Implementing Cardiovascular Risk Reduction in Patients with Cardiovascular Disease and Diabetes Mellitus
2011; Elsevier BV; Volume: 108; Issue: 3 Linguagem: Inglês
10.1016/j.amjcard.2011.03.015
ISSN1879-1913
AutoresShailesh Nandish, Jamison Wyatt, Oscar Bailon, Mike Smith, Rene Oliveros, Robert Ćhilton,
Tópico(s)Cardiac Imaging and Diagnostics
ResumoThe role of cardiovascular risk reduction in patients with diabetes mellitus is significant as several factors have been found to promote accelerated atherosclerosis in persons with diabetes including hyperglycemia-induced endothelial dysfunction, impaired fibrinolysis, increased platelet aggregation, plaque instability, dysfunctional arterial remodeling, and fibrotic and calcified coronary arteries. Recent attention has focused on identifying a cardiovascular biomarker that would propose a better noninvasive way to detect or visualize subclinical cardiovascular disease and prevent cardiovascular events. This article reviews the use of commonly used cardiovascular risk assessment tools and emerging biomarkers including coronary artery calcium scanning, metabolomics, genomics, and the role of optimal revascularization and risk reduction strategies and their impact on reducing risk in patients with cardiovascular disease and diabetes. The role of cardiovascular risk reduction in patients with diabetes mellitus is significant as several factors have been found to promote accelerated atherosclerosis in persons with diabetes including hyperglycemia-induced endothelial dysfunction, impaired fibrinolysis, increased platelet aggregation, plaque instability, dysfunctional arterial remodeling, and fibrotic and calcified coronary arteries. Recent attention has focused on identifying a cardiovascular biomarker that would propose a better noninvasive way to detect or visualize subclinical cardiovascular disease and prevent cardiovascular events. This article reviews the use of commonly used cardiovascular risk assessment tools and emerging biomarkers including coronary artery calcium scanning, metabolomics, genomics, and the role of optimal revascularization and risk reduction strategies and their impact on reducing risk in patients with cardiovascular disease and diabetes. It is estimated that 1 in 3 individuals in the United States will develop diabetes mellitus in their lifetime, with a consequent loss of approximately 10–15 years of their life span. Diabetes is the seventh leading cause of death in the United States, and the risk of morbidity is about twice that of age-matched nondiabetic patients. More than half of patients with diabetes will die as a result of cardiovascular disease (CVD), which accounts for 85% of the cumulative costs of treating diabetes-related complications over the initial 5 years.1Caro J.J. Ward A.J. O'Brien J.A. Lifetime costs of complications resulting from type 2 diabetes in the U.S..Diabetes Care. 2002; 25: 476-481Crossref PubMed Scopus (220) Google ScholarThe metabolic disorders accompanying diabetes (dysglycemia, dyslipidemia, excess fat) seem to fuel the progression of atherosclerosis. Even patients with diabetes who appear otherwise free of coronary artery disease (CAD) are at significant risk for subsequent cardiovascular (CV) events and already have extensive coronary atherosclerosis disease. The total ischemic burden in patients with diabetes is more difficult to assess, because they are likely to present with silent ischemia.The challenge faced by clinicians is to accurately identify the patient at the highest risk and to develop a global CV risk reduction plan accordingly. The key is to implement an aggressive risk reduction strategy in the high-risk group, where the benefit of the preventive or treatment strategy outweigh the risks, while avoiding unnecessary exposure and risks to the individual who may not progress to a CV event.In this review, we discuss the synergistic effects of the traditional CV risk factors in the presence of diabetes and examine the commonly used CV risk assessment tools. We also consider the value of emerging biomarkers for CVD, reevaluate optimal revascularization for patients with CVD and diabetes, and review the effectiveness of risk reduction strategies.Multiplicative Effect