The joint consensus panel of the European Atherosclerosis Society (EAS) and the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) recently addressed present and future challenges in the laboratory diagnostics of atherogenic lipoproteins. Total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDLC), LDL cholesterol (LDLC), and calculated non-HDLC (=total – HDLC) constitute the primary lipid panel for estimating risk of atherosclerotic cardiovascular disease (ASCVD) and can be measured in the nonfasting state. LDLC is the primary target of lipid-lowering therapies. For on-treatment follow-up, LDLC shall be measured or calculated by the same method to attenuate errors in treatment decisions due to marked between-method variations. Lipoprotein(a) [Lp(a)]-cholesterol is part of measured or calculated LDLC and should be estimated at least once in all patients at risk of ASCVD, especially in those whose LDLC declines poorly upon statin treatment. Residual risk of ASCVD even under optimal LDL-lowering treatment should be also assessed by non-HDLC or apolipoprotein B (apoB), especially in patients with mild-to-moderate hypertriglyceridemia (2–10 mmol/L). Non-HDLC includes the assessment of remnant lipoprotein cholesterol and shall be reported in all standard lipid panels. Additional apoB measurement can detect elevated LDL particle (LDLP) numbers often unidentified on the basis of LDLC alone. Reference intervals of lipids, lipoproteins, and apolipoproteins are reported for European men and women aged 20–100 years. However, laboratories shall flag abnormal lipid values with reference to therapeutic decision thresholds.
In the new era of very low LDL-cholesterol (LDLC) concentrations, achievable with more intensive and novel lipid-lowering therapies, increasing attention is being focused on the assessment of lipid-related residual risk of atherosclerotic cardiovascular disease (ASCVD) using additional biomarkers beyond LDLC .
An important prerequisite to address present and future challenges of ASCVD prevention is the harmonization of serum lipid and lipoprotein profiles produced by established and emerging laboratory tests and techniques. To that end, the multidisciplinary consensus panel of the European Atherosclerosis Society (EAS) and the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) recently published recommendations on the quantification of atherogenic lipoproteins in nonfasting and fasting blood samples , . This article summarizes the consensus-based recommendations of this expert panel which aimed to provide appropriate guidance on the pre-analytical, analytical, and post-analytical phases of laboratory testing of atherogenic lipoproteins.
The key recommendations are given in Table 1. Based on the Copenhagen General Population Study , reference nonfasting concentrations for lipids and (apo)lipoproteins are reported for 54,129 European women and 42,126 European men aged 20–100 years and not on lipid-lowering therapy in Tables 2 and 3.
|Pre-preanalytical phase (test ordering)|
|Comprehensive testing of atherogenic lipoproteins should include tests to assess the risk conferred by LDL particles, remnant particles, and, in selected cases, Lp(a).|
|Preanalytical phase (test sampling)|
| Fasting is not routinely required for assessing the lipid profile.
Consider fasting sample when nonfasting TG are ≥4.5 mmol/L (400 mg/dL); however, this is not a requirement.
Take 2–3 serial blood specimens, at least 1 week apart, to allow to average for biological variation (importantly when test results are near the treatment decision thresholds).a
|Analytical phase (test measurement)|
| Follow-up of measured or calculated LDLC and non-HDLC of a patient, from baseline to on-treatment measurements, should be ideally performed with the same method (and preferably the same laboratory).b
Clinicians should be notified when the laboratory test changes from a method to another.
The Martin-Hopkins equation may be preferable for calculation of LDLC in patients with low LDLC concentration <1.8 mmol/L (70 mg/dL) and/or TG concentration 2.0–4.5 mmol/L (175–400 mg/dL), and in nonfasting samples.
Direct LDLC assays should be used for calculation of RemnantC and for assessment of LDLC when TG concentration is ≥4.5 mmol/L (400 mg/dL).
Lp(a)-corrected LDLC should be assessed at least once in patients with suspected or known high Lp(a), or if the patient shows a poor response to LDL-lowering therapy.
ApoB assays currently provide the most accurate measurement of overall burden of atherogenic particles in the fasting and nonfasting state.
|Postanalytical phase (test reporting)|
| Laboratories should automatically calculate and report non-HDLC on all lipid profiles; RemnantC could also be reported.
Laboratory reports should flag abnormal concentrations based on decision thresholds.
Extremely high concentrations beyond the reference limits should alert clinicians (interpretative commenting on test report).
|Post-postanalytical phase (test interpretation and use)|
| LDLC is the primary target of lipid-lowering therapy.
When LDLC goal is achieved, non-HDLC or apoB should be preferred as secondary treatment targets in patients with TG 2–10 mmol/L (175–880 mg/dL), diabetes, obesity or metabolic syndrome.
aAvoid measurements within ~2 months after acute myocardial infarction, acute trauma, surgery, acute infection or inflammatory illness, or pregnancy. Patients should maintain their usual diet in the preceding 2 weeks, and avoid strenuous exercise. bRemove serum from cells (centrifugation) within 3 h of blood sampling, and perform lipid measurements within 1–2 days of collection. However, before measurement specimens can safely be stored at 4 °C for 3 days, at –20 °C for 1 month, and at −80 °C for 1–2 years.
Nonfasting triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDLC), and apolipoprotein B concentrations were measured by automated assays (Thermo Scientific Konelab, Vantaa, Finland). Low-density lipoprotein cholesterol (LDLC) was calculated by the Friedewald equation when TG were <4 mmol/L and was measured directly (Konelab) when TG were ≥4 mmol/L. Non-HDL cholesterol was calculated as TC minus HDLC. Remnant cholesterol was calculated as TC minus LDLC minus HDLC. The first 5592 individuals included in the Copenhagen General Population Study had lipoprotein(a) total mass measured using a sensitive immunoturbidimetric assay from DiaSys (DiaSys Diagnostic Systems, Holzheim, Germany), while all remaining individuals in the study had lipoprotein(a) measurements done using the apolipoprotein(a) isoform insensitive Denka Seiken assay (Denka Seiken, Tokyo, Japan) or the Roche second generation lipoprotein(a) assay developed by Denka Seiken (Roche Diagnostics, Rotkreuz, Switzerland) .
I. Which atherogenic lipoproteins should be measured?
Assessment of LDLC is a key component of the management of risk of ASCVD , , . Circulating LDL particles (LDLPs) are highly atherogenic and there is a direct, graded relationship between LDLC concentration and the incidence of ASCVD observed in randomized controlled trials, prospective epidemiological cohort studies, and Mendelian randomization studies , .
Despite the overwhelming evidence that LDLC-targeted therapies effectively reduce ASCVD in the population, many individuals experience ASCVD-related events or progression of atherosclerosis despite not having elevated LDLC or even concentrations <1.8 mmol/L . This residual risk indicates that a focus solely on the measurement of LDLC is not an optimal strategy for all patients, in part explained by accumulating evidence that the number of LDLPs measured by lipoprotein subfractionation techniques is more strongly causally related to ASCVD than the cholesterol content of the particles , .
All LDLPs are atherogenic, but their concentration is not always reflected by LDLC measurement because the cholesterol content in the particles can vary widely between individuals according to the continuous remodeling of lipoproteins in blood . Small lipid-depleted LDL subfractions contain less cholesterol than larger ones. They are typically predominant in patients with moderately elevated triglyceride (TG) concentrations or related conditions, such as diabetes and the metabolic syndrome, without necessarily having high LDLC concentration . These compacted LDLPs are the products of exchange of cholesteryl esters with TG from larger, TG-rich very-low-density lipoprotein (VLDL) particles (Figure 1). Concomitantly, smaller high-density lipoprotein (HDL) particles are also formed in this pathway, as typically manifested in the atherogenic dyslipidemic triad involving hypertriglyceridemia, increased small dense LDLP, and low HDL cholesterol (HDLC) concentration .
While earlier studies emphasized the atherogenicity of small LDLPs, it is now recognized that all LDLPs are atherogenic, regardless of size . Thus, the primary focus of treatment should remain the reduction of the number (concentration) of LDLPs, without efforts to distinguish between large and small LDL subfractions . Measurement of apolipoprotein B (apoB), the major protein component of LDL, or advanced measurement of LDLP (not yet widely available) can also be used to assess the number of LDLPs , .
The total cholesterol (TC) to HDLC ratio has been proposed as a surrogate marker of the number of LDLPs – associated with low HDLC and hence higher TC/HDLC ratio in individuals with atherogenic dyslipidemia . The TC/HDLC ratio may be considered as an alternative to LDLP for risk estimation, but not for diagnosis or as therapeutic decision limit because a low ratio due to high HDLC can be misleading: this may lead to assuming a low risk even if the patient has high LDLC. The components of the ratio, TC and HDLC, have to be managed separately.
Postprandial accumulation of TG-rich remnant particles in blood is an important factor in atherogenesis , . These lipoproteins contain a higher load of cholesterol that is not accounted for in typical fasting lipid profiles. Nonfasting lipid profiles, therefore, can potentially be more relevant to the estimation of an individual’s cardiovascular risk than fasting lipids as in real life the postprandial state predominates most of our 24-h cycle , .
TG-rich chylomicrons secreted from the intestine, and VLDL secreted from the liver, are rapidly depleted of part of their TG content and acquire cholesteryl esters from HDL in the circulation (Figure 1). These cholesterol-enriched remnant particles may enter the arterial intima and contribute to atherosclerosis, whereas nascent chylomicrons and very large VLDL particles do not cross the endothelial layer . Mendelian randomization studies suggest that life-long high serum concentrations of TG-rich lipoproteins or their remnants are causally associated with increased risk of ASCVD and all-cause mortality , .
Direct “homogeneous” assays have been developed to specifically measure cholesterol in remnant particles (RemnantC) and some have revealed significant associations of RemnantC with ASCVD , . An alternative is to calculate RemnantC as TC – HDLC – LDLC, because RemnantC corresponds to all cholesterol not found in LDL and HDL, that is, in all VLDL and intermediate-density lipoproteins (IDL). In the nonfasting state a relatively small amount of cholesterol can also be found in chylomicron remnants. Because both newly secreted chylomicrons and VLDL rapidly undergo lipolysis, any residual circulating chylomicrons and VLDL can be considered remnants . Direct LDLC (dLDLC) measurement should preferably be used in the calculation of RemnantC; otherwise RemnantC simply equals TG/2.2 (in mmol/L) when Friedewald-calculated LDLC is used, i.e. TC – HDLC – (TC – HDLC – TG/2.2), and it does not add clinical information beyond TG concentration ; however, it focuses the attention on the cholesterol content of remnants rather than the TG content.
RemnantC also contributes to non-HDLC which is calculated as TC – HDLC . This term is independent of the Friedewald term and therefore not correlated as tight with TG concentrations as calculated RemnantC, and thus represents an additional clinically valuable marker. RemnantC, measured or calculated, differs from non-HDLC in that non-HDLC contains RemnantC plus LDLC and does not differentiate between these two causal risk factors . Non-HDLC also includes the cholesterol of lipoprotein(a) [Lp(a)].
Lp(a) is an LDL-like particle with one molecule of apoB to which an additional apolipoprotein, apo(a), is attached. This apolipoprotein shows considerable size polymorphism originating from a variable number of kringle IV type 2 (KIV-2) repeats of apo(a) , . This size polymorphism is the most important determinant of the hepatic production rate of Lp(a): serum Lp(a) concentrations and number of KIV-2 repeats are inversely correlated, which results in marked genetic variation of Lp(a) concentrations , . Elevated baseline and on-statin treatment Lp(a) concentration above the 80th percentile of the general population (50 mg/dL) is a strong genetic risk factor for cardiovascular disease independent of LDLC , . This is recognized by the codes of International Classification of Diseases for elevated Lp(a) and family history of elevated Lp(a), introduced in response to the US National Heart, Lung, and Blood Institute recommendation . High Lp(a) concentration through a correspondingly low number of KIV-2 repeats are also associated with a higher risk of mortality in the general population .
