Improve Coronary Heart Disease Risk Prediction Research Articles

Circulating Metabolite Profiles and Risk of Coronary Heart Disease Among Racially and Geographically Diverse Populations.

Metabolomics may reveal novel biomarkers for coronary heart disease (CHD). We aimed to identify circulating metabolites and construct a metabolite risk score (MRS) associated with incident CHD among racially and geographically diverse populations. Untargeted metabolomics was conducted using baseline plasma samples from 900 incident CHD cases and 900 age-/sex-/race-matched controls (300 pairs of Black Americans, White Americans, and Chinese adults, respectively), which detected 927 metabolites with known identities among ≥80% of samples. After quality control, 896 case-control pairs remained and were randomly divided into discovery (70%) and validation (30%) sets within each race. In the discovery set, conditional logistic regression and least absolute shrinkage and selection operator over 100 subsamples were applied to identify metabolites robustly associated with CHD risk and construct the MRS. The MRS-CHD association was evaluated using conditional logistic regression and the C-index. Mediation analysis was performed to examine if MRS mediated associations between conventional risk factors and incident CHD. The results from the validation set were presented as the main findings. Twenty-four metabolites selected in ≥90% of subsamples comprised the MRS, which was significantly associated with incident CHD (odds ratio per 1 SD, 2.21 [95% CI, 1.62-3.00] after adjusting for sociodemographics, lifestyles, family history, and metabolic health status). MRS could distinguish incident CHD cases from matched controls (C-index, 0.69 [95% CI, 0.63-0.74]) and improve CHD risk prediction when adding to conventional risk factors (C-index, 0.71 [95% CI, 0.65-0.76] versus 0.67 [95% CI, 0.61-0.73]; P<0.001). The odds ratios and C-index were similar across subgroups defined by race, sex, socioeconomic status, lifestyles, metabolic health, family history, and follow-up duration. The MRS mediated large portions (46.0%-74.2%) of the associations for body mass index, smoking, diabetes, hypertension, and dyslipidemia with incident CHD. In a diverse study sample, we identified 24 circulating metabolites that, when combined into an MRS, were robustly associated with incident CHD and modestly improved CHD risk prediction beyond conventional risk factors.

Serum lipoprotein(a) and reclassification of coronary heart disease risk; application of prediction in a cross-sectional analysis of an ongoing Iranian cohort

IntroductionRecent studies have introduced elevated lipoprotein(a) (Lp(a)) as a risk factor for coronary heart disease (CHD). This study investigated whether the addition of Lp(a) as a novel biomarker to the Framingham Risk Score (FRS) model improves CHD risk prediction.MethodsThe study included 1101 Iranian subjects (443 non-diabetic and 658 diabetic patients) who were followed for 10 years (2003–2013). Lp(a) levels and CHD events were recorded for each participant.ResultsThe Net Reclassification Index (NRI) after adding Lp(a) to the FRS model was 19.57% and the discrimination slope was improved (0.160 vs. 0.173). The Akaike Information Criterion (AIC), a measure of model complexity, decreased significantly after adding Lp(a) to the FRS model (691.9 vs. 685.4, P value: 0.007).ConclusionsThe study concluded that adding Lp(a) to the FRS model improves CHD risk prediction in an Iranian population without making the model too complex. This could help clinicians to better identify individuals who are at risk of developing CHD and to implement appropriate preventive measures.

Abstract 11652: The Addition of Social Determinants of Health to Coronary Heart Disease Risk Prediction

Introduction: Coronary heart disease (CHD) remains a leading cause of death worldwide, with an unequal distribution of this burden placed on individuals of lower socioeconomic status. Individual social determinants of health (SDoH) have been shown to be associated with CHD; however, whether the addition of SDoH can improve current CHD risk prediction algorithms has not been delineated. Hypothesis: We hypothesize that the addition of SDoH to the Multi-Ethnic Study of Atherosclerosis (MESA) risk score improves the prediction of 10-year CHD events. Methods: In 6,286 individuals from the MESA cohort, we evaluated the additional predictive value of SDoH beyond the traditional factors included in the existing MESA Risk Score. SDoH considered were employment, family income, house tenure, financial strain, education, marital status, social support, chronic stress, discrimination, neighborhood characteristics, healthcare access, and health insurance, which were organized into five composite domains. A penalized Cox proportional hazards model was used to select SDoH variables with the potential to improve prediction, then an unpenalized Cox regression model was fitted with traditional risk factors plus the selected SDoH variables. Results: Statistically significant differences in the distribution of SDoH were observed between racial and ethnic groups. Notably, unfavorable SDoH were concentrated in Black and Hispanic/Latino persons. Economic stability, education, and community and social context were associated with CHD events. The area under the curve (AUC) for prediction of a 10-year CHD event was 0.816 without SDoH factors and 0.818 with the addition of SDoH. Conclusions: Racial and ethnic differences in social determinants of health were obsserved. Economic stability, education, and community and social context were significantly associated with cardiovascular events, however the addition of SDoH did not significantly improve CHD risk prediction.