of Diabetes Mellitus On Traditional Risk FactorsSeveral factors have been found to promote accelerated atherosclerosis in persons with diabetes, including hyperglycemia-induced endothelial dysfunction, impaired fibrinolysis, increased platelet aggregation, plaque instability, dysfunctional arterial remodeling, and fibrotic and calcified coronary arteries. These processes, together with the dyslipidemia and hypertension that often cluster with diabetes, synergistically raise the CV event risk. For example, patients with diabetes who have a history of smoking and hypertension have an 8-fold increased risk for myocardial infarction (MI) compared with their nondiabetic counterparts.2Wilson P.W. D'Agostino R.B. Levy D. Belanger A.M. Silbershatz H. Kannel W.B. Prediction of coronary heart disease using risk factor categories.Circulation. 1998; 97: 1837-1847Crossref PubMed Scopus (7326) Google ScholarThe INTERHEART Study, a large case-control study of the incidence of acute MI, identified 9 modifiable risk factors (smoking, dyslipidemia, hypertension, diabetes, obesity, diet, physical activity, alcohol consumption, and psychosocial factors) that can explain >90% of acute MI in young and older individuals, women (94%) and men (90%), and across all major ethnic groups.3Yusuf S. Hawken S. Ounpuu S. Dans T. Avezum A. Lanas F. McQueen M. Budaj A. Pais P. Varigos J. Lisheng L. INTERHEART Study InvestigatorsEffect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study.Lancet. 2004; 364: 937-952Abstract Full Text Full Text PDF PubMed Scopus (8271) Google Scholar Current smoking and abnormal lipids (elevated apolipoprotein B/ apolipoprotein A1 ratio) were the 2 strongest risk factors, followed by a history of diabetes, hypertension, and psychosocial factors. Daily consumption of fruits or vegetables, moderate or strenuous level of physical exercise, and consumption of alcohol ≥3 or more times per week, were found to be protective, highlighting the importance of lifestyle modifications for CVD prevention.The INTERHEART study confirmed the multiplicative effect when diabetes is added to the mix of traditional risk factors. The investigators founds that the presence of diabetes or hemoglobin A1c (HbA1c) ≥6.5%, combined with ≥2 metabolic syndrome–related factors, is predictive of MI among men and women, young and old, and across all geographic regions and ethnic groups. The combination of diabetes with hypertension confers a particularly dangerous risk for MI; and the combined impact on the incidence of MI appears to be similar to that of having all 4 metabolic syndrome component factors.4Stamler J. Vaccaro O. Neaton J.D. Wentworth D. Diabetes, other risk factors, and 12-yr cardiovascular mortality for men screened in the multiple risk factor intervention trial.Diabetes Care. 1993; 16: 434-444Crossref PubMed Scopus (3566) Google Scholar, 5Mente A. Yusuf S. Islam S. McQueen M.J. Tanomsup S. Onen C.L. Rangarajan S. Gerstein H.C. Anand S.S. INTERHEART InvestigatorsMetabolic syndrome and risk of acute myocardial infarction: a case-control study of 26,903 subjects from 52 countries.J Am Coll Cardiol. 2010; 55: 2390-2398Abstract Full Text Full Text PDF PubMed Scopus (166) Google Scholar The US National Cholesterol Education Program Adult Treatment Panel III (2001) requires at least three of the following: central obesity: waist circumference ≥ 102 cm or 40 inches (male), ≥ 88 cm or 36 inches (female); dyslipidemia: TG ≥ 1.7 mmol/L (150 mg/dl); HDL-C < 40 mg/dL (male), < 50 mg/dL (female); blood pressure ≥ 130/85 mmHg; or fasting plasma glucose ≥ 5.6 mmol/L (100 mg/dl). Additionally, the majority of subjects in INTERHEART with diabetes (77%) had abdominal obesity and intra-abdominal fat deposition that was associated with a greater CVD risk than obesity alone.6Peiris A.N. Sothmann M.S. Hoffmann R.G. et al.Adiposity, fat distribution, and cardiovascular risk.Ann Intern Med. 1989; 110: 867-872Crossref PubMed Scopus (407) Google ScholarValue and Limitations of Cardiovascular Risk Assessment ToolsThe decision to implement an aggressive CVD risk reduction strategy should be guided primarily by the potential for benefit, which is likely directly proportional to the magnitude of the individual's risk for developing