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Original Investigation |

The Metabolic Syndrome, Its Component Risk Factors, and Progression of Coronary Atherosclerosis FREE

Ozgur Bayturan, MD; E. Murat Tuzcu, MD; Andrea Lavoie, MD; Tingfei Hu, MS; Kathy Wolski, MPH; Paul Schoenhagen, MD; Samir Kapadia, MD; Steven E. Nissen, MD; Stephen J. Nicholls, MBBS, PhD
[+] Author Affiliations

Author Affiliations: Department of Cardiovascular Medicine, Heart & Vascular Institute (Drs Bayturan, Tuzcu, Lavoie, Schoenhagen, Kapadia, Nissen, and Nicholls; Mr Hu; and Ms Wolski), Department of Cell Biology (Dr Nicholls), the Imaging Institute (Dr Schoenhagen), and the Center for Cardiovascular Diagnostics and Prevention (Dr Nicholls), Cleveland Clinic, Cleveland, Ohio.


Arch Intern Med. 2010;170(5):478-484. doi:10.1001/archinternmed.2009.551.
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Published online

Background  The mechanism that confers adverse cardiovascular prognosis in patients with the metabolic syndrome (MetS) remains unclear. We sought to investigate the association of MetS and its component risk factors with progression of coronary atherosclerosis.

Methods  We performed a systematic review of 3459 patients who participated in 7 clinical trials that monitored coronary atheroma progression with intravascular ultrasonography. Patients with or without MetS were compared with regard to clinical characteristics, coronary atheroma burden at baseline, and change on serial evaluation. Relationships between plaque progression (≥5% increase in percent atheroma volume [PAV]), MetS, and its component risk factors were investigated.

Results  The metabolic syndrome was highly prevalent and was associated with greater progression of PAV (+0.51% ± 0.23% vs +0.23% ± 0.24%; P = .003). Multivariable analysis showed that MetS was associated with a greater likelihood of undergoing progression of PAV (adjusted odds ratio [OR], 1.25; 95% confidence interval [CI], 1.05-1.48; P = .01). When the individual components were used in the model instead of MetS, hypertriglyceridemia (OR, 1.26; 95% CI, 1.06-1.49; P = .008) and a body mass index of 30 or higher (1.18, 1.00-1.40; P = .05) predicted progression of PAV. However, after adjusting for its individual components, MetS was no longer an independent predictor (OR, 1.04; 95% CI, 0.79-1.37; P = .79).

Conclusion  Although accelerated disease progression is observed in the setting of MetS, this is owing to the presence of individual component risk factors rather than to the presence of the syndrome itself.

Figures in this Article

Considerable interest has focused on the association between the metabolic syndrome (MetS) and atherosclerotic cardiovascular disease. The MetS and its constituent components have been reported to play a pivotal role in the development of atherosclerosis and type 2 diabetes mellitus. Although the magnitude of risk varies according to which syndrome components are present, multiple studies have reported an association between MetS and prospective cardiovascular risk.13 The specific mechanisms that promote this increased clinical risk remain to be elucidated.

It continues to be debated whether MetS is a distinct pathophysiologic entity or simply reflects an association of cardiovascular risk factors.4 Some clinical studies demonstrate that the increased cardiovascular mortality in patients with MetS is driven by its individual components and that the presence of MetS provides no incremental risk prediction of mortality beyond that provided by its component risk factors.59

Intravascular ultrasonography (IVUS) has been used in clinical trials to evaluate the impact of medical therapies on the progression of coronary atherosclerosis. These studies have demonstrated that therapies that target established risk factors slow disease progression.1012 In a recent pooled analysis, subjects with type 2 diabetes mellitus demonstrated accelerated disease progression and impaired arterial wall remodeling.13 Whether similar effects are observed in the setting of patients with MetS is unknown. As a result, the present analysis was performed to determine whether disease progression is accelerated in patients with a diagnosis of MetS and to characterize the relative contribution of MetS beyond its individual component risk factors.

