1
|
Liu Z, Wang J, Guo F, Xu T, Yu F, Deng Q, Tan W, Duan S, Song L, Wang Y, Sun J, Zhou L, Wang Y, Zhou X, Xia H, Jiang H. Role of S100β in Patients with Unstable Angina Pectoris: Insights from Quantitative Flow Ratio. Arch Med Res 2024; 55:103034. [PMID: 38972195 DOI: 10.1016/j.arcmed.2024.103034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 06/03/2024] [Accepted: 06/17/2024] [Indexed: 07/09/2024]
Abstract
BACKGROUND AND OBJECTIVE Disturbed autonomic nervous system (ANS) may promote inflammatory, immune, and oxidative stress responses, which may increase the risk of acute coronary events. S100β has been proposed as a biomarker of neuronal injury that would provide an insightful understanding of the crosstalk between the ANS, immune-inflammatory cells, and plaques that drive atherosclerosis. This study investigates the correlation between S100β, and functional coronary stenosis as determined by quantitative flow ratio (QFR). METHODS Patients with unstable angina pectoris (UAP) scheduled for coronary angiography and QFR were retrospectively enrolled. Serum S100β levels were determined by enzyme-linked immunosorbent assay. The Gensini score was used to estimate the extent of atherosclerotic lesions and the cumulative sum of three-vessel QFR (3V-QFR) was calculated to estimate the total atherosclerotic burden. RESULTS Two hundred thirty-three patients were included in this study. Receiver operator characteristic (ROC) curve indicated that S100β>33.28 pg/mL predicted functional ischemia in patients with UAP. Multivariate logistic analyses showed that a higher level of S100β was independently correlated with a functional ischemia-driven target vessel (QFR ≤0.8). This was also closely correlated with the severity of coronary lesions, as measured by the Gensini score (OR = 5.058, 95% CI: 2.912-8.793, p <0.001). According to 3V-QFR, S100β is inversely associated with total atherosclerosis burden (B = -0.002, p <0.001). CONCLUSIONS S100β was elevated in the functional ischemia stages of UAP. It was independently associated with coronary lesion severity as assessed by Gensini score and total atherosclerosis burden as estimated by 3V-QFR in patients with UAP.
Collapse
Affiliation(s)
- Zhihao Liu
- Department of Cardiology, Wuhan No.1 Hospital, Wuhan, Hubei, China; Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, Hubei, China; Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, Hubei, China; Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei, China; Institute of Molecular Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Cardiovascular Research Institute, Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Cardiology, Wuhan, Hubei, China
| | - Jun Wang
- Department of Cardiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, China
| | - Fuding Guo
- Department of Cardiology, Yan'an Hospital, Kunming Medical University, Kunming, China
| | - Tianyou Xu
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, Hubei, China; Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, Hubei, China; Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei, China; Institute of Molecular Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Cardiovascular Research Institute, Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Cardiology, Wuhan, Hubei, China
| | - Fu Yu
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, Hubei, China; Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, Hubei, China; Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei, China; Institute of Molecular Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Cardiovascular Research Institute, Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Cardiology, Wuhan, Hubei, China
| | - Qiang Deng
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, Hubei, China; Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, Hubei, China; Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei, China; Institute of Molecular Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Cardiovascular Research Institute, Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Cardiology, Wuhan, Hubei, China
| | - Wuping Tan
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, Hubei, China; Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, Hubei, China; Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei, China; Institute of Molecular Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Cardiovascular Research Institute, Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Cardiology, Wuhan, Hubei, China
| | - Shoupeng Duan
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, Hubei, China; Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, Hubei, China; Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei, China; Institute of Molecular Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Cardiovascular Research Institute, Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Cardiology, Wuhan, Hubei, China
| | - Lingpeng Song
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, Hubei, China; Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, Hubei, China; Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei, China; Institute of Molecular Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Cardiovascular Research Institute, Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Cardiology, Wuhan, Hubei, China
| | - Yijun Wang
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, Hubei, China; Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, Hubei, China; Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei, China; Institute of Molecular Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Cardiovascular Research Institute, Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Cardiology, Wuhan, Hubei, China
| | - Ji Sun
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, Hubei, China; Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, Hubei, China; Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei, China; Institute of Molecular Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Cardiovascular Research Institute, Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Cardiology, Wuhan, Hubei, China
| | - Liping Zhou
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, Hubei, China; Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, Hubei, China; Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei, China; Institute of Molecular Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Cardiovascular Research Institute, Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Cardiology, Wuhan, Hubei, China
| | - Yueyi Wang
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, Hubei, China; Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, Hubei, China; Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei, China; Institute of Molecular Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Cardiovascular Research Institute, Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Cardiology, Wuhan, Hubei, China
| | - Xiaoya Zhou
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, Hubei, China; Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, Hubei, China; Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei, China; Institute of Molecular Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Cardiovascular Research Institute, Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Cardiology, Wuhan, Hubei, China
| | - Hao Xia
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, Hubei, China; Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, Hubei, China; Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei, China; Institute of Molecular Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Cardiovascular Research Institute, Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Cardiology, Wuhan, Hubei, China
| | - Hong Jiang
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, Hubei, China; Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, Hubei, China; Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei, China; Institute of Molecular Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, China; Cardiovascular Research Institute, Wuhan University, Wuhan, Hubei, China; Hubei Key Laboratory of Cardiology, Wuhan, Hubei, China.
