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Relationship between Cystatin C, Retinol-binding Protein 4 and Framingham Risk Score in Healthy Postmenopausal Women.
Archives of Iranian Medicine 2016 December
BACKGROUND: We aimed to examine the relationship between high levels of cystatin C, retinol-binding protein 4 (RBP4) and cardiovascular risk score [determined by Framingham Risk Score (FRS)] in postmenopausal women.
METHODS: A total of apparently healthy 129 postmenopausal women (mean age 57.1 ± 4.6 years) were included. Serum cystatin C, RBP4, glucose, lipid parameters, creatinine, uric acid and high sensitivity C-reactive protein (hsCRP) were determined. Anthropometric parameters and blood pressure were also obtained. FRS was calculated. Multiple linear regression analysis (MLR) was performed to identify independent factors affecting FRS and to estimate the final predictors of its variability. Receiver Operating Characteristic (ROC) curve analysis was used with the purpose of testing discriminatory potential of a group of parameters selected in MLR analysis, with FRS level as dependent variable.
RESULTS: We found significantly higher levels of both proteins, cystatin C (P = 0.001) and RBP4 (P = 0.006), in the FRS higher (medium and high) risk groups (FRS ≥ 10%) compared to low risk FRS group (FRS < 10%). MLR revealed the best model consisting of 4 parameters (e.g., body mass index (BMI) (P < 0.001), triglycerides (TG) (P = 0.004), RBP4 (P = 0.021), and cystatin C (P = 0.046), R2-adjusted = 0.347) for FRS prediction. Construction of a model consisted of those 4 FRS formula independent parameters (BMI, TG, cystatin C and RBP4) using logistic regression analysis showed that new ROC curve had excellent discriminatory capability (area under the curve = 0.820).
CONCLUSION: High cystatin C and retinol-binding protein 4 may contribute significantly to cardiovascular risk burden in addition to traditional cardiovascular markers.
METHODS: A total of apparently healthy 129 postmenopausal women (mean age 57.1 ± 4.6 years) were included. Serum cystatin C, RBP4, glucose, lipid parameters, creatinine, uric acid and high sensitivity C-reactive protein (hsCRP) were determined. Anthropometric parameters and blood pressure were also obtained. FRS was calculated. Multiple linear regression analysis (MLR) was performed to identify independent factors affecting FRS and to estimate the final predictors of its variability. Receiver Operating Characteristic (ROC) curve analysis was used with the purpose of testing discriminatory potential of a group of parameters selected in MLR analysis, with FRS level as dependent variable.
RESULTS: We found significantly higher levels of both proteins, cystatin C (P = 0.001) and RBP4 (P = 0.006), in the FRS higher (medium and high) risk groups (FRS ≥ 10%) compared to low risk FRS group (FRS < 10%). MLR revealed the best model consisting of 4 parameters (e.g., body mass index (BMI) (P < 0.001), triglycerides (TG) (P = 0.004), RBP4 (P = 0.021), and cystatin C (P = 0.046), R2-adjusted = 0.347) for FRS prediction. Construction of a model consisted of those 4 FRS formula independent parameters (BMI, TG, cystatin C and RBP4) using logistic regression analysis showed that new ROC curve had excellent discriminatory capability (area under the curve = 0.820).
CONCLUSION: High cystatin C and retinol-binding protein 4 may contribute significantly to cardiovascular risk burden in addition to traditional cardiovascular markers.
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