an event. Predicting which patient will progress to MI or stroke is a challenging task, because single CV risk factors taken in isolation are poor predictors of future events. Growing evidence supports the value of a global CVD risk approach that takes into account all relevant factors and their intertwined relationships to better predict who is at risk.The Framingham risk score remains the most widely used tool for assessment of risk for CV events.7Grundy S.M. Balady G. Criqui M. Fletcher G. Greenland P. Hiratzka L. Houston-Miller N. Kris-Etherton P. Krumholz H. LaRosa J. et al.Primary prevention of coronary heart disease: guidance from Framingham.Circulation. 1998; 97: 1876-1887Crossref PubMed Scopus (510) Google Scholar Most of the current risk estimation systems include the traditional risk factors—age, sex, smoking, blood pressure, and lipid levels (Table 1) .8D'Agostino Sr, R.B. Vasan R.S. Pencina M.J. Wolf P.A. Cobain M. Massaro J.M. Kannel W.B. General cardiovascular risk profile for use in primary care: the Framingham Heart Study.Circulation. 2008; 117: 743-753Crossref PubMed Scopus (4490) Google Scholar, 9Fox C.S. Sullivan L. D'Agostino Sr, R.B. Wilson P.W. Framingham Heart StudyThe significant effect of diabetes duration on coronary heart disease mortality: the Framingham Heart Study.Diabetes Care. 2004; 27: 704-708Crossref PubMed Scopus (220) Google Scholar, 10Stevens R.J. Kothari V. Adler A.I. Stratton I.M. United Kingdom Prospective Diabetes Study (UKPDS) GroupThe UKPDS risk engine: a model for the risk of coronary heart disease in Type II diabetes (UKPDS 56) [published correction appears in Clin Sci (Lond) 2002;102:679].Clin Sci (Lond). 2001; 101: 671-679Crossref PubMed Scopus (552) Google Scholar, 11Simmons R.K. Coleman R.L. Price H.C. Holman R.R. Khaw K.T. Wareham N.J. Griffin S.J. Performance of the UK Prospective Diabetes Study Risk Engine and the Framingham Risk Equations in estimating cardiovascular disease in the EPIC-Norfolk cohort.Diabetes Care. 2009; 32: 708-713Crossref PubMed Scopus (117) Google Scholar, 12Ridker P.M. Buring J.E. Rifai N. Cook N.R. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score [published correction appears in JAMA 2007;297:1433].JAMA. 2007; 297: 611-619Crossref PubMed Scopus (1408) Google Scholar, 13Balkau B. Hu G. Qiao Q. Tuomilehto J. Borch-Johnsen K. Pyörälä K. DECODE Study Group and the European Diabetes Epidemiology GroupPrediction of the risk of cardiovascular mortality using a score that includes glucose as a risk factor: the DECODE Study.Diabetologia. 2004; 47: 2118-2128Crossref PubMed Scopus (148) Google Scholar, 14DECODE Study GroupAge- and sex-specific prevalences of diabetes and impaired glucose regulation in 13 European cohorts.Diabetes Care. 2003; 26: 61-69Crossref PubMed Scopus (454) Google Scholar, 15Cederholm J. Eeg-Olofsson K. Eliasson B. Zethelius B. Nilsson P.M. Gudbjörnsdottir S. Swedish National Diabetes RegisterRisk prediction of cardiovascular disease in type 2 diabetes: a risk equation from the Swedish National Diabetes Register.Diabetes Care. 2008; 31: 2038-2043Crossref PubMed Scopus (128) Google Scholar, 16Donnan P.T. Donnelly L. New J.P. Morris A.D. Derivation and validation of a prediction score for major coronary heart disease events in a U.K. type 2 diabetic population.Diabetes Care. 2006; 29: 1231-1236Crossref PubMed Scopus (58) Google Scholar To improve the predictability of scores for estimating global CVD risk, the incorporation of biomarkers of inflammation and early family history of CAD, which are known to independently predict future vascular events, has been suggested. In most cases, going beyond the traditional risk factors adds only minor improvement in prediction of risk and is often associated with higher costs.17Pencina M.J. D'Agostino Sr, R.B. D'Agostino Jr, R.B. Vasan R.S. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond.Stat Med. 2008; 27 (1576–172; discussion 207–212)PubMed Google ScholarTable 1Cardiovascular disease (CVD)/coronary artery disease (CAD) risk calculators in type 2 diabetes mellitusStudyPopulationMain OutcomeRisk Factors IncludedFramingham8D'Agostino Sr, R.B. Vasan R.S. Pencina M.J. Wolf P.A. Cobain M. Massaro J.M. Kannel W.B. General cardiovascular risk profile for use in primary care: the Framingham Heart Study.Circulation. 