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Amini M, Bagheri A, P Paulus M, Delen D. Multimorbidity in neurodegenerative diseases: a network analysis. Inform Health Soc Care 2024:1-15. [PMID: 39363570 DOI: 10.1080/17538157.2024.2405869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/05/2024]
Abstract
The socioeconomic costs of neurodegenerative diseases (NDs) are highly affected by comorbidities. This study aims to enhance our understanding of the prevalent complications of NDs through the lens of network analysis. A multimorbidity network (MN) was constructed based on a longitudinal EHR dataset of 93,647,498 diagnoses of 824,847 patients. The association between the conditions was measured by two metrics, i.e. Phi-correlation and Cosine Index (CI). Based on multiple network centrality measures, a fused ranking list of the prevalent multimorbidities was provided. Finally, class-level networks depicting the prevalence and strength of diseases in different classes were constructed. The general MN included 928 diseases and 337,253 associations. Considering a 99% confidence level, two networks of 575 relationships were constructed based on Phi-correlations (73 diseases) and CI (102 diseases). Five out of 19 ICD-9 categories did not appear in either of the networks. Also, ND's immediate MNs for the top 50% of the significant associations included 42 relationships, whereas the Phi-correlation and CI networks included 36 and 34 diseases, respectively. Thirteen diseases were identified as the most notable multimorbidities based on various centrality measures. The analysis framework helps practitioners toward better resource allocations, more effective preventive screenings, and improved quality of life for ND patients and caregivers.
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Affiliation(s)
- Mostafa Amini
- Department of Information Systems, College of Business, California State University, Long Beach, California, USA
| | - Ali Bagheri
- Department of Management Science and Information Systems, Spears School of Business, Oklahoma State University, Stillwater, Oklahoma, USA
| | - Martin P Paulus
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
- Oxley College of Health and Natural Sciences, University of Tulsa, Tulsa, Oklahoma, USA
| | - Dursun Delen
- Center for Health Systems Innovation, Department of Management Science and Information Systems, Spears School of Business, Oklahoma State University, Tulsa, Oklahoma, USA
- Department of Industrial Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Sariyer/Istanbul, Türkiye
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2
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Zhang S, Strayer N, Vessels T, Choi K, Wang GW, Li Y, Bejan CA, Hsi RS, Bick AG, Velez Edwards DR, Savona MR, Phillips EJ, Pulley JM, Self WH, Hopkins WC, Roden DM, Smoller JW, Ruderfer DM, Xu Y. PheMIME: an interactive web app and knowledge base for phenome-wide, multi-institutional multimorbidity analysis. J Am Med Inform Assoc 2024:ocae182. [PMID: 39127052 DOI: 10.1093/jamia/ocae182] [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: 01/22/2024] [Revised: 06/03/2024] [Accepted: 07/18/2024] [Indexed: 08/12/2024] Open
Abstract
OBJECTIVES To address the need for interactive visualization tools and databases in characterizing multimorbidity patterns across different populations, we developed the Phenome-wide Multi-Institutional Multimorbidity Explorer (PheMIME). This tool leverages three large-scale EHR systems to facilitate efficient analysis and visualization of disease multimorbidity, aiming to reveal both robust and novel disease associations that are consistent across different systems and to provide insight for enhancing personalized healthcare strategies. MATERIALS AND METHODS PheMIME integrates summary statistics from phenome-wide analyses of disease multimorbidities, utilizing data from Vanderbilt University Medical Center, Mass General Brigham, and the UK Biobank. It offers interactive and multifaceted visualizations for exploring multimorbidity. Incorporating an enhanced version of associationSubgraphs, PheMIME also enables dynamic analysis and inference of disease clusters, promoting the discovery of complex multimorbidity patterns. A case study on schizophrenia demonstrates its capability for generating interactive visualizations of multimorbidity networks within and across multiple systems. Additionally, PheMIME supports diverse multimorbidity-based discoveries, detailed further in online case studies. RESULTS The PheMIME is accessible at https://prod.tbilab.org/PheMIME/. A comprehensive tutorial and multiple case studies for demonstration are available at https://prod.tbilab.org/PheMIME_supplementary_materials/. The source code can be downloaded from https://github.com/tbilab/PheMIME. DISCUSSION PheMIME represents a significant advancement in medical informatics, offering an efficient solution for accessing, analyzing, and interpreting the complex and noisy real-world patient data in electronic health records. CONCLUSION PheMIME provides an extensive multimorbidity knowledge base that consolidates data from three EHR systems, and it is a novel interactive tool designed to analyze and visualize multimorbidities across multiple EHR datasets. It stands out as the first of its kind to offer extensive multimorbidity knowledge integration with substantial support for efficient online analysis and interactive visualization.
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Affiliation(s)
- Siwei Zhang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | | | - Tess Vessels
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Karmel Choi
- Psychiatric & Neuro Developmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, United States
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Geoffrey W Wang
- Department of Statistics, North Carolina State University, Raleigh, NC 27695, United States
| | - Yajing Li
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Cosmin A Bejan
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Ryan S Hsi
- Department of Urology, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Alexander G Bick
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Digna R Velez Edwards
- Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Michael R Savona
- Division of Hematology and Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Elizabeth J Phillips
- Center for Drug Safety and Immunology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, WA 6150, Australia
| | - Jill M Pulley
- Vanderbilt Institute for Clinical and Translational Science, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Wesley H Self
- Vanderbilt Institute for Clinical and Translational Science, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Wilkins Consuelo Hopkins
- Vanderbilt Institute for Clinical and Translational Science, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Dan M Roden
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Jordan W Smoller
- Psychiatric & Neuro Developmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, United States
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, United States
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA 02142, United States
| | - Douglas M Ruderfer
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37212, United States
| | - Yaomin Xu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
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Gregg EW, Pratt A, Owens A, Barron E, Dunbar-Rees R, Slade ET, Hafezparast N, Bakhai C, Chappell P, Cornelius V, Johnston DG, Mathews J, Pickles J, Bragan Turner E, Wainman G, Roberts K, Khunti K, Valabhji J. The burden of diabetes-associated multiple long-term conditions on years of life spent and lost. Nat Med 2024:10.1038/s41591-024-03123-2. [PMID: 39090411 DOI: 10.1038/s41591-024-03123-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 06/11/2024] [Indexed: 08/04/2024]
Abstract
Diabetes mellitus is a central driver of multiple long-term conditions (MLTCs), but population-based studies have not clearly characterized the burden across the life course. We estimated the age of onset, years of life spent and loss associated with diabetes-related MLTCs among 46 million English adults. We found that morbidity patterns extend beyond classic diabetes complications and accelerate the onset of severe MLTCs by 20 years earlier in life in women and 15 years earlier in men. By the age of 50 years, one-third of those with diabetes have at least three conditions, spend >20 years with them and die 11 years earlier than the general population. Each additional condition at the age of 50 years is associated with four fewer years of life. Hypertension, depression, cancer and coronary heart disease contribute heavily to MLTCs in older age and create the greatest community-level burden on years spent (813 to 3,908 years per 1,000 individuals) and lost (900 to 1,417 years per 1,000 individuals). However, in younger adulthood, depression, severe mental illness, learning disabilities, alcohol dependence and asthma have larger roles, and when they occur, all except alcohol dependence were associated with long periods of life spent (11-14 years) and all except asthma associated with many years of life lost (11-15 years). These findings provide a baseline for population monitoring and underscore the need to prioritize effective prevention and management approaches.
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Affiliation(s)
- Edward W Gregg
- RCSI University of Medicine and Health Sciences, Dublin, Ireland.
- School of Public Health, Imperial College London, London, UK.
| | - Adrian Pratt
- NHS Arden & GEM Commissioning Support Unit, Leicester, UK
| | - Alex Owens
- NHS Arden & GEM Commissioning Support Unit, Leicester, UK
| | - Emma Barron
- NHS England, London, UK
- Chelsea and Westminster Hospital NHS Foundation Trust, London, UK
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | | | | | | | - Chirag Bakhai
- NHS England, London, UK
- Bedfordshire, Luton and Milton Keynes Integrated Care Board, Luton, UK
| | | | | | - Desmond G Johnston
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
- Department of Diabetes & Endocrinology, St Mary's Hospital, Imperial College Healthcare NHS Trust, London, UK
| | - Jacqueline Mathews
- National Institute for Health and Care Research Clinical Research Network National Coordination Centre, Faculty of Medicine and Health, University of Leeds, Leeds, UK
| | | | | | | | - Kate Roberts
- National Institute for Health and Care Research Clinical Research Network National Coordination Centre, Faculty of Medicine and Health, University of Leeds, Leeds, UK
| | - Kamlesh Khunti
- Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Jonathan Valabhji
- NHS England, London, UK
- Chelsea and Westminster Hospital NHS Foundation Trust, London, UK
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
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4
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Jørgensen IF, Haue AD, Placido D, Hjaltelin JX, Brunak S. Disease Trajectories from Healthcare Data: Methodologies, Key Results, and Future Perspectives. Annu Rev Biomed Data Sci 2024; 7:251-276. [PMID: 39178424 DOI: 10.1146/annurev-biodatasci-110123-041001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/25/2024]
Abstract
Disease trajectories, defined as sequential, directional disease associations, have become an intense research field driven by the availability of electronic population-wide healthcare data and sufficient computational power. Here, we provide an overview of disease trajectory studies with a focus on European work, including ontologies used as well as computational methodologies for the construction of disease trajectories. We also discuss different applications of disease trajectories from descriptive risk identification to disease progression, patient stratification, and personalized predictions using machine learning. We describe challenges and opportunities in the area that eventually will benefit from initiatives such as the European Health Data Space, which, with time, will make it possible to analyze data from cohorts comprising hundreds of millions of patients.
