(Circulation. 2000;101:477.)
© 2000 American Heart Association, Inc.
Clinical Investigation and Reports |
Relation Between Baseline and On-Treatment Lipid Parameters and First Acute Major Coronary Events in the Air Force/Texas Coronary Atherosclerosis Prevention Study (AFCAPS/TexCAPS)
From Weill Medical College of Cornell University, New York, NY (A.M.G., J.Y.J.); The Heart and Vascular Institute of Texas, San Antonio, Tex (E.W.); Medical Research Laboratories, Highland Heights, Ky (E.A.S.); Merck & Co, Inc, Blue Bell, Pa (D.R.S., A.L., P.A.B., D.J.W.); University of North Texas Health Science Center, Fort Worth, Tex (M.C., S.W.); Wilford Hall Medical Center, Lackland Air Force Base, San Antonio, Tex (J.R.D.); and University of Pennsylvania (emeritus), Philadelphia, Pa (J.S.d.C.).
Correspondence to Antonio M. Gotto, c/o Jesse Jou, Weill Medical College of Cornell University, 445 E 69th St, Olin Hall Room 205, New York, NY 10021. E-mail amg_editorial{at}mail.med.cornell.edu
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Abstract |
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Background—The Air Force/Texas Coronary Atherosclerosis Prevention Study (AFCAPS/TexCAPS) is the first primary-prevention study in a cohort with average total cholesterol (TC) and LDL cholesterol (LDL-C) and below-average HDL cholesterol (HDL-C). Treatment with lovastatin (20 to 40 mg/d) resulted in a 25% reduction in LDL-C and a 6% increase in HDL-C, as well as a 37% reduction in risk for first acute major coronary event (AMCE), defined as fatal or nonfatal myocardial infarction, unstable angina, or sudden cardiac death. This article describes the relation between baseline and on-treatment lipid and apolipoprotein (apo) parameters and subsequent risk for AMCEs.
Methods and Results—With all available data from the entire 6605-patient cohort, a prespecified Cox backward stepwise regression model identified outcome predictors, and logistic regression models examined the relation between lipid variables and AMCE risk. Baseline LDL-C, HDL-C, and apoB were significant predictors of AMCE; only on-treatment apoB and the ratio of apoB to apoAI were predictive of subsequent risk; on-treatment LDL-C was not. When event rates were examined across tertiles of baseline lipids, a consistent benefit of treatment with lovastatin was observed.
Conclusions—Persons with average TC and LDL-C levels and below-average HDL-C may obtain significant clinical benefit from primary-prevention lipid modification. On-treatment apoB, especially when combined with apoAI to form the apoB/AI ratio, may be a more accurate predictor than LDL-C of risk for first AMCE.
Key Words: lipids • coronary disease • prevention • risk factors • apolipoproteins
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Introduction |
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The benefit of modification of serum cholesterol has been confirmed in both primary- and secondary-prevention trials of individuals with hypercholesterolemia.1 2 In secondary prevention, studies have indicated that this benefit can be extended to coronary heart disease (CHD) patients who have mild to moderate elevations in serum total cholesterol (TC) and LDL cholesterol (LDL-C).3 4
In primary prevention, data from the Air Force/Texas Coronary Atherosclerosis Prevention Study (AFCAPS/TexCAPS) extended the benefit to those with average TC and LDL-C concentrations who also have below-average HDL cholesterol (HDL-C).5 Although the AFCAPS/TexCAPS cohort was at lower CHD risk than cohorts in previous CHD primary-prevention trials, their level of risk was similar to that of a comparable US reference population from the third National Health and Nutrition Examination Survey (NHANES III), defined as men 45 to 73 years old and women 55 to 73 years old without prior history of CHD. In brief, after a mean follow-up of 5.2 years, lovastatin therapy reduced the risk for the first acute major coronary event (AMCE), defined as the composite end point of fatal or nonfatal myocardial infarction, unstable angina, or sudden cardiac death, by 37% (P<0.001).5
In light of these findings, we were interested in examining the relation between baseline and on-treatment lipid parameters (in terms of both percent reduction and 1-year, on-treatment values) and first AMCE incidence in the AFCAPS/TexCAPS cohort. In addition to exploring the relation between coronary risk and TC, LDL-C, HDL-C, and triglycerides, we were also able to investigate the relation between coronary risk and apolipoprotein (apo) B and apoAI. Previous trials suggest a relation between reduction of apoB concentration and beneficial therapeutic response,6 and animal models suggest that increased concentrations of apoAI are antiatherogenic.7
A prespecified Cox backward stepwise regression model was performed to identify variables that were related to the risk for a first AMCE and served as a starting point for other analyses.
