1
|
Saqib M, Perswani P, Muneem A, Mumtaz H, Neha F, Ali S, Tabassum S. Machine learning in heart failure diagnosis, prediction, and prognosis: review. Ann Med Surg (Lond) 2024; 86:3615-3623. [PMID: 38846887 PMCID: PMC11152866 DOI: 10.1097/ms9.0000000000002138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 04/24/2024] [Indexed: 06/09/2024] Open
Abstract
Globally, cardiovascular diseases take the lives of over 17 million people each year, mostly through myocardial infarction, or MI, and heart failure (HF). This comprehensive literature review examines various aspects related to the diagnosis, prediction, and prognosis of HF in the context of machine learning (ML). The review covers an array of topics, including the diagnosis of HF with preserved ejection fraction (HFpEF) and the identification of high-risk patients with HF with reduced ejection fraction (HFrEF). The prediction of mortality in different HF populations using different ML approaches is explored, encompassing patients in the ICU, and HFpEF patients using biomarkers and gene expression. The review also delves into the prediction of mortality and hospitalization rates in HF patients with mid-range ejection fraction (HFmrEF) using ML methods. The findings highlight the significance of a multidimensional approach that encompasses clinical evaluation, laboratory assessments, and comprehensive research to improve our understanding and management of HF. Promising predictive models incorporating biomarkers, gene expression, and consideration of epigenetics demonstrate potential in estimating mortality and identifying high-risk HFpEF patients. This literature review serves as a valuable resource for researchers, clinicians, and healthcare professionals seeking a comprehensive and updated understanding of the role of ML diagnosis, prediction, and prognosis of HF across different subtypes and patient populations.
Collapse
Affiliation(s)
| | | | - Abraar Muneem
- College of Medicine, The Pennsylvania State University, Hershey, United States
| | | | - Fnu Neha
- Jinnah Sindh Medical University, Karachi
| | | | | |
Collapse
|
2
|
Hossain S, Hasan MK, Faruk MO, Aktar N, Hossain R, Hossain K. Machine learning approach for predicting cardiovascular disease in Bangladesh: evidence from a cross-sectional study in 2023. BMC Cardiovasc Disord 2024; 24:214. [PMID: 38632519 PMCID: PMC11025260 DOI: 10.1186/s12872-024-03883-2] [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: 11/18/2023] [Accepted: 04/08/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Cardiovascular disorders (CVDs) are the leading cause of death worldwide. Lower- and middle-income countries (LMICs), such as Bangladesh, are also affected by several types of CVDs, such as heart failure and stroke. The leading cause of death in Bangladesh has recently switched from severe infections and parasitic illnesses to CVDs. MATERIALS AND METHODS The study dataset comprised a random sample of 391 CVD patients' medical records collected between August 2022 and April 2023 using simple random sampling. Moreover, 260 data points were collected from individuals with no CVD problems for comparison purposes. Crosstabs and chi-square tests were used to determine the association between CVD and the explanatory variables. Logistic regression, Naïve Bayes classifier, Decision Tree, AdaBoost classifier, Random Forest, Bagging Tree, and Ensemble learning classifiers were used to predict CVD. The performance evaluations encompassed accuracy, sensitivity, specificity, and area under the receiver operator characteristic (AU-ROC) curve. RESULTS Random Forest had the highest precision among the five techniques considered. The precision rates for the mentioned classifiers are as follows: Logistic Regression (93.67%), Naïve Bayes (94.87%), Decision Tree (96.1%), AdaBoost (94.94%), Random Forest (96.15%), and Bagging Tree (94.87%). The Random Forest classifier maintains the highest balance between correct and incorrect predictions. With 98.04% accuracy, the Random Forest classifier achieved the best precision (96.15%), robust recall (100%), and high F1 score (97.7%). In contrast, the Logistic Regression model achieved the lowest accuracy of 95.42%. Remarkably, the Random Forest classifier achieved the highest AUC value (0.989). CONCLUSION This research mainly focused on identifying factors that are critical in impacting patients with CVD and predicting CVD risk. It is strongly advised that the Random Forest technique be implemented in a system for predicting cardiac diseases. This research may change clinical practice by providing doctors with a new instrument to determine a patient's CVD prognosis.
