Dataset Information

Lipoprotein metabolism indicators improve cardiovascular risk prediction.

ABSTRACT:

Background

Cardiovascular disease risk increases when lipoprotein metabolism is dysfunctional. We have developed a computational model able to derive indicators of lipoprotein production, lipolysis, and uptake processes from a single lipoprotein profile measurement. This is the first study to investigate whether lipoprotein metabolism indicators can improve cardiovascular risk prediction and therapy management.

Methods and results

We calculated lipoprotein metabolism indicators for 1981 subjects (145 cases, 1836 controls) from the Framingham Heart Study offspring cohort in which NMR lipoprotein profiles were measured. We applied a statistical learning algorithm using a support vector machine to select conventional risk factors and lipoprotein metabolism indicators that contributed to predicting risk for general cardiovascular disease. Risk prediction was quantified by the change in the Area-Under-the-ROC-Curve (?AUC) and by risk reclassification (Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI)). Two VLDL lipoprotein metabolism indicators (VLDLE and VLDLH) improved cardiovascular risk prediction. We added these indicators to a multivariate model with the best performing conventional risk markers. Our method significantly improved both CVD prediction and risk reclassification.

Conclusions

Two calculated VLDL metabolism indicators significantly improved cardiovascular risk prediction. These indicators may help to reduce prescription of unnecessary cholesterol-lowering medication, reducing costs and possible side-effects. For clinical application, further validation is required.

SUBMITTER: van Schalkwijk DB

PROVIDER: S-EPMC3965475 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Lipoprotein metabolism indicators improve cardiovascular risk prediction.

van Schalkwijk Daniël B DB de Graaf Albert A AA Tsivtsivadze Evgeni E Parnell Laurence D LD van der Werff-van der Vat Bianca J C BJ van Ommen Ben B van der Greef Jan J Ordovás José M JM

PloS one 20140325 3

<h4>Background</h4>Cardiovascular disease risk increases when lipoprotein metabolism is dysfunctional. We have developed a computational model able to derive indicators of lipoprotein production, lipolysis, and uptake processes from a single lipoprotein profile measurement. This is the first study to investigate whether lipoprotein metabolism indicators can improve cardiovascular risk prediction and therapy management.<h4>Methods and results</h4>We calculated lipoprotein metabolism indicators fo ...[more]

PMID: 24667559

Similar Datasets

Project description:Lipoprotein(a) is a highly heritable biomarker independently associated with atherosclerotic cardiovascular disease (ASCVD). It is unclear whether measured lipoprotein(a) or genetic factors associated with lipoprotein(a) can provide comparable or additional prognostic information for primary prevention. To determine whether a genetic risk score (GRS) comprising 43 variants at the LPA gene, which encodes apolipoprotein(a), has clinical utility in assessing ASCVD risk compared with and in addition to lipoprotein(a) measurement. The UK Biobank is a prospective observational study of approximately 500 000 volunteers aged 40 to 69 years who were recruited from 22 sites across the United Kingdom between 2006 and 2010. Using externally derived weights, an LPA GRS was calculated for 374 099 unrelated individuals with array-derived genotypes and lipoprotein(a) measures. Data were analyzed from April 2020 to March 2020. Measured lipoprotein(a) and LPA GRS. We estimated the associations between measured lipoprotein(a) and LPA GRS with the incidence of ASCVD (peripheral arterial disease, coronary artery disease, myocardial infarction, ischemic stroke, and cardiovascular mortality) using Cox proportional hazards models. To determine the utility of using measured lipoprotein(a) and LPA GRS as risk enhancers for ASCVD, we assessed the potential improvement in ASCVD risk discrimination by QRISK3 and Pooled Cohort Equations among individuals with borderline to intermediate risk (n = 113 703 and 144 350, respectively). The mean age of the overall study population was 57.6 years, and 204 355 individuals were female (54.6%). During a median follow-up of 11.1 years (interquartile range, 1.4 years), 15 444 individuals developed an incident ASCVD event (5.1%). The LPA GRS explained approximately 60% of the variation in measured lipoprotein(a) for White/European individuals. Independently, both lipoprotein(a) and LPA GRS were associated with incident, composite ASCVD (hazard ratio per 120 nmol/L increase, 1.26; 95% CI, 1.23-1.28 vs hazard ratio, 1.29; 95% CI, 1.26-1.33; P < .001). The association between LPA GRS and ASCVD was substantially attenuated after adjusting for measured lipoprotein(a). Adding measured lipoprotein(a) or LPA GRS to QRISK3 provided modest improvements to the risk discrimination of incident ASCVD events (area under the receiver operating curve, 0.640; 95% CI, 0.633-0.647 vs 0.642; 95% CI, 0.635-0.649 for both; P = .005 and P = .01, respectively). When indicated, cardiovascular risk assessment with lipoprotein(a) at middle-age may include direct measurement or an LPA GRS.

