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During the congress, E-Posters will be accessible to all participants on the congress website 24/7, as well as in the E-poster stations in the congress center.
Preparing your E-Poster
Please review the E-Poster format requirements carefully when preparing your E-Poster. Should your E-Poster not meet the mentioned requirements, it may not be displayed as described above.
E-Poster Submission Deadline
Please prepare and upload your E-Poster no later than March 14, 2026 11.59PM CET. After this date, you will no longer be able to prepare and upload your E-poster and it will not be displayed and accessible on the congress website.
Please follow the instructions below to input your abstract title.
Abstract titles should be brief and reflect the content of the abstract.
Sarcopenia is a highly prevalent and serious complication in maintenance hemodialysis (MHD) patients, arising from complex interactions among nutritional, inflammatory, metabolic, and dialysis-related factors. Conventional statistical models struggle to systematically reveal the intricate associations hidden within multimodal clinical data. This study aimed to construct a knowledge graph integrating multimodal data from multiple centers to visualize the clinical network of sarcopenia and explore its underlying pathophysiological patterns, thereby providing new insights for precise risk identification and intervention.
A total of 820 MHD patients from three tertiary medical centers were included in this retrospective study. Sarcopenia was diagnosed according to the EWGSOP2 criteria, combining assessments of muscle strength (handgrip strength, HGS), muscle quantity (bioelectrical impedance analysis, BIA), and physical performance (Short Physical Performance Battery, SPPB). Multimodal clinical data collected over a 6-month period included:
Demographics and comorbidities (age, diabetes, cardiovascular disease)
Laboratory parameters (albumin, C-reactive protein, interleukin-6, hemoglobin, potassium)
Dialysis-specific metrics (Kt/V, nPNA, interdialytic weight gain percentage)
Body composition data (phase angle, skeletal muscle mass index via BIA)
Medication records (erythropoiesis-stimulating agents, active vitamin D)
To ensure data consistency across centers, we established standardized data collection protocols and conducted rigorous data harmonization. The knowledge graph was constructed using the Neo4j graph database platform. Clinical entities (e.g., Patient, LaboratoryTest, Comorbidity) and their relationships (e.g., HAS_LEVEL, ASSOCIATED_WITH, LEADS_TO) were formally defined based on clinical expertise and ontological principles. Graph algorithms were applied for advanced analysis: the Louvain method for community detection to identify patient subtypes, and PageRank algorithm for centrality analysis to pinpoint key risk factors in the network.
The knowledge graph successfully integrated 14,236 entities and 28,950 relationships from the multicenter dataset. Community detection revealed three distinct clinical phenotypes of sarcopenia:
1.Inflammatory-Metabolic Type: Characterized by strong interconnections between elevated inflammatory markers CRP, IL-6), hypoalbuminemia, and reduced phase angle.
2.Nutritional-Deficiency Type: Centered around severely low protein intake (nPNA) and albumin levels, with relatively weaker inflammatory links.
3.Frailty-Dominant Type: Primarily associated with advanced age and multiple comorbidities, showing strong connections to poor physical performance.
Pathway analysis identified interleukin-6 as a central hub node connecting inflammation, malnutrition, and declined muscle function. Additionally, the graph uncovered clinically meaningful association subgraphs, such as the co-occurrence of high interdialytic weight gain and hypokalemia significantly linked to muscle cramps and reduced SPPB scores. The multicenter validation confirmed the robustness and generalizability of these findings across different patient populations.
This multicenter study innovatively constructed and applied a knowledge graph for sarcopenia in MHD patients, achieving deep integration and visualization of multimodal clinical data. The graph not only revealed complex risk patterns and patient subtypes that are difficult to detect with traditional methods but also precisely identified potential key intervention targets like IL-6. This work provides a novel framework for understanding the heterogeneity of sarcopenia and establishes a methodological foundation for future personalized prevention and management strategies based on risk patterns. The use of multicenter data significantly enhances the reliability and clinical applicability of our findings.