SELECTION OF SUBJECTS AND STUDY DESIGN

The present analysis pooled data from 7 prospective atherosclerosis progression/regression IVUS trials that included a total population of 3459 patients with established coronary heart disease. These studies included a wide range of pharmacologic interventions, including CAMELOT (Comparison of Amlodipine vs Enalapril to Limit Occurrences of Thrombosis),10 REVERSAL (Reversal of Atherosclerosis with Aggressive Lipid Lowering),11 ACTIVATE (Acyl-Coenzyme A:Cholesterol Acyltransferase Intravascular Atherosclerosis Treatment Evaluation),14 ASTEROID (a Study to Evaluate the Effect of Rosuvastatin on Intravascular Ultrasound-Derived Coronary Atheroma Burden),12 ILLUSTRATE (Investigation of Lipid Level Management Using Coronary Ultrasound to Assess Reduction of Atherosclerosis by Cholesteryl Ester Transfer Protein Inhibition and High-Density Lipoprotein Elevation),15 PERISCOPE (Comparison of Pioglitazone vs Glimepiride on Progression of Coronary Atherosclerosis in Patients With Type 2 Diabetes),16 and STRADIVARIUS (Effect of Rimonabant on Progression of Atherosclerosis in Patients With Abdominal Obesity and Coronary Artery Disease).17 These were clinical trials that used serial IVUS examination to assess the impact of intensive lipid lowering, antihypertensive therapy, acyl-coenzyme A:cholesterol acyltransferase inhibition, cholesteryl ester transfer protein inhibition, oral glucose-lowering agents, and an endocannabinoid type 1 receptor antagonist on the progression of coronary atherosclerosis. All patients were required to have coronary artery disease, defined as having at least 1 lumen with narrowing of greater than 20% in a major epicardial coronary artery on a diagnostic coronary angiogram performed for a clinical indication.

ACQUISITION AND ANALYSIS OF IVUS IMAGES

The acquisition and analysis of ultrasonic images have been described in detail previously.1017 In brief, after anticoagulation and administration of intracoronary nitroglycerin, an imaging catheter containing a high-frequency ultrasound transducer (30-40 MHz) was inserted as far as distally possible within a coronary artery. The target vessel for imaging was required to have a segment at least 30 mm long that contained no lumen with narrowing of greater than 50%, had not undergone previous revascularization, and was not considered to be the culprit vessel for a previous myocardial infarction. Continuous ultrasonographic imaging was acquired during withdrawal of the catheter (pullback) through the segment of artery at a constant rate of 0.5 mm/s. Images were stored on videotape and subsequently digitized for analysis in a single core laboratory by individuals who were blinded to the clinical characteristics and treatment status of the patients. Matching arterial segments were defined from the images acquired at the baseline and follow-up studies on the basis of the anatomic location of proximal and distal side branches (fiduciary points). Images spaced precisely 1 mm apart in the segment of interest were selected for analysis. The leading edge of the lumen and the external elastic membrane (EEM) were defined by manual planimetry. The plaque area was defined as the difference in area occupied by the lumen and EEM borders. The total atheroma volume (TAV) was calculated by summation of the plaque area as calculated by the following equation for each measured image and subsequently normalized to account for differences in segment length between subjects:

The percent atheroma volume (PAV) was calculated by the following equation as the proportion of vessel wall volume occupied by atherosclerotic plaque:

Volumes occupied by the lumen and EEM were similarly calculated by summation of their respective areas in each measured image and subsequently normalized to account for differences in segment length between patients.

Patients were stratified according to the presence or absence of MetS at baseline, as defined by the National Cholesterol Education Program's Adult Treatment Panel III criterion,18 and by a body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) of 30 or higher.19 Hence, subjects were classified as having MetS if they fulfilled 3 or more of the following: BMI of 30 or higher, triglyceride levels of 150 mg/dL or greater, low levels of high-density lipoprotein cholesterol (HDL-C) (<40 mg/dL in men and <50 mg/dL in women), elevated fasting glucose (≥100 mg/dL or a diagnosis of diabetes mellitus), and high systolic or diastolic blood pressure (≥130/85 mm Hg or current use of antihypertensive medication). (To convert triglycerides to millimoles per liter, multiply by 0.0113; HDL-C to millimoles per liter, multiply by 0.0259; and glucose to millimoles per liter, multiply by 0.0555.)

