Stratification of Patients With Sjögren's Syndrome and Patients With Systemic Lupus Erythematosus According to Two Shared Immune Cell Signatures, With Potential Therapeutic Implications

Systemic lupus in the era of machine learning medicine

Artificial intelligence and machine learning applications are emerging as transformative technologies in medicine. With greater access to a diverse range of big datasets, researchers are turning to these powerful techniques for data analysis. Machine learning can reveal patterns and interactions between variables in large and complex datasets more accurately and efficiently than traditional statistical methods. Machine learning approaches open new possibilities for studying SLE, a multifactorial, highly heterogeneous and complex disease. Here, we discuss how machine learning methods are rapidly being integrated into the field of SLE research. Recent reports have focused on building prediction models and/or identifying novel biomarkers using both supervised and unsupervised techniques for understanding disease pathogenesis, early diagnosis and prognosis of disease. In this review, we will provide an overview of machine learning techniques to discuss current gaps, challenges and opportunities for SLE studies. External validation of most prediction models is still needed before clinical adoption. Utilisation of deep learning models, access to alternative sources of health data and increased awareness of the ethics, governance and regulations surrounding the use of artificial intelligence in medicine will help propel this exciting field forward.

New investigational drugs to treat Sjogren's syndrome: lessons learnt from immunology

INTRODUCTION

Sjögren's syndrome is a heterogeneous autoimmune condition that impairs quality of life because of dryness, fatigue, pain, and systemic involvements. Current treatment largely depends on empirical evidence, with no effective therapy approved. Clinical trials on targeted drugs often fail to report efficacy due to common factors.

AREAS COVERED

This review summarizes the pathogenesis and what caused the failure of new investigational drugs in clinical trials, highlighting solutions for more effective investigations, with greater consistency between research outcomes, clinical use, and patient needs.

EXPERT OPINION

Unlinked pathobiology with symptoms resulted in misidentified targets and disappointing trials. Useful stratification tools are necessary for the heterogeneous SS patients. Composite endpoints or improvements in ESSDAI scores are needed, considering the high placebo response, and the unbalance between symptom burden and disease activity. Compared to classic biologics, targeted cell therapy will be a more promising field of investigation in the coming years.

Juvenile Primary Sjögren Syndrome in a 15-Year-Old Boy with Renal Involvement: A Case Report and Review of the Literature

Juvenile primary Sjögren syndrome (pSS) with renal involvement is extremely rare, reported approximately in 50 children, predominantly girls. Here, we present the first reported case of a male child with juvenile pSS with ocular surface disease (previously keratoconjunctivitis sicca), submandibular salivary gland involvement, and tubulointerstitial nephritis. First, two symptoms were clinically apparent at presentation. We illustrate here that kidney involvement in pSS should be actively looked for, as juvenile pSS may be associated with asymptomatic renal involvement. Immunophenotyping of peripheral blood cells using multicolor flow cytometry revealed at the time of diagnosis changes in both adaptive (T memory cells and B memory cells), and innate immunity (an increased activation of natural killer cells, as well as monocytes and neutrophils, and an increased representation of intermediate monocytes). Our case report points to the importance of kidney examination, early diagnosis and therapy in juvenile pSS, as well as highlights international collaboration to obtain more data for this rare disease.

Circulating CD8+ T Cell Subsets in Primary Sjögren's Syndrome

Currently, multiple studies have indicated that CD8+ T lymphocytes play a role in causing damage to the exocrine glands through acinar injury in primary Sjögren's syndrome (pSS). The aim of this research was to assess the imbalance of circulating CD8+ T cell subsets. We analyzed blood samples from 34 pSS patients and 34 healthy individuals as controls. We used flow cytometry to enumerate CD8+ T cell maturation stages, using as markers CD62L, CD28, CD27, CD4, CD8, CD3, CD45RA and CD45. For immunophenotyping of 'polarized' CD8+ T cell subsets, we used the following monoclonal antibodies: CXCR5, CCR6, CXCR3 and CCR4. The findings revealed that both the relative and absolute numbers of 'naïve' CD8+ T cells were higher in pSS patients compared to the healthy volunteers. Conversely, the proportions of effector memory CD8+ T cells were notably lower. Furthermore, our data suggested that among patients with pSS, the levels of cytotoxic Tc1 CD8+ T cells were reduced, while the frequencies of regulatory cytokine-producing Tc2 and Tc17 CD8+ T cells were significantly elevated. Simultaneously, the Tc1 cell subsets displayed a negative correlation with immunoglobulin G, rheumatoid factor, the Schirmer test and unstimulated saliva flow. On the other hand, the Tc2 cell subsets exhibited a positive correlation with these parameters. In summary, our study indicated that immune dysfunction within CD8+ T cells, including alterations in Tc1 cells, plays a significant role in the development of pSS.