One of the major differences between Lp(a) and LDLPs is that LDLs are effectively lowered by statins, whereas Lp(a) is typically resistant to this treatment . In statin-treated patients the Lp(a)-associated risk for ASCVD becomes an even better predictor for residual risk as soon as the LDL-associated risk is decreased by statin therapy . Although proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors and other novel therapies reduce Lp(a) and may contribute to reduction in ASCVD , , it is yet unknown whether Lp(a) lowering per se contributes to the clinical benefit of these novel therapies . Although recent studies with PCSK9 inhibitors were not designed to target patients with high Lp(a) concentrations, they nevertheless revealed that patients with higher baseline Lp(a) concentration experienced greater absolute reductions in Lp(a) and tended to derive greater clinical benefit from PCSK9 inhibition , . Drugs on the horizon that specifically target Lp(a) with an Lp(a)-lowering potential of 80% and more will have to prove the benefit of an isolated lowering of Lp(a) on ASCVD outcomes .
Recommendation – I
Comprehensive testing of atherogenic lipoproteins should use a biomarker, or a panel of multiple markers, to assess the risk of ASCVD associated not only with LDLPs, but also remnant particles and, in selected cases, Lp(a) particles.
Recommendations for selection of atherogenic lipoprotein tests in different clinical settings are summarized in Table 4.
|ASCVD risk estimation||Dyslipidemia characterization||Treatment choice||Treatment target|
|Lp(a)||YESi||YESi||Not yetj||Not yetj|
aIn nonfasting samples this will also include cholesterol in chylomicrons and their remnants; however, in the majority of individuals chylomicrons are rapidly converted into remnants (within 5–10 min) after delivery from lymphs to the blood stream. bTo be considered in a minimal lipid profile (TC and TG only) or when LDLC is not available. cOr ApoA-I if available. dIn combination with TC, if HDLC is entered as a separate variable in the risk estimation model. Ratios of TC/HDLC, non-HDLC/HDLC, or apoB/apoA-I which reflect the balance between atherogenic and neutral lipoproteins can be considered as an alternative for risk estimation, but not for diagnosis or as treatment targets. The components of the ratio have to be managed separately. eRemnantC, calculated as TC – HDLC – LDLC, is all cholesterol found in TG-rich lipoproteins VLDL, IDL, and, in the nonfasting state, additionally chylomicron remnants. RemnantC is included in non-HDLC, but non-HDLC does not differentiate between LDLC and RemnantC. RemnantC is the part of non-HDLC in addition to LDLC that needs reduction in some patients. Directly measured LDLC should preferably be used in the calculation of RemnantC, or RemnantC could be measured directly. fNon-HDLC, calculated as TC – HDLC, is all cholesterol in atherogenic lipoproteins: LDL, remnants, and Lp(a). None of the hyperlipidemias can be characterized by non-HDLC because the composite marker does not differentiate between the atherogenic lipoprotein-cholesterol fractions. gIn patients with mild-to-moderate hypertriglyceridemia, 2–10 mmol/L (175–880 mg/dL), diabetes, obesity or metabolic syndrome. hOr advanced LDLP measurement if available. iAt least once in each adult person’s lifetime, especially in patients with premature ASCVD (men <55 years, women <60 years), family history of premature ASCVD and/or elevated Lp(a), FH, recurrent ASCVD despite optimal lipid-lowering treatment. jUnless approved treatment is available to substantially reduce Lp(a) concentration and Lp(a)-related risk.
II. What is the standard lipid profile?
The traditional lipid profile of TC, TG, HDLC, and LDLC remains the primary approach for diagnosis and ASCVD risk classification . A cost-efficient approach is to employ measurements of three markers (TC, TG, HDLC) and from these calculate LDLC and non-HDLC. RemnantC can also be calculated if directly measured LDLC is used in the equation.
ApoB measurement is not usually part of the standard lipid profile and ASCVD risk estimation models. Monogenic disorders such as familial hypercholesterolemia (FH) can be easily recognized from the standard lipid panel without the need to measure apoB , . In patients with mild-to-moderate hypertriglyceridemia, defined as 2–10 mmol/L , elevated baseline and on-treatment apoB helps identify the atherogenic dyslipidemia related to remnant lipoproteins combined with small dense LDLPs that is not reflected by LDLC and non-HDLC.
Lp(a) measurement should be considered at least once in each adult person’s lifetime to identify those with high inherited Lp(a) concentrations, in particular among patients with premature ASCVD, with FH, with a family history of premature ASCVD and/or elevated Lp(a), or with recurrent ASCVD despite statin treatment ; Lp(a) measurement may also be considered in those with aortic valve stenosis. However, Lp(a) measurement should not be included in repeated lipid profile measurements within the same patient, as Lp(a) concentrations exhibit little variation over a lifetime. Exceptions from this rule are transition to menopause, pregnancy, oral contraceptive use, renal impairment, or when specific Lp(a)-lowering treatment is administered . Lp(a) concentrations do not change in response to normal food intake and are minimally increased in inflammation, although the influence of acute illness or acute phase reactions on Lp(a) concentration is discussed controversially .
Recommendation – II
The ‘standard lipid profile’ used for cardiovascular risk prediction includes TC, TG, HDLC, LDLC, non-HDLC, and optionally – if directly measured LDLC is used – calculated RemnantC. A ‘minimal lipid profile’ including only TC and TG can be considered in countries where costs are a major issue such as developing countries .
An ‘expanded lipid profile’ including Lp(a) or apoB should be used in selected cases (Table 4) . ‘Advanced lipid profiles’ such as lipoprotein subclasses and apolipoprotein profiles have been introduced in some laboratories but their added value in a clinical setting still needs to be validated .
III. When to use fasting and nonfasting blood samples?
Fasting blood samples have previously been the standard for measuring TG, because the fasting state reduces variability of TG concentrations and allows for a slightly more standardized LDLC estimation with the Friedewald equation; however, a fasting sample does not capture the average atherogenic lipid profile seen in the patient over a 24-h period . In consequence, extended (8–12 h) fasting is no longer routinely required for the determination of a lipid profile . Findings from population studies showed that despite minor postprandial increases in TG and RemnantC, quantitative changes in other lipids, lipoproteins, and apolipoproteins appear to be negligible in response to the habitual meal intake for most individuals . For patients, laboratories, and clinicians alike, nonfasting lipid profiles represent an operational simplification without significant negative implications for prognostic, diagnostic, and therapeutic options for ASCVD prevention . Regardless, it may remain prudent to counsel the patients to avoid an extremely high-fat or fast-food meal (e.g. a burger, fries) in the preceding 12 h .
Nonfasting lipid profiles are now endorsed by several guidelines including those in Europe, the UK, Canada, Brazil, and the US , , , , . Nonfasting and fasting measurements of the lipid profiles must be viewed as complementary and not mutually exclusive. Fasting is certainly not critical for first-time screening and general risk estimation, or to diagnose an isolated hypercholesterolemia such as FH or elevated Lp(a) without concomitant high TG . Fasting is an option when nonfasting TG are ≥4.5 mmol/L, a concentration seen in ~3–5% of nonfasting individuals in the general population (Tables 2 and 3) , and for the phenotypic diagnosis or therapeutic follow-up of mixed dyslipidemia or isolated hypertriglyceridemia; however, this is not a requirement and a random nonfasting blood sample will still best capture the average TG concentration in a given patient. Fasting may also be recommended for starting medications that cause severe hypertriglyceridemia (e.g. isotretinoin) in genetically predisposed individuals, for patients recovering from hypertriglyceridemic pancreatitis, and when additional laboratory tests that require fasting or morning samples (e.g. fasting glucose, or markers with circadian rhythm) are requested . The EFLM Preanalytical Phase Working Group recently produced guidance on how to standardize fasting blood sampling if needed , .
Recommendation – III
Fasting is not routinely required for the determination of a lipid profile.
In patients in whom an initial nonfasting lipid profile reveals a TG concentration ≥4.5 mmol/L, a repeat lipid profile in the fasting state could be performed to assess fasting TG concentration; however, this is not a requirement.
IV. Are LDLC measurements or calculations reliable?
Operational definition of LDL
Beta-quantification, the US Centers of Disease Control (CDC) Reference Method for LDLC, combines ultracentrifugation to remove VLDL and chylomicrons and heparin-Mn2+ precipitation to separate LDLPs, including Lp(a), from HDL . With beta-quantification, the lipoprotein fraction in the density range of 1.006–1.063 g/mL is defined as LDL, and the fraction in the density range of 1.063–1.21 g/mL is defined as HDL . However, it is not widely recognized that the LDLC fraction on beta-quantification also contains the cholesterol from IDL with density 1.006–1.019 g/mL and Lp(a) with density 1.04–1.13 g/mL. LDLC assays that attempt to specifically measure cholesterol in LDL may, therefore, show discordant results compared to the reference method .
Direct LDLC and HDLC assays
The “homogeneous” or “direct” LDLC (dLDLC) and HDLC (dHDLC) assays have largely replaced the older ultracentrifugation and precipitation techniques, particularly for HDLC, but these measurements cannot be generally assumed to provide the same clinical information , . The total error of measurement combines systematic bias (deviation from “true” value) and random imprecision. Despite improved analytical precision due to automation, data indicate that results can vary significantly between dLDLC and dHDLC assays from different manufacturers . Most discrepancies – with marked biases between assays and the CDC Reference Methods – are observed in samples from patients with hypertriglyceridemia >2 mmol/L, mixed dyslipidemia, or other conditions involving altered lipoprotein composition and remodeling, such as diabetes and chronic kidney disease . Direct measurements of dLDLC and dHDLC in normolipidemic samples usually meet the National Cholesterol Education Program (NCEP) total error goals of ≤12% and ≤13%, respectively, but total error ranged from −26% to +32% for dLDLC and −20% to +36% for dHDLC in a comprehensive study of different assays in dyslipidemic samples . Most discordances in dyslipidemic samples are observed at lower concentration ranges of LDLC (<1.8 mmol/L) and HDLC (<1.0 mmol/L) . These errors result in misclassifications with respect to ASCVD risk assessment depending on the type of assay, as observed in accuracy-based external quality assessment surveys of hypertriglyceridemic samples organized across different laboratories . The biases noted in dHDLC measurements affect the calculations of LDLC and non-HDLC, as HDLC is used in the calculations .
The biases noted in dLDLC and dHDLC assays when analyzing dyslipidemic samples suggest that non-specific cross-reaction takes place, reflecting difficulties in selectively measuring the cholesterol in LDL or HDL fractions when atypical lipoproteins are present. The different manufacturers’ direct methods do not measure the same LDL and HDL subfractions , . This non-selectivity error is of major concern in the contemporary treatment era in which very low LDLC concentrations <1.8 mmol/L are increasingly seen with highly efficacious LDL-lowering therapies, and in which hypertriglyceridemic samples –TG >2 mmol/L seen in ~25% of individuals in the general population (Tables 2 and 3)  – cause a greater analytical problem due to the increasing prevalence of obesity, metabolic syndrome, and diabetes mellitus .
