Identification of Hypertension Subgroups through Topological Analysis of Symptom-Based Patient Similarity.
10.1007/s11655-021-3336-3
- Author:
Yi-Fei WANG
1
;
Jing-Jing WANG
2
;
Wei PENG
1
;
Yong-Hao REN
3
;
Chao GAO
4
;
Yun-Lun LI
5
;
Rui WANG
4
;
Xiao-Feng WANG
4
;
Song-Jun HAN
6
;
Jia-Yu LYU
7
;
Jia-Ming HUAN
6
;
Cui CHEN
8
;
Hai-Yan WANG
6
;
Zi-Xin SHU
2
;
Xue-Zhong ZHOU
2
;
Wei LI
9
Author Information
1. Clinical Research Base, the Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250014, China.
2. Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China.
3. Department of Urology, the Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250014, China.
4. Information Center, the Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250014, China.
5. The First College of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250014, China.
6. College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250014, China.
7. College of Acupuncture and Massage, Shandong University of Traditional Chinese Medicine, Jinan, 250014, China.
8. Cardiovascular Department, Pingyin Hospital of Traditional Chinese Medicine, Jinan, 250400, China.
9. Department of Urology, the Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250014, China. lweidw@163.com.
- Publication Type:Journal Article
- Keywords:
hypertension;
network medicine;
patient subgroup;
precision medicine;
symptom phenotype
- From:
Chinese journal of integrative medicine
2021;27(9):656-665
- CountryChina
- Language:English
-
Abstract:
OBJECTIVE:To obtain the subtypes of the clinical hypertension population based on symptoms and to explore the relationship between hypertension and comorbidities.
METHODS:The data set was collected from the Chinese medicine (CM) electronic medical records of 33,458 hypertension inpatients in the Affiliated Hospital of Shandong University of Traditional Chinese Medicine between July 2014 and May 2017. Then, a hypertension disease comorbidity network (HDCN) was built to investigate the complicated associations between hypertension and their comorbidities. Moreover, a hypertension patient similarity network (HPSN) was constructed with patients' shared symptoms, and 7 main hypertension patient subgroups were identified from HPSN with a community detection method to exhibit the characteristics of clinical phenotypes and molecular mechanisms. In addition, the significant symptoms, diseases, CM syndromes and pathways of each main patient subgroup were obtained by enrichment analysis.
RESULTS:The significant symptoms and diseases of these patient subgroups were associated with different damaged target organs of hypertension. Additionally, the specific phenotypic features (symptoms, diseases, and CM syndromes) were consistent with specific molecular features (pathways) in the same patient subgroup.
CONCLUSION:The utility and comprehensiveness of disease classification based on community detection of patient networks using shared CM symptom phenotypes showed the importance of hypertension patient subgroups.
