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Open Access Original investigation

Discriminant ratio and biometrical equivalence of measured vs. calculated apolipoprotein B100 in patients with T2DM

Michel P Hermans1*, Sylvie A Ahn2 and Michel F Rousseau2

Author Affiliations

1 Endocrinology & Nutrition, Cliniques universitaires St-Luc and Institut de Recherche Expérimentale et Clinique (IREC), Université catholique de Louvain, Brussels, Belgium

2 Cardiology Division, Cliniques universitaires St-Luc, and Institut de Recherche Expérimentale et Clinique (IREC), Université catholique de Louvain, Brussels, Belgium

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Cardiovascular Diabetology 2013, 12:39  doi:10.1186/1475-2840-12-39

Published: 27 February 2013

Abstract

Background

Apolipoprotein B100 (ApoB100) determination is superior to low-density lipoprotein cholesterol (LDL-C) to establish cardiovascular (CV) risk, and does not require prior fasting. ApoB100 is rarely measured alongside standard lipids, which precludes comprehensive assessment of dyslipidemia.

Objectives

To evaluate two simple algorithms for apoB100 as regards their performance, equivalence and discrimination with reference apoB100 laboratory measurement.

Methods

Two apoB100-predicting equations were compared in 87 type 2 diabetes mellitus (T2DM) patients using the Discriminant ratio (DR). Equation 1: apoB100 = 0.65*non-high-density lipoprotein cholesterol + 6.3; and Equation 2: apoB100 = −33.12 + 0.675*LDL-C + 11.95*ln[triglycerides]. The underlying between-subject standard deviation (SDU) was defined as SDU = √ (SD2B - SD2W/2); the within-subject variance (Vw) was calculated for m (2) repeat tests as (Vw) = Σ(xj -xi)2/(m-1)), the within-subject SD (SDw) being its square root; the DR being the ratio SDU/SDW.

Results

All SDu, SDw and DR’s values were nearly similar, and the observed differences in discriminatory power between all three determinations, i.e. measured and calculated apoB100 levels, did not reach statistical significance. Measured Pearson’s product-moment correlation coefficients between all apoB100 determinations were very high, respectively at 0.94 (measured vs. equation 1); 0.92 (measured vs. equation 2); and 0.97 (equation 1 vs. equation 2), each measurement reaching unity after adjustment for attenuation.

Conclusion

Both apoB100 algorithms showed biometrical equivalence, and were as effective in estimating apoB100 from routine lipids. Their use should contribute to better characterize residual cardiometabolic risk linked to the number of atherogenic particles, when direct apoB100 determination is not available.

Keywords:
ApoB100; LDL-C; Non-HDL-C; Discriminant ratio; Type 2 diabetes; Cardiovascular risk