ArticleArchives of osteoporosis2023
Network analysis of osteoporosis provides a global view of associated comorbidities and their temporal relationships.
Article in Archives of osteoporosis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Network Analysis of Legg-Calve-Perthes Disease and Its Comorbidities.Journal of clinical medicine · 2025Article
- Unraveling the Mechanism of Impaired Osteogenic Differentiation in Osteoporosis: Insights fromCells · 2024Article
- SNPs in GPCR Genes and Impaired Osteogenic Potency in Osteoporotic Patient Lines-Based Study.International journal of molecular sciences · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We performed comorbidity-network analysis to obtain global view of comorbidity related with osteoporosis. We selected 10000-patients with osteoporosis registered in the National-Health-Insurance Service cohort-database. We found 45-significant disease-clusters. Of these, 14-disease-clusters were related to fra, while 10 were related to musculoskeletal diseases. Our findings will serve as basic data for further studies. PURPOSE: Osteoporosis causes devastating fractures; however, its exact etiology remains unknown. Elucidating associated comorbidities and their temporal relationships could provide better insights into its pathogenesis. Comorbidity-network analysis was performed to obtain global view of these associations.
methodsWe randomly selected 10000-patients with osteoporosis registered in the National-Health-Insurance Service cohort-database. These patients were identified using ICD-10 codes M81-M82, which represent osteoporosis without pathological fractures. Control group was created through propensity score matching. The comorbidities in each group were grouped into similar classifications to form "disease cluster"; 126 such clusters were identified. To create a comorbidity network, we selected disease clusters with high associations (i.e., odds ratios and relative risks ranked in the upper 50
resultsFinally, we found 45 significant disease clusters. Of these, 14 disease clusters were related to fractures or injuries, while 10 were related to musculoskeletal diseases. Temporal analysis revealed that 15 disease clusters preceded osteoporosis; these included the following three with the strongest associations: "other fracture", "disorders of bone density and structure (M83-M85)", and "sequelae of injuries of neck and trunk (T91)". Thirty disease clusters followed osteoporosis; these included the following three with the strongest associations: "spine fracture," "spondylopathies (M45-M49)", and "pelvic region and thigh fracture,".
conclusionWe obtained a global view of the osteoporosis comorbidity network, which is otherwise difficult to achieve through study of individual diseases. Our findings will serve as the basic data for further studies.
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