Comparison of cardiometabolic risk biomarkers from a national clinical laboratory with the US adult population.

BACKGROUND: Clinical laboratory patient databases are an untapped source of valuable diagnostic and prognostic information. However, the lack of associated clinical and/or demographic information and questionable generalizability to nonpatient populations often limit utility of these data. OBJECTIVES: This study compared levels of cardiometabolic biomarkers between a national clinical laboratory patient cohort (Health Diagnostic Laboratory [HD Lab]) and the US population as inferred from the National Health and Nutrition Examination Survey (NHANES, 2011-2012). METHODS: Sample sizes for HD Lab ranged from 199,000 to 739,000 and for NHANES from 2200 to 5300. The latter were weighted to represent the adult US population (∼220 million). Descriptive statistics were compared for body mass index, 5 lipid biomarkers, and 3 glycemic biomarkers. RESULTS: Using age- and sex-matched data, mean biomarker values (mg/dL unless noted) and percent differences (%) for HD Lab vs NHANES were body mass index (kg/m(2)), 29.1 vs 28.6 (1.7%); total cholesterol, 185 vs 193 (-4.1%); apolipoprotein B, 92 vs 90 (2.2%); low-density lipoprotein cholesterol, 107 vs 115 (-7%); high-density lipoprotein cholesterol, 53 vs 53 (0%); triglycerides, 128 vs 127 (0.8%); glucose, 99 vs 108 (-8.3%); insulin (uU/mL), 13.7 vs 13.4 (2.2%); and hemoglobin A1c (%), 5.6 vs 5.8 (-3.4%). Although all differences were statistically significant, only low-density lipoprotein cholesterol and glucose differed by more than 5%. These may reflect a greater use of medications among HD Lab patients and/or preanalytical factors. CONCLUSIONS: Cardiometabolic risk markers from a national clinical laboratory were broadly similar to those of the US population; thus, with certain caveats, data from the former may be generalizable to the latter.

Discordance between apolipoprotein B and low-density lipoprotein particle number is associated with insulin resistance in clinical practice.

BACKGROUND: Discordance between measures of atherogenic lipoprotein particle number (apolipoprotein B [ApoB] and low-density lipoprotein [LDL] particle number by nuclear magnetic resonance spectroscopy [LDL-PNMR]) is not well understood. Appropriate treatment considerations in such cases are unclear. OBJECTIVES: To assess discordance between apoB determined by immunoassay and LDL-PNMR in routine clinical practice, and to characterize biomarker profiles and other clinical characteristics of patients identified as discordant. METHODS: Two retrospective cohorts were evaluated. First, 412,013 patients with laboratory testing performed by Health Diagnostic Laboratory, Inc., as part of routine care; and second, 1411 consecutive patients presenting for risk assessment/reduction at 6 US outpatient clinics. Discordance was quantified as a percentile difference (LDL-PNMR percentile - apoB percentile) and attainment of percentile cutpoints (LDL-PNMR ≥ 1073 nmol/L or apoB ≥ 69 mg/dL). A wide range of cardiovascular risk factors were compared. RESULTS: ApoB and LDL-PNMR values were highly correlated (R(2) = 0.79), although substantial discordance was observed. Similar numbers of patients were identified as at-risk by LDL-PNMR when apoB levels were < 69 mg/dL (5%-6%) and by apoB values when LDL-PNMR was < 1073 nmol/L (6%-7%). Discordance (LDL-PNMR > apoB) was associated with insulin resistance, smaller LDL particle size, increased systemic inflammation, and low circulating levels of "traditional" lipids, whereas discordance (apoB > LDL-PNMR) was associated with larger LDL particle size, and elevated levels of lipoprotein(a) and lipoprotein-associated phospholipase A2 (Lp-PLA2). CONCLUSION: Discordance between apoB and LDL-PNMR in routine clinical practice is more widespread than currently recognized and may be associated with insulin resistance.

Validation of a lipoprotein(a) particle concentration assay by quantitative lipoprotein immunofixation electrophoresis.

