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Circulation. 2005;112:3375-3383
doi: 10.1161/CIRCULATIONAHA.104.532499
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(Circulation. 2005;112:3375-3383.)
© 2005 American Heart Association, Inc.

Coronary Heart Disease

Non–High-Density Lipoprotein Cholesterol and Apolipoprotein B in the Prediction of Coronary Heart Disease in Men

Tobias Pischon, MD, MPH; Cynthia J. Girman, DrPH; Frank M. Sacks, MD; Nader Rifai, PhD; Meir J. Stampfer, MD, DrPH; Eric B. Rimm, ScD

From the Departments of Nutrition (T.P., F.M.S., M.J.S., E.B.R.) and Epidemiology (T.P., M.J.S., E.B.R.), Harvard School of Public Health, Boston, Mass; Channing Laboratory (F.M.S., M.J.S., E.B.R.), Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass; Department of Laboratory Medicine (N.R.), Children’s Hospital and Department of Pathology, Harvard Medical School, Boston, Mass; the Department of Epidemiology (T.P.), German Institute of Human Nutrition, Potsdam-Rehbruecke, Germany; and the Department of Epidemiology (C.J.G.), Merck Research Laboratories, West Point, Pa.

Correspondence to Dr Tobias Pischon, Department of Epidemiology, German Institute of Human Nutrition (DIfE), Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany. E-mail pischon{at}mail.dife.de

Received December 27, 2004; revision received August 4, 2005; accepted August 8, 2005.


*    Abstract
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Background— Apolipoprotein B (apoB) plasma levels reflect the concentration of proatherogenic lipoproteins very low-density lipoprotein and low-density lipoprotein (LDL), whereas non–high-density lipoprotein cholesterol (non–HDL-C) levels reflect the concentration of cholesterol transported by these particles.

Methods and Results— The aim of our study was to compare apoB, non–HDL-C, LDL cholesterol (LDL-C), and other lipid markers as predictors of coronary heart disease (CHD) in a nested case-control study among 18 225 participants in the Health Professionals Follow-up Study. Among men who were free of diagnosed cardiovascular disease at the time of blood collection, 266 had nonfatal myocardial infarction or fatal CHD during 6 years of follow-up. Through the use of risk set sampling, control subjects were selected at a 2:1 ratio and matched with regard to age, date of blood collection, and smoking status. After adjustment for matching factors, the relative risk of CHD in the highest quintile compared with the lowest quintile was 2.76 (95% confidence interval [CI], 1.66 to 4.58) for non–HDL-C, 3.01 (95% CI, 1.81 to 5.00) for apoB, 1.81 (95% CI, 1.12 to 2.93) for LDL-C, 0.31 (95% CI, 0.18 to 0.52) for HDL-C, 2.41 (95% CI, 1.43 to 4.07) for triglycerides (all P trend <0.001), and 1.42 (95% CI, 0.86 to 2.32, P trend =0.19) for lipoprotein(a). When non–HDL-C and LDL-C were mutually adjusted, only non–HDL-C was predictive of CHD. When non–HDL-C and apoB were mutually adjusted, only apoB was predictive; the relative risk was 4.18 (95% CI, 1.30 to 13.49; P trend =0.02) for apoB compared with 0.70 (95% CI, 0.21 to 2.27; P trend =0.72) for non–HDL-C. Triglycerides added significant information to non–HDL-C but not to apoB for CHD risk prediction.

Conclusions— Although non–HDL-C and apoB were both strong predictors of CHD in this male cohort, more so than LDL-C, the findings support the concept that the plasma concentration of atherogenic lipoprotein particles measured by apoB is more predictive in development of CHD than the cholesterol carried by these particles, measured by non–HDL-C.


