Investigating the Association Between Central Sensitization and Breathing Pattern Disorders

<b>Background/Objectives:</b> Central sensitization (CS) is identified as a cause of pain in various musculoskeletal diseases, and breathing pattern disorders (BPDs) are reported to be correlated with chronic pain. This study aimed to analyze the relationship between CS and BPDs through regression analysis. <b>Methods:</b> A cross-sectional study was designed according to the strengthening the reporting of observational studies in epidemiology (STROBE) guidelines. Forty participants with moderate to extreme CS (central sensitization inventory for Koreans; CSI-K ≥ 40) were enrolled, and their respiratory motion (manual assessment of respiratory motion; MARM), respiratory function (self-evaluation of breathing questionnaire; SEBQ), respiratory muscle strength (maximal inspiratory pressure; MIP, maximal expiratory pressure; MEP), pain intensity (numeric pain rating scale; NPRS), pain cognition (Korean version of pain catastrophizing scale; K-PCS), muscle tone and stiffness were measured. <b>Results:</b> Among participants with moderate to extreme CS, 82.5% showed BPDs and 42.5% reported severe pain intensity. Regression analysis revealed significant relationships between respiratory and pain variables. K-PCS demonstrated significant negative relationships with MARM area (β = -0.437, R² = 0.191) and positive relationships with SEBQ (β = 0.528, R² = 0.279). In the subgroup with BPDs, strong regression relationships were found between MARM area and NPRS usual pain (β = -0.486, R² = 0.237) and K-PCS (β = -0.605, R² = 0.366). Multiple regression analysis showed that MARM area and SEBQ together explained 41.2% of variance in pain catastrophizing. The comprehensive muscle stiffness prediction model using CSI-K, K-PCS, and muscle tone showed remarkably high explanatory power (R² = 0.978). <b>Conclusions:</b> In individuals with moderate to extreme CS, respiratory dysfunction was prevalent and significantly predictable through regression models with pain intensity and pain cognition. These quantitative regression relationships between breathing mechanics, pain measures, and muscle properties provide clinical prediction tools and suggest the importance of assessing breathing patterns in CS management.