BACKGROUND: Low-density lipoprotein (LDL) particle (P, or molar) concentration has been shown to be a more sensitive marker of cardiovascular disease (CVD) risk than LDL cholesterol. Although elevated circulating lipoprotein(a) [Lp(a)] cholesterol and mass have been associated with CV risk, no practicable method exists to measure Lp(a)-P. We have developed a method of determining Lp(a)-P suitable for routine clinical use. METHODS: Lipoprotein immunofixation electrophoresis (Lipo-IFE) involves rigidly controlled electrophoretic separation of serum lipoproteins, probing with polyclonal apolipoprotein B antibodies, then visualization after staining with a nonspecific protein stain (Acid Violet). Lipo-IFE was compared to the Lp(a) mass assay for 1086 randomly selected patient samples, and for 254 samples stratified by apo(a) isoform size. RESULTS: The Lipo-IFE method was shown to be precise (CV <10% above the 50 nmol/l limit of quantitation) and linear across a 16-fold range. Lipo-IFE compared well with the mass-based Lp(a) assay (r=0.95), but was not affected by variations in apo(a) isoform size. With a throughput of 100 samples in 90 min, the assay is suitable for use in the clinical laboratory. CONCLUSIONS: The Lipo-IFE method will allow Lp(a)-P to be readily tested as a CVD risk factor in large-scale clinical trials.

Lipoprotein(a) mass: a massively misunderstood metric.

The importance of lipoprotein (a)-Lp(a)-as a cardiovascular (CV) risk marker has been underscored by recent findings that CV risk is directly related to baseline Lp(a) levels, even in well-treated patients. Although there is currently little that can be done pharmacologically to lower Lp(a) levels, knowledge of its serum concentration is important in overall risk assessment. This review focuses on 1 aspect of Lp(a) that is rarely discussed directly: how to express its levels in serum. There is considerable confusion on this point, and a fuller understanding of what the concentration units mean will help improve study-to-study comparisons and thereby advance our understanding of the pathobiology of this lipoprotein particle. As discussed here, the term Lp(a) mass refers to the entire mass of the particle: lipids, proteins, and carbohydrates combined. At present, there are no commercially available assays that are completely insensitive to the variability in particle mass, which arises not only from differences in apo(a) isoform mass but also from variations in lipid mass. Because lipoprotein "particle number" (molar concentration) has been found to be superior to component-based metrics (ie, low-density lipoprotein particle vs cholesterol concentrations) for CV disease risk prediction, the development of a mass-insensitive Lp(a) assay should be a high priority.

Serum α-hydroxybutyrate (α-HB) predicts elevated 1†h glucose levels and early-phase ß-cell dysfunction during OGTT.

OBJECTIVE: Serum α-hydroxybutyrate (α-HB) is elevated in insulin resistance and diabetes. We tested the hypothesis that the α-HB level predicts abnormal 1 h glucose levels and ß-cell dysfunction inferred from plasma insulin kinetics during a 75 g oral glucose tolerance test (OGTT). RESEARCH DESIGN AND METHODS: This cross-sectional study included 217 patients at increased risk for diabetes. 75 g OGTTs were performed with multiple postload glucose and insulin measurements over a 30-120 min period. OGTT responses were analyzed by repeated measures analysis of variance (ANOVA). Multivariable logistic regression was used to predict 1 h glucose ≥155 mg/dL with α-HB added to traditional risk factors. RESULTS: Mean±SD age was 51±15 years (44% male, 25% with impaired glucose tolerance). Fasting glucose and insulin levels, but not age or body mass index (BMI), were significantly higher in the second/third α-HB tertiles (>3.9 µg/mL) than in the first tertile. Patients in the second/third α-HB tertiles exhibited a higher glucose area under the receiver operating characteristics curve (AUC) and reduced initial slope of insulin response during OGTT. The AUC for predicting 1 h glucose ≥155 mg/dL was 0.82 for a base model that included age, gender, BMI, fasting glucose, glycated hemoglobin (HbA1c), and insulin, and increased to 0.86 with α-HB added (p=0.015), with a net reclassification index of 52% (p<0.0001). CONCLUSIONS: Fasting serum α-HB levels predicted elevated 1 h glucose during OGTT, potentially due to impaired insulin secretion kinetics. This association persisted even in patients with an otherwise normal insulin-glucose homeostasis. Measuring serum α-HB could thus provide a rapid, inexpensive screening tool for detecting early subclinical hyperglycemia, ß-cell dysfunction, and increased risk for diabetes.

