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Network analysis of osteoporosis provides a global view of associated comorbidities and their temporal relationships

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Abstract

Summary

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.

Methods

We 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 50th percentile). To identify the temporal relationships between these clusters and osteoporosis, trajectories of directions were identified.

Results

Finally, 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,”.

Conclusion

We 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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Acknowledgement

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2021R1A4A3023587, 2022R1A2C2005916, 2022R1F1A1074057).

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Authors and Affiliations

  1. Department of Orthopaedic Surgery, Ilsan Paik Hospital, Inje University College of Medicine, Goyang-si, Gyeonggi-do, 10380, Republic of Korea

    Hyun Il Lee & Jin Hwan Kim

  2. Department of Orthopedic Surgery, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam-si, Gyeonggi-do, 13496, Republic of Korea

    Siyeong Yoon, Wooyeol Ahn & Soonchul Lee

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  1. Hyun Il Lee
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Contributions

S.Y.: Data curation; formal analysis; writing original draft

H.L.: Project administration; data curation; formal analysis; writing original draft

J.K.: Project administration; writing original draft

W.A.: Manuscript review and editing

S.L.: Conceptualization; funding acquisition; investigation; methodology; manuscript review and editing

Corresponding author

Correspondence to Soonchul Lee.

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Ethical approval

The study had been approved by the appropriate institutional research ethics committee and has been performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. For this type of study formal consent is not required.

Conflicts of interest

Competing interest: Hyun Il Lee, Siyeong Yoon, Jin Hwan Kim, Wooyeol Ahn, and Soonchul Lee declare that they have no conflict of interest.

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Supplementary information

ESM 1

Supplementary Fig. 1 Undirected network map of all comorbidities (126 disease cluster). This figure represents the global view of association among total 126 disease-clusters. All links whose p-value is less than 0.05 after Fisher’s exact test, were included. The size of nodes is proportional to the degree (number of link to other disease-clusters). The color of nodes represents the type of ICD-10 (major classification according to initial alphabet character). The thickness of edges indicates the odds ratio between disease clusters. (TIF 1600 kb) (PNG 1118 kb)

High resolution image (TIF 1600 kb)

ESM 2

Supplementary Fig. 2 The network of disease clusters with high-interconnectivity. The most highly connected 11 disease-clusters were selected to make another network graph. Surprisingly only 3 disease-clusters were omitted in this network graph compared to original graph made by 45 disease-clusters (C73 Malignant neoplasm of thyroid gland; D10-D36 Benign neoplasms; E00-E07 Disorders of thyroid gland) showing the vital role of highly interconnected disease cluster as hub. (PNG 1535 kb)

High resolution image (TIF 1776 kb)

ESM 3

(DOCX 31.5 KB)

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Lee, H.I., Yoon, S., Kim, J.H. et al. Network analysis of osteoporosis provides a global view of associated comorbidities and their temporal relationships. Arch Osteoporos 18, 79 (2023). https://doi.org/10.1007/s11657-023-01290-2

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