Browsing by Author "Cicek, Mutlu"

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    Serum cystatin C and neutrophil gelatinase-associated lipocalin in predicting the severity of coronary artery disease in diabetic patients
    (2016) Okyay, Kaan; Yildirir, Aylin; Cicek, Mutlu; Aydinalp, Alp; Muderrisoglu, Haldun; 0000-0002-9635-6313; 0000-0001-8750-5287; 0000-0002-3761-8782; 0000-0001-6134-8826; 27182610; AAG-8233-2020; A-4947-2018; AAD-5841-2021; AAK-7355-2020
    Objective: Cystatin C and neutrophil gelatinase-associated lipocalin (NGAL) are biomarkers of renal functions. We evaluated their roles in predicting the severity of coronary artery disease (CAD). Methods: Fifty-two consecutive type 2 diabetic patients (32 males, 65.7 +/- 8.6 years) who underwent coronary angiography (CAG) for stable CAD were included in this single-center, prospective, cross-sectional study. Patients with an estimated glomerular filtration rate <60mL/min/1.73m(2) and with a history of by-pass surgery and/or coronary stent implantation were excluded. The vessel score and Gensini score were calculated to assess the presence and severity of CAD. Mann-Whitney U test, Spearman test, and multiple linear regression analysis were used for the main statistical analyses. Results: Serum cystatin C levels were higher in patients with multivessel disease than in those with single vessel disease [1260 ng/mL (953-1640) vs. 977 ng/mL (599-1114), p=0.017]. According to the median Gensini score, the higher score group also had higher cystatin C levels than the lower score group [1114 ng/mL (948-1567) vs. 929 ng/mL (569-1156), p=0.009]. However, serum NGAL levels were similar between these subgroups. There was a positive correlation between cystatin C and Gensini score (r=0.334, p=0.016). Multiple linear regression analysis revealed serum cystatin C as an independent predictor of the Gensini score (beta=0.360, t=2.311, p=0.026). These results may aid in defining cystatin C as a surrogate marker of the extent of CAD in further clinical trials. Conclusion: Serum Cystatin C, but not NGAL levels, could predict the severity of CAD in diabetic patients.