRCT according to the American Thoracic Society/European Respiratory Criteria [6], and pulmonary progression was defined as worsening FVC >10% or DLCO >15% [4]. Inflammation variables included inflammatory activity data measured by DAS28-ESR and a cytokine multiplex including Th1/Th2 function, inflammatory cytokines, and chemokines. Other clinical, RA severity and therapeutic variables were also studied: rheumatoid factors (RF), anti-cyclic citrullinated peptide antibodies (ACPA), radiological erosions, and Health Assessment Questionnaire (HAQ) values. A descriptive analysis and two Cox regression models were performed to identify factors associated with ILD and ILD progression in RA, adjusting for time to development of ILD-RA and to ILD progression, respectively.ResultsA total of 70 subjects were included, 35 RA-ILD cases and 35 RA controls without ILD (Table 1). A higher percentage of patients with RA-ILD, compared to the rest, presented elevated RF (p=0.089) and ACPA levels (p=0.031), higher DAS28-ESR values (p=0.032), number of swollen joints (p=0.040) and worse quality of life measured by HAQ (p=0.003). The variables that were independently associated with RA-ILD in the Cox regression adjusted for time of evolution of RA (Figure 1) were DAS28-ESR of moderate-high activity (OR [95% CI], 2,474 [1,173-5,220]; p= 0.017), elevated ACPA levels (OR [95% CI], 2.905 [1.244-6.786]; p=0.014), IL-18 (OR [95% CI], 1.063 [1.002-1.127]; p=0.044), MCP1CCL2 (OR [95% CI], 1.031 [1.001-1.064]; p=0.049) and SDF1 (OR [95% CI], 1.001 [1.001-1.002]; p=0.010). In the other COX regression model adjusted for time to ILD progression, the only variable associated with progression was IL18 (OR [95% CI], 1.254 [1.074-1.465]; p=0.004).Table 1.Baseline characteristics of the study populationVARIABLERA-ILD n=35RA without ILD n=35P-valueRF+ (>10), n (%)33 (94.3)31 (88.6)0.393High RF (>60)24 (68.6)17 (48.6)0.089ACPA+ (>20), n (%)32 (91.4)31 (88.6)0.690High ACPA (>340), n (%)22 (63.0)14 (40.0)0.039Erosions, n (%)21 (60.0)19 (55.6)0.705DAS28-ESR, mean (SD)3.1 (0.9)2.6 (0.9)0.032Remission/low activity, n (%)19 (54.3)27 (77.1)0.044Moderate/high activity, n (%)16 (45.7)8 (22.9)0.044Number of swollen joints, median (IQR)0.0 (0.0-1.0)0.0 (0.0-0.0)0.040HAQ, mean (SD)1.2 (0.6)0.8 (0.6)0.003Figure 1.Cox regression analysis adjusted for time of evolution of RAConclusionPatients with RA-ILD show higher inflammatory activity than RA patients without ILD. Some cytokines are associated both with diagnosis and with a worse prognosis in patients with RA-ILD, so they could be potential biomarkers for this entity. Future studies are needed to validate these data and confirm the findings.References[1] Aguilar-Hurtado MC, et al. J Clin Med. 2021 Feb;10(4)[2] Castellví I, et al. Reumatol Clin. 2022 Sep[3] Fischer A, et al. Eur Respir J. 2016 Feb;47(2):588–96[4] Nieto MA, et al. Reumatol Clin. 2022 Oct;18(8):443–52[5] Aggarwal R, et al. Arthritis Rheum. 2010 Sep;62(9):2582–91[6] Lederer DJ, et al. Am J Respir Crit Care Med. 2018 Sep;198(5):e44–68AcknowledgementsThis work was supported by Youth Guarantee Aid 2020 (UMA, SNGJ5Y6–12) and PAIDI Study Group for Inflammatory Rheumatic Diseases (CTS-1034)Disclosure of InterestsNone Declared.