LDLC calculated with the Friedewald formula, cLDLC=TC – HDLC – VLDL cholesterol (VLDLC), has its limitations. The equation uses a fixed TG:cholesterol ratio (TG/2.2 in mmol/L or TG/5 in mg/dL) to estimate VLDLC and assumes lack of chylomicrons which are more TG-rich than VLDL . Because the TG:cholesterol ratio in TG-rich lipoproteins progressively increases as hypertriglyceridemia becomes more severe, the equation overestimates VLDLC and therefore underestimates LDLC at high TG concentrations . The equation is increasingly inaccurate at TG concentrations from 2.3 to 4.5 mmol/L . The error is regarded as unacceptably large when TG are ≥4.5 mmol/L by NCEP and EAS guidelines or already at ≥4.0 mmol/L by national consensus in certain countries, and fasting blood samples should be used in this condition . At TG <4.5 mmol/L nonfasting and fasting lipid profiles can be used alike for Friedewald cLDLC, when LDLC is not very low.
At very low LDLC concentration ranges <1.8 mmol/L, in which VLDLC constitutes a relatively larger fraction of the blood cholesterol, VLDLC overestimation at high TG concentration >2.3 mmol/L introduces a more significant error in the calculation of cLDLC . The resulting underestimation of LDLC may translate to downward misclassification when using guideline-recommended 1.8 mmol/L or 1.4 mmol/L thresholds for patients at high and very high cardiovascular risk, respectively . This may erroneously exclude >20% of patients for initiation or intensification of lipid-lowering therapy .
A modified equation, Martin-Hopkins cLDLC=TC – HDLC – TG/adjustable factor, which adjusts the TG/VLDLC ratio dynamically for concentrations of TG and non-HDLC has been developed . Rather than dividing TG by a fixed factor of 5, the Martin-Hopkins equation requires the use of a 180-cell table to match each patient’s TG and non-HDLC with one of 180 different factors ranging from 3.1 to 9.5, to give a more personalized estimation of VLDLC in mg/dL . These personalized TG/VLDLC ratios were determined by direct comparison of TG to VLDLC concentrations directly measured after lipoprotein separation by ultracentrifugation in >106 US individuals from the Very Large Database of Lipids Study . In this database, TG and non-HDLC are the two variables that explained most of the variance in TG/VLDLC .
The novel Martin-Hopkins formula improves the accuracy of cLDLC at various conditions including very low LDLC<1.8 mmol/L and in nonfasting samples , . We recommend to use it preferably in the range of TG concentrations 2.0−4.5 mmol/L wherein the Friedewald equation is less accurate . However, it is not trivial to install the complex 180-cell approach of this formula into automated laboratory information systems. A smartphone application (“LDL Cholesterol Calculator”) has been developed to provide immediate and automated calculation of Martin-Hopkins cLDLC by simple input of a patient’s TC, HDLC, and TG data from the standard lipid profile .
dLDLC assays should always be used when TG concentration is ≥4.5 mmol/L, which is the limit of use of Friedewald and Martin-Hopkins equations , although above this concentration the direct assays may also show discordances with the CDC Reference Method and will not necessarily result in accurate LDLC measurement in every patient. Even in normotriglyceridemic samples and in samples with moderately elevated TG concentrations 2.0–4.5 mmol/L, dLDLC may not always agree with Friedewald cLDLC and translate to discrepant risk classifications as observed in fasting as well as nonfasting individuals . It should be noted that most clinical trials demonstrating the evidence base for clinical benefit of LDLC lowering have used Friedewald cLDLC; thus it cannot be excluded that dLDLC rather than cLDLC is the method that misclassifies risk . However, the fact that cLDLC calculated by either Friedewald or Martin-Hopkins equation depends upon three laboratory assays, that is, TG, TC, and dHDLC, means that three measurement errors are involved which inevitably introduce calculation variability (Table 5).
|Test||Assumed total error||Defined concentration in model patient mg/dL (mmol/L)||Range of uncertainty mg/dL (mmol/L)|
|TC||9%a||200 (5.2)||182–218 (4.7–5.7)|
|TG||15%a||250 (2.8)||212–288 (2.4–3.3)|
|dHDLC||−20% to +36%b||40 (1.0)||32–54 (0.8–1.4)|
|Non-HDLC||(derived from TC and dHDLC)||160 (4.1)||128–186 (3.3–4.8)|
|cLDLC (Friedewald)||(derived from TC, dHDLC, and TG)||110 (2.8)||70–144 (1.8–3.7)|
|cLDLC (Martin-Hopkins)||(derived from TC, dHDLC, TG, and non-HDLC)||122 (3.2)||91–151 (2.4–3.9)|
|dLDLC||−26% to +32%b||122 (3.2)||90–161 (2.3–4.2)|
aBased on National Cholesterol Education Program (NCEP) analytical performance criteria . bTotal error ranges observed by Miller et al.  across different dLDLC and dHDLC methods in dyslipidemic samples. The total error combines systematic bias and random imprecision. The table is not relevant for the monitoring of a patient by the same laboratory/method over time. In this situation the bias remains constant and only the (inevitable) imprecision is relevant. Bias should ideally be ≤4% for LDLC and ≤5% for HDLC to allow the methods to meet the NCEP total error goals, ≤12% and ≤13%, respectively.
Effect of Lp(a)-cholesterol on LDLC
The cLDLC equations and also most dLDLC assays include the cholesterol content of Lp(a) . Considering that an Lp(a) particle is composed of about 30−45% of cholesterol by weight, a significant overestimation of LDLC concentration occurs in subjects with high and very high Lp(a) concentrations ; for example, Lp(a)-corrected LDLC is only ~55−70 mg/dL in a person with an LDLC concentration of 100 mg/dL and an Lp(a) concentration of 100 mg/dL.
Lp(a)-corrected LDLC should be calculated at least once in patients with suspected high Lp(a), particularly in African Americans, in patients with nephrotic syndrome, in those undergoing peritoneal dialysis, and in any patient who does not respond sufficiently to statin therapy . If a high Lp(a) concentration is indeed the cause for an apparent non-response or low response of LDLC, then it might not be useful to increase the dosage of statin under such conditions . The correction is as follows with Lp(a) values reported in mg/dL:
At this point of time, we do not provide alternative formulas for Lp(a) reported in nmol/L (despite a proposed rough estimate of 2–2.5× conversion factor from mg/dL to nmol/L) . It has to be recognized that a simple conversion of Lp(a) from mg/dL to nmol/L or vice versa has its limitations as probably most of the available immunoassays cannot measure Lp(a) in strict molar terms due to the size heterogeneity of apo(a) isoforms and the high probability that antibodies against apo(a) recognize a repetitive KIV-2 epitope of apo(a) .
Recommendation – IV
The Martin-Hopkins modified equation may be preferable for calculation of cLDLC, most importantly in patients with low LDLC concentration <1.8 mmol/L and/or TG concentrations 2.0–4.5 mmol/L, and in nonfasting samples .
dLDLC assays should be used for calculation of RemnantC and for assessment of LDLC when TG concentration is ≥4.5 mmol/L (or ≥4.0 mmol/L by national consensus in certain countries). However, the direct assays will not necessarily result in more accurate LDLC assessment in every patient.
Lp(a)-cholesterol correction of measured or calculated LDLC should be applied in patients with known or suspected high Lp(a) concentration, or if the patient shows a poor response to LDL-lowering therapy.
V. LDLC test errors: are they clinically relevant?
The ranges of uncertainty across different LDLC methods are not negligible (Table 5). Between-laboratory variation of a certain patient’s measured or calculated LDLC can range widely over the guideline-driven critical values that determine the decision of therapeutic intervention (Table 6). Depending on the method used, different treatment decisions may be taken, or confusion may arise if the patient’s samples for monitoring are sent to different laboratories using different methods or when a laboratory changes the method. Not uncommonly, changes in a patient’s LDLC test result over time are within the range of uncertainty of laboratory method variation and may not be due to therapeutic intervention .
|Risk (SCORE)a||LDLC mmol/L (mg/dL)||Non-HDLCb mmol/L (mg/dL)||ApoBb g/L (mg/dL)|
|Very high||<1.4 (55) and ≥50% reduction in LDLC||<2.2 (85)c||<0.65 (65)d|
|High||<1.8 (70) and ≥50% reduction in LDLC||<2.6 (100)||<0.80 (80)|
|Moderate||<2.6 (100)||<3.3 (130)||<1.00 (100)|
a10-year risk of fatal ASCVD as estimated using SCORE (Systematic COronary Risk Evaluation) . Very high risk=documented ASCVD, diabetes with target organ damage (proteinuria, retinopathy, or neuropathy), or early onset type 1 diabetes of >20 years’ duration, severe chronic kidney disease (GFR <30 mL/min/1.73 m2), SCORE ≥10%; High risk=TC >8 mmol/L (310 mg/dL), LDLC >5 mmol/L (190 mg/dL), FH, hypertension ≥180/110 mmHg, diabetes of ≥10 years’ duration without target organ damage, moderate chronic kidney disease (GFR 30–59 mL/min/1.73 m2), SCORE ≥5% and <10%; Moderate risk=young patients with diabetes (type 1 <35 years, type 2 <50 years) of <10 years’ duration, SCORE ≥1% and <5%; Low risk=SCORE <1%. bSecondary target in patients with mild-to-moderate hypertriglyceridemia, 2–10 mmol/L (175–880 mg/dL) including those with obesity or metabolic syndrome, diabetes, or chronic kidney disease. cDiscordant high non-HDLC at optimal LDLC goal reflects elevated RemnantC >0.8 mmol/L (30 mg/dL). dDiscordant high apoB at optimal LDLC goal reflects elevated numbers of small, cholesterol-depleted LDL particles. To convert mmol/L to mg/dL, multiply by 38.6 for LDL and non-HDL cholesterol.
These issues are, however, less relevant for the monitoring of the patient by the same laboratory and method over time. In this situation, the non-specificity bias remains constant over time and only the assay imprecision (random error) and lot-to-lot variation are relevant, which may not be important given that clinicians are not aiming to achieve the LDLC targets exactly but often concentrations below it. In the present era, the percentage of LDLC reduction is more important than achieving specific targets, and indeed recent guidelines suggest that achievement of a >50% reduction in high- and very-high-risk patients is paramount irrespective of baseline LDLC concentration .
The risk that errors in LDLC measurement or calculation affect the clinical decision is further attenuated by the recommendation that decision to initiate a treatment, or adjusting or shifting to another treatment, should not be taken on one LDLC test, but rather after multiple repeated testing (at least 2 times) to allow averaging for intra-individual (biological) variation . The EFLM Biological Variation Working Group recently revised biological variation data , .
Recommendation – V
Follow-up of on-treatment lipid profiles in a patient should ideally be undertaken with the same method (and preferably in the same laboratory) to minimize misinterpretation of treatment effect.
Clinicians should be notified by the laboratory when the test changes from one method to another, e.g. through newsletters. This will enhance clinicians’ awareness of changes in methods as a potential cause of implausible test results.
Assay methods and their limitations should always be described in any publication of clinical trials or epidemiological studies. In meta-analyses of associations of lipid tests with outcomes, it is critical to verify the comparability of quantitative data of the assays used in each trial.
A patient’s LDLC value close to therapeutic decision thresholds should ideally be confirmed by repeated measurement(s) (≥2) by the same method and then averaged. Often the repeated test value is lower due to improved diet after the first test if the patient is told that LDLC is elevated; in this case the second value should be accepted for decision-making.
VI. Are other measurements of atherogenic lipoproteins reliable?
Calculated by subtracting HDLC from TC, non-HDLC represents the cholesterol in all particles causing cardiovascular disease, that is, LDL, VLDL, IDL, and Lp(a); in the nonfasting state this additionally includes the cholesterol in chylomicrons and their remnant particles . Non-HDLC provides a more comprehensive risk assessment than LDLC in certain individuals with hypertriglyceridemia because it adds RemnantC to LDLC and, therefore, takes into account the atherogenic potential of remnant lipoproteins . However, non-HDLC cannot substitute for RemnantC because it does not differentiate between RemnantC and LDLC and Lp(a)-cholesterol. Some individuals with high RemnantC have low LDLC and thus will have relatively low non-HDLC if Lp(a) is also low, and if interpreting non-HDLC instead of RemnantC, the high RemnantC will be masked in these individuals .