| |
Collapse
|
2
|
Woerner J, Sriram V, Nam Y, Verma A, Kim D. Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network. Bioinformatics 2024; 40:btae126. [PMID: 38527901 PMCID: PMC10963079 DOI: 10.1093/bioinformatics/btae126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 01/17/2024] [Indexed: 03/27/2024] Open
Abstract
MOTIVATION Many diseases, particularly cardiometabolic disorders, exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with intermediate endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions. RESULTS Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between diseases and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and heart failure. Triglycerides, another blood lipid with known genetic causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. This work demonstrates how association with clinical biomarkers can better explain the shared genetics between cardiometabolic disorders. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities. AVAILABILITY AND IMPLEMENTATION The generated ssDDN+ can be explored at https://hdpm.biomedinfolab.com/ddn/biomarkerDDN.
Collapse
Affiliation(s)
- Jakob Woerner
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Vivek Sriram
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Yonghyun Nam
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Anurag Verma
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104, United States
| |
Collapse
|
3
|
Cruz-Ávila HA, Ramírez-Alatriste F, Martínez-García M, Hernández-Lemus E. Comorbidity patterns in cardiovascular diseases: the role of life-stage and socioeconomic status. Front Cardiovasc Med 2024; 11:1215458. [PMID: 38414921 PMCID: PMC10897012 DOI: 10.3389/fcvm.2024.1215458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 01/29/2024] [Indexed: 02/29/2024] Open
Abstract
Cardiovascular diseases stand as a prominent global cause of mortality, their intricate origins often entwined with comorbidities and multimorbid conditions. Acknowledging the pivotal roles of age, sex, and social determinants of health in shaping the onset and progression of these diseases, our study delves into the nuanced interplay between life-stage, socioeconomic status, and comorbidity patterns within cardiovascular diseases. Leveraging data from a cross-sectional survey encompassing Mexican adults, we unearth a robust association between these variables and the prevalence of comorbidities linked to cardiovascular conditions. To foster a comprehensive understanding of multimorbidity patterns across diverse life-stages, we scrutinize an extensive dataset comprising 47,377 cases diagnosed with cardiovascular ailments at Mexico's national reference hospital. Extracting sociodemographic details, primary diagnoses prompting hospitalization, and additional conditions identified through ICD-10 codes, we unveil subtle yet significant associations and discuss pertinent specific cases. Our results underscore a noteworthy trend: younger patients of lower socioeconomic status exhibit a heightened likelihood of cardiovascular comorbidities compared to their older counterparts with a higher socioeconomic status. By empowering clinicians to discern non-evident comorbidities, our study aims to refine therapeutic designs. These findings offer profound insights into the intricate interplay among life-stage, socioeconomic status, and comorbidity patterns within cardiovascular diseases. Armed with data-supported approaches that account for these factors, clinical practices stand to be enhanced, and public health policies informed, ultimately advancing the prevention and management of cardiovascular disease in Mexico.
Collapse
Affiliation(s)
- Héctor A Cruz-Ávila
- Graduate Program in Complexity Sciences, Autonomous University of México City, México City, Mexico
- Immunology Department, National Institute of Cardiology 'Ignacio Chávez', México City, Mexico
| | | | - Mireya Martínez-García
- Immunology Department, National Institute of Cardiology 'Ignacio Chávez', México City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, México City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, México City, Mexico
| |
Collapse
|
4
|
Liu Z, Xu J, Tan J, Li X, Zhang F, Ouyang W, Wang S, Huang Y, Li S, Pan X. Genetic overlap for ten cardiovascular diseases: A comprehensive gene-centric pleiotropic association analysis and Mendelian randomization study. iScience 2023; 26:108150. [PMID: 37908310 PMCID: PMC10613921 DOI: 10.1016/j.isci.2023.108150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 08/13/2023] [Accepted: 10/02/2023] [Indexed: 11/02/2023] Open
Abstract
Recent studies suggest that pleiotropic effects may explain the genetic architecture of cardiovascular diseases (CVDs). We conducted a comprehensive gene-centric pleiotropic association analysis for ten CVDs using genome-wide association study (GWAS) summary statistics to identify pleiotropic genes and pathways that may underlie multiple CVDs. We found shared genetic mechanisms underlying the pathophysiology of CVDs, with over two-thirds of the diseases exhibiting common genes and single-nucleotide polymorphisms (SNPs). Significant positive genetic correlations were observed in more than half of paired CVDs. Additionally, we investigated the pleiotropic genes shared between different CVDs, as well as their functional pathways and distribution in different tissues. Moreover, six hub genes, including ALDH2, XPO1, HSPA1L, ESR2, WDR12, and RAB1A, as well as 26 targeted potential drugs, were identified. Our study provides further evidence for the pleiotropic effects of genetic variants on CVDs and highlights the importance of considering pleiotropy in genetic association studies.
