Browsing by Author "Thompson, Dana K"

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Critical appraisal of four IL-6 immunoassays.
    (PLoS One, 2012) Thompson, Dana K; Huffman, Kim M; Kraus, William E; Kraus, Virginia Byers
    BACKGROUND: Interleukin-6 (IL-6) contributes to numerous inflammatory, metabolic, and physiologic pathways of disease. We evaluated four IL-6 immunoassays in order to identify a reliable assay for studies of metabolic and physical function. Serial plasma samples from intravenous glucose tolerance tests (IVGTTs), with expected rises in IL-6 concentrations, were used to test the face validity of the various assays. METHODS AND FINDINGS: IVGTTs, administered to 14 subjects, were performed with a single infusion of glucose (0.3 g/kg body mass) at time zero, a single infusion of insulin (0.025 U/kg body mass) at 20 minutes, and frequent blood collection from time zero to 180 minutes for subsequent Il-6 measurement. The performance metrics of four IL-6 detection methods were compared: Meso Scale Discovery immunoassay (MSD), an Invitrogen Luminex bead-based multiplex panel (LX), an Invitrogen Ultrasensitive Luminex bead-based singleplex assay (ULX), and R&D High Sensitivity ELISA (R&D). IL-6 concentrations measured with MSD, R&D and ULX correlated with each other (Pearson Correlation Coefficients r = 0.47-0.94, p<0.0001) but only ULX correlated (r = 0.31, p = 0.0027) with Invitrogen Luminex. MSD, R&D, and ULX, but not LX, detected increases in IL-6 in response to glucose. All plasma samples were measurable by MSD, while 35%, 1%, and 4.3% of samples were out of range when measured by LX, ULX, and R&D, respectively. Based on representative data from the MSD assay, baseline plasma IL-6 (0.90 ± 0.48 pg/mL) increased significantly as expected by 90 minutes (1.29 ± 0.59 pg/mL, p = 0.049), and continued rising through 3 hours (4.25 ± 3.67 pg/mL, p = 0.0048). CONCLUSION: This study established the face validity of IL-6 measurement by MSD, R&D, and ULX but not LX, and the superiority of MSD with respect to dynamic range. Plasma IL-6 concentrations increase in response to glucose and insulin, consistent with both an early glucose-dependent response (detectable at 1-2 hours) and a late insulin-dependent response (detectable after 2 hours).
  • Thumbnail Image
    ItemOpen Access
    Genome-wide linkage analysis of cardiovascular disease biomarkers in a large, multigenerational family.
    (PLoS One, 2013) Nolan, Daniel; Kraus, William E; Hauser, Elizabeth; Li, Yi-Ju; Thompson, Dana K; Johnson, Jessica; Chen, Hsiang-Cheng; Nelson, Sarah; Haynes, Carol; Gregory, Simon G; Kraus, Virginia B; Shah, Svati H
    Given the importance of cardiovascular disease (CVD) to public health and the demonstrated heritability of both disease status and its related risk factors, identifying the genetic variation underlying these susceptibilities is a critical step in understanding the pathogenesis of CVD and informing prevention and treatment strategies. Although one can look for genetic variation underlying susceptibility to CVD per se, it can be difficult to define the disease phenotype for such a qualitative analysis and CVD itself represents a convergence of diverse etiologic pathways. Alternatively, one can study the genetics of intermediate traits that are known risk factors for CVD, which can be measured quantitatively. Using the latter strategy, we have measured 21 cardiovascular-related biomarkers in an extended multigenerational pedigree, the CARRIAGE family (Carolinas Region Interaction of Aging, Genes, and Environment). These biomarkers belong to inflammatory and immune, connective tissue, lipid, and hemostasis pathways. Of these, 18 met our quality control standards. Using the pedigree and biomarker data, we have estimated the broad sense heritability (H2) of each biomarker (ranging from 0.09-0.56). A genome-wide panel of 6,015 SNPs was used subsequently to map these biomarkers as quantitative traits. Four showed noteworthy evidence for linkage in multipoint analysis (LOD score ≥ 2.6): paraoxonase (chromosome 8p11, 21), the chemokine RANTES (22q13.33), matrix metalloproteinase 3 (MMP3, 17p13.3), and granulocyte colony stimulating factor (GCSF, 8q22.1). Identifying the causal variation underlying each linkage score will help to unravel the genetic architecture of these quantitative traits and, by extension, the genetic architecture of cardiovascular risk.