Like LDLC, non-HDLC is treatable with existing lipid-lowering agents and there is a direct, consistent relationship between the magnitude of non-HDLC lowering and cardiovascular risk reduction observed in meta-analyses and trials with statins and other lipid-lowering agents , . Guideline-recommended therapeutic goals for non-HDLC are arbitrarily set typically at 0.8 mmol/L (30 mg/dL) higher than LDLC goals (Table 6); this value is based on the assumption that the “optimal” VLDLC concentration associated with the fasting TG threshold 1.7 mmol/L is 0.8 mmol/L, as estimated by the Friedewald formula (TG/2.2) , .
Non-HDLC can be obtained in the nonfasting state and does not require TG to be less than 4.5 mmol/L, which is the limitation for calculation of cLDLC . However, dHDLC measurement errors in hypertriglyceridemic samples still affect the calculation of non-HDLC and most assays limit the measurement of dHDLC to TG<10 mmol/L. Despite this limitation, non-HDLC yields more accurate cardiovascular risk classification than either Friedewald cLDLC or dLDLC, and also more consistent risk scores using different manufacturers’ assays for dHDLC in the calculation . Non-HDLC shows better correlation and concordance with cLDLC when using the Martin-Hopkins formula compared to the Friedewald equation in patients with atherogenic dyslipidemia . When compared to Martin-Hopkins cLDLC, non-HDLC translates to only modest improvement for risk classification that could change clinical management (in ~2% of individuals in the general population), although discordance of Martin-Hopkins cLDLC and, as a consequence, risk underestimation is still common (80–90%) in patients with TG ≥4.5 mmol/L .
In contrast to the heterogeneous LDLC fraction, apoB is a clearly defined measurand. It is the structural protein for all non-HDL lipoproteins and exists as two isoforms: apoB100, the major isoform in VLDL, IDL, LDL, and Lp(a), and apoB48 in chylomicrons and chylomicron remnants . ApoB100 contains the ligand that binds to the LDL receptor. As each atherogenic particle contains one molecule of apoB, concentrations of apoB are therefore considered to be a direct measure of the total number of lipoproteins causing cardiovascular disease, that is, LDL, remnants, and Lp(a) .
ApoB quantification by automated immunoassays such as immunonephelometry and immunoturbidimetry can be easily implemented in the clinical laboratories. These immunoassays obviate the need for lipoprotein subfractionation techniques such as nuclear magnetic resonance (NMR) spectroscopy or ion mobility to quantify LDLP and have been shown to be at least equivalent to LDLP in predicting cardiovascular risk , . However, apoB cannot substitute for NMR- or ion mobility-based particle size measurements and does not differentiate between LDLP and VLDL particle numbers , . A major impediment to LDLP testing is its limited availability in the clinical laboratories, higher cost, and lack of standardization , although it does provide additional information on other lipoproteins VLDL and HDL, beyond LDLP.
ApoB can be measured in the nonfasting state because even at peak postprandial concentrations, the number of chylomicron-apoB48 particles in healthy individuals is usually <1% and the number of VLDL-apoB100 particles is <10% of the number of LDL-apoB100 particles . Thus, even when cross-reactivity of immunoassays to apoB48 occurs in nonfasting measurements, apoB quantification is essentially an estimate of LDLP number if TG and Lp(a) concentrations are low . Far higher than the maximum allowed TG concentration of 4.5 mmol/L for cLDLC, most nephelometric and turbidimetric apoB measurements are limited to TG <10 mmol/L due to interference of light scattering or absorption caused by chylomicrons and large VLDLs; a TG concentration above this limit in nonfasting blood samples is only seen in ~0.1% of individuals in the general population . Nevertheless, it is cautious to consider fasting apoB measurement when TG concentration in the nonfasting sample is ≥4.5 mmol/L .
The International Federation of Clinical Chemistry (IFCC) and World Health Organization (WHO) developed the SP3 reference material, value-assigned using immunonephelometry as interim reference method for apoB . Common calibration with the IFCC/WHO SP3 reference material has reduced between-laboratory variability of apoB measurements from >19% to <10%, although concerns about the variability among immunoassays and their comparability with apoB100 derived from NMR and other methods still exist , , . Liquid chromatography tandem-mass spectrometry (LC-MSMS)-based quantification of apolipoproteins has the potential to further improve apoB100 standardization and between-method comparability . The IFCC has therefore initiated development of LC-MSMS as candidate definitive reference method for apoB . Another advantage of LC-MSMS is that it enables the simultaneous (multiplexed) measurement of multiple apolipoproteins in addition to apoB100 in a single run of the assay, thus making it possible to achieve a complete apolipoprotein profile in the patient, including HDL- and VLDL-associated apolipoproteins for comprehensive characterization of dyslipidemias . Although nowadays throughput of LC-MSMS is low compared to immunoassays, automated LC-MSMS systems are developed and will become available for implementation in high-throughput clinical laboratories.
Given the improvements of standardization initiated by the IFCC , measurements of apolipoproteins have the potential to meet analytical performance criteria including accuracy, harmonization across laboratories, unambiguous definition of the measurand, and unequivocal test results in both normo- and dyslipidemic sera – important prerequisites for medical use of a test which cannot be met with LDLC and non-HDLC measurements or calculations.
Recommendation – VI
ApoB measurement is superior to LDLC and non-HDLC measurements and calculations for the assessment of exposure to atherogenic lipoprotein particle numbers in the circulation.
ApoB is recommended for risk assessment and may be preferred over non-HDLC, if available, in persons with mild-to-moderate hypertriglyceridemia (2–10 mmol/L), diabetes, obesity or metabolic syndrome, or very low LDLC <1.8 mmol/L .
Like non-HDLC, apoB can always be measured in the nonfasting state and is not affected by biological TG variability.
VII. Can apoB measurement replace the standard lipid profile for monitoring of lipid-lowering therapies?
Although the traditional lipid profile of TC, TG, HDLC, and LDLC remains essential for dyslipidemia diagnosis and ASCVD risk categorization, the position of LDLC as treatment target is challenged by the analytical performance of apoB. However, the apoB test has not yet been completely validated for this clinical purpose according to key criteria defined by the EFLM Test Evaluation Working Group  – clinical performance, clinical effectiveness, and cost-effectiveness – beyond analytical performance to become a medically useful test (Table 7).
|Accurate assays (method independency)||No||No||Yes||No|
|Nonfasting measurement possible||cLDLC at TG<4.5 mmol/L||Yes||Yes||Yes|
|Widely accessible assays||Yes||Yes||Yes||No|
|Reasonable operational costs||Yes||No extra measurement||Yes||Not yet|
|Robust associations with incident ASCVD?||Yes||Yes||Yes||Yes|
|Novel information beyond existing markers?||(Reference)||Yes||Yes||Yes|
|Validated decision thresholds?||No||No||No||No|
|Superiority to existing tests?||(Reference)||Probably||Probably||Probably|
|Modifiable risk association (treatment target)?||Yes||Yes||Yes||Yes|
|Test-guided treatment reduces ASCVD risk?||Yes||Probably||Probably||Unknown|
|Test-guided treatment saves healthcare costs?||Yes||Unknown||Unknown||Unknown|
Test characteristics defined by the EFLM Test Evaluation Working Group : (a) Analytical validity: ability of the test to conform to predefined quality specifications to measure the marker of interest. (b) Diagnostic or prognostic accuracy: ability of the test to consistently discriminate patients with increased risk from those with lower risk for developing ASCVD. (c) Clinical utility: ability of the test to improve health outcomes of the patient under standard clinical care. (d) Health-economic advantage of introducing the test in medical practice (value for money).
Clinical performance – risk estimation
Data and meta-analyses of prospective population-based cohort studies , , ,  and statin trials ,  suggest that the clinical performance of apoB and non-HDLC, although superior to LDLC in some studies, is on average comparable to dLDLC, Friedewald cLDLC, or Martin-Hopkins cLDLC to predict risk of fatal or nonfatal ASCVD on the population level. Risk associations are similar in nonfasting study populations than in those who did fast , .
For a majority of patients in whom apoB tests correlate with LDLC, measurement of traditional lipids should suffice to estimate risk. However, in ~20% of individuals in whom apoB is discordantly high with respect to population percentiles of LDLC, cardiovascular risk tracks with apoB, suggesting that replacing LDLC by apoB would identify more individuals with increased risk of ASCVD  – this implies an overall better clinical performance compared to LDLC in particular among this smaller subset of individuals with discordant apoB.
Clinical effectiveness – risk reduction
The substantial residual risk that persists in LDLC-targeted therapies even at LDLC <1.8 mmol/L has fueled the debate about considering using apoB rather than LDLC as treatment target. Meta-analyses of lipid-lowering trials showed that statins and other therapies which clear apoB-containing lipoproteins by upregulating LDL receptor expression, such as PCSK9 inhibitors, reduce cardiovascular risk proportional to the decrease in apoB concentration observed in these trials , . Furthermore, Mendelian randomization studies demonstrated that the reduction in cardiovascular risk associated with genetic variation in clearance and processing of apoB-containing lipoproteins, including LDLPs as well as TG-rich VLDL particles and their remnants, was correlated with the change of concentration of these particles as measured by apoB rather than the cholesterol mass carried by those particles as measured by LDLC , . These findings suggest a potential role of apoB as therapeutic target.
Cost-effectiveness – health-economic benefit
Implementation of apoB assays in follow-up of lipid-lowering therapies would impose healthcare systems and patients with yearly extra cost, although one might consider replacing the standard lipid profile (needed for calculation of LDLC and non-HDLC) by single follow-up measurement of apoB in order to attenuate the rise of expenses. The suggested use of apoB would be cost-effective if these tests guide therapy to reduce the healthcare costs of ASCVD to a greater extent than standard therapy guided by LDLC, yet the evidence base of this approach is still incomplete (Table 7). To evaluate the cost-effectiveness of apoB-guided treatment, randomized trials should be designed in which patients are randomized to receive the apoB test vs. the standard LDLC test and the health economic outcome(s) of identifying and treating more patients at high risk (i.e. not identified or treated when using the standard test) is assessed .
Recommendation – VII
At this point of time, there is insufficient evidence of benefit from outcome studies to support the option to replace the standard lipid profile (with calculation of cLDLC and non-HDLC) by single follow-up measurement of apoB to guide lipid-lowering therapies.
The clinical effectiveness of LDLC-guided management of cardiovascular risk is most strongly evidence-based , . All guidelines concur that LDLC remains the primary target of lipid-lowering strategies to prevent ASCVD , , . Lowering LDLC to concentrations below a target of 1.8 mmol/L in patients at high cardiovascular risk or 1.4 mmol/L in patients at very high risk (or by ≥50% if these targets cannot be attained) is of critical importance .
VIII. Should non-HDLC or apoB be used as additional tests to LDLC in lipid-lowering strategies?
Data from concordance/discordance analyses suggest that calculation of non-HDLC is at least equally good at predicting ASCVD compared with measurement or calculation of LDLC in the overall population and statin-treated patients; it may also be superior to LDLC if discordantly high, especially at normal or low LDLC concentrations and in individuals with hypertriglyceridemia because it includes RemnantC , , . LDLC does not provide incremental risk prediction of ASCVD relative to non-HDLC , , . The only concern is the threshold concentration selected in some studies: it may be the more sensitive threshold for non-HDLC as compared to LDLC rather than the biomarker which makes the difference. Guideline-based non-HDLC thresholds have been arbitrarily defined by consensus of expert groups, based on the assumption that a normal VLDLC concentration exists when TG are <1.7 mmol/L, which is <0.8 mmol/L as estimated by the Friedewald formula , . Lowering non-HDLC thresholds leads to upward reclassification of patients (if the goal is to reduce undertreatment), and higher thresholds lead to downward reclassification (if the goal is to reduce overtreatment). Threshold values need to be validated in clinical performance studies, to evaluate which values most accurately classify patients within risk categories .
