An exploratory data-driven approach to classify subgroups of patients with temporomandibular disorders based on pain mechanisms.

Temporomandibular disorders (TMDs) are a common musculoskeletal condition, presenting treatment challenges due to their non-specific nature. Categorizing patients with TMDs into clusters based on neurobiological pain mechanisms could provide a promising approach to facilitate targeted treatments. This observational study (1) used a network analysis (NA) to explore the complexity of TMDs by investigating relationships among biopsychosocial variables, and (2) validated potential TMD subgroups based on mechanism-specific pain categories. One hundred and two patients with TMD were included. Biopsychosocial variables covered: general health, psychosocial features, TMD pain, and TMD characteristics. A NA evaluated the associations between variables and determined the role of each feature within the network. Hierarchical clustering was used to identify TMD subgroups. The NA revealed significant correlations primarily within the same feature domains, indicating a strong interplay between symptoms and psychological factors. Cluster analysis identified two subgroups driven by nociceptive and nociplastic pain mechanisms; the nociplastic group exhibited higher levels of anxiety, depression, pain catastrophization, central sensitization, pain duration, and more pain locations, along with poorer sleep quality, quality of life, and health status. In contrast, the nociceptive group exhibited restricted maximal mouth opening (MMO), heightened pain during TMJ palpation and mouth opening, and a greater positive response to manual therapy. Across all features, psychological factors, pain locations, and MMO primarily contributed to the separation of subgroups. By adopting a data-driven approach, these results support the significant role of considering the neurobiological basis of pain to improve patient classification. This knowledge may facilitate clinical reasoning and personalized treatments.