Low-density lipoprotein cholesterol levels in adults with type 2 diabetes: An alternative equation for accurate estimation and improved cardiovascular risk classification. (November 2014)

• Record Type: Journal Article
• Authors: Chai Kheng, Ester Yeoh; Chee Fang, Sum; Chang, Su; Kiat Mun, Serena Low; Su Chi, Lim; Lee Ying, Yeoh; Xiao Wei, Ng; Wern Ee, Tang; Biing Ming, Simon Lee; Tavintharan, S
• Is Part Of: Diabetes & vascular disease research. Volume 11:Number 6(2014:Nov.)
• Page Start: 431
• Page End: 439
• Publication Date: 2014-11
• Subjects: Low-density lipoprotein cholesterol -- Friedewald equation -- type 2 diabetes -- estimation; Diabetic angiopathies -- Periodicals; 616.462005
• DOI: 10.1177/1479164114547703
• Languages: English
• ISSNs: 1479-1641

Abstract

Low-density lipoprotein cholesterol (LDL-C) is a major risk factor for atherosclerotic disease. Despite its limitations, Friedewald-calculated LDL-C (F-LDL-C) remains widely used for LDL-C determination. In this observational study of 1999 adults with type 2 diabetes mellitus (T2DM), we compare the accuracy of F-LDL-C to directly measured LDL-C (M-LDL-C) and derived and validated a new [SMART2D (Singapore Study of MAcro-angiopathy and Micro-Vascular Reactivity in Type 2 Diabetes)] formula to estimate LDL-C. From 1000 randomly selected patients, M-LDL-C was compared to F-LDL-C. Using multiple linear regression to identify independent predictors for M-LDL-C, the SMART2D equation was derived and subsequently validated in the next 981 patients. F-LDL-C was 0.367 (0.216) mmol/L lower than M-LDL-C. This difference was −0.009 (0.189) for SMART2D-LDL-C. Using F-LDL-C, 27% with M-LDL-C ≥2.6 mmol/L were classified as LDL-C <2.6 mmol/L, reduced to 2.1% when using SMART2D-LDL-C. With F-LDL-C, misclassification was greater when triglycerides were ≥2.2 mmol/L, especially for the lower LDL-C cut-offs (1.8 and 2.6 mmol/L), and this was markedly improved with SMART2D-LDL-C. In conclusion, in T2DM, F-LDL-C underestimates M-LDL-C, with misclassifications that may potentially have an impact on therapeutic decisions in T2DM. The SMART2D equation improves accuracy of estimate, reducing misclassifications. Trials will be needed to ascertain the clinical significance of these findings.