Key Words: apolipoproteins • coronary disease • follow-up studies • lipids • lipoproteins


*    Introduction
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 *Introduction
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Low-density lipoprotein cholesterol (LDL-C) levels are currently recommended as the primary target for lipid-lowering therapy for prevention of cardiovascular disease (CVD).1 The role of LDL in the development of atherosclerosis, the relation between blood LDL-C levels and risk of CVD, and the beneficial effects of LDL-C–lowering therapy are well established.1 Similarly, it is well known that low levels of high-density lipoprotein cholesterol (HDL-C) are associated with an increased risk for CVD independent of LDL-C levels, and raising HDL-C has been shown to significantly lower cardiovascular risk.2–5 Nevertheless, HDL-C levels are currently considered a secondary treatment target only.1

Editorials pp 3366 and 3368

LDL-C levels incompletely measure atherogenic lipoproteins because very low-density lipoprotein (VLDL) remnants also are likely to contribute to coronary heart disease (CHD).6 Two approaches have been proposed to provide a single measurement that includes all atherogenic lipoproteins. One is to measure the concentration of apolipoprotein B (apoB), which is a direct measurement of the concentration of proatherogenic particles, because each VLDL and LDL particle has 1 molecule of apoB.7 Alternatively, the National Cholesterol Education Program guidelines recommend measuring non–HDL-C, calculated by subtracting the protective HDL-C from the total cholesterol (TC). Non–HDL-C is thus the cholesterol concentration of atherogenic lipoproteins and has been recommended as a target especially among subjects with high triglyceride (TG) levels.1 Animal experiments suggest that a high apoB particle concentration may indeed be more important than the cholesterol concentration.8 Nevertheless, these 3 measures of atherogenic lipoproteins, LDL-C, non–HDL-C, and apoB, have not been compared directly in a large prospective study. The present study compares these lipoproteins for strength and independence as CHD risk factors and addresses the long-debated question as to whether the cholesterol content or the particle concentration of atherogenic lipoproteins is more closely linked to development of CHD.


*    Methods
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Study Population
Details on this nested case-control study have been published previously.9 Briefly, the Health Professionals Follow-up Study (HPFS) began in 1986, with recruitment of 51 529 US male health professionals who were 40 to 75 years of age.10 Information about anthropometry, lifestyle behavior, health, and disease was assessed by self-administered questionnaires in 2-year cycles.11 The questionnaires and the validity and reproducibility of the collected data and measurements have been reported in detail elsewhere.11–14 After exclusion of men with a history of CVD from the 18 225 participants who had provided a blood sample between 1993 and 1995, we identified 266 participants with incident nonfatal myocardial infarction (MI) or fatal CHD during 6 years of follow-up. Control subjects were randomly selected at a 2:1 ratio and matched with regard to age, date of blood draw, and smoking status to participants with a blood sample and without history of CVD at the time of case ascertainment (risk set sampling).15 MI was confirmed by medical record review if it met the World Health Organization’s (WHO) criteria (symptoms plus either diagnostic ECG changes or elevated levels of cardiac enzymes).16 Deaths were identified from state vital records and the National Death Index or reported by the men’s next of kin or the postal system. Fatal CHD was considered to have occurred if there was fatal MI confirmed by hospital records or on autopsy or if CHD was listed as the cause of death on the death certificate, if it was the underlying and most plausible cause, and if evidence of previous CHD was available. The Harvard School of Public Health Human Subjects Committee Review Board approved the study protocol.

Measurement of Biochemical Variables
Blood samples were collected in liquid EDTA blood tubes, returned on ice to our laboratory, centrifuged, and aliquoted for storage in the vapor phase of liquid nitrogen freezers (–130°C or colder). TC was measured enzymatically,17 LDL-C by a homogenous direct method from Genzyme Corporation,18 HDL-C by means of a direct enzymatic colorimetric assay,19 and TG enzymatically with correction for endogenous glycerol20; all coefficients of variation (CVs) were <6%. Total apoB100 was measured by an immunoturbidimetric technique on the Hitachi 911 analyzer (Roche Diagnostics), with CVs of 5%. The assay used is standardized by the WHO/International Federation of Clinical Chemistry and Laboratory Medicine standard for apoB. Lipoprotein(a) [Lp(a)] was measured by a latex-enhanced immunoturbidimetric method on the Hitachi 911 system with reagents from Denka-Saiken. The antibodies used are not affected by the multiple repeat of kringle 4 type 2.21 The overall CV was <5%. The laboratory is certified by the Centers for Disease Control and Prevention/National Heart, Lung, and Blood Institute Lipid Standardization Program.

