Predictive Value of Updating te Score Cardiovascular Risk Assessment Engine with Novel Biomarkers in a Type 2 Diabetes Population

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Abstract

Background and Aims: The aim of the study was to estimate the predictive value of some new biomarkers in the assessment of cardiovascular disease (CVD) risk in a type 2 diabetes (T2DM) population and to perform a correlation between the SCORE risk results and the risk profile estimated by the use of these biomarkers. Finally, we aimed to establish if the CVD risk assessment can be improved by adding the biomarkers into the SCORE risk equation. Material and Methods: In the study population the CVD risk assessment was performed using the SCORE High Risk Chart. The new individual biomarkers were: estimated glomerular filtration rate (eGFR), urinary albumin excretion (UAE) rate, albumin/creatinine ratio (UACR), cystatin C, plasminogen activator inhibitor-1 (PAI-1), high-sensitivity C-reactive protein (hs-CRP), high density lipoprotein cholesterol (HDL) and apolipoprotein B (apo-B). Results: The SCORE risk prediction model results were significantly altered by adding in the equation apo-B and HDLc values. An increase of one standard deviation of the apo-B values caused the increase of the SCORE results with 0.19 standard deviations while an increase of one standard deviation of the HDLc values decreased the SCORE results with 0.26 standard deviations. Conclusions: Advanced lipid testing, including the measurement of apo-B, provides a more comprehensive cardiac risk assessment and should be used in the development of specifically designed risk-scores for T2DM individuals.

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