Patients with or without MetS were compared with regard to clinical characteristics, the extent of coronary atherosclerosis at baseline, and their serial changes. In addition, the percentage of images containing plaque and calcification were investigated to evaluate differences between groups with regard to the diffuse pattern and degree of calcification of disease, respectively.

STATISTICAL ANALYSES

All analyses were conducted using SAS statistical software (version 8.2; SAS Institute Inc, Cary, North Carolina). Demographic data are expressed in percentages for categorical variables and compared using the χ2 statistic. Continuous laboratory data are expressed as mean ± SD and compared with a standard t test for factors with a normal distribution or expressed as median and interquartile range and compared with the Wilcoxon rank sum test for factors that are nonnormally distributed. Changes in atheroma burden and vascular dimensions on a continuous scale were evaluated using an analysis of variance model for random effects with the baseline value as a covariate, classification group (MetS or no MetS) as a factor, and trial as a random effect to account for the heterogeneity between the different studies. A multivariable logistic regression quantified the relative odds of undergoing substantial plaque progression (defined as a percentage increase in PAV ≥5%) associated with MetS after adjusting for age, sex, race, baseline low-density lipoprotein cholesterol (LDL-C) level, statin use, and study. Separate adjusted models were generated for each component of MetS, and odds ratios (ORs) were calculated for the patients having MetS with or without the component compared with the reference group (no MetS). In addition, each of the components of MetS were forced together into a multivariable logistic model to assess the relative odds of plaque progression for each component in the presence of the others. A 2-sided P < .05 was considered statistically significant.

BASELINE CHARACTERISTICS

Patient characteristics are summarized in Table 1. More than half of the patients (57.8%) met the criteria for MetS at baseline. The prevalence of components of MetS in both patient groups is illustrated in the Figure. Patients with MetS were younger (57.3 ± 9.2 vs 58.5 ± 9.7 years; P < .001) and more likely to be female (32.3% vs 25.1%; P < .001). Patients with MetS were less likely to be treated with statins (93.1% vs 96.5%; P < .001) and more likely to receive β-blockers (79.1% vs 72.8%; P < .001) and angiotensin-converting enzyme inhibitors (62.2% vs 45.0%; P < .001).

Place holder to copy figure label and caption
Figure.

Percentage of patients with or without the metabolic syndrome (MetS) with each defining component risk factor. HDL-C indicates high-density lipoprotein cholesterol.

Graphic Jump Location
Table Graphic Jump LocationTable 1. Clinical Characteristics of and Medication Use by Patients With and Without MetS

The degrees of risk factor control at baseline and follow-up are given in Table 2. Predictably, each diagnostic criterion of MetS was more prevalent in patients with MetS (P < .001). Patients with MetS also had higher baseline levels of non–HDL-C (144.9 ± 44.4 vs 131.2 ± 40.6 mg/dL; P < .001). During the course of the studies, the observed decrease in LDL-C was less (−7.0% ± 32.7% vs −12.1% ± 30.5%; P < .001) in patients with MetS. (To convert non–HDL-C and LDL-C to millimoles per liter, multiply by 0.0259.) Triglyceride levels decreased in patients with MetS by 11.4% but in patients not meeting the diagnostic criteria for MetS by only 2.2% (P < .001 between groups).

Table Graphic Jump LocationTable 2. Biochemical Factors and Their Percentage Change in Subjects With and Without MetS
BASELINE ATHEROSCLEROTIC BURDEN AND VASCULAR DIMENSIONS

Measures of atheroma burden and vessel wall dimensions at baseline are provided in Table 3. There was no difference between the groups with regard to the extent of atherosclerosis at baseline, with a similar PAV (38.7% ± 9.0% vs 38.5% ± 9.0%; P = .57) and TAV (193.6 ± 85.6 vs 188.5 ± 80.0 mm3; P = .19) in patients with and without MetS, respectively. Similarly, no difference was observed between the groups in vascular dimensions throughout the imaged segment.

