Clinical phenotyping in patients with anti-synthetase antibodies using cluster analysis

Objectives

To characterize clinically distinct subgroups among unselected patients with anti-synthetase antibodies using cluster analysis.

Methods

This study evaluated patients with anti-synthetase antibodies registered to two independent cohorts; 106 consecutive patients from a prospective, single-centre cohort of the Scleroderma/Myositis Centre of Excellence (SMCE) were used as a derivation cohort and 125 patients from the Multicentre Retrospective Cohort of Japanese Patients with Myositis-Associated Interstitial Lung Disease (JAMI) were used as a validation cohort. Anti-synthetase antibodies were identified by RNA immunoprecipitation. A multiple correspondence analysis followed by hierarchical clustering was performed to aggregate the patients into homogeneous subgroups. Subsequently, a simple-to-use classification tree was generated using classification and regression tree analysis.

Results

Three clusters were identified in the SMCE cohort: cluster 1 (n = 48), the interstitial pneumonia with autoimmune features/amyopathic dermatomyositis cluster, associated with older age at diagnosis and a higher frequency of malignancy; cluster 2 (n = 46), the DM cluster, corresponded to a younger age at diagnosis with a higher prevalence of myositis, arthritis, DM pathognomonic rashes, mechanic's hands and fever; and cluster 3 (n = 12), the SSc cluster, characterized by chronic interstitial lung disease. There was no significant difference in overall survival or progression-free survival between the clusters. A simple classification tree using myositis and RP was created in the SMCE cohort. Clusters 1 and 2 were successfully reproduced and the classification tree demonstrated favourable performance in the JAMI cohort.

Conclusion

Patients with anti-synthetase antibodies were classified into three distinct phenotypes, indicating substantial heterogeneity within this patient group.

Factors Associated With the Development of Bacterial Pneumonia Related to Seasonal Influenza Virus Infection: A Study Using a Large-scale Health Insurance Claim Database

Background

Influenza-related bacterial pneumonia is a leading complication of influenza infection. However, the differences in the incidence rates and risk factors associated with concomitant viral/bacterial pneumonia (CP) and secondary bacterial pneumonia following influenza (SP) remain unclear. This study aimed to clarify the incidence rates of CP and SP following seasonal influenza and identify factors associated with their development.

Methods

This retrospective cohort study was conducted using the JMDC Claims Database, a health insurance claims database in Japan. All patients aged <75 years who developed influenza during 2 consecutive epidemic seasons, 2017/2018 and 2018/2019, were analyzed. CP was defined as bacterial pneumonia diagnosed between 3 days before and 6 days after the date of influenza diagnosis, and SP was defined as pneumonia diagnosed 7-30 days after the date of diagnosis. Multivariable logistic regression analyses were performed to identify factors associated with the development of CP and SP.

Results

Among the 10 473 014 individuals registered in the database, 1 341 355 patients with influenza were analyzed. The average age at diagnosis (SD) was 26.6 (18.6) years. There were 2901 (0.22%) and 1262 (0.09%) patients who developed CP and SP, respectively. Age 65-74 years, asthma, chronic bronchitis/emphysema, cardiovascular disease, renal disease, malignant tumor, and immunosuppression were significant risk factors for both CP and SP, whereas cerebrovascular disease, neurological disease, liver disease, and diabetes were risk factors specific to CP development.

Conclusions

The results determined the incidence rates of CP and SP and identified their risk factors, such as older age and comorbidities.

OUP accepted manuscript

Cell therapy using induced pluripotent stem cell (iPSC) derivatives may result in abnormal tissue generation because the cells undergo numerous cycles of mitosis before clinical application, potentially increasing the accumulation of genetic abnormalities. Therefore, genetic tests may predict abnormal tissue formation after transplantation. Here, we administered iPSC derivatives with or without single-nucleotide variants (SNVs) and deletions in cancer-related genes with various genomic copy number variant (CNV) profiles into immunodeficient mice and examined the relationships between mutations and abnormal tissue formation after transplantation. No positive correlations were found between the presence of SNVs/deletions and the formation of abnormal tissues; the overall predictivity was 29%. However, a copy number higher than 3 was correlated, with an overall predictivity of 86%. Furthermore, we found CNV hotspots at 14q32.33 and 17q12 loci. Thus, CNV analysis may predict abnormal tissue formation after transplantation of iPSC derivatives and reduce the number of tumorigenicity tests.