Cardiology最新文献

{"title":"Identification and Validation of Aging-Related Genes in the Comorbidity of Coronary Heart Disease and Colorectal Cancer.","authors":"Nandi Bao, Yixuan Xu, Yunfeng Bai, Jianying Li, Wenhao Hu, Jiayi Yu, Ran Zhang, Guoxin Mo","doi":"10.1159/000547088","DOIUrl":"10.1159/000547088","url":null,"abstract":"<p><strong>Introduction: </strong>Coronary heart disease (CHD) and colorectal cancer (CRC) are common comorbidities among the elderly population. However, there is a lack of clinical prediction tools that utilize aging-related genes to forecast the onset and outcomes of these conditions in elderly patients.</p><p><strong>Methods: </strong>Gene expression data related to CHD and CRC were examined using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information (NCBI). The differentially expressed genes (DEGs) associated with aging, CHD, and CRC were identified. Predictive models for CHD diagnosis and prognostic risk prediction for CRC were constructed using the LASSO, random forest, and SVM-RFE techniques. Nomogram models have been developed to assess the prognosis of patients with CRC. Drug repositioning was performed to evaluate the shared predictive genes for diagnosing CHD and predicting CRC outcomes.</p><p><strong>Results: </strong>MYL9 and UL16-binding protein 2 (ULBP2) were identified as DEGs associated with aging, CHD, and CRC. Predictive models for CHD diagnosis and CRC risk prediction have been constructed. We developed a nomogram model to assess CRC prognosis and to identify MYL9 and ULBP2 as predictive genes. We assessed the potential of MYL9 and ULBP2 as therapeutic targets in elderly patients with CHD and CRC using a drug repositioning analysis.</p><p><strong>Conclusion: </strong>We identified MYL9 and ULBP2 as aging-related markers for the diagnosis of CHD and the prognosis of CRC. In addition, we developed clinical tool models to facilitate the diagnosis of CHD and predict the prognosis of CRC, specifically in the elderly population.</p>","PeriodicalId":9391,"journal":{"name":"Cardiology","volume":" ","pages":"1-18"},"PeriodicalIF":1.7,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144728100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Polypill: Shifting Paradigm from Concept to Practice in the Prevention of Myocardial Infarction.","authors":"Dhan Bahadur Shrestha, Prakash Raj Oli, Mustafain Meghani","doi":"10.1159/000547098","DOIUrl":"https://doi.org/10.1159/000547098","url":null,"abstract":"","PeriodicalId":9391,"journal":{"name":"Cardiology","volume":" ","pages":"1-3"},"PeriodicalIF":1.9,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144697688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Clinical and Imaging Factors of Native T1 Values in Patients with Stable Coronary Artery Disease.","authors":"Runlan Luo, Qi Apos An Sun, Yi Zhao, Aihua Li, Hang Qu, Xiaoping Yu, Wei Wang","doi":"10.1159/000547345","DOIUrl":"10.1159/000547345","url":null,"abstract":"<p><strong>Introduction: </strong>Stable coronary artery disease (CAD) is a leading cause of cardiac morbidity and mortality worldwide, with elevated native T1 value linked to major adverse cardiovascular events. However, predictors of elevated native T1 value in stable CAD still need to be studied. This study aimed to identify clinical and imaging predictors of elevated native T1 values in CAD patients.</p><p><strong>Methods: </strong>A total of 316 consecutive stable CAD patients (median age 58 years, 91.8% male) undergoing coronary computed tomography angiography and cardiovascular magnetic resonance native T1 mapping were included, along with 30 age- and sex-matched healthy controls. Patients were divided into normal and elevated native T1 groups based on the normal global native T1 reference; logistic regressions were used to identify predictors.</p><p><strong>Results: </strong>Patients with elevated native T1 values were more likely to be aged ≥60 years, abstain from alcohol, have abnormal electrocardiogram findings, multivessel disease, noncalcified plaques, greater degrees of stenosis, stenosis ≥50% in two or three coronary arteries, and computed tomography-derived fractional flow reserve ≤0.8 (p < 0.05). Multivariate logistic regression identified age ≥60 years (odd ratio [OR]: 2.23, 95% confidence interval [95% CI]: 1.15-4.30, p = 0.018), stenosis ≥50% in two (OR: 13.27, 95% CI: 3.38-56.94, p < 0.001) or three coronary arteries (OR: 114.19, 95% CI: 20.53-276.59, p < 0.001), and left anterior descending FFR ≤0.8 (OR: 2.69, 95% CI: 1.16-6.29, p = 0.021) as independent risk factors, whereas alcohol consumption (OR: 0.47, 95% CI: 0.25-0.88, p = 0.019) was a predictor of normal native T1 values, with strong predictive performance (area under the curve = 0.832, Brier score = 0.142).