Lipid Monitoring for Cardiovascular Risk
A Practical Guide
Forever Healthy Foundation gGmbH
D-76227 Karlsruhe, Germany
December 8, 2020
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This Practical Guide is part of Forever Healthy's "Maximizing Health" initiative that seeks to holistically review the world's leading medical knowledge on various health-related topics and turn it into actionable information.
Numerous aspects of lipid metabolism have been implicated in the genesis of cardiovascular disease (CVD). Given the prevalence and mortality of CVD, it is essential to identify optimal biomarkers of lipid metabolism that may provide better risk prediction. Several lipid biomarkers have been proposed for monitoring, and several methods have been developed for measuring them. This Practical Guide aims to identify those markers, evaluate their relevance to CVD risk prediction, and determine the best lipid monitoring protocol available at the moment.
This Practical Guide seeks to answer the following questions:
- Which lipid parameters are useful for monitoring CVD risk?
- Which test methods are available?
- Which test method is best for each parameter?
- What is the reference range for each parameter?
- What is the total error (imprecision + inaccuracy)?
- Which factors affect each parameter?
- At what age should testing commence?
- How often should testing be repeated?
- What is the optimal lipid monitoring protocol based on the currently available evidence?
Impatient readers may choose to skip directly to the Presentation of results section for tips on practical application.
A list of potential lipid parameters was compiled from medical and laboratory resources. A literature search was then conducted on PubMed for each parameter using the search terms shown in Table 1 and includes results available as of August 14, 2020. Titles and abstracts of the resulting studies were screened and relevant articles downloaded in full text. The references of the full-text articles were manually searched in order to identify additional studies that may have been missed by the search terms.
Table 1: Literature Search
|"parameter" (ie. total cholesterol) [Title/Abstract] AND cardiovascular risk[Title/Abstract] filter: review, systematic review|
|"parameter" (ie. total cholesterol) [Title/Abstract] AND analytical method [Title/Abstract] filter: review, systematic|
|"parameter" (ie. total cholesterol) [Title/Abstract] AND precision AND accuracy [Title/Abstract] filter: review, systematic|
|"parameter" (ie. total cholesterol) [Title/Abstract] AND cardiovascular risk [Title/Abstract] AND hazard ratio [Title/Abstract] filter: meta-analyses, systematic review|
|lipid management AND clinical practice guidelines|
|A manual search of the reference lists of the selected papers|
Summary of Recommendations made in Clinical Practice Guidelines
Several clinical practice guidelines (CPGs) on lipid monitoring and management have been published in recent years and the main recommendations are summarized in the table below:
Table 2. Summary of Guideline Recommendations
|Guideline||Recommendation||Strength of Recommendation||Level of Evidence|
|European Society of Cardiology (ESC)||Total cholesterol is to be used for the estimation of total CV risk by means of the SCORE system|
|HDL-C analysis is recommended to further refine risk estimation using the online SCORE system||I |
|LDL-C analysis is recommended as the primary lipid analysis method for screening, diagnosis, and management||I |
|Triglyceride (TG) analysis is recommended as part of the routine lipid analysis process||I |
|Non-HDL-C evaluation is recommended for risk assessment, particularly in people with high TG levels, diabetes mellitus (DM), obesity, or very low LDL-C levels||I |
|ApoB analysis is recommended for risk assessment, particularly in people with high TG levels, DM, obesity, metabolic syndrome, or very low LDL-C levels. It can be used as an alternative to LDL-C, if available, as the primary measurement for screening, diagnosis, and management, and may be preferred over non-HDL-C in people with high TG levels, DM, obesity, or very low LDL-C levels||I |
|Lp(a) measurement should be considered at least once in each adult person’s lifetime to identify those with very high inherited Lp(a) levels >180 mg/dL (>430 nmol/L) who may have a lifetime risk of atherosclerotic CVD equivalent to the risk associated with heterozygous familial hypercholesterolemia.||IIa |
|Lp(a) should be considered in selected patients with a family history of premature CVD, and for reclassification in people who are borderline between moderate and high-risk||IIa |
|Risk factor screening including the lipid profile should be considered in men >40 years old, and in women >50 years of age or post-menopausal.||I |
|Screen between the ages of 40-75||-||-|
|Canadian Family Physician (CFP)||Initiating screening: In patients without CVD (primary prevention), we suggest lipid testing as part of global CVD risk estimation in men at age ≥40 y and women at age ≥50 y (moderate-level evidence)||-|
|Repeat screening: For patients not taking lipid-lowering therapy, we suggest lipid testing as part of global CVD risk estimation, performed no more than every 5 y (moderate-level evidence). Global CVD risk estimation can be repeated sooner if other CVD risk factors develop in the interim||-||moderate|
|Patients do not need to fast for lipid testing. Nonfasting lipid levels can be used to calculate global CVD risk (moderate-level evidence).||-||moderate|
|American Heart Association/American College of Cardiology |
|Screen between the ages of 20-75||-||-|
|In adults who are 20 years of age or older and not on lipid-lowering therapy, measurement of either a fasting or a nonfasting plasma lipid profile is effective in estimating atherosclerotic CVD risk and documenting baseline LDL-C.||I |
|In adults who are 20 years of age or older and in whom an initial nonfasting lipid profile reveals a TG level of 400 mg/dL or higher, a repeat lipid profile in the fasting state should be performed for assessment of fasting triglyceride levels and baseline LDL-C||I|
|For adults with an LDL-C level less than 70 mg/dL, the measurement of direct LDL-C or modified LDL-C estimate is reasonable to improve accuracy over the Friedewald formula.||IIa|
|Adherence to changes in lifestyle. and effects of LDL-C lowering medication should be assessed by measurement of fasting lipids and appropriate safety indicators 4-12 weeks after statin initiation or dose adjustment and every 3-12 months thereafter based on the need to assess adherence or safety||I |
In patients with a family history of early cardiovascular disease or significant hypercholesterolemia, you may measure a lipoprotein profile when the patient is as young as 2 years old to detect familial hypercholesterolemia (FH) or rare forms of hypercholesterolemia.
In patients without cardiovascular risk factors or a family history of early cardiovascular disease, you may measure a fasting lipid profile or nonfasting non–HDL-C once between the ages of 9 and 11 years, and again between the ages of 17 and 21 years, to detect lipid abnormalities
In adults 20 years or older who are free from ASCVD (and not on lipid-lowering therapy), measure LDL-C with either a fasting or nonfasting plasma lipid profile when estimating ASCVD risk, and document baseline LDL-C. For adults 20 years or older who have an initial nonfasting lipid profile with triglycerides 400 mg/dL or higher, repeat the lipid profile with the patient fasting to establish fasting triglyceride levels and baseline LDL-C.
|A fasting or nonfasting lipoprotein profile including at least total-C and HDL-C should be obtained at least every 5 y.||Moderate||E |
|If non–HDL-C and LDL-C are in the desirable range, lipoprotein lipid measurement and atherosclerotic CVD risk assessment should be repeated every 5 y, or sooner based on clinical judgment||Moderate||E |
|When intervention beyond public health recommendations for long-term atherosclerotic CVD risk reduction is used, levels of atherogenic cholesterol (non–HDL-C and LDL-C) should be the primary targets for therapies.||High||A |
|Apo B is considered an optional, secondary target for treatment after the patient has been treated to goal levels for atherogenic cholesterol||Moderate||E |
|Clinicians may consider measuring LDL particle concentration as an alternative to apo B||Moderate||E |
|HDL-C is not recommended as a target of therapy per se, but the level is often raised as a consequence of efforts to reduce atherogenic cholesterol through lifestyle and drug therapies.||Moderate||N |
|Elevated triglyceride level is not a target of therapy per se, except when very high (>500 mg/dL)||Moderate||B |
Examination of the recommendations made in the current CPGs reveals that many "strong" recommendations are made on the basis of a weak level of evidence. Additionally, guidelines that were developed from the same evidence base provide differing recommendations. Approximately 50% of the recommendations made in all major clinical guidelines are based on the lowest level of evidence (primarily expert opinion) (Allan et al., 2015). All of the evidence underlying recommendations as to which patients should receive lipid profiling and which parameters to screen is low (based on expert opinion, observational or retrospective studies) (Mach et al., 2019).
A review and appraisal (according to AGREE II criteria) of 19 lipid-management-related guidelines determined that the CPGs are generally of good quality (Zhou et al., 2019). The majority of CPGs chose LDL-C as the primary target for lipid management and some recommend non-HDL-C and apoB as secondary targets. In general, the newer guidelines prefer a nonfasting sample for screening. The appraisal identified inconsistencies between the guidelines related to the timing/frequency of blood tests and the target biomarker (Zhou et al., 2019).
Conflicts of interest are also a significant problem with many guideline expert panel members having close ties to pharmaceutical companies that produce cholesterol-lowering medications (chriskresser.com; Zhou et al., 2019).
Additionally, none of the current guidelines contain any mention of which test method is optimal or the level of error to be expected for the recommended biomarkers.
Review of Lipid Biomarkers
Our literature search identified 21 lipid biomarkers ranging from those included in a standard lipid panel to those that are just entering clinical practice. For each biomarker, the main evidence for and against its use in CVD risk prediction is summarized. The available information on test types, standardization & error, reference values, factors affecting measurement, and frequency of testing for each parameter is also analyzed.
The review begins with markers that are typically reported on a standard lipid panel as well as markers that can be derived from it:
- Total cholesterol
Additionally, we review the newer, less commonly measured markers collectively known as "advanced lipid tests":
- LDL subfractions
- HDL subfractions
- HDL functional assays
- Remnant lipoprotein particles
- Fatty acids
Total cholesterol (TC) has been a major component of cardiovascular risk screening models for several years (Peters et al., 2016). In addition to its association with CVD risk, abnormal levels of cholesterol have been observed in Smith-Lemli-Opitz syndrome, type II diabetes, neurological diseases, and cancer (Li et al., 2019).
Many high-quality studies have linked elevated TC levels and increased intraindividual variation of TC to CVD risk. A systematic review (n=884,416) found that a 39 mg/dL (1 mmol/L) increase in TC was associated with a 20% increase in the relative risk of CVD in women and 24% in men (Peters et al., 2016). The top quartile of intraindividual variation in TC has been associated with an over 60% increase in all-cause mortality, a 40% increase in myocardial infarctions, and a 50% increase in strokes (Simpson, 2019).
In the Framingham study, subjects with elevated serum total cholesterol (>275 mg/dl) had an increased risk of adverse outcomes, whether healthy or with CAD. Compared with persons with cholesterol levels <200 mg/dl (<5.17 mmol/liter), the risk ratios for patients with elevated cholesterol levels were 3.8 for reinfarction, 2.6 for CAD mortality, and 1.9 for overall mortality (Kannel, 1995). A large meta-analysis (n= 256,259) examining the correlation of TC with cardiovascular disease events found a hazard ratio (per 1 standard deviation increase) of 1.24 (Perera et al., 2015).
A 1% drop in TC is associated with a 2-3% CVD risk reduction (Holme, 1992), at least in those under 50 years of age. Reducing high TC has been shown to reduce morbidity and mortality in young and middle-aged men (Naito et al., 1992).
However, TC is evidently not the whole story as the prevalence of cholesterol levels ≥240 mg/dl (≥6.21 mmol/liter) in persons who had sustained myocardial infarction was only 35–52% in men and 66% in women and 20% of myocardial infarctions occurred in people who had cholesterol levels <200 mg/dl (<5.17 mmol/liter) (Kannel, 1995). Several studies have also observed that individuals with low TC were just as atherosclerotic as those with high TC (Ravnskov et al., 2018).
Some studies have shown that TC performs worse than cholesterol subfractions (LDL-C, HDL-C, etc...) in terms of clinical validity, responsiveness to treatment changes, and detectability of long-term changes (Glasziou et al., 2014).
Up to certain levels, cholesterol is beneficial for the human body. It has crucial roles in the building of cell membranes and as a precursor of steroid hormones (Li et al., 2019), and high TC has been inversely correlated with all-cause mortality in people aged >65 (Liang et al., 2017). Over the age of 50, falling cholesterol levels are associated with decreased longevity. Each 1 mg/dL drop in TC per year, was associated with an 11-14% increase in coronary and total mortality (Anderson et al., 1987).
There are three main categories of analytical methods for cholesterol level determination: 1) classical chemical methods, 2) fluorometric and colorimetric enzymatic assays, and 3) analytical instrument approaches. While classical methods are simple and inexpensive to perform, multi-step procedures are required. Enzymatic assays are costly. Chromatographic and mass spectrometric methods are the most accurate and sensitive but are costly and require extensive sample pretreatment (Li et al., 2019).
The National Institute of Standards and Technology has approved two methods, isotope-dilution mass spectrometry, and a modified Abell-Kendall protocol, as reference measurement procedures for blood cholesterol quantification (Li et al., 2019). Although the modified Abell-Kendall reaction is considered a standard reference-method, its use is not widely accepted for routine tests since highly corrosive agents are used (Li et al., 2019). For routine analysis, many labs use commercial quantification assay kits, handheld point of care testing devices, and automated analyzers based on enzymatic assays.
Standardization & error
Among the lipid and lipoprotein analytes, TC was the first to undergo standardization (a process that makes the results comparable across measurement procedures ) and is now the most straightforward and well-standardized of the analytes (Warnick et al., 2008).
The expected total error for TC is ± 8.9-10% ( Warnick et al., 2008 ) where the total error is the sum of errors in accuracy (closeness to the true value) and precision (closeness of repeat measurements).
The National Cholesterol Education Program ( NCEP) performance goal for total error is ≤ 8.9%. That is, 95% of individual TC measurements should fall within 8.9% of the reference method procedure (RMP) value over repeated measurements (Warnick et al., 2008). However, a study on error levels of 5 methods for TC screening found that none of the methods met the NCEP performance recommendations. The errors led to a risk level misclassification in 5-18% of patients (Miller et al., 1993).