Collapse
Affiliation(s)
- Zeye Liu
- Department of Structural Heart Disease, National Center for Cardiovascular Disease, China & Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100037, China
- National Health Commission Key Laboratory of Cardiovascular Regeneration Medicine, Beijing 100037, China
- Key Laboratory of Innovative Cardiovascular Devices, Chinese Academy of Medical Sciences, Beijing 100037, China
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Jing Xu
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College, Beijing, China
| | - Jiangshan Tan
- Key Laboratory of Pulmonary Vascular Medicine, National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Xiaofei Li
- Department of Cardiology, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Fengwen Zhang
- Department of Structural Heart Disease, National Center for Cardiovascular Disease, China & Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100037, China
- National Health Commission Key Laboratory of Cardiovascular Regeneration Medicine, Beijing 100037, China
- Key Laboratory of Innovative Cardiovascular Devices, Chinese Academy of Medical Sciences, Beijing 100037, China
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Wenbin Ouyang
- Department of Structural Heart Disease, National Center for Cardiovascular Disease, China & Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100037, China
- National Health Commission Key Laboratory of Cardiovascular Regeneration Medicine, Beijing 100037, China
- Key Laboratory of Innovative Cardiovascular Devices, Chinese Academy of Medical Sciences, Beijing 100037, China
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Shouzheng Wang
- Department of Structural Heart Disease, National Center for Cardiovascular Disease, China & Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100037, China
- National Health Commission Key Laboratory of Cardiovascular Regeneration Medicine, Beijing 100037, China
- Key Laboratory of Innovative Cardiovascular Devices, Chinese Academy of Medical Sciences, Beijing 100037, China
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Yuan Huang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Pediatric Cardiac Surgery Center, Fuwai Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College, Beijing, China
| | - Shoujun Li
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Pediatric Cardiac Surgery Center, Fuwai Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College, Beijing, China
| | - Xiangbin Pan
- Department of Structural Heart Disease, National Center for Cardiovascular Disease, China & Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100037, China
- National Health Commission Key Laboratory of Cardiovascular Regeneration Medicine, Beijing 100037, China
- Key Laboratory of Innovative Cardiovascular Devices, Chinese Academy of Medical Sciences, Beijing 100037, China
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
| |
Collapse
|
5
|
Zhao B, Huepenbecker S, Zhu G, Rajan SS, Fujimoto K, Luo X. Comorbidity network analysis using graphical models for electronic health records. Front Big Data 2023; 6:846202. [PMID: 37663273 PMCID: PMC10470017 DOI: 10.3389/fdata.2023.846202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 07/25/2023] [Indexed: 09/05/2023] Open
Abstract
Importance The comorbidity network represents multiple diseases and their relationships in a graph. Understanding comorbidity networks among critical care unit (CCU) patients can help doctors diagnose patients faster, minimize missed diagnoses, and potentially decrease morbidity and mortality. Objective The main objective of this study was to identify the comorbidity network among CCU patients using a novel application of a machine learning method (graphical modeling method). The second objective was to compare the machine learning method with a traditional pairwise method in simulation. Method This cross-sectional study used CCU patients' data from Medical Information Mart for the Intensive Care-3 (MIMIC-3) dataset, an electronic health record (EHR) of patients with CCU hospitalizations within Beth Israel Deaconess Hospital from 2001 to 2012. A machine learning method (graphical modeling method) was applied to identify the comorbidity network of 654 diagnosis categories among 46,511 patients. Results Out of the 654 diagnosis categories, the graphical modeling method identified a comorbidity network of 2,806 associations in 510 diagnosis categories. Two medical professionals reviewed the comorbidity network and confirmed that the associations were consistent with current medical understanding. Moreover, the strongest association in our network was between "poisoning by psychotropic agents" and "accidental poisoning by tranquilizers" (logOR 8.16), and the most connected diagnosis was "disorders of fluid, electrolyte, and acid-base balance" (63 associated diagnosis categories). Our method outperformed traditional pairwise comorbidity network methods in simulation studies. Some strongest associations between diagnosis categories were also identified, for example, "diagnoses of mitral and aortic valve" and "other rheumatic heart disease" (logOR: 5.15). Furthermore, our method identified diagnosis categories that were connected with most other diagnosis categories, for example, "disorders of fluid, electrolyte, and acid-base balance" was associated with 63 other diagnosis categories. Additionally, using a data-driven approach, our method partitioned the diagnosis categories into 14 modularity classes. Conclusion and relevance Our graphical modeling method inferred a logical comorbidity network whose associations were consistent with current medical understanding and outperformed traditional network methods in simulation. Our comorbidity network method can potentially assist CCU doctors in diagnosing patients faster and minimizing missed diagnoses.
