1075 Data-Driven Sleep Phenotypes and Associated Psychological Characteristics in Painful Temporomandibular Disorders

Abstract Introduction Chronic orofacial pain, including temporomandibular disorders (TMD), is influenced by sleep disturbance and psychological distress, both of which contribute to pain vulnerability and maintenance. However, subjective sleep patterns are heterogeneous, and the psychological characteristics associated with distinct sleep profiles remain unclear. Identifying these phenotypes may clarify biopsychosocial mechanisms relevant to chronic pain. Methods Eighty-four adults (TMD pain and controls) completed the Pittsburgh Sleep Quality Index (PSQI), SCL-90-R subscales, PANAS, Brief Vulnerability Scale, and STAI-T. PSQI components were entered into a principal component analysis followed by hierarchical clustering (Ward’s method). A three-cluster solution emerged; Cluster 2 (n=4) was excluded due to minimal sample size. Analyses focused on Cluster 1 (Multidomain Sleep Disturbance Profile; n=38) and Cluster 3 (Low-Disturbance Sleep Profile; n=42). Sleep differences were examined using ANOVAs. A MANOVA tested overall psychological differences, followed by univariate ANOVAs with Bonferroni correction. Results Cluster membership differed significantly by TMD pain status (χ²(1)=8.77, p=0.003), with more pain participants in Cluster 1. The MANOVA was significant (Pillai’s Trace=0.39, F(17,62)=2.65, p=0.004), indicating a distinct multivariate psychological profile. Follow-up ANOVAs showed that Cluster 1 demonstrated higher somatization, obsessive–compulsive symptoms, interpersonal sensitivity, depression, anxiety, hostility, negative affect, and global distress after Bonferroni correction. Cluster 3 demonstrated the lowest overall symptom burden. Participants in the Multidomain Sleep Disturbance Profile exhibited greater psychological distress and were more likely to report TMD pain, whereas individuals in the Low-Disturbance Sleep Profile reported fewer symptoms and demonstrated a more resilient psychological pattern. Conclusion Poorer sleep quality was associated with elevated psychological symptoms and greater likelihood of TMD pain, whereas good sleepers exhibited fewer symptoms and markers of resilience. These findings highlight meaningful sleep–psychological phenotypes that may inform mechanisms of vulnerability and resilience in TMJ-related pain. Understanding distinct sleep profiles may support more targeted approaches to assessment and intervention in chronic orofacial pain. Support (if any)

Authors

Institutions

Publication Details

Journal
SLEEP
Published
2026-05-01
DOI
https://doi.org/10.1093/sleep/zsag091.1074
Primary Topic
Temporomandibular Joint Disorders
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

1075 Data-Driven Sleep Phenotypes and Associated Psychological Characteristics in Painful Temporomandibular Disorders

Soamy Montesino Goicolea, Margarete Ribeiro Dasilva, Yenisel Cruz-Almeida
SLEEP
Temporomandibular Joint Disorders
article

1075 Data-Driven Sleep Phenotypes and Associated Psychological Characteristics in Painful Temporomandibular Disorders

Soamy Montesino Goicolea, Margarete Ribeiro Dasilva, Yenisel Cruz-Almeida
article en

Abstract

Abstract Introduction Chronic orofacial pain, including temporomandibular disorders (TMD), is influenced by sleep disturbance and psychological distress, both of which contribute to pain vulnerability and maintenance. However, subjective sleep patterns are heterogeneous, and the psychological characteristics associated with distinct sleep profiles remain unclear. Identifying these phenotypes may clarify biopsychosocial mechanisms relevant to chronic pain. Methods Eighty-four adults (TMD pain and controls) completed the Pittsburgh Sleep Quality Index (PSQI), SCL-90-R subscales, PANAS, Brief Vulnerability Scale, and STAI-T. PSQI components were entered into a principal component analysis followed by hierarchical clustering (Ward’s method). A three-cluster solution emerged; Cluster 2 (n=4) was excluded due to minimal sample size. Analyses focused on Cluster 1 (Multidomain Sleep Disturbance Profile; n=38) and Cluster 3 (Low-Disturbance Sleep Profile; n=42). Sleep differences were examined using ANOVAs. A MANOVA tested overall psychological differences, followed by univariate ANOVAs with Bonferroni correction. Results Cluster membership differed significantly by TMD pain status (χ²(1)=8.77, p=0.003), with more pain participants in Cluster 1. The MANOVA was significant (Pillai’s Trace=0.39, F(17,62)=2.65, p=0.004), indicating a distinct multivariate psychological profile. Follow-up ANOVAs showed that Cluster 1 demonstrated higher somatization, obsessive–compulsive symptoms, interpersonal sensitivity, depression, anxiety, hostility, negative affect, and global distress after Bonferroni correction. Cluster 3 demonstrated the lowest overall symptom burden. Participants in the Multidomain Sleep Disturbance Profile exhibited greater psychological distress and were more likely to report TMD pain, whereas individuals in the Low-Disturbance Sleep Profile reported fewer symptoms and demonstrated a more resilient psychological pattern. Conclusion Poorer sleep quality was associated with elevated psychological symptoms and greater likelihood of TMD pain, whereas good sleepers exhibited fewer symptoms and markers of resilience. These findings highlight meaningful sleep–psychological phenotypes that may inform mechanisms of vulnerability and resilience in TMJ-related pain. Understanding distinct sleep profiles may support more targeted approaches to assessment and intervention in chronic orofacial pain. Support (if any)

SLEEPVol. 49(Supplement_1)
University of Florida (US)
Openalex Percentile: Top 5%
Temporomandibular Joint Disorders
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.