Enzymatic assays are subject to additional challenges as they may not be strictly selective for cholesterol (react with other sterols). Some chemicals present in a sample (ascorbic acid or bilirubin) may consume hydrogen peroxide, creating bias during indirect cholesterol quantification ( Li et al., 2019 ).
Gas and liquid chromatography are widely accepted to be more reliable, sensitive, and accurate than the other methods (Li et al., 2019).
On average, the intraindividual variation of TC is 5-6.4% (Warnick et al., 2008; da Silva et al., 2018). TC variation is estimated to be about 2.8% within-day, 7.9% within-month, and 3.9-10.9% over the long term (Naito et al., 1992). At the extreme, day-to-day cholesterol values in the same individual have been shown to vary by 15%, and an 8% difference has been identified within the same day (Naito et al., 1992).
|NCEP||> 240 mg/dL||200-239 mg/dL||< 200 mg/dL|
|Chris Kresser (ADAPT Practitioner training)||-||-|
males: 150-220 mg/dL
females: 150-230 mg/dL
|German Lipid Guidelines||> 240 mg/dL||200 - 239 mg/dL||< 190 mg/dL|
|-||< 155 mg/dL|
Factors affecting measurement
- Blood lipid levels exhibit mild seasonal variation (3-5%) with a peak in total cholesterol levels in the winter and a trough in the summer. One study found that the amplitude of seasonal variation of total cholesterol concentration was 3.9 mg/dL (0.10 mmol/L) in men and 5.4 mg/dL (0.14 mmol/L) in women (Uptodate.com; Naito et al., 1992).
- Positional changes can affect cholesterol levels. Levels can decrease by as much as 15% in the recumbent (horizontal) position. As a result, hospitalized patients can be expected to have lower levels than outpatients (Mosby's Manual of Diagnostic and Laboratory Tests, 2017).
Sex & Age
- Adult males 20-45 years generally have higher levels than age-matched females. Women <20 years of age and postmenopausal women generally have higher levels than age-matched males. In both genders, the levels increase 1.5 mg/dL/year with levels in males plateauing at age 50 and in females increasing exponentially (Naito et al., 1992).
- May influence TC by about 10% (Naito et al., 1992). Maintain a stable weight and usual diet for 2 weeks before lipid assessment (Naito et al., 1992).
- Excessive alcohol will slightly increase TC (Naito et al., 1992).
- Habitual vigorous activity causes a decrease of 0-15% but the effects may not be apparent for 3-6 weeks (Naito et al., 1992).
- Certain illnesses can affect cholesterol levels. For example, patients with acute myocardial infarction (AMI) may have as much as a 50% reduction in cholesterol level for as long as 6 to 8 weeks ( Mosby's Manual of Diagnostic and Laboratory Tests, 2017 ).
- Mental stress can raise cholesterol by 10-50% in the course of half an hour (Ravnskov et al., 2018).
- adrenocorticotropic hormone, anabolic steroids, beta-adrenergic blocking agents, corticosteroids, cyclosporine, epinephrine, oral contraceptives, phenytoin (Dilantin), sulfonamides, thiazide diuretics, and vitamin D may increase TC ( Mosby's Manual of Diagnostic and Laboratory Tests, 2017 )
- allopurinol, androgens, bile salt-binding agents, captopril, chlorpropamide, clofibrate, colchicine, colestipol, erythromycin, isoniazid, liothyronine (Cytomel), monoamine oxidase inhibitors, niacin, nitrates, and statins may decrease cholesterol ( Mosby's Manual of Diagnostic and Laboratory Tests, 2017 )
- Fasting is not required (Oxford Handbook of Clinical and Laboratory Investigation, 2017)
- The test can be performed on serum or using a whole blood spot (Oxford Handbook of Clinical and Laboratory Investigation, 2017)
Assuming 5% analytical imprecision and a mean biological variation of 6.5%, it was determined that duplicate analyses of 4 specimens per patient would be optimal to determine the true TC value (Naito et al., 1992). For practicality, the lipid standardization panel recommends two measurements taken at least one week apart but within two months (Naito et al., 1992).
Current guidelines by organizations such as the American Heart Association recommend measurements once every 4-6 years because recent evidence has suggested that, due to the weak signal–noise (SN) ratio of cholesterol levels, frequent screening and/or monitoring – as part of long-term management – more often captures measurement error rather than true changes (Perera et al., 2015). However, a detailed analysis showed that annual monitoring was both cost-saving and effective (Perera et al., 2015).
Total Cholesterol Summary Table
|Summary||Although an important CVD risk factor, serum TC on its own is a relatively poor predictor of who will go on to have an event. However, the test is well-standardized with an acceptable error. Potentially useful for identifying the risk associated with intraindividual variations over time. Necessary for the calculation of non-HDL-C.|
|Best test type||Gas & liquid chromatography, mass spectrometry|
|Hazard ratio for CVD events||1.24|
|Frequency||Duplicate analyses of 4 specimens (fasting unnecessary), obtained within 2 months but at least one week apart, repeated annually|
It is widely accepted that low-density lipoprotein (LDL) has a causal role in CVD. Patient-level meta-analyses (n=170,000) by the Cholesterol Treatment Trialists’ Collaboration found that for every 40 mg/dL of LDL-C reduction with statins, there was a 10% relative decrease in all-cause mortality, 20% in coronary heart disease mortality, and a 22% proportional reduction in the risk of major cardiovascular events over a median 5 year period of treatment. Further analyses also showed reductions in the need for myocardial revascularization and ischemic stroke (17%) during each year the treatment was continued (Rached & Santos, 2020; da Silva et al., 2018).
Intraindividual LDL-C variability has also been linked to increased CVD risk. Each standard deviation of LDL-C variability is associated with around a 10-20% increase in CVD events. An analysis of 9 coronary intravascular US trials showed that coronary atheroma progression was associated with higher variability of LDL-C. In patients with Type II DM, being in the top quartile of LDL-C variability was associated with an approximately 10% greater carotid intima-media thickness (cIMT) (Simpson, 2019).
Despite its wide acceptance as an ideal marker of CVD risk, several studies bring into question the causal role of LDL-C in CVD. It has been argued that if LDL-C is atherogenic, people with high LDL-C should have more atherosclerosis than those with low LDL-C. At least four studies have shown a lack of an association between LDL-C and the degree of atherosclerosis. In a study of 304 women, no association was found between LDL-C and coronary calcification (Ravnskov et al., 2018) and numerous Japanese studies have found that high LDL-C is not a risk factor for CVD mortality in women of any age (Ravnskov et al., 2018).
I n a large American study, that included almost 140,000 patients with acute myocardial infarction, LDL-C at the time of admission was lower than average (Ravnskov et al., 2018). Additionally, despite having LDL-C levels at the goal defined by the guidelines, patients often present with elevated levels of other atherogenic lipoproteins (ApoB 25%, non-HDL-C 31.1%, and ox-LDL 49.2%) (Paredes et al., 2019) and suffer CVD events that thus correlate more strongly with other markers.
A large meta-analysis comparing 10 lipid biomarkers found a hazard ratio of only 1.16 with elevated LDL-C for CVD events, amongst the lowest of the markers analyzed (Perera et al., 2015). Furthermore, the most important outcome – an increase in overall life expectancy – has never been mentioned in any cholesterol-lowering trial. As calculated from available information, statin treatment does not prolong lifespan by more than an average of a few days (Ravnskov et al., 2018).
Ultracentrifugation (reference method)
- The combination of ultracentrifugation and heparin-Mn2+ precipitation, with the generic term ‘β quantification’ (BQ) has been proposed as the LDL-C reference method by the NCEP (Ramasamy, 2018; Nakamura et al., 2014). The BQ method includes the cholesterol in intermediate-density lipoproteins (IDL) and the cholesterol in lipoprotein(a) (Lp(a)) in addition to the cholesterol in the classical LDL fraction. Moreover, very-low-density lipoprotein (VLDL) remnants can be found in both the VLDL range and the IDL range, which is measured as LDL by BQ (Contois et al., 2011). Measuring LDL-C by ultracentrifugation is time-intensive and expensive.
- The Friedewald formula is used as the primary method to calculate LDL-C from TC, triglycerides (TG), and HDL-C provided the patient is sampled after an overnight fast, and the TG values are < 400 mg/dL (Ferraro et al., 2019). This formula calculates LDL-C in mg/dL by subtracting the sum of HDL-C and VLDL-C (TG/5) from TC.
- By homogeneous methods, LDL-C is separated from other cholesterol fractions with the characteristics of surfactants, and LDL-C is directly measured using an automatic analyzer. Several different homogeneous LDL-C assays are used for routine clinical analysis (Yano et al., 2019). The potential advantages of direct measurement of LDL-C include the ability to measure LDL-C at high levels of TG, the ability to measure LDL-C in nonfasting samples, and the reduction of imprecision by a single measurement instead of a value that is calculated from 3 different results.
Standardization & error
The Clinical Laboratory Improvement Amendments of 1988 (CLIA) evaluation criteria recommended LDL values should be within ± 30% of the reference method (Warnick et al., 2008). The NCEP criteria for LDL-C expect inaccuracy ≤ ±4% of the RMP, imprecision of ≤ ±4%, and a total error ≤ ±12% ( Warnick et al., 2008).
The Friedewald calculated value of LDL-C has well-established limitations: 1) methodological errors may accumulate since the formula necessitates three separate analyses of TC, TGs, and HDL-C; and 2) a constant cholesterol/TG ratio in VLDL is assumed. With high TG values (>4.5 mmol/L or >400 mg/dL) the formula cannot be used (escardio.org). It has been suggested that Lp(a) cholesterol should also be subtracted for a more accurate determination of LDL-C contribution to cardiovascular risk (Ramasamy, 2018). Additionally, t he equation has not been validated for use in patients using statins or other lipid-lowering drugs (Preiss & Neely, 2015). Calculated LDL-C exhibits an underestimation bias of about 5% compared with direct assays (Schaefer et al., 2016).
Several attempts to modify the formula to achieve a more accurate measurement have been attempted. A study (n= >1.3 million) that replaced the fixed ratio of 5 for VLDL-C estimation with one of 180 adaptable ratios based on a patient's individual non-HDL-C and TG values (Martin's formula) found that i n patients with LDL-C <70 mg/dL and triglycerides of 150-199 mg/dL, LDL-C accuracy was 92% compared to 61% with the Friedewald equation. In those with TG levels of 200-399 mg/dL, the accuracy was 83% compared to only 40% with Friedewald calculation (Ferraro et al., 2019). The overall accuracy of the equation compared to direct ultracentrifugation (the RMP) was 92% in contrast to 85% accuracy for Friedewald estimation (Ferraro et al., 2019).
However, there are inaccuracies using Martin's formula as well. It has been reported that Martin’s equation was not accurate for estimating LDL-C unless TG was between 300 and 400 mg/dl and another formula was recommended in its place (Cordova & Cordova formula) as an alternative when direct LDL-C measurement is not available (Cordova et al., 2020).
The risk-based cut points for LDL cholesterol defined by NCEP are determined from epidemiological studies, many of which used calculated LDL-C rather than the reference method (Contois et al., 2011).
Direct LDL-C assays use proprietary chemical-based methods and are not necessarily reliable. They are not standardized and, in some cases, can be even less accurate than the Friedewald equation (Ferraro et al., 2019). The reaction specificities of homogeneous methods to LDL and VLDL subfractions have been shown to vary, with reduced reactivity to small dense LDL and nonspecific reactivity to VLDL.
Direct LDL-C assays have been reported to give falsely low LDL-C in patients with various causes of cholestasis (Ramasamy, 2018). One study reported that 7 of 8 direct LDL-C methods failed to show improved CVD risk score classification over the corresponding calculated LDL-C methods ( van Deventer et al., 2011 ). Another study comparing direct LDL-C assessment found that the total error ranged from 13.5% in healthy individuals to -26.6% and + 31.9% in those with known cardiovascular disease or dyslipidemias (Ferraro et al., 2019). In contrast, a comparison of four different homogeneous direct methods for LDL-C with the β quantitation reference method found that all had acceptable assay precision within the NCEP performance guidelines. However, all four methods exhibited a progressively poor performance as the TG concentrations increased, though the performance was superior to the Friedewald calculation (Ramasamy, 2018).
|Guideline||Optimal||Low-risk patients||Moderate-risk patients||High-risk patients||Very high-risk patients|
|ESC||-||< 116 mg/dL||< 100 mg/dL||< 70 mg/dL||<55 mg/dL|
|AHA/ACC||-||-||< 160 mg/dL||< 70 mg/dL||-|
< 100 mg/dL
< 70 mg/dL
|AACC||-||< 160 mg/dL||< 130 mg/dL||< 100 mg/dL||-|
|Functional Medicine||50-140 mg/dL||-||-||-||-|
ESC guidelines suggest that for low-risk populations, LDL-C <116 mg/dL is recommended. However, the value is based on a paper from 2012 that never proposed any LDL-C target goal, much less a specific one of 116 mg/dL (Rodrı´guez, 2020).
Factors affecting measurement
- Calculated LDL-C has been found to underestimate LDL-C at concentrations of TG ≥2 mmol/L (177 mg/dL) (Fawwad et al., 2016). Equally, at very low levels of LDL-C, calculated LDL-C may be misleading.
- Certain medicines, including steroids, some blood pressure medicines, and HIV/AIDS medicines, can raise LDL (medlineplus.gov): Loop diuretics (5-10%) Thiazide diuretics (high dose) (5-10%) Cyclosporine and Tacrolimus (0-50%)
Other medical conditions
- Diseases such as chronic kidney disease, diabetes, and HIV/AIDS can cause a higher LDL level (medlineplus.gov).