Precision phenotyping, panomics, and system-level bioinformatics to delineate complex biologies of atherosclerosis: rationale and design of the "Genetic Loci and the Burden of Atherosclerotic Lesions" study.

BACKGROUND: Complex biological networks of atherosclerosis are largely unknown. OBJECTIVE: The main objective of the Genetic Loci and the Burden of Atherosclerotic Lesions study is to assemble comprehensive biological networks of atherosclerosis using advanced cardiovascular imaging for phenotyping, a panomic approach to identify underlying genomic, proteomic, metabolomic, and lipidomic underpinnings, analyzed by systems biology-driven bioinformatics. METHODS: By design, this is a hypothesis-free unbiased discovery study collecting a large number of biologically related factors to examine biological associations between genomic, proteomic, metabolomic, lipidomic, and phenotypic factors of atherosclerosis. The Genetic Loci and the Burden of Atherosclerotic Lesions study (NCT01738828) is a prospective, multicenter, international observational study of atherosclerotic coronary artery disease. Approximately 7500 patients are enrolled and undergo non-contrast-enhanced coronary calcium scanning by CT for the detection and quantification of coronary artery calcium, as well as coronary artery CT angiography for the detection and quantification of plaque, stenosis, and overall coronary artery disease burden. In addition, patients undergo whole genome sequencing, DNA methylation, whole blood-based transcriptome sequencing, unbiased proteomics based on mass spectrometry, as well as metabolomics and lipidomics on a mass spectrometry platform. The study is analyzed in 3 subsequent phases, and each phase consists of a discovery cohort and an independent validation cohort. For the primary analysis, the primary phenotype will be the presence of any atherosclerotic plaque, as detected by cardiac CT. Additional phenotypic analyses will include per patient maximal luminal stenosis defined as 50% and 70% diameter stenosis. Single-omic and multi-omic associations will be examined for each phenotype; putative biomarkers will be assessed for association, calibration, discrimination, and reclassification.

Comprehensive biomarker testing of glycemia, insulin resistance, and beta cell function has greater sensitivity to detect diabetes risk than fasting glucose and HbA1c and is associated with improved glycemic control in clinical practice.

Blood-based biomarker testing of insulin resistance (IR) and beta cell dysfunction may identify diabetes risk earlier than current glycemia-based approaches. This retrospective cohort study assessed 1,687 US patients at risk for cardiovascular disease (CVD) under routine clinical care with a comprehensive panel of 19 biomarkers and derived factors related to IR, beta cell function, and glycemic control. The mean age was 53 ± 15, 42 % were male, and 25 % had glycemic indicators consistent with prediabetes. An additional 45 % of the patients who had normal glycemic indicators were identified with IR or beta cell abnormalities. After 5.3 months of median follow-up, significantly more patients had improved than worsened their glycemic status in the prediabetic category (35 vs. 9 %; P < 0.0001) and in the "high normal" category (HbA1c values of 5.5-5.6; 56 vs. 18 %, p < 0.0001). Biomarker testing can identify IR early, enable and inform treatment, and improve glycemic control in a high proportion of patients.

Does APOE genotype modify the relations between serum lipid and erythrocyte omega-3 fatty acid levels?

Earlier reports indicated that patients with the apolipoprotein APOE ε4 allele responded to fish oil supplementation with a rise in serum low-density lipoprotein cholesterol (LDL-C) compared to ε3 homozygotes. In this study, we used clinical laboratory data to test the hypothesis that the cross-sectional relation between RBC omega-3 fatty acid status (the Omega-3 Index) and LDL-C was modified by APOE genotype. Data from 136,701 patients were available to compare lipid biomarker levels across Omega-3 Index categories associated with heart disease risk in all APOE genotypes. We found no adverse interactions between APOE genotype and the Omega-3 Index for LDL-C, LDL particle number, apoB, HDL-C, or triglycerides. However, we did find evidence that ε2 homozygotes lack an association between omega-3 status and LDL-C, apoB, and LDL particle number. In summary, we found no evidence for a deleterious relationship between lipid biomarkers and the Omega-3 Index by APOE genotype.