</p><p><strong>Conclusion: </strong>Our findings could help clinicians make individualized diagnosis and treatment of stable CAD patients, which also provide a foundation for predicting prognosis. This research has been registered in the National Medical Research Registration and Filing Information System, numbered MR-32-24-030226.</p>","PeriodicalId":9391,"journal":{"name":"Cardiology","volume":" ","pages":"1-13"},"PeriodicalIF":1.7,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144658430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction and Risk Factor Analysis of in-Stent Restenosis and Revascularization after Coronary Stenting Based on Machine Learning.","authors":"Hao Ling, Chunli Song","doi":"10.1159/000547438","DOIUrl":"10.1159/000547438","url":null,"abstract":"<p><strong>Introduction: </strong>Effective prediction of in-stent restenosis and revascularization after coronary stent implantation and interventions targeting risk factors that may lead to these events are crucial for their prevention and management.</p><p><strong>Methods: </strong>Based on a C5.0 decision tree approach, data from 2,326 patients from two centers were included. We comprehensively analyzed 34 risk factors that may affect in-stent restenosis and revascularization after stent implantation and conducted predictions and risk factor analyses for in-stent restenosis and revascularization following coronary stent implantation.</p><p><strong>Results: </strong>The accuracy of predicting in-stent restenosis following coronary stent implantation with a median follow-up period of 30 months was as follows: area under the curve (AUC) in the training set, 0.996; AUC in the internal validation set, 0.988; and AUC in the external validation set, 0.889, with an f1 value of 0.95, a sensitivity of 99.16%, and a specificity of 91.72%. Additionally, the accuracy of revascularization prediction was as follows: AUC in the training set, 0.984; AUC in the internal validation set, 0.956; and AUC in the external validation set, 0.876, with an f1 value of 0.84, a sensitivity of 96.43%, and a specificity of 25%. We also conducted a risk factor analysis.</p><p><strong>Conclusion: </strong>We successfully constructed a predictive and risk factor analysis model for in-stent restenosis and revascularization following coronary stent implantation. This model may be helpful for clinical decision-making.</p>","PeriodicalId":9391,"journal":{"name":"Cardiology","volume":" ","pages":"1-8"},"PeriodicalIF":1.7,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144625397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Major Adverse Cardiovascular Events in Patients with Hypertrophic Cardiomyopathy by Deep Learning and Radiomics.","authors":"Jiangtao Wang, Biaohu Liu, Caiyun Xia, Sensen Wang","doi":"10.1159/000547232","DOIUrl":"10.1159/000547232","url":null,"abstract":"<p><strong>Introduction: </strong>Hypertrophic cardiomyopathy (HCM) patients may be at risk for major adverse cardiovascular events (MACEs), making risk stratification essential for implementing interventions in high-risk individuals. Deep transfer learning (DTL) and radiomics have made significant advances in the medical field; however, to date, no studies have combined echocardiography in HCM patients with DTL and radiomics to develop predictive models for identifying individuals at risk for MACE.</p><p><strong>Methods: </strong>This study is a retrospective analysis that included 210 HCM patients, with a mean follow-up time of 29.44 ± 16.21 months. Among the patients, 59 experienced MACE and 151 non-MACE. The patients were randomly divided into training and validation sets in an 8:2 ratio. We collected chest parasternal left ventricular long-axis and short-axis images, with the left ventricular myocardial region defined as the region of interest. Radiomic features were extracted using the PyRadiomics software package, and DTL features were obtained through the pre-trained Resnet50 model. These radiomic and DTL features were then combined, and feature selection was conducted using the least absolute shrinkage and selection operator. The selected features were used to construct the DTL-RAD predictive model with machine learning algorithms. The model's diagnostic performance was evaluated using the receiver operating characteristic curve and decision curve analysis (DCA). Finally, we compared the prediction performance of the DTL-RAD model with those of models built using only radiomic features or only DTL features.