Exploring the shared molecular mechanisms between systemic lupus erythematosus and primary Sjögren's syndrome based on integrated bioinformatics and single-cell RNA-seq analysis

Background

Systemic lupus erythematosus (SLE) and primary Sjögren's syndrome (pSS) are common systemic autoimmune diseases that share a wide range of clinical manifestations and serological features. This study investigates genes, signaling pathways, and transcription factors (TFs) shared between SLE and pSS.

Methods

Gene expression profiles of SLE and pSS were obtained from the Gene Expression Omnibus (GEO). Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis were conducted to identify shared genes related to SLE and pSS. Overlapping genes were then subject to Gene Ontology (GO) and protein-protein interaction (PPI) network analyses. Cytoscape plugins cytoHubba and iRegulon were subsequently used to screen shared hub genes and predict TFs. In addition, gene set variation analysis (GSVA) and CIBERSORTx were used to calculate the correlations between hub genes and immune cells as well as related pathways. To confirm these results, hub genes and TFs were verified in microarray and single-cell RNA sequencing (scRNA-seq) datasets.

Results

Following WGCNA and limma analysis, 152 shared genes were identified. These genes were involved in interferon (IFN) response and cytokine-mediated signaling pathway. Moreover, we screened six shared genes, namely IFI44L, ISG15, IFIT1, USP18, RSAD2 and ITGB2, out of which three genes, namely IFI44L, ISG15 and ITGB2 were found to be highly expressed in both microarray and scRNA-seq datasets. IFN response and ITGB2 signaling pathway were identified as potentially relevant pathways. In addition, STAT1 and IRF7 were identified as common TFs in both diseases.

Conclusion

This study revealed IFI44L, ISG15 and ITGB2 as the shared genes and identified STAT1 and IRF7 as the common TFs of SLE and pSS. Notably, the IFN response and ITGB2 signaling pathway played vital roles in both diseases. Our study revealed common pathogenetic characteristics of SLE and pSS. The particular roles of these pivotal genes and mutually overlapping pathways may provide a basis for further mechanistic research.

Artificial intelligence-driven drug development against autoimmune diseases

Artificial intelligence (AI)-based predictive models are being used to foster a precision medicine approach to treat complex chronic diseases such as autoimmune and autoinflammatory disorders (AIIDs). In the past few years the first models of systemic lupus erythematosus (SLE), primary Sjögren syndrome (pSS), and rheumatoid arthritis (RA) have been produced by molecular profiling of patients using omic technologies and integrating the data with AI. These advances have confirmed a complex pathophysiology involving multiple proinflammatory pathways and also provide evidence for shared molecular dysregulation across different AIIDs. I discuss how models are used to stratify patients, assess causality in pathophysiology, design drug candidates in silico, and predict drug efficacy in virtual patients. By relating individual patient characteristics to the predicted properties of millions of drug candidates, these models can improve the management of AIIDs through more personalized treatments.

Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review

Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.

Artificial Intelligence and laboratory data in rheumatic diseases

Artificial intelligence (AI)-based medical technologies are rapidly evolving into actionable solutions for clinical practice. Machine learning (ML) algorithms can process increasing amounts of laboratory data such as gene expression immunophenotyping data and biomarkers. In recent years, the analysis of ML has become particularly useful for the study of complex chronic diseases, such as rheumatic diseases, heterogenous conditions with multiple triggers. Numerous studies have used ML to classify patients and improve diagnosis, to stratify the risk and determine disease subtypes, as well as to discover biomarkers and gene signatures. This review aims to provide examples of ML models for specific rheumatic diseases using laboratory data and some insights into relevant strengths and limitations. A better understanding and future application of these analytical strategies could facilitate the development of precision medicine for rheumatic patients.

