Collaborative for research to advancetmd evidence (create) - temporomandibular disorders (tmds) involve complex and poorly understood pathophysiological processes, limiting prevention, diagnosis,…

The National Institute of Dental and Craniofacial Research awarded the University of Pittsburgh a cooperative agreement worth $8,154,330 on September 9, 2025, under the Oral Diseases and Disorders Research program (CFDA 93.121) to establish the Collaborative for Research to Advance TMD Evidence (CREATE), a multi-site research initiative addressing temporomandibular disorders through integrated clinical and preclinical research.

CREATE operates three functional cores plus a central longitudinal study. The Administrative Core manages scientific, administrative, and operational functions, ensuring alignment with NIH goals, standardizing data collection across sites, and implementing FAIR principles for data and biospecimen management. The Bioinformatics and Data Science Core employs AI and machine learning to analyze multi-modal data, identify TMD subtypes, implicate therapeutic targets, and establish common data elements and ontologies to enhance data integration. The Outreach, Training, and Education Core expands the TMD research and clinical workforce, fosters stakeholder collaboration, and organizes biannual public workshops to disseminate findings.

The Deep Pain Phenotyping Project—the initiative's centerpiece—is a large-scale longitudinal study enrolling 1,000 TMD patients and 300 age-matched controls across multiple U.S. clinical centers. Standardized assessments and mechanistic deep phenotyping will enable identification of TMD subtypes with high reliability and selectivity to facilitate diagnosis, prognosis, and future clinical trials. The clinical dataset will inform reverse translation to preclinical studies to elucidate mechanisms and validate therapeutic hypotheses.

Performance occurs in Pennsylvania. The period of performance extends from September 9, 2025, through June 30, 2030.

Sub-recipients include the University of Florida, the Research Foundation for the State University of New York (administering for SUNY institutions), University of Texas Health Science Center at Houston, Texas A&M University System Health Science Center, Health Research at Roswell Park, Healthpartners Institute, and Missouri State University. These institutions contribute as research sites and partner organizations within the CREATE consortium.

Collaborative for research to advancetmd evidence (create) - temporomandibular disorders (tmds) involve complex and poorly understood pathophysiological processes, limiting prevention, diagnosis, prognosis and the development or application of effective, evidence-based treatments. The collaborative for research to advance TMD evidence (create) aims to address these critical gaps through a bidirectional approach that integrates clinical and preclinical research to identify underlying mechanisms, develop personalized therapies, and improve patient outcomes. The administrative core (AC) will manage create's scientific, administrative, and operational functions, ensuring alignment with NIH goals, standardizing data collection across sites, and adhering to fair principles for data and biospecimen management. The bioinformatics and data science core (bdsc) will employ the latest AI and machine learning techniques to analyze multi-modal data, identify TMD subtypes, implicate therapeutic targets, and establish frameworks such as common data elements (cdes) and ontologies to enhance data integration. The outreach, training, and education core (otec) will expand the TMD research and clinical workforce, foster collaboration with stakeholders, and organize biannual public workshops to disseminate findings and guide the collaborative's efforts. At the heart of create is the deep pain phenotyping project, a large-scale longitudinal study involving 1,000 TMD patients and 300 age-matched controls across multiple US clinical centers, designed to enable identification of TMD subtypes through standardized assessments and mechanistic deep phenotyping. The identification of TMD subtypes with high reliability and selectivity will facilitate diagnosis and prognosis and future clinical trials, and may suggest more effective preventive and therapeutic strategies. The clinical dataset will also inform reverse translation to preclinical studies to further elucidate mechanisms and validate therapeutic targets. By leveraging the expertise of a diverse, multidisciplinary team, create seeks to bridge the GAP between clinical data and preclinical insights, paving the way for improved diagnostics, treatments, and patient care in tmds.