LDL cholesterol variability in patients with Type 2 diabetes taking atorvastatin and simvastatin: a comparison of two formulae for LDL-C estimation

Best practice for LDL-cholesterol: when and how to calculate

The lipid profile is important in the risk assessment for cardiovascular disease. The lipid profile includes total cholesterol, high-density lipoprotein (HDL)-cholesterol, triglycerides (TGs) and low-density lipoprotein (LDL)-cholesterol (LDL-C). LDL-C has traditionally been calculated using the Friedewald equation (invalid with TGs greater than 4.5 mmol/L and is based on the assumption that the ratio of TG to cholesterol in very- low-density lipoprotein (VLDL) is 5 when measured in mg /dL). LDL-C can be quantified with a reference method, beta-quantification involving ultracentrifugation and this is unsuitable for routine use. Direct measurement of LDL-C was expected to provide a solution with high TGs. However, this has some challenges because of a lack of standardisation between the reagents and assays from different manufacturers as well as the additional costs. Furthermore, mild hypertriglyceridaemia also distorts direct LDL-C measurements. With the limitations of the Friedewald equation, alternatives have been derived. Newer equations include the Sampson-National Institutes of Health (NIH) equation 2 and the Martin-Hopkins equation. The Sampson-NIH2 equation was derived using beta-quantification in a population with high TG and multiple least squares regression to calculate VLDL-C, using TGs and non-HDL-C as independent variables. These data were used in a second equation to calculate LDL-C. The Sampson-NIH2 equation can be used with TGs up to 9 mmol/L. The Martin-Hopkins equation uses a 180 cell stratification of TG/non-HDL-C to determine the TG:VLDL-C ratio and can be used with TGs up to 4.5 mmol/L. Recently, an extended Martin-Hopkins equation has become available for TGs up to 9.04 mmol/L.This article discusses the best practice approach to calculating LDL-C based on the available evidence.

Risk stratification of cardiac arrhythmias and sudden cardiac death in type 2 diabetes mellitus patients receiving insulin therapy: A population-based cohort study

INTRODUCTION

Metabolic abnormalities may exacerbate the risk of adverse outcomes in patients with type 2 diabetes mellitus. The present study aims to assess the predictive value of HbA1c and lipid variability on the risks of sudden cardiac death (SCD) and incident atrial fibrillation (AF).

METHODS

The retrospective observational study consists of type 2 diabetic patients prescribed with insulin, who went to publicly funded clinics and hospitals in Hong Kong between January 1, 2009 and December 31, 2009. Variability in total cholesterol, low-density lipoprotein-cholesterol (LDL-C), high-density lipoprotein-cholesterol (HDL-C), triglyceride, and HbA1c were assessed through their SD and coefficient of variation. The primary outcomes were incident (1) ventricular tachycardia/ventricular fibrillation, actual or aborted SCD and (2) AF.

RESULTS

A total of 23 329 patients (mean ± SD age: 64 ± 14 years old; 51% male; mean HbA1c 8.6 ± 1.3%) were included. On multivariable analysis, HbA1c, total cholesterol, LDL-C and triglyceride variability were found to be predictors of SCD (p < .05).

CONCLUSION

HbA1c and lipid variability were predictive of SCD. Therefore, poor glucose control and variability in lipid parameters in diabetic patients are associated with aborted or actual SCD. These observations suggest the need to re-evaluate the extent of glycemic control required for outcome optimization.

Impact of glucose and lipid markers on the correlation of calculated and enzymatic measured low-density lipoprotein cholesterol in diabetic patients with coronary artery disease

BACKGROUND AND AIMS

Low-density lipoprotein cholesterol (LDL-C) is widely estimated by Friedewald equation (FE) and Enzymatic test (ET), which are affected by several factors. The aim of this study was to observe the impact of diabetic lipid and glucose patterns on the correlation between FE LDL-C (F-LDL) and ET LDL-C (E-LDL) in patients with coronary artery disease (CAD).

