This project in response to RFA-DE-22-011 aims to analyze existing data representing a subset of participants with Temporomandibular disorders (TMD) who underwent in-depth clinical, behavioral, and psychological phenotyping under R01 DE025946 (PI: Colloca, ending September 30, 2022). The proposed aims are substantially different from the original R01-work exploring genetic variants associated with expectancy-induced analgesia in chronic orofacial pain, psychological factors predicting placebo responders, and genes-related neuronal changes in the prefrontal and limbic areas associated with expectancy-induced analgesia. Using Dean’s Initiative Funds allocated to Dr. Colloca, we collected blood and extracted RNA-seq data from a subset of 74 TMD participants. With the behavioral, psychological, clinical and now, transcriptomic data from this subset of TMD participants, the central hypothesis is distinct Differently Expressed Genes (DEG) and pathways associated with Endogenous Pain Modulation (EPM) characterize those TMD participants who show the highest placebo effects. We will compare transcriptomic profiles associated with high versus low EPM via placebo effects tested in TMD participants (AIM1) and we will predict high EPM integrating transcriptomic, sociodemographic, clinical and psychological data (Exploratory Specific Aim 2) using machine learning models. In order to identify transcriptomic profiles of high placebo responsiveness, TMD participants will be divided into High Placebo Responders (HLR) and Low Placebo Responders (LPR) based on an average reported pain score cut-off of 30 on a visual analogue scale (VAS) anchored from zero=no pain to 100=maximum imaginable pain. Based on our prior published results, informative preliminary results, and DEG power calculation, we expect enough power to identify key DEG associations in HPR compared to those TMD who do not respond and/or have lower placebo responses while controlling for sex, age and pain severity. Importantly, unbiased enrichment analyses will be conducted to identify transcriptomic processes associated with EPM. Machine learning approaches (e.g., generalized boosted models) will allow us to integrate sociodemographic, clinical and psychological with transcriptomic markers to further characterize HPR in TMD participants. Our team is strong with complementary expertise, ensuring that this research will provide integrative models towards step- by-step discoveries of molecular mechanisms characterizing those who show the largest activation of EPM via placebo effects. This is the first project to use transcriptomic profiling and machine learning models to predict EPM in an understudied TMD population. Findings will have high clinical relevance and will inform more extensive studies generating knowledge that will be critical to guide future steps towards integrative and translational precision medicine.