Collapse
Affiliation(s)
- Bo Zhao
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center, Houston, TX, United States
| | - Sarah Huepenbecker
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Gen Zhu
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center, Houston, TX, United States
| | - Suja S. Rajan
- Department of Management, Policy and Community Health, School of Public Health, The University of Texas Health Science Center, Houston, TX, United States
| | - Kayo Fujimoto
- Department of Health Promotion and Behavioral Sciences, School of Public Health, The University of Texas Health Science Center, Houston, TX, United States
| | - Xi Luo
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center, Houston, TX, United States
| |
Collapse
|
6
|
Lanzer JD, Valdeolivas A, Pepin M, Hund H, Backs J, Frey N, Friederich HC, Schultz JH, Saez-Rodriguez J, Levinson RT. A network medicine approach to study comorbidities in heart failure with preserved ejection fraction. BMC Med 2023; 21:267. [PMID: 37488529 PMCID: PMC10367269 DOI: 10.1186/s12916-023-02922-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 06/05/2023] [Indexed: 07/26/2023] Open
Abstract
BACKGROUND Comorbidities are expected to impact the pathophysiology of heart failure (HF) with preserved ejection fraction (HFpEF). However, comorbidity profiles are usually reduced to a few comorbid disorders. Systems medicine approaches can model phenome-wide comorbidity profiles to improve our understanding of HFpEF and infer associated genetic profiles. METHODS We retrospectively explored 569 comorbidities in 29,047 HF patients, including 8062 HFpEF and 6585 HF with reduced ejection fraction (HFrEF) patients from a German university hospital. We assessed differences in comorbidity profiles between HF subtypes via multiple correspondence analysis. Then, we used machine learning classifiers to identify distinctive comorbidity profiles of HFpEF and HFrEF patients. Moreover, we built a comorbidity network (HFnet) to identify the main disease clusters that summarized the phenome-wide comorbidity. Lastly, we predicted novel gene candidates for HFpEF by linking the HFnet to a multilayer gene network, integrating multiple databases. To corroborate HFpEF candidate genes, we collected transcriptomic data in a murine HFpEF model. We compared predicted genes with the murine disease signature as well as with the literature. RESULTS We found a high degree of variance between the comorbidity profiles of HFpEF and HFrEF, while each was more similar to HFmrEF. The comorbidities present in HFpEF patients were more diverse than those in HFrEF and included neoplastic, osteologic and rheumatoid disorders. Disease communities in the HFnet captured important comorbidity concepts of HF patients which could be assigned to HF subtypes, age groups, and sex. Based on the HFpEF comorbidity profile, we predicted and recovered gene candidates, including genes involved in fibrosis (COL3A1, LOX, SMAD9, PTHL), hypertrophy (GATA5, MYH7), oxidative stress (NOS1, GSST1, XDH), and endoplasmic reticulum stress (ATF6). Finally, predicted genes were significantly overrepresented in the murine transcriptomic disease signature providing additional plausibility for their relevance. CONCLUSIONS We applied systems medicine concepts to analyze comorbidity profiles in a HF patient cohort. We were able to identify disease clusters that helped to characterize HF patients. We derived a distinct comorbidity profile for HFpEF, which was leveraged to suggest novel candidate genes via network propagation. The identification of distinctive comorbidity profiles and candidate genes from routine clinical data provides insights that may be leveraged to improve diagnosis and identify treatment targets for HFpEF patients.
Collapse
Affiliation(s)
- Jan D Lanzer
- Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Bioquant, Heidelberg, Germany.
- Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg, Germany.
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.
- Informatics for Life, Heidelberg, Germany.
| | - Alberto Valdeolivas
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Mark Pepin
- Institute of Experimental Cardiology, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Heidelberg/Mannheim, Im Neuenheimer Feld 669, 69120, Heidelberg, Germany
| | - Hauke Hund
- Department of Cardiology, Internal Medicine III, Heidelberg University Hospital, Heidelberg, Germany
| | - Johannes Backs
- Institute of Experimental Cardiology, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Heidelberg/Mannheim, Im Neuenheimer Feld 669, 69120, Heidelberg, Germany
| | - Norbert Frey
- Department of Cardiology, Internal Medicine III, Heidelberg University Hospital, Heidelberg, Germany
| | - Hans-Christoph Friederich
- Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg, Germany
- Informatics for Life, Heidelberg, Germany
| | - Jobst-Hendrik Schultz
- Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg, Germany
- Informatics for Life, Heidelberg, Germany
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Bioquant, Heidelberg, Germany
- Informatics for Life, Heidelberg, Germany
| | - Rebecca T Levinson
- Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Bioquant, Heidelberg, Germany.
- Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg, Germany.
- Informatics for Life, Heidelberg, Germany.
