A network-based study reveals multimorbidity patterns in people with type 2 diabetes
Summary: Patients with type 2 diabetes mellitus (T2DM) are at a heightened risk of living with multiple comorbidities. However, the comprehension of the multimorbidity characteristics of T2DM is still scarce. This study aims to illuminate T2DM’s prevalent comorbidities and their interrelationships u...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004223020564 |
| _version_ | 1797647814702923776 |
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| author | Zizheng Zhang Ping He Huayan Yao Renjie Jing Wen Sun Ping Lu Yanbin Xue Jiying Qi Bin Cui Min Cao Guang Ning |
| author_facet | Zizheng Zhang Ping He Huayan Yao Renjie Jing Wen Sun Ping Lu Yanbin Xue Jiying Qi Bin Cui Min Cao Guang Ning |
| author_sort | Zizheng Zhang |
| collection | DOAJ |
| description | Summary: Patients with type 2 diabetes mellitus (T2DM) are at a heightened risk of living with multiple comorbidities. However, the comprehension of the multimorbidity characteristics of T2DM is still scarce. This study aims to illuminate T2DM’s prevalent comorbidities and their interrelationships using network analysis. Using electronic medical records (EMRs) from 496,408 Chinese patients with T2DM, we constructed male and female global multimorbidity networks and age- and sex-specific networks. Employing diverse network metrics, we assessed the structural properties of these networks. Furthermore, we identified hub, root, and burst diseases within these networks while scrutinizing their temporal trends. Our findings uncover interconnected T2DM comorbidities manifesting as emergence in clusters or age-specific outbreaks and core diseases in each sex that necessitate timely detection and intervention. This data-driven methodology offers a comprehensive comprehension of T2DM’s multimorbidity, providing hypotheses for clinical considerations in the prevention and therapeutic strategies. |
| first_indexed | 2024-03-11T15:21:55Z |
| format | Article |
| id | doaj.art-4ac58f695ae24502929c708012b72b97 |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-03-11T15:21:55Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-4ac58f695ae24502929c708012b72b972023-10-28T05:09:12ZengElsevieriScience2589-00422023-10-012610107979A network-based study reveals multimorbidity patterns in people with type 2 diabetesZizheng Zhang0Ping He1Huayan Yao2Renjie Jing3Wen Sun4Ping Lu5Yanbin Xue6Jiying Qi7Bin Cui8Min Cao9Guang Ning10Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaLink Healthcare Engineering and Information Department, Shanghai Hospital Development Center, Shanghai, ChinaComputer Net Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaWonders Information Co. Ltd., Shanghai, ChinaWonders Information Co. Ltd., Shanghai, ChinaComputer Net Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Corresponding authorDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Corresponding authorDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Corresponding authorSummary: Patients with type 2 diabetes mellitus (T2DM) are at a heightened risk of living with multiple comorbidities. However, the comprehension of the multimorbidity characteristics of T2DM is still scarce. This study aims to illuminate T2DM’s prevalent comorbidities and their interrelationships using network analysis. Using electronic medical records (EMRs) from 496,408 Chinese patients with T2DM, we constructed male and female global multimorbidity networks and age- and sex-specific networks. Employing diverse network metrics, we assessed the structural properties of these networks. Furthermore, we identified hub, root, and burst diseases within these networks while scrutinizing their temporal trends. Our findings uncover interconnected T2DM comorbidities manifesting as emergence in clusters or age-specific outbreaks and core diseases in each sex that necessitate timely detection and intervention. This data-driven methodology offers a comprehensive comprehension of T2DM’s multimorbidity, providing hypotheses for clinical considerations in the prevention and therapeutic strategies.http://www.sciencedirect.com/science/article/pii/S2589004223020564EndocrinologyPathophysiologyMedical informatics |
| spellingShingle | Zizheng Zhang Ping He Huayan Yao Renjie Jing Wen Sun Ping Lu Yanbin Xue Jiying Qi Bin Cui Min Cao Guang Ning A network-based study reveals multimorbidity patterns in people with type 2 diabetes iScience Endocrinology Pathophysiology Medical informatics |
| title | A network-based study reveals multimorbidity patterns in people with type 2 diabetes |
| title_full | A network-based study reveals multimorbidity patterns in people with type 2 diabetes |
| title_fullStr | A network-based study reveals multimorbidity patterns in people with type 2 diabetes |
| title_full_unstemmed | A network-based study reveals multimorbidity patterns in people with type 2 diabetes |
| title_short | A network-based study reveals multimorbidity patterns in people with type 2 diabetes |
| title_sort | network based study reveals multimorbidity patterns in people with type 2 diabetes |
| topic | Endocrinology Pathophysiology Medical informatics |
| url | http://www.sciencedirect.com/science/article/pii/S2589004223020564 |
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