Comparative effects of an acute dose of fish oil on omega-3 fatty acid levels in red blood cells versus plasma: implications for clinical utility.

BACKGROUND: Omega-3 fatty acid (n-3 FA) biostatus can be estimated with red blood cell (RBC) membranes or plasma. The matrix that exhibits the lower within-person variability and is less affected by an acute dose of n-3 FA is preferred in clinical practice. OBJECTIVE: We compared the acute effects of a large dose of n-3 FA on RBC and plasma levels of eicosapentaenoic acid (EPA) plus docosahexaenoic acid (DHA). METHODS: Healthy volunteers (n = 20) were given 4 capsules containing 3.6 g of n-3 FA with a standardized breakfast. Blood samples were drawn at 0, 2, 4, 6, 8, and 24 hours. The EPA + DHA content of RBC membranes and plasma (the latter expressed as a percentage of total FA and as a concentration) were determined. General linear mixed models were used to analyze the mean response profiles in FA changes over time for plasma and RBCs. RESULTS: At 6 hours after load, the plasma concentration of EPA + DHA had increased by 47% (95% confidence interval [CI], 24% to 73%) and the plasma EPA + DHA percentage of total FA by 19% (95% CI, 4.7% to 36%). The RBC EPA + DHA percentage of composition was unchanged [-0.6% (95% CI, -2.6% to 1.5%)]. At 24 hours, the change in both of the plasma EPA + DHA markers was 10-fold greater than that in RBCs. CONCLUSIONS: An acute dose of n-3 FA (eg, a meal of oily fish or fish oil supplements) taken within a day before a doctor's visit can elevate levels of EPA + DHA in plasma, whether expressed as a percentage or a concentration, but not in RBC membranes. Similar to hemoglobin A1c, which is not affected by an acute glycemic deviation, RBCs provide a more reliable estimate of a patient's chronic EPA + DHA status than does plasma.

Association between high-normal levels of alanine aminotransferase and risk factors for atherogenesis.

BACKGROUND & AIMS: Liver disease has been associated with cardiovascular disorders, but little is known about the relationship between serum levels of alanine aminotransferase (ALT) and markers of atherogenesis. We investigated the relationship between low-normal and high-normal levels of ALT and an extended panel of cardiovascular risk factors among individuals with no known diseases in a primary care setting. METHODS: We performed a retrospective analysis of data collected from 6442 asymptomatic patients at wellness visits to a primary care setting in central Virginia from 2010 through 2011. Serum levels of ALT were compared with levels of lipids and lipoproteins, as well as metabolic, inflammatory, and coagulation-related factors associated with risk for cardiovascular disease. RESULTS: Serum levels of ALT were higher than 40 IU/L in 12% of subjects, and in the high-normal range (19-40 IU/L in women and 31-40 IU/L in men) in 25% of subjects. ALT level was associated with the apolipoprotein B level, concentration and particle size of very-low-density lipoproteins, concentration of low-density lipoprotein (LDL) particles (LDL-P), and percentages of small dense LDL (sdLDL) and sdLDL-cholesterol (sdLDL-C) (P < .0001 for all). A high-normal level of ALT was associated with higher levels of LDL-C, LDL-P, sdLDL-C, and sdLDL particles (P < .001 for all). These effects were independent of age, body mass index, and hyperinsulinemia. Increasing levels of ALT and fasting hyperinsulinemia (>12 µU/mL) synergized with increasing levels of triglycerides, very-low-density lipoprotein particles, LDL-P, sdLDL-C, and percentage of sdLDL-C. Levels of APOA1, high-density lipoprotein-cholesterol, and high-density lipoprotein-class 2 were associated inversely with serum level of ALT (P < .0001 for all). CONCLUSIONS: In an analysis of asymptomatic individuals, increased serum levels of ALT (even high-normal levels) are associated with markers of cardiovascular disease.