Table Graphic Jump LocationTable 3. Baseline and Serial Changes of Atheroma Burden and Vascular Dimensions in Patients With and Without MetS
SERIAL CHANGES OF ATHEROSCLEROTIC BURDEN AND VASCULAR DIMENSIONS

Changes in atheroma burden and vessel wall dimensions during serial evaluation are given in Table 3. Despite a high rate of medical therapy use, patients with MetS demonstrated accelerated progression of PAV (+0.51% ± 0.23% vs +0.23% ± 0.24%; P = .003) and less regression in TAV (−2.3 ± 1.7 vs −3.4 ± 1.7 mm3; P = .07), although the latter failed to meet statistical significance. Patients with MetS were more likely to demonstrate substantial atheroma progression (≥5% increase in PAV, 27.2% vs 22.2%; P = .001) and less likely to demonstrate substantial regression (≥5% decrease in PAV, 17.7% vs 20.6%; P = .03). The baseline and serial changes of EEM and lumen volumes were similar in patients with and without MetS.

INDEPENDENT PREDICTORS OF DISEASE BURDEN AND PROGRESSION

Table 4 provides the odds of having substantial atheroma progression for those with MetS, with or without the individual components, compared with those without MetS. Individuals with MetS, including the components of elevated BMI or hypertriglyceridemia, tended to have the highest odds of progression (OR, 1.32; 95% confidence interval [CI], 1.10-1.58 and 1.31, 1.10-1.58, respectively).

Table Graphic Jump LocationTable 4. Likelihood of Substantial Progression According to Each Component of MetS

In a multivariable model adjusting for age, sex, race, baseline LDL-C level, and baseline statin use, having MetS demonstrated a greater likelihood for substantial progression (OR, 1.25; 95% CI, 1.05-1.48; P = .01) (Table 5). When the individual components were used in the adjusted model instead of the syndrome, hypertriglyceridemia (P = .008) and a BMI of 30 or higher (P = .05) were again shown to be associated with substantial progression of PAV (Table 5). Baseline C-reactive protein levels were not an independent predictor in any of the models (data not shown). When MetS and its individual components were considered in the model together, MetS as a cluster was not an independent predictor (OR, 1.04; 95% CI, 0.79-1.37; P = .79), and the only component that remained significant was hypertriglyceridemia (1.23, 1.02-1.49; P = .03).

Table Graphic Jump LocationTable 5. Likelihood of Undergoing Substantial Plaque Progressiona

Further multivariable analysis that considered the change in PAV as a continuous outcome variable showed that elevated glucose levels (P = .002) and hypertriglyceridemia (P < .001), but not MetS (P = .15), were independent predictors for progression (data not shown).

In this pooled analysis of prospective atherosclerosis progression/regression IVUS trials that included 3459 patients with established coronary artery disease, we examined the association of MetS and its component risk factors with plaque progression. Metabolic syndrome was highly prevalent (57.8%) in this cohort and was associated with higher rates of disease progression. However, multivariable analysis showed that the excess plaque progression appeared to be driven more by its individual risk factors. These results have important implications for understanding the relationship between MetS and cardiovascular disease. A major impetus for developing the concept of MetS involved the observation of a clustering of risk factors for coronary heart disease and diabetes mellitus in patients with abdominal obesity. However, it remains to be unequivocally established whether MetS is a distinct pathologic entity or simply represents an association of risk factors. Debate continues to be supported by the observation that the adverse outcome appears to be driven by the presence of individual risk factors.47,9 As a result, it remains to be demonstrated that the impact of the syndrome exceeds that of the sum of its individual components. We used atherosclerosis imaging to characterize the association between MetS and atherosclerotic disease progression. The finding of more progression and less regression of disease in patients with MetS on univariate analysis confirms previous observations from studies that used carotid intima-media thickness.20,21

However, to our knowledge, no previous imaging study has compared the relative contribution of MetS and its individual components with disease progression. Notably, multivariable analysis showed that accelerated disease progression was more likely to be driven by individual risk factors. Although each of the components predicted greater disease progression, hypertriglyceridemia and elevated BMI had the highest hazard ratios for atheroma progression. These findings provide a mechanistic link between the burden of atherosclerosis and reported outcome data. A study based on Third National Health and Nutrition Examination Survey data has found that, among MetS components, hypertriglyceridemia has the strongest relation to prevalent myocardial infarction and stroke in patients with MetS.22 Moreover, Miller et al23 demonstrated that patients receiving statin therapy after acute coronary syndrome, for whom triglyceride levels while receiving therapy were less than 150 mg/dL, had a lower risk of recurrent coronary heart disease events independent of the level of LDL-C.

