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Myocardiocyte senescence in ischemic heart disease and breathome changes 认领 引用
1
作者 Basheer Abdullah Marzoog Philipp Kopylov 《World Journal of Experimental Medicine》 2026年第1期120-129,共10页
Aging is an irreversible and continuous process characterized by metabolic alterations induced by epigenomic changes.Myocardiocytes,a type of cardiac cell,are among the cells affected by this process.These changes aff... Aging is an irreversible and continuous process characterized by metabolic alterations induced by epigenomic changes.Myocardiocytes,a type of cardiac cell,are among the cells affected by this process.These changes affect cardiometabolic homeostasis at both cellular and subcellular levels.Consequently,dysregulation occurs between the protective and aggressive systems of myocardiocytes,leading to an increased prevalence of the aggressive system.This imbalance weakens the protective system against harmful factors,such as ischemia.As a result,ischemic heart disease develops,and pathological cardiometabolic changes in myocardiocytes progress with each ischemia-reperfusion event.These cardiometabolic alterations serve as biomarkers(outcomes)of ischemic myocardiocytes released into the bloodstream.The detection of these biomarkers in exhaled breath,in the form of volatile organic compounds(VOCs),is feasible using various types of mass spectrometers,including the proton transfer reaction time of flight mass spectrometer.Exhaled VOCs can be utilized as biomarkers of the biological age of myocardiocytes by measuring the concentration of specific VOCs associated with cardiometabolic changes and ischemic myocardiocytes.This article explores the relationship between myocardiocyte aging and the development of ischemic heart disease,as well as the changes in exhaled VOCs. 展开更多
关键词 Ischemic heart disease Breathome Aging Atherosclerosis Metabolome Lipidome Myocardiocytes
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Machine learning-based detection of diabetes mellitus from singlelead electrocardiography:A phenotype-stratified approach 认领 引用 被引量：2
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作者 Anna Dmitrievna Karbovskaya Basheer Abdullah Marzoog +9 位作者 Anastasia Stroeva Alexander Suvorov Peter Chomakhidze Daria Gognieva Natalia Kuznetsova Philipp Kopylov Abromavich Syrkin Valentin V Fadeev Sevindzh M Ismailova Irina V Poluboyarinova 《World Journal of Cardiology》 2026年第3期78-92,共15页
BACKGROUND Diabetes mellitus(DM)and the related sequalae remains one of the most frequently reported cause of morbidity and mortality in our era.This returns to the non-sufficient screening methods for DM at early sta... BACKGROUND Diabetes mellitus(DM)and the related sequalae remains one of the most frequently reported cause of morbidity and mortality in our era.This returns to the non-sufficient screening methods for DM at early stages.AIM To assess the diagnostic capabilities of the parameters of single lead electrocardiography(ECG)in the diagnosis of DM utilizing machine learning model.METHODS A single center study involved 629 participants with vs without DM.All the study participants passed transthoracic echocardiography,fasting blood glucose measurement,standard 12-lead ECG recording,and single lead ECG registration using the Cardio-Qvark®device.A gradient boosting machine model,specifically the XGBoost implementation,was developed using R v4.2 and Python v3.10.The model was trained and validated using a novel cluster-stratified approach-training on three phenotypic clusters and testing on the fourth-to isolate DM-specific ECG signatures from confounding cardiovascular disease.RESULTS The cluster-stratified analysis revealed that the model performed best in cluster 4(patients with high DM prevalence and significant comorbidities),achieving a sensitivity of 75%,specificity of 83%,and an area under the curve of 88%.CONCLUSION This study demonstrates that a phenotype-stratified approach is crucial for effective ECG-based DM screening.By identifying a specific clinical profile(cluster 4:High comorbidity burden with preserved cardiac function),we developed a model that accurately detects DM from a single-lead ECG.This phenotype-specific strategy overcomes the confounding effect of cardiovascular disease,moving beyond one-size-fits-all algorithms towards a precise and clinically viable tool for non-invasive DM detection in high-risk populations. 展开更多
关键词 Diabetes mellitus Machine learning model Diagnosis Single lead electrocardiography Hyperglycemia Metabolic syndrome
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Discriminating diabetes mellitus from single-lead electrocardiography using machine learning and multinomial regression 认领 引用 被引量：1
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作者 Anna Dmitrievna Karbovskaya Basheer Abdullah Marzoog +9 位作者 Anastasia Stroeva Peter Chomakhidze Daria Gognieva Natalia Kuznetsova Alexander Suvorov Philipp Kopylov Abromavich Syrkin Valentin V Fadeev Irina V Poluboyarinova Sevindzh M Ismailova 《World Journal of Cardiology》 2026年第3期93-106,共14页