Small Dense LDL: Scientific Background, Clinical Relevance, and Recent Evidence Still a Risk Even with 'Normal' LDL-C Levels.

Residual cardiovascular disease event risk, following statin use and low-density lipoprotein cholesterol (LDL-C) reduction, remains an important and common medical conundrum. Identifying patients with significant residual risk, despite statin drug use, is an unmet clinical need. One pathophysiologic disorder that contributes to residual risk is abnormal distribution in lipoprotein size and density, which is referred to as lipoprotein heterogeneity. Differences in low density lipoprotein (LDL) composition and size have been linked to coronary heart disease (CHD) risk and arteriographic disease progression. The clinical relevance has been investigated in numerous trials since the 1950s. Despite this long history, controversy remains regarding the clinical utility of LDL heterogeneity measurement. Recent clinical trial evidence reinforces the relevance of LDL heterogeneity measurement and the impact on CHD risk prediction and outcomes. The determination of LDL subclass distribution improves CHD risk prediction and guides appropriate treatment.

Metabolomic Pattern Predicts Incident Coronary Heart Disease.

Objective- Alterations in the serum metabolome may be detectable in at-risk individuals before the onset of coronary heart disease (CHD). Identifying metabolomic signatures associated with CHD may provide insight into disease pathophysiology and prevention. Approach and Results- Metabolomic profiling (chromatography-mass spectrometry) was performed in 2232 African Americans and 1366 European Americans from the ARIC study (Atherosclerosis Risk in Communities). We applied Cox regression with least absolute shrinkage and selection operator to select metabolites associated with incident CHD. A metabolite risk score was constructed to evaluate whether the metabolite risk score predicted CHD risk beyond traditional risk factors. After 30 years of follow-up, we observed 633 incident CHD cases. Thirty-two metabolites were selected by least absolute shrinkage and selection operator to be associated with CHD, and 19 of the 32 showed significant individual associations with CHD, including a sugar substitute, erythritol. Theophylline (hazard ratio [95% CI] =1.16 [1.09-1.25]) and gamma-linolenic acid (hazard ratio [95% CI] =0.89 [0.81-0.97]) showed the greatest positive and negative associations with CHD, respectively. A 1 SD greater standardized metabolite risk score was associated with a 1.37-fold higher risk of CHD (hazard ratio [95% CI] =1.37 [1.27-1.47]). Adding the metabolite risk score to the traditional risk factors significantly improved model predictive performance (30-year risk prediction: Δ C statistics [95% CI] =0.016 [0.008-0.024], continuous net reclassification index [95% CI] =0.522 [0.480-0.556], integrated discrimination index [95% CI] =0.038 [0.019-0.065]). Conclusions- We identified 19 metabolites from known and novel metabolic pathways that collectively improved CHD risk prediction. Visual Overview- An online visual overview is available for this article.

Genomic prediction of coronary heart disease.

AimsGenetics plays an important role in coronary heart disease (CHD) but the clinical utility of genomic risk scores (GRSs) relative to clinical risk scores, such as the Framingham Risk Score (FRS), is unclear. Our aim was to construct and externally validate a CHD GRS, in terms of lifetime CHD risk and relative to traditional clinical risk scores.Methods and resultsWe generated a GRS of 49 310 SNPs based on a CARDIoGRAMplusC4D Consortium meta-analysis of CHD, then independently tested it using five prospective population cohorts (three FINRISK cohorts, combined n = 12 676, 757 incident CHD events; two Framingham Heart Study cohorts (FHS), combined n = 3406, 587 incident CHD events). The GRS was associated with incident CHD (FINRISK HR = 1.74, 95% confidence interval (CI) 1.61–1.86 per S.D. of GRS; Framingham HR = 1.28, 95% CI 1.18–1.38), and was largely unchanged by adjustment for known risk factors, including family history. Integration of the GRS with the FRS or ACC/AHA13 scores improved the 10 years risk prediction (meta-analysis C-index: +1.5–1.6%, P < 0.001), particularly for individuals ≥60 years old (meta-analysis C-index: +4.6–5.1%, P < 0.001). Importantly, the GRS captured substantially different trajectories of absolute risk, with men in the top 20% of attaining 10% cumulative CHD risk 12–18 y earlier than those in the bottom 20%. High genomic risk was partially compensated for by low systolic blood pressure, low cholesterol level, and non-smoking.ConclusionsA GRS based on a large number of SNPs improves CHD risk prediction and encodes different trajectories of lifetime risk not captured by traditional clinical risk scores.