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Affiliation(s)
- Isabella Friis Jørgensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;
| | - Amalie Dahl Haue
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;
| | - Davide Placido
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;
| | - Jessica Xin Hjaltelin
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;
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5
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Griffith LE, Brini A, Muniz-Terrera G, St John PD, Stirland LE, Mayhew A, Oyarzún D, van den Heuvel E. A call for caution when using network methods to study multimorbidity: an illustration using data from the Canadian Longitudinal Study on Aging. J Clin Epidemiol 2024; 172:111435. [PMID: 38901709 DOI: 10.1016/j.jclinepi.2024.111435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 06/11/2024] [Accepted: 06/14/2024] [Indexed: 06/22/2024]
Abstract
OBJECTIVES To examine the impact of two key choices when conducting a network analysis (clustering methods and measure of association) on the number and type of multimorbidity clusters. STUDY DESIGN AND SETTING Using cross-sectional self-reported data on 24 diseases from 30,097 community-living adults aged 45-85 from the Canadian Longitudinal Study on Aging, we conducted network analyses using 5 clustering methods and 11 association measures commonly used in multimorbidity studies. We compared the similarity among clusters using the adjusted Rand index (ARI); an ARI of 0 is equivalent to the diseases being randomly assigned to clusters, and 1 indicates perfect agreement. We compared the network analysis results to disease clusters independently identified by two clinicians. RESULTS Results differed greatly across combinations of association measures and cluster algorithms. The number of clusters identified ranged from 1 to 24, with a low similarity of conditions within clusters. Compared to clinician-derived clusters, ARIs ranged from -0.02 to 0.24, indicating little similarity. CONCLUSION These analyses demonstrate the need for a systematic evaluation of the performance of network analysis methods on binary clustered data like diseases. Moreover, in individual older adults, diseases may not cluster predictably, highlighting the need for a personalized approach to their care.
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Affiliation(s)
- Lauren E Griffith
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; McMaster Institute for Research on Aging, McMaster University, Hamilton, Ontario, Canada.
| | - Alberto Brini
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | - Philip D St John
- Section of Geriatric Medicine, Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Lucy E Stirland
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, Scotland, UK; Global Brain Health Institute, University of California, San Francisco, CA, USA
| | - Alexandra Mayhew
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; McMaster Institute for Research on Aging, McMaster University, Hamilton, Ontario, Canada
| | - Diego Oyarzún
- School of Informatics, University of Edinburgh, Edinburgh, UK; School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - Edwin van den Heuvel
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
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6
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Baneshi MR, Dobson A, Mishra GD. Choices of measures of association affect the visualisation and composition of the multimorbidity networks. BMC Med Res Methodol 2024; 24:157. [PMID: 39044152 PMCID: PMC11265466 DOI: 10.1186/s12874-024-02286-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 07/15/2024] [Indexed: 07/25/2024] Open
Abstract
BACKGROUND Network analysis, commonly used to describe the patterns of multimorbidity, uses the strength of association between conditions as weight to classify conditions into communities and calculate centrality statistics. Our aim was to examine the robustness of the results to the choice of weight. METHODS Data used on 27 chronic conditions listed on Australian death certificates for women aged 85+. Five statistics were calculated to measure the association between 351 possible pairs: odds ratio (OR), lift, phi correlation, Salton cosine index (SCI), and normalised-joint frequency of pairs (NF). Network analysis was performed on the 10% of pairs with the highest weight according to each definition, the 'top pairs'. RESULTS Out of 56 'top pairs' identified, 13 ones were consistent across all statistics. In networks of OR and lift, three of the conditions which did not join communities were among the top five most prevalent conditions. Networks based on phi and NF had one or two conditions not part of any community. For the SCI statistics, all three conditions which did not join communities had prevalence below 3%. Low prevalence conditions were more likely to have high degree in networks of OR and lift but not SCI. CONCLUSION Use of different statistics to estimate weights leads to different networks. For exploratory purposes, one may apply alternative weights to identify a large list of pairs for further assessment in independent studies. However, when the aim is to visualise the data in a robust and parsimonious network, only pairs which are selected by multiple statistics should be visualised.
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Affiliation(s)
- Mohammad Reza Baneshi
- Australian Women and Girls' Health Research Centre, School of Public Health, Faculty of Medicine, The University of Queensland, Level 3 Public Health Building, 288 Herston Rd, Herston, QLD, 4006, Australia.
| | - Annette Dobson
- Australian Women and Girls' Health Research Centre, School of Public Health, Faculty of Medicine, The University of Queensland, Level 3 Public Health Building, 288 Herston Rd, Herston, QLD, 4006, Australia
| | - Gita D Mishra
- Australian Women and Girls' Health Research Centre, School of Public Health, Faculty of Medicine, The University of Queensland, Level 3 Public Health Building, 288 Herston Rd, Herston, QLD, 4006, Australia
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Lai LC, Huang DM, Peng J, Cao XY, Feng XL, Tao PY, Pan X, Pan QN, Fan DJ, Lu SY, Li CL, Pan YF, Dong PX, Chai YD, Huang P, Wu HC, Huang HQ. Depressive symptom mediates the association between the number of chronic diseases and cognitive impairment: a multi-center cross-sectional study based on community older adults. Front Psychiatry 2024; 15:1404229. [PMID: 39086730 PMCID: PMC11288913 DOI: 10.3389/fpsyt.2024.1404229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 06/27/2024] [Indexed: 08/02/2024] Open
Abstract
Objective The purpose of this study was to understand the relationship between the multiple chronic conditions (MCC), mental health and cognitive function of older adults in the community, and to propose a hypothesis that depressive symptom mediate the number of chronic diseases and cognitive impairment in older adults. Method Participants aged 65 years and older from 35 communities in 14 cities in Guangxi, China were recruited. The residents' depressive symptom (PHQ-9) and cognitive status (AD-8) were evaluated, Chi-square test was used to explore the effects of different socio-demographic characteristics on depressive symptom and cognitive impairment. Pearson correlation analysis and the process model 4 were used to explore the relationship between the number of chronic diseases, depressive symptom and cognitive impairment. Result A total of 11,582 older adults were included in our analysis. The rate of MCC reaching 26.53%. Hypertension combined with diabetes accounts for the highest proportion of two chronic diseases (13.2%). Among the combination of three chronic diseases, the highest incidence of coexisting hypertension combined with cervical/lumbar spondylosis, and rheumatoid arthritis (7.1%). In this study, depression symptoms accounted for 12.9% of older adults aged 65 and above, and cognitive impairment accounted for 27.4%. Female, older age, reside in urban areas, lower educational levels, no spouse, live alone, and MCC were risk factors for depressive symptom and cognitive impairment in older adults (P<0.05). Depressive symptom had a mediating effect in the number of chronic diseases and cognitive impairment, and the mediating effect (1.109) accounted for 44.13% of the total effect (0.247). Conclusion The mental health of the older adult needs to be taken seriously, and improving depressive symptom can reduce the occurrence of cognitive impairment in older patients with MCC to a certain extent.
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Affiliation(s)
- Li-Chong Lai
- Nursing Department, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Dong-Mei Huang
- Rehabilitation Department, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Jie Peng
- Nursing Department, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Xiao-Ying Cao
- Nursing Department, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Xiao-Ling Feng
- Nursing Department, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Pin-Yue Tao
- Anesthesiology Department, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Xiao Pan
- Ear, Nose, Throat, Head and Neck Surgery, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Qi-Ni Pan
- Nursing Department, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Deng-Jing Fan
- Nursing Department, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Shu-Yu Lu
- Anesthesiology Department, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Cai-Li Li
- Nursing Department, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Yan-Fei Pan
- Nursing Department, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Peng-Xin Dong
- Nursing Department, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Yi-Dan Chai
- Nursing Department, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Ping- Huang
- Nursing Department, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Hai-Chen Wu
- Nursing Department, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Hui-Qiao Huang
- Party Committee Office, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
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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.