Collapse
Affiliation(s)
- Sorif Hossain
- Department of Statistics, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh.
| | - Mohammad Kamrul Hasan
- Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh
| | - Mohammad Omar Faruk
- Department of Statistics, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh
| | - Nelufa Aktar
- Department of Statistics, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh
| | - Riyadh Hossain
- Department of Statistics, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh
| | - Kabir Hossain
- Department of Statistics, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh
| |
Collapse
|
3
|
Potter E, Huynh Q, Haji K, Wong C, Yang H, Wright L, Marwick TH. Use of Clinical and Echocardiographic Evaluation to Assess the Risk of Heart Failure. JACC. HEART FAILURE 2024; 12:275-286. [PMID: 37498272 DOI: 10.1016/j.jchf.2023.06.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 04/20/2023] [Accepted: 06/07/2023] [Indexed: 07/28/2023]
Abstract
BACKGROUND Clinical and echocardiographic features predict incident heart failure (HF), but the optimal strategy for combining them is unclear. OBJECTIVES This study sought to define an effective means of using echocardiography in HF risk evaluation. METHODS The same clinical and echocardiographic evaluation was obtained in 2 groups with HF risk factors: a training group (n = 926, followed to 7 years) and a validation group (n = 355, followed to 10 years). Clinical risk was categorized as low, intermediate, and high using 4-year ARIC (Atherosclerosis Risk In Communities) HF risk score cutpoints of 9% and 33%. A risk stratification algorithm based on clinical risk and echocardiographic markers of stage B HF (SBHF) (abnormal global longitudinal strain [GLS], diastolic dysfunction, or left ventricular hypertrophy) was developed using a classification and regression tree analysis and was validated. RESULTS HF developed in 12% of the training group, including 9%, 18%, and 73% of low-, intermediate-, and high-risk patients. HF occurred in 8.6% of stage A HF and 19.4% of SBHF (P < 0.001), but stage A HF with clinical risk of ≥9% had similar outcome to SBHF. Abnormal GLS (HR: 2.92 [95% CI: 1.95-4.37]; P < 0.001) was the strongest independent predictor of HF. Normal GLS and diastolic function reclassified 61% of the intermediate-risk group into the low-risk group (HF incidence: 12%). In the validation group, 11% developed HF over 4.5 years; 4%, 17%, and 39% of low-, intermediate-, and high-risk groups. Similar results were obtained after exclusion of patients with known coronary artery disease. The echocardiographic parameters also provided significant incremental value to the ARIC score in predicting new HF admission (C-statistic: 0.78 [95% CI: 0.71-0.84] vs 0.83 [95% CI: 0.77-0.88]; P = 0.027). CONCLUSIONS Clinical risk assessment is adequate to classify low and high HF risk. Echocardiographic evaluation reclassifies 61% of intermediate-risk patients.
Collapse
Affiliation(s)
- Elizabeth Potter
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Quan Huynh
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Kawa Haji
- Western Health, Melbourne, Victoria, Australia
| | - Chiew Wong
- Northern Health, Melbourne, Victoria, Australia
| | - Hong Yang
- Menzies Institute for Medical Research, Hobart, Tasmania, Australia
| | - Leah Wright
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Thomas H Marwick
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia; Western Health, Melbourne, Victoria, Australia; Menzies Institute for Medical Research, Hobart, Tasmania, Australia.
| |
Collapse
|
4
|
Rabkin SW, Wong CN. Epigenetics in Heart Failure: Role of DNA Methylation in Potential Pathways Leading to Heart Failure with Preserved Ejection Fraction. Biomedicines 2023; 11:2815. [PMID: 37893188 PMCID: PMC10604152 DOI: 10.3390/biomedicines11102815] [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: 08/23/2023] [Revised: 09/21/2023] [Accepted: 09/26/2023] [Indexed: 10/29/2023] Open
Abstract
This review will focus on epigenetic modifications utilizing the DNA methylation mechanism, which is potentially involved in the pathogenesis of heart failure with preserved ejection fraction (HFpEF). The putative pathways of HFpEF will be discussed, specifically myocardial fibrosis, myocardial inflammation, sarcoplasmic reticulum Ca2+-ATPase, oxidative-nitrosative stress, mitochondrial and metabolic defects, as well as obesity. The relationship of HFpEF to aging and atrial fibrillation will be examined from the perspective of DNA methylation.
