This study demonstrates that in patients with T2DM, two simple equations published to date were as effective to calculate apoB100 concentration from routine lipids [7, 8]. In addition, as the underlying correlation between apoB100 levels estimated by the two formulas reached unity, once preanalytical and analytical attenuation was taken into account, these two algorithms may be used interchangeably to assess an equivalent underlying biological variable. Even though the two algorithms were developed from lipid values obtained in different populations and conditions, each formula can substitute for each other, being as precise and interchangeable.
Although equation 1 was computed using lipid values from a small cohort (n = 45) of Caucasian T2DM patients, and equation 2 used fasting lipids from an extensive cohort (n = 73047) of healthy Koreans representative of a general Asian population, the two means of estimating apoB100 were perfectly correlated, and as effective and precise. This illustrates the performance of the comparison of measurements methods based on the DR methodology, which requires only limited samples (n ≥20 for 2 replicates), as long as the sample represents a meaningful clinical range for the variable under study [see appendix of  for a detailed discussion on sample size requirements for estimating DRs].
ApoB100 metabolism and physiology are comparable in diabetic and nondiabetic subjects, as well as in different ethnic groups. The equations appear applicable across the two major ethnic groups that provided the source data, and uninfluenced by baseline TG values. An inherent advantage of equation 1 is the inclusion of lipid values that do not require sampling in the fasting state, whereas equation 2 requires a fasting lipid panel [7, 8]. Another limitation of equation 2, in terms of routine clinical practice, is that LDL-C is usually calculated from Friedewald’s formula, which induces systematic and linear underestimation once fasting TG rise above 200 mg/dl, confining the applicability of equation 2 to patients with fasting TG <400 mg/dL, unless direct LDL-C measurement is available .
For use in equation 1, non-HDL-C offers the added advantage of being derived from the compute of two robust, well-established measurements methods, namely total cholesterol and HDL-C . Although Cho et al. used direct, and hence more expensive measurements of LDL-C, contributing to enhance accuracy of their algorithm , there is an intrinsic rationale to opt for equation 1 in populations with high prevalence of hypertriglyceridemia, such as patients with AD [6, 10, 15, 17–19]. They belong to the highest cardiometabolic risk category, for which it is recommended to assess (and bring to target), both non-HDL-C and apoB100, on top of LDL-C [6, 10, 20].
There is still no consensus among the various players on the ultimate relevance to measure apoB100, non-HDL-C, or both, for baseline or residual CV risk classification, One might wonder what is the advantage of being able to dispose of apoB100 from an equation that incorporates non-HDL-C, since the latter is considered by ATPIII as adequate and sufficient. Notwithstanding the ongoing debate, the apoB100 concept is intrinsically easier to apprehend than non-HDL-C, which contains in itself a ferment of educational failure because it represents a state of otherness defined by a non-number, instead of a single atherogenic lipid variable .
In conclusion, this study demonstrates the biometrical equivalence of two original apoB100 algorithms, which are as effective in estimating the concentration of apoB100 from routine lipids, and may be used interchangeably. One approach requires fasting blood lipids, while the other is not influenced by fasting status, and therefore independent of food intake prior to sampling. These algorithms should contribute to better characterize residual cardiometabolic risk linked to the number of atherogenic particles in patients with available standard lipids, but in whom apoB100 assay was not performed for various reasons. The practical implications of these findings are directly relevant to routine clinical practice.