METHODS AND RESULTS

A total of 8155 CAD patients were consecutively enrolled and their lipid profiles were measured. The impacts of triglyceride (TG), glycosylated hemoglobin A1c (HbA1c), and high-density lipoprotein cholesterol (HDL-C) on the correlation of F-LDL and E-LDL were examined. The difference value (DV) between F-LDL and E-LDL was compared using ANOVA test. The CAD patients with DM were elder and had higher body mass index, plasma TG compared with those without DM (P < .05 separately). In the whole population, F-LDL was lower than E-LDL but showed a high correlation with E-LDL (r = .970, P = .000). Moreover, as the TG concentrations increased, the DV increased accordingly but the correlation between F-LDL and E-LDL decreased (P < .01). The similar trend was also found in both DM and non-DM patients comparing with different TG groups. However, in patients with DM, there was no significant difference of DV in different HbA1c groups or HDL-C concentrations (P > .05).

CONCLUSION

Although F-LDL might underestimate the value of LDL-C, the correlation between F-LDL and E-LDL was clinically acceptable (r = .97), suggesting the LDL-C values measured by two methods were similarly reliable in CAD patients with or without DM.

Evaluation of a new equation for LDL-c estimation and prediction of death by cardiovascular related events in a German population-based study cohort

A simple equation established by Cordova & Cordova (LDL-COR) was developed to provide an improved estimation of LDL-cholesterol in a large Brazilian laboratory database. We evaluated this new equation in a general population cohort in Pomerania, north-eastern Germany (SHIP Study) compared to other existing formulas (Anandaraja, Teerakanchana, Chen, Hattori, Martin, Friedewald and Ahmadi), and its power in the prediction of death by atherosclerosis related events as the primary outcome. Analysis was conducted on a cohort of 4075 individuals considering age, gender, use of lipid lowering therapy and associated co-morbidities such as diabetes, hepatic, kidney and thyroid disease. LDL-COR values had a lower standard deviation compared to the previously published equations: 0.92 versus 1.02, 1.02, 1.03, 1.04, 1.09, 1.10 and 1.74 mmol/L, respectively. All of the factors known to affect the results obtained by the Friedewald's equation (LDL-FW), except fibrate use, were associated with the difference between LDL-COR and LDL-FW (p < .01), with TSH being borderline (p = .06). LDL-COR determined a higher hazard ratio (1.23 versus 1.12, 1.19, 1.21, 1.19, 1.21 and 1.19) for cardiovascular disease related mortality, incident stroke or myocardial infarction compared to the other evaluated formulas, except for Ahmadi's (1.24), and the same adjusted predictive power considering all confounding factors. The proposed simple equation was demonstrated to be suitable for a more precise LDL-c estimation in the studied population. Since LDL-c is a parameter frequently requested by medical laboratories in clinical routine, and will probably remain so, precise methods for its estimation are needed when direct measurement is not available.

Risk of misclassification with a non-fasting lipid profile in secondary cardiovascular prevention

AIMS

Routinely fasting is not necessary for measuring the lipid profile according to the latest European consensus. However, LDL-C tends to be lower in the non-fasting state with risk of misclassification. The extent of misclassification in secondary cardiovascular prevention with a non-fasting lipid profile was investigated.

METHODS AND RESULTS

329 patients on lipid lowering therapy for secondary cardiovascular prevention measured a fasting and non-fasting lipid profile. Cut-off values for LDL-C, non-HDL-C and apolipoprotein B were set at <1.8mmol/l, <2.6mmol/l and <0.8g/l, respectively. Study outcomes were net misclassification with non-fasting LDL-C (calculated using the Friedewald formula), direct LDL-C, non-HDL-C and apolipoprotein B. Net misclassification <10% was considered clinically irrelevant. Mean age was 68.3±8.5years and the majority were men (79%). Non-fasting measurements resulted in lower LDL-C (-0.2±0.4mmol/l, P<0.001), direct LDL-C (-0.1±0.2mmol/l, P=0.001), non-HDL-C (-0.1±0.4mmol/l, P=0.004) and apolipoprotein B (-0.02±0.10g/l, P=0.004). 36.0% of the patients reached a fasting LDL-C target of <1.8mmol/l with a significant net misclassification of 10.7% (95% CI 6.4-15.0%) in the non-fasting state. In the non-fasting state net misclassification with direct LDL-C was 5.7% (95% CI 2.1-9.2%), 4.0% (95% CI 1.0-7.4%) with non-HDL-C and 4.1% (95% CI 1.1-9.1%) with apolipoprotein B.

CONCLUSION

Use of non-fasting LDL-C as treatment target in secondary cardiovascular prevention resulted in significant misclassification with subsequent risk of undertreatment, whereas non-fasting direct LDL-C, non-HDL-C and apolipoprotein B are reliable parameters.