Standard deviation of carotid young's modulus and presence or absence of plaque improves prediction of coronary heart disease risk.

The stiffness of large arteries and the presence or absence of plaque are associated with coronary heart disease (CHD). Because arterial walls are biologically heterogeneous, the standard deviation of Young's modulus (YM-std) of the large arteries may better predict coronary atherosclerosis. However, the role of YM-std in the occurrence of coronary events has not been addressed so far. Therefore, this study investigated whether the carotid YM-std and the presence or absence of plaque improved CHD risk prediction. One hundred and three patients with CHD (age 66±11years) and 107 patients at high risk of atherosclerosis (age 61±7years) were recruited. Carotid YM was measured by the vessel texture matching method, and YM-std was calculated. Carotid intima-media thickness was measured by the MyLab 90 ultrasound Platform employed dedicated software RF-tracking technology. In logistic regression analysis, YM-std (OR=1·010; 95% CI=1·003-1·016), carotid plaque (OR=16·759; 95% CI=3·719-75·533) and YM-std plus plaque (OR=0·989; 95% CI=0·981-0·997) were independent predictors of CHD. The traditional risk factors (TRF) plus YM-std plus plaque model showed a significant improvement in area under the receiver-operating characteristic curve (AUC), which increased from 0·717 (TRF only) to 0·777 (95% CI for the difference in adjusted AUC: 0·010-0·110). Carotid YM-std is a powerful independent predictor of CHD. Adding plaque and YM-std to TRF improves CHD risk prediction.

C-reactive protein and serum creatinine, but not haemoglobin A1c, are independent predictors of coronary heart disease risk in non-diabetic Chinese.

In western populations, high-sensitivity C-reactive protein (hsCRP), and to a lesser degree serum creatinine and haemoglobin A1c, predict risk of coronary heart disease (CHD). However, data on Asian populations that are increasingly affected by CHD are sparse and it is not clear whether these biomarkers can be used to improve CHD risk classification. We conducted a nested case-control study within the Singapore Chinese Health Study cohort, with incident 'hard' CHD (myocardial infarction or CHD death) as an outcome. We used data from 965 men (298 cases, 667 controls) and 528 women (143 cases, 385 controls) to examine the utility of hsCRP, serum creatinine and haemoglobin A1c in improving the prediction of CHD risk over and above traditional risk factors for CHD included in the ATP III model. For each sex, the performance of models with only traditional risk factors used in the ATP III model was compared with models with the biomarkers added using weighted Cox proportional hazards analysis. The impact of adding these biomarkers was assessed using the net reclassification improvement index. For men, loge hsCRP (hazard ratio 1.25, 95% confidence interval: 1.05; 1.49) and loge serum creatinine (hazard ratio 4.82, 95% confidence interval: 2.10; 11.04) showed statistically significantly associations with CHD risk when added to the ATP III model. We did not observe a significant association between loge haemoglobin A1c and CHD risk (hazard ratio 1.83, 95% confidence interval: 0.21; 16.06). Adding hsCRP and serum creatinine to the ATP III model improved risk classification in men with a net gain of 6.3% of cases (p-value = 0.001) being reclassified to a higher risk category, while it did not significantly reduce the accuracy of classification for non-cases. For women, squared hsCRP was borderline significantly (hazard ratio 1.01, 95% confidence interval: 1.00; 1.03) and squared serum creatinine was significantly (hazard ratio 1.81, 95% confidence interval: 1.49; 2.21) associated with CHD risk. However, the association between squared haemoglobin A1c and CHD risk was not significant (hazard ratio 1.05, 95% confidence interval: 0.99; 1.12). The addition of hsCRP and serum creatinine to the ATP III model resulted in 3.7% of future cases being reclassified to a higher risk category (p-value = 0.025), while it did not significantly reduce the accuracy of classification for non-cases. Adding hsCRP and serum creatinine, but not haemoglobin A1c, to traditional risk factors improved CHD risk prediction among non-diabetic Singaporean Chinese. The improved risk estimates will allow better identification of individuals at high risk of CHD than existing risk calculators such as the ATP III model.