Evaluation of four different equations for calculating LDL-C with eight different direct HDL-C assays.

BACKGROUND: Low-density lipoprotein cholesterol (LDL-C) is often calculated (cLDL-C) by the Friedewald equation, which requires high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG). Because there have been considerable changes in the measurement of HDL-C with the introduction of direct assays, several alternative equations have recently been proposed. METHODS: We compared 4 equations (Friedewald, Vujovic, Chen, and Anandaraja) for cLDL-C, using 8 different direct HDL-C (dHDL-C) methods. LDL-C values were calculated by the 4 equations and determined by the ß quantification reference method procedure in 164 subjects. RESULTS: For normotriglyceridemic samples (TG<200mg/dl), between 6.2% and 24.8% of all results exceeded the total error goal of 12% for LDL-C, depending on the dHDL-C assay and cLDL-C equation used. Friedewald equation was found to be the optimum equation for most but not all dHDL-C assays, typically leading to less than 10% misclassification of cardiovascular risk based on LDL-C. Hypertriglyceridemic samples (>200mg/dl) showed a large cardiovascular risk misclassification rate (30%-50%) for all combinations of dHDL-C assays and cLDL-C equations. CONCLUSION: The Friedewald equation showed the best performance for estimating LDL-C, but its accuracy varied considerably depending on the specific dHDL-C assay used. None of the cLDL-C equations performed adequately for hypertriglyceridemic samples.

Association of apolipoprotein B and nuclear magnetic resonance spectroscopy-derived LDL particle number with outcomes in 25 clinical studies: assessment by the AACC Lipoprotein and Vascular Diseases Division Working Group on Best Practices.

BACKGROUND: The number of circulating LDL particles is a strong indicator of future cardiovascular disease (CVD) events, even superior to the concentration of LDL cholesterol. Atherogenic (primarily LDL) particle number is typically determined either directly by the serum concentration of apolipoprotein B (apo B) or indirectly by nuclear magnetic resonance (NMR) spectroscopy of serum to obtain NMR-derived LDL particle number (LDL-P). CONTENT: To assess the comparability of apo B and LDL-P, we reviewed 25 clinical studies containing 85 outcomes for which both biomarkers were determined. In 21 of 25 (84.0%) studies, both apo B and LDL-P were significant for at least 1 outcome. Neither was significant for any outcome in only 1 study (4.0%). In 50 of 85 comparisons (58.8%), both apo B and LDL-P had statistically significant associations with the clinical outcome, whereas in 17 comparisons (20.0%) neither was significantly associated with the outcome. In 18 comparisons (21.1%) there was discordance between apo B and LDL-P. CONCLUSIONS: In most studies, both apo B and LDL-P were comparable in association with clinical outcomes. The biomarkers were nearly equivalent in their ability to assess risk for CVD and both have consistently been shown to be stronger risk factors than LDL-C. We support the adoption of apo B and/or LDL-P as indicators of atherogenic particle numbers into CVD risk screening and treatment guidelines. Currently, in the opinion of this Working Group on Best Practices, apo B appears to be the preferable biomarker for guideline adoption because of its availability, scalability, standardization, and relatively low cost.

Reliability of low-density lipoprotein cholesterol, non-high-density lipoprotein cholesterol, and apolipoprotein B measurement.

There is little understanding of the reliability of laboratory measurements among clinicians. Low-density lipoprotein cholesterol (LDL-C) measurement is the cornerstone of cardiovascular risk assessment and prevention, but it is fraught with error. Therefore, we have reviewed issues related to accuracy and precision for the measurement of LDL-C and the related markers non-high-density lipoprotein cholesterol (non-HDL-C) and apolipoprotein B. Despite the widespread belief that LDL-C is standardized and reproducible, available data suggest that results can vary significantly as the result of methods from different manufacturers. Similar problems with direct HDL-C assays raise concerns about the reliability of non-HDL-C measurement. The root cause of method-specific bias relates to the ambiguity in the definition of both LDL and HDL, and the heterogeneity of LDL and HDL particle size and composition. Apolipoprotein B appears to provide a more reliable alternative, but assays for it have not been as rigorously tested as direct LDL-C and HDL-C assays.