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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
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9
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Chen H, Wang B, Lv R, Zhou T, Shen J, Song H, Xu X, Ma Y, Yuan C. Progression and trajectory network of age-related functional impairments and their combined associations with mortality. iScience 2023; 26:108368. [PMID: 38058300 PMCID: PMC10696261 DOI: 10.1016/j.isci.2023.108368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 07/06/2023] [Accepted: 10/26/2023] [Indexed: 12/08/2023] Open
Abstract
Age-related functional impairments (ARFIs) contribute to the loss of independence in older adults, but their progressions, interrelations, and combined relations with mortality are largely unknown. We conducted a prospective study among 17,914 participants in the Health and Retirement Study (2000-2020). The incidence rates of visual impairment, hearing impairment, physical frailty, and cognitive impairment increased exponentially with age, while those of restless sleep and depression increased relatively slowly. These ARFIs were associated with each other in temporal sequence and constituted a hazard network. We observed a dose-response relationship between the number of ARFIs and mortality risk, and the dyads involving physical frailty demonstrated the strongest associations with mortality. Our findings may assist in the identification of individuals at higher mortality risk and highlight the potential for future investigations to explore the impact of multiple ARFIs in aging.
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Affiliation(s)
- Hui Chen
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Binghan Wang
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Rongxia Lv
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Tianjing Zhou
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jie Shen
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Huan Song
- West China Biomedical Big Data Center and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
| | - Xiaolin Xu
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Yuan Ma
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Changzheng Yuan
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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10
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Zhang Z, He P, Yao H, Jing R, Sun W, Lu P, Xue Y, Qi J, Cui B, Cao M, Ning G. A network-based study reveals multimorbidity patterns in people with type 2 diabetes. iScience 2023; 26:107979. [PMID: 37822506 PMCID: PMC10562779 DOI: 10.1016/j.isci.2023.107979] [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: 07/07/2023] [Revised: 08/20/2023] [Accepted: 09/15/2023] [Indexed: 10/13/2023] Open
Abstract
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.
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Affiliation(s)
- Zizheng Zhang
- Department 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
| | - Ping He
- Link Healthcare Engineering and Information Department, Shanghai Hospital Development Center, Shanghai, China
| | - Huayan Yao
- Computer Net Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Renjie Jing
- Department 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
| | - Wen Sun
- Wonders Information Co. Ltd., Shanghai, China
| | - Ping Lu
- Wonders Information Co. Ltd., Shanghai, China
| | - Yanbin Xue
- Computer Net Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiying Qi
- Department 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
| | - Bin Cui
- Department 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
| | - Min Cao
- Department 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
| | - Guang Ning
- Department 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
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11
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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: 1] [Impact Index Per Article: 1.0] [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.
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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
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12
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Zhang S, Strayer N, Vessels T, Choi K, Wang GW, Li Y, Bejan CA, Hsi RS, Bick AG, Velez Edwards DR, Savona MR, Philips EJ, Pulley J, Self WH, Hopkins WC, Roden DM, Smoller JW, Ruderfer DM, Xu Y. PheMIME: An Interactive Web App and Knowledge Base for Phenome-Wide, Multi-Institutional Multimorbidity Analysis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.23.23293047. [PMID: 37547012 PMCID: PMC10402210 DOI: 10.1101/2023.07.23.23293047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Motivation Multimorbidity, characterized by the simultaneous occurrence of multiple diseases in an individual, is an increasing global health concern, posing substantial challenges to healthcare systems. Comprehensive understanding of disease-disease interactions and intrinsic mechanisms behind multimorbidity can offer opportunities for innovative prevention strategies, targeted interventions, and personalized treatments. Yet, there exist limited tools and datasets that characterize multimorbidity patterns across different populations. To bridge this gap, we used large-scale electronic health record (EHR) systems to develop the Phenome-wide Multi-Institutional Multimorbidity Explorer (PheMIME), which facilitates research in exploring and comparing multimorbidity patterns among multiple institutions, potentially leading to the discovery of novel and robust disease associations and patterns that are interoperable across different systems and organizations. Results PheMIME integrates summary statistics from phenome-wide analyses of disease multimorbidities. These are currently derived from three major institutions: Vanderbilt University Medical Center, Mass General Brigham, and the UK Biobank. PheMIME offers interactive exploration of multimorbidity through multi-faceted visualization. Incorporating an enhanced version of associationSubgraphs, PheMIME enables dynamic analysis and inference of disease clusters, promoting the discovery of multimorbidity patterns. Once a disease of interest is selected, the tool generates interactive visualizations and tables that users can delve into multimorbidities or multimorbidity networks within a single system or compare across multiple systems. The utility of PheMIME is demonstrated through a case study on schizophrenia. Availability and implementation The PheMIME knowledge base and web application are accessible at https://prod.tbilab.org/PheMIME/. A comprehensive tutorial, including a use-case example, is available at https://prod.tbilab.org/PheMIME_supplementary_materials/. Furthermore, the source code for PheMIME can be freely downloaded from https://github.com/tbilab/PheMIME. Data availability statement The data underlying this article are available in the article and in its online web application or supplementary material.
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Affiliation(s)
- Siwei Zhang
- Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
| | | | - Tess Vessels
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Karmel Choi
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston MA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston MA
| | | | - Yajing Li
- Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
| | - Cosmin A Bejan
- Department of Biomedical informatics, Vanderbilt University, Nashville, TN, USA
| | - Ryan S Hsi
- Department of Urology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Alexander G Bick
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Digna R Velez Edwards
- Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Michael R Savona
- Division of Hematology and Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Elizabeth J Philips
- Center for Drug Safety and Immunology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Western Australia, Australia
| | - Jill Pulley
- Vanderbilt Institute for Clinical and Translational Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wesley H Self
- Vanderbilt Institute for Clinical and Translational Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wilkins Consuelo Hopkins
- Vanderbilt Institute for Clinical and Translational Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dan M Roden
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jordan W Smoller
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston MA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston MA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA
| | - Douglas M Ruderfer
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical informatics, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yaomin Xu
- Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical informatics, Vanderbilt University, Nashville, TN, USA
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13
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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.
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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.
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Lee HI, Yoon S, Kim JH, Ahn W, Lee S. Network analysis of osteoporosis provides a global view of associated comorbidities and their temporal relationships. Arch Osteoporos 2023; 18:79. [PMID: 37272994 DOI: 10.1007/s11657-023-01290-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 05/26/2023] [Indexed: 06/06/2023]
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. 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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Affiliation(s)
- Hyun Il Lee
- Department of Orthopaedic Surgery, Ilsan Paik Hospital, Inje University College of Medicine, Goyang-si, Gyeonggi-do, 10380, Republic of Korea
| | - Siyeong Yoon
- Department of Orthopedic Surgery, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam-si, Gyeonggi-do, 13496, Republic of Korea
| | - Jin Hwan Kim
- Department of Orthopaedic Surgery, Ilsan Paik Hospital, Inje University College of Medicine, Goyang-si, Gyeonggi-do, 10380, Republic of Korea
| | - Wooyeol Ahn
- Department of Orthopedic Surgery, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam-si, Gyeonggi-do, 13496, Republic of Korea
| | - Soonchul Lee
- Department of Orthopedic Surgery, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam-si, Gyeonggi-do, 13496, Republic of Korea.
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15
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Yu Z, Chen Y, Xia Q, Qu Q, Dai T. Identification of status quo and association rules for chronic comorbidity among Chinese middle-aged and older adults rural residents. Front Public Health 2023; 11:1186248. [PMID: 37325337 PMCID: PMC10267321 DOI: 10.3389/fpubh.2023.1186248] [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: 03/14/2023] [Accepted: 05/11/2023] [Indexed: 06/17/2023] Open
Abstract
Background Chronic comorbidity has become a major challenge in chronic disease prevention and control. This issue is particularly pronounced in rural areas of developing countries, where the prevalence of chronic disease comorbidity is high, especially among middle-aged and older adults populations. However, the health status of middle-aged and older adults individuals in rural areas of China has received inadequate attention. Therefore, it is crucial to investigate the correlation among chronic diseases to establish a reference basis for adjusting health policies aimed at promoting the prevention and management of chronic diseases among middle-aged and older adults individuals. Methods This study selected 2,262 middle-aged and older adults residents aged 50 years or older in Shangang Village, Jiangsu Province, China, as the study population. To analyze the chronic comorbidity of middle-aged and older adults residents with different characteristics, we used the χ2 test with SPSS statistical software. Data analysis was conducted using the Apriori algorithm of Python software, set to mine the strong association rules of positive correlation between chronic disease comorbidities of middle-aged and older adults residents. Results The prevalence of chronic comorbidity was 56.6%. The chronic disease comorbidity group with the highest prevalence rate was the lumbar osteopenia + hypertension group. There were significant differences in the prevalence of chronic disease comorbidity among middle-aged and older adults residents in terms of gender, BMI, and chronic disease management. The Apriori algorithm was used to screen 15 association rules for the whole population, 11 for genders, and 15 for age groups. According to the order of support, the most common association rules of comorbidity of three chronic diseases were: {lumbar osteopenia} → {hypertension} (support: 29.22%, confidence: 58.44%), {dyslipidemia} → {hypertension} (support: 19.14%, confidence: 65.91%) and {fatty liver} → {hypertension} (support: 17.82%, confidence: 64.17%). Conclusion The prevalence of chronic comorbidity among middle-aged and older adults rural residents in China is relatively high. We identified many association rules among chronic diseases, dyslipidemia is mostly the antecedent, and hypertension is primarily the result. In particular, the majority of comorbidity aggregation patterns consisted of hypertension and dyslipidemia. By implementing scientifically-proven prevention and control strategies, the development of healthy aging can be promoted.