Risk prediction by genetic risk scores for coronary heart disease is independent of self-reported family history.

AimsGenetic risk scores (GRSs) have been associated with coronary heart disease (CHD) in large studies. We asked whether expanding an established 27-variant GRS (GRS27) to a 50-variant GRS (GRS50) improved CHD prediction and whether GRSs are independent of self-reported family history of CHD.Methods and resultsThe association between GRSs and incident CHD was assessed in Cox models adjusting for established risk factors in 23 595 participants of the Malmö Diet and Cancer study—a prospective, population-based study. During a median follow-up of 14.4 years, 2213 participants experienced a first CHD event. After adjustment for established risk factors, both GRS27 and GRS50 were associated with incident CHD [hazard ratio (HR) = 1.70 for high (top quintile) vs. low (bottom quintile) of GRS27; 95% confidence interval (CI): 1.48–1.94; Ptrend = 1.6 × 10−15 and HR = 1.92 for GRS50; 95% CI: 1.67–2.20; Ptrend = 6.2 × 10−22]. Adding 23 single nucleotide polymorphisms (SNPs) to GRS27 improved risk prediction (P = 3 × 10−6). Further adjustment for self-reported family history did not appreciably change the risk estimates of either GRS27 (HR = 1.65; 95% CI: 1.45–1.89) or GRS50 (HR = 1.87; 95% CI: 1.63–2.14). The addition of GRS50 to established risk factors, including self-reported family history, improved discrimination (P < 0.0001) and reclassification (continuous net reclassification improvement index = 0.17, P < 0.0001). In young participants (below median age), those with high GRS50 had 2.4-fold greater risk (95% CI: 1.85–3.12) than those with low GRS50.ConclusionThe addition of 23 SNPs to an existing GRS27 improved CHD risk prediction and was independent of self-reported family history. Coronary heart disease risk assessment by GRS could be particularly useful in young individuals.

Biomarkers in stable coronary heart disease, their modulation and cardiovascular risk: The LIPID biomarker study

AimsIn patients with stable coronary heart disease (CHD), we aimed to assess 1. the prognostic power of biomarkers reflecting haemodynamics, micronecrosis, inflammation, coagulation, lipids, neurohumoral activity, and renal function; 2. whether changes in concentrations of these biomarkers over 12months affected subsequent CHD risk; and 3. whether pravastatin modified the change in biomarker concentrations and this influenced the risk of future events. MethodsIn the LIPID study, 9014 patients were randomised to pravastatin 40mg or placebo 3–36months after an acute coronary syndrome. Eight biomarkers were measured at baseline (n=7863) and 12months later (n=6434). ResultsDuring a median of 6.0 (IQR 5.5–6.5) years follow-up, 1100 CHD-related deaths and nonfatal myocardial infarctions occurred, 694 after biomarker measurement at 12months. Baseline BNP, CRP, cystatin C, D-dimer, midregional pro-adrenomedullin, and sensitive troponin I predicted recurrent CHD events. In a multivariable model, sensitive troponin I, BNP, and cystatin C had the strongest associations with outcome (P<0.001 for trend). The strongest improvement in risk prediction was achieved by including sensitive troponin I (net reclassification improvement (NRI) 5.5%; P=0.003), BNP (4.3%; P=0.02), history of MI (NRI 7.0%; P<0.001). In landmark analyses, among biomarkers, changes to 12months in sensitive troponin I (HR 1.32 (1.03–1.70) for T3/T1), BNP (HR 1.37 (1.10–1.69) for Q4/Q1) and Lp-PLA2 (HR 1.52 (1.16–1.97)) improved CHD risk prediction. ConclusionsBaseline levels and changes in sensitive troponin I, and BNP may have the potential to guide the intensity of secondary prevention therapy.

