Introduction

Despite widespread dissemination of the Fredrickson Levy and Lees classification scheme for lipoprotein disorders among medical school curriculums, routine clinical care does not include such phenotyping for lipoprotein abnormalities.¹ In 2008, Sniderman and others sought to reinvigorate this charge by proposing a more clinically accessible scheme for the classification of lipid disorders. The mechanistic basis for this scheme stems from the fact that atherogenic lipoproteins are composed of variable quantities of cholesterol and triglycerides, and a predictable 1 apolipoprotein B per lipoprotein.

Perhaps one of the most helpful applications of the apoB-based approach to lipid phenotyping is the identification of patients with the "type III" or familial dysbetalipoproteinemia.²

In this study by Sniderman et al.,³ published in the Journal of Clinical Lipidology in December 2018, the apoB-based approach was compared to the gold standard diagnostic method of ultracentrifugation chemical analysis to determine whether patients with type III hyperlipoproteinemia could be distinguished from mixed hyperlipidemia with a standard lipid profile.

Pathophysiology and the Algorithm

Under normal circumstances, chylomicron and VLDL particles, each of which contain a single apoB protein, are metabolized in a two-step process. First, chylomicron and VLDL particles bind to lipoprotein lipase in adipose tissue, skeletal and cardiac muscle tissue where triglyceride is rapidly removed and the particles are released into the circulation as chylomicron and VLDL remnants. Second, the liver removes both particles from the circulation. Additionally, VLDL particles can be converted to LDL particles.

In type III hyperlipoproteinemia three things appear to happen: first, VLDL and chylomicron remnants are not effectively removed by the liver and accumulate in the plasma. It is not completely clear why this occurs but apoE, a protein involved in the removal of normal chylomicron and VLDL remnants in the liver is implicated. Second, as their plasma half-lives are prolonged both particles have more time to become cholesterol enriched. Third, VLDL particles are not converted in their normal proportion to LDL.^6,7 These changes provide the basis for the ratios in the algorithm.

• Increased numbers of remnant chylomicron and VLDL particles with resultant:
  • High TG
• Excess cholesterol-rich remnant particles manifesting as:
  • High cholesterol/triglyceride (C/TG ratio)
  • High cholesterol/apoB (C/apoB ratio)
• Normal total plasma ApoB levels as less VLDL particles converted to low-density lipoprotein (LDL) particles resulting in:
  • Increased VLDL apoB/LDL apoB ratio
  • Increased VLDL apoB/total apoB
  • Increased TG/apoB ratio

Figure 1: The ApoB Diagnostic Algorithm

Sequential steps involving apoB, triglyceride (TG) level, the triglyceride to apoB ratio (TG:ApoB) and total cholesterol to apoB ratio (TC:ApoB) allow identification of the principal lipoproteins which are elevated, from which the likely primary etiology can be determined. As the study took place in Canada triglycerides, cholesterol and apoB were reported in mmol/L, mmol/L and g/L, respectively. In the United States, triglycerides, cholesterol and apoB are all typically reported in mg/dL. The ratios used in the algorithm cannot be used until the units are converted to those in the study. Fortunately, the author has released a smartphone app to assist frontline clinician with both the ratios and units.

Figure 2: The Critical Steps Required for Diagnosis of Type III Hyperlipoproteinemia

Key Methods and Findings of the Study

The study included 2,178 people who attended the Lipid Clinic of the Laval Medical Center in Quebec City between 1995 and 2005. Comparisons between the different groups were calculated using ANOVA, Tukey's honest significant difference and Spearman correlation coefficients were determined to assess the significance of the associations.

Of the 2,178 individuals included in the study, 1,351 (62%) were hypertriglyceridemic, defined as a plasma TG ≥1.5 mmol/L (≥133 mg/dL). Of these, 637 (47%) had a normal cholesterol defined as TC ≤75th percentile for age and sex. The remaining 714 (53%) had hypercholesterolemia in addition to hypertriglyceridemia and therefore had combined hyperlipidemia. ApoB levels were measured and the diagnostic algorithm was applied.