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Affiliation(s)
- Zijing Yu
- Institute of Medical Information/Library, Chinese Academy of Medical Sciences, Beijing, China
- Peking Union Medical College, Beijing, China
| | - Yuquan Chen
- Institute of Medical Information/Library, Chinese Academy of Medical Sciences, Beijing, China
- Peking Union Medical College, Beijing, China
| | - Qianhang Xia
- Institute of Medical Information/Library, Chinese Academy of Medical Sciences, Beijing, China
- Peking Union Medical College, Beijing, China
| | - Qingru Qu
- PBC School of Finance, Tsinghua University, Beijing, China
| | - Tao Dai
- Institute of Medical Information/Library, Chinese Academy of Medical Sciences, Beijing, China
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16
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Kuan V, Denaxas S, Patalay P, Nitsch D, Mathur R, Gonzalez-Izquierdo A, Sofat R, Partridge L, Roberts A, Wong ICK, Hingorani M, Chaturvedi N, Hemingway H, Hingorani AD. Identifying and visualising multimorbidity and comorbidity patterns in patients in the English National Health Service: a population-based study. Lancet Digit Health 2023; 5:e16-e27. [PMID: 36460578 DOI: 10.1016/s2589-7500(22)00187-x] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 09/10/2022] [Accepted: 09/19/2022] [Indexed: 12/03/2022]
Abstract
BACKGROUND Globally, there is a paucity of multimorbidity and comorbidity data, especially for minority ethnic groups and younger people. We estimated the frequency of common disease combinations and identified non-random disease associations for all ages in a multiethnic population. METHODS In this population-based study, we examined multimorbidity and comorbidity patterns stratified by ethnicity or race, sex, and age for 308 health conditions using electronic health records from individuals included on the Clinical Practice Research Datalink linked with the Hospital Episode Statistics admitted patient care dataset in England. We included individuals who were older than 1 year and who had been registered for at least 1 year in a participating general practice during the study period (between April 1, 2010, and March 31, 2015). We identified the most common combinations of conditions and comorbidities for index conditions. We defined comorbidity as the accumulation of additional conditions to an index condition over an individual's lifetime. We used network analysis to identify conditions that co-occurred more often than expected by chance. We developed online interactive tools to explore multimorbidity and comorbidity patterns overall and by subgroup based on ethnicity, sex, and age. FINDINGS We collected data for 3 872 451 eligible patients, of whom 1 955 700 (50·5%) were women and girls, 1 916 751 (49·5%) were men and boys, 2 666 234 (68·9%) were White, 155 435 (4·0%) were south Asian, and 98 815 (2·6%) were Black. We found that a higher proportion of boys aged 1-9 years (132 506 [47·8%] of 277 158) had two or more diagnosed conditions than did girls in the same age group (106 982 [40·3%] of 265 179), but more women and girls were diagnosed with multimorbidity than were boys aged 10 years and older and men (1 361 232 [80·5%] of 1 690 521 vs 1 161 308 [70·8%] of 1 639 593). White individuals (2 097 536 [78·7%] of 2 666 234) were more likely to be diagnosed with two or more conditions than were Black (59 339 [60·1%] of 98 815) or south Asian individuals (93 617 [60·2%] of 155 435). Depression commonly co-occurred with anxiety, migraine, obesity, atopic conditions, deafness, soft-tissue disorders, and gastrointestinal disorders across all subgroups. Heart failure often co-occurred with hypertension, atrial fibrillation, osteoarthritis, stable angina, myocardial infarction, chronic kidney disease, type 2 diabetes, and chronic obstructive pulmonary disease. Spinal fractures were most strongly non-randomly associated with malignancy in Black individuals, but with osteoporosis in White individuals. Hypertension was most strongly associated with kidney disorders in those aged 20-29 years, but with dyslipidaemia, obesity, and type 2 diabetes in individuals aged 40 years and older. Breast cancer was associated with different comorbidities in individuals from different ethnic groups. Asthma was associated with different comorbidities between males and females. Bipolar disorder was associated with different comorbidities in younger age groups compared with older age groups. INTERPRETATION Our findings and interactive online tools are a resource for: patients and their clinicians, to prevent and detect comorbid conditions; research funders and policy makers, to redesign service provision, training priorities, and guideline development; and biomedical researchers and manufacturers of medicines, to provide leads for research into common or sequential pathways of disease and inform the design of clinical trials. FUNDING UK Research and Innovation, Medical Research Council, National Institute for Health and Care Research, Department of Health and Social Care, Wellcome Trust, British Heart Foundation, and The Alan Turing Institute.
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Affiliation(s)
- Valerie Kuan
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK.
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK; UCL BHF Research Accelerator, University College London, London, UK; Alan Turing Institute, London, UK; University College London Hospitals NIHR Biomedical Research Centre, London, UK; British Heart Foundation Data Science Centre, HDR UK, London, UK
| | - Praveetha Patalay
- Centre for Longitudinal Studies, University College London, London, UK; MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - Dorothea Nitsch
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Rohini Mathur
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; Centre for Primary Care, Wolfson Institute of Primary Care, Queen Mary University of London, London, UK
| | - Arturo Gonzalez-Izquierdo
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK
| | - Reecha Sofat
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK; British Heart Foundation Data Science Centre, HDR UK, London, UK
| | - Linda Partridge
- Institute of Healthy Ageing, Department of Genetics, Evolution and Environment, University College London, London, UK; Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Amanda Roberts
- Nottingham Support Group for Carers of Children with Eczema, Nottingham, UK
| | - Ian C K Wong
- School of Pharmacy, University College London, London, UK; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China; Aston Pharmacy School, Aston University, Birmingham, UK
| | | | - Nishi Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK; University College London Hospitals NIHR Biomedical Research Centre, London, UK
| | - Aroon D Hingorani
- UCL BHF Research Accelerator, University College London, London, UK; Institute of Cardiovascular Science, University College London, London, UK; University College London Hospitals NIHR Biomedical Research Centre, London, UK
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17
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Mulick AR, Henderson AD, Prieto-Merino D, Mansfield KE, Matthewman J, Quint JK, Lyons RA, Sheikh A, McAllister DA, Nitsch D, Langan SM. Novel multimorbidity clusters in people with eczema and asthma: a population-based cluster analysis. Sci Rep 2022; 12:21866. [PMID: 36529816 PMCID: PMC9760185 DOI: 10.1038/s41598-022-26357-x] [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: 04/27/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Eczema and asthma are allergic diseases and two of the commonest chronic conditions in high-income countries. Their co-existence with other allergic conditions is common, but little research exists on wider multimorbidity with these conditions. We set out to identify and compare clusters of multimorbidity in people with eczema or asthma and people without. Using routinely-collected primary care data from the U.K. Clinical Research Practice Datalink GOLD, we identified adults ever having eczema (or asthma), and comparison groups never having eczema (or asthma). We derived clusters of multimorbidity from hierarchical cluster analysis of Jaccard distances between pairs of diagnostic categories estimated from mixed-effects logistic regressions. We analysed 434,422 individuals with eczema (58% female, median age 47 years) and 1,333,281 individuals without (55% female, 47 years), and 517,712 individuals with asthma (53% female, 44 years) and 1,601,210 individuals without (53% female, 45 years). Age at first morbidity, sex and having eczema/asthma affected the scope of multimorbidity, with women, older age and eczema/asthma being associated with larger morbidity clusters. Injuries, digestive, nervous system and mental health disorders were more commonly seen in eczema and asthma than control clusters. People with eczema and asthma of all ages and both sexes may experience greater multimorbidity than people without eczema and asthma, including conditions not previously recognised as contributing to their disease burden. This work highlights areas where there is a critical need for research addressing the burden and drivers of multimorbidity in order to inform strategies to reduce poor health outcomes.
