Objective. To design and validate a new questionnaire for identifying patients with methotrexate (MTX) intolerance, and to determine the prevalence of MTX intolerance in patients with juvenile idiopathic arthritis (JIA) using this questionnaire.Methods. The MTX Intolerance Severity Score (MISS) questionnaire was constructed, consisting of 5 domains: stomach ache, nausea, vomiting, sore mouth, and behavioral symptoms. The domains each consisted of 3 questions pertaining to the presence of a symptom upon, prior to (anticipatory), and when thinking of (associative) MTX intake. The MISS questionnaire was validated in 86 patients by determining its discriminative power between patients with and those without MTX intolerance, identified as such by a gold standard (physician's opinion). Using the MISS questionnaire, the prevalence of MTX intolerance was determined in 297 JIA patients.Results. The MISS questionnaire discriminated well between MTX-intolerant and MTX-tolerant patients. A cutoff score of 6 yielded the best sensitivity (88%) and specificity (80%). MTX intolerance was found in 150 (50.5%) of 297 patients. Of 220 patients receiving oral MTX, 98 (44.5%) experienced MTX intolerance, whereas 67.5% of 77 patients receiving parenteral MTX experienced intolerance to the drug (P ‫؍‬ 0.001).Conclusion. Our findings indicate that the MISS questionnaire is a highly sensitive and specific tool for the diagnosis of MTX intolerance, and that there is a high prevalence of MTX intolerance among JIA patients. The prevalence of intolerance in patients receiving parenteral MTX exceeds that in patients receiving oral MTX. The frequent occurrence of anticipatory and associative symptoms suggests that classic conditioning plays an important role in MTX intolerance.

ObjectivesTo study whether clinical remission (CR) and Low Lupus Disease Activity State (LLDAS) are achievable goals in childhood-onset SLE.MethodsData on medication use and disease activity were prospectively collected. LLDAS was defined as Safety of Estrogen in Lupus Erythematosus National Assesment-SLE disease Activity Index (SELENA-SLEDAI) ≤4 with zero scores for renal, Central Nervous System (CNS), serositis, vasculitis and constitutional components, no increase in any SLEDAI component since the previous visit, PGA ≤1, and prednisone dose ≤7.5 mg/day. CR on treatment (Tx) was defined as a Physician Global Assessment <0.5, SELENA-SLEDAI=0, with prednisone ≤5 mg/day and maintenance treatment with immunosuppressives. CR off Tx was the same but without prednisone or other immunosuppressive usage.Results51 patients (700 visits) were included. Within 3 months after diagnosis, 94.1% of children were treated with hydroxychloroquine and 60.8% with prednisone. Prednisone dosage decreased from a median of 0.74 mg/kg/day at diagnosis to 0.44 mg/kg/day at 3 months and 0.16 mg/kg/day at 6 months after diagnosis. Use of mycophenolate mofetil increased from 25.5% to 56.9% within 6 months after diagnosis. All children achieved LLDAS (median 186 days) and 72.5% remained in LLDAS >50% of time. 52.9% children achieved CR on Tx, and only 21.6% children achieved CR off Tx.ConclusionsLLDAS is an attainable treat-to-target goal in contrast to CR on and off Tx. Even more, LLDAS can be reached with limited use of corticosteroids with early introduction of immunosuppressives.

Objectives Clinical phenotyping and predicting treatment responses in Systemic Lupus Erythematosus (SLE) patients is challenging. Extensive blood transcriptional profiling has identified various gene modules that are promising for stratification of SLE patients. We aimed to translate existing transcriptomic data into simpler gene signatures suitable for daily clinical practice. Methods RT-PCR of multiple genes from the Interferon M1.2, Interferon M5.12, neutrophil (NPh) and plasma cell (PLC) modules followed by a principle component analysis, was used to identify indicator genes per gene signature. Gene signatures were measured in longitudinal samples from two childhood onset SLE cohorts (n = 101 and n = 34, respectively) and associated with clinical features. Disease activity was measured using SELENA-SLEDAI. Cluster analysis subdivided patients into three mutually exclusive fingerprint-groups termed 1) all-signatures-low, 2) only IFN high (M1.2 and/or M5.12) and 3) high NPh and/or PLC. Results All gene signatures were significantly associated with disease activity in cross-sectionally collected samples. The PLC-signature showed the highest association with disease activity. Interestingly in longitudinally collected samples, the PLC-signature was associated with disease activity and showed a decrease over time. When patients were divided into fingerprints, the highest disease activity was observed in the high NPh and/or PLC group. The lowest disease activity was observed in the all-signatures-low group. The same distribution was reproduced in samples from an independent SLE cohort. Conclusions The identified gene signatures are associated with disease activity and suitable tools to stratify SLE patients into groups with similar activated immune pathways that may guide future treatment choices.

