Predictive Value of Classical and Emerging Autoantibodies for Cardiac Dysfunction in Systemic Sclerosis: Systematic...

Background: Cardiac involvement is a major cause of morbidity and mortality in systemic sclerosis (SSc). Autoantibodies may help identify patients at increased cardiovascular (CV) risk. This systematic review aimed to assess the predictive value of classical and emerging SSc-related autoantibodies for cardiac involvement and their integration with imaging and cardiac biomarkers. Methods: A comprehensive literature search was conducted in PubMed, Web of Science, Scopus, and the Cochrane Library up to 16 July 2025. Studies were included if they reported associations between specific autoantibodies and cardiac outcomes (e.g., myocardial fibrosis, conduction abnormalities, arrhythmias, ventricular dysfunction) in adult patients with SSc. Data extraction and quality assessment followed PRISMA 2020 guidelines. The review protocol was registered in PROSPERO (registration ID: CRD420251107782). Results: Anti-topoisomerase I antibodies were associated with myocardial fibrosis, subclinical systolic and diastolic dysfunction, elevated cardiac biomarkers, and pathological findings on cardiac magnetic resonance imaging. Anti-centromere antibodies were linked to conduction system abnormalities, particularly among older individuals. Anti-RNA polymerase III and anti-U3 ribonucleoprotein antibodies correlated strongly with arrhythmias and pericardial involvement. Novel autoantibodies, such as anti-heart antibodies and anti-intercalated disk antibodies, were linked to early myocardial injury, although their clinical utility requires further validation. Across studies, serological markers alone were insufficient to predict cardiac outcomes without concurrent imaging or biomarker evaluation. Conclusions: Autoantibody profiling plays an important role in CV risk stratification in SSc. Combining serological testing with cardiac biomarkers and advanced imaging enhances early detection and supports individualized monitoring. Further longitudinal studies are needed to validate predictive models and optimize patient outcomes.