Non-HDL cholesterol shows improved accuracy for cardiovascular risk score classification compared to direct or calculated LDL cholesterol in a dyslipidemic population.

BACKGROUND: Our objective was to evaluate the accuracy of cardiovascular disease (CVD) risk score classification by direct LDL cholesterol (dLDL-C), calculated LDL cholesterol (cLDL-C), and non-HDL cholesterol (non-HDL-C) compared to classification by reference measurement procedures (RMPs) performed at the CDC. METHODS: We examined 175 individuals, including 138 with CVD or conditions that may affect LDL-C measurement. dLDL-C measurements were performed using Denka, Kyowa, Sekisui, Serotec, Sysmex, UMA, and Wako reagents. cLDL-C was calculated by the Friedewald equation, using each manufacturer's direct HDL-C assay measurements, and total cholesterol and triglyceride measurements by Roche and Siemens (Advia) assays, respectively. RESULTS: For participants with triglycerides<2.26 mmol/L (<200 mg/dL), the overall misclassification rate for the CVD risk score ranged from 5% to 17% for cLDL-C methods and 8% to 26% for dLDL-C methods when compared to the RMP. Only Wako dLDL-C had fewer misclassifications than its corresponding cLDL-C method (8% vs 17%; P<0.05). Non-HDL-C assays misclassified fewer patients than dLDL-C for 4 of 8 methods (P<0.05). For participants with triglycerides≥2.26 mmol/L (≥200 mg/dL) and<4.52 mmol/L (<400 mg/dL), dLDL-C methods, in general, performed better than cLDL-C methods, and non-HDL-C methods showed better correspondence to the RMP for CVD risk score than either dLDL-C or cLDL-C methods. CONCLUSIONS: Except for hypertriglyceridemic individuals, 7 of 8 dLDL-C methods failed to show improved CVD risk score classification over the corresponding cLDL-C methods. Non-HDL-C showed overall the best concordance with the RMP for CVD risk score classification of both normal and hypertriglyceridemic individuals.

Seven direct methods for measuring HDL and LDL cholesterol compared with ultracentrifugation reference measurement procedures.

BACKGROUND: Methods from 7 manufacturers and 1 distributor for directly measuring HDL cholesterol (C) and LDL-C were evaluated for imprecision, trueness, total error, and specificity in nonfrozen serum samples. METHODS: We performed each direct method according to the manufacturer's instructions, using a Roche/Hitachi 917 analyzer, and compared the results with those obtained with reference measurement procedures for HDL-C and LDL-C. Imprecision was estimated for 35 runs performed with frozen pooled serum specimens and triplicate measurements on each individual sample. Sera from 37 individuals without disease and 138 with disease (primarily dyslipidemic and cardiovascular) were measured by each method. Trueness and total error were evaluated from the difference between the direct methods and reference measurement procedures. Specificity was evaluated from the dispersion in differences observed. RESULTS: Imprecision data based on 4 frozen serum pools showed total CVs <3.7% for HDL-C and <4.4% for LDL-C. Bias for the nondiseased group ranged from -5.4% to 4.8% for HDL-C and from -6.8% to 1.1% for LDL-C, and for the diseased group from -8.6% to 8.8% for HDL-C and from -11.8% to 4.1% for LDL-C. Total error for the nondiseased group ranged from -13.4% to 13.6% for HDL-C and from -13.3% to 13.5% for LDL-C, and for the diseased group from -19.8% to 36.3% for HDL-C and from -26.6% to 31.9% for LDL-C. CONCLUSIONS: Six of 8 HDL-C and 5 of 8 LDL-C direct methods met the National Cholesterol Education Program total error goals for nondiseased individuals. All the methods failed to meet these goals for diseased individuals, however, because of lack of specificity toward abnormal lipoproteins.

The correlation between TG vs remnant lipoproteins in the fasting and postprandial plasma of 23 volunteers.