For the present purposes, the combination of non-HDLC with Martin-Hopkins cLDLC may be considered as an appropriate strategy to guide therapy and they can be calculated from the standard lipid profile . This may compensate for the under- or overestimation of LDLC in terms of clinical decision-making, given the uncertainty of dLDLC measurements or the Friedewald calculation in dyslipidemic samples when LDLC lowering approaches 1.4 mmol/L, and non-HDLC can be used at high TG ≥4.5 mmol/L. However, the compromised accuracy of dHDLC assays in samples with hypertriglyceridemia reduces the benefit in reporting non-HDLC in some individuals in whom an apoB or LDLP measurement may be clinically useful .
Data from concordance/discordance analyses in large case-control and prospective cohort studies reveal that the addition of apoB to LDLC and even to non-HDLC has the potential to improve risk prediction by identifying more high-risk individuals , , , . This is consistent with the notion that risk of ASCVD is more directly related to the number of apoB-containing particles (reflected by apoB measurement) than to the cholesterol content of lipoproteins . The implication of discordant LDLC vs. apoB (one normal, the other high) is most evident in patients with predominant small, cholesterol-depleted LDLPs who present with “optimal” concentrations of TC and LDLC – a profile that is especially prevalent among individuals with the metabolic syndrome or diabetes and in those taking medications, such as statins and anti-PCSK9, that reduce LDLC to a greater extent than apoB , , . This necessarily results in on-treatment LDLP and VLDL particle numbers that are higher than would be anticipated from the concurrent LDLC follow-up measurement and may explain part of residual risk among statin-treated patients , .
In patients with a moderate estimated risk score, in particular those with additional metabolic risk factors, apoB (or LDLP) measurement as a “risk-enhancing factor” could be useful . Presence of risk-enhancing factors including apoB can tip the balance toward earlier initiation of drug treatment in shared decision-making between clinician and patient, especially in primary prevention if goals cannot be achieved with lifestyle advice .
Secondary treatment target: non-HDLC or apoB?
Guidelines propose using non-HDLC or apoB as a secondary treatment target in the management of patients with mild-to-moderate hypertriglyceridemia (2–10 mmol/L), including patients with diabetes , . If the primary target LDLC is at goal, but non-HDLC or apoB is still high, attainment of all three targets will require intensified lipid-lowering therapy, lifestyle (re)inforcement, and/or additional TG-lowering drugs (e.g. fibrate or omega-3 fatty acids) . Addition of PCSK9 inhibition to statin therapy allows more patients to achieve non-HDLC and apoB goals and lower risk of ASCVD, with no attenuation of benefit at lower concentrations .
Which to choose as a secondary target: non-HDLC or apoB? Although apoB demonstrates competitive clinical performance compared with non-HDLC, there is no evidence yet of significant population health-economic benefit of intensifying pharmacological intervention aiming to further reduce apoB at very low concentrations of LDLC . For now and until this issue is clarified, non-HDLC is an acceptable choice and it can be used without the additional expense of extra measurement.
Recommendation – VIII
Use of non-HDLC or apoB should be considered as an index of the efficacy of treatment targeted at LDLC. For the present purpose, every lipid profile report should automatically add non-HDLC. To improve patient comfort and compliance, there are practical advantages of this approach for follow-up without the need to fast .
At TG concentration ≥4.5 mmol/L, a condition wherein use of Friedewald cLDLC or Martin-Hopkins cLDLC is not recommended and also dLDLC is likely to be inaccurate, use of non-HDLC calculation may be considered instead of dLDLC measurement to evaluate therapeutic response.
IX. How to report the atherogenic lipid profiles?
We recommend that laboratory reports should flag abnormal concentrations based on threshold values defined by guidelines, i.e. decision threshold to trigger therapy or to identify increased risk of ASCVD (Table 8). For nonfasting samples, laboratories should flag abnormal TG concentrations as ≥2 mmol/L according to the Women’s Health Study, which found that this threshold was optimal for ASCVD prediction . The threshold for fasting TG at 1.7 mmol/L is 0.3 mmol/L lower than for nonfasting TG, corresponding to the mean maximal increase in TG following habitual food intake . Nonfasting state-adjusted thresholds for RemnantC and, consequently, also non-HDLC may be considered as an option (Table 8). In case the patient’s postprandial time in the preceding 12 h is unknown on sample reception in the laboratory, it may remain prudent to apply the lower fasting thresholds to draw attention to a potential cardiovascular risk.
|TGa||Fasting ≥1.7 mmol/L (150 mg/dL)
Nonfasting ≥2 mmol/L (175 mg/dL)
|>10 mmol/L (880 mg/dL): severe hypertriglyceridemia with high risk of acute pancreatitis|
|TC||≥5 mmol/L (190 mg/dL)|
|LDLC||≥3 mmol/L (115 mg/dL)||>13 mmol/L (500 mg/dL): consider homozygous FH
>5 mmol/L (190 mg/dL): consider heterozygous FH
|RemnantC||Fasting ≥0.8 mmol/L (30 mg/dL)
Nonfasting ≥0.9 mmol/L (35 mg/dL)
|Non-HDLC||Fasting ≥3.8 mmol/L (145 mg/dL)
Nonfasting ≥3.9 mmol/L (150 mg/dL)
|ApoB||≥1 g/L (100 mg/dL)||<0.1 g/L (10 mg/dL): genetic abetalipoproteinemia|
|HDLC||Men ≤1 mmol/L (40 mg/dL)
Women ≤1.2 mmol/L (45 mg/dL)
|ApoA-I||Men ≤1.2 g/L (120 mg/dL)
Women ≤1.4 g/l (140 mg/dL)
|<0.1 g/L (10 mg/dL): genetic hypoalphalipoproteinemia|
|Lp(a)||≥50 mg/dL (>105 nmol/L)b,c||>120 mg/dL: very high risk for myocardial infarction and aortic valve stenosis|
Values in mmol/L were converted to mg/dL by multiplication by 38.6 for cholesterol and 88.5 for TG, followed by rounding to the nearest 5 mg/dL. aTG thresholds based on assays with correction for endogenous glycerol. The free glycerol concentration in a sample, usually 1 mg/dL, equivalent to ~10 mg/dL (0.11 mmol/L) of TG, can be ignored. Increased baseline glycerol concentrations can be found in patients with diabetes and chronic kidney disease and during intravenous lipid infusion, and TG may be wrongly flagged in these patients unless glycerol-corrected TG assay is used. bThreshold value for Lp(a) should represent ≥80th percentile of the population-specific Lp(a) assay. cThere is no consensus on which threshold value in mmol/L to be used for Lp(a); however, for conversion of Lp(a) concentrations in mg/dL to nmol/L, 13,930 individuals from the Copenhagen General Population Study had measurements in both mg/dL and nmol/L by Denka Seiken assays distributed by Roche Diagnostics (Rotkreuz, Switzerland). The correlation was done by linear regression with an R2 value of 0.996, and the conversion was done by the following equation: Lp(a), nmol/L=2.18*Lp(a), mg/dL–3.83 .
For LDLC, the decision threshold for initiation of therapeutic intervention varies with the individual’s risk score (Table 6) . This personalized reporting of optimal thresholds is difficult to implement in laboratory reports because usually the clinical conditions and risk factors of the individual patients are not known to the laboratory personnel. We therefore propose a simplified flagging based on the threshold for low risk only (3 mmol/L), which may be complemented by more detailed information on risk-stratified thresholds in footnotes on the laboratory report or by references to web-based information .
Given the uncertainty of measurements and calculations of LDLC across different methods and laboratories, in hypertriglyceridemic patients the decision thresholds may not always be assumed to be universally applicable . Guideline-recommended LDLC thresholds are based on observations with Friedewald cLDLC using the older HDLC precipitation methods which differ from dHDLC assays used nowadays. This situation is challenging for new generation assays to meet regulations for in vitro diagnostic medical devices, requiring evidence of clinical performance of the assays .
Usually, in laboratory reports, results of most tests are flagged if they are below or above the age- and sex-specific reference interval (2.5th–97.5th percentiles). Because of the widespread unhealthy lifestyle, in most populations the upper reference limits of TC, LDLC, and TG are very high and far above the thresholds of increased ASCVD risk (Tables 2 and 3, Figure 2). Therefore, flagging of lipid profiles in adults should not be based on reference limits . In a pediatric setting, reporting age- and gender-specific reference intervals is relevant for early identification of children with hyperlipidemia associated with premature atherosclerosis, especially FH .
Reference nonfasting concentrations in adults from the Copenhagen General Population Study are reported in Tables 2 and 3. Reference intervals in children and adolescents are available from recent population-based cohort studies and databases , , . Countries are encouraged to establish reference intervals in their local populations, to account for differences in lifestyle and risk factors in different European regions. Ideally, these reference intervals should be regularly updated because significant changes in population lipid concentrations occur mainly related to the substantial increase in unhealthy lifestyle and obesity and changes of analytical methods over time , , . In the adoption of published reference concentrations with known biological or analytical sources of variation, the clinical laboratory should verify the reference interval with its own analytical method on 20 samples drawn from its local population under similar pre-analytical conditions , . If ≤2 of 20 values (≤10%) fall outside the reference interval, then the interval can be adopted , . For decision thresholds it is unnecessary to validate them in the laboratory .
Using decision thresholds for flagging will lead to many lipid profiles reported with flags, as with more than 50% of LDLC results (Figure 2). There is a risk that flagging too many results will make the physician ignore very high concentrations and may distract from the severe dyslipidemias, especially when used for screening in primary care. We have considered this by defining alarming values for extreme dyslipidemias, where we recommend special notification and reaction from the laboratory .
Extremely abnormal test results should be flagged with special alert notifications to quickly initiate further diagnostic and possibly therapeutic actions by the clinician (Table 8). For example, patients with severe hypertriglyceridemia ≥10 mmol/L and chylomicronemia syndrome have high risk of acute pancreatitis but usually do not develop premature atherosclerosis, probably because chylomicrons and large VLDLs do not traverse the vascular endothelial barrier . Lp(a) above the 97.5th percentile (>120 mg/dL depending on the assay) should be noted because of very high risk for myocardial infarction and aortic valve stenosis . Any LDLC >5 mmol/L in adults or >4 mmol/L in children should trigger investigations to rule out FH and, if diagnosis of FH is confirmed in the index case, cascade family screening , . In patients with mixed hyperlipidemias, routine genetic testing is not warranted . Reflective testing can proactively assist clinicians to rule out common secondary causes of hyperlipidemia using additional tests, e.g. thyrotropin, hemoglobin A1c, liver enzymes, and creatinine/eGFR, if not already known to the clinician at first-time screening .
Recommendation – IX
Flagging of lipid profiles on laboratory reports should always be based on decision thresholds. In children, reporting of reference intervals is relevant.
Extremely high concentrations should automatically trigger alerts to initiate immediate diagnostic investigations.
Conclusions and future research priorities
The consensus-based recommendations of EAS and EFLM provide guidance for the use of contemporary lipid, lipoprotein, and apolipoprotein tests to assist clinicians in their strategies to prevent ASCVD , . These recommendations take into account the strengths and weaknesses of the tests in terms of key criteria to become a medically useful test, as defined by the EFLM Test Evaluation Working Group .