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Methods |
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The trial design has been described in detail elsewhere.8 Figure 1
provides a summary of the study design. AFCAPS/TexCAPS was a
randomized, double-blind, placebo-controlled primary-prevention trial
with 6605 men and women and was conducted at 2 sites: Wilford Hall
Medical Center at Lackland Air Force Base in San Antonio, Tex, and the
University of North Texas Health Science Center in Fort Worth, Tex. All
participants provided written informed consent, and the study protocol
was approved by both institutional review boards.
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Participant Recruitment and Follow-Up
Men (aged 45 to 73 years) and postmenopausal women (aged 55 to
73 years) who met the lipid entrance criteria and had no prior history
or signs or symptoms of definite myocardial infarction, angina,
claudication, cerebrovascular accident, or transient ischemic
attack were eligible for participation. Lipid inclusion criteria were
TC 4.65 to 6.82 mmol/L (180 to 264 mg/dL), LDL-C 3.36 to 4.91
mmol/L (130 to 190 mg/dL), HDL-C 
1.16 mmol/L (45 mg/dL) for men
or 1.21 mmol/L (47 mg/dL) for women, and
triglycerides 4.52 mmol/L (400 mg/dL). In addition,
those with LDL-C between 3.23 and 3.33 mmol/L (125 to 129 mg/dL)
were included when the ratio of TC to HDL-C was >6.0.
Measurement of Lipid and Lipoprotein Components
Venous blood was collected after subjects had fasted for 12 to
14 hours, and it was allowed to coagulate for 1 to 2 hours at room
temperature. For analysis of changes in lipids, frozen sera
(-70°C) obtained immediately before the start of active treatment
and at the year 1 visit (posttreatment) were assayed at a specialized
lipid laboratory at Johns Hopkins University, Baltimore, Md. The
laboratory was standardized for lipid and lipoprotein measurements
through the Centers for Disease Control and Prevention–National Heart,
Lung, and Blood Institute Lipid Standardization Program.9
All LDL-C values were calculated on the basis of the Friedewald
estimation.10
End Point
AFCAPS/TexCAPS was designed and powered to investigate whether
chronic lipid modification with lovastatin would decrease
the rate of first AMCEs compared with placebo. The procedures for
end-point adjudication were described in detail
previously.8 Only the first end point for an individual
patient was included in the analysis. In addition, participants
who experienced an event in the first year of the study were excluded
from the analyses of the relation of on-treatment lipids with
AMCEs. Posttreatment samples from these participants were not
analyzed, because we wished to avoid the statistical dilemma of
using the year 1, or postevent, lipid measurements to predict the prior
AMCE.
Statistical Analyses
Relation of Baseline Characteristics to Risk for an Event
A number of baseline demographic characteristics, as well as
baseline lipid and apo parameters, were evaluated
individually in a Cox regression model with treatment groups combined
to examine their relation with the primary end point. The demographic
and baseline characteristics tested were sex, age (in 5-year groups),
race, diabetes mellitus, hypertension, obesity, smoking, drinking,
amount of drinking, dietary compliance, exercise, marital status,
education level, military versus civilian status, study site, prior
therapy with calcium channel blockers, parental history of premature
CHD, sibling history of premature CHD, and family (either parental or
sibling) history of premature CHD. The baseline lipid and apo
parameters tested were TC, LDL-C, HDL-C,
triglycerides, LDL-C/HDL-C, TC/HDL-C, apoAI, apoB, and
apoB/AI. The variables that showed a trend (P<0.20)
were then evaluated in a Cox regression model with a backward stepwise
strategy used to eliminate nonsignificant (P>0.05)
parameters. As a final step, each significant factor was
evaluated for its interaction with treatment one at a time in a model
with all the significant main effects. This method is a modification of
that described by Tukey.11 This analysis was
planned before study unblinding.