Assessment of serum 25-hydroxy vitamin D improves coronary heart disease risk stratification in patients with type 2 diabetes

A growing body of evidence suggests an association between lower serum 25-hydroxy vitamin D (25(OH)VitD) levels and adverse cardiovascular events. Patients with type 2 diabetes mellitus (T2DM) are at increased risk for developing coronary heart disease (CHD). 25-Hydroxy vitamin D deficiency is highly prevalent, especially among patients with T2DM. This study aimed to evaluate the predictive value of serum 25(OH)VitD in improvement of CHD risk stratification in patients with T2DM. In an open cohort, community-dwelling T2DM patients were followed up for first CHD event. Patients were divided into 4 categories, based on 25(OH)VitD quartiles. Cox regression analysis was used to obtain hazard ratios. A total number of 2,607 T2DM patients were followed up for median time of 8.5 years. During follow-up, 299 patients experienced CHD events. Patients in the lowest quartile experienced more CHD events. Adjusted hazard ratios (95% CI) for developing CHD events were 0.77 (0.55-1.07) for second quartile, 0.52 (0.38-0.73) for third quartile, and 0.43 (0.31-0.60) for fourth quartile, compared with the first quartile. The incidence rate decreased as serum 25(OH)VitD increased, which remained significant after stepwise adjustments (P value for trend ≤.001). Addition of 25(OH)VitD to traditional risk factors in Framingham Risk Score successfully reclassified 29% of study population. Serum 25(OH)VitD is an independent predictor of future adverse CHD events in patients with T2DM. Addition of 25(OH)VitD status to Framingham Risk Score improves CHD risk prediction in patients with T2DM.

Abstract P369: Left Ventricular Hypertrophy and Coronary Heart Disease Risk Reclassification in Blacks and Whites: The Atherosclerosis Risk in Communities (ARIC) Study

Introduction: Left ventricular hypertrophy (LVH) is more prevalent in blacks than whites, and is a major independent predictor of coronary heart disease (CHD)/CVD survival in blacks. We evaluated the ability of LVH to predict CHD outcomes beyond traditional cardiovascular risk factors in blacks, compared with whites from a large community-based cohort. Methods: Data were analyzed on 14,489 participants (mean age 54 +/- 5.7 years, 43.5% men, and 26% black) within the ARIC cohort, with baseline (1987-1989) electrocardiograms (ECG), followed through 2009. Risk estimates for incident CHD were assessed using the 10-year Framingham Risk Score (FRS). Model 1 was the Framingham base model, while model 2 included the base model plus LVH by any of 11 traditional ECG-LVH criteria (Table). Net reclassification improvement (NRI) was calculated, and the distribution of risk was compared using model 2 vs. model 1. Results: There were 690 (4.8%) 10-year and 1515 (10.5%) 20-year CHD events. LVH defined by any criteria was associated with CHD events in the entire cohort [HR (95% CI): 1.42 (1.20-1.7)]. LVH defined by the Framingham ECG score and LV strain criteria were the criteria most associated with CHD overall. LVH by Framingham ECG score was most associated with CHD in blacks [HR (95%CI): 2.53 (1.65-3.89)], while LV strain showed the strongest association with CHD in whites [1.73 (1.18-2.56)]. No statistically significant improvement in NRI or C-statistic was observed in model 2 [C-statistic (95% CI): 0.779 (0.763-0.794), NRI = 0.006 (p = 0.41)], compared with the base model [0.777 (0.762-0.792)]; and no racial interactions were observed. Findings were unchanged when the base model was replaced with the 10 and 20-year ARIC risk model (includes diabetes) for CHD. Conclusions: In this cohort of black and white men and women, LVH (defined by ECG) was significantly associated with CHD after adjustment for FRS; but did not significantly improve CHD risk prediction beyond the FRS. No significant black-white differences in risk prediction were observed. Table. Reclassification of Coronary Heart Disease by the addition of each Criterion for Left Ventricular Hypertrophy, based on a 10-year Framingham Risk Model * Base model factors in age, gender, current smoking, diabetes, systolic blood pressure, diastolic blood pressure, HDL cholesterol and total cholesterol as separate variables. * NRI categorized as &lt;10%, 10-20% and &gt;20%. Abbreviations: NRI = Net Reclassification Index, IDI = Integrated Discrimination Index, HR = Hazard Ratio, CI = Confidence Interval

A genetic risk score based on direct associations with coronary heart disease improves coronary heart disease risk prediction in the Atherosclerosis Risk in Communities (ARIC), but not in the Rotterdam and Framingham Offspring, Studies