Calculation of non-HDLC and RemnantC from the standard lipid profile, ‘expanded’ testing of apoB and Lp(a), and ‘advanced’ testing of LDLP have the potential to address clinical needs unmet with LDLC testing and they can always be used in nonfasting samples. The research priority is to investigate whether the diagnostic information provided by ‘expanded’ or ‘advanced’ lipid profiles can sufficiently change clinical management to reduce the risk (and cost) of ASCVD to a greater extent than the standard LDLC-centered approach.
Diabetes and abdominal obesity, disorders that underlie the clinical expression of complex dyslipidemias without elevated LDLC, are attaining epidemic proportions . Hence, emerging and advanced lipoprotein testing will likely become more and more useful in the future. This underscores the need to standardize and validate advanced lipoprotein tests, such as NMR- or ion mobility-based LDLP and VLDL particle numbers and size , , and multiplexed LC-MSMS apolipoprotein profiles , which have the potential to become widely available medical tests . These novel technologies provide complementary diagnostic information regarding the complex molecular basis of dyslipidemias and, as such, can be used to explore and evaluate precision medicine approaches for identifying better and individualized treatment options for patients at high risk of ASCVD .
We thank Ms. Jane Stock for coordination of the joint EAS-EFLM Panel meetings. We greatly appreciate the comments and suggestions received from EFLM National Societies and their assigned reviewers.
Author contributions: EAS-EFLM Joint Consensus Panel members were nominated by EAS, EFLM, and the co-chairs M.L. and B.G.N. to represent expertise across clinical and laboratory management and lipid research from across the World. The Panel met twice, organized and chaired by M.L. and B.G.N. The first meeting critically reviewed the literature while the second meeting reviewed additional literature and scrutinized the first drafts of the joint consensus statements. This summary embodies the consensus-based recommendations produced by the Panel [1,2] and was critically reviewed by independent experts of the EFLM Task Group on Cardiac Markers, chaired by P.L., who were not present at the first Panel meetings. The recommendations should be considered as consensus-based expert opinion, despite the high-quality evidence base available from systematic reviews and meta-analyses of randomized controlled trials, Mendelian randomization studies, and prospective population-based cohort studies for most recommendations. All Panel members agreed to conception and design, contributed to interpretation of available and additional novel data, and all suggested revisions for this document. All members have accepted responsibility for the entire content of the submitted manuscript and approved submission. A first version of the document underwent public consultation by the EFLM National Societies. Comments were received from 11 Societies and have been taken into account during the revision of this document. The revised version has been sent for final voting to all 40 National Societies. The document was positively voted by 29 Societies (one negative vote was received for Recommendation – IX and 10 Societies did not vote).
Research funding: The Panel was supported by unrestricted educational grants to EAS and EFLM from Merck, Roche Diagnostics, and Denka Seiken.
Employment or leadership: M.L. and B.G.N. are co-chairs of the Panel appointed by EFLM and EAS, respectively. P.L. is chair of the EFLM Task Group – Cardiac Markers.
Honorarium: None declared.
Competing interests: The funding companies were not present at the Joint Consensus Panel meetings, had no role in the design or content of the joint consensus statement and had no right to approve or disapprove of the decision to submit the final document for publication.
1. Langlois MR, Chapman MJ, Cobbaert C, Mora S, Remaley AT, Ros E, et al. European Atherosclerosis Society (EAS) and the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Joint Consensus Initiative. Quantifying Atherogenic Lipoproteins: Current and Future Challenges in the Era of Personalized Medicine and Very Low Concentrations of LDL Cholesterol. A Consensus Statement from EAS and EFLM. Clin Chem 2018;64:1006–33.10.1373/clinchem.2018.287037Search in Google Scholar
2. Nordestgaard BG, Langsted A, Mora S, Kolovou G, Baum H, Bruckert E, et al. European Atherosclerosis Society (EAS) and the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Consensus Panel. Fasting is not routinely required for a lipid profile: Clinical and laboratory implications including flagging at desirable concentration cut-points – a joint consensus statement from the European Atherosclerosis Society and European Federation of Clinical Chemistry and Laboratory Medicine. Eur Heart J 2016;37:1944–58.10.1093/eurheartj/ehw152Search in Google Scholar
4. Mach F, Baigent C, Catapano AL, Koskinas KC, Casula M, BadimonL, et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. The Task Force for the Management of Dyslipidaemias of the European Society of Cardiology (ESC) and European Atherosclerosis Society (EAS). Eur Heart J 2019 doi: 10.1093/eurheartj/ehz455 [Epub ahead of print].10.1093/eurheartj/ehz455Search in Google Scholar
5. Anderson TJ, Grégoire J, Pearson GJ, Barry AR, Couture P, Dawes M, et al. 2016 Canadian Cardiovascular Society Guidelines for the Management of Dyslipidemia for the Prevention of Cardiovascular Disease in the Adult. Can J Cardiol 2016;32:1263–82.10.1016/j.cjca.2016.07.510Search in Google Scholar
6. Grundy SM, Stone NJ, Bailey AL, Beam C, Birtcher KK, Blumenthal RS, et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol 2019;73:e285–350.10.1016/j.jacc.2018.11.003Search in Google Scholar
7. Ference BA, Ginsberg HN, Graham I, Ray KK, Packard CJ, Bruckert E, et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel. Eur Heart J 2017;38:2459–72.10.1093/eurheartj/ehx144Search in Google Scholar
8. Baigent C, Blackwell L, Emberson J, Holland LE, Reith C, Bhala N, et al. Cholesterol Treatment Trialists’ (CTT) Collaboration. Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170 000 participants in 26 randomised trials. Lancet 2010;376:1670–81.10.1016/S0140-6736(10)61350-5Search in Google Scholar
9. Mudd JO, Borlaug BA, Johnston PV, Kral BG, Rouf R, Blumenthal RS, Kwiterovich PO Jr. Beyond low-density lipoprotein cholesterol: defining the role of low-density lipoprotein heterogeneity in coronary artery disease. J Am Coll Cardiol 2007;50:1735–41.10.1016/j.jacc.2007.07.045Search in Google Scholar PubMed
10. Mora S. Advanced lipoprotein testing and subfractionation are not (yet) ready for routine clinical use. Circulation 2009;119:2396–404.10.1161/CIRCULATIONAHA.108.819359Search in Google Scholar PubMed PubMed Central
11. Miller M, Stone J, Ballantyne C, Bittner V, Criqui MH, Ginsberg HN, et al. Triglycerides and cardiovascular disease: a scientific statement from the American Heart Association. Circulation 2011;123:2292–333.10.1161/CIR.0b013e3182160726Search in Google Scholar
12. Contois JH, McConnell JP, Sethi AA, Csako G, Devaraj S, Hoefner DM, et al. Apolipoprotein B and cardiovascular disease risk: position statement from the AACC Lipoproteins and Vascular Disease Working Group on Best Practices. Clin Chem 2009;55:407–19.10.1373/clinchem.2008.118356Search in Google Scholar
13. Chapman MJ, Ginsberg HN, Amarenco P, Andreotti F, Boren J, Catapano AL, et al. Triglyceride-rich lipoproteins and high-density lipoprotein cholesterol in patients at high risk of cardiovascular disease: evidence and guidance for management. Eur Heart J 2011;32:1345–61.10.1093/eurheartj/ehr112Search in Google Scholar
15. Mora S, Rifai N, Buring JE, Ridker PM. Fasting compared with nonfasting lipids and apolipoproteins for predicting incident cardiovascular events. Circulation 2008;118:993–1001.10.1161/CIRCULATIONAHA.108.777334Search in Google Scholar PubMed PubMed Central
17. Nordestgaard BG. Triglyceride-rich lipoproteins and atherosclerotic cardiovascular disease: new insights from epidemiology, genetics, and biology. Circ Res 2016;118:547–63.10.1161/CIRCRESAHA.115.306249Search in Google Scholar PubMed
18. Thomsen M, Varbo A, Tybjaerg-Hansen A, Nordestgaard BG. Low nonfasting triglycerides and reduced all-cause mortality: a Mendelian Randomization Study. Clin Chem 2014;60:737–46.10.1373/clinchem.2013.219881Search in Google Scholar PubMed
20. Varbo A, Freiberg JJ, Nordestgaard BG. Remnant cholesterol and myocardial infarction in normal weight, overweight, and obese individuals from the Copenhagen General Population Study. Clin Chem 2018;64:219–30.10.1373/clinchem.2017.279463Search in Google Scholar PubMed
22. Nordestgaard BG, Chapman MJ, Ray K, Boren J, Andreotti F, Watts GF, et al. Lipoprotein(a) as a cardiovascular risk factor: current status. Eur Heart J 2010;31:2844–53.10.1093/eurheartj/ehq386Search in Google Scholar
23. Nordestgaard BG, Langsted A. Lipoprotein(a) as a cause of cardiovascular disease: insights from epidemiology, genetics, and biology. J Lipid Res 2016;57:1953–75.10.1194/jlr.R071233Search in Google Scholar
24. Willeit P, Ridker PM, Nestel PJ, Simes J, Tonkin AM, Pedersen TR, et al. Baseline and on-statin treatment lipoprotein(a) levels for prediction of cardiovascular events: individual patient-data meta-analysis of statin outcome trials. Lancet 2018;392:1311–20.10.1016/S0140-6736(18)31652-0Search in Google Scholar
25. Tsimikas S, Fazio S, Ferdinand KC, Ginsberg HN, Koschinsky ML, Marcovina SM, et al. NHLBI Working Group Recommendations to Reduce Lipoprotein(a) – Mediated Risk of Cardiovascular Disease and Aortic Stenosis. J Am Coll Cardiol 2018;71:177–92.10.1016/j.jacc.2017.11.014Search in Google Scholar
27. O’Donoghue ML, Fazio S, Guigliano RP, Stroes ESG, Kanevsky E, Gouni-Berthold I, et al. Lipoprotein(a), PCSK9 inhibition and cardiovascular risk: insights from the FOURIER Trial. Circulation 2019;139:1483–92.10.1161/CIRCULATIONAHA.118.037184Search in Google Scholar
28. Ray KK, Vallejo-Vaz AJ, Ginsberg HN, Davidson MH, Louie MJ, Bujas-Bobanovic M, et al. Lipoprotein(a) reductions from PCSK9 inhibition and major adverse cardiovascular events: pooled analysis of alirocumab phase 3 trials. Atherosclerosis 2019;288:194–202.10.1016/j.atherosclerosis.2019.06.896Search in Google Scholar
29. Viney NJ, van Capelleveen JC, Geary RS, Xia S, Tami JA, Yu RZ, et al. Antisense oligonucleotides targeting apolipoprotein(a) in people with raised lipoprotein(a): two randomised, double-blind, placebo-controlled, dose-ranging trials. Lancet 2016;388:2239–53.10.1016/S0140-6736(16)31009-1Search in Google Scholar
