Leucine-rich alpha-2-glycoprotein: A novel predictor of diastolic dysfunction

Leucine-rich alpha 2-glycoprotein (LRG1) mediates cardiac fibrocyte activation. It is upregulated in inflammatory conditions, atherosclerosis, and fibrosis. Diastolic dysfunction (DD) is due to myocardial fibrosis. This cross-sectional study examined the relationship between LRG1 and DD. Patients wi...

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Main Authors: Loch, Alexander, Tan, Kok Leng, Danaee, Mahmoud, Idris, Iskandar, Ng, Mei Li
Format: Article
Published: MDPI 2023
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Online Access:http://eprints.um.edu.my/38527/
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Summary:Leucine-rich alpha 2-glycoprotein (LRG1) mediates cardiac fibrocyte activation. It is upregulated in inflammatory conditions, atherosclerosis, and fibrosis. Diastolic dysfunction (DD) is due to myocardial fibrosis. This cross-sectional study examined the relationship between LRG1 and DD. Patients with symptoms of chronic coronary ischemia were recruited. Patients with symptoms of overt heart failure, ejection fraction (EF) < 55%, impaired renal function, infection, and recent trauma were excluded from the study. Clinical parameters examined were SYNergy between percutaneous coronary intervention with TAXus and cardiac surgery (SYNTAX) score, echocardiographic assessment, and LRG1 levels. Binary stepwise logistic regression was used to evaluate the association between LRG1 and DD. Receiver Operating Characteristic (ROC) analysis was used to determine optimal cut-off values and predictive performance of LRG1. A total of 94 patients were enrolled in the study, with 47 having a clinical diagnosis of DD. Plasma LRG1 was significantly (U = 417.00, p < 0.001) higher in the DD group (M = 14) compared to the No-DD group (M = 8) by Mann-Whitney U test. There were higher SYNTAX scores in the DD group (M = 24.5) compared with No-DD (M = 7). LRG1 had significant predictability of DD (OR = 1.32 (95% CI: 1.14-1.53)). The ROC showed an AUC = 0.89 (95% CI: 0.82-0.95). LRG1 had a 78% sensitivity (95% CI: 65.3-87.7) and 72.3% specificity (95% CI: 57.4-84.4) for predicting DD at a cut-off value of ``9''. In conclusion, we identified LRG1 as a novel independent predictor of DD. Further studies are warranted to validate the utility of LRG1 in predicting DD.