ObjectiveMultiple studies have identified single-nucleotide polymorphisms (SNPs) that are associated with coronary heart disease (CHD). We examined whether SNPs selected based on predefined criteria will improve CHD risk prediction when added to traditional risk factors (TRFs). MethodsSNPs were selected from the literature based on association with CHD, lack of association with a known CHD risk factor, and successful replication. A genetic risk score (GRS) was constructed based on these SNPs. Cox proportional hazards model was used to calculate CHD risk based on the Atherosclerosis Risk in Communities (ARIC) and Framingham CHD risk scores with and without the GRS. ResultsThe GRS was associated with risk for CHD (hazard ratio [HR] = 1.10; 95% confidence interval [CI]: 1.07–1.13). Addition of the GRS to the ARIC risk score significantly improved discrimination, reclassification, and calibration beyond that afforded by TRFs alone in non-Hispanic whites in the ARIC study. The area under the receiver operating characteristic curve (AUC) increased from 0.742 to 0.749 (Δ = 0.007; 95% CI, 0.004–0.013), and the net reclassification index (NRI) was 6.3%. Although the risk estimates for CHD in the Framingham Offspring (HR = 1.12; 95% CI: 1.10–1.14) and Rotterdam (HR = 1.08; 95% CI: 1.02–1.14) Studies were significantly improved by adding the GRS to TRFs, improvements in AUC and NRI were modest. ConclusionAddition of a GRS based on direct associations with CHD to TRFs significantly improved discrimination and reclassification in white participants of the ARIC Study, with no significant improvement in the Rotterdam and Framingham Offspring Studies.

Evaluation of Newer Risk Markers for Coronary Heart Disease Risk Classification

Whether newer risk markers for coronary heart disease (CHD) improve CHD risk prediction remains unclear. To assess whether newer risk markers for CHD risk prediction and stratification improve Framingham risk score (FRS) predictions. Prospective population-based study. The Rotterdam Study, Rotterdam, the Netherlands. 5933 asymptomatic, community-dwelling participants (mean age, 69.1 years [SD, 8.5]). Traditional CHD risk factors used in the FRS (age, sex, systolic blood pressure, treatment of hypertension, total and high-density lipoprotein cholesterol levels, smoking, and diabetes) and newer CHD risk factors (N-terminal fragment of prohormone B-type natriuretic peptide levels, von Willebrand factor antigen levels, fibrinogen levels, chronic kidney disease, leukocyte count, C-reactive protein levels, homocysteine levels, uric acid levels, coronary artery calcium [CAC] scores, carotid intima-media thickness, peripheral arterial disease, and pulse wave velocity). Adding CAC scores to the FRS improved the accuracy of risk predictions (c-statistic increase, 0.05 [95% CI, 0.02 to 0.06]; net reclassification index, 19.3% overall [39.3% in those at intermediate risk, by FRS]). Levels of N-terminal fragment of prohormone B-type natriuretic peptide also improved risk predictions but to a lesser extent (c-statistic increase, 0.02 [CI, 0.01 to 0.04]; net reclassification index, 7.6% overall [33.0% in those at intermediate risk, by FRS]). Improvements in predictions with other newer markers were marginal. The findings may not be generalizable to younger or nonwhite populations. Among 12 CHD risk markers, improvements in FRS predictions were most statistically and clinically significant with the addition of CAC scores. Further investigation is needed to assess whether risk refinements using CAC scores lead to a meaningful change in clinical outcome. Whether to use CAC score screening as a more routine test for risk prediction requires full consideration of the financial and clinical costs of performing versus not performing the test for both persons and health systems. Netherlands Organization for Health Research and Development (ZonMw).

The 9p21 genetic variant is additive to carotid intima media thickness and plaque in improving coronary heart disease risk prediction in white participants of the Atherosclerosis Risk in Communities (ARIC) Study

ObjectiveWe evaluated whether the addition of carotid intima media thickness and plaque (CIMT-P), and a single nucleotide polymorphism on chromosome 9p21 (9p21) together improve coronary heart disease (CHD) risk prediction in the ARIC study. MethodsTen year CHD risk was estimated using the ARIC coronary risk score (ACRS) alone and in combination with CIMT-P and 9p21 individually and together in White participants (n=9338). Area under the receiver operating characteristic curve (AUC), model calibration, net reclassification index (NRI), integrated discrimination index (IDI) and number of individuals reclassified were estimated. ResultsThe AUC of the ACRS, ACRS+9p21, ACRS+CIMT-P and ACRS+CIMT-P+9p21 models were 0.748, 0.751, 0.763 and 0.766 respectively. The percentage of individuals reclassified, model calibration, NRI and IDI improved when CIMT-P and 9p21 were added to the ACRS only model (see manuscript). ConclusionAddition of 9p21 allele information to CIMT-P minimally improves CHD risk prediction in whites in the ARIC study.