- May cause an acute increase in the level of TC, TG, and LDL-C (3%, 9.1%, and 1.7%) and a decrease in HDL (Jha, 2019).
A fasting sample (9-12 hours) is required for calculated LDL-C but not for direct LDL-C (Contois et al., 2011). For patients with an LDL-C level <70 mg/dL (<1.8 mmol/L), the measurement of direct LDL-C or modified LDL-C estimate is reasonable to improve accuracy over the Friedewald formula (labtestsonline.org).
- Many guidelines recommend a lipid panel with LDL-C every four to six years in adults with no risk factors for heart disease, and that youth should be tested at least once between the ages of 9 and 11 and again between the ages of 17 and 21 (labtestsonline.org).
- According to most guidelines, monitoring may be done more frequently and at regular intervals when risk factors for heart disease are present, when prior results showed high-risk levels, and/or when undergoing treatment for unhealthy lipid levels (labtestsonline.org).
- 4-12 weeks after beginning a treatment plan, a fasting or nonfasting lipid panel should be performed and then retested every 3 to 12 months if needed (AHA/ACC).
LDL-C Summary Table
|Summary||Despite widespread use, several other markers have shown better results in the prediction of CVD risk with a lower error. Not recommended but calculated LDL-C comes as part of a standard lipid panel.|
|Error||± 12% ideally (but -26.6% - + 31.9% reported in practice)|
|Hazard ratio for CVD events||1.16|
Many beneficial functions of HDL such as antioxidative activity on LDL, improvement of endothelial functions, anti-inflammatory properties, anti-thrombotic effects, and cholesterol efflux capacity (CEC) have been reported. HDL protects against CVD by removing excess cholesterol from macrophages through the ATP-binding cassette transporter A1 (ABCA1) and ATP-binding cassette transporter G1 (ABCG1) pathways of reverse cholesterol transport (NCEP).
The inverse association between plasma HDL-C and the risk of CVD is widely considered to be one of the most consistent and reproducible associations in observational epidemiology. The NCEP estimates that each 1% increase in HDL-C is associated with a 2–4% decrease in the risk of coronary heart disease (CHD), and clinical trials on low-density lipoprotein-lowering therapies have shown that concomitant increases in HDL-C confer an additional independent reduction in the risk of CHD (Nakamura et al., 2015). An extensive meta-analysis found a hazard ratio of 1.19 for CVD events with low HDL-C (Perera et al., 2015).
However, a recent review concluded that HDL-C correlated with CVD risk only in individuals with no history of CVD (Niisuke et al., 2018). A major challenge in assessing CVD risk associated with low HDL-C is its close linkage with insulin resistance (Riggs & Rohatgi, 2019). It has also recently been shown that the measurement of HDL-C does not improve risk prediction beyond the conventional European Systematic Coronary Risk Evaluation (SCORE) risk algorithm (Barter & Genest, 2019). Another study found primary low HDL-C was associated with CVD events but not associated with all-cause mortality (Riggs & Rohatgi, 2019).
Although the relation between CVD events and HDL-C has typically been described as a linear increase association, recent studies have found a U-shaped relation (Riggs & Rohatgi, 2019). The lowest all-cause mortality was observed at an HDL-C level of 73 mg/dL in men and 93 mg/dL in women. All-cause mortality was increased by 36% for men with an HDL-C level of 97–115 mg/dL and over 2x with levels of 116 mg/dL. For women, a 10% increase in mortality was observed for HDL-C of 116–134 mg/dL and 68% increased risk of mortality for HDL-C of 135 mg/dL (Barter & Genest, 2019).
Additionally, there is no evidence from randomized trials that therapeutically increasing plasma HDL-C reduces the risk of CVD events. Cholesteryl ester transfer protein (CETP) inhibitors induce potent increases in HDL-C but 3 out of 4 clinical trials were discontinued due to either futility or harm. The fourth trial did show increased HDL-C and a decrease in CVD events, however, further analysis suggested that the benefit was actually driven by the reduction of ApoB (Riggs & Rohatgi, 2019).
It has been determined that about 50% of the variability in HDL-C levels is a result of genetic factors (NCEP).
Ultracentrifugation (Reference Method)
- The Center for Disease Control (CDC) reference method for HDL-C combines a precipitation technique with ultracentrifugation in a three-step procedure. This method involves separation and removal of VLDL and chylomicrons, if present, by 18.5 hours of ultracentrifugation and separation of HDL by selective precipitation of non-HDL (including Lp(a), IDL, and LDL) from the beta-quantification bottom fraction (d ≥1.006 kg/L) using 46 mmol/L heparin-manganese (Mn+2) (Warnick et al., 2008). The reference method is technically demanding which precludes its use as a routine reference method.
- HDL-C is separated by precipitating apoB containing lipoproteins from serum by using a combination of polyanions, typically such as heparin–MnCl2, dextran sulfate–MgCl2 or phosphotungstate–MgCl2, and a divalent cation, such as magnesium, heparin–manganese, or calcium. Subsequently, HDL is quantified as cholesterol in the supernatant (Hafiane & Genest, 2015).
Vertical Auto Profiling
- VAP is an inverted rate zonal density gradient ultracentrifugation technique that sequentially measures the cholesterol content of all five lipoprotein classes. Unlike most other ultracentrifugation methods, the VAP method separates all lipoproteins in less than 1 h at 65,000 rpm (Hafiane & Genest, 2015).
High-Performance Liquid Chromatography
- HPLC is a method for classifying and quantifying lipoproteins according to size. In this method, lipoproteins are separated by permeation columns (exclusion chromatography) and the lipid components (mainly cholesterol and triglycerides) are detected enzymatically. Various columns containing nonporous polymer-based gels are used for the separation of major classes of human lipoproteins in serum and plasma. For lipoprotein analysis, a Superose 6 column is most frequently used (Hafiane & Genest, 2015).
- Most laboratories now use the direct (homogeneous, single-step) assays without the need for manual pre-treatment to separate non-HDL. There are several different direct HDL-C assays. One example is the use of polyethylene glycol (PEG)-modified cholesterol esterase and cholesterol oxidase as well as sulfated α-cyclodextrin to produce a catalytic activity that is specific for the cholesterol in HDL particles (Okada et al., 2001).
Standardization & error
HDL-C measurement is challenging because the reference range is narrow and even small analytical errors can contribute to misclassification (Hafiane & Genest, 2015). Unfortunately, with some of the newer assays, there appears to be a poor agreement with reference methods and significant method-specific bias.
The NCEP accepts a total error of ± 13% for HDL-C as tolerable (Warnick et al., 2008) while CLIA evaluation criteria define a value of ± 25% (Warnick et al., 2008) as acceptable. Physiological variation is reportedly between 7.1-7.5% (da Silva et al., 2018).
A study that compared seven direct methods with the ultracentrifugation reference method found that none of the methods met the NCEP minimum total error goal of <13%, which was attributed to a lack of specificity of the methods for abnormal lipoproteins (Miller et al., 2010). Total variability ranged from 2.6% to 16.4%, total error ranged from -8.2% to 36.3%, and mean bias ranged from -8.6% to 8.8% between assays and the reference method.
Precipitation methods for HDL cholesterol measurement appear to provide more accurate results than direct methods and should be considered the method of choice for laboratories that desire accuracy (Contois et al., 2012), although the method also has significant possibilities for error. Incomplete precipitation of apoB lipoproteins is the major drawback of this method. Supernatant turbidity, observed with hypertriglyceridemia, inflammatory conditions, and cryopreservation may lead to discordant results between methods (Hafiane & Genest, 2015 ).
One source of inaccuracy in the VAP method is that shear forces generated by the ultracentrifugal field (57 × 107 g average/min) may strip off proteins associated with lipoproteins. Another drawback is that lipoproteins, especially HDL particles are subjected to high ionic strengths (5 to 20 times above those of human plasma and lymph) during the measurement procedure. These conditions can alter the labile proteins on the HDL surface and cause minor structural disruption to the HDL particles (Hafiane & Genest, 2015 ). VAP has been validated in the measurement of HDL-2 and HDL-3, but only limited studies have compared it with lipoprotein subfraction measurement techniques (Hafiane & Genest, 2015).
HPLC can be contaminated by plasma proteins that co-elute with HDL, especially albumin. HPLC has the benefit of being less tedious for large studies compared to ultracentrifugation (Hafiane & Genest, 2015).
men: <40 mg/dL
women: <50 mg/dL
|NCEP||<40 mg/dL in both men and women|
Functional reference range: 50–85 mg/dL
Factors affecting measurement
- Measurements in hypertriglyceridemic serum showed a negative mean bias of 15% that resulted in false risk reclassification in 70% of women and 43% of men to a high-risk (SCORE >5%) in hypertriglyceridemic serum (Langois et al., 2014)
- can interfere with HDL-C measurement (Ramasamy, 2018)
HDL-C Summary Table
|Summary||Testing is reasonably well-standardized with acceptable error for precipitation methods and is widely used in CVD risk models, although data suggest that other HDL measures provide superior information. Measurement of HDL-C is necessary for the calculation of non-HDL-C.|
|Best test type||Precipitation (CDC or DCM)|
|Error||± 13% (but up to - 8.2- +36.3%)|
|Frequency||as per standard lipid panel (annually)|
|Hazard ratio for CVD events|
TG are measured as part of a standard lipid panel. Epidemiological evidence suggests that both fasting and nonfasting TG concentrations are associated with cardiovascular events. In a meta-analysis, both fasting and non-fasting TG were equally good at predicting an increased risk of CHD (Ramasamy, 2018). In addition, extreme hypertriglyceridemia (HTG) is a frequent cause of pancreatitis (Laufs et al., 2020).
Non-fasting TG of 584.6 mg/dL vs. 70.86 mg/dL (6.6 vs. 0.8 mmol/L) was associated with a five-, three-, and two-fold increased adjusted risk for myocardial infarction, ischaemic stroke, and all-cause mortality, respectively. TG levels also independently predict long- and short-term CVD risk in patients post-acute coronary syndrome who are treated with a statin and therefore represent a potential target in secondary prevention (Laufs et al., 2020).
Although circulating levels of TG-rich lipids predict increased CVD risk, it is less clear whether TG themselves contribute to atherogenesis. In contrast to cholesterol that accumulates in intimal foam cells and atherosclerotic plaques, TG are degraded by most cells. However, TG-rich lipoproteins (TRLs) promote atherogenesis via infiltration into the vessel wall and pro-inflammatory and pro-thrombotic pathways. In addition, elevated TG are frequently associated with pathological HDL particles that may contribute to CVD risk (Laufs et al., 2020).
The correlation of plasma TG with CVD risk in epidemiological studies is attenuated or lost after adjusting for non-high-density lipoprotein cholesterol (non-HDL-C) or apo B. After adjusting for non-HDL-C, a study reported a nonsignificant hazard ratio of 1.14 for cardiovascular events (Aberra et al., 2020).
Atherosclerotic cardiovascular disease risk mediated by TRLs appears to be determined by the circulating concentration of apoB-containing particles rather than their TG content and that the clinical benefit of lowering TG correlates with the reduction in apoB, rather than the change in plasma TG concentration (Laufs et al., 2020).
The analytical “gold standard” for measuring TG is density gradient ultracentrifugation. This technique is labor-intensive and involves a 24-h analysis to fractionate the plasma lipoproteins. Only a few samples can be processed at the same time in each ultracentrifuge, precluding the use of this method in large studies (Tsai et al., 2004).
Serum TG concentrations are generally determined from total glycerol with or without subtraction of free glycerol. For serum TG determination, there are a variety of fluorometric or enzymatic analytical methodologies and commercial kits provided by different manufacturers on the market (Meng et al., 2017).
Standardization & error
TG measurement, even in the fasting state, shows extreme intra-individual biological variability (Langlois & Sniderman, 2020). Variation among serial samples for TGs is much higher than what is seen with TC (25% vs 8%) (Aberra et al., 2020).
Greater than 95% of laboratories in the United States use enzymatic methods for TG that nonspecifically measure TG, diglyceride, monoglyceride, and free glycerol (ie. total glycerides) (Warnick et al., 2008).
TG levels > 200 mg/dL have been classified as elevated and levels > 1000 mg/dL are associated with an increased risk of pancreatitis (Schaefer et al., 2016).
For non-fasting samples, TG ≥ 175 mg/dL should be flagged as abnormal (Laufs et al., 2020).
Factor affecting measurement
- Measure after a 12 hour fast to clear chylomicrons (Oxford Handbook of Clinical and Laboratory Investigation, 2017)
- Corticosteroids, thiazides, non-selective beta-blockers, estrogen, tamoxifen, bile acid sequestrants, cyclophosphamide, antiretroviral drugs, and second-generation antipsychotic agents may raise TG (Laufs et al., 2020).
- Habitual exercise causes a decrease of 20-50% in TG, particularly in overweight individuals (Naito et al., 1992).
TG Summary Table
|Summary||Although TG levels, may be a useful marker on a population level, the intraindividual variation and test errors are too high to be a useful marker of CVD in individuals. Many studies have shown that TG are not an independent CVD risk variable. Not recommended for assessing CVD risk.|
|Hazard ratio for CVD events||1.14 not significant after adjustment for non-HDL-C, based on limited data|
VLDL-C levels have been positively associated with increased coronary artery calcification (CAC) after adjusting for age, race, gender, Framingham risk score, body mass index, C-reactive protein, exercise, medication, alcohol use, hemoglobin A1c, and diabetes duration even after the inclusion of apoB data. An approximately 3-fold stronger effect was observed in women than in men. Plasma VLDL-C was related more strongly than TG levels to CAC scores and had a stronger CAC association in individuals with hypertriglyceridemia (Prenner et al., 2014).