We excluded 59 participants who reported intake of cholesterol-lowering drugs (8.7% of cases, 6.8% of control subjects, P=0.34). Information on LDL-C levels was missing from 1 subject and Lp(a) information was missing from 2 men; these values were replaced by the median concentrations in this cohort. Fifty-nine percent of the participants in the present analysis provided fasting blood samples (>8 hours since last meal), 63% among cases, 58% among control subjects (P=0.14).

Statistical Analyses
Variables were compared between cases and control subjects by using the Student’s unpaired t test, Wilcoxon’s unpaired rank sum test, or the {chi}2 test. Associations between lipid marker levels were examined in control subjects by the age-adjusted Spearman’s partial correlation coefficient.

Men were categorized on the basis of quintiles of the lipid markers calculated among the control subjects. We analyzed the association between lipid levels and risk of CHD by using both conditional and unconditional logistic regression with adjustment for matching factors (age [5-year categories], smoking status [never, past, current], and month of blood draw [5 categories]). Because we excluded individuals who reported intake of cholesterol-lowering drugs, their matched samples also would have to be excluded from conditional regression. Further, the use of conditional logistic regression also would have led to exclusion of participants from stratified analyses. Therefore, to include as many subjects in the analyses as possible, and because both analyses provided essentially the same results, we present unconditional logistic regression. To test for linear trend across categories, we used the median lipid marker levels within quintiles (based on the control subjects) as a continuous variable. In our multivariable model, we further adjusted for parental history of MI before the age of 60 (yes/no), alcohol intake (nondrinker, 0.1 to 4.9, 5.0 to 14.9, 15.0 to 29.9, or ≥30.0 g/d), body mass index (<20, 20 to 24, 25 to 29, 30 to 34, ≥35 kg/m2), physical activity (quintiles), and history of diabetes (yes/no) and hypertension (yes/no) at baseline. Analyses that included TG levels were additionally adjusted for fasting status. In separate analyses, we also examined TG in fasting and nonfasting individuals only. We also tested for goodness of fit of the model by using the Hosmer and Lemeshow test. In these analyses, only the crude trend model for TG (adjusted for matching factors only) suggested some significant deviance (P=0.03).

To examine to what extent non–HDL-C captures information about CHD risk compared with related measurements of atherogenic lipoproteins, we ran different models that included non-HDL and either LDL-C, TG, or apoB. Lipid markers were included as quintiles with 4 dummy variables in the model. For each marker, we calculated the relative risk (RR) in the highest to lowest quintile, the P for trend across quintiles, and the probability value of the likelihood ratio statistic with 4 degrees of freedom for adding the marker to the final model (ie, the model that already includes 1 other lipid marker). Using similar principles, we combined apoB with either LDL-C, TG, or non–HDL-C. HDL-C was also included in certain specified models.

All probability values presented are 2-tailed, and probability values <0.05 were considered statistically significant. Analyses were performed with the use of SAS 8.2 (SAS Institute).


*    Results
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Patients had significantly higher levels of TC, LDL-C, non–HDL-C, TG, and apoB and significantly lower levels of HDL-C than control subjects (Table 1). Cases had higher Lp(a) levels, although this was not statistically significant.


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  		TABLE 1. Baseline Characteristics of 1994 of Men With Incident CHD (1994 to 2000) and Matched Control Subjects From the Health Professionals Follow-up Study

We found strong correlations among TC, LDL-C, apoB, and non–HDL-C (Table 2), ranging from 0.83 to 0.93. HDL-C was inversely associated with TG (r=–0.58) and apoB (r=–0.22). Lp(a) was only weakly associated with any of the markers.