Analysis of Event Rates Across Baseline Tertiles of
Lipid Parameters
Primary end-point event rates (number of first AMCEs per 100
person-years at risk) were calculated by tertiles of baseline lipid
parameters. In the calculation of case rates, the number of
person-years contributed by each participant was the number of years
measured from the date of randomization to the earliest of (1) the
first occurrence of an AMCE or (2) the date of censoring. The date of
censoring was the last date that complete end-point information was
available for the participant. All analyses were performed
according to the intention-to-treat principle. Summaries by tertiles
were prespecified in the study protocol.
Logistic Regression Analyses of Baseline and On-Treatment
Lipids and Apos and Events
Logistic regression models were examined to evaluate the
relation of AMCEs to lipids and apos measured at baseline and year 1
and the percent change from baseline at year 1. These models included
treatment group, the lipid or apo parameter, and the
interaction between treatment and the lipid/apo parameter,
and it also included as covariates the demographic factors that were
found to be associated with outcome in the Cox backward stepwise
regression model described above. For percent change from baseline,
models with and without the baseline value of the parameter
as an additional covariate were examined. Probability values reported
were from the main effects model (model without interaction) unless the
interaction was significant (P<0.100). Logistic regression
plots were from models with treatment groups evaluated separately.
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Results |
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Relation of Baseline Characteristics to Risk for an Event
When the preplanned analysis of baseline demographic characteristics was first run, apoAI and apoB had not been assayed, and therefore the only lipid parameters considered were TC, LDL-C, HDL-C, triglycerides, TC/HDL-C, and LDL-C/HDL-C. The final model from this initial analysis included treatment group (P<0.001), sex (P<0.001), age (P<0.001), hypertension (P<0.001), premature family history of CHD (P=0.002), smoking (P<0.001), baseline LDL-C (P=0.029), and baseline HDL-C (P=0.010). For a 0.5 mmol/L (
19
mg/dL) increment in baseline LDL-C between individuals, there was a
16% (95% CI 2% to 32%) incremental risk for an AMCE; for a
0.125 mmol/L (5 mg/dL) decrement in HDL-C between individuals,
there was a 14% (95% CI 3% to 27%) incremental risk.
This analysis was repeated with the apo data when they became
available. Baseline and 1-year, on-treatment concentrations of the
lipid and apo parameters are reported in Table 1. With the inclusion of these
baseline data, the final model again included treatment group
(P<0.001), sex (P<0.001), age
(P<0.001), hypertension (P<0.001), premature
family history of CHD (P=0.002), and smoking
(P<0.001). It also included marital status
(P=0.0499) (previously, marital status narrowly missed
inclusion, with P=0.052) and apoB/AI ratio
(P<0.001), which replaced LDL-C (P=0.513) and
HDL-C (P=0.517), which were no longer significant in the
second model. For a 0.25 increment in baseline apoB/apoAI ratio between
individuals, there was a 36% (95% CI 16% to 59%) incremental risk
for an AMCE.
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Analysis of Event Rates Across Baseline Tertiles of
Lipid Parameters
In AFCAPS/TexCAPS, risk reduction for the first AMCE was evident
in all tertiles of baseline LDL-C (Figure 2). The placebo event rate appeared
greatest for both the highest tertile of LDL-C and the lowest tertile
of HDL-C (Figure 2). For both LDL-C and HDL-C tertiles, the
magnitude of improvement with lovastatin within the
tertiles suggests a trend; however, when these baseline lipids were
tested as continuous variables in the previously described Cox
models, the magnitude of benefit was independent of baseline LDL-C or
HDL-C. Event rates as broken down by the LDL-C/HDL-C ratio likewise
suggest benefit in each tertile of this parameter (Figure 2), with lovastatin-treated participants
experiencing fewer events than placebo participants and with placebo
participants in the higher tertiles at greater risk for an event.