30. Langlois MR, Nordestgaard BG. Which lipids should be analyzed for diagnostic workup and follow-up of patients with hyperlipidemias? Curr Cardiol Rep 2018;20:88.10.1007/s11886-018-1036-1Search in Google Scholar PubMed
31. Nordestgaard BG, Chapman MJ, Humphries SE, Ginsberg HN, Masana L, Descamps OS, et al. European Atherosclerosis Society Consensus Panel. Familial hypercholesterolaemia is underdiagnosed and undertreated in the general population: guidance for clinicians to prevent coronary heart disease: consensus statement of the European Atherosclerosis Society. Eur Heart J 2013;34:3478–90a.10.1093/eurheartj/eht273Search in Google Scholar
32. Wiegman A, Gidding SS, Watts GF, Chapman MJ, Ginsberg HN, Cuchel M, et al. European Atherosclerosis Society Consensus Panel. Familial hypercholesterolaemia in children and adolescents: gaining decades of life by optimizing detection and treatment. Eur Heart J 2015;36:2425–37.10.1093/eurheartj/ehv157Search in Google Scholar
33. Hegele RA, Ginsberg HN, Chapman MJ, Nordestgaard BG, Kuivenhoven JA, Averna M, et al. European Atherosclerosis Society Consensus Panel. The polygenic nature of hypertriglyceridaemia: implications for definition, diagnosis, and management. Lancet Diabetes Endocrinol 2014;2:655–66.10.1016/S2213-8587(13)70191-8Search in Google Scholar
34. Langsted A, Kamstrup PR, Nordestgaard BG. Lipoprotein(a): fasting and nonfasting levels, inflammation, and cardiovascular risk. Atherosclerosis 2014;234:95–101.10.1016/j.atherosclerosis.2014.01.049Search in Google Scholar PubMed
36. Mora S, Chang CL, Moorthy MV, Sever PS. Association of nonfasting vs fasting lipid Levels with risk of major coronary events in the Anglo-Scandinavian Cardiac Outcomes Trial-lipid lowering arm. J Am Med Assoc Intern Med 2019;179:898–905.10.1001/jamainternmed.2019.0392Search in Google Scholar PubMed PubMed Central
37. Simundic AM, Cornes M, Grankvist K, Lippi G, Nybo M. Standardization of collection requirements for fasting samples: for the Working Group on Preanalytical Phase (WG-PA) of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM). Clin Chim Acta 2014;432:33–7.10.1016/j.cca.2013.11.008Search in Google Scholar PubMed
38. National Institute for Health and Care Excellence (NICE). NICE Guidance. Cardiovascular disease: risk assessment and reduction, including lipid modification. NICE CG181. Updated 2016. Available at https://www.nice.org.uk/guidance/cg181. Accessed 30 January 2019.Search in Google Scholar
39. Simundic AM, Bölenius K, Cadamuro J, Church S, Cornes MP, van Dongen-Lases EC, et al. Joint EFLM-COLABIOCLI Recommendation for venous blood sampling. Clin Chem Lab Med 2018;56:2015–38.10.1515/cclm-2018-0602Search in Google Scholar PubMed
41. Contois JH, Warnick GR, Sniderman AD. Reliability of low-density lipoprotein cholesterol, non-high density lipoprotein cholesterol, and apolipoprotein B measurement. J Clin Lipidol 2011;5:264–72.10.1016/j.jacl.2011.05.004Search in Google Scholar PubMed
42. Miller WG, Myers GL, Sakurabayashi I, Bachmann LM, Caudill SP, Dziekonski A, et al. Seven direct methods for measuring HDL and LDL cholesterol compared with ultracentrifugation reference measurement procedures. Clin Chem 2010;56:977–86.10.1373/clinchem.2009.142810Search in Google Scholar PubMed PubMed Central
43. Langlois MR, Descamps OS, van der Laarse A, Weykamp C, Baum H, Pulkki K, et al. Clinical impact of direct HDLc and LDLc method bias in hypertriglyceridemia. A simulation study of the EAS-EFLM Collaborative Project Group. Atherosclerosis 2014;233:83–90.10.1016/j.atherosclerosis.2013.12.016Search in Google Scholar PubMed
44. Nauck M, Warnick GR, Rifai N. Methods for measurement of LDL-cholesterol: a critical assessment of direct measurement by homogeneous assays versus calculation. Clin Chem 2002;48:236–25.10.1093/clinchem/48.2.236Search in Google Scholar
45. Martin SS, Blaha MJ, Elshazly MB, Brinton EA, Toth PP, McEvoy JW, et al. Friedewald-estimated versus directly measured low-density lipoprotein cholesterol and treatment implications. J Am Coll Cardiol 2013;62:732–9.10.1016/j.jacc.2013.01.079Search in Google Scholar PubMed
46. Martin SS, Blaha MJ, Elshazly MB, Toth PP, Kwiterovich PO, Blumenthal RS, Jones SR. Comparison of a novel method vs the Friedewald equation for estimating low-density lipoprotein cholesterol levels from the standard lipid profile. J Am Med Assoc 2013;310:2061–8.10.1001/jama.2013.280532Search in Google Scholar PubMed PubMed Central
47. Sathiyakumar V, Park J, Golozar A, Lazo M, Quispe R, Guallar E, et al. Fasting versus nonfasting and low-density lipoprotein cholesterol accuracy. Circulation 2018;137:10–19.10.1161/CIRCULATIONAHA.117.030677Search in Google Scholar PubMed
48. Martin SS, Giugliano RP, Murphy SA, Wasserman SM, Stein EA, Ceška R, et al. Comparison of low-density lipoprotein cholesterol assessment by Martin/Hopkins estimation, Friedewald estimation, and preparative ultracentrifugation: insights from the FOURIER trial. J Am Med Assoc Cardiol 2018;3:749–53.10.1001/jamacardio.2018.1533Search in Google Scholar PubMed PubMed Central
49. Johns Hopkins Medicine. Johns Hopkins medical apps. LDL Cholesterol Calculator. Available at https://www.hopkinsmedicine.org/apps/all-apps/ldl-cholesterol-calculator. Accessed 26 August 2019.Search in Google Scholar
50. Palmer MK, Barter PJ, Lundman P, Nicholls SJ, Toth PP, Karlson BW. Comparing a novel equation for calculating low-density lipoprotein cholesterol with the Friedewald equation: a VOYAGER analysis. Clin Biochem 2019;64:24–9.10.1016/j.clinbiochem.2018.10.011Search in Google Scholar PubMed
51. Mora S, Rifai N, Buring JE, Ridker PM. Comparison of LDL cholesterol concentrations by Friedewald calculation and direct measurement in relation to cardiovascular events in 27,331 women. Clin Chem 2009;55:888–94.10.1373/clinchem.2008.117929Search in Google Scholar PubMed PubMed Central
52. Yeang C, Witztum JL, Tsimikas S. ‘LDLC’=LDL-C+Lp(a)-C: implications of achieved ultra-low LDL-C levels in the proprotein convertase subtilisin/kexin type 9 era of potent LDL-C lowering. Curr Opin Lipidol 2015;26:169–78.10.1097/MOL.0000000000000171Search in Google Scholar PubMed
53. Tsimikas S, Fazio S, Viney NJ, Xia S, Witztum JL, Marcovina SM. Relationship of lipoprotein(a) molar concentrations and mass according to lipoprotein(a) thresholds and apolipoprotein(a) isoform size. J Clin Lipidol 2018;12:1313–23.10.1016/j.jacl.2018.07.003Search in Google Scholar PubMed
54. Aarsand AK, Díaz-Garzón J, Fernandez-Calle P, Guerra E, Locatelli M, Bartlett WA, et al. European Federation of Clinical Chemistry and Laboratory Medicine Working Group on Biological Variation. The EuBIVAS: within- and between-subject biological variation data for electrolytes, lipids, urea, uric acid, total protein, total bilirubin, direct bilirubin, and glucose. Clin Chem 2018;64:1380–93.10.1373/clinchem.2018.288415Search in Google Scholar PubMed
55. Díaz-Garzón J, Fernández-Calle P, Minchinela J, Aarsand AK, Bartlett WA, Aslan B, et al. Biological variation data for lipid cardiovascular risk assessment biomarkers. A systematic review applying the biological variation data critical appraisal checklist (BIVAC). Clin Chim Acta 2019;495:467–75.10.1016/j.cca.2019.05.013Search in Google Scholar PubMed
56. Robinson JG, Wang S, Smith BJ, Jacobson TA. Meta-analysis of the relationship between non-high-density lipoprotein cholesterol reduction and coronary heart disease. J Am Coll Cardiol 2009;53:316–22.10.1016/j.jacc.2008.10.024Search in Google Scholar PubMed
57. Silverman MG, Ference BA, Im K, Wiviott SD, Giugliano RP, Grundy SM, et al. Association between lowering LDL-C and cardiovascular risk reduction among different therapeutic interventions: a systematic review and meta-analysis. J Am Med Assoc 2016;316:1289–97.10.1001/jama.2016.13985Search in Google Scholar PubMed
58. van Deventer HE, Miller WG, Myers GL, Sakurabayashi I, Bachmann LM, Caudill SP, et al. Non-HDL-cholesterol shows improved accuracy for cardiovascular risk score classification compared to direct or calculated LDL cholesterol in a dyslipidemic population. Clin Chem 2011;57:490–501.10.1373/clinchem.2010.154773Search in Google Scholar PubMed PubMed Central
59. Mehta R, Reyes-Rodríguez E, Bello-Chavolla OY, Guerrero-Díaz AC, Vargas-Vázquez A, Cruz-Bautista I, et al. Performance of LDL-C calculated with Martin’s formula compared to the Friedewald equation in familial combined hyperlipidemia. Atherosclerosis 2018;277:204–10.10.1016/j.atherosclerosis.2018.06.868Search in Google Scholar PubMed
60. Sathiyakumar V, Park J, Quispe R, Elshazy MB, Michos ED, Banach M, et al. Impact of novel low-density lipoprotein-cholesterol assessment on the utility of secondary non-high-density lipoprotein-cholesterol and apolipoprotein B targets in selected worldwide dyslipidemia guidelines. Circulation 2018;138:244–54.10.1161/CIRCULATIONAHA.117.032463Search in Google Scholar PubMed
61. Cole TG, Contois JH, Csako C, McConnell JP, Remaley AT, Devaraj S, et al. Association of apolipoprotein B and nuclear magnetic resonance spectroscopy-derived LDL particle number with outcomes in 25 clinical studies: assessment by the AACC Lipoprotein and Vascular Diseases Division Working Group on Best Practices. Clin Chem 2013;59:752–70.10.1373/clinchem.2012.196733Search in Google Scholar PubMed
62. Mora S, Caulfield MP, Wohlgemuth J, Chen Z, Superko HR, Rowland CM, et al. Atherogenic lipoprotein subfractions determined by ion mobility and first cardiovascular events after random allocation to high-intensity statin or placebo. The Justification for the Use of Statins in Prevention: An Intervention Trial Evaluating Rosuvastatin (JUPITER) Trial. Circulation 2015;132:2220–9.10.1161/CIRCULATIONAHA.115.016857Search in Google Scholar PubMed PubMed Central
63. Clouet-Foraison N, Gaie-Levrel F, Gillery P, Delatour V. Advanced lipoprotein testing for cardiovascular diseases risk assessment: a review of the novel approaches in lipoprotein profiling. Clin Chem lab Med 2017;55:1453–64.10.1515/cclm-2017-0091Search in Google Scholar PubMed
64. Marcovina SM, Albers JJ, Kennedy H, Mei JV, Henderson LO, Hannon WH. International Federation of Clinical Chemistry standardization project for measurements of apolipoproteins A-I and B. IV. Comparability of apolipoprotein B values by use of International Reference Material. Clin Chem 1994;40:586–92.10.1093/clinchem/40.4.586Search in Google Scholar