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  		TABLE 2. Age-Adjusted Spearman Partial Correlation Coefficients Between Lipid Markers Among 496 Control Subjects in the Health Professionals Follow-Up Study

Table 3 shows the RRs of CHD during 6 years of follow-up across quintiles of lipid levels at baseline. After multivariable adjustment, apoB showed the strongest association with CHD. Non–HDL-C was also strongly predictive of CHD, with a multivariable relative risk similar to that of apoB, ie, 2.75 versus 2.98. The association of HDL-C with CHD was similar to that seen with apoB or non–HDL-C, in which participants in the lowest compared with the highest quintile had a 2.78-fold (1/0.36) increased RR. LDL-C and TG were also highly significant predictors of CHD; the multivariable relative risks were 2.07 and 2.12, respectively. The RR estimates for non–HDL-C and apoB were not significantly affected by TG levels, as determined by stratified analysis (TG levels ≥100 versus <100; ≥150 versus <150; or ≥200 versus <200 mg/dL). There was no significant interaction of apoB levels with age (≥ versus < median [65.75 years]) or LDL-C levels (≥ versus < median [127.15 mg/dL]) on CHD risk.


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  		TABLE 3. Relative Risks (and 95% Confidence Intervals) of CHD During 6 Years of Follow-Up According to Quintiles of Baseline Biomarker Levels in 1994 in the Health Professionals Follow-Up Study (n=739)*

ApoB and non–HDL-C both predominated over LDL-C as predictors of CHD (Table 4, model 2 RRs: apoB 3.86, LDL-C 0.70; model 3 RRs: non–HDL-C 2.99, LDL-C 0.86). TG did not add significant information to apoB when adjusted for matching factors (age, smoking, and month of blood draw) only or in multivariable adjusted analyses (Table 4, model 4 RRs: apoB 2.61, TG 1.22). For non-HDL, TG added additional information when adjusted for matching factors only but not in multivariable adjusted models. TG added significant information to LDL-C when adjusted for matching factors and in multivariable adjusted analyses. Fasting had little influence on risk of high TG (P for interaction =0.15). When we restricted our analysis to fasting subjects, the RR in the highest compared with the lowest quintile of TG levels was 2.72 (95% confidence interval [CI], 1.42 to 5.21; P trend <0.001) when adjusted for matching factors and 2.25 (95% CI, 1.13 to 4.51; P trend =0.003) when multivariable adjusted, compared with 2.44 (95% CI, 0.90 to 6.61; P trend =0.08) and 3.22 (95% CI, 1.07 to 9.70; P trend =0.07) for TG among nonfasting individuals.


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  		TABLE 4. Combination of Two Lipid Markers Adjusted for Matching Factors and in Multivariable Adjusted Models

As expected, HDL-C was a significant predictor beyond apoB or non–HDL-C (Table 4, models 7 and 8). For example, when HDL-C was added to the multivariable adjusted model with apoB, the RR between quintiles of apoB was 2.69 (95% CI, 1.57 to 4.62; P trend <0.001) and the RR for HDL-C was 0.45 (95% CI, 0.24 to 0.82; P trend =0.01). Within each tertile of HDL-C, the risk of CHD increased with increasing tertiles of apoB, whereas within each tertile of apoB, the risk of CHD decreased with increasing tertile of HDL-C (Figure 1). Results were similar when HDL-C and non–HDL-C were studied together.



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  		Figure 1. Relative risk of CHD during 6 years of follow-up according to tertiles of apoB and HDL-C levels adjusted for matching factors (age, smoking status, and month of blood draw), body mass index, parental history of MI before age 60, history of diabetes, history of hypertension, alcohol intake, and physical activity. Numbers indicate relative risks; numbers in parentheses indicate number of subjects. Cutoff points for tertiles of apoB were <81.6 mg/dL, 81.6 to 100.6 mg/dL, and ≥100.7 mg/dL. Cutoff points for tertiles of HDL-C were <39.4 mg/dL, 39.4 to 49.1 mg/dL, and ≥49.2 mg/dL. The number of cases limited the number of cells in the figures; therefore, we chose to cross-classify subjects on the basis of tertiles (3x3=9 cells) instead of quintiles (5x5=25 cells).

Lp(a) was not significantly related to risk of CHD in the whole group, nor was the Lp(a) association substantially different between men with LDL-C levels ≥130 mg/dL (multivariable-adjusted RR in the highest versus lowest quintile, 1.46; 95% CI, 0.68 to 3.12; P trend =0.99) or those with LDL levels <130 mg/dL (multivariable-adjusted, 1.44; 95% CI, 0.65 to 3.17; P trend =0.20).