Memory CD4+T cell profile is associated with unfavorable prognosis in IgG4-related disease: Risk stratification by machine-learning

IgG4-related disease (IgG4-RD) is a chronic immune-mediated disease with heterogeneity. In this study, we used machine-learning approaches to characterize the immune cell profiles and to identify the heterogeneity of IgG4-RD. The XGBoost model discriminated IgG4-RD from HCs with an area under the receiver operating characteristic curve of 0.963 in the testing set. There were two clusters of IgG4-RD by k-means clustering of immunological profiles. Cluster 1 featured higher proportions of memory CD4⁺T cell and were at higher risk of unfavorable prognosis in the follow-up, while cluster 2 featured higher proportions of naïve CD4⁺T cell. In the multivariate logistic regression, cluster 2 was shown to be a protective factor (OR 0.30, 95% CI 0.10-0.91, P = 0.011). Therefore, peripheral immunophenotyping might potentially stratify patients with IgG4-RD and predict those patients with a higher risk of relapse at early time.

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).

Clinical and immunological phenotypes of systemic lupus erythematosus, identified based on cluster analysis of data from 400 patients from V.A. Nasonova Research Institute of Rheumatology

Objective: to identify clinical and immunological variants (phenotypes) of systemic lupus erythematosus (SLE) using cluster analysis.

Patients and methods. The study included 400 patients with diagnosis of SLE according to the 2012 SLICC classification criteria. Patients underwent laboratory and immunological workup according to accepted standards of medical care for patients with SLE, and therapy was prescribed in accordance with disease activity.

Results and discussion. Among patients, most were females (ratio of men and women – 1:10), and people of young age (34.2±11.5 years), with an average duration of illness of 6 [3; 12] years. In 98 (25%) patients with SLE, the disease debuted before the age of 18 years. Lupus nephritis (LN) was detected in 192 (48%) patients, SLE with antiphospholipid syndrome (APS) – in 48 (12%), SLE with Sjцgren's syndrome – in 44 (11%). For cluster analysis 30 clinical, 4 laboratory, 12 immunological and 10 therapeutic parameters were selected and a dendrogram was constructed with the calculation of the Euclidean distance using the Ward method. As a result, five clusters of SLE were identified: with the development of LN; with predominantly extrarenal manifestations; SLE combined with APS; SLE combined with Sjцgren's syndrome; SLE with a debut in childhood (up to 18 years of age). Clusters differed in clinical, laboratory and immunological parameters, as well as in therapy.

Conclusion. Cluster analysis data made it possible to group the selected signs into five clinical and immunological variants (phenotypes) of SLE. Identification of SLE phenotypes as a set of characteristics that, individually or in combination, make it possible to determine differences between patients based on clinical, laboratory and immunological parameters, variants of the onset and course of the disease, response to therapy and prognosis, will contribute to a personalized approach in choosing the therapy, improving its long-term results, as well as quality of life and prognosis in patients with SLE.

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.

Recent advances in the use of machine learning and artificial intelligence to improve diagnosis, predict flares, and enrich clinical trials in lupus

PURPOSE OF REVIEW

Machine learning is a computational tool that is increasingly used for the analysis of medical data and has provided the promise of more personalized care.

RECENT FINDINGS

The frequency with which machine learning analytics are reported in lupus research is comparable with that of rheumatoid arthritis and cancer, yet the clinical application of these computational tools has yet to be translated into better care. Considerable work has been applied to the development of machine learning models for lupus diagnosis, flare prediction, and classification of disease using histology or other medical images, yet few models have been tested in external datasets and independent centers. Application of machine learning has yet to be reported for lupus clinical trial enrichment and automated identification of eligible patients. Integration of machine learning into lupus clinical care and clinical trials would benefit from collaborative development between clinicians and data scientists.

SUMMARY

Although the application of machine learning to lupus data is at a nascent stage, initial results suggest a promising future.