VLDL concentrations also appear to be independently associated with hypertension that is related to hyperhomocysteinemia (Chen et al., 2020). A statistically significant correlation between VLDL and carotid IMT has also been found where there was no significant correlation between cIMT and LDL-C, IDL-C, or HDL-C levels. After adjusting for age, systolic blood pressure, body mass index, smoking habits, glucose plasma concentration, and Lp(a) levels, multivariate analysis showed that women in the third tertile of VLDL-C, compared with those in the first tertile, had higher cIMT (Gentile et al., 2020).
VLDL-C is usually estimated as a percentage of the TG value (mayoclinic.org). To estimate VLDL-C, the TG value is divided by 5 if the value is in mg/dL or by 2.2 if the value is in mmol/L. In most cases, this formula provides a good estimate of VLDL-C. However, estimating VLDL this way does not work if TG levels are very high (medlineplus.gov).
There's no simple, direct way to measure VLDL-C, which is why it's normally not reported during a routine cholesterol screening. However, ultracentrifugation (VAP) or electrophoresis may be used.
Standardization & error
Estimates are influenced by the error in TG measurements. The imprecision of VLDL-C measurement is reportedly high (15.6-29.8%) (Cathcart & Dominiczak, 1990).
An elevated VLDL cholesterol level is >30 mg/dL (mayoclinic.org).
VLDL-C Summary Table
|Summary||VLDL-C level may provide useful information when measured directly but has a high error. VLDL-P is potentially more useful in a clinical setting. Not recommended.|
|Hazard ratio for CVD events||1.11 (mortality) (Xie et al., 2017)|
|Error||± 15.6 - 29.8% limited data|
Non-HDL-C includes the cholesterol in all lipoprotein particles that participate in causing CVD, that is, LDL, VLDL, VLDL remnants, and Lp(a). Several studies have suggested it is one of the most promising and practical measurements for assessing CVD risk, superior to the more commonly used LDL-C (Harada et al., 2014; Glasziou et al., 2014 ). An extensive meta-analysis comparing 10 lipid biomarkers found a hazard ratio of 1.25 for cardiovascular events with elevated non-HDL-C (Perera et al., 2015).
Overall, non-HDL cholesterol correlates strongly and positively with total apoB (Grundy et al., 2011). A meta-analysis of randomized controlled trials indicated that on-statin levels of non-HDL-C were more strongly associated with future risk of CVD events than TC, apoB, and LDL-C (Allaire et al., 2017; Glasziou et al., 2014). A study (n= 4019) reported a strong correlation of LDL-P (0.87) with non-HDL-C (Cromwell et al., 2007). However, another study found significant discordance between LDL-P and non-HDL-C levels in 44% of subjects where LDL-P was more closely associated with cIMT (deGoma et al., 2013).
A 1% decrease in non-HDLC has been associated with a 1% decrease in relative risk (RR) for coronary heart disease (Langlois & Sniderman, 2020).
An analysis of 9 coronary intravascular US trials showed that coronary atheroma progression was associated with higher variability in non-HDL-C (Simpson, 2019).
In the nonfasting state, non-HDLC additionally includes the cholesterol in chylomicrons and their remnant particles that are not accounted for in traditional fasting lipid profiles. Nonfasting non-HDL-C, therefore, can potentially be more relevant to the estimation of an individual’s cardiovascular risk than fasting non-HDL-C since, in real life, the postprandial state predominates during most of our 24-h cycle.
Suggested disadvantages of non-HDL-C are that in some individuals, high remnant cholesterol may be masked (if LDL-C is low), none of the dyslipoproteinemias can be characterized by non-HDLC, and that analytical errors of HDL-C measurement affect the calculation of non-HDL-C (Langlois & Sniderman, 2020).
Non-HDL-C (calculated by subtracting HDL-C from total cholesterol levels) reflects the cholesterol in all the apoB-containing particles and is readily accessible from standard lipid testing.
Standardization & error
As a calculation, non-HDL-C is affected by errors in TC and HDL-C measurement.
Population studies (n=222,738 ) of non-HDL-C indicate that values > 160 mg/dL are associated with increased CVD risk (Schaefer et al., 2016).
Guideline-recommended goals for non-HDL-C are arbitrarily set 30 mg/dL higher than LDL-C goals. This value is based on the assumption that the “optimal” remnant cholesterol concentration associated with the desirable fasting TG value of < 150 mg/dL is 30 mg/dL, as estimated by the Friedewald formula assuming a fixed TG/cholesterol ratio (TG/5 in mg/dL) in VLDL particles (Langlois & Sniderman, 2020).
very high risk: < 100 mg/dL
high risk: < 130mg/dL
|130-160 mg/dL||> 190 mg/dL|
|AACC||< 120 mg/dL||-||-|
very high risk: <85 mg/dL
high risk: < 100 mg/dL
moderate risk: < 130 mg/dL
|AHA/ACC||-||> 100 mg/dL||-|
Non-HDL-C Summary Table
|Summary||Testing is reasonably well-standardized since it is based on TC and HDL-C measurements. It is the most useful measurement derived from a standard lipid panel as it closely reflects apoB.|
|Best test type||N/A calculation (TC - HDL)|
< 130 mg/dL
|Frequency||as per standard lipid panel (annually)|
|Hazard ratio for CVD event||1.28|
|Error||± 21.9% (error from TC and HDL-C)|
Several studies have shown that LDL particle number (LDL-P) has a robust and independent association with atherosclerosis development (CAC, cIMT) and CVD events (Schaefer et al., 2016; Rosenson & Underberg, 2013; Zaid et al., 2016; Ip et al., 2009). The risk of a CVD event has been shown to increase by 4% for each 100 nmol/L increase in the LDL-P level (n=15,569) (Toth et al., 2014). The hazard ratio of LDL-P for CVD events in a large systematic review was 1.24 ( Ip et al., 2009).
In some studies, LDL-P has been found to provide better predictions of risk than LDL-C or non-HDL-C (Hopkins et al., 2015; Cromwell et al., 2007). However, there is also evidence to the contrary with some studies reporting that changes in LDL-C and non-HDL-C were both independently correlated with cIMT regression, while changes in LDL-P showed borderline significance to cIMT regression (Rosenson & Underberg, 2013).
As the amount of cholesterol carried by each LDL-P can vary more than two-fold between individuals, estimating LDL-P concentration by LDL-C may be misleading in some circumstances. This is particularly evident in patients with diabetes, metabolic syndrome, hypertriglyceridemia, and low HDL-C, as their LDL-P carry less cholesterol and more triglycerides, and hence LDL-C may underestimate LDL-P (Harada et al., 2014). Several studies have shown that while many patients have LDL-C levels within the target goal for high-risk patients, LDL-P levels for most patients are higher than goals. In one study, only 18% had LDL-P levels <1000 nmol/L (20th percentile) and 6% had LDL-P levels <80 nmol/L (10th percentile) (Malave et al., 2012). When LDL-C and LDL-P measurements are discordant, LDL-P has been shown to be more strongly associated with incident CVD events than LDL-C (Matyus et al., 2014).
Recent data suggest that there may even be significant discordance between apoB and LDL-P concentrations , despite the fact that these two measures are both considered to reflect LDL-P . Cases of discordance in which LDL-P concentrations are greater than the corresponding apoB concentration have been associated with insulin resistance, small dense (sdLDL), and low-grade systemic inflammation. Conversely, discordance in which apoB concentrations are greater than the corresponding LDL-P concentration has been associated with larger LDL particles and elevated levels of Lp(a) (Allaire et al., 2017).
A review of 25 clinical studies in which the strength of association of apoB and LDL-P with specific cardiovascular outcomes was evaluated found that LDL-P measured by NMR was more strongly and more frequently associated with clinical outcomes, provided more information on other lipoprotein classes, and was more precise than apoB (coefficient of variation (CoV) of apoB = 5-11% vs. NMR LDL= 2-4%) (Morris et al., 2014).
Patients with low apoB levels and higher than desirable LDL-P levels may have considerable residual CVD risk ( Morris et al., 2014 ).
There is no mention of LDL-P measurement in the 2019 ESC guideline or 2018 AHA/ACC guideline for assessing CVD risk. The NLA guidelines state that clinicians can consider measuring LDL-P as an alternative to apoB (German & Shapiro, 2020).
Nuclear magnetic resonance spectroscopy (NMR)
- This measurement is based on the proton magnetic resonance signals from terminal methyl groups on lipids within the core and shell of lipoprotein particles and the fact that the amplitude of the methyl NMR signal is proportional to the concentration of the particles (Matyus et al., 2014). Smaller particles resonate at lower frequencies (Mallol et al., 2015). Values are provided in nmol/L (Schaefer et al., 2016).
- We identified several protocols that use 1 or 2D NMR to characterize lipoprotein particles. These include the commercial assays NMR LipoProfile (LipoScience Inc.), AXINON lipoFIT (Numares AG, Regensburg, Germany), Brainshake Ltd., Vantera (LipoScience Inc.), and the Liposcale test (Biosfer Teslab SL) (Aru et al., 2017; Mallol et al., 2015).
Differential ion mobility analysis
- Ion mobility lipoprotein fractionation is a technology that uses gas-phase (laminar flow) electrophoresis to separate unmodified lipoproteins on the basis of size. Following the separation, each lipoprotein particle is directly detected and counted as it exits the separation chamber (questdiagnostics.com).
- Agarose gel electrophoresis to separate LDL followed by immunostaining. It uses the sum of LDL-P and Lp(a) to provide a comparison to other methods that don't resolve Lp(a) (Guadagno et al., 2015).
Vertical lipoprotein profile
- The vertical lipoprotein profile (VLP) method is based upon the separation of lipoproteins using single vertical spin density gradient ultracentrifugation followed by determination of LDL-P concentration using multi-angle laser light scattering flow through a detector positioned in the VLP analyzer (Kulkarni et al., 2014).
Standardization & error
A major issue with NMR measurement is that all of the methods are proprietary and lack standardization. The first device to receive FDA approval for in vitro diagnostics was the NMR Profiler (LipoScience) in 2008. The FDA approval data defined a total error of < 20% as acceptable (fda.gov).
The limit of detection for the first FDA-approved device for clinical use (2012), the Vantera Analyzer, has been reported as 41 nmol/L and the limit of quantitation as 132 nmol/L to maintain a <10% CoV (Matyus et al., 2014). The within lab precision was 3.9-5.3% (2.6-4.9% for repeatability and 2.7-5.8% for within-run precision) and the CoV was 4-5% (Matyus et al., 2014). Another study on the same device reported coefficients of variation ranging from 2%–4% (Morris et al., 2014). LDL-P values measured by the Vantera Clinical Analyzer and the more well-established NMR Profiler have a 96% correlation (Hopkins et al., 2015).
A study that compared multiple LDL-P measurement methods found within-in subject deviation was least for the ultracentrifugation method and greatest for the NMR methods (Hopkins et al., 2015). The ultracentrifugation method had the lowest CoV, which was significantly less than either of the NMR methods but not significantly less than the electrophoresis method. The LipoScience-NMR method (Vantera Clinical Analyzer) had a significantly greater CoV when compared to any of the 3 other methods. The HealthDiagnostics-NMR method (ASCEND 600 Bruker Biospin) measured a mean value of 1543 nmol/L compared to LS-NMR of 1243 nmol/L; the ultracentrifugation method had a mean of 1764 nmol/L (Hopkins et al., 2015). However, the study was funded by the company that offered 2 of the 4 tested methods.
The CoV of LDL-P was shown to be similar for two NMR methods: 31% using Liposcale (BrukerAvance III 600 + diffusion ordered spectroscopy) and 32.3% using the LipoProfile test (Vantera Clinical Analyzer)(Mallol et al., 2015). The difference between methods was 288.1 nmol/L.
LDL-P values measured by NMR were 24.5% lower compared to the same samples measured by ion mobility (Kulkarni et al., 2014).
The CoV of the VLP method has been measured between 2.1-7.4% It showed a bias of -3.2% compared with NMR and -26.9% when compared with ion mobility (Kulkarni et al., 2014). LDL-P measured by ion mobility was substantially higher compared to both NMR and VLP (Kulkarni et al., 2014).
Reference range (nmol/L)
|Test/Guideline||Machine||Optimal/Low (25th%)||Moderate||Borderline High||High (75%)||Very High||Reference Population|
|NMR Lipoprofile (Liposcience)||NMR Profiler||< 1000||1000-1299||1300-1599||1600-2000||> 2000||n=5,362 men & women not on lipid medication|
|NMR Liposcale||Bruker Avance + diffusion ordered spectroscopy||1120||1300||1500||n=6000 men & women aged 15-85|
|Lipocomplete||The test is based on the CE-marked IVD test lipoFIT-S100 (Numares)||693||-||1910||-||-||-|
|Boston Heart Diagnostics||The test is based on the CE-marked IVD test lipoFIT-S100 (Numares)||<1200||-||1200-1800||>1800||-||-|
|American Academy of Clinical Chemistry||< 1100 (20th percentile)|
A high-risk value has been classified as > 1600 nmol/L and an optimal value < 1000 nmol/L, corresponding approximately to the 75th and 25th percentile values ( Schaefer et al., 2016). It is recommended that LDL-P be < 700, < 1000, and < 1150 for high, moderate, and low-risk patients respectively.
The reference interval and mean values for LDL-P show significant differences between genders (men: reference range = 372–2365; mean= 1279; women: reference range = 480–2057 nmol/L; mean 1148 nmol/L (Matyus et al., 2014). The reference range may also change according to the method as the differences between methods are significant (Hopkins et al., 2015).
