The Relationship between Glucose Tolerance and Severity of Coronary Artery Disease Using the Gensini Score

Background and Aim. Nonalcoholic fatty liver disease (NAFLD) is an independent risk factor for cardiovascular disease. Hepatic fibrosis is the most significant determinant of all-cause- and liver -related mortality in NAFLD. However, the relationship between NAFLD fibrosis and severe coronary artery disease (CAD) remains unclear. Methods and Results. We conducted a retrospective study of 531 patients with ultrasonogram-confirmed NAFLD who underwent percutaneous coronary intervention (PCI). Then, all patients were separated into four categories by Gensini score (0, 0-9, 9-48, and ≥48) for use in ordinal logistic regression analysis to determine whether NAFLD fibrosis was associated with increased Gensini scores. Mediation analysis was used to investigate whether systemic inflammation is a mediating factor in the association between NAFLD fibrosis and CAD severity. FIB − 4 > 2.67 ( OR = 5.67 , 95% CI 2.59-12.38) and APRI > 1.5 ( OR = 14.8 , 95% CI 3.24-67.60) remained to be independent risk factors for the severity of CAD after adjusting for conventional risk factors, whereas among the inflammation markers, only neutrophils and neutrophil-to-lymphocyte ratio (NLR) were independently associated with CAD. Multivariable ordinal regression analysis suggested that increasing Gensini score (0, 0-9, 9-48, and ≥48) was associated with advanced NAFLD fibrosis. ROC curve showed that either fibrosis markers or inflammation markers, integrating with traditional risk factors, could increase the predictive capacity for determining CAD. Inflammation markers, especially neutrophils and NLR, were mediators of the relationship between NAFLD fibrosis and CAD severity. Conclusions. NAFLD patients with advanced fibrosis are at a high risk of severe coronary artery stenosis, and inflammation might mediate the association between NAFLD fibrosis and CAD severity.

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Stabil Angina Pektorisli Hastalarda Arteriyel Sertlik Ve Aort Augmentasyon İndeksinin Koroner Arter Hastalığı İle İlişkisi

Gevher Nesibe Journal, IESDR ◽

10.46648/gnj.236 ◽

2021 ◽

Vol 6 (13) ◽

pp. 61-67

Author(s):

Ali UYSAL ◽

Serhat ÇALIŞKAN ◽

Şeyda GÜNAY ◽

Mehmet ATAY ◽

Osman Akın SERDAR

Keyword(s):

Coronary Artery Disease ◽

Coronary Artery ◽

Arterial Stiffness ◽

Patient Group ◽

Augmentation Index ◽

Area Under The Curve ◽

Control Group ◽

Gensini Score ◽

Artery Disease ◽

The Relationship

Objective : Many studies have indicated arterial stiffness as an independent predictor of hypertension, stroke, cardiovascular events, and mortality. However, the relationship between the severity of coronary artery disease(CAD) and arterial stiffness is still unclear. In this study, it was aimed to examine the relationship between augmentation index which is a marker of arterial stiffness with CAD and its prevalence. Methods: A total of 80 patients (22 women, mean age 59±8) who were scheduled for coronary angiography with a preliminary diagnosis of coronary artery disease by evaluating their symptoms and findings and coronary risk profiles were included. The other group have 20 healthy patients without any chronic disease history. The augmentation index (AIX), which is an indicator of arterial stiffness, was measured in all cases with the BP+ Central Blood Pressure CardioScope II device in all groups. Gensini score of the patient group was calculated. Results: It was found to be 131% (79-198mmHg) in the AIX patient group and 72.5% (69-96mmHg) in the control group (p<0.001). A positive correlation was observed between AIX and Gensini score, LDL-cholesterol, body mass index and HbA1c in the patient group. In the ROC analysis, the area under the curve (AUC) for AIX was calculated as 0.701 (P<0.001). While AIX was 121% above normal, it predicted coronary artery disease with 94.8% sensitivity and 80.9% specificity. Conclusion: In our study, a relationship was found between coronary artery disease with its severity and arterial stiffness. Augmentation index can be used effectively with low cost in predicting coronary artery disease.

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