Measuring TG represents an easy means to estimate the combined mass of fasting or non-fasting TRLs, as surrogate for their cholesterol load. An excess of TRLs, including RLPs, throughout the nycthemeron epitomizes CMR conditions such the MetS, IR and the common form of T2DM. Elevated levels of TRL-C contribute, alongside LDL-C, to plaque formation and progression. TRL-C is a modifiable driver of RVR. Whereas TG as such are not atherogenic, the well-demonstrated association between fasting or non-fasting TG and CVD is underlied by the atherogenicity of TRLs, especially that of RLPs [2–6, 8–11, 32, 33].

In *fasting conditions*, hypertriglyceridemia >150 mg/dL is categorized as “elevated” VLDL, corresponding to TRL-C levels >30 mg/dL. Such assumption of equivalence is valid only if the composition of VLDL, in terms of C and TG, is in a ratio of 1/5. This is not necessarily the case in CMR states where non-VLDL TRLs (among which numerous RLPs) co-exist alongside VLDL. Likewise, in *non-fasting conditions*, TRLs are further heterogeneous, in size and composition, being populated by various TG-enriched and relatively TG-depleted lipoproteins (including remnants from the endogenous and exogenous pathways). The C/TG ratio of non-fasting TRLs substantially differs from that of fasting TRLs, the latter essentially consisting of standard VLDL with 20% cholesterol [1, 3, 13–17, 20, 21].

For this reason, recent articles on the usefulness of “RC” as residual risk marker relied by default on Friedewald’s formula to estimate the LDL-C component of the equation. Doing so, their authors did not distinguish TRL-C from RC. Such an oversimplification ascribes to RC all the observed risk of non-fasting TRL-C. Besides, their rationale for extending the use of Friedewald’s equation to determine LDL-C in non-fasting samples, in place of a direct assay, relied upon a linear relationship between calculated and measured LDL-C in a reference subgroup, such a relationship being a self-fulfilling prophecy [8, 10, 11, 14].

The present results provide unbiased and physiologically-consistent equations to determine TRL-C from non-fasting lipids, regardless apoB_{100} availability. As expected, non-fasting TRL-C and *log*[TG] were highly correlated, with adjusted Pearson’s coefficient reaching unity. Since both measures have uniform precision and discrimination, they provide similar information for ranking patients according to non-fasting TRLs, and are interchangeable. Yet, conceptually and educationally, determining TRL-C as surrogate for TRLs is more attractive, since it quantifies the atherogenic component directly involved in driving CV risk, including all the cholesterol load from RLPs. In this context, the DR method provides an unbiased equivalence equation allowing to predict TRL-C from *log*[TG], or *vice-versa*.

While the concept of low HDL-C as unconditional RF is strongly debated, the coexistence of elevated fasting TG together with low fasting HDL-C allows identifying patients with AD, in whom residual risk is particularly high, even when on-statin LDL-C is controlled [28–30, 33, 34]. An explanation for the accrued RVR from AD in the fasting state is that it could be a marker for high numbers of postprandial TRLs and elevated non-fasting TRL-C. This is supported by results from an ACCORD-Lipid sub-study, in which fenofibrate similarly lowered non-fasting TG in all T2DM participants, while reducing postprandial apoB_{48} excursions only in individuals with elevated fasting TG at baseline, a subgroup in which fenofibrate reduced CV outcomes [35].

In the presence of fasting hypertriglyceridemia (>150 mg/dL), the HDL-C cutoffs defining AD (≤40 mg/dL [men] and ≤50 mg/dL [women]) are transposable to non-fasting conditions, because remnant TRLs have little influence on HDL-C. Contrariwise, there are currently no standards or agreement defining (*i*) the upper physiological value for non-fasting TG; (*ii*) the sampling time after meal; and (*iii*) the lipid content and composition of the previous meal. For all these reasons we suggest to use either [TRL-C/apoA-I] or [*log*[TG]/HDL-C] to assess postprandial AD as a continuous variable.

As the underlying correlation between TRL-C and *log*[TG] on one hand, and between TRL-C/apoA-I and *log*[TG]/HDL-C on the other hand, reached unity once pre-analytical and analytical attenuation were taken into account, these two approaches may be used interchangeably to assess equivalent biological measures. While there was no significant difference between the discriminating performance of *log*[TG] compared to TRL-C, the discrimination of the ratio *log*[(TG]/HDL-C was clearly and significantly higher than the TRL-C/apoA-I ratio to quantify the severity of non-fasting AD in patients at high CMR. Given the perfect concordance between pairs of measurements, the clinician may prefer to expressing CV risk linked to TRL-C (intuitively more educational than *log*[TG]), and to determine CV risk related to AD by calculating *log*[TG]/HDL-C, which is superior to TRL-C/apoA-I. The latter has the inconvenience to require apoA-I determination on top of routine lipids. Regarding biometric equivalence between atherogenic-antiatherogenic ratios, we previously reported that non-HDL-C/HDL-C provides CV risk stratification similar to the apoB_{100}/apoA-I ratio [36].

In this study, the performance of the above measures to that of a direct measurement of RC was not assessed, since the latter is not part of routine risk assessment. As regards cohort’s size, we compared the performance of two means to assess the burden of atherogenic TRL in 120 patients, an ample number given the DR methodology, which only requires ≥20 samples with 2 replicates as long as they represent a clinically-meaningful range for the variable under study [*see Appendix of*[22]*for a detailed discussion on sample size requirements for estimating DRs*]. The fact that patients had T2DM in this study does not limit the applicability of the findings, since the metabolic and pathophysiological fundamentals of TRL, CMR and RC are similar in diabetic and nondiabetic subjects, at increasing levels along a *continuum*, from normal to impaired fasting glucose, and from prediabetes to T2DM [37, 38].

In conclusion, estimating TRL-C requires formulas which reflect the complex compositional changes in non-fasting TRLs, the latter consisting of particles not exclusively generated along the VLDL pathway, in which TG-content is heterogeneous and changes dynamically. We provide two unbiased equations to estimate the burden of TRL-C based on routine nonfasting lipids, depending on apoB_{100} measurement availability. Our results show that TRL-C and *log*[TG] are as effective and interchangeable to assess the atherogenic load of nonfasting TRLs. However, to grade TRL-related AD, it is better to use *log*[TG]/HDL-C, which is inherently superior to TRL-C/apoA-I, while measuring the same underlying variable.