BACKGROUND: Two recent publications report that non-fasting triglycerides concentrations in plasma are more predictive of cardiovascular events than conventional measurements of fasting triglycerides. While these observations are consistent with the previous studies, direct correlations between remnant lipoprotein triglyceride (RLP-TG) and remnant lipoprotein cholesterol (RLP-C), which are also considered to be risk factors for cardiovascular disease, and fasting and postprandial TG have not been investigated. METHODS: On four different days, both fasting and postprandial blood samples were collected from twenty-three overweight to obese men and women at UC Davis and analyzed for plasma concentrations of TG, RLP-C and RLP-TG. RESULTS: Significantly higher correlations between plasma TG and RLPs were observed in the postprandial state (RLP-C r2 = 0.85; RLP-TG r2 = 0.92) than in the fasting state (RLP-C r2 = 0.61; RLP-TG r2 = 0.73). The differences in the correlations between the fasting and postprandial TG and RLPs were statistically significant (p < 0.001). The increase of RLP-TG (postprandial RLP-TG minus fasting RLP-TG) consisted of approximately 80% of the total increase of TG (postprandial TG minus fasting TG). CONCLUSION: Postprandial TG vs remnant lipoprotein concentrations were significantly more correlated when compared with fasting TG vs RLP concentrations. The increased TG in the postprandial state mainly consisted of TG in remnant lipoproteins. Therefore, the increased sensitivity of non-fasting TG in predicting the risk for cardiovascular events may be directly explained by the increase of remnant lipoproteins in the postprandial state.

Apolipoprotein B and cardiovascular disease risk: position statement from the AACC Lipoproteins and Vascular Diseases Division Working Group on Best Practices.

BACKGROUND: Low-density lipoprotein cholesterol (LDL-C) has been the cornerstone measurement for assessing cardiovascular risk for nearly 20 years. CONTENT: Recent data demonstrate that apolipoprotein B (apo B) is a better measure of circulating LDL particle number (LDL-P) concentration and is a more reliable indicator of risk than LDL-C, and there is growing support for the idea that addition of apo B measurement to the routine lipid panel for assessing and monitoring patients at risk for cardiovascular disease (CVD) would enhance patient management. In this report, we review the studies of apo B and LDL-P reported to date, discuss potential advantages of their measurement over that of LDL-C, and present information related to standardization. CONCLUSIONS: In line with recently adopted Canadian guidelines, the addition of apo B represents a logical next step to National Cholesterol Education Program Adult Treatment Panel III (NCEP ATPIII) and other guidelines in the US. Considering that it has taken years to educate physicians and patients regarding the use of LDL-C, changing perceptions and practices will not be easy. Thus, it appears prudent to consider using apo B along with LDL-C to assess LDL-related risk for an interim period until the superiority of apo B is generally recognized.

Estimation of LDL-associated apolipoprotein B from measurements of triglycerides and total apolipoprotein B.

BACKGROUND: VLDL and chylomicrons may interfere with measurements of apolipoprotein B (apo B) on LDL particles. Ultracentrifugation of samples enriched in chylomicrons and VLDL and subsequent measurement of apo B in the infranate fraction [density (d) = 1.006] removes this interference. This apo B fraction is called "LDL-apo B." METHODS: We retrospectively analyzed 64 895 measurements of triglycerides, total apo B, and LDL-apo B. Samples were ultracentrifuged, and 3 commercially available immunoassays that use different antibodies were used to measure LDL-apo B in the 1.006 infranate fraction. RESULTS: After adjusting for triglyceride concentration, we found total apo B and LDL-apo B measurements to be strongly correlated. We derived a simple linear equation for calculating LDL-apo B concentration (in milligrams per deciliter) from measurements of total apo B and triglycerides: LDL-apo B = apo B - 10 mg/dL - triglycerides/32. This equation accurately predicts LDL-apo B values within +/- 12% of the measured value in 75% of cases. CONCLUSIONS: Our equation provides a convenient means of estimating LDL-apo B from commonly available measurements of total apo B and triglycerides without the need for ultracentrifugation. LDL-apo B measurements were also independent of the different apo B antibodies in the 3 assays used in this study. An equation that predicts LDL-apo B particle number may be useful, regardless of the apo B assay used.