Cell-free plasma telomere length correlated with the risk of cardiovascular events using machine learning classifiers
Abstract This retrospective study explored the association between circulating cell-free plasma telomere length (cf-TL) and coronary artery disease (CAD) and heart failure (HF). Data from 518 participants were collected, including clinical and laboratory data. cf-TL was measured in plasma samples an...
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Nature Portfolio
2024-12-01
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| Online Access: | https://doi.org/10.1038/s41598-024-76686-2 |
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| author | Mengjun Dai Kangbo Li Mesud Sacirovic Claudia Zemmrich Oliver Ritter Peter Bramlage Anja Bondke Persson Eva Buschmann Ivo Buschmann Philipp Hillmeister |
| author_facet | Mengjun Dai Kangbo Li Mesud Sacirovic Claudia Zemmrich Oliver Ritter Peter Bramlage Anja Bondke Persson Eva Buschmann Ivo Buschmann Philipp Hillmeister |
| author_sort | Mengjun Dai |
| collection | DOAJ |
| description | Abstract This retrospective study explored the association between circulating cell-free plasma telomere length (cf-TL) and coronary artery disease (CAD) and heart failure (HF). Data from 518 participants were collected, including clinical and laboratory data. cf-TL was measured in plasma samples and machine learning (ML) classification models were developed to differentiate between CAD, HF and control conditions. Our results showed that cf-TL was significantly prolonged in HF patients compared to controls, but no significant difference was observed between CAD patients and controls. Additionally, cf-TL was significantly correlated with nitric oxide metabolites (NOx) and flow-mediated dilation (FMD), suggesting a potential link with endothelial function. To avoid data leakage and ensure the model captured only relationships relevant to the research question, we utilized a temporal data split, holding out the last year’s data for testing (n = 81) and using the remaining data for training (n = 324) and validation (n = 109). The ML models using four variables achieved an area under the curve (AUC) of 0.795 in the validation dataset and 0.717 in the test dataset for CAD classification, and 0.829 in the validation dataset and 0.806 in the test dataset for HF classification. SHAP analysis revealed that cf-TL had minimal impact on the predictions of the CAD model, as indicated by consistently low SHAP values, whereas in the HF model, cf-TL exhibited a broader range of SHAP values, indicating a greater contribution to the model’s classification. These findings suggest that cf-TL may play a more prominent role in HF pathophysiology and could serve as a valuable biomarker for predicting HF risk. Further studies are warranted to explore cf-TL’s diagnostic and prognostic potential across different cardiovascular diseases. |
| format | Article |
| id | doaj-art-c753d46b6db245fbb150fe2af4e9497e |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c753d46b6db245fbb150fe2af4e9497e2025-08-20T02:30:58ZengNature PortfolioScientific Reports2045-23222024-12-0114111110.1038/s41598-024-76686-2Cell-free plasma telomere length correlated with the risk of cardiovascular events using machine learning classifiersMengjun Dai0Kangbo Li1Mesud Sacirovic2Claudia Zemmrich3Oliver Ritter4Peter Bramlage5Anja Bondke Persson6Eva Buschmann7Ivo Buschmann8Philipp Hillmeister9Department for Angiology, Center for Internal Medicine I, Deutsches Angiologie Zentrum Brandenburg – Berlin (DAZB), University Clinic Brandenburg, Brandenburg Medical School Theodor FontaneDepartment for Angiology, Center for Internal Medicine I, Deutsches Angiologie Zentrum Brandenburg – Berlin (DAZB), University Clinic Brandenburg, Brandenburg Medical School Theodor FontaneDepartment for Angiology, Center for Internal Medicine I, Deutsches Angiologie Zentrum Brandenburg – Berlin (DAZB), University Clinic Brandenburg, Brandenburg Medical School Theodor FontaneInstitute for Pharmacology and Preventive MedicineDepartment for Cardiology, Center for Internal Medicine I, Brandenburg Medical School Theodor Fontane, University Clinic BrandenburgInstitute for Pharmacology and Preventive MedicineCharité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu BerlinDepartment of Cardiology, University Clinic GrazDepartment for Angiology, Center for Internal Medicine I, Deutsches Angiologie Zentrum Brandenburg – Berlin (DAZB), University Clinic Brandenburg, Brandenburg Medical School Theodor FontaneDepartment for Angiology, Center for Internal Medicine I, Deutsches Angiologie Zentrum Brandenburg – Berlin (DAZB), University Clinic Brandenburg, Brandenburg Medical School Theodor FontaneAbstract This retrospective study explored the association between circulating cell-free plasma telomere length (cf-TL) and coronary artery disease (CAD) and heart failure (HF). Data from 518 participants were collected, including clinical and laboratory data. cf-TL was measured in plasma samples and machine learning (ML) classification models were developed to differentiate between CAD, HF and control conditions. Our results showed that cf-TL was significantly prolonged in HF patients compared to controls, but no significant difference was observed between CAD patients and controls. Additionally, cf-TL was significantly correlated with nitric oxide metabolites (NOx) and flow-mediated dilation (FMD), suggesting a potential link with endothelial function. To avoid data leakage and ensure the model captured only relationships relevant to the research question, we utilized a temporal data split, holding out the last year’s data for testing (n = 81) and using the remaining data for training (n = 324) and validation (n = 109). The ML models using four variables achieved an area under the curve (AUC) of 0.795 in the validation dataset and 0.717 in the test dataset for CAD classification, and 0.829 in the validation dataset and 0.806 in the test dataset for HF classification. SHAP analysis revealed that cf-TL had minimal impact on the predictions of the CAD model, as indicated by consistently low SHAP values, whereas in the HF model, cf-TL exhibited a broader range of SHAP values, indicating a greater contribution to the model’s classification. These findings suggest that cf-TL may play a more prominent role in HF pathophysiology and could serve as a valuable biomarker for predicting HF risk. Further studies are warranted to explore cf-TL’s diagnostic and prognostic potential across different cardiovascular diseases.https://doi.org/10.1038/s41598-024-76686-2Coronary artery diseaseMachine learningHeart failureSHAPTelomere length |
| spellingShingle | Mengjun Dai Kangbo Li Mesud Sacirovic Claudia Zemmrich Oliver Ritter Peter Bramlage Anja Bondke Persson Eva Buschmann Ivo Buschmann Philipp Hillmeister Cell-free plasma telomere length correlated with the risk of cardiovascular events using machine learning classifiers Scientific Reports Coronary artery disease Machine learning Heart failure SHAP Telomere length |
| title | Cell-free plasma telomere length correlated with the risk of cardiovascular events using machine learning classifiers |
| title_full | Cell-free plasma telomere length correlated with the risk of cardiovascular events using machine learning classifiers |
| title_fullStr | Cell-free plasma telomere length correlated with the risk of cardiovascular events using machine learning classifiers |
| title_full_unstemmed | Cell-free plasma telomere length correlated with the risk of cardiovascular events using machine learning classifiers |
| title_short | Cell-free plasma telomere length correlated with the risk of cardiovascular events using machine learning classifiers |
| title_sort | cell free plasma telomere length correlated with the risk of cardiovascular events using machine learning classifiers |
| topic | Coronary artery disease Machine learning Heart failure SHAP Telomere length |
| url | https://doi.org/10.1038/s41598-024-76686-2 |
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