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Similar analyses of baseline apoB and apoAI concentrations
support the above observations (Figure 3). Event rates were highest in
participants in the highest tertile of apoB (the major apo of LDL and
VLDL) and the lowest tertile of apoAI (a major apo of HDL).
Furthermore, participants in the highest tertile for the apoB/AI ratio
appeared to be at the greatest risk for an event (Figure 3).
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Logistic Regression Analyses of Baseline and On-Treatment
Lipids and Apos and Events
The probability value of various logistic regression models
performed with baseline and 1-year, on-treatment lipid
parameters are shown in Table 2. These models included as covariates
those demographic and CHD risk factors found to be associated with
outcome in the Cox backward stepwise regression model reported above,
namely, treatment group, age, sex, smoking, hypertension, family
history of premature CHD, and marital status. Interestingly, LDL-C and
TC did not achieve significance as predictors of risk either at
baseline or at year 1. Instead, baseline concentrations of HDL-C,
apoAI, and apoB and the ratios of LDL-C/HDL-C, TC/HDL-C, and apoB/AI
were significantly associated with the primary outcome. The value at
year 1 was significantly associated with primary outcome only for
apoAI, apoB, and the ratio of apoB/AI. For percent change from
baseline, only apoAI had a significant relation with the primary
outcome in the models with or without baseline apoAI. Percent change in
apoB/AI was also significant when baseline apoB/AI was included in the
model.
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In Figure 4, logistic regression plots,
adjusted for covariates, demonstrate a flattening of the risk curve for
developing a primary end point in the lovastatin-treated
group compared with the placebo group with respect to baseline HDL-C
level (Figure 4a) and apoAI levels (Figure 4b). This
difference implies that lipid modification with lovastatin
abolishes the excess risk for CHD associated with having a low HDL-C
level at baseline. At year 1, there were inverse relations between the
change in concentrations of apoAI (data not shown) and HDL-C (data not
shown) and the subsequent risk for developing a primary end point among
those treated with lovastatin, although only on-treatment
apoAI achieved statistical significance as a predictor of outcome.
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Figure 5 illustrates the effect of
lowering LDL-C and apoB on subsequent risk. At baseline, there was a
similar relation between LDL-C (data not shown) and apoB (data not
shown) levels and the risk for developing a primary end point. However,
at year 1, little relation (P=0.162) between on-treatment
LDL-C (Figure 5a) and risk for an AMCE was observed.
On-treatment apoB (Figure 5b), on the other hand, proved to be a
strong predictor of outcome (P<0.001). The overlap of the
regression lines for this parameter suggests a continuous
relation between on-treatment apoB concentration and CHD risk,
independent of treatment group. A similarly positive relation was
observed with the on-treatment ratio of apoB/AI (Figure 6b).