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Affiliation(s)
- Amy R. Mulick
- grid.8991.90000 0004 0425 469XDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT UK
| | - Alasdair D. Henderson
- grid.8991.90000 0004 0425 469XDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT UK
| | - David Prieto-Merino
- grid.8991.90000 0004 0425 469XDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT UK
| | - Kathryn E. Mansfield
- grid.8991.90000 0004 0425 469XDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT UK
| | - Julian Matthewman
- grid.8991.90000 0004 0425 469XDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT UK
| | - Jennifer K. Quint
- grid.7445.20000 0001 2113 8111National Heart and Lung Institute, Imperial College London, London, UK
| | - Ronan A. Lyons
- grid.4827.90000 0001 0658 8800National Centre for Population Health and Wellbeing Research, Swansea University Medical School, Swansea, UK ,grid.4827.90000 0001 0658 8800Administrative Data Research UK, Swansea University Medical School, Swansea, UK
| | - Aziz Sheikh
- grid.4305.20000 0004 1936 7988Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, EH8 9DX UK
| | - David A. McAllister
- grid.8756.c0000 0001 2193 314XInstitute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Dorothea Nitsch
- grid.8991.90000 0004 0425 469XDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT UK
| | - Sinéad M. Langan
- grid.8991.90000 0004 0425 469XDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT UK ,grid.507332.00000 0004 9548 940XHealth Data Research UK, Gibbs Building, 215 Euston Road, London, NW1 2BE UK
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18
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Martinez-De la Torre A, Perez-Cruz F, Weiler S, Burden AM. Comorbidity clusters associated with newly treated type 2 diabetes mellitus: a Bayesian nonparametric analysis. Sci Rep 2022; 12:20653. [PMID: 36450743 PMCID: PMC9712684 DOI: 10.1038/s41598-022-24217-2] [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: 05/26/2022] [Accepted: 11/11/2022] [Indexed: 12/05/2022] Open
Abstract
Type 2 diabetes mellitus (T2DM) is associated with the development of chronic comorbidities, which can lead to high drug utilization and adverse events. We aimed to identify common comorbidity clusters and explore the progression over time in newly treated T2DM patients. The IQVIA Medical Research Data incorporating data from THIN, a Cegedim database of anonymized electronic health records, was used to identify all patients with a first-ever prescription for a non-insulin antidiabetic drug (NIAD) between January 2006 and December 2019. We selected 58 chronic comorbidities of interest and used Bayesian nonparametric models to identify disease clusters and model their progression over time. Among the 175,383 eligible T2DM patients, we identified the 20 most frequent comorbidity clusters, which were comprised of 14 latent features (LFs). Each LF was associated with a primary disease (e.g., 98% of patients in cluster 2, characterized by LF2, had congestive heart failure [CHF]). The presence of certain LFs increased the probability of having another LF active. For example, LF2 (CHF) frequently appeared with LFs related to chronic kidney disease (CKD). Over time, the clusters associated with cardiovascular diseases, such as CHF, progressed rapidly. Moreover, the onset of certain diseases led to further complications. Our models identified established T2DM complications and previously unknown connections, thus, highlighting the potential for Bayesian nonparametric models to characterize complex comorbidity patterns.
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Affiliation(s)
- Adrian Martinez-De la Torre
- grid.5801.c0000 0001 2156 2780Institute of Pharmaceutical Sciences, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1-5/10, 8093 Zurich, Switzerland
| | - Fernando Perez-Cruz
- grid.5801.c0000 0001 2156 2780Swiss Data Science Center, ETH Zurich and EPFL, Zurich, Switzerland ,grid.5801.c0000 0001 2156 2780Institute of Machine Learning, Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | - Stefan Weiler
- grid.5801.c0000 0001 2156 2780Institute of Pharmaceutical Sciences, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1-5/10, 8093 Zurich, Switzerland
| | - Andrea M. Burden
- grid.5801.c0000 0001 2156 2780Institute of Pharmaceutical Sciences, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1-5/10, 8093 Zurich, Switzerland
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19
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Multimorbidity and chronic co-prescription networks and potential interactions in adult patients with epilepsy: MorbiNet study. Neurol Sci 2022; 43:6889-6899. [DOI: 10.1007/s10072-022-06375-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 08/27/2022] [Indexed: 10/14/2022]
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20
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Bernabe-Ortiz A, Borjas-Cavero DB, Páucar-Alfaro JD, Carrillo-Larco RM. Multimorbidity Patterns among People with Type 2 Diabetes Mellitus: Findings from Lima, Peru. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:9333. [PMID: 35954689 PMCID: PMC9367906 DOI: 10.3390/ijerph19159333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/19/2022] [Accepted: 07/27/2022] [Indexed: 11/17/2022]
Abstract
BACKGROUND Type 2 diabetes (T2DM) is a chronic condition with a high disease burden worldwide, and individuals with T2DM often have other morbidities. Understanding the local multimorbidity profile of patients with T2DM will inform precision medicine and public health, so that tailored interventions can be offered according to the different profiles. METHODS An analysis was conducted of electronic health records (2016-2021) in one hospital in Lima, Peru. Based on ICD-10 codes and the available measurements (e.g., body mass index), we identified all T2DM cases and quantified the frequency of the most common comorbidities (those in ≥1% of the sample). We also conducted k-means analysis that was informed by the most frequent comorbidities, to identify clusters of patients with T2DM and other chronic conditions. RESULTS There were 9582 individual records with T2DM (mean age 58.6 years, 61.5% women). The most frequent chronic conditions were obesity (29.4%), hypertension (18.8%), dyslipidemia (11.3%), hypothyroidism (6.4%), and arthropathy (3.6%); and 51.6% had multimorbidity: 32.8% had only one, 14.1% had two, and 4.7% had three or more extra chronic conditions in addition to T2DM. The cluster analysis revealed four unique groups: T2DM with no other chronic disease, T2DM with obesity only, T2DM with hypertension but without obesity, and T2DM with all other chronic conditions. CONCLUSIONS More than one in two people with T2DM had multimorbidity. Obesity, hypertension, and dyslipidemia were the most common chronic conditions that were associated with T2DM. Four clusters of chronic morbidities were found, signaling mutually exclusive profiles of patients with T2DM according to their multimorbidity profile.
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Affiliation(s)
- Antonio Bernabe-Ortiz
- CRONICAS Center of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima 15074, Peru;
- School of Medicine, Universidad Científica del Sur, Lima 15067, Peru
| | | | | | - Rodrigo M. Carrillo-Larco
- CRONICAS Center of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima 15074, Peru;
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London SW7 2AZ, UK
- Universidad Continental, Lima 15046, Peru
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21
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Kim HJ, Shin SY, Jeong SH. Nature and Extent of Physical Comorbidities Among Korean Patients With Mental Illnesses: Pairwise and Network Analysis Based on Health Insurance Claims Data. Psychiatry Investig 2022; 19:488-499. [PMID: 35753688 PMCID: PMC9233950 DOI: 10.30773/pi.2022.0068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 04/29/2022] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE The nature of physical comorbidities in patients with mental illness may differ according to diagnosis and personal characteristics. We investigated this complexity by conventional logistic regression and network analysis. METHODS A health insurance claims data in Korea was analyzed. For every combination of psychiatric and physical diagnoses, odds ratios were calculated adjusting age and sex. From the patient-diagnosis data, a network of diagnoses was constructed using Jaccard coefficient as the index of comorbidity. RESULTS In 1,017,024 individuals, 77,447 (7.6%) were diagnosed with mental illnesses. The number of physical diagnoses among them was 11.2, which was 1.6 times higher than non-psychiatric groups. The most noticeable associations were 1) neurotic illnesses with gastrointestinal/pain disorders and 2) dementia with fracture, Parkinson's disease, and cerebrovascular accidents. Unexpectedly, the diagnosis of metabolic syndrome was only scarcely found in patients with severe mental illnesses (SMIs). However, implicit associations between metabolic syndrome and SMIs were suggested in comorbidity networks. CONCLUSION Physical comorbidities in patients with mental illnesses were more extensive than those with other disease categories. However, the result raised questions as to whether the medical resources were being diverted to less serious conditions than more urgent conditions in patients with SMIs.
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Affiliation(s)
- Ho Joon Kim
- Department of Psychiatry, Daejeon Eulji Medical Center, Eulji University School of Medicine, Daejeon, Republic of Korea
| | - Sam Yi Shin
- Department of Psychiatry, The Healer's Hospital, Busan, Republic of Korea
| | - Seong Hoon Jeong
- Department of Psychiatry, Daejeon Eulji Medical Center, Eulji University School of Medicine, Daejeon, Republic of Korea
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22
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Jones I, Cocker F, Jose M, Charleston M, Neil AL. Methods of analysing patterns of multimorbidity using network analysis: a scoping review. J Public Health (Oxf) 2022. [DOI: 10.1007/s10389-021-01685-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
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23
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Wesołowski S, Lemmon G, Hernandez EJ, Henrie A, Miller TA, Weyhrauch D, Puchalski MD, Bray BE, Shah RU, Deshmukh VG, Delaney R, Yost HJ, Eilbeck K, Tristani-Firouzi M, Yandell M. An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records. PLOS DIGITAL HEALTH 2022; 1:e0000004. [PMID: 35373216 PMCID: PMC8975108 DOI: 10.1371/journal.pdig.0000004] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 11/17/2021] [Indexed: 11/19/2022]
Abstract
Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children's Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications. Using explainable Artificial Intelligence (AI) methodologies, we then tease apart the intertwined, conditionally-dependent impacts of comorbid conditions and demography upon cardiovascular health, focusing on the key areas of heart transplant, sinoatrial node dysfunction and various forms of congenital heart disease. The resulting multimorbidity networks make possible wide-ranging explorations of the comorbid and demographic landscapes surrounding these cardiovascular outcomes, and can be distributed as web-based tools for further community-based outcomes research. The ability to transform enormous collections of EHRs into compact, portable tools devoid of Protected Health Information solves many of the legal, technological, and data-scientific challenges associated with large-scale EHR analyses.
