Global Prevalence of Myocardial Fibrosis among Individuals with Cardiometabolic Conditions: A Systematic Review and Meta-Analysis

Abstract Background Cardiometabolic conditions including hypertension, diabetes, hyperlipidaemia and obesity are significant risk factors for cardiovascular diseases. Myocardial fibrosis (MF) is a complication and final common pathway of these conditions, potentially leading to heart failure, arrhythmias and sudden death. Existing reviews explored pathophysiological changes and treatment of MF, but the global prevalence of MF among individuals with cardiometabolic conditions remain limited. Objectives To evaluate the global prevalence of MF in individuals with cardiometabolic conditions and explore factors influencing its rate. Methods CINAHL, Cochrane Library, Embase, PubMed, ProQuest Theses and Dissertations, Scopus, and Web of Science were systematically reviewed until January 2024. Studies included individuals with hypertension, type 2 diabetes mellitus, hyperlipidaemia, and obesity, with MF prevalence assessed via biopsy or Late Gadolinium Enhancement-Cardiac Magnetic Resonance (LGE-CMR). Meta-analysis was conducted using jamovi and factors associated with MF were synthesised narratively. This review is registered on PROSPERO, CRD42024544632. Results The meta-analysis included 52 articles involving 5,921 individuals. 32.7% of individuals with cardiometabolic conditions developed MF, with hypertension demonstrating the highest prevalence [35.2%(95%CI:25.5-45.0)]. Biopsy-based studies reported a higher prevalence [75.6%(95%CI:53.6-97.6)] compared to LGE-CMR studies [26.8%(95%CI:20.6-33.0)]. Key factors associated with MF included increased LV mass/LV hypertrophy, reduced LV function, and myocardial stiffness. Conclusions This first global review estimates that one-third of individuals with cardiometabolic conditions develop MF, with the rate expected to rise. Standardized CMR measures cut-offs are needed to address prevalence inconsistencies. Future research should explore MF prevalence using diverse samples, combined CMR measures, considering socio-demographic and clinical factors for more accurate estimates.