Collapse
Affiliation(s)
- Simon W. Rabkin
- Department of Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Division of Cardiology, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Chenille N. Wong
- Department of Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| |
Collapse
|
5
|
Durak-Nalbantic A, Begic E, Begic A, Dzubur A, Lepara O, Baljic R, Hamzic-Mehmedbasic A, Rebic D, Hodzic E, Halimic M, Badnjevic A. Biomarkers and their combination in a prediction of decompensation after an index hospitalization for acute heart failure. J Family Med Prim Care 2023; 12:1158-1164. [PMID: 37636186 PMCID: PMC10451570 DOI: 10.4103/jfmpc.jfmpc_1456_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/29/2022] [Accepted: 02/14/2023] [Indexed: 08/29/2023] Open
Abstract
Introduction Heart failure (HF) still remains as one of the most common causes of hospital admission with a high mortality rate. Aim To investigate the possible prognostic role of brain natriuretic peptide (BNP), high-sensitivity (hs) cardiac troponin (cTn) I, cystatin C, and cancer antigen 125 (CA125) in the prediction of decompensation after an index hospitalization and to investigate their possible additive prognostic value. Patients and Methods Two hundred twenty-two patients hospitalized with acute HF were monitored and followed for 18 months. Results BNP at discharge has the highest sensitivity and specificity in the prediction of decompensation. For a cutoff value of 423.3 pg/ml, sensitivity was 64.3% and specificity was 64.5%, with a positive predictive value of 71.6% and an area under the curve (AUC) of 0.69 (P < 0.001). The hazard risk (HR) for decompensation when the discharge BNP was above the cutoff value was 2.18. Cystatin C, at a cutoff value of 1.46 mg/L, had a sensitivity of 57% and specificity of 57.8%, with a positive predictive value of 65.8% and an AUC of 0.59 (P = 0.028). CA125, in the prediction of decompensation in patients with acute heart failure (AHF) and at a cutoff value of 80.5 IU/L, had a sensitivity of 60.5% and specificity of 53.3%, with a positive predictive value of 64.5% and an AUC of 0.59 (P = 0.022). The time till onset of decompensation was significantly shorter in patients with four versus three elevated biomarkers (P = 0.047), with five versus three elevated biomarkers (P = 0.026), and in patients with four versus two elevated biomarkers (P = 0.026). The HR for decompensation in patients with five positive biomarkers was 3.7 (P = 0.001) and in patients with four positive biomarkers was 2.5 (P = 0.014), compared to patients who had fewer positive biomarkers. Conclusion BNP, cystatin C, and CA125 are predictors of decompensation, and their combined usage leads to better prediction of new decompensation.
