Elevated Th17 cells are associated with cardiovascular complications in ankylosing spondylitis

Ding, Tingting; Li, Baochen; Su, Rui; Su, R.; Wang, Yanyan; Gao, Chong; Li, Xiaofeng; Wang, Caihong

doi:10.1093/rheumatology/keab888

Cited by 9 publications

(15 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…JAK−STAT signaling was one of the primary pathways investigated in the treatment of AS [ 53 ]. In AS, the Th17 and Th2 pathways have opposing regulatory functions [ 54 ], and elevated Th17 cells are also associated with cardiovascular problems [ 55 ]. In IBD, Th1 cells degrade IEC programming by inducing IEC death, attracting immune cells, increasing IEC adhesion molecule expression, and developing into epithelial cell adhesion molecule–specific interferon γ –positive Th1 cells.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics Analysis of Immune Cell Infiltration and Diagnostic Biomarkers between Ankylosing Spondylitis and Inflammatory Bowel Disease

Zhang

Chen

Qian

et al. 2023

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Background. Ankylosing spondylitis (AS) and inflammatory bowel disease (IBD) are both autoimmune diseases, and they often occur together in clinical practice, but the pathogenesis is unclear. This study is aimed at identifying the hub genes and explore the related immune molecular mechanisms between AS and IBD by bioinformatics analysis. Methods. From the public Gene Expression Omnibus (GEO) database, the AS and IBD datasets (GSE73754, GSE59071, GSE25101, and GSE36807) were obtained. The immune cell infiltration in the peripheral blood tissues of GSE73754 and GSE59071 was assessed using the CIBERSORT algorithm. Then, we used the Weighted Gene Coexpression Network Analysis (WGCNA) to identify the Differentially Expressed Genes (DEGs) related to AS and IBD. Then, the immune genes from the ImmPort database intersected with the DEGs to obtain hub genes. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyzed the functional correlation of hub genes. Then, hub genes were verified in GSE25101 and GSE36807. The clusterProfiler software and Gene Set Enrichment Analysis (GSEA) were used to conduct functional enrichment and pathway enrichment studies. Finally, the diagnostic efficacy was assessed using Receiver Operating Characteristic (ROC) curve analysis. Results. The analysis of immune characteristics showed that both AS and IBD were related to immunity, and neutrophils were positively correlated in both diseases. Nine coexpressed genes, including FCGRT, S100A11, IFNGR1, NFKBIZ, JAK2, LYN, PLAUR, ADM, and IL1RN, were linked to immune cells. The GO and KEGG analyses results showed that enrichment analysis was mainly related to cell transport and migration. Finally, the ROC curve was verified with the validation set, and it was found that PLAUR has clinical diagnostic significance and the most excellent specificity and sensitivity, respectively. Conclusions. PLAUR (uPAR) is a promising biomarker and will be an underlying genetic biomarker for diagnosing AS comorbid IBD. Inflammation and immunological modulation mediated by neutrophil infiltration were important in the development of AS and IBD and may be diagnostic and therapeutic targets.

show abstract

Section: Discussionmentioning

confidence: 99%

Bioinformatics Analysis of Immune Cell Infiltration and Diagnostic Biomarkers between Ankylosing Spondylitis and Inflammatory Bowel Disease

Zhang

Chen

Qian

et al. 2023

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…AS is thought to be an immune-mediated illness accompanied by inflammatory cell infiltration ( 1 , 3 ). AS is accompanied by several comorbidities, chronic pain, functional impairment, and resource consumption which places an economic burden on the nation and society ( 4 – 6 ). Early, consistent therapy may successfully slow the disease progression.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive AI-assisted tool for ankylosing spondylitis based on multicenter research outperforms human experts

Xiang²,

Liang³

et al. 2023

Front. Public Health

View full text Add to dashboard Cite

IntroductionThe diagnosis and treatment of ankylosing spondylitis (AS) is a difficult task, especially in less developed countries without access to experts. To address this issue, a comprehensive artificial intelligence (AI) tool was created to help diagnose and predict the course of AS.MethodsIn this retrospective study, a dataset of 5389 pelvic radiographs (PXRs) from patients treated at a single medical center between March 2014 and April 2022 was used to create an ensemble deep learning (DL) model for diagnosing AS. The model was then tested on an additional 583 images from three other medical centers, and its performance was evaluated using the area under the receiver operating characteristic curve analysis, accuracy, precision, recall, and F1 scores. Furthermore, clinical prediction models for identifying high-risk patients and triaging patients were developed and validated using clinical data from 356 patients.ResultsThe ensemble DL model demonstrated impressive performance in a multicenter external test set, with precision, recall, and area under the receiver operating characteristic curve values of 0.90, 0.89, and 0.96, respectively. This performance surpassed that of human experts, and the model also significantly improved the experts' diagnostic accuracy. Furthermore, the model's diagnosis results based on smartphone-captured images were comparable to those of human experts. Additionally, a clinical prediction model was established that accurately categorizes patients with AS into high-and low-risk groups with distinct clinical trajectories. This provides a strong foundation for individualized care.DiscussionIn this study, an exceptionally comprehensive AI tool was developed for the diagnosis and management of AS in complex clinical scenarios, especially in underdeveloped or rural areas that lack access to experts. This tool is highly beneficial in providing an efficient and effective system of diagnosis and management.

show abstract

“…AS starts gradually, and its early symptoms are mild. Patients with AS often have nephropathy and cardiovascular disease [10,11]. Some hospitals lack HLA-B27 and related imaging instruments to assist in the diagnosis of AS.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Machine Learning-Based Nomogram for Prediction of Ankylosing Spondylitis

Zhu

Liang

et al. 2022

Rheumatol Ther

View full text Add to dashboard Cite

Introduction: Ankylosing spondylitis (AS) is a chronic progressive inflammatory disease of the spine and its affiliated tissues. AS mainly affects the axial bone, sacroiliac joint, hip joint, spinal facet, and adjacent ligaments. We used machine learning (ML) methods to construct diagnostic models based on blood routine examination, liver function test, and kidney function test of patients with AS. This method will help clinicians enhance diagnostic efficiency and allow patients to receive systematic treatment as soon as possible.

show abstract

Elevated Th17 cells are associated with cardiovascular complications in ankylosing spondylitis

Cited by 9 publications

References 38 publications

Bioinformatics Analysis of Immune Cell Infiltration and Diagnostic Biomarkers between Ankylosing Spondylitis and Inflammatory Bowel Disease

Bioinformatics Analysis of Immune Cell Infiltration and Diagnostic Biomarkers between Ankylosing Spondylitis and Inflammatory Bowel Disease

Comprehensive AI-assisted tool for ankylosing spondylitis based on multicenter research outperforms human experts

Development and Validation of a Machine Learning-Based Nomogram for Prediction of Ankylosing Spondylitis

Contact Info

Product

Resources

About