Browsing by Author "Nelson, Sarah"

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    Approaches for enhancing the informativeness and quality of clinical trials: Innovations and principles for implementing multicenter trials from the Trial Innovation Network.
    (Journal of clinical and translational science, 2023-01) Lane, Karen; Palm, Marisha E; Marion, Eve; Kay, Marie T; Thompson, Dixie; Stroud, Mary; Boyle, Helen; Hillery, Shannon; Nanni, Angeline; Hildreth, Meghan; Nelson, Sarah; Burr, Jeri S; Edwards, Terri; Poole, Lori; Waddy, Salina P; Dunsmore, Sarah E; Harris, Paul; Wilkins, Consuelo; Bernard, Gordon R; Dean, J Michael; Dwyer, Jamie; Benjamin, Daniel K; Selker, Harry P; Hanley, Daniel F; Ford, Daniel E
    One challenge for multisite clinical trials is ensuring that the conditions of an informative trial are incorporated into all aspects of trial planning and execution. The multicenter model can provide the potential for a more informative environment, but it can also place a trial at risk of becoming uninformative due to lack of rigor, quality control, or effective recruitment, resulting in premature discontinuation and/or non-publication. Key factors that support informativeness are having the right team and resources during study planning and implementation and adequate funding to support performance activities. This communication draws on the experience of the National Center for Advancing Translational Science (NCATS) Trial Innovation Network (TIN) to develop approaches for enhancing the informativeness of clinical trials. We distilled this information into three principles: (1) assemble a diverse team, (2) leverage existing processes and systems, and (3) carefully consider budgets and contracts. The TIN, comprised of NCATS, three Trial Innovation Centers, a Recruitment Innovation Center, and 60+ CTSA Program hubs, provides resources to investigators who are proposing multicenter collaborations. In addition to sharing principles that support the informativeness of clinical trials, we highlight TIN-developed resources relevant for multicenter trial initiation and conduct.
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    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.