| |
Collapse
|
7
|
Zhou D, Qiu H, Wang L, Shen M. Risk prediction of heart failure in patients with ischemic heart disease using network analytics and stacking ensemble learning. BMC Med Inform Decis Mak 2023; 23:99. [PMID: 37221512 DOI: 10.1186/s12911-023-02196-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 05/15/2023] [Indexed: 05/25/2023] Open
Abstract
BACKGROUND Heart failure (HF) is a major complication following ischemic heart disease (IHD) and it adversely affects the outcome. Early prediction of HF risk in patients with IHD is beneficial for timely intervention and for reducing disease burden. METHODS Two cohorts, cases for patients first diagnosed with IHD and then with HF (N = 11,862) and control IHD patients without HF (N = 25,652), were established from the hospital discharge records in Sichuan, China during 2015-2019. Directed personal disease network (PDN) was constructed for each patient, and then these PDNs were merged to generate the baseline disease network (BDN) for the two cohorts, respectively, which identifies the health trajectories of patients and the complex progression patterns. The differences between the BDNs of the two cohort was represented as disease-specific network (DSN). Three novel network features were exacted from PDN and DSN to represent the similarity of disease patterns and specificity trends from IHD to HF. A stacking-based ensemble model DXLR was proposed to predict HF risk in IHD patients using the novel network features and basic demographic features (i.e., age and sex). The Shapley Addictive exPlanations method was applied to analyze the feature importance of the DXLR model. RESULTS Compared with the six traditional machine learning models, our DXLR model exhibited the highest AUC (0.934 ± 0.004), accuracy (0.857 ± 0.007), precision (0.723 ± 0.014), recall (0.892 ± 0.012) and F1 score (0.798 ± 0.010). The feature importance showed that the novel network features ranked as the top three features, playing a notable role in predicting HF risk of IHD patient. The feature comparison experiment also indicated that our novel network features were superior to those proposed by the state-of-the-art study in improving the performance of the prediction model, with an increase in AUC by 19.9%, in accuracy by 18.7%, in precision by 30.7%, in recall by 37.4%, and in F1 score by 33.7%. CONCLUSIONS Our proposed approach that combines network analytics and ensemble learning effectively predicts HF risk in patients with IHD. This highlights the potential value of network-based machine learning in disease risk prediction field using administrative data.
Collapse
Affiliation(s)
- Dejia Zhou
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, 611731, P.R. China
| | - Hang Qiu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, 611731, P.R. China.
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.
| | - Liya Wang
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China
| | - Minghui Shen
- Health Information Center of Sichuan Province, Chengdu, China
| |
Collapse
|
8
|
Woerner J, Sriram V, Nam Y, Verma A, Kim D. Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.11.23289852. [PMID: 37293013 PMCID: PMC10246076 DOI: 10.1101/2023.05.11.23289852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Many diseases exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions. Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between disease phenotypes and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and diabetic retinopathy. Triglycerides, another blood lipid with known genetics causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities.
Collapse
Affiliation(s)
- Jakob Woerner
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Vivek Sriram
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yonghyun Nam
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Anurag Verma
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| |
Collapse
|
9
|
Ung CY, Warwick A, Onoufriadis A, Barker JN, Parsons M, McGrath JA, Shaw TJ, Dand N. Comorbidities of Keloid and Hypertrophic Scars Among Participants in UK Biobank. JAMA Dermatol 2023; 159:172-181. [PMID: 36598763 PMCID: PMC9857738 DOI: 10.1001/jamadermatol.2022.5607] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 10/26/2022] [Indexed: 01/05/2023]
Abstract
Importance Keloids and hypertrophic scars (excessive scarring) are relatively understudied disfiguring chronic skin conditions with high treatment resistance. Objective To evaluate established comorbidities of excessive scarring in European individuals, with comparisons across ethnic groups, and to identify novel comorbidities via a phenome-wide association study (PheWAS). Design, Setting, and Participants This multicenter cross-sectional population-based cohort study used UK Biobank (UKB) data and fitted logistic regression models for testing associations between excessive scarring and a variety of outcomes, including previously studied comorbidities and 1518 systematically defined disease categories. Additional modeling was performed within subgroups of participants defined by self-reported ethnicity (as defined in UK Biobank). Of 502 701 UKB participants, analyses were restricted to 230078 individuals with linked primary care records. Exposures Keloid or hypertrophic scar diagnoses. Main Outcomes and Measures Previously studied disease associations (hypertension, uterine leiomyoma, vitamin D deficiency, atopic eczema) and phenotypes defined in the PheWAS Catalog. Results Of the 972 people with excessive scarring, there was a higher proportion of female participants compared with the 229 106 controls (65% vs 55%) and a lower proportion of White ethnicity (86% vs 95%); mean (SD) age of the total cohort was 64 (8) years. Associations were identified with hypertension and atopic eczema in models accounting for age, sex, and ethnicity, and the association with atopic eczema (odds ratio [OR], 1.68; 95% CI, 1.36-2.07; P < .001) remained statistically significant after accounting for additional potential confounders. Fully adjusted analyses within ethnic groups revealed associations with hypertension in Black participants (OR, 2.05; 95% CI, 1.13-3.72; P = .02) and with vitamin D deficiency in Asian participants (OR, 2.24; 95% CI, 1.26-3.97; P = .006). The association with uterine leiomyoma was borderline significant in Black women (OR, 1.93; 95% CI, 1.00-3.71; P = .05), whereas the association with atopic eczema was significant in White participants (OR, 1.68; 95% CI, 1.34-2.12; P < .001) and showed a similar trend in Asian (OR, 2.17; 95% CI, 1.01-4.67; P = .048) and Black participants (OR, 1.89; 95% CI, 0.83-4.28; P = .13). The PheWAS identified 110 significant associations across disease systems; of the nondermatological, musculoskeletal disease and pain symptoms were prominent. Conclusions and Relevance This cross-sectional study validated comorbidities of excessive scarring in UKB with comprehensive coverage of health outcomes. It also documented additional phenome-wide associations that will serve as a reference for future studies to investigate common underlying pathophysiologic mechanisms.