The associations between lipids and risk of CHD were not substantially different between men with and without the metabolic syndrome, which is defined as having 3 or more of the following 5 abnormalities: (1) Body mass index ≥25 kg/m2; (2) TG levels ≥150 mg/dL; (3) HDL-C levels <40 mg/dL; (4) history of hypertension; and (5) history of diabetes, development of diabetes during follow-up, or hemoglobin A1c levels ≥7% at baseline. For comparison with other studies, we also calculated the RR of CHD in quintiles of TC/HDL-C. In this analysis, the RR of CHD of men in the highest compared with the lowest quintiles of TC/HDL-C was 3.57 (95% CI, 2.07 to 6.16; P trend <0.001) when adjusted for matching factors and 3.49 (95% CI, 1.93 to 6.31; P trend <0.001) when multivariable adjusted. Further adjustment of the models presented in Table 3 for C-reactive protein levels only modestly attenuated the risk estimates. For example, after further adjustment for C-reactive protein levels, the relative risk in the highest compared with lowest quintile was 2.62 (95% CI, 1.53 to 4.48; P trend <0.001) for non–HDL-C and 2.73 (95% CI, 1.60 to 4.66; P trend <0.001) for apoB.

When the 2 strongest lipid measurements, non–HDL-C and apoB, were included, the RR between extreme quintiles for apoB was 3.99 (95% CI, 1.22 to 13.04; P trend =0.02), but the RR for non–HDL-C was 0.73 (95% CI, 0.22 to 2.41; P trend =0.76); thus, non HDL-C did not add information on risk to the model (P=0.83; Table 4, model 1). We further cross-classified men on the basis of tertiles of apoB and non–HDL-C among the control subjects (Figure 2). As expected, there were almost no men with extreme opposite apoB and non–HDL-C levels; thus, no subject was in the group defined as the lowest tertile of non–HDL-C and highest tertile of apoB, and only one subject was in the group defined as the highest tertile of non–HDL-C and lowest tertile of apoB. Despite the high correlation between apoB and non–HDL-C, however, there was some dissociation between the 2 variables. As can be seen from Figure 2, within each tertile of non–HDL-C, the risk of CHD increased with increasing tertiles of apoB, whereas within each tertile of apoB, the risk of CHD did not increase by tertiles of non–HDL-C. For example, men with low non–HDL-C levels had either low or intermediate levels of apoB, whereas men with low apoB had either low or intermediate levels of non–HDL-C. In these situations, only apoB was related to higher risk, whereas no such association was observed for non–HDL-C. The same held true for men with intermediate non–HDL-C levels who had either low, intermediate, or high apoB and men with intermediate apoB who had either low, intermediate, or high non–HDL-C. Only apoB predicted CHD.



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  		Figure 2. Relative risk of CHD during 6 years of follow-up according to tertiles of apoB and non–HDL-C levels adjusted for matching factors (age, smoking status, and month of blood draw), body mass index, parental history of MI before age 60, history of diabetes, history of hypertension, alcohol intake, and physical activity. No subject was in the group defined as the lowest tertile of non–HDL-C and highest tertile of apoB. Only one subject was in the group defined as the highest tertile of non–HDL-C and lowest tertile of apoB. Numbers indicate relative risks; numbers in parentheses indicate number of subjects. Cutoff points for tertiles of apoB were <81.6 mg/dL, 81.6 to 100.6 mg/dL, and ≥100.7 mg/dL. Cutoff points for tertiles of non–HDL-C were <139.6 mg/dL, 139.6 to 171.3 mg/dL, and ≥171.4 mg/dL. The number of cases limited the number of cells in the figures; therefore, we chose to cross-classify subjects based on tertiles (3x3=9 cells) instead of quintiles (5x5=25 cells).