Collapse
Affiliation(s)
- Azra Durak-Nalbantic
- Clinic for Heart and Vessel Disease and Rheumatism, Clinical Center University of Sarajevo, Bolnicka 25, Sarajevo, Bosnia and Herzegovina
| | - Edin Begic
- Medical School, Sarajevo School of Science and Tecnology, Hrasnička Cesta 3a, Sarajevo, Bosnia and Herzegovina
| | - Alden Begic
- Clinic for Heart and Vessel Disease and Rheumatism, Clinical Center University of Sarajevo, Bolnicka 25, Sarajevo, Bosnia and Herzegovina
| | - Alen Dzubur
- Clinic for Heart and Vessel Disease and Rheumatism, Clinical Center University of Sarajevo, Bolnicka 25, Sarajevo, Bosnia and Herzegovina
| | - Orhan Lepara
- Medical Faculty, University of Sarajevo, Cekalusa 90, Sarajevo, Bosnia and Herzegovina
| | - Rusmir Baljic
- Clinic for Infective Disease, Clinical Center University of Sarajevo, Bolnicka 25, Sarajevo, Bosnia and Herzegovina
| | - Aida Hamzic-Mehmedbasic
- Medical School, Sarajevo School of Science and Tecnology, Hrasnička Cesta 3a, Sarajevo, Bosnia and Herzegovina
| | - Damir Rebic
- Clinic for Nephrology, Clinical Center University of Sarajevo, Bolnicka 25, Sarajevo, Bosnia and Herzegovina
| | - Enisa Hodzic
- Clinic for Heart and Vessel Disease and Rheumatism, Clinical Center University of Sarajevo, Bolnicka 25, Sarajevo, Bosnia and Herzegovina
| | - Mirza Halimic
- Pediatric Clinic, Clinical Center University of Sarajevo, Patriotske Lige, 87, Sarajevo, Bosnia and Herzegovina
| | - Almir Badnjevic
- International Burch University, Faculty of Engineering and Natural Sciences, Genetics and Bioengineering Department, Sarajevo, Bosnia and Herzegovina
| |
Collapse
|
6
|
Sopic M, Robinson EL, Emanueli C, Srivastava P, Angione C, Gaetano C, Condorelli G, Martelli F, Pedrazzini T, Devaux Y. Integration of epigenetic regulatory mechanisms in heart failure. Basic Res Cardiol 2023; 118:16. [PMID: 37140699 PMCID: PMC10158703 DOI: 10.1007/s00395-023-00986-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/27/2023] [Accepted: 04/10/2023] [Indexed: 05/05/2023]
Abstract
The number of "omics" approaches is continuously growing. Among others, epigenetics has appeared as an attractive area of investigation by the cardiovascular research community, notably considering its association with disease development. Complex diseases such as cardiovascular diseases have to be tackled using methods integrating different omics levels, so called "multi-omics" approaches. These approaches combine and co-analyze different levels of disease regulation. In this review, we present and discuss the role of epigenetic mechanisms in regulating gene expression and provide an integrated view of how these mechanisms are interlinked and regulate the development of cardiac disease, with a particular attention to heart failure. We focus on DNA, histone, and RNA modifications, and discuss the current methods and tools used for data integration and analysis. Enhancing the knowledge of these regulatory mechanisms may lead to novel therapeutic approaches and biomarkers for precision healthcare and improved clinical outcomes.
Collapse
Affiliation(s)
- Miron Sopic
- Department of Medical Biochemistry, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia
| | - Emma L Robinson
- Division of Cardiology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Costanza Emanueli
- National Heart & Lung Institute, Imperial College London, London, UK
| | | | - Claudio Angione
- School of Computing, Engineering & Digital Technologies, Teesside University, Tees Valley, Middlesbrough, TS1 3BA, UK
- Centre for Digital Innovation, Teesside University, Campus Heart, Tees Valley, Middlesbrough, TS1 3BX, UK
- National Horizons Centre, Darlington, DL1 1HG, UK
| | - Carlo Gaetano
- Laboratorio di Epigenetica, Istituti Clinici Scientifici Maugeri IRCCS, Via Maugeri 10, 27100, Pavia, Italy
| | - Gianluigi Condorelli
- IRCCS-Humanitas Research Hospital, Via Manzoni 56, 20089, Rozzano, MI, Italy
- Institute of Genetic and Biomedical Research, National Research Council of Italy, Arnold-Heller-Str.3, 24105, Milan, Italy
| | - Fabio Martelli
- Molecular Cardiology Laboratory, IRCCS-Policlinico San Donato, Via Morandi 30, San Donato Milanese, 20097, Milan, Italy
| | - Thierry Pedrazzini
- Experimental Cardiology Unit, Division of Cardiology, Department of Cardiovascular Medicine, University of Lausanne Medical School, 1011, Lausanne, Switzerland
| | - Yvan Devaux
- Cardiovascular Research Unit, Department of Population Health, Luxembourg Institute of Health, L-1445, Strassen, Luxembourg.