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Discussion |
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Analyses based on lipid parameters in the AFCAPS/TexCAPS cohort have yielded a number of interesting insights into lipid-based risk in this population. Most individuals with characteristics similar to the AFCAPS/TexCAPS cohort would not be recommended for lipid-modifying drug treatment and would be considered to be "at goal" with an LDL-C level <4.14 mmol/L (160 mg/dL). Treatment with lovastatin (20 to 40 mg/d) resulted in mean on-treatment LDL-C values of 2.96 mmol/L (114.6 mg/dL) and HDL-C 1.02 mmol/L (39.3 mg/dL) at year 1, as a result of a mean reduction in LDL-C of 25% and a mean increase in HDL-C of 6%. As previously reported, lovastatin-mediated changes in these lipids were associated with a 37% decrease in AMCEs (defined as fatal or nonfatal myocardial infarction, unstable angina, or sudden cardiac death).5 These results, taken in aggregate with other large-scale clinical trials of HMG-CoA reductase inhibitors, demonstrate the benefit of reducing LDL-C and increasing HDL-C across a broad range of baseline cholesterol values. Somewhat more controversial is the relation between the degree of LDL-C reduction and achievable clinical benefit.12
There is no evidence from AFCAPS/TexCAPS to support a threshold of benefit below which LDL-C reduction is not of clinical benefit. The magnitude of risk reduction was comparable across tertiles of baseline LDL-C, HDL-C, apos B and AI, and the ratio of apoB to apoAI. The absence of a threshold of benefit in a cohort consisting of relatively healthy men and women, middle-aged and older, at lower risk for CHD has important implications for primary prevention of coronary disease and supports the evidence from epidemiological studies.13
Although the Cox regression model did show a significant relation
between risk and baseline LDL-C, it was predictive only if baseline
HDL-C was also included in the model (data not shown). The lack of a
statistically significant association with the logistic regression
models between baseline TC or LDL-C, as individual analyses,
and CHD risk in this cohort may be explained in several ways. First,
these participants would not have been considered at high risk for a
coronary event based solely on the TC or LDL-C concentration.
In fact, under current US guidelines, a fasting lipid profile would not
have been recommended for 2137 (32%) of the AFCAPS/TexCAPS cohort with
TC <6.21 mmol/L (240 mg/dL).14 The Framingham risk
score based on the AFCAPS/TexCAPS annual placebo event rate of 1%
would define this population as "average" risk.15 It
may be that within these ranges, TC and LDL-C are less specific for
risk prediction. Second, in the logistic regression models, the
baseline ratios of TC/HDL-C and LDL-C/HDL-C were significant predictors
of outcome, which suggests that for a cohort with average LDL-C levels,
LDL-C is not predictive unless considered in conjunction with HDL-C. In
other words, HDL-C measurement is an essential component of risk
assessment in these individuals.
Although on-treatment LDL-C failed to predict risk in the
AFCAPS/TexCAPS cohort, on-treatment apoB proved to be a significant
predictor of coronary risk. ApoB was in fact the single most
significant and consistent lipid measurement to predict risk
both at baseline and on treatment. In the logistic models, it was only
slightly improved by the incorporation of a second measurement, apoAI,
to form the apoB/AI ratio. The apoB/AI ratio was the best discriminator
of baseline risk. When analyzed by tertiles for the placebo
group, the ratio identified not only the highest-risk group, with
roughly 1.6 events per 100 patient-years at risk, but also the
lowest-risk group, with 0.8 events per 100 patient-years (Figure 3
).
To explore this finding further, we assessed the relative
importance of lipids when analyzed on a paired basis in
predicting event risk (data not shown), in an analysis similar
to one performed in 4S.16 In this model, on-treatment
LDL-C was not a significant primary predictor of risk, whereas
on-treatment apoB and the ratio of on-treatment apoB to on-treatment
apoAI were. When evaluated together with a second lipid
parameter, only the percent change in apoAI added
significant additional predictive information for these 2 measures.
This suggests that the mechanism of the benefit associated with
lovastatin-mediated changes in LDL-C and HDL-C may be, in
part, a function of the changes in apoB and apoAI.