Table 1

Results demonstrate considerable overlap of plasma cholesterol levels between individuals with types I, IIb, IV and type III. As expected, given the elevated chylomicrons typical of type I and type V, plasma triglycerides were markedly elevated compared with types IIb, III and IV. Although triglycerides in type III hyperlipoproteinemia were increased compared to the remaining types (IIb and IV) there was insufficient separation for this finding to be conclusive from a diagnostic standpoint. Taken together, these results demonstrate that the combination of total cholesterol and triglyceride levels alone cannot make the diagnosis of dysbetalipoproteinemia.

The individuals identified as having type III hyperlipoproteinemia using the algorithm met several ultracentrifugation criteria (the gold standard) for the diagnosis. This included VLDL apoB particles that were abnormally enriched with cholesterol and markedly elevated VLDL apoB with a comparatively lower LDL apoB. Of note, the classically cited ratio of TC/TG (mg/dL) of close to 1 was not useful in the diagnosis.

Conclusion

The diagnosis of type III hyperlipoproteinemia is critical because of the highly atherogenic nature of the condition. It cannot be diagnosed using the traditional lipid panel. Establishing a diagnosis enables deeper understanding of prognosis, since prognosis varies between different hypertriglyceridemias. This study provides further information on an algorithm that can assist in easier diagnosis of type III hyperlipoproteinemia, via measurement of apoB in combination with a standard lipid profile. A smartphone app is one step that has been made to simplify the algorithm for frontline clinicians particularly given the units for cholesterol, triglycerides and apoB reported in the paper are different to those routinely used in the US. However, there is a paucity of high-quality clinical trial evidence or comparative effectiveness research evaluating therapeutic strategies in this disorder. Such work will be critical in an era of precision medicine to inform us on the right treatment for the right patient at the right time.

References

1. Fredrickson DS, Levy RI, Lees RS. Fat transport in lipoproteins—an integrated approach to mechanisms and disorders. N Engl J Med 1967;276:148-56.
2. Blom DJ, O'Neill FH, Marais AD. Screening for dysbetalipoproteinemia by plasma cholesterol and apolipoprotein B concentrations. Clin Chem 2005;51:904-7.
3. Sniderman AD, de Graaf J, Thanassoulis G, Tremblay AJ, Martin SS, Couture P. The spectrum of type III hyperlipoproteinemia. J Clin Lipidol 2018;12:1383-9.
4. Hopkins PN, Brinton EA, Nanjee MN. Hyperlipoproteinemia type 3: the forgotten phenotype. Curr Atheroscler Rep 2014;16:440.
5. Huebbe P, Rimbach G. Evolution of human apolipoprotein E (APOE) isoforms: gene structure, protein function and interaction with dietary factors. Ageing Res Rev 2017;37:146-61.
6. de Graaf J, Couture P, Sniderman A. A diagnostic algorithm for the atherogenic apolipoprotein B dyslipoproteinemias. Nat Clin Pract Endocrinol Metab 2008;4:608-18.
7. Marais AD, Solomon GA, Blom DJ. Dysbetalipoproteinaemia: a mixed hyperlipidaemia of remnant lipoproteins due to mutations in apolipoprotein E. Crit Rev Clin Lab Sci 2014;51:46-62.

Clinical Topics: Diabetes and Cardiometabolic Disease, Dyslipidemia, Homozygous Familial Hypercholesterolemia, Hypertriglyceridemia, Lipid Metabolism, Nonstatins, Primary Hyperlipidemia

Keywords: Dyslipidemias, Hyperlipoproteinemia Type III, Apolipoproteins B, Chylomicron Remnants, Lipoprotein Lipase, Triglycerides, Hypercholesterolemia, Apolipoproteins E, Chylomicrons, Hypertriglyceridemia, Hyperlipidemia, Familial Combined, Cholesterol, Lipoproteins, LDL, Adipose Tissue, Ultracentrifugation, Myocardium, Algorithms, Analysis of Variance

< Back to Listings