CLINICAL RESEARCH: CORONARY DISEASE RISK
Meta-Analysis of the Relationship Between Non–High-Density Lipoprotein Cholesterol Reduction and Coronary Heart Disease Risk
Jennifer G. Robinson, MD, MPH*,*,
Songfeng Wang, MS*,
Brian J. Smith, PhD* and
Terry A. Jacobson, MD
* Lipid Research Clinic, University of Iowa, Iowa City, Iowa
Emory University, Atlanta, Georgia
Manuscript received June 16, 2008;
revised manuscript received October 6, 2008,
accepted October 7, 2008.
* Reprint requests and correspondence: Dr. Jennifer G. Robinson, Lipid Research Clinic, 200 Hawkins Drive SE, 226 GH, Iowa City, Iowa 52242 (Email: jennifer-g-robinson{at}uiowa.edu).
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Abstract
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Objectives: To determine the relationship between non–high-density lipoprotein cholesterol (HDL-C) lowering and coronary heart disease (CHD) risk reduction for various lipid-modifying therapies.
Background: Non–HDL-C is the second lipid target of therapy after low-density lipoprotein cholesterol (LDL-C).
Methods: Randomized placebo or active-controlled trials were evaluated. The effect of mean non–HDL-C reduction on the relative risk of nonfatal myocardial infarction and CHD death was estimated using Bayesian random-effects meta-analysis models adjusted for study duration. Cochrane's Q was used to test for heterogeneity.
Results: Inclusion criteria were met by 14 statin (n = 100,827), 7 fibrate (n = 21,647), and 6 niacin (n = 4,445) trials, and 1 trial each of a bile acid sequestrant (n = 3,806), diet (n = 458), and ileal bypass surgery (n = 838). For statins, each 1% decrease in non–HDL-C resulted in an estimated 4.5-year CHD relative risk of 0.99 (95% Bayesian confidence interval: 0.98 to 1.00). The fibrate model did not differ from the statin model (Bayes factor K = 0.49) with no evidence of heterogeneity. The niacin model was moderately different from the statin model (K = 7.43), with heterogeneity among the trials (Q = 11.8, 5 df; p = 0.038). The only niacin monotherapy trial (n = 3,908) had a 1:1 relationship between non–HDL-C and risk reduction. No consistent relationships were apparent for the 5 small trials of niacin in combination. The 95% confidence intervals for the single trials of diet, bile acid sequestrants, and surgery also included the 1:1 relationship.
Conclusions: Non–HDL-C is an important target of therapy for CHD prevention. Most lipid-modifying drugs used as monotherapy have an  1:1 relationship between percent non–HDL-C lowering and CHD reduction.
Key Words: non–HDL-cholesterol • coronary heart disease • meta-analysis • statins • fibrates • niacin
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Abbreviations and Acronyms
| | CHD = coronary heart disease | | CI = confidence interval | | HDL-C = high-density lipoprotein cholesterol | | LDL-C = low-density lipoprotein cholesterol |
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The National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) identified non–high-density lipoprotein cholesterol (HDL-C) as the second target of therapy after low-density lipoprotein cholesterol (LDL-C) goals have been achieved in patients with elevated triglyceride levels between 200 and 500 mg/dl (1). Non–HDL-C is calculated by subtracting HDL-C from total cholesterol, and it reflects circulating levels of the atherogenic apolipoprotein-B–containing lipoproteins including LDL-C, very low-density lipoprotein cholesterol, intermediate-density lipoprotein cholesterol, chylomicron remnants, and Lp(a). In epidemiologic studies, non–high-density lipoprotein is a superior predictor of cardiovascular risk compared with LDL-C (2). The 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors, or statins, the foundation for lipid-lowering therapy to reduce cardiovascular risk, also lower non–HDL-C (3). A recent analysis of the TNT (Treating to New Targets) and IDEAL (Incremental Decrease in End Points Through Aggressive Lipid Lowering) trials found that non–HDL-C levels on statin therapy were a better predictor of cardiovascular risk than LDL-C (4). Other nonstatin lipid-lowering therapies, which have varying effects on LDL-C, HDL-C, and triglycerides, also influence non–HDL-C levels. This analysis will explore the relationship between non–HDL-C reduction and CHD risk reduction, and evaluate whether fibrates, niacin, bile acid sequestrants, and ileal bypass surgery have a similar magnitude of effect as do statins.
