Impaired fibrinolysis in patients with atrial fibrillation and elevated circulating lipopolysaccharide.
Marcin SadowskiMichał ZąbczykAnetta UndasPublished in: Journal of thrombosis and thrombolysis (2024)
It is unknown whether elevated gut-derived serum lipopolysaccharide (LPS) can affect thrombin generation, fibrinolysis, and fibrin clot properties in atrial fibrillation (AF). We aimed to evaluate associations of circulating LPS with prothrombotic markers in AF patients. A total of 157 (women, 57.3%) ambulatory anticoagulant-naïve AF patients aged from 42 to 86 years were recruited. Clinical data together with serum LPS, inflammation, endothelial injury, coagulation and fibrinolysis markers, including fibrin clot permeability (K s ) and clot lysis time (CLT), were analyzed. A median LPS concentration was 73.0 (58.0-100.0) pg/mL and it showed association with CLT (r = 0.31, p < 0.001) and plasminogen activator inhibitor-1 (PAI-1, r = 0.57, p < 0.001), but not other fibrinolysis proteins, thrombin generation, inflammatory markers, or K s . There were weak associations of LPS with von Willebrand factor (vWF, r = 0.2, p = 0.013), cardiac troponin I (r = 0.16, p = 0.045), and growth differentiation factor-15 (r = 0.27, p < 0.001). No associations of LPS and CHA 2 DS 2 -VASc or other clinical variables were observed. Multivariable regression adjusted for potential confounders showed that serum LPS ≥ 100 pg/mL was an independent predictor of prolonged CLT. This study is the first to demonstrate antifibrinolytic effects of elevated LPS in AF patients largely driven by enhanced PAI-1 release.
Keyphrases
- atrial fibrillation
- inflammatory response
- end stage renal disease
- anti inflammatory
- newly diagnosed
- ejection fraction
- prognostic factors
- heart failure
- blood pressure
- toll like receptor
- peritoneal dialysis
- patient reported outcomes
- coronary artery disease
- skeletal muscle
- machine learning
- metabolic syndrome
- electronic health record
- climate change
- mitral valve
- artificial intelligence
- big data
- patient reported
- data analysis