Factors that affect the measurement
- On average, men have higher levels of LDL-P. A study found the mean value of LDL-P was 1279 nmol/L in men and only 1148 nmol/L in women (Matyus et al., 2014)
- Some substances exhibit a potential for interference: ibuprofen, naproxen, albumin, fenofibrate, salicylic acid, and clopidogrel of which only aspirin and clopidogrel occurred at therapeutic ranges (Matyus et al., 2014)
- Aspirin resulted in 10-15% lower LDL-P values
- Clopidogrel resulted in an approximately 10-20% increase in LDL-P
Sample collection tube type
- Results were 3-7% lower in EDTA plasma tubes than results from specimens collected in LipoTubes (Matyus et al., 2014)
Delay in analysis
- Values are stable up to day 6 with value changes of less than 10% (Matyus et al., 2014)
LDL-P Summary Table
|Summary||LDL-P is a potentially valuable addition to a CVD risk assessment. The test is not widely available and results between methods are highly variable. Precise but not accurate (differs significantly by method). Provides additional information on subfractions.|
|Best test type||NMR|
|Error||6% (precision); 12.5- 29% (accuracy) total error around 25%|
|Hazard ratio for CVD events||1.32 (Otvos et al., 2011) based on limited data|
< 1000 nmol/L
|Frequency||as per standard lipid panel (annually)|
Elevated levels of small dense LDL (sdLDL) have been reported in many conditions linked to atherosclerosis, including dyslipidemia, diabetes, metabolic syndrome, and peripheral arterial disease ( Khalil et al., 2017; Musunuru et al., 2009).
sdLDL particles have altered chemical contents, containing decreased amounts of phospholipids, free cholesterol, and cholesterol esters (Ivanova et al., 2017). The circulation time of sdLDL is proposed to be longer than larger LDL-particles (3 days vs. 1.5) ( da Silva et al., 2018) due to their impaired interaction with the LDL-receptor which may allow more sdLDL to become modified and taken up by macrophage scavenger receptors in the artery wall (Kjellmo et al., 2018; Khalil et al., 2017; Schaefer et al., 2016 ). Additionally, they are deficient in vitamin E ( Khalil et al., 2017) and exhibit increased susceptibility to atherogenic modifications such as desialyation, glycation, and oxidation. In vitro studies have shown that sdLDL particles are more avidly taken up by macrophages, have a greater propensity for transport into the artery wall, and have a greater binding potential to proteoglycans in the artery wall (Kjellmo et al., 2018; Chung et al., 2009; Ivanova et al., 2017 ).
sdLDL particles are associated with incident CVD independently of 23 traditional risk factors, including standard lipids (Shiffman et al., 2017). Whether this relationship is independent of LDL-P remains controversial (Hopkins et al., 2015). Baseline sdLDL is concordantly related to plasma triglyceride concentration and inversely correlated with plasma HDL-C, whereas large LDL shows the opposite relationship (Williams et al, 2014). NMR estimates of sdLDL have been positively associated with arterial stenosis independently of traditional lipid and lipoprotein risk factors (Williams et al, 2014).
On the other hand, NMR estimates of LDL subclasses were not found to significantly improve the prediction of incident cardiovascular disease during an 11-year follow-up of the Women’s Health Study when adjusted for traditional lipoprotein risk factors (Williams et al, 2014) and it is now believed that all LDL particles are equally atherogenic, regardless of their size (Langlois & Sniderman, 2020). A systematic review found no significant relation between LDL subfractions and CVD event risk (Ip et al, 2009).
LDL particles are heterogeneous with respect to size, density, and composition, and can be separated based on various physicochemical properties depending on the protein purification technique used. The methods used in the published literature on LDL subfractions are usually based on gel electrophoresis (GE), nuclear magnetic resonance (NMR), ultracentrifugation (UC), or ion mobility (IM), but several other methods are also available (Kjellmo et al., 2018; Ramasamy, 2018). The different methods separate LDL particles based on different characteristics that are not directly comparable; GE separates LDL particles based on size and charge, UC based on density, NMR measures methyl group signals from lipoprotein particles and calculates the LDL particle number and size, while IM separates lipoprotein particles using gas-phase electrophoresis and directly counting the size-lipoprotein particles using gas-phase.
- Analysis of the plasma LDL profile can be performed by ultracentrifugation that can separate the LDL particles based on their density (Ivanova et al., 2017). LDL particles are separated based on their flotation rate into 3 classes (Ivanova et al., 2017). VAP ultracentrifugation is based on the deconvolution into subfractions of the direct cholesterol quantitation of lipoproteins separated by flotation rate (a function of size and hydrated density) (Williams et al, 2014).
- NMR assessment of LDL size is rapid but access to such sophisticated methodology is limited.
- Different lipoprotein subclasses may be deconvoluted within the 1D NMR spectrum according to their chemical shift positions, but it has proven to be complicated in practice due to significant spectral overlap as different lipoprotein subclasses contribute to NMR resonance at the same frequency (especially for LDL subclasses due to the effect of neighboring lipoprotein classes such as VLDL and HDL) ( Mallol et al., 2015) .
- The addition of a second dimension in the NMR spectrum by means of diffusion ordered spectroscopy (DOSY) helps to better characterize the different lipoprotein subclasses. DOSY allows the separation of the lipoprotein subclasses according to their diffusion coefficient, and with the use of the Stokes-Einstein equation, DOSY NMR yields an objective separation of lipoprotein subclasses based on their size ( Mallol et al., 2015).
- Another widely used method of LDL subfraction analysis is GE under nondenaturing conditions. In this method, LDL subclasses are separated by their electrophoretic mobility, which is determined by the size and shape of the lipoprotein. Studies using GE separation of LDL define 4 subclasses: LDL I (large LDL, peak diameter 26.0–28.5 nm), LDL II (intermediate LDL, 25.5–26.4 nm), LDL III A and B (small LDL, 24.2–25.5 nm), and LDL IV A and B (very small LDL, 22.0–24.1 nm) (Ivanova et al., 2017).
- The Lipoprint LDL system (Hoefner et al., 2001) and the LipoPhor system are two GE-based methods that have been adapted for clinical use. The Lipoprint system has been validated as an alternative to the traditional GE method for the assessment of LDL size distribution (Allaire et al., 2017).
- Ion mobility uses an electrospray procedure to obtain direct lipoprotein particle counts as a function of particle size. It is based on the principle that when carried in a laminar airflow subjected to an electric field, particles of a certain size and uniform charge behave in a predictable way (Williams et al, 2014).
Other methods of LDL subfraction analysis include HPLC with gel filtration columns, dynamic light scattering, and homogenous assay analysis. Homogenous assay analysis is of particular interest because of its high reproducibility and suitability for large-scale use. In homogenous assay methods, sdLDL (particle size 15.0–20.0 nm) is separated from large LDL using detergent and sphingomyelinase treatment, and sdLDL-cholesterol concentration is measured. The method separates sdLDL fraction with a density from 1.044 to 1.063 g/ml using standard clinical laboratory equipment (Ivanova et al., 2017).
Standardization & error
There is no gold standard method for LDL particle determination and direct comparison between the techniques is limited. Within a specific method, there is also no standardized protocol that is universally used for defining or describing the LDL subfractions (Chung et al., 2009).
A study (n=40) that compared 4 methods (VAP, gradient GE, NMR, tube gel electrophoresis) with results categorized according to LDL phenotypes (pattern A, B, or AB), showed complete agreement in only 8% of analyses. Another study also reported a 7-94% range of agreement between the different methods (Chung et al., 2009). A review of nine articles that compared the measurement of LDL subfractions using at least two methods, led to the conclusion that the currently available literature does not provide adequate data about comparability in terms of test performance between methods (Ramasamy, 2018).
Another study concluded that the results of particle size measurement by NMR differ significantly from the electrophoresis data in the same patients and can not be directly compared (Ivanova et al., 2017). Even among the NMR studies, which mostly evaluated LDL particle number and particle size, different cut-points were used for the various LDL subfractions (Ip et al., 2009). Finally, the characterization of IDL by NMR spectroscopy is not straightforward due to its low concentration range and that its NMR response arises between the small VLDL and large LDL in terms of chemical shift (Mallol et al., 2015).
The coefficients of inter- and intra-assay variation for cholesterol distribution in the 7 LDL species defined in Lipoprint were less than 10% (Gentile et al., 2017).
In another study, the within- and between-run CoV for sdLDL-C were 4.99% and 4.67% (Ai et al., 2010).
sdLDL > 40-50 mg/dl has been associated with increased CHD risk. It has been suggested that the optimal value for sdLDL-C is < 20 mg/dl ( Schaefer et al., 2016). However, another study reported the average normolipidemic sdLDL-C was 31 mg/dL (Hirano et al., 2004).
LDL Subfractions Summary Table
|Summary||Although LDL subfractions are potentially useful markers of cardiovascular risk, the lack of standardization and a wide range of agreement between methods make the results unreliable. Currently not recommended.|
|Error||± 50% rough estimate based on a range of agreement between methods of 7-94%, no RMP established|
|Hazard ratio for CVD events (sdLDL)||1.51 incident cardiovascular disease, limited data (Hoogeveen et al., 2014)|
The majority of studies comparing HDL-P and HDL-C found HDL-P to be comparable or better than HDL-C for the prediction of CVD (Harada et al., 2014; Master et al., 2013). Comparison of the HDL-related measures of cholesterol efflux, HDL-C, apolipoprotein A-I (apoA-I) concentrations, and HDL-P have shown HDL-P as the strongest inverse predictor of incident CVD (Riggs & Rohatgi, 2019).
HDL-P has the benefit of not being associated with measures of adiposity or insulin resistance unlike low plasma levels of HDL-C which are confounded by coexistent insulin resistance (Riggs & Rohatgi, 2019).
Standardization & error
The various methods give different estimates of HDL particle concentration and size. A comparison of two methods yielded concentrations of HDL particles that differ > 5 fold (Pallarés-Carratalá et al., 2020).
Suggested levels for abnormally low concentrations of HDL- P are < 24 μmol/L (Pallarés-Carratalá et al., 2020).
HDL-P Summary Table
|Summary||Although HDL-P is likely superior to measures of HDL-C, the tests are not well-standardized and show high method variability. Currently not recommended.|
|Error||Precision: ± 10% No RMP for accuracy - total error unknown|
|Hazard ratio for CVD events||0.64|
HDL is a heterogenous group of lipoproteins that have a density of more than 1.063 and a small size (5-12 nm) (Rizzo et al., 2014). Various subclassification schemes exist depending on the method used.
|Separation Principle||HDL Subclasses|
|Density||HDL-2 (density range 1.063-1.125 g/mL) |
HDL-3 (density range 1.125-1.210 g/mL)
|Electrophoretic mobility|| α -migrating HDL |
pre-β migrating HDL
|Electrophoretic mobility and size||HDL-2b (9.7-12 nm) |
HDL-2a (8.8-9.7 nm)
HDL-3a (8.2-8.8 nm)
HDL-3b (7.8-8.2 nm)
HDL-3c (7.2-7.8 nm)
|2D gel electrophoresis||Alpha 1, 2, 3, 4 |
Pre-β 1 HDL
|Apolipoprotein composition||LpAI |
|NMR and size range||Large HDL-P (9.4-14 nm) |
Medium HDL-P (8.2-9.4 nm)
Small HDL-P (7.3-8.2 nm)
|Ion mobility and size range||HDL-2b (10.5-14.5 nm) |
HDL-2a + 3 (7.65-10.5 nm)
|Lipoprint System||Large (1-3), intermediate |
(4-7) and small (8-10
(Adapted from Rizzo et al., 2014)
Current evidence regarding anti- or pro-atherogenic effects of specific HDL subpopulations in humans is conflicting and confusing. In some studies, medium and large HDL-P have been associated with a decreased risk of incident CVD, and smaller sizes have been shown to correlate with an increased risk of CVD (Akinkuolie et al., 2013). In other studies, all HDL particles were associated with reduced risk for CVD. Some studies suggest that small HDL may promote cholesterol efflux more than large HDL based on their phospholipid content (Rizzo et al., 2014).
Long-term follow-up of a cohort study showed that being in the lowest quartile of HDL-2 mass increased the risk of CHD and that the increased risk remained when adjusted for total HDL, HDL-3, and other risk factors (Ramasamy, 2018). The Jackson Heart and Framingham Offspring Cohort studies reported that HDL-3 is the chief determinant of the inverse HDL-C association with CHD (Joshi et al., 2016).
At this time there is no consensus as to the role of HDL subclasses in CVD risk assessment or as to which technique for HDL subclass measurement is the best ( Ramasamy, 2018).
Methods for quantification of HDL subclasses are ultracentrifugation, non-denaturing polyacrylamide gradient gel electrophoresis (ND-PAGGE), 2D polyacrylamide gradient gel electrophoresis (2D-PAGGE), VAP, and NMR. Ultracentrifugation is the gold standard (Ramasamy, 2018).
ND-PAGGE has been used as a standard laboratory technique for the past three decades. This technique can identify various HDL subspecies separable on the basis of average diameter into 6 distinct subclasses. Although ND-PAGGE is considered a sensitive and reproducible approach for quantifying the size distribution of HDL subpopulations, there appears to be minimal additional benefit when compared with the more standard measurement of HDL-C. The method is labor-intensive and standardization between laboratories is poor, limiting its broad application (Hafiane & Genest, 2015).
2D-PAGGE is another technique used to separate HDL based on their charge: mass ratio. This method combines ND-PAGGE with agarose gel electrophoresis, which uses surface charge density in the first dimension and particle size in the second dimension (Hafiane & Genest, 2015).