The finding that hypertriglyceridemia independently predicted disease progression supports an increasing body of evidence demonstrating that fasting hypertriglyceridemia is an established cardiovascular risk factor.2426 This has been extended to the observations of triglyceride measurements obtained using samples collected in the nonfasting state.2729 The variability in these findings between the fasting and nonfasting samples may also reflect differences in dietary fat and carbohydrate intake, potentially confounding the association to a greater degree. The finding that elevated triglyceride levels predict disease progression and cardiovascular risk, even in patients with LDL-C levels considered to be normal, may result from both the atherogenicity of triglyceride-rich remnant particles and effects on the relative functionality of circulating LDL-C and HDL-C particles.30,31 These observations also suggest that a more aggressive approach to management of even mild triglyceride elevations should be integrated into risk-reduction strategies.

The additional finding that obesity is an independent predictor of disease progression warrants further comment. The presence of central adiposity is commonly encountered in patients with MetS. An abdominal distribution of adipose tissue is associated with a number of factors that are likely to have a detrimental influence on the artery wall, including dysregulation of metabolic factors, activation of inflammatory cascades, and potentially direct effects of adipocytokines.32 It is therefore not surprising that anthropometric factors consistent with abdominal obesity are associated with an adverse cardiovascular outcome. Accordingly, targeting the abdominally obese patient for aggressive preventive measures may be of greater utility than the identification of MetS. Abdominal adiposity may also be the source of targets for new therapies. Although the use of the endocannabinoid receptor antagonist rimonabant did not slow progression of coronary atherosclerosis, the strategy did appear to be beneficial in abdominally obese patients with hypertriglyceridemia.16 This further underscores the interaction between obesity and the development of an atherogenic dyslipidemic phenotype in promoting cardiovascular risk.

The current findings do not diminish the importance of other risk factors previously reported to be associated with disease progression. In particular, LDL-C remains an important promoter of atherosclerosis formation and propagation. The high prevalence of MetS in the current cohort—which is more typically associated with an atherogenic dyslipidemic phenotype—and the high rate of statin therapy use are likely to influence the relationship between LDL-C and disease progression. This is consistent with the observation that the absence of statin therapy remains an independent predictor of disease progression. Similarly, the importance of diabetes mellitus, even after controlling for associated risk factors, underscores the severity of that disease process in the setting of disordered glucose homeostasis beyond the insulin resistance commonly encountered in patients with MetS.

The results do not diminish the importance of the other MetS components. When each component was considered individually in combination with MetS, a greater chance of progression was observed. This supports the importance of hyperglycemia, hypertension, and low HDL-C level as risk factors. Each of these factors remains a potential target for cardiovascular prevention in these patients. Nevertheless, a more robust association was observed between atheroma progression and baseline hypertriglyceridemia and elevated BMI levels. This underscores the finding that hypertriglyceridemia and elevated BMI levels were stronger predictors of progression on multivariable analysis. In addition, the analysis reflects associations with baseline measurements. It is also possible that changes in some factors, such as HDL-C level, may be more important predictors of progression than their baseline levels, as previously reported in statin-treated patients.33

A number of caveats with regard to the current analysis should be noted. All patients had established coronary artery disease on an angiogram performed for a clinical indication. As a result, it is unknown whether the same findings can be extrapolated to asymptomatic patients in the primary prevention setting. Although the data resulted from pooling of multiple clinical trials, all trials recruited similar patients and the coronary imaging was performed and analyzed according to standardized criteria as set forth by a single core laboratory. No data were collected with regard to the composition of atherosclerotic plaque. This may have an important influence on the propensity to undergo plaque rupture. The relationship between the burden and progression of coronary atherosclerosis and clinical outcome also requires ongoing investigation.

