The association between the gut microbiota and the development of lupus is unclear. We investigated alterations in the gut microbiota after induction of lupus in a murine model using viral peptide of human cytomegalovirus (HCMV). Three treatment arms for the animals were prepared: intraperitoneal injection of HCMVpp65 peptide, adjuvant alone, and PBS injection. Feces were collected before and after lupus induction biweekly for 16S rRNA sequencing. HCMVpp65 peptide immunization induced lupus-like effects, with higher levels of anti-dsDNA antibodies, creatinine, proteinuria, and glomerular damage, compared with mice treated with nothing or adjuvant only. The Simpson diversity value was higher in mice injected with HCMVpp65 peptide, but there was no difference in ACE or Chao1 among the three groups. Statistical analysis of metagenomic profiles showed a higher abundance of various families (Saccharimonadaceae, Marinifiaceae, and Desulfovibrionaceae) and genera (Candidatus Saccharimonas, Roseburia, Odoribacter, and Desulfovibrio) in HCMVpp65 peptide-treated mice. Significant correlations between increased abundances of related genera (Candidatus Saccharimonas, Roseburia, Odoribacter, and Desulfovibrio) and HCMVpp65 peptide immunization-induced lupus-like effects were observed. This study provides insight into the changes in the gut microbiota after lupus onset in a murine model.

Objectives: To examine the comorbidity burden in patients with rheumatoid arthritis (RA) patients using a nationwide population-based cohort by assessing the Charlson Comorbidity Index (CCI), Elixhauser Comorbidity Index (ECI), Multimorbidity Index (MMI), and Rheumatic Disease Comorbidity Index (RDCI) scores and to investigate their predictive ability for all-cause mortality. Methods: We identified 24,767 RA patients diagnosed from 1998 to 2008 in Taiwan and followed up until 31 December 2013. The incidence of comorbidities was estimated in three periods (before, during, and after the diagnostic period). The incidence rate ratios were calculated by comparing during vs. before and after vs. before the diagnostic period. One- and 5-year mortality rates were calculated and discriminated by low and high-score groups and modified models for each index. Results: The mean score at diagnosis was 0.8 in CCI, 2.8 in ECI, 0.7 in MMI, and 1.3 in RDCI, and annual percentage changes are 11.0%, 11.3%, 9.7%, and 6.8%, respectively. The incidence of any increase in the comorbidity index was significantly higher in the periods of “during” and “after” the RA diagnosis (incidence rate ratios for different indexes: 1.33–2.77). The mortality rate significantly differed between the high and low-score groups measured by each index (adjusted hazard ratios: 2.5–4.3 for different indexes). CCI was slightly better in the prediction of 1- and 5-year mortality rates. Conclusions: Comorbidities are common before and after RA diagnosis, and the rate of accumulation accelerates after RA diagnosis. All four comorbidity indexes are useful to measure the temporal changes and to predict mortality.

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BackgroundRheumatoid arthritis (RA) is characterized by altered bone microarchitecture (radiographically referred to as ‘texture’) of periarticular regions. We hypothesize that deep learning models can quantify periarticular texture changes to aid in the classification of early RA.MethodsThe second, third, and fourth distal metacarpal areas from hand radiographs of 892 early RA and 1236 non-RA patients were segmented for the Deep Texture Encoding Network (Deep-TEN; texture-based) and residual network-50 (ResNet-50; texture and structure-based) models to predict the probability of RA. The performances were measured using the area under the curve of the receiver operating characteristics curve (AUROC). Multivariate logistic regression was used to estimate the odds ratio (OR) with 95% confidence intervals (CIs) for RA.ResultsThe AUROC for RA was 0.69 for the Deep-TEN and 0.73 for the ResNet-50 model. The positive predictive values of a high texture score to classify RA using the Deep-TEN and ResNet-50 models were 0.64 and 0.67, respectively. High mean texture scores were associated with age- and sex-adjusted ORs (95% CI) for RA of 3.42 (2.59–4.50) and 4.30 (3.26–5.69) using the Deep-TEN and ResNet-50 models, respectively. The moderate and high RA risk groups determined by the Deep-TEN model were associated with adjusted ORs (95% CIs) of 2.48 (1.78–3.47) and 4.39 (3.11–6.20) for RA, respectively, and those using the ResNet-50 model were 2.17 (1.55–3.04) and 6.91 (4.83–9.90), respectively.ConclusionFully automated quantitative assessment for periarticular texture by deep learning models can help the classification of early RA.