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
Diabetes Centre, Khoo Teck Puat Hospital, Singapore Division of Endocrinology, Medicine Unit 2, Department of Medicine, Khoo Teck Puat Hospital, Singapore.
Clinical Research Unit, Khoo Teck Puat Hospital, Singapore.
Diab Vasc Dis Res. 2014 Nov;11(6):431-9. doi: 10.1177/1479164114547703. Epub 2014 Sep 9.
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.
低密度脂蛋白胆固醇（LDL-C）是动脉粥样硬化性疾病的主要危险因素。尽管存在局限性，但通过Friedewald公式计算的LDL-C（F-LDL-C）仍被广泛用于LDL-C的测定。在这项对1999例2型糖尿病（T2DM）成人患者的观察性研究中，我们比较了F-LDL-C与直接测量的LDL-C（M-LDL-C）的准确性，并推导并验证了一个新的[SMART2D（新加坡2型糖尿病大血管病变和微血管反应性研究）]公式来估算LDL-C。从1000例随机选择的患者中，将M-LDL-C与F-LDL-C进行比较。使用多元线性回归确定M-LDL-C的独立预测因素，推导了SMART2D方程，随后在接下来的981例患者中进行了验证。F-LDL-C比M-LDL-C低0.367（0.216）mmol/L。SMART2D-LDL-C的这一差异为-0.009（0.189）。使用F-LDL-C时，M-LDL-C≥2.6 mmol/L的患者中有27%被分类为LDL-C<2.6 mmol/L，而使用SMART2D-LDL-C时这一比例降至2.1%。使用F-LDL-C时，当甘油三酯≥2.2 mmol/L时，错误分类更大，尤其是对于较低的LDL-C临界值（1.8和2.6 mmol/L），而使用SMART2D-LDL-C时这一情况明显改善。总之，在T2DM中，F-LDL-C低估了M-LDL-C，其错误分类可能会对T2DM的治疗决策产生潜在影响。SMART2D方程提高了估算的准确性，减少了错误分类。需要进行试验来确定这些发现的临床意义。