In summary, although accelerated disease progression is observed in patients with MetS, this appears to be largely driven by individual component risk factors rather than by the presence of the syndrome itself. This provides further support for the concept that conferring a diagnosis of MetS highlights a patient with multiple atherogenic risk factors. In particular, hypertriglyceridemia and possibly obesity in these patients provide important targets for potential therapeutic intervention to more effectively reduce cardiovascular risk.

Correspondence: Stephen J. Nicholls, MBBS, PhD, Department of Cardiovascular Medicine, Heart & Vascular Institute, Mail Code JJ-65, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44195 (nichols1@ccf.org).

Accepted for Publication: September 3, 2009.

Author Contributions:Study concept and design: Bayturan, Tuzcu, Schoenhagen, Nissen, and Nicholls. Acquisition of data: Bayturan, Hu, and Nissen. Analysis and interpretation of data: Bayturan, Tuzcu, Lavoie, Hu, Wolski, Schoenhagen, Kapadia, Nissen, and Nicholls. Drafting of the manuscript: Bayturan, Tuzcu, and Nicholls. Critical revision of the manuscript for important intellectual content: Bayturan, Tuzcu, Lavoie, Hu, Wolski, Schoenhagen, Kapadia, and Nissen. Statistical analysis: Bayturan, Hu, Wolski, and Nissen. Administrative, technical, and material support: Tuzcu, Nissen, and Nicholls. Study supervision: Tuzcu, Schoenhagen, Kapadia, Nissen, and Nicholls.

Financial Disclosure: Dr Lavoie has received honoraria from Pfizer Inc, AstraZeneca, Bayer, Servier, and Bristol Myers-Squibb. Dr Nissen has received research support from AstraZeneca, Eli Lilly and Company, Pfizer Inc, Takeda Pharmaceutical Company Limited, Sankyo, and Sanofi-Aventis. He has consulted for a number of pharmaceutical companies without financial compensation; all honoraria, consulting fees, or any other payments from any for-profit entity are paid directly to charity, so that neither income nor any tax deduction is received. Dr Nicholls has received honoraria from Pfizer Inc, AstraZeneca, Takeda Pharmaceutical Company Limited, and Merck/Schering-Plough Pharmaceuticals; consultancy fees from AstraZeneca, Pfizer Inc, Roche, Novo Nordisk, Merck/Schering-Plough Pharmaceuticals, LipoScience Inc, and Anthera Pharmaceuticals Inc; and research support from AstraZeneca, Lipid Sciences Inc, Novartis, and Resverlogix Corp.

Funding/Support: Dr Bayturan is supported by the Scientific and Technological Research Council of Turkey. Internal departmental funding for this analysis was provided. The REVERSAL, CAMELOT, and ILLUSTRATE studies were funded by Pfizer Inc; ACTIVATE was funded by Sankyo; ASTEROID was funded by AstraZeneca; PERISCOPE was funded by Takeda Pharmaceutical Company Limited; and STRADIVARIUS was funded by Sanofi-Aventis.

Role of the Sponsor: None of the pharmaceutical companies that provided funding sponsored the present analysis.

Additional Contributions: Ugur Kemal Tezcan, MD, reviewed the manuscript and provided scientific advice.

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Freiberg  JJTybjaerg-Hansen  AJensen  JSNordestgaard  BG Nonfasting triglycerides and risk of ischemic stroke in the general population. JAMA 2008;300 (18) 2142- 2152
PubMed Link to Article
Nordestgaard  BGLangsted  AFreiberg  JJ Nonfasting hyperlipidemia and cardiovascular disease. Curr Drug Targets 2009;10 (4) 328- 335
PubMed Link to Article
Imke  CRodriguez  BLGrove  JS  et al.  Are remnant-like particles independent predictors of coronary heart disease incidence? the Honolulu Heart study. Arterioscler Thromb Vasc Biol 2005;25 (8) 1718- 1722
PubMed Link to Article
Després  JPLemieux  IBergeron  J  et al.  Abdominal obesity and the metabolic syndrome: contribution to global cardiometabolic risk [published correction appears in Arterioscler Thromb Vasc Biol. 2008;28(7):e151]. Arterioscler Thromb Vasc Biol 2008;28 (6) 1039- 1049
PubMed Link to Article
Nicholls  SJTuzcu  EMSipahi  I  et al.  Statins, high-density lipoprotein cholesterol, and regression of coronary atherosclerosis. JAMA 2007;297 (5) 499- 508
PubMed Link to Article

Figures

Place holder to copy figure label and caption
Figure.

