# Secondary Analysis and Integration of Existing Data Related to Chronic Orofacial Pain and Placebo Effects

> **NIH NIH R21** · UNIVERSITY OF MARYLAND BALTIMORE · 2022 · $424,875

## Abstract

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.

## Key facts

- **NIH application ID:** 10597861
- **Project number:** 1R21DE032532-01
- **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE
- **Principal Investigator:** Luana Colloca
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $424,875
- **Award type:** 1
- **Project period:** 2022-09-22 → 2024-09-21

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10597861

## Citation

> US National Institutes of Health, RePORTER application 10597861, Secondary Analysis and Integration of Existing Data Related to Chronic Orofacial Pain and Placebo Effects (1R21DE032532-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10597861. Licensed CC0.

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