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Affiliation(s)
- Sergiusz Wesołowski
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
| | - Gordon Lemmon
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
| | - Edgar J. Hernandez
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
| | - Alex Henrie
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
| | - Thomas A. Miller
- Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Derek Weyhrauch
- Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Michael D. Puchalski
- Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Bruce E. Bray
- Division of Cardiovascular Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America
- University of Utah, Biomedical Informatics, Salt Lake City, UT 84108, United States of America
| | - Rashmee U. Shah
- Division of Cardiovascular Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Vikrant G. Deshmukh
- University of Utah Health Care CMIO Office, Salt Lake City, UT, United States of America
| | - Rebecca Delaney
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, United States of America
| | - H. Joseph Yost
- Molecular Medicine Program, University of Utah, Salt Lake City, UT, United States of America
| | - Karen Eilbeck
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, United States of America
| | - Martin Tristani-Firouzi
- Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, United States of America
- Nora Eccles Harrison CVRTI, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Mark Yandell
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
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24
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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: 12] [Impact Index Per Article: 6.0] [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.
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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
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25
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Siah KW, Wong CH, Gupta J, Lo AW. Multimorbidity and mortality: A data science perspective. JOURNAL OF MULTIMORBIDITY AND COMORBIDITY 2022; 12:26335565221105431. [PMID: 35668849 PMCID: PMC9163746 DOI: 10.1177/26335565221105431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 05/15/2022] [Indexed: 11/26/2022]
Abstract
Background With multimorbidity becoming the norm rather than the exception, the management of multiple chronic diseases is a major challenge facing healthcare systems worldwide. Methods Using a large, nationally representative database of electronic medical records from the United Kingdom spanning the years 2005–2016 and consisting over 4.5 million patients, we apply statistical methods and network analysis to identify comorbid pairs and triads of diseases and identify clusters of chronic conditions across different demographic groups. Unlike many previous studies, which generally adopt cross-sectional designs based on single snapshots of closed cohorts, we adopt a longitudinal approach to examine temporal changes in the patterns of multimorbidity. In addition, we perform survival analysis to examine the impact of multimorbidity on mortality. Results The proportion of the population with multimorbidity has increased by approximately 2.5 percentage points over the last decade, with more than 17% having at least two chronic morbidities. We find that the prevalence and the severity of multimorbidity, as quantified by the number of co-occurring chronic conditions, increase progressively with age. Stratifying by socioeconomic status, we find that people living in more deprived areas are more likely to be multimorbid compared to those living in more affluent areas at all ages. The same trend holds consistently for all years in our data. In general, hypertension, diabetes, and respiratory-related diseases demonstrate high in-degree centrality and eigencentrality, while cardiac disorders show high out-degree centrality. Conclusions We use data-driven methods to characterize multimorbidity patterns in different demographic groups and their evolution over the past decade. In addition to a number of strongly associated comorbid pairs (e.g., cardiac-vascular and cardiac-metabolic disorders), we identify three principal clusters: a respiratory cluster, a cardiovascular cluster, and a mixed cardiovascular-renal-metabolic cluster. These are supported by established pathophysiological mechanisms and shared risk factors, and largely confirm and expand on the results of existing studies in the medical literature. Our findings contribute to a more quantitative understanding of the epidemiology of multimorbidity, an important pre-requisite for developing more effective medical care and policy for multimorbid patients.
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Affiliation(s)
- Kien Wei Siah
- Laboratory for Financial Engineering, Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Chi Heem Wong
- Laboratory for Financial Engineering, Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Digital Catalyst, Swiss Re, Cambridge, MA, USA
| | - Jerry Gupta
- Digital Catalyst, Swiss Re, Cambridge, MA, USA
| | - Andrew W Lo
- Laboratory for Financial Engineering, Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Sante Fe Institute, Santa Fe, NM, USA
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Puri P, Singh SK. Exploring the non-communicable disease (NCD) network of multi-morbid individuals in India: A network analysis. PLOS GLOBAL PUBLIC HEALTH 2022; 2:e0000512. [PMID: 36962702 PMCID: PMC10021153 DOI: 10.1371/journal.pgph.0000512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 06/09/2022] [Indexed: 11/18/2022]
Abstract
Nationally representative evidence discussing the interplay of non-communicable diseases (diseases) are scarce in India. Therefore, the present study aims to fill this research void by providing empirical evidence on disease networking using a large nationally representative cross-sectional sample segregated by gender among older adults in India. The analysis utilized data on 10,606 multimorbid women and 7,912 multimorbid men from the Longitudinal Ageing Study in India (LASI), 2017-18. Multimorbidity was defined as the co-occurrence of two or more diseases in an individual using a list of 16 self-reported diseases. Weighted networks were visualized to illustrates the complex relationships between the diseases using network analysis. The findings suggest that women possess a higher burden of multimorbidity than men. Hypertension, musculoskeletal disorder, gastrointestinal disorder, diabetes mellitus, and skin diseases were reported as the most recurrent diseases. 'Hypertension-musculoskeletal disorder', 'diabetes mellitus-hypertension', 'gastrointestinal disorders-hypertension' and 'gastrointestinal disorders- musculoskeletal disorder' were recurrent disease combinations among the multimorbid individuals. The study generated compelling evidence to establish that there are statistically significant differences between the prevalence of diseases and how they interact with each other between women and men. These findings further accentuate that disease networks are slightly more complex among women. In totality, the study visualizes disease association, identifies the most influential diseases to the network, and those which acts as a bridge between other diseases, causing multimorbidity among the older adult population in India.
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Affiliation(s)
- Parul Puri
- Department of Survey Research and Data Analytics, International Institute for Population Sciences, Mumbai, Maharashtra, India
| | - Shri Kant Singh
- Department of Survey Research and Data Analytics, International Institute for Population Sciences, Mumbai, Maharashtra, India
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27
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Napoli C, Benincasa G, Ellahham S. Precision Medicine in Patients with Differential Diabetic Phenotypes: Novel Opportunities from Network Medicine. Curr Diabetes Rev 2022; 18:e221221199301. [PMID: 34951369 DOI: 10.2174/1573399818666211222164400] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 10/05/2021] [Accepted: 10/28/2021] [Indexed: 11/22/2022]
Abstract
INTRODUCTION Diabetes mellitus (DM) comprises differential clinical phenotypes ranging from rare monogenic to common polygenic forms, such as type 1 (T1DM), type 2 (T2DM), and gestational diabetes, which are associated with cardiovascular complications. Also, the high- -risk prediabetic state is rising worldwide, suggesting the urgent need for early personalized strategies to prevent and treat a hyperglycemic state. OBJECTIVE We aim to discuss the advantages and challenges of Network Medicine approaches in clarifying disease-specific molecular pathways, which may open novel ways for repurposing approved drugs to reach diabetes precision medicine and personalized therapy. CONCLUSION The interactome or protein-protein interactions (PPIs) is a useful tool to identify subtle molecular differences between precise diabetic phenotypes and predict putative novel drugs. Despite being previously unappreciated as T2DM determinants, the growth factor receptor-bound protein 14 (GRB14), calmodulin 2 (CALM2), and protein kinase C-alpha (PRKCA) might have a relevant role in disease pathogenesis. Besides, in silico platforms have suggested that diflunisal, nabumetone, niflumic acid, and valdecoxib may be suitable for the treatment of T1DM; phenoxybenzamine and idazoxan for the treatment of T2DM by improving insulin secretion; and hydroxychloroquine reduce the risk of coronary heart disease (CHD) by counteracting inflammation. Network medicine has the potential to improve precision medicine in diabetes care and enhance personalized therapy. However, only randomized clinical trials will confirm the clinical utility of network- oriented biomarkers and drugs in the management of DM.
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Affiliation(s)
- Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", 80138- Naples, Italy
- Clinical Department of Internal and Specialty Medicine (DAI), University Hospital (AOU), University of Campania "Luigi Vanvitelli", 80138 Naples, Italy
| | - Giuditta Benincasa
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", 80138- Naples, Italy
| | - Samer Ellahham
- Department of Cardiology, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates
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28
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Benedetti R, Benincasa G, Glass K, Chianese U, Vietri MT, Congi R, Altucci L, Napoli C. Effects of novel SGLT2 inhibitors on cancer incidence in hyperglycemic patients: a meta-analysis of randomized clinical trials. Pharmacol Res 2021; 175:106039. [PMID: 34929299 DOI: 10.1016/j.phrs.2021.106039] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 12/10/2021] [Accepted: 12/15/2021] [Indexed: 02/06/2023]
Abstract
Epidemiological evidence shows that diabetic patients have an increased cancer risk and a higher mortality rate. Glucose could play a central role in metabolism and growth of many tumor types, and this possible mechanism is supported by the high rate of glucose demand and uptake in cancer. Thus, growing evidence suggests that hyperglycemia contributes to cancer progression but also to its onset. Many mechanisms underlying this association have been hypothesized, such as insulin resistance, hyperinsulinemia, and increased inflammatory processes. Inflammation is a common pathophysiological feature in both diabetic and oncological patients, and inflammation linked to high glucose levels sensitizes microenvironment to tumorigenesis, promoting the development of malignant lesions by altering and sustaining a pathological condition in tissues. Glycemic control is the first goal of antidiabetic therapy, and glucose level reduction has also been associated with favorable outcomes in cancer. Here, we describe key events in carcinogenesis focusing on hyperglycemia as supporter in tumor progression and in particular, related to the role of a specific hypoglycemic drug class, sodium-glucose linked transporters (SGLTs). We also discuss the use of SGLT2 inhibitors as a novel potential cancer therapy. Our meta-analysis showed that SGLT-2 inhibitors were significantly associated with an overall reduced risk of cancer as compared to placebo (RR = 0.35, CI 0.33-0.37, P = 0. 00) with a particular effectiveness for dapaglifozin and ertuglifozin (RR = 0. 06, CI 0. 06-0. 07 and RR = 0. 22, CI 0. 18-0. 26, respectively). Network Medicine approaches may advance the possible repurposing of these drugs in patients with concomitant diabetes and cancer.