Percentage of patients with or without the metabolic syndrome (MetS) with each defining component risk factor. HDL-C indicates high-density lipoprotein cholesterol.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Clinical Characteristics of and Medication Use by Patients With and Without MetS
Table Graphic Jump LocationTable 2. Biochemical Factors and Their Percentage Change in Subjects With and Without MetS
Table Graphic Jump LocationTable 3. Baseline and Serial Changes of Atheroma Burden and Vascular Dimensions in Patients With and Without MetS
Table Graphic Jump LocationTable 4. Likelihood of Substantial Progression According to Each Component of MetS
Table Graphic Jump LocationTable 5. Likelihood of Undergoing Substantial Plaque Progressiona

References

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PubMed Link to Article
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Nissen  SETardif  JCNicholls  SJ  et al. ILLUSTRATE Investigators, Effect of torcetrapib on the progression of coronary atherosclerosis [published correction appears in N Engl J Med. 2007;357(8):835]. N Engl J Med 2007;356 (13) 1304- 1316
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Nissen  SENicholls  SJWolski  K  et al. STRADIVARIUS Investigators, Effect of rimonabant on progression of atherosclerosis in patients with abdominal obesity and coronary artery disease: the STRADIVARIUS randomized controlled trial. JAMA 2008;299 (13) 1547- 1560
PubMed Link to Article
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PubMed Link to Article
Tzou  WSDouglas  PSSrinivasan  SR  et al.  Increased subclinical atherosclerosis in young adults with metabolic syndrome: the Bogalusa Heart Study. J Am Coll Cardiol 2005;46 (3) 457- 463
PubMed Link to Article
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PubMed Link to Article
Miller  MCannon  CPMurphy  SAQin  JRay  KKBraunwald  EPROVE IT-TIMI 22 Investigators, Impact of triglyceride levels beyond low-density lipoprotein cholesterol after acute coronary syndrome in the PROVE IT-TIMI 22 trial. J Am Coll Cardiol 2008;51 (7) 724- 730
PubMed Link to Article
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PubMed Link to Article
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PubMed Link to Article
Nordestgaard  BGBenn  MSchnohr  PTybjaerg-Hansen  A Nonfasting triglycerides and risk of myocardial infarction, ischemic heart disease, and death in men and women. JAMA 2007;298 (3) 299- 308
PubMed Link to Article
Bansal  SBuring  JERifai  NMora  SSacks  FMRidker  PM Fasting compared with nonfasting triglycerides and risk of cardiovascular events in women. JAMA 2007;298 (3) 309- 316
PubMed Link to Article
Freiberg  JJTybjaerg-Hansen  AJensen  JSNordestgaard  BG Nonfasting triglycerides and risk of ischemic stroke in the general population. JAMA 2008;300 (18) 2142- 2152
PubMed Link to Article
Nordestgaard  BGLangsted  AFreiberg  JJ Nonfasting hyperlipidemia and cardiovascular disease. Curr Drug Targets 2009;10 (4) 328- 335
PubMed Link to Article
Imke  CRodriguez  BLGrove  JS  et al.  Are remnant-like particles independent predictors of coronary heart disease incidence? the Honolulu Heart study. Arterioscler Thromb Vasc Biol 2005;25 (8) 1718- 1722
PubMed Link to Article
Després  JPLemieux  IBergeron  J  et al.  Abdominal obesity and the metabolic syndrome: contribution to global cardiometabolic risk [published correction appears in Arterioscler Thromb Vasc Biol. 2008;28(7):e151]. Arterioscler Thromb Vasc Biol 2008;28 (6) 1039- 1049
PubMed Link to Article
Nicholls  SJTuzcu  EMSipahi  I  et al.  Statins, high-density lipoprotein cholesterol, and regression of coronary atherosclerosis. JAMA 2007;297 (5) 499- 508
PubMed Link to Article

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