Another interesting observation was that of an apparent
difference in risk between active treatment and placebo within the
overlapping range of on-treatment LDL-C (Figure 6a). This
appeared to be similar to findings from WOSCOPS.17
However, this apparent gap was markedly reduced when apoB was
substituted for LDL-C and was totally eliminated when on-treatment
apoB/AI ratio was assessed (Figure 5b and Figure 6b, respectively). It is well documented by many observational studies,
including most recently the Quebec Cardiovascular
Study,18 that apoB is a more powerful independent
predictor of CHD than LDL-C. Although apoB is associated with known
atherogenic lipoprotein species, such as IDL remnants and small, dense
LDL (a distinct, highly atherogenic subpopulation), LDL has a
variable cholesterol content.19 20 This
variability in the composition of LDL has been hypothesized to explain
the clinically observed variation in risk that appears to be
independent of LDL-C.18 Our results suggest that it may be
more valid to use apoB rather than LDL-C to assess the on-treatment
effect of reducing the atherogenic burden, especially when LDL-C is not
markedly elevated. In the present analysis, the use of the
apoB/AI ratio, which takes into consideration most, if not all, of the
beneficial changes in lipoprotein metabolism produced by
statins, provides a remarkable continuum of risk, with no apparent
threshold to benefit (Figure 6b). Furthermore, in the last few
years, the measurement of apos B and AI has become more widely
available, lower in cost, and, because of international efforts, more
standardized. These results suggest that reconsideration should be
given to apos B and AI in risk assessment and that treatment goals
based on apoB and/or the apoB/AI ratio be further explored in certain
populations.
AFCAPS/TexCAPS has important implications for the optimal
identification of persons at low to moderate CHD risk who may achieve
significant clinical benefit with lipid modification. The study’s
results confirm the importance of a treatment strategy that allows for
individualization of dose to target an LDL-C goal of 3.36 mmol/L
(130 mg/dL). Titration enabled less-responsive persons to achieve
clinically meaningful LDL-C reduction while exposing those who did not
require titration to the lowest clinically effective dose and resulted
in an overall relative benefit that was similar to what has been
observed in studies of cohorts at much greater absolute risk. These
findings suggest that it may be possible to refine primary-prevention
guidelines by improving identification of at-risk individuals and
populations who may benefit from lipid-modifying intervention.
Specifically, consideration should be given to HDL-C and apos B and AI
in risk assessment, and recommendations may be made for appropriate
goals and the treatment that would most likely achieve clinical
benefit.
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Acknowledgments |
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Funding for this study was provided by Merck & Co, Inc. The authors acknowledge Qinfen Yu for providing statistical programming support for the described analyses, and Dr Paul Bachorik and Kat Lovejoy for their involvement in the measurements of lipid and lipoprotein parameters in the study. We are grateful to Lynne O’Donnell for her assistance in coordinating communications between the authors.
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Footnotes |
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Funding for this study was provided by Merck & Co, Inc, which manufactures and markets Mevacor (lovastatin). D.R.S., A.L., P.A.B., and D.J.W. are employees of Merck and as such may own stock and/or stock options in Merck. A.M.G. is a consultant and speaker for Merck, and E.A.S. is a consultant, speaker, and funded researcher for Merck. The opinions stated in the article are those of the authors and do not represent those of the United States Department of Defense or the US Air Force.
Received March 3, 1999; revision received August 26, 1999; accepted September 15, 1999.
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R. A.H. Stewart, H. D. White, A. C. Kirby, S. R. Heritier, R. J. Simes, P. J. Nestel, M. J. West, D. M. Colquhoun, A. M. Tonkin, and for the Long-Term Intervention With Pravastatin in White Blood Cell Count Predicts Reduction in Coronary Heart Disease Mortality With Pravastatin Circulation, April 12, 2005; 111(14): 1756 - 1762. [Abstract] [Full Text] [PDF]
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I. Shai, E. B. Rimm, S. E. Hankinson, G. Curhan, J. E. Manson, N. Rifai, M. J. Stampfer, and J. Ma Multivariate Assessment of Lipid Parameters as Predictors of Coronary Heart Disease Among Postmenopausal Women: Potential Implications for Clinical Guidelines Circulation, November 2, 2004; 110(18): 2824 - 2830. [Abstract] [Full Text] [PDF]
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N. Sattar, K. Williams, A. D. Sniderman, R. D'Agostino Jr, and S. M. Haffner Comparison of the Associations of Apolipoprotein B and Non-High-Density Lipoprotein Cholesterol With Other Cardiovascular Risk Factors in Patients With the Metabolic Syndrome in the Insulin Resistance Atherosclerosis Study Circulation, October 26, 2004; 110(17): 2687 - 2693. [Abstract] [Full Text] [PDF]
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