Lymphocyte subset clustering analysis in treatment-naive patients with systemic lupus erythematosus

Immunohistochemical expression of perforin in adult systemic lupus erythematosus associated macrophage activation syndrome: Clinicohematological correlation and literature review

OBJECTIVE

To study the bone marrow (BM) immunohistomorphological characteristics in adult systemic lupus erythematosus (SLE) associated macrophage activation syndrome (SLE-MAS).

MATERIALS AND METHODS

Immunohistochemical (IHC) expression of CD3, CD8, perforin (PFN), and CD163 was studied on BM trephine biopsies from 30 cytopenic adult SLE cases (male: female = 1:5, age; 24 years, range; 19-32) and compared them with ten age matched controls. Clinicopathological parameters were compared among the cases likely (L) or unlikely (U) to have MAS using probability scoring criteria. The best cut off laboratory parameters to discriminate between the two were obtained through receiver operator curve (ROC) analysis.

RESULTS

MAS occurred in 12/30 (40%) cases and was more commonly associated with prior immunosuppressive therapy (p = .07), ≥ 3 system involvement (p = .09), lower fibrinogen (p < .01), increased triglyceride (p = .002), increased BM hemophagocytosis (p = .002), and higher MAS score [185 (176-203) vs. 105 (77-119), p < .01] than MAS-U subgroup. Although PFN+CD8+ T lymphocytes significantly decreased among cases than controls (p < .05), it was comparable between MAS-L and MAS-U subgroups. Fibrinogen (< 2.4 g/L, AUC; 0.93, p < .01), hemophagocytosis score (> 1.5, AUC; 0.71, p = .03), and an MAS probability score of ≥ 164 (AUC; 1, p < .01) discriminated MAS from those without MAS.

CONCLUSION

We noted a decrease in perforin mediated CD8 + T cell cytotoxicity in SLE. Immunohistochemical demonstration of the same along with histiocytic hemophagocytosis on BM biopsy may be useful adjunct in early diagnosis and management of MAS in SLE.

Application of Machine Learning Models in Systemic Lupus Erythematosus

Systemic Lupus Erythematosus (SLE) is a systemic autoimmune disease and is extremely heterogeneous in terms of immunological features and clinical manifestations. This complexity could result in a delay in the diagnosis and treatment introduction, with impacts on long-term outcomes. In this view, the application of innovative tools, such as machine learning models (MLMs), could be useful. Thus, the purpose of the present review is to provide the reader with information about the possible application of artificial intelligence in SLE patients from a medical perspective. To summarize, several studies have applied MLMs in large cohorts in different disease-related fields. In particular, the majority of studies focused on diagnosis and pathogenesis, disease-related manifestations, in particular Lupus Nephritis, outcomes and treatment. Nonetheless, some studies focused on peculiar features, such as pregnancy and quality of life. The review of published data demonstrated the proposal of several models with good performance, suggesting the possible application of MLMs in the SLE scenario.

Current state and completeness of reporting clinical prediction models using machine learning in systemic lupus erythematosus: A systematic review

OBJECTIVE

We carried out a systematic review (SR) of adherence in diagnostic and prognostic applications of ML in SLE using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement.

METHODS

A SR employing five databases was conducted from its inception until December 2021. We identified articles that evaluated the utilization of ML for prognostic and/or diagnostic purposes. This SR was reported based on the PRISMA guidelines. The TRIPOD statement assessed adherence to reporting standards. Assessment for risk of bias was done using PROBAST tool.

RESULTS

We included 45 studies: 29 (64.4%) diagnostic and 16 (35.5%) prognostic prediction- model studies. Overall, articles adhered by between 17% and 67% (median 43%, IQR 37-49%) to TRIPOD items. Only few articles reported the model's predictive performance (2.3%, 95% CI 0.06-12.0), testing of interaction terms (2.3%, 95% CI 0.06-12.0), flow of participants (50%, 95% CI; 34.6-65.4), blinding of predictors (2.3%, 95% CI 0.06-12.0), handling of missing data (36.4%, 95% CI 22.4-52.2), and appropriate title (20.5%, 95% CI 9.8-35.3). Some items were almost completely reported: the source of data (88.6%, 95% CI 75.4-96.2), eligibility criteria (86.4%, 95% CI 76.2-96.5), and interpretation of findings (88.6%, 95% CI 75.4-96.2). In addition, most of model studies had high risk of bias.

CONCLUSIONS

The reporting adherence of ML-based model developed for SLE, is currently inadequate. Several items deemed crucial for transparent reporting were not fully reported in studies on ML-based prediction models.

REVIEW REGISTRATION

PROSPERO ID# CRD42021284881. (Amended to limit the scope).

