Abstract Objective HLA-DRB1 alleles, particularly the shared epitope (SE) alleles, are strongly associated with RA. Different genetic structures underlie the production of the various autoantibodies in RA. While extensive genetic analyses have been conducted to generate a detailed profile of ACPA, a representative autoantibody in RA, the genetic architecture underlying subfractions of RF other than IgM-RF, namely IgG-RF, known to be associated with rheumatoid vasculitis, is not well understood. Methods We enrolled a total of 743 RA subjects whose detailed autoantibody (IgG-RF, IgM-RF, and ACPA) data were available. We evaluated co-presence and correlations of the levels of these autoantibodies. We analysed associations between the presence or levels of the autoantibodies and HLA-DRB1 alleles for the 743 RA patients and 2008 healthy controls. Results We found both IgG-RF(+) and IgG-RF(–) RA subjects showed comparable associations with SE alleles, which was not observed for the other autoantibodies. Furthermore, there was a clear difference in SE allele associations between IgG-RF(+) and (–) subsets: the association with the IgG-RF(+) subsets was solely driven by HLA-DRB1*04:05, the most frequent SE allele in the Japanese population, while not only HLA-DRB1*04:05 but also HLA-DRB1*04:01, less frequent in the Japanese population but the most frequent SE allele in Europeans, were main drivers of the association in the IgG-RF(–) subset. We confirmed that these associations were irrespective of ACPA presence. Conclusion We found a unique genetic architecture for IgG-RF(–) RA, which showed a strong association with a SE allele not frequent in the Japanese population but the most frequent SE allele in Europeans. The findings could shed light on uncovered RA pathology.
A "cation pool" of an N- acyliminium ion was found to serve as an effective initiator of cationic polymerization of vinyl ethers in a microflow system consisting of two micromixers (IMM micromixer) and two microtube reactors. The cationic polymerization of n-butyl vinyl ether in CH(2)Cl(2) at -78 degrees C led to very narrow molecular weight distribution (M(w)/M(n)=1.14). The molecular weight (M(n)) increased linearly with an increase in monomer/initiator ratio. The carbocationic polymer end was effectively trapped by allyltrimethylsilane. Additionally, the synthesis of block polymers was accomplished by the present microflow system controlled method. The polymerization was also conducted using commercially available trifluoromethanesulfonic acid (TfOH) as an initiator. A high level of molecular weight control was attained even at -25 degrees C. The TfOH-initiated polymerization could be conducted using a microflow system based on T-shaped micromixers, which serves as a practical tool for microflow system controlled carbocationic polymerization.
Objectives There are often discrepancies in the evaluation of disease activity between patients and physicians in systemic lupus erythematosus (SLE). In this study, we examined the factors that affect those evaluations. Methods Physician visual analogue scale (Ph-VAS), patient VAS (Pt-VAS), Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2k), glucocorticoid (GC) usage and dose, age, Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index, and three patient-reported outcomes (SLE symptom checklist [SSC], short-form 36 questionnaire [SF-36], and LupusPRO) were obtained from a study performed in 2019 using 225 SLE outpatients of the Kyoto Lupus Cohort at Kyoto University Hospital. Correlations among Ph-VAS, Pt-VAS, or dif (Pt-VAS-Ph-VAS) (Pt-VAS minus Ph-VAS) and other factors were examined. Results We found a significant discrepancy between Pt-VAS (median 38.0 mm) and Ph-VAS (median 18.7 mm) scores ( p < 0.001). SSC score showed a significant correlation with Pt-VAS and dif (Pt-VAS-Ph-VAS) ( p < 0.001). Among SSC items, fatigue showed the most significant correlation with dif (Pt-VAS-Ph-VAS). We also showed that higher dif (Pt-VAS-Ph-VAS) was associated with lower quality of life (QOL) evaluated by SF-36 and LupusPRO. Conclusions Pt-VAS scores tended to be higher than Ph-VAS scores, and the discrepancy was influenced mainly by fatigue. Higher dif (Pt-VAS-Ph-VAS) was associated with lower patient QOL.
Although the SLE Disease Activity Score (SLE-DAS) and its definitions to classify disease activity have been recently developed to overcome the drawbacks of the SLE Disease Activity Index 2000 (SLEDAI-2K), the performance of the SLE-DAS for patient-reported outcomes (PROs) has not been fully examined. We aimed to compare SLE-DAS with SLEDAI-2K and validate the classifications of disease activity based on SLE-DAS in terms of PROs.We assessed generic quality of life (QoL) using the Medical Outcome Survey 36-Item Short-Form Health Survey (SF-36), disease-specific QoL using the lupus patient-reported outcome tool (LupusPRO), burden of symptoms using the SLE Symptom Checklist (SSC), patient global assessment (PtGA) and physician global assessment (PhGA).Of the 335 patients with SLE, the magnitudes of the mean absolute error, root mean square error, Akaike information criterion, and Bayesian information criterion were comparable for most PROs between the SLE-DAS and SLEDAI-2K. In contrast, SLEDAI-2K had a higher predictive value for health-related QoL of LupusPRO and PtGA than SLE-DAS. Low disease activity, Boolean and index-based remission and categories of disease activity (remission, mild and moderate/severe activity) were significantly associated with health-related QoL in LupusPRO, SSC and PhGA, but not SF-36 or PtGA.No clear differences were identified in the use of the SLE-DAS over the SLEDAI-2K in assessing PROs in patients with SLE. The classification of disease activity based on the SLE-DAS was validated against several PROs. SLE-DAS and its categories of disease activity effectively explain some of the PROs.
Genetic variations influence the levels of blood metabolites. We present analytical pipelines for assessing genetic influences on human blood metabolites. We describe steps for the normalization of metabolome data, genome-wide association studies, and the identification of metabolite quantitative trait loci (mQTLs). We then detail procedures for functional enrichment analysis of mQTLs. This protocol could be applicable to other quantitative traits, such as clinical measurements or proteome data. For complete details on the use and execution of this protocol, please refer to Iwasaki et al.