Project description:BackgroundAlthough N-terminal pro-B-type natriuretic peptide (NT-proBNP) has a strong relationship with incident cardiovascular disease (CVD), few studies have examined whether NT-proBNP adds to risk prediction algorithms, particularly in women.ObjectivesThis study sought to evaluate the relationship between NT-proBNP and incident CVD in women.MethodsUsing a prospective case-cohort within the WHI (Women's Health Initiative) observational study, we selected 1,821 incident cases of CVD (746 myocardial infarctions, 754 ischemic strokes, 160 hemorrhagic strokes, and 161 other cardiovascular [CV] deaths) and a randomly selected reference cohort of 1,992 women without CVD at baseline.ResultsMedian levels of NT-proBNP were higher at study entry among incident cases (120.3 ng/l [interquartile range (IQR): 68.1 to 219.5 ng/l]) than among control subjects (100.4 ng/l [IQR: 59.7 to 172.6 ng/l]; p < 0.0001). Women in the highest quartile of NT-proBNP (≥140.8 ng/l) were at 53% increased risk of CVD versus those in the lowest quartile after adjusting for traditional risk factors (1.53 [95% confidence interval (CI): 1.21 to 1.94]; p for trend <0.0001). Similar associations were observed after adjustment for Reynolds Risk Score covariables (1.53 [95% CI: 1.20 to 1.95]; p for trend <0.0001); the association remained in separate analyses of CV death (2.66 [95% CI: 1.48 to 4.81]; p for trend <0.0001), myocardial infarction (1.39 [95% CI: 1.02 to 1.88]; p for trend = 0.008), and stroke (1.60 [95% CI: 1.22 to 2.11]; p for trend <0.0001). When added to traditional risk covariables, NT-proBNP improved the c-statistic (0.765 to 0.774; p = 0.0003), categorical net reclassification (0.08; p < 0.0001), and integrated discrimination (0.0105; p < 0.0001). Similar results were observed when NT-proBNP was added to the Reynolds Risk Score.ConclusionsIn this multiethnic cohort of women with numerous CV events, NT-proBNP modestly improved measures of CVD risk prediction.