BACKGROUND Current advances in diagnostic and therapeutic strategies remain insufficient to reduce the prevalence and incidence rate of diabetes mellitus(DM).AIM To investigate any association between single-lead elec... BACKGROUND Current advances in diagnostic and therapeutic strategies remain insufficient to reduce the prevalence and incidence rate of diabetes mellitus(DM).AIM To investigate any association between single-lead electrocardiography(ECG)parameters and the diagnosis of DM.METHODS A single center study involved participants of Caucasian origin for the period between May 2,2022 and August 23,2025 with or without DM and aged≥18 years.All participants participating in the study passed the cardiologist's,random glucose measurement using a glucometer,single lead-ECG registration(using Cardio-Qvark®)and transthoracic echocardiography.Statistical analysis conducted using the R programming language(version 4.5).RESULTS The built logistic regression machine learning model demonstrated diagnostic performance in discriminating(area under the curve)type 1 DM 0.84(95%CI:0.76-0.91),type 2 DM 0.69(95%CI:0.61-0.76),and healthy control 0.82(95%CI:0.76-0.87).CONCLUSION The developed model demonstrates an association between single-lead ECG parameters and diabetes status that can support the clinical identification of individuals who would benefit from confirmatory testing.This is probably attributable to relatively stable and long-term physiological alterations associated with the state of the disease. 展开更多
关键词 Machine learning Diabetes mellitus Diagnosis Hyperglycemia Glycated hemoglobin Glucose
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Development and validation of a machine learning model for diagnosis of ischemic heart disease using single-lead electrocardiogram parameters 认领 引用 被引量：5
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作者 Basheer Abdullah Marzoog Peter Chomakhidze +11 位作者 Daria Gognieva Artemiy Silantyev Alexander Suvorov Magomed Abdullaev Natalia Mozzhukhina Darya Alexandrovna Filippova Sergey Vladimirovich Kostin Maria Kolpashnikova Natalya Ershova Nikolay Ushakov Dinara Mesitskaya Philipp Kopylov 《World Journal of Cardiology》 2025年第4期76-92,共17页
BACKGROUND Ischemic heart disease(IHD)impacts the quality of life and has the highest mortality rate of cardiovascular diseases globally.AIM To compare variations in the parameters of the single-lead electrocardiogram... BACKGROUND Ischemic heart disease(IHD)impacts the quality of life and has the highest mortality rate of cardiovascular diseases globally.AIM To compare variations in the parameters of the single-lead electrocardiogram(ECG)during resting conditions and physical exertion in individuals diagnosed with IHD and those without the condition using vasodilator-induced stress computed tomography(CT)myocardial perfusion imaging as the diagnostic reference standard.METHODS This single center observational study included 80 participants.The participants were aged≥40 years and given an informed written consent to participate in the study.Both groups,G1(n=31)with and G2(n=49)without post stress induced myocardial perfusion defect,passed cardiologist consultation,anthropometric measurements,blood pressure and pulse rate measurement,echocardiography,cardio-ankle vascular index,bicycle ergometry,recording 3-min single-lead ECG(Cardio-Qvark)before and just after bicycle ergometry followed by performing CT myocardial perfusion.The LASSO regression with nested cross-validation was used to find the association between Cardio-Qvark parameters and the existence of the perfusion defect.Statistical processing was performed with the R programming language v4.2,Python v.3.10[^R],and Statistica 12 program.RESULTS Bicycle ergometry yielded an area under the receiver operating characteristic curve of 50.7%[95%confidence interval(CI):0.388-0.625],specificity of 53.1%(95%CI:0.392-0.673),and sensitivity of 48.4%(95%CI:0.306-0.657).In contrast,the Cardio-Qvark test performed notably better with an area under the receiver operating characteristic curve of 67%(95%CI:0.530-0.801),specificity of 75.5%(95%CI:0.628-0.88),and sensitivity of 51.6%(95%CI:0.333-0.695).CONCLUSION The single-lead ECG has a relatively higher diagnostic accuracy compared with bicycle ergometry by using machine learning models,but the difference was not statistically significant.However,further investigations are required to uncover the hidden capabilities of single-lead ECG in IHD diagnosis. 展开更多
关键词 Ischemic heart disease Single-lead electrocardiography Computed tomography myocardial perfusion Prevention Risk factors Stress test Machine learning model
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Volatilome and machine learning in ischemic heart disease:Current challenges and future perspectives 认领 引用 被引量：1
5
作者 Basheer Abdullah Marzoog Philipp Kopylov 《World Journal of Cardiology》 2025年第4期138-144,共7页
Integrating exhaled breath analysis into the diagnosis of cardiovascular diseases holds significant promise as a valuable tool for future clinical use,particularly for ischemic heart disease(IHD).However,current resea... Integrating exhaled breath analysis into the diagnosis of cardiovascular diseases holds significant promise as a valuable tool for future clinical use,particularly for ischemic heart disease(IHD).However,current research on the volatilome(exhaled breath composition)in heart disease remains underexplored and lacks sufficient evidence to confirm its clinical validity.Key challenges hindering the application of breath analysis in diagnosing IHD include the scarcity of studies(only three published papers to date),substantial methodological bias in two of these studies,and the absence of standardized protocols for clinical imple-mentation.Additionally,inconsistencies in methodologies—such as sample collection,analytical techniques,machine learning(ML)approaches,and result interpretation—vary widely across studies,further complicating their reprodu-cibility and comparability.To address these gaps,there is an urgent need to establish unified guidelines that define best practices for breath sample collection,data analysis,ML integration,and biomarker annotation.Until these challenges are systematically resolved,the widespread adoption of exhaled breath analysis as a reliable diagnostic tool for IHD remains a distant goal rather than an immi-nent reality. 展开更多
关键词 Volatilome Breathome Ischemic heart disease Mass spectrometer Machine learning
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