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Affiliation(s)
- Rosaria Benedetti
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Via L. De Crecchio 7, 80138 Naples, Italy
| | - Giuditta Benincasa
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Pz. Miraglia 2, 80138 Naples, Italy.
| | - Kimberly Glass
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Ugo Chianese
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Via L. De Crecchio 7, 80138 Naples, Italy
| | - Maria Teresa Vietri
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Via L. De Crecchio 7, 80138 Naples, Italy
| | - Raffaella Congi
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Via L. De Crecchio 7, 80138 Naples, Italy
| | - Lucia Altucci
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Via L. De Crecchio 7, 80138 Naples, Italy; Biogem Institute of Molecular and Genetic Biology, 83031 Ariano Irpino, Italy.
| | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Pz. Miraglia 2, 80138 Naples, Italy; Clinical Department of Internal Medicine and Specialistics, Division of Clinical Immunology, Transfusion Medicine and Transplant Immunology, AOU University of Campania "Luigi Vanvitelli", Via L. De Crecchio 7, 80138 Naples, Italy.
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Fränti P, Sieranoja S, Wikström K, Laatikainen T. Clustering Diagnoses from 58M Patient Visits in Finland 2015–2018 (Preprint). JMIR Med Inform 2021; 10:e35422. [PMID: 35507390 PMCID: PMC9118010 DOI: 10.2196/35422] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 02/25/2022] [Accepted: 03/02/2022] [Indexed: 12/21/2022] Open
Affiliation(s)
- Pasi Fränti
- Machine Learning Group, School of Computing, University of Eastern Finland, Joensuu, Finland
| | - Sami Sieranoja
- Machine Learning Group, School of Computing, University of Eastern Finland, Joensuu, Finland
| | - Katja Wikström
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
- The Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Tiina Laatikainen
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
- The Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
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30
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Yu J, Li Y, Zheng Z, Jia H, Cao P, Qiangba Y, Yu X. Analysis of multimorbidity networks associated with different factors in Northeast China: a cross-sectional analysis. BMJ Open 2021; 11:e051050. [PMID: 34732482 PMCID: PMC8572406 DOI: 10.1136/bmjopen-2021-051050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVES This study aimed to identify and study the associations and co-occurrence of multimorbidity, and assessed the associations of diseases with sex, age and hospitalisation duration. DESIGN Cross-sectional. SETTING 15 general hospitals in Jilin Province, China. PARTICIPANTS A total of 431 295 inpatients were enrolled through a cross-sectional study in Jilin Province, China. PRIMARY OUTCOME MEASURES The complex relationships of multimorbidity were presented as weighted networks. RESULTS The distributions of the numbers of diseases differed significantly by sex, age and hospitalisation duration (p<0.001). Cerebrovascular diseases (CD), hypertensive diseases (HyD), ischaemic heart diseases (IHD) and other forms of heart disease (OFHD) showed the highest weights in the multimorbidity networks. The connections between different sexes or hospitalisation duration and diseases were similar, while those between different age groups and diseases were different. CONCLUSIONS CD, HyD, IHD and OFHD were the central points of disease clusters and directly or indirectly related to other diseases or factors. Thus, effective interventions for these diseases should be adopted. Furthermore, different intervention strategies should be developed according to multimorbidity patterns in different age groups.
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Affiliation(s)
- Jianxing Yu
- Social Medicine and Health Service Management, School of Public Health, Jilin University, Changchun, China
| | - Yingying Li
- Social Medicine and Health Service Management, School of Public Health, Jilin University, Changchun, China
| | - Zhou Zheng
- Social Medicine and Health Service Management, School of Public Health, Jilin University, Changchun, China
| | - Huanhuan Jia
- Social Medicine and Health Service Management, School of Public Health, Jilin University, Changchun, China
| | - Peng Cao
- Social Medicine and Health Service Management, School of Public Health, Jilin University, Changchun, China
| | - Yuzhen Qiangba
- Social Medicine and Health Service Management, School of Public Health, Jilin University, Changchun, China
| | - Xihe Yu
- Social Medicine and Health Service Management, School of Public Health, Jilin University, Changchun, China
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Pearson-Stuttard J, Buckley J, Cicek M, Gregg EW. The Changing Nature of Mortality and Morbidity in Patients with Diabetes. Endocrinol Metab Clin North Am 2021; 50:357-368. [PMID: 34399950 DOI: 10.1016/j.ecl.2021.05.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
The number of adults living with diabetes has increased substantially globally over the past 40 years, driven by a combination of increased age-standardized prevalence, population growth, aging, and increases in obesity prevalence. Patients with diabetes in high-income countries are living longer, with large declines in vascular disease mortality rates. This appears to be resulting in a diversification of cause of death, complications, and comorbidities that those with diabetes live with. This has large implications for prevention and management approaches, which should be reviewed to update the breadth of conditions that patients with diabetes are at excess risk of throughout their life. These trends have not yet been seen in low- and middle-income countries, where evidence is also more scarce.
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Affiliation(s)
- Jonathan Pearson-Stuttard
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK; MRC Centre for Environment and Health, Imperial College London, Norfolk Place, London W2 1PG, UK; Health Analytics, Lane Clark & Peacock LLP, 95 Wigmore Street, London W1U 1DQ, UK; Northumbria Healthcare NHS Foundation Trust, North Shields NE27 0QJ, UK.
| | - James Buckley
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK; MRC Centre for Environment and Health, Imperial College London, Norfolk Place, London W2 1PG, UK
| | - Meryem Cicek
- Department of Primary Care and Public Health, School of Public Health, Imperial College London, St Dunstan's Road, London W6 8RP, UK
| | - Edward W Gregg
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK; MRC Centre for Environment and Health, Imperial College London, Norfolk Place, London W2 1PG, UK
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Cicek M, Buckley J, Pearson-Stuttard J, Gregg EW. Characterizing Multimorbidity from Type 2 Diabetes: Insights from Clustering Approaches. Endocrinol Metab Clin North Am 2021; 50:531-558. [PMID: 34399960 PMCID: PMC8383848 DOI: 10.1016/j.ecl.2021.05.012] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Patients with type 2 diabetes mellitus (T2DM) often live with and develop multiple co-occurring conditions, namely multimorbidity, with diffuse impacts on clinical care and patient quality of life. However, literature characterizing T2DM-related multimorbidity patterns is limited. This review summarizes the findings from the emerging literature characterizing and quantifying the association of T2DM with multimorbidity clusters. The authors' findings reveal 3 dominant cluster types appearing in patients with T2DM-related multimorbidity, such as cardiometabolic precursor conditions, vascular conditions, and mental health conditions. The authors recommend that holistic patient care centers around early detection of other comorbidities and consideration of wider risk factors.
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Affiliation(s)
- Meryem Cicek
- Department of Primary Care and Public Health, School of Public Health, Imperial College London, Charing Cross Campus, Reynolds Building, St Dunstan's Road, London W6 8RP, UK.
| | - James Buckley
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Medical School Building, St Mary's Campus, Norfolk Place, London W2 1PG, UK; MRC Centre for Environment and Health, School of Public Health, Imperial College London, Medical School Building, St Mary's Campus, Norfolk Place, London W2 1PG, UK
| | - Jonathan Pearson-Stuttard
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Medical School Building, St Mary's Campus, Norfolk Place, London W2 1PG, UK; MRC Centre for Environment and Health, School of Public Health, Imperial College London, Medical School Building, St Mary's Campus, Norfolk Place, London W2 1PG, UK
| | - Edward W Gregg
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Medical School Building, St Mary's Campus, Norfolk Place, London W2 1PG, UK; MRC Centre for Environment and Health, School of Public Health, Imperial College London, Medical School Building, St Mary's Campus, Norfolk Place, London W2 1PG, UK
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Experience of Patients with Diabetes and Other Cardiovascular Risk Factors with Health Professionals and Healthcare in Spain. J Clin Med 2021; 10:jcm10132831. [PMID: 34206918 PMCID: PMC8267612 DOI: 10.3390/jcm10132831] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 06/10/2021] [Accepted: 06/24/2021] [Indexed: 12/21/2022] Open
Abstract
We aimed to evaluate the experience of patients with type 2 diabetes (T2DM) with healthcare received in Spain. This was a retrospective, observational study in patients with T2DM cared for in primary healthcare (PHC) centers. A cross-sectional analysis of the patients' experience data was performed using the Instrument for the Evaluation of the Experience of Chronic Patients (IEXPAC). A total of 475 patients with T2DM were recruited from 36 PHC centers, of which 248 (52.2%) completed the IEXPAC questionnaire. The IEXPAC total mean score (range 0-10) was 7 points, with an average "new relational model" score of 2.5 points. The mean continuity of care score after hospital discharge was 6.2 points. The results showed that 8% of the patients always or almost always used the internet to check their medical history, appointments or other data from their healthcare service, and 15% responded that healthcare professionals always or almost always informed them of forums or other reliable internet sites to obtain information about their illness. The study results show that there is a wide margin for improvement in the experience of patients with T2DM with healthcare in Spain, especially regarding the information patients receive or can obtain.