</p><p><strong>Results: </strong>The diagnostic performance of the DTL-RAD model in both the training and validation sets was excellent, with AUC values of 0.936 and 0.918, specificity values of 0.852 and 0.767, and sensitivity values of 0.892 and 0.929, respectively. It significantly outperformed models that used only radiomics or DTL features. Furthermore, the DCA demonstrated that the DTL-RAD model exhibited superior clinical applicability and effectiveness, surpassing the performance of other models.</p><p><strong>Conclusion: </strong>The DTL-RAD model demonstrated exceptional performance in identifying HCM patients at risk of MACE, accurately detecting high-risk individuals among HCM patients at an early stage. This provides a basis for precise clinical intervention, effectively reducing the incidence of MACE in HCM patients.</p>","PeriodicalId":9391,"journal":{"name":"Cardiology","volume":" ","pages":"1-15"},"PeriodicalIF":1.7,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144625398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Pump and the Pipes: Another Step towards the Non-Invasive Evaluation of Left Ventricular-Arterial Coupling in the Spectrum of Heart Failure.","authors":"Nicolò De Biase, Nicola Riccardo Pugliese, Kristina Kopeva","doi":"10.1159/000546902","DOIUrl":"https://doi.org/10.1159/000546902","url":null,"abstract":"","PeriodicalId":9391,"journal":{"name":"Cardiology","volume":" ","pages":"1-3"},"PeriodicalIF":1.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144607433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Body-Wide Expression Profiles of Commonly Assessed Cardiac Biomarkers in a Large Cohort of Human Tissue Donors.","authors":"Grant C O'Connell, Christine G Smothers, Jing Wang, Bethany L Armentrout","doi":"10.1159/000547289","DOIUrl":"10.1159/000547289","url":null,"abstract":"<p><strong>Introduction: </strong>Blood biomarkers of myocardial damage or stress are routinely used to guide the diagnosis and management of both acute and chronic cardiac conditions. The circulating levels of these proteins are directly influenced by source tissue expression levels, and understanding where and to what degree they are expressed throughout the body can yield insights into their properties as biomarkers. Thus, in this descriptive study, we sought to comprehensively map the expression of twelve clinically established or emerging cardiac biomarkers across a broad spectrum of human tissues and comparatively assess expression characteristics that could impact diagnostic performance.</p><p><strong>Methods: </strong>Existing genome-wide mRNA sequencing data originating from 16,357 cardiac and noncardiac tissue specimens harvested from 946 donors were used to quantify the expression levels of genes coding for the twelve proteins of interest (cTnT, cTnI, H-FABP, proANP, proBNP, Mb, CKTOTAL, CK-MB, LDHTOTAL, LDH-1, sST2, and cMyBP-C). Cardiac abundance, atrioventricular heterogeneity, and cardiac enrichment were subsequently assessed and compared between genes, both in the total pool of specimens and subsets of specimens grouped by donor sex and age.</p><p><strong>Results: </strong>When considering the entirety of our analyses, the spatial expression characteristics of cMyBP-C, one of the emerging biomarkers we investigated, compared favorably to those of established biomarkers such as the troponins, suggesting that it may be a viable supplement to markers currently in clinical use. However, several other emerging biomarkers we assessed, including sST2 and H-FABP, displayed high expression in numerous noncardiac tissues that could serve as diagnostic confounds and limit their clinical value. In addition, we also observed differences between the expression profiles of closely related established biomarkers that have often been used interchangeably, including cTnI and cTnT, and proANP and proBNP, that could explain recent reports of discordant blood measures. Finally, we observed notable age and sex-related differences in the expression of proANP and proBNP within cardiac tissue specifically that support calls for the use of tiered diagnostic cutoffs.</p><p><strong>Conclusion: </strong>Our findings provide insights into the potential utility of several notable emerging cardiac biomarkers and new information that could mechanistically explain previously reported phenomena or highlight possible diagnostic advantages, disadvantages, or use-caveats regarding others currently measured in clinical care.