2008; 117: 743-753Crossref PubMed Scopus (4490) Google Scholar, 9Fox C.S. Sullivan L. D'Agostino Sr, R.B. Wilson P.W. Framingham Heart StudyThe significant effect of diabetes duration on coronary heart disease mortality: the Framingham Heart Study.Diabetes Care. 2004; 27: 704-708Crossref PubMed Scopus (220) Google ScholarGeneral population 3,969 men and 4,522 women (n = 588 with diabetes)Risk of CAD (MI and coronary death)Sex, age, TC, HDL-C, SBP, smoking status, diabetes, hypertension RxUKPDS10Stevens R.J. Kothari V. Adler A.I. Stratton I.M. United Kingdom Prospective Diabetes Study (UKPDS) GroupThe UKPDS risk engine: a model for the risk of coronary heart disease in Type II diabetes (UKPDS 56) [published correction appears in Clin Sci (Lond) 2002;102:679].Clin Sci (Lond). 2001; 101: 671-679Crossref PubMed Scopus (552) Google Scholar, 11Simmons R.K. Coleman R.L. Price H.C. Holman R.R. Khaw K.T. Wareham N.J. Griffin S.J. Performance of the UK Prospective Diabetes Study Risk Engine and the Framingham Risk Equations in estimating cardiovascular disease in the EPIC-Norfolk cohort.Diabetes Care. 2009; 32: 708-713Crossref PubMed Scopus (117) Google Scholar4,540 men and women aged 25–65 yr with diabetes, without history of MI, angina, or heart failure (UK)Fatal or nonfatal MI or sudden death (verified by 2 independent clinical assessors)Age at diagnosis of diabetes, sex, ethnicity, smoking status, HbA1c, SBP, TC:HDL-C ratio, duration of diabetesReynolds12Ridker P.M. Buring J.E. Rifai N. Cook N.R. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score [published correction appears in JAMA 2007;297:1433].JAMA. 2007; 297: 611-619Crossref PubMed Scopus (1408) Google ScholarWHS 24,558 women and 10,724 men (n = 442 with diabetes)10-yr risk of incident MI, stroke, coronary revascularization, or CV deathAge, SBP, HbA1c (in patients with diabetes), smoking, TC, HDL-C, hs-CRP, parental history of MIDECODE13Balkau B. Hu G. Qiao Q. Tuomilehto J. Borch-Johnsen K. Pyörälä K. DECODE Study Group and the European Diabetes Epidemiology GroupPrediction of the risk of cardiovascular mortality using a score that includes glucose as a risk factor: the DECODE Study.Diabetologia. 2004; 47: 2118-2128Crossref PubMed Scopus (148) Google Scholar, 14DECODE Study GroupAge- and sex-specific prevalences of diabetes and impaired glucose regulation in 13 European cohorts.Diabetes Care. 2003; 26: 61-69Crossref PubMed Scopus (454) Google Scholar16,506 men and 8,907 women from 14 European studies (n = 1,325 with diabetes)5-yr and 10-yr CV mortalityAge, smoking status, arterial BP, TC concentration, BMI, FPG, 2-hr glucose concentration, diabetes statusSwedish NDR15Cederholm J. Eeg-Olofsson K. Eliasson B. Zethelius B. Nilsson P.M. Gudbjörnsdottir S. Swedish National Diabetes RegisterRisk prediction of cardiovascular disease in type 2 diabetes: a risk equation from the Swedish National Diabetes Register.Diabetes Care. 2008; 31: 2038-2043Crossref PubMed Scopus (128) Google Scholar5,823 men and women aged 18–70 yr with diabetes and no previous CVD (Sweden)Fatal or nonfatal CVD (CAD or stroke, including coronary intervention) retrieved by data linkage at 5.6 yrAge at diabetes diagnosis. sex, smoking status, duration of diabetes, BMI, SBP, HbA1c, antihypertensive Rx, lipid-lowering RxDARTS16Donnan P.T. Donnelly L. New J.P. Morris A.D. Derivation and validation of a prediction score for major coronary heart disease events in a U.K. type 2 diabetic population.Diabetes Care. 2006; 29: 1231-1236Crossref PubMed Scopus (58) Google Scholar4,569 men and women of any age with diabetes without previous CV events (Scotland)First major CAD event (fatal or nonfatal acute MI or CAD death) at 4.7 yrAge at diabetes diagnosis, sex, SBP, duration of diabetes, smoking status, TC, HbA1c, treated hypertension, heightBMI = body mass index; BP = blood pressure; CV = cardiovascular; DARTS = Diabetes Audit and Research in Tayside, Scotland; DECODE = Diabetes Epidemiology: Collaborative Analysis of Diagnostic Criteria in Europe); FPG = fasting plasma glucose; MI = myocardial infarction; NDR = National Diabetes Register; HbA1c = hemoglobin A1c; HDL-C = high-density lipoprotein