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Methods
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Articles were identified by a literature search of the MEDLINE database (1966 to May 8, 2008), English language journals, a manual search of the author's reference files, and reference lists of original articles, reviews, and meta-analyses. Two abstractors independently extracted information on sample size, treatment type and duration, participant characteristics at baseline, reduction in lipids, and outcomes using a standardized protocol and reporting form. Disagreements were resolved by consensus. Authors were not contacted for additional study information. For inclusion in the meta-analyses, a study must have met these criteria: 1) Studies were designed to evaluate the effect of diet, statins, niacin, fibrates, bile acid sequestrants, or surgery compared with an active or placebo control. 2) Studies had random, blinded (except for diet studies) allocation of study participants to the treatment or control group. 3) Total cholesterol and HDL-C, or non–HDL-C, were measured at least once after baseline; measured non–HDL-C was used in the few studies in which it was available, otherwise non–HDL-C was calculated from total cholesterol minus HDL-C; measured and calculated non–HDL-C were within 1 mg/dl for every study in which non–HDL-C was measured; the interval for lipid measurement was not fixed, and in some cases the values could represent the average during the trial. 4) For the statin trials, primary outcomes of the trial were clinical events; a previous analysis found a similar relationship between LDL-C and CHD risk reduction in statin trials with imaging as the primary end point compared with those trials with cardiovascular events as the primary end point (5); statin trials of 2 or more years, duration were included to provide a stable estimate of relative risk reduction (6). 5) Study population did not have serious noncardiovascular diseases or conditions (e.g., renal or heart failure, organ transplantation). 6) The CHD end points were blindly adjudicated according to standardized criteria; coronary revascularization and unstable angina diagnoses were excluded because of greater temporal and regional variability in utilization and classification (7). The analysis was confined to CHD events because earlier trials did not report stroke outcomes.
Statistical methods.
Because relative risks and hazard ratios were not consistently reported across studies, the study-specific crude relative risk and associated standard error was estimated from the published total number of subjects and incident cases in the treatment and control groups for nonfatal myocardial infarction and CHD death. A random effects model was used for the meta-analyses. The natural log-transformed relative risk was modeled as a linear function of the study-specific mean difference in non–HDL-C between the 2 treatment groups and the mean length of follow-up. For the comparative models, treatment-non–HDL-C interaction was added to allow the effect of non–HDL-C lowering to vary between the 2 types of treatments. Gaussian errors were specified as a combination of the within- and between-study variation. The study-specific standard errors for the estimated relative risks were used to account for within-study variation. Between-study variation was estimated in the analysis. Bayesian methods were used to fit the random-effects meta-analysis models (8). A prior odds of unity was assumed to indicate no prior preference for the null or alternative hypothesis. Vague prior specifications were used for all regression parameters. Additional Bayesian models evaluated the relationships between non–HDL-C and CHD risk by type of study (predominant study population primary prevention, secondary prevention [CHD or cardiovascular disease], or diabetes mellitus) as well as between non–HDL-C and/or LDL-C and CHD risk.
Models were fit with the WinBUGS statistical software (9), and the methods of Chib (10) were used to compute the Bayes factor to compare the effect of non–HDL-C between statin and nonstatin trials. A Bayes factor of <1 provides evidence that the types of trials are similar, and a Bayes factor between 3.2 and 15 provides moderate evidence that types of trials differ (11). Cochrane's Q was used to test for heterogeneity with the Meta package in R 2.8 (12).
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Results
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For statin trials, 14 met inclusion criteria (n = 100,827); all used statins as monotherapy, and 10 were placebo-controlled (Table 1) (13–26). It should be noted that the PROVE-IT–TIMI 22 (Pravastatin or Atorvastatin Evaluation and Infection Therapy–Thrombolysis In Myocardial Infarction 22) trial was excluded because of missing baseline and on-treatment total cholesterol and HDL-C levels despite a review of multiple publications (27–29). The MEGA (Management of Elevated Cholesterol in the Primary Prevention Group of Adult Japanese) trial was also excluded because it was an unblinded trial comparing diet and diet plus pravastatin (30). For fibrate trials, 7 met inclusion criteria (n = 21,647), and all used a fibrate as monotherapy compared with placebo: 2 used gemfibrozil (both event trials, n = 6,612), 2 used fenofibrate (1 event trial, n = 10,213), and 3 used bezafibrate (1 event trial, n = 3,090) (31–37). Only 2 small niacin trials met all inclusion criteria (n = 176) (38,39). Therefore, the niacin category was expanded to include 1 large trial of niacin monotherapy compared with placebo that was adjudicated by an executive secretary (n = 3,908), (40) and 4 trials using niacin in combination with a statin or colestipol that did not report whether events were adjudicated (n = 458) (41–44). One of the niacin trials reported a total of 1 event in the 2 treatment groups, and the constant correction method was used (44,45). One diet (n = 458) (46,47), 1 ileal bypass surgery (n = 838) (48), and 1 bile acid sequestrant trial (n = 3,806) (47,49) met inclusion criteria but were not included in the analysis comparing statins to other drugs (i.e., fibrates and niacin). The mean duration of trials was 4.5 years.