NMR employs the characteristic lipid methyl signal broadcast by HDL subclasses whose amplitudes can be measured. This technique uses proton (1H, 13C, and 32P) spectroscopy to directly estimate the different sizes of lipoprotein subfractions rapidly. Current NMR methods allow for the separation of 26 subpopulations of HDL. However, NMR based techniques use mathematical assumptions that do not take into account variations in the protein and lipid cargoes of the particles. The most important limitation of this method is the requirement for specialized equipment not found in routine clinical laboratories (Hafiane & Genest, 2015).
Error & standardization
2D-PAGGE quantifies preβ-1-HDL but this technique does not correlate precisely with sandwich ELISA and may overestimate preβ-1 concentrations in plasma (Hafiane & Genest, 2015).
Conflicting results between 2D-PAGGE and NMR for HDL subfractions in the prediction of CVD risk have been reported (Hafiane & Genest, 2015).
HDL Subfractions Summary Table
|Summary||So far, there is little evidence that HDL subfractions provide valuable information on CVD risk and the tests are not well-standardized. Currently not recommended.|
|Error||Precision: ± 10%; No RMP to compare for accuracy - total error unknown|
|Hazard ratio for CVD||0.65 (NMR)|
HDL functional assays
Reverse cholesterol transport (RCT) is considered to be the most relevant antiatherosclerotic function of HDL (Riggs & Rohatgi, 2019). Cholesterol efflux capacity (CEC) has been inversely associated with prevalent and incident atherosclerotic CVD in multiple large cohorts (Jin et al., 2019; Riggs & Rohatgi, 2019).
CEC is measured using artificially prepared foam cells composed of cultured macrophage and 3H-cholesterol. However, this conventional method is not suitable for clinical laboratory use due to poor repeatability, complexity, and low safety (Horiuchi et al., 2020).
A novel CEC assay called the immobilized liposome-bound gel beads (ILG) method is an alternative to foam cells, comprised of gel beads and 4,4-difluoro-4-bora-3a,4a-s-indacene labeled cholesterol (BODIPY-cholesterol) instead of macrophage and 3H-cholesterol, respectively. The ILG method has shown adequate basic properties and a strong correlation with the conventional method. The ILG method had far better reproducibility than the conventional method (Horiuchi et al., 2020).
An assay for endogenous lecithin–cholesterol acyltransferase activity known as the fractional esterification rate (FER) measures the esterification of radiolabeled free cholesterol that has been equilibrated with HDL. The FER is calculated as the ratio of radioactive unesterified to radioactive esterified cholesterol per unit of time and is expressed in (%/h) (Hafiane & Genest, 2015).
Error & standardization
Measurement of cholesterol efflux capacity (CEC) is not standardized (Riggs & Rohatgi, 2019). Many variations exist and attempts are being made to standardize this assay, by posting protocols on-line. Protocols differ by the type of cell, acceptor milieu, efflux times, and the specificity of the transporter examined (Hafiane & Genest, 2015).
Macrophages can efflux cholesterol not only onto lipid-free apoA-I, but also to apoE, and to nascent HDL particles via the ABCA1 transporter, or onto mature spherical HDL particles via the ABCG1 or SR-BI transporters. These confounders could strongly influence the cholesterol efflux capacity of serum independently of HDL composition and functionality (Hafiane & Genest, 2015).
Inaccuracy in the FER may be due to alterations in the substrate properties of the plasma that occur during the preincubation and equilibration phases or due to the radiolabeled exogenous cholesterol not being in total equilibrium with endogenous cholesterol (Hafiane & Genest, 2015).
HDL Functional Assays Summary Table
|Summary||Even though some HDL biomarkers, such as cholesterol efflux capacity look promising, it is too early to embrace these measurements in the clinical realm as they are not yet standardized.|
|Error||Precision: 9.5%; Accuracy: no RMP - total error unknown|
Lipoprotein(a) (Lp(a)) is a well-established and probably causal risk factor for CVD, independent of LDL-C or other risk factors (Harada et al., 2014). It consists of an LDL particle with an apoA moiety covalently bound to its apoB component and is <70 nm in diameter allowing it to freely move across the endothelial barrier, where it can become—similar to LDL—retained within the arterial wall (Mach et al., 2019). The pro-atherogenic effects of Lp(a) may also be due to both its pro-coagulant effects as it has a similar structure to plasminogen and its pro-inflammatory effects which are most likely related to the oxidized phospholipid load (Mach et al., 2019).
Current estimates suggest that Lp(a) has a detrimental effect on the cardiac health of 1 out of 3 individuals, world-wide (Kouvari et al., 2019). The emerging risk factors collaboration meta-analysis reported a 27% increased risk for myocardial infarction or fatal coronary events after adjustment for CVD risk factors for the top vs. the lowest third of Lp(a) levels (Waldeyer et al., 2017) and individuals with Lp(a) levels above the 90th percentile had the highest risk for future CVD events with an HR of 1.49 (Waldeyer et al., 2017). Unadjusted models showed that participants with Lp(a) over 50 mg/dL presented with about 1.6 times higher CVD event rate (Kouvari et al., 2019). A meta-analysis (n= 72683) showed that 1 SD higher Lp(a) was associated with a 14% increased risk of CVD mortality (Kouvari et al., 2019).
Plasma levels of Lp(a) are >90% genetically determined in an autosomal co-dominant fashion, with adult levels achieved by about 5 years of age. They remain stable throughout life regardless of lifestyle thus, routine evaluations are not required (da Silva et al., 2018). Interestingly, there is a strongly established link between Lp(a) and calcific aortic valve stenosis though the mechanism remains unclear (German & Shapiro, 2020).
Randomized trials evaluating therapies that lower Lp(a) by 20-30% (including niacin and CETP inhibitors) have not provided evidence that lowering Lp(a) reduces the risk of CVD beyond that which would be expected from the observed reduction in apoB-containing lipoproteins (Mach et al., 2019).
A variety of immunochemical methods, such as ELISA, nephelometry, immunoturbidimetry, and dissociation-enhanced lanthanide fluorescent immunoassay, are used to measure Lp(a) in human plasma or sera (Marina & Albers, 2016). Lp(a) can be measured by a latex-enhanced turbidimetric immunoassay (Kouvari et al., 2019) or by using electrophoresis followed by immunoblotting (but this has never been clinically validated) ( Schaefer et al., 2016).
Standardization & error
A consensus RMP for standardizing Lp(a) measurements, an ELISA method that uses a KIV9 MAb, was chosen by the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Working Group (Warnick et al., 2008). The limit of detection (LOD) is 0.38 mg/dL and measurements are linear in the range of 1.3–90.0 mg/dL (Warnick et al., 2008).
The complex molecular structure of Lp(a) and the variation in size of apoA have been challenges in the development of analytical methods for Lp(a). Available methods are, to a varying degree, influenced by the apoA isoform. As well, the concentration of Lp(a) can be reported either as a molar concentration (nmol/L) or as a mass concentration (mg/dL), and conversion between molar and mass concentrations has been found to be both size- and concentration-dependent ( Tsimikas et al., 2018; Mach et al., 2019).
The lack of standardization of Lp(a) measurements has confounded the interpretation of results across different clinical studies to determine the role of Lp(a) as a risk factor for coronary heart disease (Warnick et al., 2008). The largest meta-analysis of 26 cohort studies using a wide variety of different assays showed a range of Lp(a) medians between 3.0 and 23.0 mg/dL across the studies (Waldeyer et al., 2017). 75th percentile values ranging from 73 nmol/L in a Caucasian population to 130 nmol/L in an African American population to 40 nmol/L in a Japanese population have been identified (Schaefer et al., 2016).
Precision is high with an intra-assay CoV <4% and an inter-assay CoV <9% (Waldeyer et al., 2017).
Factors affecting measurement
Lp(a) measurement should be considered at least once in each adult person’s lifetime to identify those with very high inherited Lp(a) levels >180 mg/dL (>430 nmol/L) as they may have a lifetime risk of CVD equivalent to the risk associated with heterozygous familial hypercholesterolemia (Mach et al., 2019). This strategy can also help identify people with less-extreme Lp(a) elevations who may have a higher risk of CVD, which is not reflected by the SCORE system, or by other lipid biomarkers. Measurement of Lp(a) has been shown to provide clinically significant improved risk reclassification in some cases, and should therefore be considered in patients with an estimated 10-year risk of CVD that is close to the threshold between high and moderate risk (Mach et al., 2019).
Lp(a) Summary Table
|Summary||Lp(a) measurement should be considered at least once in each adult person’s lifetime to identify those with very high inherited Lp(a) levels >180 mg/dL|
|Best test type||Immunoassay|
< 30 mg/dL or 75 nmol/L
|Hazard ratio for CVD events||1.3|
|Error||Precision: ± 9%; Accuracy: unknown; Total error: unknown - one study found ± 20% related to heterogeneity in size|
Apolipoprotein B (apoB) is a structural surface protein of non-HDL lipoprotein particles (LDL, IDL, VLDL, Lp(a) and chylomicron derived particles). One molecule of apoB is embedded within the phospholipid monolayer of each lipoprotein particle secreted by the liver or the intestine, remaining with each particle for its lifetime. More than 90% of apoB in plasma is associated with LDL when TG concentration and Lp(a) are not very high. Therefore, the measurement of apoB is generally a good estimate of the LDL particle number (Langlois & Sniderman, 2020; Harada et al., 2014 ).
Many studies have found that apoB is superior to TC, LDL-C, and non-HDL-C for CVD risk prediction (Allaire et al., 2017; Langlois et al., 2019). In two study cohorts representing the general US population, only 1–2% of individuals with LDL-C < 70 mg/dL had non-HDL-C above guideline-recommended targets. By comparison, 40% had apoB values above the population-equivalent target after meeting the LDLC (< 70 mg/dL) and non-HDLC (< 100 mg/dL) targets (Langlois & Sniderman, 2020).
Discordance analysis consistently demonstrates apoB and LDL-P are more accurate measures of cardiovascular risk than LDL-C/non-HDL-C (Sniderman et al., 2016). A meta-analysis based on all previously published epidemiologic studies that contained estimates of relative risks of non-HDL-C, LDL-C and apoB, suggested that apoB was the most potent CVD risk marker, followed by non-HDL-C and lastly, LDL-C (Cardiac Biomarkers, 2016).
However, other studies found similar risk prediction abilities for apoB and non-HDL-C ( Harada et al., 2014). A meta-analysis of seven randomized controlled trials (n=>60,000) has also shown that changes in LDL-C, apoB, and non-HDL-C all predicted similar CVD risk reduction after 1-year of statin therapy (20, 24, and 20% risk reduction, respectively). According to the same data, a 42% reduction in apoB concentrations by statin therapy would produce a 39% CVD risk reduction, whereas a corresponding reduction in LDL-C and non-HDL-C concentrations would be expected to reduce CVD risk by 30 and 39%, respectively (Allaire et al., 2017).
Like many of the other parameters, high intraindividual variability of apoB has been associated with coronary atheroma progression (Simpson, 2019).
Incorporation of particle number, either apoB or LDL-P, into CVD risk assessment has been recommended by several authors (Master et al., 2013). The European Society of Cardiology (ESC) and European Atherosclerosis Society (EAS) stated that apoB may be preferred over non-HDLC in patients with hypertriglyceridemia, obesity, or diabetes and can be used as an alternative to LDL-C for CVD risk assessment (Mach et al., 2019; Langlois & Sniderman, 2020). The American Association of Clinical Chemistry lipoprotein and vascular diseases division working group on best practices concluded in 2013 that apoB was preferable to LDL-P because of its wide-based availability, scalability, standardization, and relatively low cost.
There are several techniques available for apoB measurement. The most widely used method is the immunoassay, which has 3 variants: immunoturbidimetric assay, immunonephelometric assay, and radial immunodiffusion. A second method isolates lipoproteins by VAP and provides cholesterol concentrations in subfractions of apoB–containing lipoproteins. A third method measures the number of apoB–containing lipoprotein particles by nuclear magnetic resonance (NMR) (Grundy et al., 2011).
Liquid chromatography-tandem mass spectrometry (LC-MSMS)-based quantification of apoB has been proposed as a candidate reference method by the IFCC (Langlois & Sniderman, 2020).
Standardization & error
Reports on the error level of apoB analytical measurements vary widely. As reported by one author, the typical intra-assay CoV for apoB ranges from 5%–11% (Morris et al., 2014). However, another paper reports a CoV of apoB concentration biochemically assayed to be 26.5%. ApoB concentration obtained by NMR yielded CoVs of 31.0 and 32.3%, respectively (Mallol et al., 2015). ApoB analytical measurements have shown to have good reproducibility across laboratories (6%–8% CoV in 2012 College of American Pathologists survey), although a number of preanalytical biological confounders, including diurnal and seasonal effects, have been described (Master et al., 2013). The across-method expected total error of apoB is 12% (Langlois et al., 2018).
Common calibration with the IFCC/WHO reference material (SP3-07) has reduced between-laboratory variability of apoB measurements from >19% to <10%, although concerns about the variability among immunoassays and their comparability with apoB derived from NMR and other methods still exist ( Langlois et al., 2020) . One study found that the between-method CoV (NMR, VAP, and immunonephelometry) was 14.1% (Delatour et al., 2018). ApoB levels were found to be highest when measured by immunoassay, lower by NMR, and lowest by VAP (German & Shapiro, 2020).
Another potential source of error is due to the heterogeneity in apoB-containing particles that may potentially interfere with the immunological recognition of the antigenic sites used for apoB quantitation. It is possible that variations in the folding of the apoB molecule between small and large LDL particles and differences in the concentrations of other lipids, such as phospholipids on the particle surface, may interfere with detection by immunoassay (Morris et al., 2014).
Liquid chromatography-tandem mass spectrometry (LC-MSMS)-based quantification of apolipoproteins has the potential to further improve apoB standardization and between-method comparability. The IFCC has therefore initiated the development of LC-MSMS as the 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 apoB 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 (Langlois et al., 2020; Langlois & Sniderman, 2020).
