Low-density lipoproteins (LDL) and their very-low density lipoprotein (VLDL) precursors represent the major atherogenic particles. Each contains a single apolipoprotein B100 (apoB100) molecule, which ensures the structural integrity of the lipoprotein, and binds to the hepatic receptor for catabolic removal of LDL. The “small-dense” LDL phenotype confers a higher cardiovascular (CV) risk than that resulting from their cholesterol load. Therefore circulating apoB100 level more accurately reflects the number of atherogenic particles, 90% of apoB100 belonging to LDL irrespective of their size. The determination of apoB100 does not require prior fasting, unlike estimation of LDL-cholesterol (LDL-C) by Friedewald’s formula [1–8].
Numerous studies have demonstrated the superiority of apoB100 relative to LDL-C to establish CV risk, and improvement of outcomes after lipid-lowering drug (LLD) therapy [2–6, 9]. The Adult Treatment Panel III proposed that in individuals with elevated triglycerides (TG), non-high-density lipoprotein cholesterol (non-HDL-C) should be treated as secondary therapy goal, after targeting LDL-C; moreover non-HDL-C appears a better predictor of CV risk than LDL-C, especially in statin-treated patients [2, 6]. However, the relationships between LDL-C, non-HDL-C and apoB100 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), non-HDL-C 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) LDL-C; (ii) non-HDL-C; and (iii) apoB100[6, 10]. In real life however, apoB100 determination is rarely performed alongside routine lipids, which precludes such comprehensive assessment of residual dyslipidemia. Consequently, simple algorithms were proposed to estimate apoB100 level from routine lipids, based on LDL-C and non-HDL-C as freely-available biometrical equivalent to apoB100[7, 8].
The aim of this study was to compare the performance and true equivalence of two apoB100-predicting algorithms in T2DM patients considered at high cardiometabolic risk, with reference to laboratory determination of apoB100 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]. Cross-validation of these algorithms should prompt potential users to increasingly rely on them, to derive unbiased apoB100 values from landmark epidemiological or interventional databases, or from current standard lipids in specific situations where it is desirable to know the levels of non-HDL-C and that of apoB100.
Methods and statistical analysis
We studied 87 consecutive (84% white Caucasians; 6% North-Africans; 5% sub-Saharan Africans) patients with T2DM, treated or not with lipid-lowering drug(s) (LLD). All lipid values were obtained in the fasting state, on two non-consecutive days. The time-span between sampling, obtained during regular outpatients’ follow-up visits, was 2–6 months. The following biologic variables were recorded: glycated hemoglobin (HbA1c), fasting lipids (total cholesterol [C], HDL-C, and TG). Fasting duration was ≥10-hours, 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). HDL-C was determined with the ULTRA-N-geneous reagent (Genzyme Corporation, Cambridge, MA). ApoB100 was measured with immunonephelometry on BNII Analyzer (Siemens Healthcare Products GmbH, Marburg, Germany) from the same blood samples destined for routine lipids determination. The within-subject coefficients of variation were as follows: 5.4% [total C]; 7.1% [HDL-C]; and 6.9% [apoB100]. LDL-C was computed with Friedewald’s formula ; non-HDL-C by subtracting HDL-C from total C.
Besides direct measurement, ApoB100 level was calculated from routine lipids using the two following equations:
based on fasting or nonfasting lipids [ref. 7]
based on fasting lipids only [ref. 8]
The presence of atherogenic dyslipidemia (AD) was defined as the combined occurrence of decreased HDL-C (<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) . Glomerular filtration rate was estimated using the Modified Diet in Renal Disease formula .
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 within-subject variation [7, 11–14]. In a comparison study where duplicates measurements are performed in each subject, the measured between-subject standard deviation (SDB) is calculated as the SD of the subject mean values calculated from the 2 replicates.
The standard mathematical adjustment to yield the underlying between-subject SD (SDU) is: SDU = √ (SD2B - SD2W/2);
The within-subject variance (Vw) is calculated for m repeat tests as (Vw) = Σ(xj -xi)2/(m-1)), the within-subject SD (SDw) being its square root;
The DR represents the ratio SDU/SDW
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 apoB100 (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 within-subject variation .
The study was performed in accordance with the institutional review board of St-Luc Academic Hospital.