cholesterol; hs-CRP = high-sensitivity C-reactive protein; Rx = medication; SBP = systolic blood pressure; TC = total cholesterol; UKPDS = United Kingdom Prospective Diabetes Study; WHS = Women's Health Study. Open table in a new tab The Reynolds Risk score incorporates the traditional Framingham variables plus stroke and adds high-sensitivity C-reactive protein (hs-CRP) and parental history of MI before age 60 years. This risk score has been shown to improve global CVD risk prediction for both men and women without diabetes. In an internal validation study of 10,724 men, use of the Reynolds Risk score reclassified 20% of subjects into higher or lower risk categories as compared with the traditional Framingham variables. Such improvement in classification allows more accurate targeting of preventive therapies to individuals with the most appropriate levels of risk, decreasing toxicity and increasing benefit.18Ridker P. Paynter N. Rifai N. Gaziano J.M. Cook N. C-Reactive protein and parental history improve global cardiovascular risk prediction.Circulation. 2008; 118: 2243-2251Crossref PubMed Scopus (679) Google ScholarThe risk estimate calculator's external validity is highly dependent on time, region, and population. Differences in the baseline rates of CVD over time (current event rates are much higher compared with rates in 1950), comparing countries such as the United States and United Kingdom or comparing populations with or without diabetes, will affect the overall estimate. A risk score calibrated in one region will lead to over- or underestimation of risk in another.19Brindle P. Beswick A. Fahey T. Ebrahim S. Accuracy and impact of risk assessment in the primary prevention of cardiovascular disease: a systematic review.Heart. 2006; 92: 1752-1759Crossref PubMed Scopus (340) Google Scholar For example, only 4% of 5,573 patients had diabetes in the original Framingham cohort. For that reason, when applying the Framingham risk score to a diabetes-specific population in the 10- year United Kingdom Prospective Diabetes Study (UKPDS), fatal CAD events were underestimated by 32%.20Coleman R. Stevens R. Retnakaran R. Holman R. Framingham, SCORE, and DECODE risk equations do not provide reliable cardiovascular risk estimates in Type II diabetes.Diabetes Care. 2007; 30: 1292-1294Crossref PubMed Scopus (151) Google Scholar Limitations applicable to all risk assessment tools are that they assume constant effects of the risk factors at all ages and ignore the potential for synergistic effects between risk factors such as the impact of diabetes on traditional CVD risk factors.21Hippisley-Cox J. Coupland C. Vinogradova Y. Robson J. Minhas R. Sheikh A. Brindle P. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2.BMJ. 2008; 336: 1475-1482Crossref PubMed Scopus (990) Google ScholarThe UKPDS risk calculator is a type 2 diabetes–specific calculator, based on 53,000 patient-years from the UKPDS database, that provides risk estimates and 95% confidence intervals (CIs) for the prediction of fatal and nonfatal CAD events, as well as fatal and nonfatal stroke.22Stevens R. Kothari V. Adler A. Stratton I. Holman R. The UKPDS risk engine: a model for the risk of coronary heart disease in Type II diabetes (UKPDS 56).Clin Sci. 2001; 101: 671-679Crossref PubMed Scopus (1000) Google Scholar It incorporates age and duration of diabetes, HbA1c measurement, systolic blood pressure, total and high-density lipoprotein (HDL) cholesterol, sex, presence or absence of atrial fibrillation, ethnicity, and smoking history, and incorporates these variables into a 10-year risk calculator.Controversy remains as to whether one tool is better than another. The UKPDS risk calculator, the Framingham score, and other multivariate risk assessment tools all have important limitations, but they are useful in helping practicing physicians implement global CVD risk reduction.Emerging Cardiovascular BiomarkersCoronary artery calciumThe current CVD risk calculators, while helpful, remain imperfect. Attention has focused on identifying a CV biomarker that would propose a better noninvasive way to detect or visualize subclinical CVD and prevent CV events. This is the focus of several new research platforms that focus on the