Co-PIs

The goal of the Rheumatic and Musculoskeletal Disease (RMD) Epidemiology and Outcomes Training Program at the University of North Carolina is to provide state-of-the-art resources and a rich environment to train independent researchers who will improve our understanding of the magnitude, etiology, impact, and treatment of RMDs, and who will assume leadership roles in RMD epidemiology and outcomes research.

This T32 includes:

• Clear core methodologic and content- related competencies to be met by trainees and supported by the existing infrastructure at UNC and TARC, including our NIAMS-funded Core Center for Clinical Research.
• Linkages to formal training programs in epidemiologic methods and biostatistics to provide a strong foundation in research design and analytic techniques, including those through the North Carolina Translational and Clinical Sciences Institute.
• Emphasis on design, execution, analysis, and publication of research projects to enhance the ability of the trainee to conceptualize and think through research problems with increasing independence.
• Experienced and dedicated mentors to guide the developing investigator.
• An emphasis on rigor and reproducibility, data science principles, and the responsible conduct of research in an inclusive and supportive environment.
• The program benefits from strong leadership, outstanding faculty from Schools and Departments across the university, and the strong research infrastructure of RAI, TARC, and UNC.
• Focus on state-of-the-art training in artificial intelligence, machine learning, precision medicine, bioinformatics, and use of real-world data sources.

Download this 2-page overview of the training program and research experience for more information.

Eligibility:

At the time of the award, the trainee must be:

• A citizen of the United States
• or a non-citizen national
• or must have been lawfully admitted for permanent residence and possess an Alien Registration Receipt Card (1-151 or 1-551) or some other verification of legal admission as a permanent resident

For more information about the eligibility criteria for this program, please visit the NIH Grants Policy Statement website. Guidance on payback obligation requirements can also be found online in section 11.4.3.1 Service Payback of the NIH Grants Policy Statement.                                                                     

Current Trainees

Ashley N. Buck, MS                                      Danae C. Gross, MS, RDN, LDN                 Reece Blay, PhD
Pre-Doctoral Trainee                                     Pre-Doctoral Trainee                                    Post-Doctoral Trainee

Michelle Ramirez, PhD, DPT, PT                                                         
Post-Doctoral Trainee     

Past Trainees

Helal El-Zaatari, PhD
Post-Doctoral Trainee

T32 Products

1. Buck AN, Moore SR, Smith-Ryan AE, Schwartz TA, Nelson AE, Davis-Wilson H, Blackburn JT, Pietrosimone B. Body Composition, Not Body Mass Index, Is Associated with Clinical Outcomes Following ACL Reconstruction. Med Sci Sports Exerc. 2025 Feb 10. doi: 10.1249/MSS.0000000000003670. Epub ahead of print. PMID: 39929144.
2. Lee H, Buck AN, Armitano-Lago C, Creighton RA, Kamath GM, Spang JT, Li X, Lalush D, Franz JR, Blackburn JT, Pietrosimone B. Aberrant Gait Biomechanics Linked to Cartilage Changes After ACL Reconstruction in Those With High Body Mass Index. J Orthop Res. 2025 Aug;43(8):1413-1422. doi: 10.1002/jor.26099. Epub 2025 May 19. PMID: 40384518; PMCID: PMC12258122.
3. Buck AN, Gross DC, Kim JJ, Rauff EL, Dinallo JM, Abbate LM, Schwartz TA, Beresic NJ, Newman CB, Shultz SP. BMI-Specific Nutritional Education Priorities for Weight Management in Osteoarthritis. Nutrients. 2025 Jun 20;17(13):2056. doi: 10.3390/nu17132056. PMID: 40647161; PMCID: PMC12250860.
4. El-Zaatari H, Arbeeva L, Nelson AE. Applying binary mixed model to predict knee osteoarthritis pain. PLoS One. 2025 Jul 15;20(7):e0325678. doi: 10.1371/journal.pone.0325678. PMID: 40663584.