Collapse
Affiliation(s)
- Chuin Y. Ung
- St John’s Institute of Dermatology, School of Basic and Medical Biosciences, King’s College London, London, United Kingdom
- Centre for Inflammation Biology & Cancer Immunology, King’s College London, London, United Kingdom
| | - Alasdair Warwick
- University College London Institute of Cardiovascular Science, London, United Kingdom
| | - Alexandros Onoufriadis
- St John’s Institute of Dermatology, School of Basic and Medical Biosciences, King’s College London, London, United Kingdom
| | - Jonathan N. Barker
- St John’s Institute of Dermatology, School of Basic and Medical Biosciences, King’s College London, London, United Kingdom
| | - Maddy Parsons
- Randall Division of Cell and Molecular Biophysics, King’s College London, London, United Kingdom
| | - John A. McGrath
- St John’s Institute of Dermatology, School of Basic and Medical Biosciences, King’s College London, London, United Kingdom
| | - Tanya J. Shaw
- Centre for Inflammation Biology & Cancer Immunology, King’s College London, London, United Kingdom
| | - Nick Dand
- Department of Medical and Molecular Genetics, School of Basic & Medical Biosciences, Faculty of Life Sciences & Medicine, King’s College London, London, United Kingdom
| |
Collapse
|
10
|
Yang X, Xu W, Leng D, Wen Y, Wu L, Li R, Huang J, Bo X, He S. Exploring novel disease-disease associations based on multi-view fusion network. Comput Struct Biotechnol J 2023; 21:1807-1819. [PMID: 36923471 PMCID: PMC10009443 DOI: 10.1016/j.csbj.2023.02.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 02/02/2023] [Accepted: 02/22/2023] [Indexed: 03/06/2023] Open
Abstract
Established taxonomy system based on disease symptom and tissue characteristics have provided an important basis for physicians to correctly identify diseases and treat them successfully. However, these classifications tend to be based on phenotypic observations, lacking a molecular biological foundation. Therefore, there is an urgent to integrate multi-dimensional molecular biological information or multi-omics data to redefine disease classification in order to provide a powerful perspective for understanding the molecular structure of diseases. Therefore, we offer a flexible disease classification that integrates the biological process, gene expression, and symptom phenotype of diseases, and propose a disease-disease association network based on multi-view fusion. We applied the fusion approach to 223 diseases and divided them into 24 disease clusters. The contribution of internal and external edges of disease clusters were analyzed. The results of the fusion model were compared with Medical Subject Headings, a traditional and commonly used disease taxonomy. Then, experimental results of model performance comparison show that our approach performs better than other integration methods. As it was observed, the obtained clusters provided more interesting and novel disease-disease associations. This multi-view human disease association network describes relationships between diseases based on multiple molecular levels, thus breaking through the limitation of the disease classification system based on tissues and organs. This approach which motivates clinicians and researchers to reposition the understanding of diseases and explore diagnosis and therapy strategies, extends the existing disease taxonomy. Availability of data and materials The preprocessed dataset and source code supporting the conclusions of this article are available at GitHub repository https://github.com/yangxiaoxi89/mvHDN.
Collapse
Affiliation(s)
- Xiaoxi Yang
- Clinical Medicine Institute, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.,Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Wenjian Xu
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China.,Rare Disease Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China.,MOE Key Laboratory of Major Diseases in Children, Beijing 100045, China.,Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute, Beijing 100045, China
| | - Dongjin Leng
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Yuqi Wen
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Lianlian Wu
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Ruijiang Li
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Jian Huang
- Clinical Medicine Institute, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Xiaochen Bo
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Song He
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| |
Collapse
|
11
|
Phenotypic Disease Network-Based Multimorbidity Analysis in Idiopathic Cardiomyopathy Patients with Hospital Discharge Records. J Clin Med 2022; 11:jcm11236965. [PMID: 36498544 PMCID: PMC9736397 DOI: 10.3390/jcm11236965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/18/2022] [Accepted: 11/21/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Idiopathic cardiomyopathy (ICM) is a rare disease affecting numerous physiological and biomolecular systems with multimorbidity. However, due to the small sample size of uncommon diseases, the whole spectrum of chronic disease co-occurrence, especially in developing nations, has not yet been investigated. To grasp the multimorbidity pattern, we aimed to present a multidimensional model for ICM and differences among age groups. METHODS Hospital discharge records were collected from a rare disease centre of ICM inpatients (n = 1036) over 10 years (2012 to 2021) for this retrospective analysis. One-to-one matched controls were also included. First, by looking at the first three digits of the ICD-10 code, we concentrated on chronic illnesses with a prevalence of more than 1%. The ICM and control inpatients had a total of 71 and 69 chronic illnesses, respectively. Second, to evaluate the multimorbidity pattern in both groups, we built age-specific cosine-index-based multimorbidity networks. Third, the associated rule mining (ARM) assessed the comorbidities with heart failure for ICM, specifically. RESULTS The comorbidity burden of ICM was 78% larger than that of the controls. All ages were affected by the burden, although those over 50 years old had more intense interactions. Moreover, in terms of disease connectivity, central, hub, and authority diseases were concentrated in the metabolic, musculoskeletal and connective tissue, genitourinary, eye and adnexa, respiratory, and digestive systems. According to the age-specific connection, the impaired coagulation function was required for raising attention (e.g., autoimmune-attacked digestive and musculoskeletal system disorders) in young adult groups (ICM patients aged 20-49 years). For the middle-aged (50-60 years) and older (≥70 years) groups, malignant neoplasm and circulatory issues were the main confrontable problems. Finally, according to the result of ARM, the comorbidities and comorbidity patterns of heart failure include diabetes mellitus and metabolic disorder, sleeping disorder, renal failure, liver, and circulatory diseases. CONCLUSIONS The main cause of the comorbid load is aging. The ICM comorbidities were concentrated in the circulatory, metabolic, musculoskeletal and connective tissue, genitourinary, eye and adnexa, respiratory, and digestive systems. The network-based approach optimizes the integrated care of patients with ICM and advances our understanding of multimorbidity associated with the disease.