possibility of offering individualized medicine in the near future. The motivation is to identify high-risk patients for CVD by looking for traditional risk factors and to ascertain whether therapeutic options will be of benefit.Probably the most debated biomarker for CV event prediction is the use of coronary artery calcium (CAC) scanning. Recently, Polonsky et al23Polonsky T.S. McClelland R.L. Jorgensen N.W. Bild D.E. Burke G.L. Guerci A.D. Greenland P. Coronary artery calcium score and risk classification for coronary heart disease prediction.JAMA. 2010; 303: 1610-1616Crossref PubMed Scopus (817) Google Scholar studied 6,814 subjects in the Multi-Ethnic Study of Atherosclerosis (MESA), a population-based cohort without known CVD, using CAC scoring by computed tomography to determine the added benefit of CAC scan scores on traditional risk factors in a prediction model to improve risk assessment. The medium duration of their trial was 5.8 years, with 209 CAD events (122 MI, death from CAD or cardiac arrest). Two models were used to assess the benefit of CAC scans. Model 1 used the standard Framingham risk factors (age, sex, and smoking, systolic blood pressure, use of antihypertensive medications, and HDL and total cholesterol) and race/ethnicity. Model 2 used these standard risk factors plus CAC scores (expressed as ln[CACS +1]). Using a Cox proportional hazards model, risk estimates were divided from 0% to <3% (low risk), 3% to <10%, and ≥10% (high risk). The addition of the CAC score to the assessment of traditional risk factors significantly improved the risk classification: 23% of patients who did have an event were reclassified as high risk, and 13% who did not have events were reclassified as low risk when the CAC score was used. In addition some physicians suggest that, in addition to these findings, the use of CAC scores helps patients better adhere to their medication; however, this was not supported by a randomized controlled trial in Army personnel.24O'Malley P.G. Feuerstein I.M. Taylor A.J. Impact of electron beam tomography, with or without case management, on motivation, behavioral change, and cardiovascular risk profile: a randomized controlled trial.JAMA. 2003; 289: 2215-2223Crossref PubMed Scopus (157) Google Scholar A meta-analysis reported that the risk of CV event was 4.3 (95% CI, 3.5–5.2, p <0.0001) with a CAC score of 100 to 400, 7.2 (95% CI, 5.2–9.9, p <0.0001) with a CAC score of 401 to 1,000, and 10.8 (95% CI, 4.2–27.7, p 1,000. Moreover, subjects with no CAC score had a very low rate (approximately 0.4%) of a hard event over 3 to 5 years of observation. In patients with a zero CAC score, the 10-year event rate is probably <1% and the use of a 3-hydroxy-3 methylglutaryl coenzyme A reductase inhibitor (statin) or other compounds with potential side effects in this group of patients should be carefully weighed.25Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in AdultsExecutive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III).JAMA. 2001; 285: 2486-2497Crossref PubMed Scopus (24019) Google Scholar However, the utility of CAC to assess atherosclerosis progression appears to be limited. A recent analysis of the Carotid Intima-Media Thickness in Atherosclerosis Using Pioglitazone (CHICAGO) study showed that pioglitazone did not alter CAC progression compared with glimepiride, even though proglitazone reduced carotid intima-media thickness (CIMT) progression. In the CHICAGO study, the best on-treatment predictor of CAC progression was apolipoprotein B.26Davidson M.H. Beam C.A. Haffner S. et al.Pioglitazone versus glimepiride on coronary artery calcium progression in patients with type 2 diabetes mellitus: a secondary end point of the CHICAGO study.Arterioscler Thromb Vasc Biol. 2010; 30: 1873-1876Crossref PubMed Scopus (8) Google Scholar Noninvasive CAC score is not without risk, however. Kim and associates estimate a lifetime excess cancer risk from a single examination at the age of 40 years of 9 cancers per 100,000 men and 28 cancers per 100,000 women.27Kim K.P. Einstein A.J. Berrington de González A. Coronary artery calcification screening: estimated radiation dose and cancer risk.Arch Intern Med. 2009; 169: 1188-1194Crossref PubMed Scopus (190) Google