Objectives:Cross-sectional observational study of a series of SLE patients selected from the Rheumatology consultations.Methods:age ≥18 years with SLE (ACR 1997 criteria) capable of understanding and willing to take the questionnaires. Protocol: All patients with SLE undergoing follow-up in the rheumatology clinic are recorded in a database. A telephone call was made to all the patients included in the database and those patients who responded to the call and gave their verbal consent for the collection of data from their clinical history and completed the Goldberg questionnaire were finally included. The nurse was in charge of explaining the questionnaire to the patients. Variables: the main outcome variable was depression assessed by Goldberg (≥2 depression) and other variables were: previous diagnosis of depression, Charlson index, polypharmacy, psychiatric medication, referral to mental health or primary care, SLEDAI and SLICC. Descriptive, bivariate statistical analysis and multivariate logistic regression analysis (VD: Goldberg depression).Results:89 patients with SLE were included (95.5% women, mean age 49.44 ± 13.2 years and 18.28 ± 9.19 years of disease). The mean (SD) of the Goldberg scale in all the patients was 3.2 ± 2.9 and a total of 45 patients (50.4%) met criteria of depression according to Goldberg’s screening, of which 19 (21.3%) patients had a previous diagnosis of depression. Only 9 patients (10.1%) had had a mental health follow-up and 22 patients (24.7%) were being followed by the family doctor. A total of 87 patients (97.8%) presented polypharmacy: severe polypharmacy 59 (66.3%) and 33 (37.1%) psychiatric medication. The most used psychiatric medication was: 7 (7.8%) bromazepam, 6 (6.7%) citalopram, 5 (5.6%) diazepam. Regarding comorbidities, the Charlson index was 1.82 ± 1.21, also highlighting that 34 (27%) of the sample had Sjögren syndrome. In the multivariate analysis, polypharmacy (OR, 1.8 [95% CI, 1.0-3.1]) and Sjogren’s syndrome (OR, 3.8 [95% CI, 1.0-10.7]) were independently associated with depression by Goldberg.Conclusion:Depression is underdiagnosed and undertreated in patients with SLE. Depression is associated with polypharmacy and the perception of patients with SLE of being ill. It is important to correctly treat depression in the context of SLE comorbidity due to its great impact on quality of life.Disclosure of Interests:None declared
Background:In recent years, several studies show contradictory results regarding body composition in juvenile idiopathic arthritis. Adiposity in Rheumatoid Arthritis and Psoriasic Arthritis has been associated to inflammatory activity, but it is not clear what happens in JIA.Objectives:To describe the body composition and anthropometric parameters of patients with JIA compared with healthy controls and analyze associated risk factors in JIA patients.Methods:Observational cross-sectional study in spanish children aged 4-15 years with JIA compared with healthy controls matched for age and sex. We recorded epidemiological variablesanthropometric parameters, clinical data and validated physical activity questionnaires. Body composition was measured using dual-energy x-ray absorptiometry (DXA), and included total mass (kg), fat mass (g), lean mass (g), and lean mass and android and gynoid fat mass. The fat mass index (FMI) was defined as fat mass (kg)/height squared (m2) and fat-free mass index (FFMI) as fat-free mass (kg)/height squared (m2). Descriptive, bivariete and two multivariate models were constructed to identify factors associated with obesity and fat mass in JIA patients.Results:We analyze 160 subjects: 80 patients with JIA and 80 healthy controls.The baseline characteristics of both groups are shown in Table 1. No differences were found between both groups in BMI (p=0.936), fat mass (p = 0.449), lean mass (p = 0.793) and in fat and lean mass of legs, arms and trunk, or in physical activity questionnaire (p = 0.582). The factors associated with obesity in patients with JIA were: time with biological drug (OR [95% CI] = 1.12 [1.01-1.04]; p = 0.042) and sedentary lifestyle (OR [95% CI] = 3.50 [1.18-7.35]; p = 0.023); while the factors associated with the fat mass index were: age (ß [95% CI] = 0.30 [0.16-1.41]; p= 0.014), inflammatory activity (JDAS) (ß [95% CI] = 0.44 [0.16-1.08]; p= 0.009) and physical activity (ß [95% CI] = -0.22 [-0.10,-0.28]; p = 0.031).Table 1.Baseline characteristics of patients with JIA and controlsVariableJIA (n=80)Controls (n=80)P-value Sex, girls n (%)56 (70.0)57 (71.3)0.862 Age, mean (SD)10.7 (3.2)10.2 (3.2)0.893Disease duration (years) JIA, mean (SD)6.5 (3.7)JIA subtype Systemic, n (%)9 (11.3) Oligoarticular persistent, n (%)38 (47.5) Oligoarticular extended, n (%)13 (16.3) Rheumatoid