Lowdensity lipoproteins (LDL) and their verylow density lipoprotein (VLDL) precursors represent the major atherogenic particles. Each contains a single apolipoprotein B_{100} (apoB_{100}) molecule, which ensures the structural integrity of the lipoprotein, and binds to the hepatic receptor for catabolic removal of LDL. The “smalldense” LDL phenotype confers a higher cardiovascular (CV) risk than that resulting from their cholesterol load. Therefore circulating apoB_{100} level more accurately reflects the number of atherogenic particles, 90% of apoB_{100} belonging to LDL irrespective of their size. The determination of apoB_{100} does not require prior fasting, unlike estimation of LDLcholesterol (LDLC) by Friedewald’s formula [1–8].
Numerous studies have demonstrated the superiority of apoB_{100} relative to LDLC to establish CV risk, and improvement of outcomes after lipidlowering drug (LLD) therapy [2–6, 9]. The Adult Treatment Panel III proposed that in individuals with elevated triglycerides (TG), nonhighdensity lipoprotein cholesterol (nonHDLC) should be treated as secondary therapy goal, after targeting LDLC; moreover nonHDLC appears a better predictor of CV risk than LDLC, especially in statintreated patients [2, 6]. However, the relationships between LDLC, nonHDLC and apoB_{100} are often less convergent than expected, and therefore less predictable in patients at high cardiometabolic risk, including those with high TG and/or the metabolic syndrome. In these patients, including those with type 2 diabetes mellitus (T2DM), nonHDLC and apoB100 are therefore less interchangeable than the reading of the general recommendations for the treatment of hypercholesterolemia would suggest.
In the absence of consensual guidelines, the current recommendation for hypercholesterolemic patients at high cardiometabolic risk is to bring at target three key modifiable variables: (i) LDLC; (ii) nonHDLC; and (iii) apoB_{100}[6, 10]. In real life however, apoB_{100} determination is rarely performed alongside routine lipids, which precludes such comprehensive assessment of residual dyslipidemia. Consequently, simple algorithms were proposed to estimate apoB_{100} level from routine lipids, based on LDLC and nonHDLC as freelyavailable biometrical equivalent to apoB_{100}[7, 8].
The aim of this study was to compare the performance and true equivalence of two apoB_{100}predicting algorithms in T2DM patients considered at high cardiometabolic risk, with reference to laboratory determination of apoB_{100} and against each other. We used the Discriminant Ratio (DR) methodology, which standardises comparisons between measurements by taking into account fundamental properties for assessing imprecision and practical performance of tests designed to quantify similar variables [7, 11–14]. Crossvalidation of these algorithms should prompt potential users to increasingly rely on them, to derive unbiased apoB_{100} values from landmark epidemiological or interventional databases, or from current standard lipids in specific situations where it is desirable to know the levels of nonHDLC and that of apoB_{100}.
Methods and statistical analysis
We studied 87 consecutive (84% white Caucasians; 6% NorthAfricans; 5% subSaharan Africans) patients with T2DM, treated or not with lipidlowering drug(s) (LLD). All lipid values were obtained in the fasting state, on two nonconsecutive days. The timespan between sampling, obtained during regular outpatients’ followup visits, was 2–6 months. The following biologic variables were recorded: glycated hemoglobin (HbA_{1c}), fasting lipids (total cholesterol [C], HDLC, and TG). Fasting duration was ≥10hours, with last intake of food allowed at dinner the day before sampling. No change in LLD(s) was allowed during the interval that separated the two days of sampling.