ScholarThe US Preventive Services Task Force recommends against routine CAC screening with computed tomography. The American Heart Association (AHA) has indicated that CAC scanning may be reasonable in selected, intermediate-risk patients (class IIb recommendation) but does not recommend it in low- or high-risk patients (class III recommendation).28Budoff M.J. Achenbach S. Blumenthal R.S. Carr J.J. Goldin J.G. Greenland P. Guerci A.D. Lima J.A. Rader D.J. Rubin G.D. Shaw L.J. Wiegers S.E. American Heart Association Committee on Cardiovascular Imaging and Intervention, the American Heart Association Council on Cardiovascular Radiology and Intervention, the American Heart Association Committee on Cardiac Imaging, Council on Clinical CardiologyAssessment of coronary artery disease by cardiac computed tomography: a scientific statement from the American Heart Association Committee on Cardiovascular Imaging and Intervention, Council on Cardiovascular Radiology and Intervention, and Committee on Cardiac Imaging, Council on Clinical Cardiology.Circulation. 2006; 114: 1761-1791Crossref PubMed Scopus (1182) Google Scholar The AHA and American College of Cardiology Foundation (ACCF) guidelines indicate that CAC scan can be a useful tool in refining risk prediction in intermediate-risk adults. Intermediate risk is currently defined by national guidelines as a 10-year risk of 10% to 20% of a hard event.29Greenland P. Bonow R.O. Brundage B.H. Budoff M.J. Eisenberg M.J. Grundy S.M. Lauer M.S. Post W.S. Raggi P. Redberg R.F. et al.American College of Cardiology Foundation Clinical Expert Consensus Task Force (ACCF/AHA Writing Committee to Update the 2000 Expert Consensus Document on Electron Beam Computed Tomography), the Society of Atherosclerosis Imaging and Prevention, and the Society of Cardiovascular Computed TomographyACCF/AHA 2007 clinical expert consensus document on coronary artery calcium scoring by computed tomography in global cardiovascular risk assessment and in evaluation of patients with chest pain: a report of the American College of Cardiology Foundation Clinical Expert Consensus Task Force (ACCF/AHA Writing Committee to Update the 2000 Expert Consensus Document on Electron Beam Computed Tomography) developed in collaboration with the Society of Atherosclerosis Imaging and Prevention and the Society of Cardiovascular Computed Tomography.J Am Coll Cardiol. 2007; 49: 378-402Abstract Full Text Full Text PDF PubMed Scopus (807) Google ScholarMetabolomicsMetabolomic profiling may be the risk assessment tool of the future. One such consideration is the use of molecular tools to assess CV risk. Metabolomics studies small molecule metabolites to assess human disease. A recent study by Shah et al30Shah S.H. Bain J.R. Muehlbauer M.J. Stevens R.D. Crosslin D.R. Haynes C. Dungan J. Newby L.K. Hauser E.R. Ginsburg G.S. Newgard C.B. Kraus W.E. Association of a peripheral blood metabolic profile with coronary artery disease and risk of subsequent cardiovascular events.Circ Cardiovasc Genet. 2010; 3: 207-214Crossref PubMed Scopus (332) Google Scholarprofiled 69 metabolites in patients from the CATHGEN biorepository. They assessed the ability of metabolites to discriminate for CAD in a case-control study. Cases were combined into "event groups" that experienced 74 incidents of MI or death during the follow-up period. There were 2 main components analyzed, branched chain metabolites and urea cycle metabolites. Branched chain amino acid metabolites ("factor 4") included phenylalanine, tyrosine, leucine/isoleucine, methionine, valine, alanine, isovaleryl carnitine, and 3-methylbutyryl carnitine or 2-methylbutyryl carnitine. This factor seems to report on branched-chain amino acid catabolism, previously implicated in obesity and insulin signaling; this group of metabolites appears to provide biological insight into the branched-chain amino acid pathway in CAD. The urea cycle metabolites ("factor 9") included arginine, histidine, citrulline, and methylmalonyl carnitine or succinyl carnitine. This factor may represent amino acid catabolism and later metabolism of ammonia derived from these amino acids in the urea cycle. Another important fi
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