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34
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A patient network-based machine learning model for disease prediction: The case of type 2 diabetes mellitus. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02533-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Moratalla-Navarro F, Moreno V, López-Simarro F, Aguado A. MorbiNet Study: Hypothyroidism Comorbidity Networks in the Adult General Population. J Clin Endocrinol Metab 2021; 106:e1179-e1190. [PMID: 33319230 DOI: 10.1210/clinem/dgaa927] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Indexed: 11/19/2022]
Abstract
PURPOSE Multimorbidity impacts quality of life. We constructed hypothyroidism comorbidity networks to identify positive and negative associations with other prevalent diseases. METHODS We analyzed data of 285 342 patients with hypothyroidism from 3 135 948 adults with multimorbidity in a population-based study in Catalonia, Spain, (period: 2006-2017). We constructed hypothyroidism comorbidity networks using logistic regression models, adjusted by age and sex, and for men and women separately. We considered relevant associations those with odds ratios (OR) >1.2 or <0.8 and P value < 1e-5 to identify coexistence greater (or smaller) than the expected by the prevalence of diseases. Multivariate models considering comorbidities were used to further adjust OR values. RESULTS The conditions associated included larynx cancer (adjusted OR: 2.48), congenital anomalies (2.26), thyroid cancer (2.13), hyperthyroidism (1.66), vitamin B12/folate deficiency anemia (1.57), and goiter (1.56). The network restricted to men had more connections (mental, cardiovascular, and neurological) and stronger associations with thyroid cancer (7.26 vs 2.55), congenital anomalies (5.11 vs 2.13), hyperthyroidism (4.46 vs 1.69), larynx cancer (3.55 vs 1.67), and goiter (3.94 vs 1.64). After adjustment for comorbidities, OR values were more similar in men and women. The strongest negative associations after adjusting for comorbidities were with HIV/AIDS (OR: 0.71) and tobacco abuse (0.77). CONCLUSIONS Networks show direct and indirect hypothyroidism multimorbidity associations. The strongest connections were thyroid and larynx cancer, congenital anomalies, hyperthyroidism, anemia, and goiter. Negative associations included HIV/AIDS and tobacco abuse. The network restricted to men had more and stronger associations, but not after adjusting for comorbidities, suggesting important indirect interactions.
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Affiliation(s)
- Ferran Moratalla-Navarro
- Oncology Data Analytics Program, Catalan Institute of Oncology (ICO), Barcelona, Spain
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
- CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine and Health Sciences, University of Barcelona, Campus Bellvitge, Barcelona, Spain
| | - Victor Moreno
- Oncology Data Analytics Program, Catalan Institute of Oncology (ICO), Barcelona, Spain
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
- CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine and Health Sciences, University of Barcelona, Campus Bellvitge, Barcelona, Spain
| | | | - Alba Aguado
- CAP Sagrada Familia, Consorci Sanitari Integral, Barcelona, Spain
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36
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Lee HA, Park H. Comorbidity network analysis related to obesity in middle-aged and older adults: findings from Korean population-based survey data. Epidemiol Health 2021; 43:e2021018. [PMID: 33677857 PMCID: PMC8060529 DOI: 10.4178/epih.e2021018] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 03/05/2021] [Indexed: 12/21/2022] Open
Abstract
OBJECTIVES We conducted a comorbidity network analysis using data from the seventh Korea National Health and Nutrition Examination Survey to systematically quantify obesity-related comorbidities. METHODS The study included 11,712 subjects aged 45 to 80 (5,075 male and 6,637 female). A prevalent disease was defined as a specific disease for which a subject had been diagnosed by a doctor and was being treated. Comorbidity network analysis was performed for diseases with a prevalence of 1% or more, including overweight and obesity. We estimated the observed-to-expected ratio of all possible disease pairs with comorbidity strength and visualized the network of obesity-related comorbidities. RESULTS In subjects over 45 years old, 37.3% of people had a body mass index over 25.0 kg/m2. The most common prevalent disease was hypertension (42.3%), followed by dyslipidemia (17.4%) and diabetes (17.0%). Overweight and obese subjects were 2.1 times (95% confidence interval, 1.9 to 2.3) more likely to have a comorbidity (i.e., 2 or more diseases) than normal-weight subjects. Metabolic diseases such as hypertension, dyslipidemia, diabetes, and osteoarthritis were directly associated with overweight and obesity. The probability of coexistence for each of those 4 diseases was 1.3 times higher than expected. In addition, hypertension and dyslipidemia frequently coexisted in overweight and obese female along with other diseases. In obese male, dyslipidemia and diabetes were the major diseases in the comorbidity network. CONCLUSIONS Our results provide evidence justifying the management of metabolic components in obese individuals. In addition, our results will help prioritize interventions for comorbidity reduction as a public health goal.
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Affiliation(s)
- Hye Ah Lee
- Clinical Trial Center, Ewha Womans University Mokdong Hospital, Seoul, Korea
| | - Hyesook Park
- Department of Preventive Medicine, Ewha Womans University College of Medicine, Seoul, Korea
- Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, Korea
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37
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Hassaine A, Salimi-Khorshidi G, Canoy D, Rahimi K. Untangling the complexity of multimorbidity with machine learning. Mech Ageing Dev 2020; 190:111325. [PMID: 32768443 PMCID: PMC7493712 DOI: 10.1016/j.mad.2020.111325] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 07/28/2020] [Accepted: 07/30/2020] [Indexed: 12/20/2022]
Abstract
The prevalence of multimorbidity has been increasing in recent years, posing a major burden for health care delivery and service. Understanding its determinants and impact is proving to be a challenge yet it offers new opportunities for research to go beyond the study of diseases in isolation. In this paper, we review how the field of machine learning provides many tools for addressing research challenges in multimorbidity. We highlight recent advances in promising methods such as matrix factorisation, deep learning, and topological data analysis and how these can take multimorbidity research beyond cross-sectional, expert-driven or confirmatory approaches to gain a better understanding of evolving patterns of multimorbidity. We discuss the challenges and opportunities of machine learning to identify likely causal links between previously poorly understood disease associations while giving an estimate of the uncertainty on such associations. We finally summarise some of the challenges for wider clinical adoption of machine learning research tools and propose some solutions.
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Affiliation(s)
- Abdelaali Hassaine
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom; Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - Gholamreza Salimi-Khorshidi
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - Dexter Canoy
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom; Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - Kazem Rahimi
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom; Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom.
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38
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Kalgotra P, Sharda R, Croff JM. Examining multimorbidity differences across racial groups: a network analysis of electronic medical records. Sci Rep 2020; 10:13538. [PMID: 32782346 PMCID: PMC7419498 DOI: 10.1038/s41598-020-70470-8] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 07/22/2020] [Indexed: 02/07/2023] Open
Abstract
Health disparities across ethnic or racial groups are typically examined through single behavior at a time. The syndemics and multimorbidity health disparities have not been well examined by race. In this study, we study health disparities by identifying the networks of multimorbidities among individuals from seven population groups based on race, including White, African American, Asian, Hispanic, Native American, Bi- or Multi-racial and Pacific Islander. We examined a large electronic medical record (EMR) containing health records of more than 18.7 million patients and created multimorbidity networks considering their lifetime history from medical records in order to compare the network properties among seven population groups. In addition, the networks at organ system level depicting the relationship among disorders belonging to different organ systems are also compared. Our macro analysis at the organ-level indicates that African-Americans have a stronger multimorbidity network followed by Whites and Native Americans. The networks of Asians and Hispanics are sparse. Specifically, the relationship of infectious and parasitic disorders with respiratory, circulatory and genitourinary system disorders is stronger among African Americans than others. On the other hand, the relationship of mental disorders with respiratory, musculoskeletal system and connective tissue disorders is more prevalent in Whites. Similar other disparities are discussed. Recognition and explanation of such differences in multimorbidities inform the public health policies, and can inform clinical decisions as well. Our multimorbidity network analysis identifies specific differences in diagnoses among different population groups, and presents questions for biological, behavioral, clinical, social science, and policy research.
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Affiliation(s)
- Pankush Kalgotra
- Raymond J. Harbert College of Business, Auburn University, Auburn, AL, USA.
| | - Ramesh Sharda
- Spears School of Business, Oklahoma State University, Stillwater, OK, USA
| | - Julie M Croff
- Center for Health Sciences, National Center for Wellness and Recovery, Oklahoma State University, Tulsa, USA
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