Factor-positive polyarticular, n (%)1 (1.3) Rheumatoid Factor-negative polyarticular, n (%)19(23.8)CRP (mg/l), mean (SD)4,8 (9,5)ESR (mm/h), mean (SD)8.8 (7,3)JADAS27, mean (SD)2 (4.0)CHAQ, mean (SD)0.17 (0.4)Treatment DMARDs (synthetic), n (%)42 (52.5) DMARDs (biological), n (%)24 (30.0) Anti IL-1, n (%)4 (16.7) Anti IL-6, n (%)2 (8.3) Anti TNF-α, n (%)18 (75.0)Treatment duration DMARDs synthetic, (months), mean (±SD)51 (37.5) DMARDs biological, (months), mean (±SD)19.7 (28.4) DMARDs total (months), mean (±SD)55.8 (38.0)Cumulative corticoisteroids dose, median (range)11.3 (0.12-870)Abreviaturas; JIA: juvenile idhipathic arthritis; SD: standart deviation; CRP: C-reactive protein; VSG:erytrocyte sedimentation rate; JADAS27: Juvenile Arthritis Disease Activity Score; CHAQ: Childhood Health Assessment; DMARD: disease-modifyng anti-rheumatic drug.Conclusion:Children with JIA have adiposity similar to healthy controls. Inflammatory activity measured by JDAS is associated with fat mass but not to anthropometric measurements such as body mass index (BMI).References:[1]Grönlund et al Juvenile idiopathic arthritis patients with low inflammatory activity have increased adiposity. Scand J Rheumatol 2014.[2]Giani et al. The Influence of Overweight and Obesity on Treatment Response in Juvenile Idiopathic Arthritis. Front Pharmacol 2019[3]Wiech et al. Body composition and phase angle as an indicator of nutritional status in children with juvenile idiopathic arthritis. Pediatric Rheumatology 2018[4]Alvarez-Nemegyei et al. Association between Overweight/Obesity and Clinical Activity in rheumatoid arthritis. Reumatol Clin 2020Disclosure of Interests:None declared
Background: The influence of body mass index (BMI) on Juvenile Idiopathic Arthritis (JIA) disease activity is poorly understood. In adults with Rheumatoid Arthritis, obesity has been associated with higher disease activity, while in JIA patients, a previous study has failed to find any association.Objectives: To investigate the relationship between BMI and JIA disease activity. Methods: This is an international, multicenter, observational, cross-sectional study. JIA patients (according to ILAR criteria) aged £ 18 years, registered at Reuma.pt in Portugal and Brazil were included. Data was analysed upon records from the first registered visit. Age-and sex-specific BMI percentiles (P) were calculated based on WHO growth standard charts and categorized into underweight (P<3), normal weight (3£P£85), overweight (85 97). Disease activity was assessed by Juvenile Arthritis Disease Activity Score (JADAS-27). Univariate linear regression was used to examine the association of JADAS-27 with BMI categories. Two multivariate regression models were performed a) adjusting for age, gender, race, country, disease duration and JIA category (model 1); b) adjusting for those covariates plus use of DMARDs (model 2). Results: 255 patients included, mean age 10.1±4.7 years, mean disease duration 6.3±4.9 years; 62% female; 85% Caucasian. Thirty-two percent were persistent oligoarticular, 9% extended oligoarticular, 34% polyarticular RF+, 6% systemic, 13% enthesitis-related arthritis, 5% psoriatic arthritis and 1% undifferentiated arthritis. The prevalence of underweight, normal weight, overweight and obesity was 7.5%, 65.9%, 15.7% and 11%, respectively. In the univariate linear regression, underweight was significantly associated with higher JADAS-27, compared to normal weight (B=-9.563, p<0.001), overweight (B=-10.661, p<0.001) and obesity (B=-7.422, p=0.004). Lower age (B=-0.299, p=0.012), shorter disease duration (B=-0.396, p=0.001), black race (B=6.852, p=0.033), RF+ polyarthritis (B=7.101, p<0.001), living in Brazil (B=5.357, p=0.002) and the absence of DMARD therapy (B=4.831, p<0.001) were also associated with higher JADAS-27. In the model 1 of multivariate analysis, the same variables, except the country, remained significantly associated with higher disease activity. When DMARD therapy was added to the model (model 2), RF+ polyarthritis (B=4.447, p=0.001) and living in Brazil (B=4.728, p=0.013) were associated with higher JADAS-27. Patients with normal weight (B=-9.964, p<0.001), overweight (B=-10.316, p<0.001) and obesity (B=-9.502, p=0.001) had significantly lower activity disease, compared to underweight patients, as well as those under DMARD therapy (B=-4.858, p<0.001). Conclusion: Despite the lack of adjustment for corticosteroids use, there seems to be an independent association between underweight and higher disease activity in JIA patients. Importantly, these results suggest that active disease can impair child's weight gain. Further studies are needed to confirm these findings and understand the u...
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