Artery compliance in patients with rheumatoid arthritis: results from a case-control study

Multi-modal transcriptomics: integrating machine learning and convolutional neural networks to identify immune biomarkers in atherosclerosis

BACKGROUND

Atherosclerosis, a complex chronic vascular disorder with multifactorial etiology, stands as the primary culprit behind consequential cardiovascular events, imposing a substantial societal and economic burden. Nevertheless, our current understanding of its pathogenesis remains imprecise. In this investigation, our objective is to establish computational models elucidating molecular-level markers associated with atherosclerosis. This endeavor involves the integration of advanced machine learning techniques and comprehensive bioinformatics analyses.

MATERIALS AND METHODS

Our analysis incorporated data from three publicly available the Gene Expression Omnibus (GEO) datasets: GSE100927 (104 samples, 30,558 genes), which includes atherosclerotic lesions and control arteries from carotid, femoral, and infra-popliteal arteries of deceased organ donors; GSE43292 (64 samples, 23,307 genes), consisting of paired carotid endarterectomy samples from 32 hypertensive patients, comparing atheroma plaques and intact tissues; and GSE159677 (30,498 single cells, 33,538 genes), examining single-cell transcriptomes of calcified atherosclerotic core plaques and adjacent carotid artery tissues from patients undergoing carotid endarterectomy. Utilizing single-cell sequencing, highly variable atherosclerotic monocyte subpopulations were systematically identified. We analyzed cellular communication patterns with temporal dynamics. The bioinformatics approach Weighted Gene Co-expression Network Analysis (WGCNA) identified key modules, constructing a Protein-Protein Interaction (PPI) network from module-associated genes. Three machine-learning models derived marker genes, formulated through logistic regression and validated via convolutional neural network(CNN) modeling. Subtypes were clustered based on Gene Set Variation Analysis (GSVA) scores, validated through immunoassays.

RESULTS

Three pivotal atherosclerosis-associated genes-CD36, S100A10, CSNK1A1-were unveiled, offering valuable clinical insights. Profiling based on these genes delineated two distinct isoforms: C2 demonstrated potent microbicidal activity, while C1 engaged in inflammation regulation, tissue repair, and immune homeostasis. Molecular docking analyses explored therapeutic potential for Estradiol, Zidovudine, Indinavir, and Dronabinol for clinical applications.

CONCLUSION

This study introduces three signature genes for atherosclerosis, shaping a novel paradigm for investigating clinical immunological medications. It distinguishes the high biocidal C2 subtype from the inflammation-modulating C1 subtype, utilizing identified signature gene as crucial targets.

Cardiovascular outcomes of patients with rheumatoid arthritis prescribed disease modifying anti-rheumatic drugs: a review

INTRODUCTION

Rheumatoid arthritis (RA) is associated with a heightened risk of cardiovascular disease (CVD), with both traditional CV risk factors and inflammation contributing to this risk.

AREAS COVERED

This review highlights the burden of CVD in RA and associated traditional CV risk factors, including the complexity of dyslipidemia in RA and the so-called 'lipid paradox.' Furthermore, the recognized RA-disease-specific factors associated with higher risk of CVD and the role of systemic inflammation in the pathogenesis of CVD in RA will be addressed. With the advent of biologic and targeted synthetic therapies in the treatment of RA, the effect of conventional and newer generation disease modifying anti-rheumatic therapies (DMARDs) on CV risk and associated risk factors will also be discussed.

EXPERT OPINION

Identifying the RA phenotype at greatest risk of CVD, understanding the interplay of increased traditional risk factors, common inflammatory processes and RA-specific factors, and personalized use of DMARDs according to disease phenotype and comorbidity to reduce this risk are key areas for future research.