Project description:BackgroundWe evaluated the ability of 23 novel biomarkers representing several pathophysiological pathways to improve the prediction of cardiovascular event (CVE) risk in patients with type 2 diabetes mellitus beyond traditional risk factors.Methods and resultsWe used data from 1002 patients with type 2 diabetes mellitus from the Second Manifestations of ARTertial disease (SMART) study and 288 patients from the European Prospective Investigation into Cancer and Nutrition-NL (EPIC-NL). The associations of 23 biomarkers (adiponectin, C-reactive protein, epidermal-type fatty acid binding protein, heart-type fatty acid binding protein, basic fibroblast growth factor, soluble FMS-like tyrosine kinase-1, soluble intercellular adhesion molecule-1 and -3, matrix metalloproteinase [MMP]-1, MMP-3, MMP-9, N-terminal prohormone of B-type natriuretic peptide, osteopontin, osteonectin, osteocalcin, placental growth factor, serum amyloid A, E-selectin, P-selectin, tissue inhibitor of MMP-1, thrombomodulin, soluble vascular cell adhesion molecule-1, and vascular endothelial growth factor) with CVE risk were evaluated by using Cox proportional hazards analysis adjusting for traditional risk factors. The incremental predictive performance was assessed with use of the c-statistic and net reclassification index (NRI; continuous and based on 10-year risk strata 0-10%, 10-20%, 20-30%, >30%). A multimarker model was constructed comprising those biomarkers that improved predictive performance in both cohorts. N-terminal prohormone of B-type natriuretic peptide, osteopontin, and MMP-3 were the only biomarkers significantly associated with an increased risk of CVE and improved predictive performance in both cohorts. In SMART, the combination of these biomarkers increased the c-statistic with 0.03 (95% CI 0.01-0.05), and the continuous NRI was 0.37 (95% CI 0.21-0.52). In EPIC-NL, the multimarker model increased the c-statistic with 0.03 (95% CI 0.00-0.03), and the continuous NRI was 0.44 (95% CI 0.23-0.66). Based on risk strata, the NRI was 0.12 (95% CI 0.03-0.21) in SMART and 0.07 (95% CI -0.04-0.17) in EPIC-NL.ConclusionsOf the 23 evaluated biomarkers from different pathophysiological pathways, N-terminal prohormone of B-type natriuretic peptide, osteopontin, MMP-3, and their combination improved CVE risk prediction in 2 separate cohorts of patients with type 2 diabetes mellitus beyond traditional risk factors. However, the number of patients reclassified to a different risk stratum was limited.

Project description:Disorders of lipoprotein metabolism have been linked with an increased risk of cardiovascular diseases (CVDs) but the causal association is unclear. In this study, we investigated the causal association between disorders of lipoprotein metabolism and CVDs using two-sample Mendelian randomization (MR). The exposure was obtained from Finn genome-wide association studies (14,010 cases, 197,259 controls), and the corresponding CVDs were extracted from the largest published genome-wide association studies. A random-effects inverse-variance weighted method was used for the main analyses with a complementary analysis using the weighted median and MR-Egger approaches. Multiple sensitivity analyses were performed to assess horizontal pleiotropy. The MR analysis indicated positive associations of disorders of lipoprotein metabolism with coronary artery disease (odds ratio [OR] 1.670, 95% confidence interval [CI] 1.373-2.031; p < 0.001), aortic aneurysm (OR 1.394, 95% CI 1.199-1.619; p < 0.001), heart failure (OR 1.20, 95% CI 1.115-1.294; p < 0.001), hypertension (OR 1.011, 95% CI 1.006-1.091; p < 0.001), old myocardial infarction (OR 1.004, 95% CI 1.002-1.007; p = 0.001), and stroke (OR 1.002, 95% CI 1.001-1.003; p = 0.002). There is a suggestive causal relationship between disorders of lipoprotein metabolism and atrial fibrillation (OR 1.047, 95% CI 1.006-1.091; p = 0.026) and acute myocardial infarction (OR 1.003, 95% CI 1.001-1.005; p = 0.012). There was limited evidence of a causal association between disorders of lipoprotein metabolism and peripheral vascular disease and venous thromboembolism. Our findings indicate a significant causal association between disorders of lipoprotein metabolism and many CVDs, including coronary artery disease, aortic aneurysm, heart failure, hypertension, old myocardial infarction, and stroke. These associations may be useful for development of treatment strategies that regulate lipoprotein metabolism in patients with CVD.

Dataset Information

Lipoprotein metabolism indicators improve cardiovascular risk prediction.

Background

Methods and results

Conclusions

Publications

Lipoprotein metabolism indicators improve cardiovascular risk prediction.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets