Project Details
Projekt
Coordination Funds
Applicant
Professor Dr. Hendrik Schmidt
Subject Area
Orthopaedics, Traumatology, Reconstructive Surgery
Term
since 2021
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 439742772
FOR 5177 aims to fundamentally improve the diagnosis of chronic low back pain (cLBP) through an integrated “3M” framework—Morphology, Motion, and Mechanics. In the first funding period, we demonstrated the limited validity of conventional diagnostic methods and, through everyday measurements of spinal alignment, theory-based psychosocial assessments, and lab-based electromyographic analyses of muscle activity, identified novel functional and morphological parameters that more accurately distinguish between asymptomatic individuals and those with cLBP. Strikingly, differences between cohorts did not localize to the lumbar spine itself, i.e., the reported pain site, but rather to adjacent segments, particularly the thoracic spine and pelvis, an observation corroborated by our animal studies. Moreover, an integrative, real-world diagnostic approach combining clinical findings with dynamic measurement data, MRI analyses, and psychosocial evaluations proved markedly superior. Whereas the first funding period used cross-sectional assessments to identify functional, psychological, and morphological markers of cLBP, the second funding period will capture the early biomechanical, neuromuscular, morphological, and psychosocial changes that contribute to chronification. We will employ longitudinal observational studies in humans, both in daily life, under controlled laboratory conditions, and during therapeutic interventions, augmented by in silico analyses and animal models. A central focus will be on compensatory adaptations in regions adjacent to the lumbar spine. Machine learning will define diagnostic markers, characteristic patterns, and physiological reference and threshold values, further enhancing diagnostic precision. Finally, we will determine multimodal parameter combinations that differentiate specific subpopulations of asymptomatic individuals and cLBP patients; these findings will be incorporated into standardized protocols and validated in additional cohorts. Our overarching hypothesis is that early compensatory changes in spinal-adjacent segments, combined with specific multimodal parameters, will serve as valid predictors of the transition from acute to chronic low back pain and, through algorithmic integration, enable highly accurate prediction of chronification. Through seven complementary subprojects, we will establish the foundation for a robust, evidence-based diagnostic paradigm for chronic low back pain and pave the way for individualized prevention and treatment concepts.
DFG Programme
Research Units