Authors' response to: Can information on life stress improve CHD risk prediction in clinical practice?

We are pleased that our paper on job strain and coronary heart disease (CHD) risk prediction1 has attracted the interest of IJE readers. Given that stress has been the subject of observational research for several decades,2 surprisingly few attempts have been made to explore the predictive value of stress in clinical practice. The purpose of our paper was to address this gap in knowledge. We examined whether information on work stress would improve identification of patients at high risk of developing CHD if we already knew their standard risk factors, such as raised blood pressure, lipid levels, smoking habits and the presence of diabetes. Determining a patient's risk of CHD is important as this helps a physician to decide whether there is a need to recommend lifestyle change or prescribe medication (e.g. statins). Our findings for job strain,3 the most commonly used operationalization of work stress, suggest this additional information is unlikely to enhance the predictive capacity of Framingham risk prediction algorithm (a summary of conventional risk factor levels). In his letter to the Editor, Bozorgmanesh raises important points regarding risk prediction, both in general and specific to our paper. He notes that choice of the reference method that represents current clinical practice is critical because if the best available method is not selected, the value of the new measure (here work stress) may be overestimated. The Framingham risk algorithm is a widely used tool to assess a patient's 10-year risk of CHD based on standard coronary risk factors—this was the model used in our paper. Bozorgmanesh noted that we did not specify whether it was the most recent revision4 or an earlier, less well-calibrated, version.5 We are grateful for the opportunity to clarify that we used the most recent algorithm,4 thus any concern that we used a comparator model that would have resulted in an overestimation of the value of adding job strain is unfounded. Traditional C-statistics have been found to be insensitive for detecting improvement in risk prediction;6 Net Reclassification Index (NRI) analysis is seen as a more informative alternative.7 Bozorgmanesh correctly notes that there has recently been some further developments in the NRI, but to date most clinical studies still apply the standard NRI.7 This was also the case in our paper, mainly to enable comparison with other studies that have tested new risk markers to improve CHD prediction.8,9 In this analysis, we divided the predicted risk level into four categories: <4, 4–5.9, 6–9.9 and ≥10%. An alternative approach would have been to use cut-points that are of more direct relevance to clinical decisions on risk prevention, that is, very low (risk <5%), low (5–9.9%), intermediate (10–19.9%) and high (≥20%);10 the latter indicative of a need to consider pharmacotherapy. Unfortunately, the small number of participants in the highest category in our study prevented us from doing so. This brings us to the final point made by Bozorgmanesh: the use of a 2-step analysis strategy. That is, using conventional risk prediction models first and, instead of then examining the entire population, testing whether the new measure (here job strain) improves risk stratification within the intermediate category only (people with an estimated risk of 10–20%). This group is particularly problematic for clinicians because, while some group members are truly at low risk of CHD (no interventions needed), others are at high risk and would thus benefit from medication, such as statin therapy or behaviour modification. Further research is needed to examine whether information on work stress could be useful to improve risk stratification among those who are, based on conventional risk models, at intermediate risk. Following our null findings, one might ask whether further research on this issue is likely to be worthwhile. We believe that our findings on job strain are not the final word in this field of research. Indeed, we have subsequently tested other life stress markers and found, among working populations, inclusion of information on long working hours to improve CHD risk prediction. Employees who work very long days are at increased risk of CHD and the net reclassification improvement on inclusion of this information is ~5%.11 While this finding needs to be replicated in other cohorts before firm conclusions can be drawn, it indicates a possible role for stress markers in improved CHD risk prediction in clinical practice.

