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Articles by S Blankenberg
Total Records ( 3 ) for S Blankenberg
  R. B Schnabel , K. L Lunetta , M. G Larson , J Dupuis , I Lipinska , J Rong , M. H Chen , Z Zhao , J. F Yamamoto , J. B Meigs , V Nicaud , C Perret , T Zeller , S Blankenberg , L Tiret , J. F Keaney , R. S Vasan and E. J. Benjamin
 

Background— Environmental and genetic correlates of inflammatory marker variability are incompletely understood. In the family-based Framingham Heart Study, we investigated heritability and candidate gene associations of systemic inflammatory biomarkers.

Methods and Results— In offspring participants (n=3710), we examined 11 inflammatory biomarkers (CD40 ligand, C-reactive protein, intercellular adhesion molecule-1, interleukin-6, urinary isoprostanes, monocyte chemoattractant protein-1, myeloperoxidase, P-selectin, tumor necrosis factor-, tumor necrosis factor receptor II, fibrinogen). Heritability and bivariate genetic and environmental correlations were assessed by Sequential Oligogenic Linkage Analysis routines in 1012 family members. We examined 1943 tagging single-nucleotide polymorphisms in 233 inflammatory pathway genes with ≥5 minor allele carriers using a general genetic linear model. Clinical correlates explained 2.4% (CD40 ligand) to 28.5% (C-reactive protein) of the variability in inflammatory biomarkers. Estimated heritability ranged from 10.9% (isoprostanes) to 44.8% (P-selectin). Most correlations between biomarkers were weak although statistically significant. A total of 45 single-nucleotide polymorphism-biomarker associations met the q-value threshold of 0.25. Novel top single-nucleotide polymorphisms were observed in ICAM1 gene in relation to intercellular adhesion molecule-1 concentrations (rs1799969, P=1.32x10–8) and MPO in relation to myeloperoxidase (rs28730837, P=1.9x10–5). Lowest P values for trans-acting single-nucleotide polymorphisms were observed for APCS with monocyte chemoattractant protein-1 concentrations (rs1374486, P=1.01x10–7) and confirmed for IL6R with interleukin-6 concentrations (rs8192284, P=3.36x10–5). Novel potential candidates (APCS, MPO) need to be replicated.

Conclusions— Our community-based data support the relevance of clinical and genetic factors for explaining variation in inflammatory biomarker traits.

  T Kempf , J. M Sinning , A Quint , C Bickel , C Sinning , P. S Wild , R Schnabel , E Lubos , H. J Rupprecht , T Munzel , H Drexler , S Blankenberg and K. C. Wollert
 

Background— Growth-differentiation factor-15 (GDF-15) is a stress-responsive transforming growth factor-β-related cytokine that has emerged as a prognostic biomarker in acute coronary syndrome trial populations. Its predictive role in stable coronary heart disease (CHD) has never been assessed.

Methods and Results— The circulating levels of GDF-15 were measured by immunoradiometric assay in patients with stable angina pectoris (n=1352) or acute coronary syndrome (n=877) who were followed up for a median of 3.6 years. Stable angina pectoris patients presenting with normal (<1200 ng/L), moderately elevated (1200 to 1800 ng/L), or markedly elevated (>1800 ng/L) GDF-15 levels had 3.6-year CHD mortality rates of 1.4%, 2.7%, and 15.0%, respectively (P<0.001). By backward stepwise Cox-regression analysis, which adjusted for age and gender, clinical variables, the number of diseased vessels, renal function, the levels of C-reactive protein, cardiac troponin I, and N-terminal pro-B-type natriuretic peptide, GDF-15 remained an independent predictor of CHD mortality (P<0.001). Addition of GDF-15 improved the prognostic accuracy of a clinical risk prediction model concerning CHD mortality (c-statistic, 0.84 versus 0.74; P=0.005). Analysis of the acute coronary syndrome part of the study population confirmed GDF-15 as an independent predictor of CHD mortality (P<0.001). The circulating levels of GDF-15 did not predict the future risk of nonfatal myocardial infarction in patients with stable angina pectoris or acute coronary syndrome.

Conclusion— This study identifies GDF-15 as a strong and independent predictor of CHD mortality across the broad spectrum of patients with stable and unstable CHD.

  M Preuss , I. R Konig , J. R Thompson , J Erdmann , D Absher , T. L Assimes , S Blankenberg , E Boerwinkle , L Chen , L. A Cupples , A. S Hall , E Halperin , C Hengstenberg , H Holm , R Laaksonen , M Li , W Marz , R McPherson , K Musunuru , C. P Nelson , M Susan Burnett , S. E Epstein , C. J O'Donnell , T Quertermous , D. J Rader , R Roberts , A Schillert , K Stefansson , A. F. R Stewart , G Thorleifsson , B. F Voight , G. A Wells , A Ziegler , S Kathiresan , M. P Reilly , N. J Samani , H Schunkert and on behalf of the CARDIoGRAM Consortium
  Background—

Recent genome-wide association studies (GWAS) of myocardial infarction (MI) and other forms of coronary artery disease (CAD) have led to the discovery of at least 13 genetic loci. In addition to the effect size, power to detect associations is largely driven by sample size. Therefore, to maximize the chance of finding novel susceptibility loci for CAD and MI, the Coronary ARtery DIsease Genome-wide Replication And Meta-analysis (CARDIoGRAM) consortium was formed.

Methods and Results—

CARDIoGRAM combines data from all published and several unpublished GWAS in individuals with European ancestry; includes >22 000 cases with CAD, MI, or both and >60 000 controls; and unifies samples from the Atherosclerotic Disease VAscular functioN and genetiC Epidemiology study, CADomics, Cohorts for Heart and Aging Research in Genomic Epidemiology, deCODE, the German Myocardial Infarction Family Studies I, II, and III, Ludwigshafen Risk and Cardiovascular Heath Study/AtheroRemo, MedStar, Myocardial Infarction Genetics Consortium, Ottawa Heart Genomics Study, PennCath, and the Wellcome Trust Case Control Consortium. Genotyping was carried out on Affymetrix or Illumina platforms followed by imputation of genotypes in most studies. On average, 2.2 million single nucleotide polymorphisms were generated per study. The results from each study are combined using meta-analysis. As proof of principle, we meta-analyzed risk variants at 9p21 and found that rs1333049 confers a 29% increase in risk for MI per copy (P=2x10–20).

Conclusion—

CARDIoGRAM is poised to contribute to our understanding of the role of common genetic variation on risk for CAD and MI.

 
 
 
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