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Objective To study the association of serum IFNα2 levels measured by ultrasensitive single-molecule array (Simoa) and interferon type I gene signature (IGS) with disease activity and determine whether these assays can mark disease activity states in a longitudinal cohort of childhood-onset SLE patients. Methods Serum IFNα2 levels were measured in 338 samples from 48 cSLE patients and 67 healthy controls using IFNα Simoa assay. Five gene IGS was measured by RT-PCR in paired whole blood samples. Disease activity was measured by clinical SELENA-SLEDAI and BILAG-2004. Low disease activity was defined by Low Lupus Disease Activity State (LLDAS) and flares were characterized by SELENA-SLEDAI flare index. Analysis was performed using linear mixed models. Results A clear positive correlation was present between serum IFNα2 levels and the IGS (r = 0.78, p &lt; 0.0001). Serum IFNα2 levels and IGS showed the same significant negative trend in the first three years after diagnosis. In this timeframe, mean baseline serum IFNα2 levels decreased with 55.1% (Δ 201 fg/mL, p &lt; 0.001) to a mean value of 164 fg/mL, which was below the calculated threshold of 219.4 fg/mL, which discriminated between patients and healthy controls. In the linear mixed model, serum IFNα2 levels were significantly associated with both cSELENA-SLEDAI and BILAG-2004, while the IGS did not show this association. Both IFN-I assays were able to characterize LLDAS and disease flare in ROC analysis. Conclusions Serum IFNα2 levels measured by Simoa technology are associated with disease activity scores and characterize disease activity states in cSLE.

PurposeThe aim is to investigate the association among TLR7 and TLR9 serum levels with previous viral infections, disease activity and proinflammatory cytokine levels in SLE patients. Methods Cross-sectional observational study in SLE patients (SLICC/ACR 2012 criteria) and healthy controls (HC). Previous infection data with RNA (HCV) and DNA virus (CMV, Epstein-Barr, Herpes simplex, Parvovirus B19 or HBV), disease activity and clinical data were collected. Biological samples of SLE patients and HC from the medical visit were supplied to the TLR7, TLR9, IL10 and INF1A determination by enzymelinked immunoassay. Results 94 SLE patients (91.5% female) with a mean age of 51 ( 13) years old and 35 HC (80% female) and 42 (12) years old were recruited. Mean age at diagnosis was 33 ( 14) years old and mean disease evolution was 19 (10) years. Mean SLE-DAI index was 5.35 (4.58).The 48.94% of patients reported almost one DNA virus infections, the 2.13% reported HCV infection, the 4.25% with HCV and DNA virus, and the 31.92% not reported any infection. HC had no history of acute (3 months) or lasting chronic infections with viruses of bacteria.TLR7 and TLR9 did not correlate between them. TLR9 levels were significantly higher in SLE patients than HC (P<0,001). Even though TLR7 levels did not show any difference between both groups, an association with the age of individuals was observed (P<0,001).No association among TLR7 or TLR9 levels with CRP, ESR, anti-dsDNA, ENAs or antiphospholipid antibodies was observed, and nor with disease activity, age at diagnosis and disease evolution time. In contrast, however, we reported low TLR7 levels in SLE patients and antiphospholipid syndrome in comparison to those without antiphospholipid syndrome (P=0,001).High TLR9 levels were significantly associated to increased levels of IL10 and INF1A in SLE patients (P<0,001). TLR7 levels were not associated with INF1A levels but it is noticeable that there is a tendency to increase TLR7 levels in cases with increase of IL10 levels. Conclusions TLR9 is increased in SLE patients in comparison to HC. TLR7 increases with age. No evidence of association between previous infections and TLR levels was found. Nor do we observe any difference in TLR level according to autoantibodies presence or disease activity, probably due to the long-term SLE evolution and a good control of the disease.There was, however, an association between high TLR9 levels and increase of IL10 and INF1A.

ObjectiveTo combine targeted transcriptomic and proteomic data in an unsupervised hierarchical clustering method to stratify patients with childhood-onset SLE (cSLE) into similar biological phenotypes, and study the immunological cellular landscape that characterises the clusters.MethodsTargeted whole blood gene expression and serum cytokines were determined in patients with cSLE, preselected on disease activity state (at diagnosis, Low Lupus Disease Activity State (LLDAS), flare). Unsupervised hierarchical clustering, agnostic to disease characteristics, was used to identify clusters with distinct biological phenotypes. Disease activity was scored by clinical SELENA-SLEDAI (Safety of Estrogens in Systemic Lupus Erythematosus National Assessment-Systemic Lupus Erythematosus Disease Activity Index). High-dimensional 40-colour flow cytometry was used to identify immune cell subsets.ResultsThree unique clusters were identified, each characterised by a set of differentially expressed genes and cytokines, and by disease activity state: cluster 1 contained primarily patients in LLDAS, cluster 2 contained mainly treatment-naïve patients at diagnosis and cluster 3 contained a mixed group of patients, namely in LLDAS, at diagnosis and disease flare. The biological phenotypes did not reflect previous organ system involvement and over time, patients could move from one cluster to another. Healthy controls clustered together in cluster 1. Specific immune cell subsets, including CD11c+ B cells, conventional dendritic cells, plasmablasts and early effector CD4+ T cells, differed between the clusters.ConclusionUsing a targeted multiomic approach, we clustered patients into distinct biological phenotypes that are related to disease activity state but not to organ system involvement. This supports a new concept where choice of treatment and tapering strategies are not solely based on clinical phenotype but includes measuring novel biological parameters.