David Preiss1 and Dermot Neely2. In 1972, some 20 years before prescription of ..... Blom DJ, O'Neill FH and Marais AD. Screening for dysbetalipoproteine-.
Editorial Annals of Clinical Biochemistry 2015, Vol. 52(6) 629–631 ! The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0004563215594818 acb.sagepub.com
Biochemistry laboratories should routinely report non-HDL-cholesterol David Preiss1 and Dermot Neely2
In 1972, some 20 years before prescription of statins became commonplace, Friedewald et al. published a simple equation whereby low-density lipoprotein (LDL)-cholesterol could be estimated from measurements of total cholesterol, high-density lipoprotein (HDL)-cholesterol and triglycerides, without the need for preparative ultracentrifugation.1 This was based on their observation that the mass ratio of triglyceride to cholesterol in very low-density lipoprotein (VLDL) was relatively constant among normal subjects and those with common hyperlipidaemias, at about 5 to 1 (or 2.2 to 1 when expressed in mmol/L) when chylomicrons are not present in plasma. Although the authors conceded that this ratio did not provide a very accurate estimate of VLDL-cholesterol, they considered the error to be acceptable as this is small relative to the LDL-cholesterol concentration, at least when used for the original purpose of classification of hyperlipidaemias. In the intervening years since publication, the Friedewald paper has become a citation classic and the use of the equation (LDL-cholesterol [mmol/L] ¼ total cholesterol [mmol/L] – HDL-cholesterol [mmol/L] – triglycerides [mmol/L]/2.2 where triglycerides [mmol/L]/2.2 estimates VLDL-cholesterol) and reporting of results for calculated LDL-cholesterol have become standard practice in laboratories in the US and worldwide. Emphasis on LDL-cholesterol appears sound given that this biomarker is predictive of cardiovascular disease2 and, as shown most notably by statins3 but also by other medications,4,5 strategies that reduce LDLcholesterol provide cardiovascular benefit. For many years, lipid-lowering guidelines have therefore recommended LDL-cholesterol as the main target of treatment. Importantly, the consistency of the relationship between LDL-cholesterol reduction and cardiovascular event reduction has also led to its acceptance by the US
FDA and NIH of LDL-cholesterol as a surrogate endpoint for the purpose of regulatory approval of new lipid-lowering drugs. Most, though not all, of these trials (e.g. WOSCOPS) used the Friedewald equation; indeed, most required randomized patients to have regular fasting lipid measurements and they typically excluded patients with fasting triglycerides >4.5 mmol/L. The equation was developed from only 448 individuals (96 with normal lipids and 352 with dyslipidaemia), none of whom were on lipid-modifying treatment. Yet, remarkably, the equation has never been validated for use in patients treated with statins or other lipid-lowering drugs. Few laboratories use direct assays or preparative ultracentrifugation to measure LDL-cholesterol. While the recommendations that the Friedewald equation can only be used in fasting individuals with triglycerides 2.3 mmol/L.7,8 It assumes that the cholesterol content of VLDL is fixed, an overly simplistic assumption which fails to take account of the variable composition of other atherogenic apolipoprotein B-containing lipoproteins like VLDL and chylomicron remnants, introducing an unpredictable error which is greatest in the context of dysbetalipoproteinaemia (Type III) as Friedewald himself recognized. Unfortunately, direct LDL-cholesterol methods are costly and have shown poor agreement in dyslipidaemic samples,9 which has led to the proposal of more advanced equations for estimating LDL-cholesterol.10 Notably, currently available tools for cardiovascular risk estimation do not require calculated or measured LDL-cholesterol values. The JBS3 (http://www.jbs3risk.com/JBS3Risk.swf) and QRISK2 (http://www.qrisk.org/) risk engines use total cholesterol:HDL-cholesterol ratio and ASSIGN (http://assign-scorecom/estimate-the-risk/) includes total cholesterol and HDL-cholesterol as separate variables Given that the primary aim of lipid measurement is to aid cardiovascular risk estimation and allocation of statin therapy, there are thus clear reasons to seek simpler approaches for testing and evaluating lipids which require no assumptions, which do not require fasting and which are valid both on and off
Annals of Clinical Biochemistry 52(6) lipid-modifying therapies. Non-HDL-cholesterol (non-HDL-cholesterol [mmol/L] ¼ total cholesterol [mmol/L] – HDL-cholesterol [mmol/L]) is an obvious and practical choice to consider as it avoids the weaknesses of the Friedewald equation as described above, and its use has long been encouraged.11 Most importantly, non-HDL-cholesterol is better than LDL-cholesterol and as good as apolipoprotein B at predicting cardiovascular disease12 regardless of triglyceride concentrations, fasting status and the presence or absence of statin treatment. This reflects the fact that it incorporates all atherogenic lipoproteins including not only LDL, IDL and VLDL, but also remnant particles and lipoprotein(a)13,14 (see Figure 1). The increasing prevalence of obesity, diabetes and consequently the higher triglyceride concentrations observed in the general population imply that a measure which includes the predictive capacity of these atherogenic lipoproteins, which would otherwise be lost by using estimated LDL-cholesterol, is increasingly important. It does not incur additional cost on top of routine analysis of total and HDL-cholesterol apart from the need to include it on the result report. It can be calculated on both fasting and non-fasting samples and its predictive capability is at least as good in the non-fasted state.12 Fasting can therefore be avoided in the vast majority of situations, which is convenient for patients. The difference in numbers obviously depends on the triglyceride concentrations, non-HDL-cholesterol being roughly 0.8 mmol/L greater in a fasted patient with ‘optimum’
Figure 1. The contribution of various lipoproteins to non-HDL-cholesterol.
Preiss and Neely triglycerides of 1.7 mmol/L if the VLDL-cholesterol:triglyceride ratio is exactly as predicted by Friedewald. Recognizing that this rough conversion is not valid in patients on statins, in whom VLDL-cholesterol is depleted and in whom the difference is typically smaler (e.g. 0.5 mmol/L), a treatment target of 7.5 mmol/L in whom a diagnosis of familial hypercholesterolaemia (FH) is considered or for LDL-cholesterol-based cascade testing in relatives of an individual with FH, fasted blood sampling with use of the Friedewald equation is still clinically useful. Numerous current clinical trials of lipid-modifying therapies have recognized the value of non-HDL-cholesterol and are using it as an inclusion criterion rather than calculated LDL-cholesterol. Prediction of cardiovascular events in those already on statins remains an important goal but one which is not addressed by the use of tools like the JBS3, QRISK2 or ASSIGN risk calculators. Notably, in an important analysis of eight statin trials involving >60,000 participants on statins, nonHDL-cholesterol was more strongly associated with the risk of cardiovascular events than either LDLcholesterol or apolipoprotein B in adjusted analyses.15 While there are advocates for the routine measurement of apolipoprotein B, it offers no advantage over the predictive data provided by measurement of lipids which is already routinely performed in the UK12,16 though it may be helpful in identifying patients with dysbetalipoproteinaemia.17 We therefore propose that biochemistry laboratories should report non-HDL-cholesterol when a fasting or random lipid profile is requested. While this will present some challenges, mainly the lack of familiarity that clinicians will have for this apparently new variable, it offers considerable advantages over the ubiquitous Friedewald equation. Acknowledgement The Figure was partly generated using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 unported license (available at http://www.servier.com/Powerpoint-image-bank).
Declaration of conflicting interests None declared.
631 Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Ethical approval Not required.
Guarantor DP.
Contributorship DP and DN contributed equally to the writing of the editorial.
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