Multi-Omic Biomarkers for Patient Stratification in Sjogren's Syndrome-A Review of the Literature

Sjögren's syndrome (SS) is a heterogeneous autoimmune rheumatic disease (ARD) characterised by dryness due to the chronic lymphocytic infiltration of the exocrine glands. Patients can also present other extra glandular manifestations, such as arthritis, anaemia and fatigue or various types of organ involvement. Due to its heterogenicity, along with the lack of effective treatments, the diagnosis and management of this disease is challenging. The objective of this review is to summarize recent multi-omic publications aiming to identify biomarkers in tears, saliva and peripheral blood from SS patients that could be relevant for their better stratification aiming at improved treatment selection and hopefully better outcomes. We highlight the relevance of pro-inflammatory cytokines and interferon (IFN) as biomarkers identified in higher concentrations in serum, saliva and tears. Transcriptomic studies confirmed the upregulation of IFN and interleukin signalling in patients with SS, whereas immunophenotyping studies have shown dysregulation in the immune cell population frequencies, specifically CD4⁺and C8⁺T activated cells, and their correlations with clinical parameters, such as disease activity scores. Lastly, we discussed emerging findings derived from different omic technologies which can provide integrated knowledge about SS pathogenesis and facilitate personalised medicine approaches leading to better patient outcomes in the future.

The promise of precision medicine in rheumatology

Systemic autoimmune rheumatic diseases (SARDs) exhibit extensive heterogeneity in clinical presentation, disease course, and treatment response. Therefore, precision medicine - whereby treatment is tailored according to the underlying pathogenic mechanisms of an individual patient at a specific time - represents the 'holy grail' in SARD clinical care. Current strategies include treat-to-target therapies and autoantibody testing for patient stratification; however, these are far from optimal. Recent innovations in high-throughput 'omic' technologies are now enabling comprehensive profiling at multiple levels, helping to identify subgroups of patients who may taper off potentially toxic medications or better respond to current molecular targeted therapies. Such advances may help to optimize outcomes and identify new pathways for treatment, but there are many challenges along the path towards clinical translation. In this Review, we discuss recent efforts to dissect cellular and molecular heterogeneity across multiple SARDs and future directions for implementing stratification approaches for SARD treatment in the clinic.

Association of primary Sjögren’s syndrome with incident heart failure: a secondary analysis of health claims data in Taiwan

Objective:

Mounting evidence has demonstrated that various chronic inflammatory diseases are associated with incident heart failure (HF). However, there is scarce evidence about the association between primary Sjögren’s syndrome (pSS) and HF. We aimed to explore this association using a nationwide database in Taiwan.

Methods:

We selected patients with incident pSS and no history of HF. Using propensity score matching based on age, sex, cohort entry time, comorbidities, and concomitant medications, cohorts of patients with and without pSS (as controls) were created in a 1:1 ratio and the groups were compared. The cumulative incidence of HF was calculated using Kaplan–Meier estimation. Cox proportional hazard regression analysis was used to measure the hazard ratio (HR) of HF-related hospitalization for the pSS cohort compared with the comparison group.

Results:

A total of 16,466 pairs of patients with pSS and those without pSS were identified. The cumulative incidence of HF-related hospitalization at 3, 5, and 10 years in patients with pSS was 1.05%, 1.89%, and 4.33%, respectively. The risk of HF-related hospitalization was not higher in patients with pSS than in the general population (HR: 0.98, 95% confidence interval [CI]: 0.84–1.14). There was no difference in survival probability after the first episode of HF-related hospitalization between pSS and non-pSS groups.

Conclusion:

Our results suggest that distinct inflammatory spectrums in each chronic inflammatory disease might have differential impacts on cardiac function and subsequent risk of HF. Future studies are needed to elucidate the complex and diverse mechanisms of HF in various chronic autoimmune diseases.

Model-based computational precision medicine to develop combination therapies for autoimmune diseases

INTRODUCTION

The complex pathophysiology of autoimmune diseases (AIDs) is being progressively deciphered, providing evidence for a multiplicity of pro-inflammatory pathways underlying heterogeneous clinical phenotypes and disease evolution.

