Inflammation biomarkers are associated with the incidence of cardiovascular disease: a meta-analysis - Liu, Yifei; Guan, Suzhen; Xu, Haiming; Zhang, Na; Huang, Min; Liu, Zhihong (Frontiers in Cardiovascular Medicine, 2023)
Abstract Background: Inflammation is a risk factor for cardiovascular disease (CVD), and particular inflammatory parameters can be used to predict the incidence of CVD. The aim of this study was to assess the association between fibrinogen (FIB), interleukin-6 (IL-6), C-reactive protein (CRP) and galectin-3 (Gal-3) and the risk of cardiovascular disease using meta-analysis. Methods: PubMed, Embase, Scopus, and Web of Science databases were searched with the appropriate strategies to identify observational studies relevant to this meta-analysis. A random-effects model was used to combine inflammation factor-associated outcomes and cardiovascular disease outcomes, except in the case of galectin-3, where a fixed-effects model was used because of less heterogeneity. Location, age, type of cardiovascular disease, and sample size factors were used to explore heterogeneity in stratification and metaregression for subgroup analysis. A case-by-case literature exclusion approach was used for sensitivity analysis. The funnel plot and Begg's test were combined to assess publication bias. Results: Thirty-three papers out of 11,456 were screened for inclusion in the analysis. Four inflammation biomarkers were significantly associated with the development of CVD: FIB (OR: 1.21, 95% CI: 1.15-1.27, P < 0.001; HR:1.04, 95% CI:1.00-1.07, P< 0.05), IL-6 (HR: 1.16, 95% CI: 1.10-1.22, P < 0.001), CRP (OR: 1.25, 95% CI: 1.15-1.35, P < 0.001; HR: 1.20, 95% CI: 1.14-1.25, P < 0.001) and Gal-3 (HR: 1.09, 95% CI: 1.05-1.14, P < 0.001). Location factors help explain the source of heterogeneity, and there is publication bias in the Gal-3 related literature. Conclusion: Taken together, the current research evidence suggests that high levels of fibrinogen, interleukin-6, C-reactive protein and galectin-3 are risk factors for cardiovascular disease and can be used as biomarkers to predict the development of cardiovascular disease to some extent.