CD8+ T Cell Phenotype and Function in Childhood and Adult-Onset Connective Tissue Disease

CD8+ T cells are cytotoxic lymphocytes that destroy pathogen infected and malignant cells through release of cytolytic molecules and proinflammatory cytokines. Although the role of CD8+ T cells in connective tissue diseases (CTDs) has not been explored as thoroughly as that of other immune cells, research focusing on this key component of the immune system has recently gained momentum. Aberrations in cytotoxic cell function may have implications in triggering autoimmunity and may promote tissue damage leading to exacerbation of disease. In this comprehensive review of current literature, we examine the role of CD8+ T cells in systemic lupus erythematosus, Sjögren's syndrome, systemic sclerosis, polymyositis, and dermatomyositis with specific focus on comparing what is known about CD8+ T cell peripheral blood phenotypes, CD8+ T cell function, and CD8+ T cell organ-specific profiles in adult and juvenile forms of these disorders. Although, the precise role of CD8+ T cells in the initiation of autoimmunity and disease progression remains to be elucidated, increasing evidence indicates that CD8+ T cells are emerging as an attractive target for therapy in CTDs.

Correlation between peripheral blood lymphocyte subpopulations and primary systemic lupus erythematosus

This study explored the correlation between peripheral blood CD3^＋, CD3^＋/CD4^＋, CD3⁺/CD8⁺, CD4^＋/CD8^＋, CD3^-/CD16⁺ CD56⁺, and CD3^-CD19⁺ and disease activity of different subtypes of systemic lupus erythematosus (SLE). The percentages of CD3⁺, CD3⁺/CD4⁺, CD3⁺/CD8⁺, CD4⁺/CD8⁺, CD3^-/CD16⁺ CD56⁺, and CD3^-CD19⁺ in the peripheral blood of patients (n = 80) classified into lupus nephritis, blood involvement, and joint involvement and SLE in different active stages were detected by flow cytometry. Their correlations with baseline clinical experimental indicators of SLE patients' SLE disease activity index score (SLEDAI) and complement C3 were analyzed. The results showed that CD3⁺, CD3⁺/CD4⁺, and CD3⁺/CD8⁺ at baseline level were negatively correlated with SLEDAI scores. These were positively correlated with C3. In conclusion, T-lymphocyte subpopulations are closely related to SLE activity and can be used as reference indicators to evaluate the SLE activity.

The proliferative activity levels of each immune cell population evaluated by mass cytometry are linked to the clinical phenotypes of systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease, and many peripheral immune cell populations (ICPs) are thought to be altered according to the course of the disease. However, it is unclear which ICPs are associated with the clinical phenotypes of SLE. We analyzed peripheral blood mononuclear cells (PBMCs) of 28 SLE patients using mass cytometry and identified 30 ICPs. We determined the proliferative activity of ICPs by measuring the proportion of cells expressing specific markers and Ki-67 among CD45+ cells (Ki-67+ proportion). We observed an increased Ki-67+ proportion for many ICPs of SLE patients and examined the association between their Ki-67+ proportions and clinical findings. The Ki-67+ proportions of five ICPs [classical monocyte (cMo), effector memory CD8+ T cell (CD8Tem), CXCR5- naive B cell (CXCR5- nB), and CXCR5- IgD-CD27- B cell (CXCR5- DNB)] were identified as clinically important factors. The SLE Disease Activity Index (SLEDAI) was positively correlated with cMo and plasma cells (PC). The titer of anti-DNA antibodies was positively correlated with cMo, CXCR5- nB, and CXCR5- DNB. The C4 level was negatively correlated with CXCR5- DNB. The bioactivity of type I interferon was also positively correlated with these ICPs. Fever and renal involvement were associated with cMo. Rash was associated with CD8Tem and CXCR5- DNB. On the basis of the proliferative activity among five ICPs, SLE patients can be classified into five clusters showing different SLE phenotypes. Evaluation of the proliferative activity in each ICP can be linked to the clinical phenotypes of individual SLE patients and help in the treatment strategy.

Big data analyses and individual health profiling in the arena of rheumatic and musculoskeletal diseases (RMDs)

Health care processes are under constant development and will need to embrace advances in technology and health science aiming to provide optimal care. Considering the perspective of increasing treatment options for people with rheumatic and musculoskeletal diseases, but in many cases not reaching all treatment targets that matter to patients, care systems bare potential to improve on a holistic level. This review provides an overview of systems and technologies under evaluation over the past years that show potential to impact diagnosis and treatment of rheumatic diseases in about 10 years from now. We summarize initiatives and studies from the field of electronic health records, biobanking, remote monitoring, and artificial intelligence. The combination and implementation of these opportunities in daily clinical care will be key for a new era in care of our patients. This aims to inform rheumatologists and healthcare providers concerned with chronic inflammatory musculoskeletal conditions about current important and promising developments in science that might substantially impact the management processes of rheumatic diseases in the 2030s.