The physiological variation of ApoB is about 6.4% (da Silva et al, 2018).
According to some studies, the high-risk total serum apoB value is classified as > 120 mg/dL, while an optimal value is < 80 mg/dL ( Schaefer et al., 2016). Values < 40 mg/dL are consistent with some form of hypobetalipoproteinemia, while undetectable values indicate the presence of abetalipoproteinemia ( Schaefer et al., 2016).
For high-risk patients, the recommended goal for apoB (80 mg/dL) corresponds to approximately the 25th percentile of the population, whereas a target level of LDL-C of 70 mg/dL corresponds to the lower reference limit (2.5th percentile) of the population The target for apoB should be the population equivalent level for LDL-C, that is, approximately 60-65 mg/dL for high-risk patients in a nonfasting population (Langlois & Sniderman, 2020).
Physiological variation is estimated at about 6.4% (da Silva et al., 2018).
very-high risk: <65 mg/dL
high risk: 80 mg/dL
moderate risk: 100 mg/dL
|AHA/ACC||< 130 mg/dL|
ApoB Summary Table
|Summary||ApoB outperforms standard lipid panel parameters in several studies. There are still issues with the error level and between method comparability, however, the method is well-standardized and nonproprietary.|
|Best test type||Immunoassay or liquid chromatography-tandem mass spectrometry|
|Hazard ratio for CVD events||1.28|
|Error||± 12% (Langlois et al., 2018)|
ApoA-I makes up about 65% of the protein mass of HDL and reflects the anti-atherogenic potential of HDL particles. Generally, the higher the value, the better the protection against CVD risk (Walldius, 2012). C oncentrations of apoA-I in HDL particles in the general population and in CVD patients have been extensively assessed (Schaefer et al., 2016). CVD patients have significant increases in apoA-I levels in very small preβ-1 HDL and significant decreases in apoA-I in large and very large α-2 and α-1 HDL (Schaefer et al., 2016).
The Framingham Offspring Study documented that for every 1 mg/dL increase in the apoA-I level in very large α-1 HDL, there was a 26% reduction in CVD risk ( Schaefer et al., 2016). Moreover, in the prospective Veterans Affairs HDL Intervention Trial (VA HIT), decreased apoA-I levels in large α migrating HDL particles and increased apoA-I levels in very small preβ-1 HDL were associated with an increased CHD risk ( Schaefer et al., 2016).
T he results of most studies (including a metanalysis of 28 studies that compared HDL-C and apoAI as markers of CVD risk) suggest that in a simple head-to-head comparison, HDL-C may be a better marker of CHD than apoA-I (Ramasamy, 2018; Schaefer et al., 2016). It has however also been reported that an increase in apoA-I, but not in HDL-C levels was associated with a reduced risk of major cardiovascular events (Barter & Genest, 2019).
- rate immunonephelometry (Kouvari et al., 2019)
Standardization & error
There are no universally accepted RMPs for the analysis of apoA-I (Warnick et al., 2008).
The loss of apoA-I from HDL during ultracentrifugal isolation is higher than other precipitation methods by as much as 50% (Hafiane & Genest, 2015).
The physiological variation of ApoA-I has been measured as 7.1% (da Silva et al., 2018).
In contrast with apoB, apoA-I does not necessarily evaluate the number of HDL particles (each HDL particle can bear one to five apoA-I molecules), but nevertheless plasma apoA-I levels < 120 mg/dL for men and <140 mg/dL for women correspond roughly to low HDL-C levels (da Silva et al., 2018).
Factors affecting measurement
ApoA-I may decrease with:
- Chronic kidney disease
- Use of drugs such as androgens, beta-blockers, diuretics, and progestins (synthetic progesterone)
- Uncontrolled diabetes
ApoA-I may increase with:
- Use of drugs such as carbamazepine, estrogens, ethanol, lovastatin, niacin, oral contraceptives, phenobarbital, pravastatin, and simvastatin
- Physical exercise
- Weight reduction
- Use of statins
ApoA-I Summary Table
|Summary||ApoA-I does not appear to add additional information to CVD risk assessment, over and above HDL-C on its own. However, measurement is recommended because the apoB/apoA-I ratio is one of the most predictive measurements currently available.|
|Best test type||Immunoassay|
|Hazard ratio for CVD events||1.16|
men: < 120 mg/dL
women: < 140 mg/dL
|Total error||Precision: ± 4% Accuracy: ± 5.4% (Dati & Tate, 2001) Estimated total error: ± 10%|
The apoB/apoA-I ratio reﬂects the two sides of the CVD risk equation: the atherogenic – apoB side, and the antiatherogenic – apoA-I side. The ratio of apoB to apoA-I thus reﬂects the balance of cholesterol transport in a simple way.
Several studies have shown an improvement in CVD risk prediction by the apoB/apoA-I ratio that remained even after adjusting for traditional risk factors (Ramasamy, 2018; Tian et al., 2019; Perera et al., 2015). The INTERHEART study showed that the non-fasting apoB/apoA-I ratio was superior to other cholesterol ratios for the estimation of risk in all ethnic groups, in both sexes at all ages (Ramasamy, 2018). The most extensive meta-analysis to date found that the hazard ratio for apoB/apoA-I was 1.36, the highest of the 10 markers that were compared (Perera et al., 2015).
Some studies have shown only comparable predictive power to other lipid measurements. In the Framingham cohort study, a prospective study with 291 CHD events, the performance of apoB/apoA-I ratio did not offer incremental utility over TC/HDL-C in the prediction of CHD risk (Ramasamy, 2018; Schaefer et al., 2016). In the MONICA/KORA study, the predictive power of the apoB/apoA-I ratio was also similar to that of TC/HDL-C. In a case-control analysis in the EPIC-Norfolk study, apoB/apoA-I was independently associated with future coronary artery disease but was no better than lipid values at discriminating between cases and controls.
ApoB and apoA-I can be measured by nephelometric and turbidimetric methods.
Standardization & error
ApoB and apoA-I are measured directly by standardized and internationally validated techniques and the methods have an error of < 5% (Walldius & Jungner, 2006) .
The methods have drawbacks due to immunoreactivity differences between different lipoproteins and differences in the reactivity of the epitopes. ApoB and apoA-I can be measured with an inter-laboratory CoV of < 7% (Ramasamy, 2018).
The cut-off value for the apoB/apoA-I ratio that defines high cardiovascular risk was proposed to be 0.9 for men and 0.8 for women ( Kaneva et al., 2015).
ApoB/ApoA-I Summary Table
|Summary||Several studies have shown an improvement in CVD risk prediction by the apoB/apoA-I ratio that remained even after adjusting for traditional risk factors. The ratio of apoB to apoA-I thus reﬂects the balance of cholesterol transport in a simple way. A total lipoprotein profile can be generated.|
|Best test type||Immunoassay|
|Hazard ratio for CVD events||1.36|
|Error||± 22% estimate based on individual errors of apoB & apoA-I|
Apolipoprotein C-III (apoC-III), resides on the surface of TG rich lipoproteins such as chylomicrons and VLDL and was first identified as a regulator of TRLs in the circulation. Suggested mechanisms by which apoC-III increases plasma TG levels are by direct inhibition of lipoprotein lipase and hepatic lipase and delayed clearance of apoB-containing particles. ApoC-III containing light LDL evolve to dense LDL faster than particles without apoC-III. It also exerts direct proatherogenic effects which result in monocyte activation and adhesion (Ramasamy, 2018; da Silva et al., 2018). This has led to apoC-III being recognized as a possible important new risk factor.
Studies have confirmed the association between apoC-III and incident coronary artery disease. Elevated TG, sdLDL, and low-grade inflammation may explain this association. It has been demonstrated that a high apoC-III concentration in plasma, independent of fasting TG and other traditional risk factors, predicts CVD mortality. A meta-analysis of 15 studies suggested that it remains to be demonstrated to what extent apoC-III is a TG independent risk factor for CVD. Much of the risk of CHD attributed to LDL appeared to result from LDL that contains apoC-III (Ramasamy, 2018).
A fraction of HDL-C that has apoC-III present on its surface also tended to be associated with a higher risk of future CHD. Genetic studies have shown that loss of function mutation in APOC3 (which encodes apoC-III) results in a favorable lipid profile characterized by reduced TG and VLDL levels and corroborated causality by directly linking genetic variation in APOC3 with coronary artery disease. Collectively these findings have led to the development of apoC-III lowering therapies (Ramasamy, 2018).
ApoC-III is not frequently measured in clinical laboratories. Using commercially available reagents and automation, an immunoturbidimetric method can be used to determine apoC-III. An automated LC-MS/MS was developed for the simultaneous measurement of apoA-I, apoB, apoC-I, apoC-II, apoC-III, and apoE (Ramasamy, 2018) .
Standardization & error
Standardized clinical immunoassays for the measurements of apoC-III concentrations are not available (da Silva et al., 2018). The LC-MS/MS assay correlated with the immunoturbidimetric assay with Conformité Européenne marking for apoC-III, apoA-I, apoB, and apoC-II. There are no reference methods or standards available for apoC-III and it is difficult to compare different methods. ApoC-III exists in different glycoforms and is differentially distributed between VLDL, LDL, and HDL (Ramasamy, 2018).
ApoC-III Summary Table
|Summary||Although apoC-III may be a useful marker of cardiovascular risk, the lack of data & standardization of methods makes the results unreliable. Currently not recommended.|
|Hazard ratio for CVD events||1.47 (van Capelleveen et al., 2017)|
Remnant lipoprotein particles
Remnants (RC) are the products of remodeling of TG-rich chylomicrons and VLDLs in the circulation through the actions of lipoprotein lipase and cholesteryl ester transfer protein. The hydrolysis of TG by lipoprotein lipase and acquisition of cholesteryl esters from HDL by cholesteryl ester transfer protein generates smaller remnant particles that are depleted of part of their TG content (Langlois & Sniderman, 2020). RC has long been considered to be atherogenic and Mendelian randomization studies have confirmed RC as pro-atherogenic lipoproteins (da Silva et al., 2018).
RC is increased in diabetic subjects, and its value increases significantly in the post-prandial state ( Schaefer et al., 2016). Studies have shown that high RC levels correlate with ischemic heart disease, major adverse cardiovascular events, and ischemic stroke. In addition to fasting RC levels, non-fasting RC level has a consistent association with the risk for incident coronary artery disease (CAD) (Chen et al., 2020).
The traditional method for measuring RC was ultracentrifugation. Various types of electrophoresis and the VAP method have also been used (Chen et al., 2020). An antibody-based precipitation assay for measuring cholesterol in remnant lipoproteins has also been developed ( Schaefer et al., 2016).
In clinical practice, RC levels are usually calculated from the standard lipid profile. RC is the cholesterol content of all non-LDL and non-HDL and can be calculated from a standard lipid profile as TC minus LDL-C minus HDL-C (Chen et al., 2020).
Nuclear magnetic resonance (NMR) can measure RC levels directly (Chen et al., 2020).
A level of remnant lipoprotein cholesterol > 30 mg/dl was associated with dysbetalipoproteinemia, and a value > 10 mg/dL was associated with an increased risk of CVD, especially in women ( Schaefer et al., 2016).
Male: 0.22-1.49 mmol/L (Ridefelt et al., 2019)
Female: 0.2-1.10 mmol/L (Ridefelt et al., 2019)
Standardization & error
A consensus method of measuring RC levels has not been established yet (Chen et al., 2020).
Notable discrepancies exist between methodologies (Chen et al., 2020).
RC Summary Table
|Summary||Although RC may be a useful marker of cardiovascular risk, the lack of data & standardization of methods makes the results unreliable. Currently not recommended.|
|Hazard ratio for CVD events||1.23|
LDL particles are extremely sensitive to oxidative damage: each LDL particle contains approximately 700 molecules of phospholipids, 600 molecules of free cholesterol, 1600 molecules of cholesterol ester, 185 molecules of triglycerides, and an apolipoprotein B-100 (apoB-100) protein with 4536 amino acids. Both lipids and protein constituents can be oxidized in a complex process multistage process (Gao & Liu, 2017). Oxidized LDL (ox-LDL) is a mixture of heterogeneously modified particles.
Circulating ox-LDL levels have been shown to be elevated in patients with coronary artery disease, transplant-associated atherosclerosis, hemodialysis, and diabetes (Itabe & Ueda, 2007). Subclinical atherosclerosis may also be predicted through ox-LDL levels. One study found a HR of 1.44 for new-onset carotid plaques at the 5-year follow-up per unit increase in ox-LDL (Gao & Liu, 2017).
Ox-LDL plays an important role in the pathogenesis of atherosclerosis. Subclinical atherosclerosis and clinical coronary heart disease (CHD) are associated with higher concentrations of circulating ox-LDL (Holvoet et al., 2006). The level of ox-LDL and the ratio of ox-LDL/TC, ox-LDL/LDL-C, and ox-LDL/HDL-C are better biomarkers than TC, TG, HDL-C, and LDL-C for discriminating between patients with coronary artery disease and healthy subjects. Patients who have a high ratio of ox-LDL/TC may have a higher risk for CAD (Huang et al., 2008). There are some reports showing that blood ox-LDL was elevated in patients with coronary artery disease, suggesting that ox-LDL may be a marker for atherosclerosis. In patients with acute myocardial infarction, the level of ox-LDL in the plasma was more than 6 times higher than in controls which strongly suggests that ox-LDL could also serve as a marker for cardiovascular events (Huang et al., 2008).