65. Cao J, Steffen BT, Guan W, Remaley AT, McConnell JP, Palamalai V, et al. A comparison of three apolipoprotein B methods and their associations with incident coronary heart disease risk over a 12-year follow-up period: The Multi-Ethnic Study of Atherosclerosis. J Clin Lipidol 2018;12:300–4.10.1016/j.jacl.2017.12.013Search in Google Scholar PubMed
66. Grundy SM, Vega GL, Tomassini JE, Tershakovec AM. Comparisons of apolipoprotein B levels estimated by immunoassay, nuclear magnetic resonance, vertical auto profile, and non-high-density lipoprotein cholesterol in subjects with hypertriglyceridemia (SAFARI Trial). Am J Cardiol 2011;108:40–6.10.1016/j.amjcard.2011.03.003Search in Google Scholar PubMed
67. Delatour V, Clouet-Foraison N, Gaie-Levrel F, Marcovina SM, Hoofnagle AN, Kuklenyik Z, et al. Comparability of lipoprotein particle number concentrations across ES-DMA, NMR, LC-MS/MS, immunonephelometry, and VAP: in search of a candidate reference measurement procedure for apoB and non-HDL-P standardization. Clin Chem 2018;64:1485–95.10.1373/clinchem.2018.288746Search in Google Scholar PubMed
68. van den Broek I, Romijn FP, Nouta J, van der Laarse A, Drijfhout JW, Smit NP, et al. Automated multiplex LC-MS/MS assay for quantifying serum apolipoproteins A-I, B, C-I, C-II, C-III, and E with qualitative apolipoprotein E phenotyping. Clin Chem 2016;62:188–97.10.1373/clinchem.2015.246702Search in Google Scholar PubMed
70. Horvath AR, Lord SJ, St John A, Sandberg S, Cobbaert CM, Lorenz S, et al. From biomarkers to medical tests: the changing landscape of test evaluation. Clin Chim Acta 2014;427:49–57.10.1016/j.cca.2013.09.018Search in Google Scholar PubMed
71. Sniderman AD, Williams K, Contois JH, Monroe HM, McQueen MJ, de Graaf J, et al. A meta-analysis of low-density lipoprotein cholesterol, non-high-density lipoprotein cholesterol, and apolipoprotein B as markers of cardiovascular risk. Circ Cardiovasc Qual Outcomes 2011;4:337–45.10.1161/CIRCOUTCOMES.110.959247Search in Google Scholar PubMed
72. Di Angelantonio E, Sarwar N, Perry P, Kaptoge S, Ray KK, Thompson A, et al. Emerging Risk Factors Collaboration. Major lipids, apolipoproteins, and risk of vascular disease. J Am Med Assoc 2009;302:1993–2000.10.1001/jama.2009.1619Search in Google Scholar PubMed PubMed Central
73. Di Angelantonio E, Gao P, Pennells L, Kaptoge S, Caslake M, Thompson A, et al. Emerging Risk Factors Collaboration. Lipid-related markers and cardiovascular disease prediction. J Am Med Assoc 2012;307:2499–506.Search in Google Scholar
74. Welsh C, Celis-Morales CA, Brown R, Mackay DF, Lewsey J, Mark PB, et al. Comparison of conventional lipoprotein tests and apolipoproteins in the prediction of cardiovascular disease. Data from UK Biobank. Circulation 2019;140:542–52.10.1161/CIRCULATIONAHA.119.041149Search in Google Scholar PubMed PubMed Central
75. Boekholdt SM, Arsenault BJ, Mora S, Pedersen TR, LaRosa JC, Nestel PJ, et al. Association of LDL cholesterol, non-HDL cholesterol, and apolipoprotein B levels with risk of cardiovascular events among patients treated with statins: a meta-analysis. J Am Med Assoc 2012;307:1302–9.10.1001/jama.2012.366Search in Google Scholar PubMed
76. Robinson JG, Wang S, Jacobson TA. Meta-analysis of comparison of effectiveness of lowering apolipoprotein B versus low-density lipoprotein cholesterol and non high-density lipoprotein cholesterol for cardiovascular risk reduction in randomized trials. Am J Cardiol 2012;110:1468–76.10.1016/j.amjcard.2012.07.007Search in Google Scholar PubMed
77. Thanassoulis G, Williams K, Ye K, Brook R, Couture P, Lawler PR, et al. Relations of change in plasma levels of LDL-C, non-HDL-C and apoB with risk reduction from statin therapy: a meta-analysis of randomized trials. J Am Heart Assoc 2014;3:e000759.10.1161/JAHA.113.000759Search in Google Scholar PubMed PubMed Central
78. Khan SU, Khan MU, Valavoor S, Khan MS, Okunrintemi V, Mamas MA, et al. Association of lowering apolipoprotein B with cardiovascular outcomes across various lipid-lowering therapies: systematic review and meta-analysis of trials. Eur J Prev Cardiol 2019. doi:10.1177/2047487319871733 [Epub ahead of print].10.1177/2047487319871733Search in Google Scholar PubMed PubMed Central
79. Ference BA, Kastelein JJP, Ginsberg HN, Chapman MJ, Nicholls SJ, Ray KK, et al. Association of genetic variants related to CETP inhibitors and statins with lipoprotein levels and cardiovascular risk. J Am Med Assoc 2017;318:947–56.10.1001/jama.2017.11467Search in Google Scholar PubMed PubMed Central
80. Ference BA, Kastelein JJP, Ray KK, Ginsberg HN, Chapman MJ, Packard CJ, et al. Association of triglyceride-lowering LPL variants and LDL-C-lowering LDLR variants with risk of coronary heart disease. J Am Med Assoc 2019;321:364–73.10.1001/jama.2018.20045Search in Google Scholar PubMed PubMed Central
81. Arsenault BJ, Rana JS, Stroes ES, Després JP, Shah PK, Kastelein JJ, et al. Beyond low-density lipoprotein cholesterol: respective contributions of non-high-density lipoprotein cholesterol levels, triglycerides, and the total cholesterol/high-density lipoprotein cholesterol ratio to coronary heart disease risk in apparently healthy men and women. J Am Coll Cardiol 2010;55:35–41.10.1016/j.jacc.2009.07.057Search in Google Scholar PubMed
82. Masana L, Ibarretxe D, Heras M, Cabré A, Ferré R, Merino J, et al. Substituting non-HDL cholesterol with LDL as a guide for lipid-lowering therapy increases the number of patients with indication for therapy. Atherosclerosis 2013;226:471–5.10.1016/j.atherosclerosis.2012.12.001Search in Google Scholar PubMed
83. Elshazly MB, Martin SS, Blaha MJ, Joshi PH, Toth PP, McEvoy JW, et al. Non-high-density lipoprotein cholesterol, guideline targets, and population percentiles for secondary prevention in 1.3 million adults: the VLDL-2 study (very large database of lipids). J Am Coll Cardiol 2013;62:1960–5.10.1016/j.jacc.2013.07.045Search in Google Scholar PubMed
84. Mora S, Buring JE, Ridker PM. Discordance of low-density lipoprotein (LDL) cholesterol with alternative LDL-related measures and future coronary events. Circulation 2014;129:553–61.10.1161/CIRCULATIONAHA.113.005873Search in Google Scholar PubMed PubMed Central
85. Lawler PR, Akinkuolie AO, Ridker PM, Sniderman AD, Buring JE, Glynn RJ, et al. Discordance between circulating atherogenic cholesterol mass and lipoprotein particle concentration in relation to future coronary events in women. Clin Chem 2017;63:870–9.10.1373/clinchem.2016.264515Search in Google Scholar PubMed PubMed Central
86. Pischon T, Girman CJ, Sacks FM, Rifai N, Stampfer MJ, Rimm EB. Non-high-density lipoprotein cholesterol and apolipoprotein B in the prediction of coronary heart disease in men. Circulation 2005;112:3375–83.10.1161/CIRCULATIONAHA.104.532499Search in Google Scholar PubMed
87. Wong ND, Chuang J, Zhao Y, Rosenblit PD. Residual dyslipidemia according to low-density lipoprotein cholesterol, non-high-density lipoprotein cholesterol, and apolipoprotein B among statin-treated US adults: National Health and Nutrition Examination Survey 2009–2010. J Clin Lipidol 2015;9:525–32.10.1016/j.jacl.2015.05.003Search in Google Scholar PubMed
88. Ray KK, Ginsberg HN, Davidson MH, Pordy R, Bessac L, Minini P, et al. Reductions in atherogenic lipids and major cardiovascular events: a pooled analysis of 10 ODYSSEY trials comparing alirocumab with control. Circulation 2016;134:1931–43.10.1161/CIRCULATIONAHA.116.024604Search in Google Scholar PubMed PubMed Central
89. Rosenson RS, Jacobson TA, Preiss D, Djedjos CS, Dent R, Bridges I, et al. Efficacy and safety of the PCSK9 inhibitor Evolocumab in patients with mixed hyperlipidemia. Cardiovasc Drugs Ther 2016;30:305–13.10.1007/s10557-016-6666-1Search in Google Scholar PubMed PubMed Central
90. Lawler PR, Akinkuolie AO, Chu AY, Shah SH, Kraus WE, Craig D, et al. Atherogenic lipoprotein determinants of cardiovascular disease and residual risk among individuals with low low-density lipoprotein cholesterol. J Am Heart Assoc 2017;6:e005549.10.1161/JAHA.117.005549Search in Google Scholar PubMed PubMed Central
91. White KT, Moorthy MV, Akinkuolie AO, Demler O, Ridker PM, Cook NR, et al. Identifying an optimal cutpoint for the diagnosis of hypertriglyceridemia in the nonfasting state. Clin Chem 2015;61:1156–63.10.1373/clinchem.2015.241752Search in Google Scholar PubMed PubMed Central
92. Dathan-Stumpf A, Vogel M, Hiemisch A, Thiery J, Burkhardt R, Kratzsch J, et al. Pediatric reference data of serum lipids and prevalence of dyslipidemia: results from a population-based cohort in Germany. Clin Biochem 2016;49:740–9.10.1016/j.clinbiochem.2016.02.010Search in Google Scholar PubMed
93. Balder JW, Lansberg PJ, Hof MH, Wiegman A, Hutten BA, Kuivenhoven JA. Pediatric lipid reference values in the general population: the Dutch Lifelines cohort study. J Clin Lipidol 2018;12:1208–16.10.1016/j.jacl.2018.05.011Search in Google Scholar PubMed
94. Colantonio DA, Kyriakopoulo L, Chan MK, Daly CH, Brinc D, Venner AA, et al. Closing the gaps in pediatric laboratory reference intervals: a CALIPER database of 40 biochemical markers in a healthy and multiethnic population of children. Clin Chem 2012;58:854–68.10.1373/clinchem.2011.177741Search in Google Scholar PubMed
95. Ichihara K, Ozarda Y, Barth JH, Klee G, Shimizu Y, Xia L, et al. Committee on Reference Intervals and Decision Limits, International Federation of Clinical Chemistry and Laboratory Medicine and Science Committee, Asia-Pacific Federation for Clinical Biochemistry. A global multicenter study on reference values: 2. Exploration of sources of variation across the countries. Clin Chim Acta 2017;467:83–97.10.1016/j.cca.2016.09.015Search in Google Scholar PubMed
97. Henny J, Vassault A, Boursier G, Vukasovic I, Mesko Brguljan P, Lohmander M, et al. Working Group Accreditation and ISO/CEN standards (WG-A/ISO) of the EFLM. Recommendation for the review of biological reference intervals in medical laboratories. Clin Chem Lab Med 2016;54:1893–900.10.1515/cclm-2016-0793Search in Google Scholar PubMed
98. Jellinger PS, Handelsman Y, Rosenblit PD, Bloomgarden ZT,Fonseca VA, Garber AJ, et al. American Association of Clinical Endocrinologists and American College of Endocrinology Guidelines for Management of Dyslipidemia and Prevention of Cardiovascular Disease. Endocrine Practice 2017;23(Suppl 2):1–87.10.2337/ds18-0009Search in Google Scholar PubMed PubMed Central
99. Ruhaak LR, van der Laarse A, Cobbaert CM. Apolipoprotein profiling as a personalized approach to the diagnosis and treatment of dyslipidaemia. Ann Clin Biochem 2019;56:338–56.10.1177/0004563219827620Search in Google Scholar PubMed PubMed Central
©2020 Walter de Gruyter GmbH, Berlin/Boston