Total C and TG were determined using the SYNCHRON system (Beckman Coulter Inc., Brea, CA). HDLC was determined with the ULTRANgeneous reagent (Genzyme Corporation, Cambridge, MA). ApoB_{100} was measured with immunonephelometry on BNII Analyzer (Siemens Healthcare Products GmbH, Marburg, Germany) from the same blood samples destined for routine lipids determination. The withinsubject coefficients of variation were as follows: 5.4% [total C]; 7.1% [HDLC]; and 6.9% [apoB_{100}]. LDLC was computed with Friedewald’s formula [1]; nonHDLC by subtracting HDLC from total C.
Besides direct measurement, ApoB_{100} level was calculated from routine lipids using the two following equations:
\mathbf{apo}{\mathbf{B}}_{\mathbf{100}}\left(\mathrm{mg}/\mathrm{dL}\right)=\left[\mathbf{0}.\mathbf{65}\phantom{\rule{0.25em}{0ex}}\mathbf{x}\phantom{\rule{0.25em}{0ex}}\mathbf{non}\mathbf{HDL}\mathrm{C}\phantom{\rule{0.5em}{0ex}}\left(\mathrm{mg}/\mathrm{dL}\right)\right]\phantom{\rule{0.5em}{0ex}}+\phantom{\rule{0.5em}{0ex}}\mathbf{6}.\mathbf{3}\phantom{\rule{0.5em}{0ex}}\left(\mathrm{mg}/\mathrm{dL}\right)
(1)
based on fasting or nonfasting lipids [ref. 7]
\mathbf{apo}{\mathbf{B}}_{\mathbf{100}}\left(\mathrm{mg}/\mathrm{dL}\right)=\phantom{\rule{1em}{0ex}}\mathbf{33}.\mathbf{12}\phantom{\rule{0.5em}{0ex}}\left(\mathrm{mg}/\mathrm{dL}\right)\phantom{\rule{0.5em}{0ex}}+\phantom{\rule{0.5em}{0ex}}\left[\mathbf{0}.\mathbf{675}\phantom{\rule{0.25em}{0ex}}\mathbf{x}\phantom{\rule{0.25em}{0ex}}\mathbf{LDL}\mathbf{C}\phantom{\rule{0.5em}{0ex}}\left(\mathrm{mg}/\mathrm{dL}\right)\right]\phantom{\rule{0.5em}{0ex}}+\phantom{\rule{0.5em}{0ex}}\left[\mathbf{11}.\mathbf{95}\phantom{\rule{0.25em}{0ex}}\mathbf{x}\phantom{\rule{0.5em}{0ex}}\mathit{ln}\phantom{\rule{0.5em}{0ex}}\left[\mathbf{TG}\right]\phantom{\rule{0.5em}{0ex}}\left(\mathrm{mg}/\mathrm{dL}\right)\right]
(2)
based on fasting lipids only [ref. 8]
The presence of atherogenic dyslipidemia (AD) was defined as the combined occurrence of decreased HDLC (<40 [males] or <50 mg/dL [females]) plus elevated fasting TG (≥150 mg/dL) using baseline lipid values (ie, before any LLD(s) in treated patients) [15]. Glomerular filtration rate was estimated using the Modified Diet in Renal Disease formula [16].
The Discriminant Ratio (DR) methodology compares different tests measuring the same underlying physiological variable by determining the ability of a test to discriminate between different subjects, and the comparison of discrimination between different tests as well as the underlying correlation between pairs of tests adjusting for the attenuating effect of withinsubject variation [7, 11–14]. In a comparison study where duplicates measurements are performed in each subject, the measured betweensubject standard deviation (SD_{B}) is calculated as the SD of the subject mean values calculated from the 2 replicates.

The standard mathematical adjustment to yield the underlying betweensubject SD (SD_{U}) is: SD_{U} = √ (SD^{2}_{B}  SD^{2}_{W}/2);

The withinsubject variance (V_{w}) is calculated for m repeat tests as (V_{w}) = Σ(x_{j} x_{i})^{2}/(m1)), the withinsubject SD (SD_{w}) being its square root;

The DR represents the ratio SD_{U}/SD_{W}
Confidence limits for DR’s and the testing for equivalence of different DR’s were calculated and differences were considered significant for p < 0.05. Given sample size and number of replicates, the minimal detectable significant difference in DR for the present study was 0.42. Coefficients of correlation between pairs of tests used to estimate apoB_{100} (measured vs. estimated) were adjusted to include an estimate of the underlying correlation, as standard coefficients tend to underestimate the true correlation between tests, due to withinsubject variation [13].
The study was performed in accordance with the institutional review board of StLuc Academic Hospital.