Three monoclonal antibodies (mAb), DLH3, E06, and 4E6 are commonly used to measure ox-LDL in the circulation. DHL3 recognizes oxidized phosphatidylcholine (ox-PC). E06 also binds to ox-PC. 4E6 binds to modified apoB. At least five types of ELISA procedures have been developed:
In this method, anti-ox-PC mAb, DLH3, and an anti-human apoB polyclonal antibody are used to detect apoB-containing particles that have been modified by ox-PC (Itabe & Ueda, 2007). The LDL fraction is separated prior to the assay by ultracentrifugation to eliminated forms of VLDL, IDL, and chylomicrons. Since the same amount of LDL fraction is applied to every microtiter well, the data are expressed as ng ox-LDL/ug LDL protein, representing the ratio of oxidatively modified particles in the LDL fraction (Itabe & Ueda, 2007). It takes at least 3 days to run a set of 8-10 samples, making the procedure unsuitable for mass screening.
MX ELISA kit
This kit is also a sandwich assay that was altered to make it applicable to a larger number of samples. The kit was manufactured to measure ox-LDL in diluted plasma using a pre-coated ELISA plate. The assay can be completed within a day (Itabe & Ueda, 2007).
In this method, the anti-PC mAb E06 and chemical luminescence detector are used to develop a dual-sandwich. The value obtained is a ratio between oxidized modifications per apoB-containing particle. WItztum's value includes the degree of modification; if 10 PC moieties appear on an LDL particle, it will be counted as 10 (Itabe & Ueda, 2007).
Monoclonal antibody (mAb 4E6)–based competitive ELISA
A sample of diluted plasma is incubated with a known amount of 4E6 mAb. The 4E6 antibody is directed against a conformational epitope in the apoB moiety of LDL that is generated as a consequence of the substitution of at least 60 lysine residues of apoB with aldehydes. This number of substituted lysines corresponds to the minimal number required for scavenger-mediated uptake of ox-LDL. The substituting aldehydes can be produced by the peroxidation of lipids of LDL, leading to the generation of ox-LDL. Aldehydes that are released by endothelial cells under oxidative stress or by activated platelets may also induce oxidative modification of apoB in the absence of peroxidation of lipids of LDL (Holvoet et al., 2006).
Standardization & error
The different methods may detect slightly different types of ox-LDL particles. These measures may not determine the absolute amount of ox-LDL but can indicate the relative changes (Itabe & Ueda, 2007).
Although some of the methods use the same mAb, the data obtained cannot be directly compared. Itabe's method provides a ratio whereas the MX kit provides the concentration of ox-LDL in plasma. The ox-LDL standards used for the procedures are also prepared under different conditions and the unit used in one assay is not equal to the unit used in the other (Itabe & Ueda, 2007). Studies have shown a very weak correlation between methods.
Interindividual variability accounted for 77% of the variation in ox-LDL levels in one study with the remaining 23% being within-person and analytical variance (Holvoet et al., 2006). Another study reported a CoV of 15% for the assay used in their analysis (Sjogren et al., 2005).
One study found the mean value of persons at high CVD risk but without any diagnosis of CVD was 15 mg/L (50 units/L) (Holvoet et al., 2006).
|Method||ox-LDL in Healthy Subjects|
|Itabe's (Sandwich DLH3 + anti apoB)||0.1 ng/ug LDL|
|Kyowa MX kit (Sandwich DLH3 + anti apoB)||10 unit/mL plasma|
|Witztum's (Dual sandwich: E06, MB47, MB24,anti apoB)||0.027-0.42 (ratio E06 / apoB)|
|Holvoet's (Competition: 4E6)||0.7 mg/dL|
|Mercodia kit (Competition: 4E6)||70 U/L|
Factors affecting measurement
Ox-LDL Summary Table
|Summary||Ox-LDL has shown value in the prediction of CVD events.|
|Best test type||ELISA|
|Hazard ratio for CVD events||1.44 (Gao & Liu, 2017)|
|Reference range||< 15 mg/L|
Sterol-based biomarkers can be useful in the assessment of the cause of hypercholesterolemia and may have an impact on therapy (Wu, 2014). Plasma levels of lathosterol and desmosterol are markers of cholesterol synthesis with approximately 80% of cholesterol synthesis occurring via lathosterol and 20% via desmosterol. Desmosterol levels are additionally determined by the rate of conversion of desmosterol to cholesterol via the enzyme 24-dehydrocholesterol reductase. Elevated desmosterol values relative to cholesterol are mainly a reflection of tissue desmosterol accumulation (Schaefer et al., 2016).
β-sitosterol and campesterol levels are markers of cholesterol absorption that have been associated with cardiovascular disease in some studies ( Schaefer et al., 2016). However, a systematic review and meta-analysis based on 17 studies (n=11,182) did not find evidence of an association between serum concentrations of β-sitosterol and campesterol and elevated risk of CVD (Genser et al., 2012).
Statins significantly lower markers of cholesterol synthesis, especially lathosterol, and increase markers of cholesterol absorption, while ezetimibe has the opposite effect. Importantly, while statin therapy has been shown to significantly lower CVD risk, it has also been reported that patients with heart disease on simvastatin got no benefit from therapy versus placebo if their baseline cholestanol levels were elevated (Schaefer et al., 2016).
Gas-liquid chromatography/mass spectrometry after lipid extraction
Error & standardization
The values of these sterols can be reported relative to total serum cholesterol as 100 x μmol per mmol of total cholesterol or in absolute concentration in mg/L ( Schaefer et al., 2016). The overall precision ranges from 8% to 10% (Wu, 2014). There are currently no assays cleared by the US Food and Drug Administration (Wu, 2014).
The analytical, intra-individual, and group inter-individual variations (CVA, CVI, and CVG, respectively) have been calculated in a group of healthy volunteers as shown in the table below (Wu et al., 2014).
|Sterol||CoV analytics||Intraindividual variation||Interindividual variation|
As seen in the table above, population-based reference values are of little use for these biomarkers. The number of measurements needed for an accurate estimate was 5 samples for β-sitosterol and campesterol and 19 samples for lathosterol (Wu et al., 2014). However, some studies have provided reference values as seen in the table below.
|Compound||Optimal (μmol x 100/mmol of TC)||Borderline (μmol x 100/mmol of TC)||High risk (μmol x 100/mmol of TC)|
|Lathosterol||< 90||90 - 160||> 160|
|Desmosterol||< 70||70 - 90||> 90|
|β-sitosterol||< 100||100 - 180||> 180|
|Campesterol||< 130||130 - 230||> 230|
|Cholestanol (True Health Diagnostics)|
≤ 2.01 μg/mL
2.02 - 3.47 μg/mL
≥ 3.48 μg/mL
Sterols Summary Table
|Summary||Not currently recommended.|
|Hazard ratio for CVD events|
Lathosterol: cholesterol = 0.60
Cholestanol: cholesterol = 1.6
Campesterol: cholesterol = 1.29
|Error||8-10% CoV + unknown accuracy|
Circulating fatty acid composition and its relation to cardiovascular disease has been assessed in several studies (Chowdhury et al., 2014). Measurements of fatty acid composition consist of saturated, monounsaturated, polyunsaturated, and trans-fatty acids, each with several subcategories. A systematic review (n= 25,721) found some evidence that circulating levels of eicosapentaenoic, docosahexaenoic, and arachidonic acids are each associated with lower CVD risk (Chowdhury et al., 2014). It found no association between total saturated or monounsaturated fatty acids and CVD risk. Total trans fatty acid intake was positively associated with CVD risk. Another study found that fasting free fatty acids were not associated with CVD, or CVD-specific mortality regardless of age, sex, race/ethnicity, or metabolic syndrome status (Nomura et al., 2020).
Other studies have reported that higher levels of linoleic acid have been associated with lower risks of total CVD, cardiovascular mortality, and ischemic stroke, with hazard ratios of 0.78-0.93 for CVD (Marklund et al., 2019). In prospective cohort studies, higher linoleic acid levels were also associated with a modestly lower risk of mortality from all causes, CVD, and cancer (Li et al., 2020). The quantification of omega-6 and omega-3 acids is still a challenging task due to the difficulties in the detection of trace levels of these analytes and the high matrix content of biological samples (Öztürk. et al., 2020).
The majority of studies used g as chromatography (GC) or high-performance liquid chromatography (HPLC) combined with mass spectrometry to measure circulating fatty acid composition (Öztürk. et al., 2020). Calorimetric methods and enzymatic methods have also been used (Chowdhury et al., 2014).
Error & standardization
Precision error is < 10% (Öztürk. et al., 2020). Accuracy unknown.
|Saturated fatty acid index||< 30%||> 33%|
|Trans-fatty acid index||-||> 0.80%|
|Monounsaturated fatty acid index||>22%||<19%|
|Omega-6 fatty acid index||41-45%||> 46%|
|Unsaturated/saturated ratio index||-||< 2.0|
|Omega 3 fat index||> 4.50%||< 1.85%|
|Plasma ALA||-||< 12.0 μg/L|
|Plasma EPA||> 150 μg/L||-|
|Plasma DHA||> 100.0 μg/L||< 45.0 μg/L|
Fatty Acids Summary Table
|Summary||Due to a lack of information on error and standardization, not currently recommended.|
Hazard ratio for CVD events
(Comparing top vs. bottom thirds)
|Error||10% precision + unknown accuracy|
Shotgun lipidomics using mass spectrometry detects 135 lipid species in 8 classes - triacylglycerols (TAGs) of low carbon number and double-bond content show the strongest and most consistent associations with CVD - molecular lipid profiling by MS results in a significant improvement in CVD risk prediction beyond classic lipid markers (Stegemann et al., 2014).
Certain TAGs rather than total TG confer increased CVD risk and saturated and MUFAs chains were most consistently associated with CVD.
Some of the identified risk markers that were only detectable within advanced plaques include cholesterol esters: CE (16:1) cholesteryl palmitoleate, PC (38.3), TAG (54:2), PE (36.5) (Stegemann et al., 2014).
Standardization & error
Study results are not very consistent and in some cases, in fact, quite contradictory. However, many of these inconsistent findings can be explained by obvious differences in disease phenotypes and analytical approaches (Laaksonen, 2016).
Lipidomics Summary Table
|Summary||Lipidomics is new and not yet standardized. Not currently recommended.|
|Hazard ratio for CVD events||unknown|
Presentation of results
This chart compares lipid biomarkers in terms of their association with CVD and the total error of the analytical technique used to measure them. Arbitrary "acceptable" thresholds of 15% for error and a 1.2 CVD hazard ratio were assigned. Only two of the biomarkers, total cholesterol, and ApoB, currently satisfy these criteria.
According to our analysis, only two markers meet the criteria of being both closely associated with cardiovascular risk and having a total error of < 15%: ApoB and TC. There are several other markers that have stronger associations with CVD risk and may present valuable information however, the test results are less reliable, due in most cases, to a lack of standardization or a low level of accuracy.
Accuracy refers to how close a measurement is to the true or accepted value. Precision refers to how close measurements of the same item are to each other. Many test methods in our analysis exhibited a high level of precision but a low-level of accuracy.
High intraindividual variability in many of the markers is also associated with CVD risk which speaks for regular measurement of these biomarkers. However, with regard to the assessment of variability, neither the number of measurements nor the time between measurements has been standardized (Simpson, 2019).
As seen by the large error levels in testing and in contrast to the cost-driven approach taken by many guidelines of recommending measurement of lipid biomarkers once every 5 years, more frequent measurement of biomarkers is required to approach a true value. Based on the level of error in lipid testing, it would be ideal to take serial, duplicate measurements of 4 specimens within two months, each at least 1 week apart. For average risk, this should be repeated annually. For higher-risk individuals, the monitoring could be repeated every 3-6 months.
Screening should be performed at least once during childhood in order to identify any inherited conditions and repeated once in early adulthood. If results are normal, annual screening should begin at age 40 and continue at least until age 79.
Lipid Monitoring Protocol
|Parameter||Recommended||Hazard ratio||Test||Error||Frequency||Reference range||Summary|
|1.28||Immunoassay or liquid chromatography-tandem mass spectrometry||Total error: ± 12%||Duplicate analyses of 4 specimens (fasting unnecessary), repeated annually||65 mg/dL||ApoB outperforms standard lipid panel parameters in several studies. There are still issues with the error level and between method comparability, however, the method is well-standardized and nonproprietary.|
|1.24||Gas & liquid chromatography, mass spectrometry||Total error: ± 8.9%||Duplicate analyses of 4 specimens (fasting unnecessary), repeated annually||150-240 mg/dL||Although an important CVD risk factor, serum TC on its own is actually a relatively poor predictor of who will go on to have an event. However, the test is relatively well-standardized with an acceptable error. Potentially useful for identifying the risk associated with intraindividual variations over time. Necessary for the calculation of non-HDL-C & usually comes with the other values on a standard lipid panel.|
Precision: ± 6%
|Duplicate analyses of 4 specimens (fasting unnecessary), repeated annually||< 1000 nmol/L||LDL-P is a valuable addition to a cardiovascular risk assessment. The test is not widely available and results between methods are highly variable. Precise but not accurate (differs significantly by method). Provides additional information on subfractions.|
Precision: ± 9%
|At least once||< 30 mg/dL|
< 75 nmol/L
|Lp(a) measurement should be considered at least once in each adult person’s lifetime to identify those with very high inherited Lp(a) levels >180 mg/dL.|
Precision: ± 4%
|Duplicate analyses of 4 specimens (fasting unnecessary), repeated annually|
men: < 120 mg/dL
women: < 140 mg/dL
|ApoA-I does not appear to add additional information to CVD risk assessment, over and above HDL-C on its own. However, measurement is recommended because the ApoB/ApoA-1 ratio is one of the most predictive measurements currently available.|
|1.44||ELISA||Total error: ± 15-23%||Duplicate analyses of 4 specimens (fasting unnecessary), repeated annually||< 15 mg/L||Ox-LDL has shown value in the prediction of CVD events.|