| |
Collapse
|
7
|
Krolevets M, Cate VT, Prochaska JH, Schulz A, Rapp S, Tenzer S, Andrade-Navarro MA, Horvath S, Niehrs C, Wild PS. DNA methylation and cardiovascular disease in humans: a systematic review and database of known CpG methylation sites. Clin Epigenetics 2023; 15:56. [PMID: 36991458 PMCID: PMC10061871 DOI: 10.1186/s13148-023-01468-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/19/2023] [Indexed: 03/31/2023] Open
Abstract
BACKGROUND Cardiovascular disease (CVD) is the leading cause of death worldwide and considered one of the most environmentally driven diseases. The role of DNA methylation in response to the individual exposure for the development and progression of CVD is still poorly understood and a synthesis of the evidence is lacking. RESULTS A systematic review of articles examining measurements of DNA cytosine methylation in CVD was conducted in accordance with PRISMA (preferred reporting items for systematic reviews and meta-analyses) guidelines. The search yielded 5,563 articles from PubMed and CENTRAL databases. From 99 studies with a total of 87,827 individuals eligible for analysis, a database was created combining all CpG-, gene- and study-related information. It contains 74,580 unique CpG sites, of which 1452 CpG sites were mentioned in ≥ 2, and 441 CpG sites in ≥ 3 publications. Two sites were referenced in ≥ 6 publications: cg01656216 (near ZNF438) related to vascular disease and epigenetic age, and cg03636183 (near F2RL3) related to coronary heart disease, myocardial infarction, smoking and air pollution. Of 19,127 mapped genes, 5,807 were reported in ≥ 2 studies. Most frequently reported were TEAD1 (TEA Domain Transcription Factor 1) and PTPRN2 (Protein Tyrosine Phosphatase Receptor Type N2) in association with outcomes ranging from vascular to cardiac disease. Gene set enrichment analysis of 4,532 overlapping genes revealed enrichment for Gene Ontology molecular function "DNA-binding transcription activator activity" (q = 1.65 × 10-11) and biological processes "skeletal system development" (q = 1.89 × 10-23). Gene enrichment demonstrated that general CVD-related terms are shared, while "heart" and "vasculature" specific genes have more disease-specific terms as PR interval for "heart" or platelet distribution width for "vasculature." STRING analysis revealed significant protein-protein interactions between the products of the differentially methylated genes (p = 0.003) suggesting that dysregulation of the protein interaction network could contribute to CVD. Overlaps with curated gene sets from the Molecular Signatures Database showed enrichment of genes in hemostasis (p = 2.9 × 10-6) and atherosclerosis (p = 4.9 × 10-4). CONCLUSION This review highlights the current state of knowledge on significant relationship between DNA methylation and CVD in humans. An open-access database has been compiled of reported CpG methylation sites, genes and pathways that may play an important role in this relationship.
Collapse
Affiliation(s)
- Mykhailo Krolevets
- Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131, Mainz, Germany
- Institute of Molecular Biology (IMB), 55128, Mainz, Germany
- Division of Molecular Embryology, DKFZ-ZMBH Alliance, 69120, Heidelberg, Germany
- Systems Medicine, Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany
| | - Vincent Ten Cate
- Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131, Mainz, Germany
- Clinical Epidemiology and Systems Medicine, Center for Thrombosis and Hemostasis (CTH), Mainz, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Rhine Main, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Jürgen H Prochaska
- Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131, Mainz, Germany
- Clinical Epidemiology and Systems Medicine, Center for Thrombosis and Hemostasis (CTH), Mainz, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Rhine Main, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Andreas Schulz
- Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Steffen Rapp
- Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131, Mainz, Germany
- Clinical Epidemiology and Systems Medicine, Center for Thrombosis and Hemostasis (CTH), Mainz, Germany
| | - Stefan Tenzer
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Miguel A Andrade-Navarro
- Institute for Immunology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | | | - Christof Niehrs
- Institute of Molecular Biology (IMB), 55128, Mainz, Germany
- Division of Molecular Embryology, DKFZ-ZMBH Alliance, 69120, Heidelberg, Germany
| | - Philipp S Wild
- Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131, Mainz, Germany.