The Role of Carotid Intimal Thickness and Plaque Imaging in Risk Stratification for Coronary Heart Disease

Improving the 10-year coronary heart disease (CHD) risk prediction beyond its current state is important as the current risk prediction schemes classify the majority of individuals who experience an incident CHD event as low or intermediate in risk. B-mode ultrasound-based carotid intima-media thickness (CIMT) measurement and carotid plaque detection is one of the surrogate markers of atherosclerosis that has shown value in CHD risk prediction. It has been shown that adding either CIMT, plaque, or both to traditional risk prediction models improves CHD risk prediction. Carotid ultrasound-based CIMT measurement and plaque identification is noninvasive, safe, and relatively inexpensive. Recent guidelines have given CIMT and plaque-based risk prediction a class II A recommendation. This article reviews the available data related to the use of CIMT and plaque information in CHD risk prediction.

Novel cardiovascular risk prediction models in patients with systemic lupus erythematosus

Women with systemic lupus erythematosus (SLE) have increased risk for coronary heart disease (CHD) which is underestimated by the Framingham risk score (FRS). We hypothesized that new risk scores that include inflammation or vascular age in the risk calculation would better identify women with SLE at risk for CHD, particularly in those with subclinical coronary atherosclerosis. We calculated the FRS and Reynolds risk score (RRS) in 121 women with SLE and 65 age-matched female controls; coronary age-modified risk scores (camFRS, camRRS) were calculated using coronary age derived from the coronary artery calcium (CAC) score. Risk scores were compared in SLE and controls, and in SLE patients with and without CAC. Although CAC was present in 21 SLE patients (17%) and four controls (6%) (p = 0.033); the FRS, camFRS, RRS, and camRRS, did not differ significantly among SLE and controls (p > 0.05), but were all significantly higher in SLE patients with CAC compared with those without (p < 0.001 for all). The camFRS (8%, p = 0.016) but not camRRS (5%, p = 0.221) assigned significantly more SLE patients to a category of ≥ 10% risk than conventional FRS (1%) and RRS (2%). The RRS was of limited use but coronary age may improve CHD risk prediction in SLE.

Carotid intima-media thickness in plaque-free site, carotid plaques and coronary heart disease risk prediction in older adults. The Three-City Study

ObjectivesWe sought to address the respective association between carotid intima-media thickness (IMT) in plaque-free sites and plaques with coronary heart disease (CHD) and their usefulness for CHD risk prediction in the Three-City Study. MethodsAt baseline, 5895 CHD-free adults aged 65–85 years underwent a bilateral ultrasound examination of carotid arteries. Mean IMT was measured in the far wall of the right and left common carotid arteries (CCA) at plaque-free site while the presence of focal plaques was assessed in the near and the far walls of the CCAs, the bifurcations and the origin of the internal carotid arteries. ResultsAfter a median follow-up of 5.4 years, 223 subjects had a first ever CHD event. In multivariate analysis, carotid plaques were independent predictors of CHD (Hazard ratio (HR)plaquesat1site=1.5; 95% confidence interval (CI)=1.0–2.2; HRplaquesat≥2sites=2.2; 95% CI=1.6–3.1; pfortrend<0.001), contrary to mean CCA-IMT (HRfifthvs.firstquintile=0.8; 95% CI=0.5–1.2; pfortrend<0.48). Adding carotid plaques to conventional risk factors significantly improved CHD risk prediction as measured by the area under the ROC curve (from 0.728 to 0.745; p=0.04), the Harrell's c (from 0.748 to 0.762; p<0.001), and the integrated discrimination improvement (IDI=0.007; p=0.002)/net reclassification improvement (NRI=13.7%; p<0.001) indices. ConclusionCarotid plaques, but not CCA-IMT measured at a plaque-free site, were independent predictors of CHD and improved CHD risk prediction in older adults.

What is the role of alternative biomarkers for coronary heart disease?

Predictive models for future risk of coronary heart disease (CHD) based on traditional risk factors, such as age, male gender, LDL cholesterol, HDL cholesterol, diabetes mellitus, hypertension, smoking and family history of premature CHD, are quite robust but leave room for further improvement. Thus, efforts are being made to assess additional biomarkers for CHD, such as, lipoprotein (a), C-reactive protein, fibrinogen, lipoprotein-associated phospholipase A2, homocysteine and others. However, none of the novel biomarkers has demonstrated improved prediction beyond traditional risk factor models in a consistent fashion across multiple cohorts. Many criteria have to be fulfilled before a biomarker can be considered clinically relevant. Another way is to develop new models predicting long-term or life-time risk of CHD. Further research using novel biomarkers and long-term predictive models has the potential to improve CHD risk prediction.