The UAB MCRC is a multidisciplinary program uniquely positioned to promote research related to the causes, diagnoses, treatments and improved care of patients with arthritis and musculoskeletal diseases. The MCRC builds on the capabilities of the UAB Comprehensive Arthritis, Musculoskeletal, and Autoimmunity Center (CAMAC) and its thematic workgroups (Experimental Therapeutics &Biomarkers;Neurobehavioral Medicine;Epidemiology, Prevention, &Outcomes;Genetics and Functional Genomics;Immunology, Autoimmunity &Inflammation;Bone, Cartilage and Connective Tissue). An outstanding MCRC Methodology Core is comprised of experts in biostatistics (Redden, Howard, McGwin, Aslibekyan), data management (Westfall), statistical genetics and bioinformatics (Cui, Lefkowitz, Liu) and health services research (Kilgore). All have a proven record of collaborative clinical investigation in musculoskeletal diseases. This proposal includes 3 innovative projects: 1. Genetic Determinants of Cell Type-Specific Gene Expression in RA (Brown/Bridges);2. Facilitating Treat-to-Target Strategies Using Novel Health Technology with Decision Support (Curtis);3. Adaptive Immune Responses to Gut Microbiota in Juvenile &Adult Spondyloarthritis (Elson). Projects 1 and 3 focus on populations for which there is a paucity of clinical research: (African-Americans with RA and juvenile spondyloarthritis). All projects leverage and expand upon substantial existing resources, including ongoing cohorts. The Administrative Core coordinates MCRC activities, sets the strategic agenda, facilitates interactions and collaborations, promotes scientific development, and performs evaluation of MCRC programs. Four advisory groups have been established to assist the MCRC Director and Associate Director in maximizing the strengths of the MCRC's projects, as well as in identifying and correcting weaknesses: 1. Executive Committee;2. Internal Advisory Committee;3. External Advisory Committee;4. Data Safety Monitoring Committee. The Scientific Development Program, which promotes the introduction and development of new techniques and nurtures young and new faculty in arthritis and musculoskeletal disease research, ensures the continued energy and vitality of this MCRC.
The UAB MCRC will support innovative research to: 1. Understand regulation of gene expression in immune cells of African-Americans with RA;2. Utilize electronic tools to help rheumatologists improve outcomes of RA patients;3. Understand the role of intestinal bacteria in children and adults with spondyloarthritis. MCRC programs will teach new research methods and encourage new investigators in rheumatic diseases.
|Stoll, M L; Kumar, R; Lefkowitz, E J et al. (2016) Fecal metabolomics in pediatric spondyloarthritis implicate decreased metabolic diversity and altered tryptophan metabolism as pathogenic factors. Genes Immun 17:400-405|
|Stoll, Matthew L; Cron, Randy Q (2016) The microbiota in pediatric rheumatic disease: epiphenomenon or therapeutic target? Curr Opin Rheumatol 28:537-43|
|Yang, Celeste; Bartolucci, Alfred A; Cui, Xiangqin (2015) Multigroup Equivalence Analysis for High-Dimensional Expression Data. Cancer Inform 14:253-63|
|Curtis, J R; Yang, S; Chen, L et al. (2015) Determining the Minimally Important Difference in the Clinical Disease Activity Index for Improvement and Worsening in Early Rheumatoid Arthritis Patients. Arthritis Care Res (Hoboken) 67:1345-53|
|Li, Peng; Redden, David T (2015) Small sample performance of bias-corrected sandwich estimators for cluster-randomized trials with binary outcomes. Stat Med 34:281-96|
|Yan, Qi; Weeks, Daniel E; CeledÃ³n, Juan C et al. (2015) Associating Multivariate Quantitative Phenotypes with Genetic Variants in Family Samples with a Novel Kernel Machine Regression Method. Genetics 201:1329-39|
|Li, Peng; Redden, David T (2015) Comparing denominator degrees of freedom approximations for the generalized linear mixed model in analyzing binary outcome in small sample cluster-randomized trials. BMC Med Res Methodol 15:38|
|Liu, Nianjun (2015) QTL mapping - Current status and challenges: Comment on "Mapping complex traits as a dynamic system" by L. Sun and R. Wu. Phys Life Rev 13:194-5|
|Cui, Xiangqin; Yu, Shaohua; Tamhane, Ashutosh et al. (2015) Simple regression for correcting Î”Ct bias in RT-qPCR low-density array data normalization. BMC Genomics 16:82|
|Yan, Qi; Weeks, Daniel E; Tiwari, Hemant K et al. (2015) Rare-Variant Kernel Machine Test for Longitudinal Data from Population and Family Samples. Hum Hered 80:126-38|
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