ArticleClinical proteomics2022
Data-independent acquisition mass spectrometry in severe rheumatic heart disease (RHD) identifies a proteomic signature showing ongoing inflammation and effectively classifying RHD cases.
Article in Clinical proteomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 29 citations in OpenAlex.
- Disease Modifying Therapies for Chronic Rheumatic Heart Disease: Promise, Mechanisms, and the Road Ahead.JACC. Asia · 2026Article
- Harnessing data science to control non-communicable diseases in Africa: a systematic review and gap analysis.Communications medicine · 2026Article
- Neutrophil Extracellular Traps and Complement Pathways: The Missing Links in Acute Rheumatic Fever and Rheumatic Heart Disease Pathogenesis.Journal of cardiovascular translational research · 2026Review
- Integration of high-throughput proteomic data and complementary omics layers with PriOmics.Genome research · 2026Article
- The "Cairo Accord"- towards the eradication of RHD: revisited (2026).Frontiers in cardiovascular medicine · 2026Review
- Rapid screening for acute rheumatic fever using machine learning analysis of host tissue reactive antibodies.Scientific reports · 2025Article
- Evaluation of Ficolin-3 deficiency as a risk factor in the development of rheumatic heart disease.BMC research notes · 2025Article
- Rheumatic heart valve disease: navigating the challenges of an overlooked autoimmune disorder.Frontiers in cardiovascular medicine · 2025Review
- Rheumatic Heart Disease Burden in Africa and the Need to Build Robust Infrastructure.JACC. Advances · 2024Review
- Making a difference: 5 years of Cardiac Surgery Intersociety Alliance (CSIA).European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery · 2024Article
- Making a difference: 5 years of Cardiac Surgery Intersociety Alliance (CSIA).Asian cardiovascular & thoracic annals · 2024Article
- Evaluation of a proteomic signature coupled with the kidney failure risk equation in predicting end stage kidney disease in a chronic kidney disease cohort.Clinical proteomics · 2024Article
- Machine Learning-Based Prediction of Binge Drinking among Adults in the United State: Analysis of the 2022 Health Information National Trends Survey.Proceedings of the 2024 9th International Conference on Mathematics and Artificial Intelligence · 2024Article
- Proteomic analysis identifies subgroups of patients with active systemic lupus erythematosus.Clinical proteomics · 2023Article
- Rationale, Design, and the Baseline Characteristics of the RHDGen (The Genetics of Rheumatic Heart Disease) Network Study†.Circulation. Genomic and precision medicine · 2023Article
- Multi-Omics Reveals Mechanisms of Partial Modulation of COVID-19 Dysregulation by Glucocorticoid Treatment.International journal of molecular sciences · 2022Article
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Authors and funding
29 authors at 10 institutions in 9 countries.
Funding
Abstract
backgroundRheumatic heart disease (RHD) remains a major source of morbidity and mortality in developing countries. A deeper insight into the pathogenetic mechanisms underlying RHD could provide opportunities for drug repurposing, guide recommendations for secondary penicillin prophylaxis, and/or inform development of near-patient diagnostics.
methodsWe performed quantitative proteomics using Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectrometry (SWATH-MS) to screen protein expression in 215 African patients with severe RHD, and 230 controls. We applied a machine learning (ML) approach to feature selection among the 366 proteins quantifiable in at least 40% of samples, using the Boruta wrapper algorithm. The case-control differences and contribution to Area Under the Receiver Operating Curve (AUC) for each of the 56 proteins identified by the Boruta algorithm were calculated by Logistic Regression adjusted for age, sex and BMI. Biological pathways and functions enriched for proteins were identified using ClueGo pathway analyses.
resultsAdiponectin, complement component C7 and fibulin-1, a component of heart valve matrix, were significantly higher in cases when compared with controls. Ficolin-3, a protein with calcium-independent lectin activity that activates the complement pathway, was lower in cases than controls. The top six biomarkers from the Boruta analyses conferred an AUC of 0.90 indicating excellent discriminatory capacity between RHD cases and controls.
conclusionsThese results support the presence of an ongoing inflammatory response in RHD, at a time when severe valve disease has developed, and distant from previous episodes of acute rheumatic fever. This biomarker signature could have potential utility in recognizing different degrees of ongoing inflammation in RHD patients, which may, in turn, be related to prognostic severity.
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