Three Clinical Clusters Identified through Hierarchical Cluster Analysis Using Initial Laboratory Findings in Korean Patients with Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is a heterogeneous disorder with diverse clinical manifestations. This study classified patients by combining laboratory values at SLE diagnosis via hierarchical cluster analysis. Linear discriminant analysis was performed to construct a model for predicting clusters. Cluster analysis using data from 389 patients with SLE yielded three clusters with different laboratory characteristics. Cluster 1 had the youngest age at diagnosis and showed significantly lower lymphocyte and platelet counts and hemoglobin and complement levels and the highest erythrocyte sedimentation rate (ESR) and anti-double-stranded DNA (dsDNA) antibody level. Cluster 2 showed higher white blood cell (WBC), lymphocyte, and platelet counts and lower ESR and anti-dsDNA antibody level. Cluster 3 showed the highest anti-nuclear antibody titer and lower WBC and lymphocyte counts. Within approximately 171 months, Cluster 1 showed higher SLE Disease Activity Index scores and number of cumulative manifestations, including malar rash, alopecia, arthritis, and renal disease, than did Clusters 2 and 3. However, the damage index and mortality rate did not differ significantly between them. In conclusion, the cluster analysis using the initial laboratory findings of the patients with SLE identified three clusters. While disease activities, organ involvements, and management patterns differed between the clusters, damages and mortalities did not.

RFID Data Analysis and Evaluation Based on Big Data and Data Clustering

The era people live in is the era of big data, and massive data carry a large amount of information. This study aims to analyze RFID data based on big data and clustering algorithms. In this study, a RFID data extraction technology based on joint Kalman filter fusion is proposed. In the system, the proposed data extraction technology can effectively read RFID tags. The data are recorded, and the KM-KL clustering algorithm is proposed for RFID data, which combines the advantages of the K-means algorithm. The improved KM-KL clustering algorithm can effectively analyze and evaluate RFID data. The experimental results of this study prove that the recognition error rate of the RFID data extraction technology based on the joint Kalman filter fusion is only 2.7%. The improved KM-KL clustering algorithm also has better performance than the traditional algorithm.

Associations of lymphocyte subpopulations with clinical phenotypes and long-term outcomes in juvenile-onset systemic lupus erythematosus

OBJECTIVE

Juvenile-onset systemic lupus erythematosus (JSLE) is a complex and heterogeneous immune-mediated disease. Cellular components have crucial roles in disease phenotypes and outcomes. We aimed to determine the associations of lymphocyte subsets with clinical manifestations and long-term outcomes in JSLE patients.

METHODS

A cohort of 60 JSLE patients provided blood samples during active disease, of whom 34 provided further samples during inactive disease. In a longitudinal study, blood samples were obtained from 49 of the JSLE patients at 0, 3, and 6 months. The healthy control (HC) group consisted of 42 age-matched children. Lymphocyte subsets were analyzed by flow cytometry.

RESULTS

The percentages of CD4+ T, γδ T, and NK cells were significantly decreased in JSLE patients compared with HC, while the percentages of CD8+ T, NKT, and CD19+ B cells were significantly increased. The percentage of regulatory T cells (Tregs) was significantly lower in JSLE patients with lupus nephritis (LN) than in non-LN JSLE patients and HC. The patients were stratified into high and low groups by the median frequency of each lymphocyte subset. The γδ T cells high group and NK cells high group were significantly related to mucosal ulcer. The CD4+ T cells high group was significantly associated with arthritis, and the NKT cells high group was substantially linked with autoimmune hemolytic anemia. The CD8+ T cells low group was mainly related to vasculitis, and the Tregs low group was significantly associated with LN. The percentage of Tregs was significantly increased at 6 months of follow-up, and the LN JSLE group had a lower Treg percentage than the non-LN JSLE group. Predictors of remission on therapy were high Tregs, high absolute lymphocyte count, direct Coombs test positivity, and LN absence at enrollment.

CONCLUSION

JSLE patients exhibited altered lymphocyte subsets, which were strongly associated with clinical phenotypes and long-term outcomes.

An introduction to machine learning and analysis of its use in rheumatic diseases

Machine learning (ML) is a computerized analytical technique that is being increasingly employed in biomedicine. ML often provides an advantage over explicitly programmed strategies in the analysis of multidimensional information by recognizing relationships in the data that were not previously appreciated. As such, the use of ML in rheumatology is increasing, and numerous studies have employed ML to classify patients with rheumatic autoimmune inflammatory diseases (RAIDs) from medical records and imaging, biometric or gene expression data. However, these studies are limited by sample size, the accuracy of sample labelling, and absence of datasets for external validation. In addition, there is potential for ML models to overfit or underfit the data and, thereby, these models might produce results that cannot be replicated in an unrelated dataset. In this Review, we introduce the basic principles of ML and discuss its current strengths and weaknesses in the classification of patients with RAIDs. Moreover, we highlight the successful analysis of the same type of input data (for example, medical records) with different algorithms, illustrating the potential plasticity of this analytical approach. Altogether, a better understanding of ML and the future application of advanced analytical techniques based on this approach, coupled with the increasing availability of biomedical data, may facilitate the development of meaningful precision medicine for patients with RAIDs.