*    Discussion
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 *Discussion
down arrowReferences
 
Both apoB and non–HDL-C have been proposed as markers to reflect the risk conferred by proatherogenic TG-rich VLDL in addition to LDL-C.1,7 We found that non–HDL-C was more strongly correlated with CHD than LDL-C; however, apoB showed the strongest association with risk of CHD. Interestingly, apoB was associated with increased risk of CHD even after adjustment for LDL-C or non–HDL-C, despite the high degree of correlation between these variables. Furthermore, TG levels provided additional information on CHD risk beyond LDL-C and non–HDL-C but not apoB levels. Because TC and HDL-C measurements are currently standard clinical practice, non–HDL-C is an easily obtainable CHD risk factor. However, our data suggest that apoB—as a single well quantified lipoprotein measurement—is more strongly linked to the incidence of CHD than non–HDL-C and LDL-C. Thus, plasma concentration of atherogenic lipoproteins may be more critical to the development of atherosclerosis than the amount of cholesterol that the lipoproteins carry into the arterial wall.

Our results confirm previous observations that non–HDL-C is superior to LDL-C in predicting CHD,22 probably because it also captures TG-rich atherogenic lipoproteins, such as VLDL.1 Our study also confirms previous reports that TG levels—fasting or nonfasting—are a strong risk marker for CHD23–25; however, in our study, TG levels did not provide significant information after taking HDL-C levels into account.

In our analysis, apoB showed the strongest association with CHD risk, which is in line with previous studies on apoB.26–29 ApoB is synthesized by the liver and secreted with VLDL. These in turn are converted in the periphery to intermediate-density lipoproteins (IDL) and then to LDL. Because there is 1 apoB molecule per lipoprotein particle, apoB reflects the total number of VLDL, IDL, and LDL particles and thus the concentration of proatherogenic particles.30 Because an antibody to apoB100 was used in this study, apoB48 in chylomicrons and chylomicron remnants do not contribute to the apoB concentrations. ApoB48 concentrations comprise <1% of total apoB concentrations in the fasting or postprandial states.31

Interestingly, apoB was significantly related to CHD risk even after adjustment for LDL-C, despite a very high correlation between both variables. This is in agreement with a large cohort study of 175 553 individuals reported by Walldius et al,27 who found that the RR of MI per change in LDL-C was substantially reduced after additional adjustment for apoB; in contrast, apoB was still associated with substantial risk when adjusted for LDL-C. However, LDL-C levels in the study by Walldius et al27 were inferred from TC, TG, and apoA1 concentrations and therefore may include compounded measurement error from the calculation based on these 3 variables. In contrast, in the present study, LDL-C was measured directly, and the CVs were similar for LDL-C and apoB, which makes it unlikely that the stronger association for apoB is due only to more precise measurement of apoB compared with LDL-C; rather, it is likely due to the biological properties of these markers. However, our results are in contrast to the Atherosclerosis Risk in Communities (ARIC) study,23 which found that LDL-C related more strongly to CHD than did apoB. In the Nurses’ Health Study,32 apoB was more strongly related to CHD than was LDL-C; however, in contrast to our study in a multivariable-adjusted model, apoB did not add significant information beyond LDL-C.

We also found that apoB was more strongly related to CHD risk than was non–HDL-C, the cholesterol concentration of all atherogenic lipoproteins. This is in line with previous studies showing that apoB is superior to non–HDL-C in predicting subclinical atherosclerosis33,34 and raises the hypothesis that direct measurement of the concentration of atherogenic particles is more biologically meaningful than the measurement of the cholesterol concentration contained in these particles. Our analysis was able to disentangle the CHD risk indicated by these 2 strongly correlated measurements because there was a sufficient number of subjects who had mildly disparate non–HDL-C and apoB concentrations, as indicated by previous findings.35 As an example, the study demonstrated that among persons with low non–HDL-C (<139.6 mg/dL) a mid-range level compared with a low-range level of apoB increased CHD risk by 55%. This clinical phenotype, normocholesterolemic hyper-apoB, has been previously described and hypothesized to have a high risk of CHD.36 In those with mid-range non–HDL-C (139.6 to 171.3 mg/dL), a high compared with a low apoB level increased risk by &2.4-fold. In contrast, when apoB was used for the primary risk classification, non–HDL-C levels did not affect risk.