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Table 1 Randomized, Placebo, or Active-Controlled Trials of at Least 2 Years' Duration With a Primary End Point of Cardiovascular Events or Atherosclerosis Imaging
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The statin trials reported 13% to 29% decreases in total cholesterol, 0% to 13% increases in HDL-C, 10% to 22% decreases in triglycerides, 20% to 43% decreases in LDL-C, and 17% to 39% decreases in non–HDL-C between the 2 treatment groups (Table 1). The fibrate trials reported 4% to 11% reductions in total cholesterol, 4% to 14% increases in HDL-C, 23% to 37% reductions in triglycerides, 0% to 12% reductions in LDL-C, and 6% to 16% reductions in non–HDL-C. The niacin trials reported 10% to 28% reductions in total cholesterol, 13% to 38% increases in HDL-C, 18% to 52% decreases in triglycerides, 1% to 41% decreases in LDL-C, and 7% to 39% decreases in non–HDL-C.
For statins, a 1% decrease in non–HDL-C was associated with an estimated 4.5-year CHD relative risk of 0.99 (95% Bayesian confidence interval [CI]: 0.98 to 1.00)—a relative risk decrease of 1%. Because the log-relative risk is modeled as a linear function of non–HDL-C in the analysis, the estimated association translates into a relative risk of 0.78 (95% Bayesian CI: 0.64 to 0.94) for a 25% decrease in non–HDL-C. The estimated relationship between decreases in relative risks and levels of non–HDL-C is summarized in Figure 1. The fibrate trials did not differ from statin trials (Bayes factor K = 0.49). Heterogeneity among the fibrate trails was not detected (Q = 3.66, 6 df; p = 0.68).
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Figure 1 Change in Relative Risk of CHD Event
Estimated change in the relative risk of a coronary heart disease (CHD) event (nonfatal myocardial infarction or CHD death) associated with non–high-density lipoprotein cholesterol (HDL-C) reduction with statins at a mean follow-up of 4.5 years (dashed line) along with the 95% Bayesian confidence interval (dashed boundary lines). The solid line indicates a 1:1 relationship. The crude risk estimates from the individual studies are plotted along with their associated 95% confidence intervals. Statin trials are in black; fibrate trials are in red; niacin trials are in blue (UCSF-SCOR was not plotted); and the POSCH, Oslo, and LRC trials are in green. The relative risks from the 3 trials, POSCH, Oslo, and LRC, were plotted but they are not included in the modeling.
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The effect of non–HDL-C was moderately different in the niacin trials relative to statin trials (K = 7.43), with evidence of heterogeneity among the 6 niacin trials (Q = 11.8, 5 df; p = 0.038). The only niacin monotherapy morbidity/mortality trial, which included 88% of subjects included in the niacin analysis (n = 3,908), had a 17% reduction in non–HDL-C and a 17% reduction in CHD risk, in other words, a 1:1 relationship, over 6.2 years (Table 1). The 95% CI of the HATS (HDL-Atherosclerosis Treatment Study) trial did not include the 1:1 line (Fig. 1). No consistent relationships were evident for the 5 very small trials (study size n = 76 to 167) of niacin in combination that reported events in both treatment groups: 2 of the 3 trials that reported a non–HDL-C/CHD relative risk reduction relationship of >1:2 used niacin in combination with a statin (1:2.6 and 1:9.6), and 1 used niacin in combination with colestipol (1:2.9). Conversely, 1 trial of a niacin-statin combination reported a relationship of 1:0.6. On-treatment LDL-C levels ranged from 75 to 129 mg/dl in those reporting a >1:1 relationship and 86 to 101 in those reporting a  1:1 relationship.
Because only 1 trial of each treatment met entry criteria, trials of diet, bile acid sequestrant, and surgery were not evaluated using the Bayesian models. However, the 95% CIs for the diet, bile acid sequestrants, and surgery trials also included a 1:1 relationship between percent non–HDL-C lowering and CHD risk (Fig. 1).