</p>","PeriodicalId":9391,"journal":{"name":"Cardiology","volume":" ","pages":"1-17"},"PeriodicalIF":1.7,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12344743/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144590535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Letter to the Editor Regarding \"Associations between Chest Pain, Diagnosis, and Clinical Outcome in Patients Hospitalized with Acute Dyspnea: Data from the ACE 2 Study\".","authors":"Çağrı Zorlu","doi":"10.1159/000547248","DOIUrl":"https://doi.org/10.1159/000547248","url":null,"abstract":"","PeriodicalId":9391,"journal":{"name":"Cardiology","volume":" ","pages":"1-3"},"PeriodicalIF":1.9,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144607432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"T2 Mapping Cardiovascular Magnetic Resonance Is an Independent Predictor of Adverse Outcome in Hypertrophic Cardiomyopathy.","authors":"Yafim Brodov, Zehavit Kirshenboim, Alexander Fardman, Rayiq Shagraui, Avi Sabbag, Eli Konen, Arkadi Beytelman, Michael Arad, Yishay Wassershtrum, Orly Goitein","doi":"10.1159/000547262","DOIUrl":"10.1159/000547262","url":null,"abstract":"<p><strong>Introduction: </strong>The outcome of patients with hypertrophic cardiomyopathy (HCM) largely depends on the presence of ventricular arrhythmias. T2 mapping is a well-documented cardiovascular magnetic resonance (CMR) sequence for edema evaluation and quantification. Our aim was to evaluate whether T2 mapping values might predict ventricular arrythmias and clinical outcome in patients with HCM.</p><p><strong>Methods: </strong>Consecutive patients (n = 48, mean age 50 ± 18 years) with HCM and healthy volunteers (n = 21, mean age 44 ± 5 years) underwent a CMR scan (3 Tesla scanner). T2 mapping values were presented using a 16-segment AHA model and divided into 3 tertiles. Late gadolinium enhancement (LGE) was calculated as percentage of left ventricular mass. Ventricular tachycardia (VT) was recorded by 24-h Holter monitoring or during exercise stress test. The clinical endpoint was defined as the presence of VT, admission due to arrhythmias, and implantable cardioverter defibrillator insertion.</p><p><strong>Results: </strong>The clinical endpoint was documented in 69%, 62%, and 50% of the third, second, and first T2 mapping tertiles, respectively (p for trend = 0.03). A multivariate analysis including age, gender, LGE, and T2 mapping demonstrated that T2 mapping was an independent predictor of the clinical endpoint as a continuous variable (OR 1.56; 1.03-2.38, p = 0.04) and the third T2 tertile (OR 22.80; 1.40-361.60, p = 0.03), respectively.</p><p><strong>Conclusion: </strong>Elevated T2 mapping values were found to be an independent predictor of the clinical endpoint in HCM patients. These results highlight the potential role of edema in HCM arrhythmias.</p>","PeriodicalId":9391,"journal":{"name":"Cardiology","volume":" ","pages":"1-8"},"PeriodicalIF":1.7,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144583081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Hydrodynamic Approach for the Evaluation of Microvascular Resistance during Provocative Spasm Testing.","authors":"Navid Freidoonimehr, Olivia Girolamo, Tam Atkins, Bo Yang, Rosanna Tavella, Christopher Zeitz, Maziar Arjomandi, John F Beltrame","doi":"10.1159/000547197","DOIUrl":"10.1159/000547197","url":null,"abstract":"<p><strong>Introduction: </strong>Intracoronary acetylcholine provocative testing is the gold standard method for the assessment of epicardial and/or microvascular spasm, with the latter diagnosed when there are acetylcholine (ACh)-induced chest pain and ischaemic electrocardiogram changes in the absence of epicardial spasm. While epicardial spasm can be visualised, microvascular spasm cannot and remains a presumed diagnosis.</p><p><strong>Methods: </strong>This article describes a hydrodynamic model developed to calculate the epicardial and microvascular resistances for both pre- and post-ACh administration. The model is based on the concept of two resistances (epicardial and microvascular) located in a series arrangement. The epicardial resistance is obtained as a hydraulic resistance, accounting for the friction resistance between the coronary blood flow and the arterial walls. The microvascular resistance is calculated by subtracting the epicardial resistance from the ratio of the pressure and flow measured using coronary guidewire-based techniques.</p><p><strong>Conclusion: </strong>This novel methodology provides key insights into the physiological characteristics of epicardial and microvascular spasm during ACh provocation testing. Further clinical validation is required to explore the clinical utility of this methodology.</p>","PeriodicalId":9391,"journal":{"name":"Cardiology","volume":" ","pages":"1-9"},"PeriodicalIF":1.7,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12503449/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144526516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}