- Systems Medicine, Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany.
- Clinical Epidemiology and Systems Medicine, Center for Thrombosis and Hemostasis (CTH), Mainz, Germany.
- German Center for Cardiovascular Research (DZHK), Partner Site Rhine Main, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
| |
Collapse
|
8
|
Ping P, Guan L, Ning C, Liu Q, Zhao Y, Zhu X, Yang T, Fu S. WGCNA and molecular docking identify hub genes for cardiac aging. Front Cardiovasc Med 2023; 10:1146225. [PMID: 37180776 PMCID: PMC10172467 DOI: 10.3389/fcvm.2023.1146225] [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: 01/17/2023] [Accepted: 04/10/2023] [Indexed: 05/16/2023] Open
Abstract
Background Cardiac aging and ageing-related cardiovascular diseases remain increase medical and social burden. Discovering the molecular mechanisms associated with cardiac aging is expected to provide new perspectives for delaying aging and related disease treatment. Methods The samples in GEO database were divided into older group and younger group based on age. Age-associated differentially expressed genes (DEGs) were identified by limma package. Gene modules significantly associated with age were mined using weighted gene co-expression network analysis (WGCNA). Protein-protein interaction networks (PPI) networks were developed using genes within modules, and topological analysis on the networks was performed to identify hub genes in cardiac aging. Pearson correlation was used to analyze the association among hub genes and immune and immune-related pathways. Molecular docking of hub genes and the anti-aging drug Sirolimus was performed to explore the potential role of hub genes in treating cardiac aging. Results We found a generally negative correlation between age and immunity, with a significant negative correlation between age and b_cell_receptor_signaling_pathway, fc_gamma_r_mediated_phagocytosis, chemokine signaling pathway, t-cell receptor signaling pathway, toll_like_receptor_signaling_pathway, and jak_stat_signaling_pathway, respectively. Finally, 10 cardiac aging-related hub genes including LCP2, PTPRC, RAC2, CD48, CD68, CCR2, CCL2, IL10, CCL5 and IGF1 were identified. 10-hub genes were closely associated with age and immune-related pathways. There was a strong binding interaction between Sirolimus-CCR2. CCR2 may be a key target for Sirolimus in the treatment of cardiac aging. Conclusion The 10 hub genes may be potential therapeutic targets for cardiac aging, and our study provided new ideas for the treatment of cardiac aging.
Collapse
Affiliation(s)
- Ping Ping
- General Station for Drug and Instrument Supervision and Control, Joint Logistic Support Force of Chinese People's Liberation Army, Beijing, China
| | - Lixun Guan
- Hematology Department, Hainan Hospital of Chinese People's Liberation Army General Hospital, Sanya, China
| | - Chaoxue Ning
- Central Laboratory, Hainan Hospital of Chinese People's Liberation Army General Hospital, Sanya, China
| | - Qiong Liu
- Medical Care Center, Hainan Hospital of Chinese People's Liberation Army General Hospital, Sanya, China
| | - Yali Zhao
- Central Laboratory, Hainan Hospital of Chinese People's Liberation Army General Hospital, Sanya, China
- Correspondence: Shihui Fu Xiang Zhu Ting Yang Yali Zhao
| | - Xiang Zhu
- Department of Infectious Disease, Army No.82 Group Military Hospital, Baoding, China
- Correspondence: Shihui Fu Xiang Zhu Ting Yang Yali Zhao
| | - Ting Yang
- Central Laboratory, Hainan Hospital of Chinese People's Liberation Army General Hospital, Sanya, China
- Correspondence: Shihui Fu Xiang Zhu Ting Yang Yali Zhao
| | - Shihui Fu
- Department of Cardiology, Hainan Hospital of Chinese People's Liberation Army General Hospital, Sanya, China
- Department of Geriatric Cardiology, Chinese People's Liberation Army General Hospital, Beijing, China
- Correspondence: Shihui Fu Xiang Zhu Ting Yang Yali Zhao
| |
Collapse
|