Collapse
|
12
|
Martínez-García M, Villegas Camacho JM, Hernández-Lemus E. Connections and Biases in Health Equity and Culture Research: A Semantic Network Analysis. Front Public Health 2022; 10:834172. [PMID: 35425756 PMCID: PMC9002348 DOI: 10.3389/fpubh.2022.834172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 03/07/2022] [Indexed: 11/27/2022] Open
Abstract
Health equity is a rather complex issue. Social context and economical disparities, are known to be determining factors. Cultural and educational constrains however, are also important contributors to the establishment and development of health inequities. As an important starting point for a comprehensive discussion, a detailed analysis of the literature corpus is thus desirable: we need to recognize what has been done, under what circumstances, even what possible sources of bias exist in our current discussion on this relevant issue. By finding these trends and biases we will be better equipped to modulate them and find avenues that may lead us to a more integrated view of health inequity, potentially enhancing our capabilities to intervene to ameliorate it. In this study, we characterized at a large scale, the social and cultural determinants most frequently reported in current global research of health inequity and the interrelationships among them in different populations under diverse contexts. We used a data/literature mining approach to the current literature followed by a semantic network analysis of the interrelationships discovered. The analyzed structured corpus consisted in circa 950 articles categorized by means of the Medical Subheadings (MeSH) content-descriptor from 2014 to 2021. Further analyses involved systematic searches in the LILACS and DOAJ databases, as additional sources. The use of data analytics techniques allowed us to find a number of non-trivial connections, pointed out to existing biases and under-represented issues and let us discuss what are the most relevant concepts that are (and are not) being discussed in the context of Health Equity and Culture.
Collapse
Affiliation(s)
- Mireya Martínez-García
- Department of Immunology, National Institute of Cardiology Ignacio Chávez, Mexico City, Mexico
| | - José Manuel Villegas Camacho
- Clinical Research Division, National Institute of Cardiology Ignacio Chávez, Mexico City, Mexico.,Social Relations Department, Universidad Autónoma Metropolitana, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico.,Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
| |
Collapse
|
13
|
Zhou D, Wang L, Ding S, Shen M, Qiu H. Phenotypic Disease Network Analysis to Identify Comorbidity Patterns in Hospitalized Patients with Ischemic Heart Disease Using Large-Scale Administrative Data. Healthcare (Basel) 2022; 10:healthcare10010080. [PMID: 35052244 PMCID: PMC8775672 DOI: 10.3390/healthcare10010080] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 12/24/2021] [Accepted: 12/29/2021] [Indexed: 02/04/2023] Open
Abstract
Ischemic heart disease (IHD) exhibits elevated comorbidity. However, few studies have systematically analyzed the comorbid status of IHD patients with respect to the entire spectrum of chronic diseases. This study applied network analysis to provide a complete picture of physical and mental comorbidities in hospitalized patients with IHD using large-scale administrative data. Hospital discharge records from a provincial healthcare database of IHD inpatients (n = 1,035,338) and one-to-one matched controls were included in this retrospective analysis. We constructed the phenotypic disease networks in IHD and control patients and further assessed differences in comorbidity patterns. The community detection method was applied to cluster diagnoses within the comorbidity network. Age- and sex-specific patterns of IHD comorbidities were also analyzed. IHD inpatients showed 50% larger comorbid burden when compared to controls. The IHD comorbidity network consisted of 1941 significant associations between 71 chronic conditions. Notably, the more densely connected comorbidities in IHD patients were not within the highly prevalent ones but the rarely prevalent ones. Two highly interlinked communities were detected in the IHD comorbidity network, where one included hypertension with heart and multi-organ failures, and another included cerebrovascular diseases, cerebrovascular risk factors and anxiety. Males exhibited higher comorbid burden than females, and thus more complex comorbidity relationships were found in males. Sex-specific disease pairs were detected, e.g., 106 and 30 disease pairs separately dominated in males and females. Aging accounts for the majority of comorbid burden, and the complexity of the comorbidity network increased with age. The network-based approach improves our understanding of IHD-related comorbidities and enhances the integrated management of patients with IHD.
