Dataset Information

Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging.

ABSTRACT: Rheumatoid arthritis (RA) is a systemic chronic inflammatory disease that affects synovial joints and has various extra-articular manifestations, including atherosclerotic cardiovascular disease (CVD). Patients with RA experience a higher risk of CVD, leading to increased morbidity and mortality. Inflammation is a common phenomenon in RA and CVD. The pathophysiological association between these diseases is still not clear, and, thus, the risk assessment and detection of CVD in such patients is of clinical importance. Recently, artificial intelligence (AI) has gained prominence in advancing healthcare and, therefore, may further help to investigate the RA-CVD association. There are three aims of this review: (1) to summarize the three pathophysiological pathways that link RA to CVD; (2) to identify several traditional and carotid ultrasound image-based CVD risk calculators useful for RA patients, and (3) to understand the role of artificial intelligence in CVD risk assessment in RA patients. Our search strategy involves extensively searches in PubMed and Web of Science databases using search terms associated with CVD risk assessment in RA patients. A total of 120 peer-reviewed articles were screened for this review. We conclude that (a) two of the three pathways directly affect the atherosclerotic process, leading to heart injury, (b) carotid ultrasound image-based calculators have shown superior performance compared with conventional calculators, and (c) AI-based technologies in CVD risk assessment in RA patients are aggressively being adapted for routine practice of RA patients.

SUBMITTER: Jamthikar AD

PROVIDER: S-EPMC7453675 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging.

Jamthikar Ankush D AD Gupta Deep D Puvvula Anudeep A Johri Amer M AM Khanna Narendra N NN Saba Luca L Mavrogeni Sophie S Laird John R JR Pareek Gyan G Miner Martin M Sfikakis Petros P PP Protogerou Athanasios A Kitas George D GD Kolluri Raghu R Sharma Aditya M AM Viswanathan Vijay V Rathore Vijay S VS Suri Jasjit S JS

Rheumatology international 20200828 12

Rheumatoid arthritis (RA) is a systemic chronic inflammatory disease that affects synovial joints and has various extra-articular manifestations, including atherosclerotic cardiovascular disease (CVD). Patients with RA experience a higher risk of CVD, leading to increased morbidity and mortality. Inflammation is a common phenomenon in RA and CVD. The pathophysiological association between these diseases is still not clear, and, thus, the risk assessment and detection of CVD in such patients is o ...[more]

PMID: 32857281

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Project description:BackgroundCommon carotid artery intima-media thickness (CIMT), as measured by ultrasound, has utility in stratification of the accelerated cardiovascular risk seen in rheumatoid arthritis (RA); however, the technique has limitations. Carotid magnetic resonance imaging (MRI) is emerging as a useful research tool in the general population, but has yet to be applied in RA populations. Our objectives were to describe the utility of carotid artery MRI (carotid-MRI) in patients with RA in comparison to healthy controls and to describe the association with RA disease phenotype.MethodsSixty-four patients with RA and no history of cardiovascular (CV) disease/diabetes mellitus were assessed for RA and CV profile, including homeostasis model assessment-estimated insulin resistance (HOMA-IR) and N-terminal pro-brain natriuretic peptide (NT-proBNP). All underwent carotid-MRI (3 T), and were compared to 24 healthy controls. Univariable analysis (UVA) and multivariable linear regression models (MVA) were used to determine associations between disease phenotype and carotid-MRI measures.ResultsThere were no significant differences in carotid arterial wall measurements between patients with RA and controls. Wall and luminal volume correlated with 10-year CV risk scores (adjusted as per 2017 European League Against Rheumatism (EULAR) guidance); rho?=?0.33 (p?=?0.012) and rho?=?0.35 (p?=?0.008), respectively, for Joint British Societies-2 risk score. In UVA, carotid-MRI volumetric measures predominantly were associated with traditional CV risk factors including age, ever-smoking and HOMA-IR (p?<?0.05). Lower body mass index was associated with wall maximum thickness (r?=?-?0.25 p?=?0.026). In MVA, age was independently associated with wall volume (B 1.13 (95% CI 0.32, 1.93), p?=?0.007) and luminal volume (B 3.69 (95% CI 0.55, 6.83, p?=?0.022), and RA disease duration was associated with luminal volume (B 3.88 (95% CI 0.80, 6.97), p?=?0.015).ConclusionsThis study demonstrates the utility of carotid-MRI in RA, reporting an association between three-dimensional measures in particular and CV risk scores, individual traditional CV risk factors and RA disease duration. Carotid-MRI in RA is a promising research tool in the investigation of CVD.

Project description:Objectives: Periodontal disease (PD) and rheumatoid arthritis (RA) are known chronic conditions with sustained inflammation leading to osteolysis. Cardiovascular diseases (CVD) are frequent comorbidities that may arise from sustained inflammation associated with both PD and RA. In order to determine CVD risk, alterations at the molecular level need to be identified. The objective of this study, therefore, was to assess the relationship of CVD associated biomarkers in RA patients and how it is influenced by PD. Methods: The study consisted of patient (26 RA with PD, 21 RA without PD, 51 patients with PD only) and systemically and periodontally healthy control (n = 20) groups. Periodontal parameters bleeding on probing, probing pocket depth, and marginal bone loss were determined to characterize the patient groups. Proteomic analysis of 92 CVD-related protein biomarkers was performed using a multiplex proximity extension assay. Biomarkers were clustered using the search tool for retrieval of interacting genes (STRING) to determine protein−protein interaction (PPI) networks. Results: RA patients with PD had higher detection levels for 47% of the measured markers (ANGPT1, BOC, CCL17, CCL3, CD4, CD84, CTRC, FGF-21, FGF-23, GLO1, HAOX1, HB-EGF, hOSCAR, HSP 27, IL16, IL-17D, IL18, IL-27, IL6, LEP, LPL, MERTK, MMP12, MMP7, NEMO, PAPPA, PAR-1, PARP-1, PD-L2, PGF, PIgR, PRELP, RAGE, SCF, SLAMF7, SRC, THBS2, THPO, TNFRSF13B, TRAIL-R2, VEGFD, VSIG2, and XCL1) as compared to RA without PD. Furthermore, a strong biological network was identified amongst these proteins (clustering coefficient = 0.52, PPI enrichment p-value < 0.0001). Coefficients for protein clusters involved in CVD (0.59), metabolic (0.53), and skeletal (0.51) diseases were strongest in the PD group. Conclusion: Periodontal disease augments CVD-related biomarkers in RA through shared pathological clusters, concurrently enhancing metabolic and skeletal disease protein interactions, independent of autoimmune status.

Dataset Information

Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging.

Publications

Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging.

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