Evaluation of statin trials by type found little support for a different relationship between non–HDL-C and CHD risk among the trials of primary prevention (14,17,20), secondary prevention (13,15,16,18,19,23–26), and diabetes populations (21,22) (K = 1.48). Only 1 fibrate trial evaluated a primary prevention population (31). A similar relationship between non–HDL-C and CHD occurred for fibrate trials in secondary prevention (32,33,35) and diabetes (34,36,37) (K = 0.41) populations. All niacin trials were performed in secondary prevention populations.
The Coronary Drug Project did not measure LDL-C levels. The remaining trials were pooled to evaluate the relationship between non–HDL-C and LDL-C and CHD risk. The model including both non–HDL-C and LDL-C was essentially similar to the model for non–HDL-C (K = 1.01), with very minimal additional risk prediction contributed by the LDL-C change. The LDL-C changes alone were about one-half as accurate as the non–high-density lipoprotein changes in predicting CHD risk reductions (LDL-C model/non–HDL-C model: K = 0.43).
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Discussion
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Along with LDL-C, non–HDL-C is an important target of therapy for CHD prevention. The relationship between non–HDL-C lowering and CHD risk reduction is similar for statins and fibrates. Most lipid-modifying drugs used as monotherapy appear to have an 1:1 relationship between percent non–HDL-C lowering and CHD reduction. Small trial sizes and design issues limit conclusions regarding niacin used in combination. Definitive conclusions regarding greater efficacy for niacin used in combination with statins await the results of ongoing trials (50).
Because only 1 trial met inclusion criteria in each of the categories of diet, bile acid sequestrants, and surgery, the relationship between non–HDL-C and CHD risk could not be evaluated in the multivariate models. However, each treatment performed about as expected for the 1:1 relationship given the percent reduction in non–HDL-C. Indeed, these findings are similar to those of a previous meta-analysis that found that diet, bile acid sequestrants, ileal bypass surgery, and statins reduce CHD events in a 1:1 relationship with LDL-C reduction (6).
Study limitations.
Limitations of this analysis include lack of access to patient-level data and the small numbers of subjects who received niacin combination therapy in shorter-term trials with unknown methods of end point adjudication. Unpublished trials were not included. We were not able to address the use of fibrates in combination with statins. Large morbidity trials of fenofibrate or niacin used in combination with simvastatin are ongoing (50).
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Conclusions
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A recent joint consensus report by the American Diabetes Association (ADA) and the American College of Cardiology (ACC) Foundation concluded that non–HDL-C was a better measure than LDL-C for identifying patients at high risk who had multiple cardiometabolic risk factors (51). They further concluded that non–HDL-C calculation should be included on all laboratory reports, and that non–HDL-C goals should be aggressively pursued in addition to aggressive LDL-C goals. They recommended non–HDL-C goals of <100 mg/dl for all CHD patients and diabetic patients with 1 other cardiovascular risk factor, and a goal of <130 mg/dl for all cardiometabolic risk patients with 2 major cardiovascular risk factors. In addition, the National Lipid Association has endorsed the importance of non–HDL-C, and recommends that all laboratories begin reporting it as an additional measure of residual cardiovascular risk (52). They further concluded that non–HDL-C is a robust laboratory test, incurs no additional expense in its calculation, and can be obtained in the nonfasting state. Although other lipid measures have been proposed as possible improvements over LDL-C measurement, non–HDL-C has as a major advantage in that it can be calculated on all lipid profiles and measures all apolipoprotein-B–containing lipoproteins that are considered atherogenic.
In summary, there is a direct, consistent relationship between the magnitude of non–HDL-C lowering and cardiovascular risk reduction. These findings support the use of non–HDL-C as an important target of therapy as recommended by both the NCEP ATP III and the ADA/ACC consensus report on lipoprotein management.
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Footnotes
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Dr. Robinson has received research grants from Abbott, Aegerion, AstraZeneca, Bristol-Myers Squibb, Daiichi-Sankyo, Hoffman La Roche, Merck, Merck Schering-Plough, and Takeda; has received speaker honoraria from Merck Schering-Plough; and has served as a consultant for AstraZeneca and Merck Schering-Plough. Dr. Jacobson has served as a consultant for Abbott, AstraZeneca, GlaxoSmithKline, Merck, Merck Schering-Plough, Pfizer, Reliant, and Sanofi-Aventis. Dr. Robinson had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
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Abstract
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