AREAS COVERED

Treatment strategies involving drug combinations are emerging as a preferred option to achieve remission in a vast majority of patients affected by systemic AIDs. The design of appropriate drug combinations can benefit from AID modeling following a comprehensive multi-omics molecular profiling of patients combined with Artificial Intelligence (AI)-powered computational analyses. Such disease models support patient stratification in homogeneous subgroups, shed light on dysregulated pro-inflammatory pathways and yield hypotheses regarding potential therapeutic targets and candidate biomarkers to stratify and monitor patients during treatment. AID models inform the rational design of combination therapies interfering with independent pro-inflammatory pathways related to either one of five prominent immune compartments contributing to the pathophysiology of AIDs, i.e. pro-inflammatory signals originating from tissues, innate immune mechanisms, T lymphocyte activation, autoantibodies and B cell activation, as well as soluble mediators involved in immune cross-talk.

EXPERT OPINION

The optimal management of AIDs in the future will rely upon rationally designed combination therapies, as a modality of a model-based Computational Precision Medicine taking into account the patients' biological and clinical specificities.

Machine Learning Techniques for Personalised Medicine Approaches in Immune-Mediated Chronic Inflammatory Diseases: Applications and Challenges

In the past decade, the emergence of machine learning (ML) applications has led to significant advances towards implementation of personalised medicine approaches for improved health care, due to the exceptional performance of ML models when utilising complex big data. The immune-mediated chronic inflammatory diseases are a group of complex disorders associated with dysregulated immune responses resulting in inflammation affecting various organs and systems. The heterogeneous nature of these diseases poses great challenges for tailored disease management and addressing unmet patient needs. Applying novel ML techniques to the clinical study of chronic inflammatory diseases shows promising results and great potential for precision medicine applications in clinical research and practice. In this review, we highlight the clinical applications of various ML techniques for prediction, diagnosis and prognosis of autoimmune rheumatic diseases, inflammatory bowel disease, autoimmune chronic kidney disease, and multiple sclerosis, as well as ML applications for patient stratification and treatment selection. We highlight the use of ML in drug development, including target identification, validation and drug repurposing, as well as challenges related to data interpretation and validation, and ethical concerns related to the use of artificial intelligence in clinical research.

Integrated Bioinformatics and Validation Reveal Potential Biomarkers Associated With Progression of Primary Sjögren's Syndrome

Background

Primary Sjögren’s syndrome (pSS) is a chronic systemic autoimmune disease of the exocrine glands characterized by specific pathological features. Previous studies have pointed out that salivary glands from pSS patients express a unique profile of cytokines, adhesion molecules, and chemokines compared to those from healthy controls. However, there is limited evidence supporting the utility of individual markers for different stages of pSS. This study aimed to explore potential biomarkers associated with pSS disease progression and analyze the associations between key genes and immune cells.

Methods

We combined our own RNA sequencing data with pSS datasets from the NCBI Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) via bioinformatics analysis. Salivary gland biopsies were collected from 14 pSS patients, 6 non-pSS patients, and 6 controls. Histochemical staining and transmission electron micrographs (TEM) were performed to macroscopically and microscopically characterize morphological features of labial salivary glands in different disease stages. Then, we performed quantitative PCR to validate hub genes. Finally, we analyzed correlations between selected hub genes and immune cells using the CIBERSORT algorithm.

Results

We identified twenty-eight DEGs that were upregulated in pSS patients compared to healthy controls. These were mainly involved in immune-related pathways and infection-related pathways. According to the morphological features of minor salivary glands, severe interlobular and periductal lymphocytic infiltrates, acinar atrophy and collagen in the interstitium, nuclear shrinkage, and microscopic organelle swelling were observed with pSS disease progression. Hub genes based on above twenty-eight DEGs, including MS4A1, CD19, TCL1A, CCL19, CXCL9, CD3G, and CD3D, were selected as potential biomarkers and verified by RT-PCR. Expression of these genes was correlated with T follicular helper cells, memory B cells and M1 macrophages.

Conclusion

Using transcriptome sequencing and bioinformatics analysis combined with our clinical data, we identified seven key genes that have potential value for evaluating pSS severity.