26dkou545 10.3205/26dkou545 urn:nbn:de:0183-26dkou5455 Meeting Abstract Qiao Qiao Yu Y

Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, TUM University Hospital, Technical University of Munich, München, Deutschland

author Hinterwimmer Hinterwimmer Florian F

Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, TUM University Hospital, Technical University of Munich, München, Deutschland TUM University Hospital, Institute for AI and Informatics in Medicine, School of Medicine and Health, München, Deutschland

author Duan Duan Xiang X

Clinical Psychology and Psychotherapy, Faculty of Psychology and Movement Science, University of Hamburg, Hamburg, Deutschland

author German Medical Science GMS Publishing House

Düsseldorf

610 20261020 engl This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). M0659 545 Deutsche Gesellschaft für Orthopädie und Unfallchirurgie Deutsche Gesellschaft für Orthopädie und Orthopädische Chirurgie Deutsche Gesellschaft für Unfallchirurgie Berufsverband für Orthopädie und Unfallchirurgie Deutscher Kongress für Orthopädie und Unfallchirurgie (DKOU 2026) Poster experimentell | Arthrose und Infektion Berlin 20261020 20261023 DKOU26_Abs_2222 TextObjectives and questions: Osteoarthritis (OA) and depressive symptoms frequently co-occur in middle-aged and older adults, yet most studies focus on disorder-level associations and rarely examine symptom-specific mechanisms or underlying molecular correlates. Understanding how OA connects to the depressive symptom may help identify targeted intervention pathways.Material and methods: Using data from 15,721 adults aged ≥45 years in CHARLS 2018, hierarchical regression and mixed graphical modeling were used to assess population-level associations between OA and ten CES-D symptoms and to estimate the symptom network. Separate EBICglasso networks were constructed for OA and non-OA groups, and structural differences were evaluated using the Network Comparison Test. Three GEO datasets were integrated to identify shared differentially expressed genes, hub genes, enriched pathways, and machine-learning-derived cross-disease feature genes.Results: After full adjustment, OA remained significantly associated with higher depressive symptom levels (β = .10, p < .001). Network analysis identified depressed mood, lethargy, loneliness, and restless sleep as central symptoms, whereas “restless sleep” and “everything is an effort” emerged as key bridge symptoms linking OA with depressive symptoms. Several affective-motivational and social pathways were stronger in the OA group. Bioinformatic analyses identified 177 shared OA-depression genes and two robust feature genes (TGFB2, ENO2), enriched in ECM remodeling, PI3K-Akt signaling, focal adhesion, and metabolic stress-related pathways.Discussion and conclusions: OA is associated with distinct depressive symptoms and bridge mechanisms involving sleep disturbance, reduced energy, and diminished positive affect. Multilevel convergence across symptom networks and molecular pathways highlights potential screening targets and supports early detection and personalized intervention for OA-depression comorbidity. 0 0 0 0