Lp(a) has a lipid composition similar to LDL and also contains one apo(a) and apoB molecule.37,38 Lp(a) concentrations vary substantially between individuals and are largely determined by genetics. Measurement of Lp(a) is challenging because of the repeating structure of apo(a) and its structural similarity to plasminogen.39 Thus, immunoassays tend to underestimate Lp(a) concentrations for smaller isoforms and overestimate for larger apo(a) isoforms when the antibodies used are directed to epitopes in the repeated apo(a) K4 type 2 sequence. In contrast, the technique used in the present study was shown not to be affected by the multiple repeat kringle 4 type 2.21 In our analysis, plasma Lp(a) levels were not significantly associated with risk of CHD, although the magnitude of the association was similar to that reported in a recent meta-analysis.40 Lp(a), measured by an isoform-independent method, was a highly significant independent predictor of CHD in a cohort of US male physicians, particularly in those who had elevated LDL-C >130 mg/dL.41

Our study has some limitations. The ranges of anthropometric parameters in the present study were quite broad, and therefore the biological relations found should be generalizable. The RR estimates for non–HDL-C and apoB, respectively, were not significantly different when we stratified subjects by TG levels (≥100 versus <100; ≥150 versus <150; or ≥200 versus <200 mg/dL). Nevertheless, our cohort included a generally healthy population, and therefore information provided by lipid markers and indexes might be different for high-risk subjects. For example, our analysis included only a limited number of subjects with TG levels ≥200 mg/dL, and non–HDL-C may be a better indicator at higher TG levels or for those with glucose abnormalities.42,43 Further, our analysis was restricted to men; a recent study suggested that apoB may be similar to non–HDL-C in predicting CHD in women.32 We also only had a single assessment of blood lipids with up to 6 years of follow-up. Ideally, multiple measures during follow-up would increase our precision. In a previous pilot study of men from the Health Professionals Follow-up Study, however, we found correlations of 0.70 to 0.76 for lipids measured in the same men 4 years apart. The area under the receiver operating characteristics curve has been suggested as a measure of individual risk prediction of a model44–46; however, the area under the receiver operating characteristics curve is a very insensitive marker that does not substantially change even when significant traditional cardiovascular risk factors are included into a model.47 Lipid indexes or ratios may improve CHD prediction beyond the information provided by single lipid markers32,48; however, the aim of our study was to add to the understanding of the pathophysiology of CHD comparing lipid markers, cholesterol, and triglyceride, with the concentration of atherogenic lipoprotein particles, ie, apoB. Of note, the regression models 1 to 3 in Table 4 each combine biomarkers that are highly correlated, leading to imprecision of the RR estimates as reflected by wide confidence intervals. For example, the RR point estimate for CHD in the highest to lowest quintile of apoB increased from 3.01 (95% CI, 1.81 to 5.00) to 4.18 (95% CI, 1.30 to 13.49) when non–HDL-C was added, although this increase in the point estimates seems biologically unlikely but probably reflects collinearity between these variables. Therefore, the RR estimates should be interpreted cautiously. Nevertheless, as also supported by Figure 2, it seems fair to conclude that apoB provides information that is not sufficiently captured by non–HDL-C or LDL-C.

The practical application of our findings would be the substitution of apoB for LDL-C and non–HDL-C for screening and treatment of CHD risk. However, whether the additional costs of switching to and subsequently measuring apoB justify the potential improvement in risk prediction over the currently available non–HDL-C, the second strongest lipoprotein risk factor in our study, needs to be further evaluated.

In conclusion, we found in a generally healthy male population that non–HDL-C is more strongly related to CHD than is LDL-C; however, our study suggests that apoB as a direct measurement of the number of atherogenic lipoprotein particles is more closely related to risk of CHD than the cholesterol concentration provided by these particles.


*    Acknowledgments
 
This study was supported by National Institutes of Health grants HL35464, CA55075, and AA11181 and additional funding from a research grant from Merck & Co, Inc (West Point, Pa). Dr Pischon is a Jetson Lincoln fellow, supported in part by an unrestricted gift from Mr Lincoln. The authors thank Lydia Liu for review of statistical programs.

Disclosure

Dr Girman is an employee of Merck & Co, Inc (West Point, Pa), manufacturer of pharmaceuticals for the treatment of lipid abnormalities. She also owns stock or stock options in Merck as well as Pfizer and Amgen.


*    References
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 up arrowAbstract
 up arrowIntroduction
 up arrowMethods
 up arrowResults
 up arrowDiscussion
 *References
 

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