Collapse
Affiliation(s)
- Dejia Zhou
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China; (D.Z.); (L.W.)
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Liya Wang
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China; (D.Z.); (L.W.)
| | - Shuhan Ding
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA;
| | - Minghui Shen
- Health Information Center of Sichuan Province, Chengdu 610041, China;
| | - Hang Qiu
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China; (D.Z.); (L.W.)
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
- Correspondence: ; Tel.: +86-28-618-302-78
| |
Collapse
|
14
|
Zhou Y, Zhu XP, Shi JJ, Yuan GZ, Yao ZA, Chu YG, Shi S, Jia QL, Chen T, Hu YH. Coronary Heart Disease and Depression or Anxiety: A Bibliometric Analysis. Front Psychol 2021; 12:669000. [PMID: 34149564 PMCID: PMC8211422 DOI: 10.3389/fpsyg.2021.669000] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 04/26/2021] [Indexed: 12/15/2022] Open
Abstract
This study aimed to conduct a bibliometric analysis of published studies on the association between coronary heart disease (CHD) and depression or anxiety. The study also aimed to identify leading authors, institutions, and countries to determine research hotspots and obtain some hints from the speculated future frontiers. Publications about CHD and depression or anxiety between 2004 and 2020 were collected from the Web of Science Core Collection (WOSCC) database. Bibliographic information, such as authorship, country, citation frequency, and interactive visualization, was generated using VOSviewer1.6.16 and CiteSpace5.6.R5. In total, 8,073 articles were identified in the WOSCC database. The United States (2,953 publications), Duke University and Harvard University (214 publications), Psychosomatic Medicine (297 publications), and Denollet Johan. (99 publications) were the most productive country, institutions, journal, and author, respectively. The three hotspots of the research were “The relationship between depression and CHD,” “depression and myocardial infarction,” and “The characteristic of women suffering depression after MI.” The four future research frontiers are predicted to be “treating depression in CHD patients with multimorbidity,” “psychometric properties of instruments for assessing depression and anxiety in CHD patients,” “depression or anxiety in post-PCI patients,” and “other mental diseases in CHD patients.” Bibliometric analysis of the association between CHD and depressive disorders might identify new directions for future research.
Collapse
Affiliation(s)
- Yan Zhou
- Department of Cardiology, Guanganmen Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing, China.,Clinical Medical School, Beijing University of Chinese Medicine, Beijing, China
| | - Xue-Ping Zhu
- Department of Cardiology, Guanganmen Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing, China
| | - Jing-Jing Shi
- Department of Cardiology, Guanganmen Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing, China
| | - Guo-Zhen Yuan
- Department of Cardiology, Guanganmen Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing, China
| | - Zi-Ang Yao
- Department of Cardiology, Guanganmen Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing, China
| | - Yu-Guang Chu
- Department of Cardiology, Guanganmen Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing, China
| | - Shuai Shi
- Department of Cardiology, Guanganmen Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing, China
| | - Qiu-Lei Jia
- Department of Cardiology, Guanganmen Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing, China
| | - Ting Chen
- Department of Cardiology, Guanganmen Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing, China
| | - Yuan-Hui Hu
- Department of Cardiology, Guanganmen Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing, China
| |
Collapse
|
15
|
Factors Associated with Mutations: Their Matching Rates to Cardiovascular and Neurological Diseases. Int J Mol Sci 2021; 22:ijms22105057. [PMID: 34064609 PMCID: PMC8151074 DOI: 10.3390/ijms22105057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/29/2021] [Accepted: 05/07/2021] [Indexed: 12/28/2022] Open
Abstract
Monogenic hypertension is rare and caused by genetic mutations, but whether factors associated with mutations are disease-specific remains uncertain. Given two factors associated with high mutation rates, we tested how many previously known genes match with (i) proximity to telomeres or (ii) high adenine and thymine content in cardiovascular diseases (CVDs) related to vascular stiffening. We extracted genomic information using a genome data viewer. In human chromosomes, 64 of 79 genetic loci involving >25 rare mutations and single nucleotide polymorphisms satisfied (i) or (ii), resulting in an 81% matching rate. However, this high matching rate was no longer observed as we checked the two factors in genes associated with essential hypertension (EH), thoracic aortic aneurysm (TAA), and congenital heart disease (CHD), resulting in matching rates of 53%, 70%, and 75%, respectively. A matching of telomere proximity or high adenine and thymine content projects the list of loci involving rare mutations of monogenic hypertension better than those of other CVDs, likely due to adoption of rigorous criteria for true-positive signals. Our data suggest that the factor–disease matching rate is an accurate tool that can explain deleterious mutations of monogenic hypertension at a >80% match—unlike the relatively lower matching rates found in human genes of EH, TAA, CHD, and familial Parkinson’s disease.
Collapse
|