Construction of a miR-15a-based risk prediction model for vascular calcification detection in patients undergoing hemodialysis
Vascular calcification (VC) is highly prevalent in patients undergoing hemodialysis, and is a significant contributor to the mortality rate. Therefore, biomarkers that can accurately predict the onset of VC are urgently required. Our study aimed to investigate serum miR-15a levels in relation to VC...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
|
| Series: | Renal Failure |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2024.2313175 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832591097761628160 |
|---|---|
| author | Chen Fu Yingjie Liu Huayu Yang Qiaojing Liang Wenhu Liu Weikang Guo |
| author_facet | Chen Fu Yingjie Liu Huayu Yang Qiaojing Liang Wenhu Liu Weikang Guo |
| author_sort | Chen Fu |
| collection | DOAJ |
| description | Vascular calcification (VC) is highly prevalent in patients undergoing hemodialysis, and is a significant contributor to the mortality rate. Therefore, biomarkers that can accurately predict the onset of VC are urgently required. Our study aimed to investigate serum miR-15a levels in relation to VC and to develop a predictive model for VC in patients undergoing hemodialysis at the Beijing Friendship Hospital hemodialysis center between 1 January 2019 and 31 December 2020. The patients were categorized into two groups: VC and non-VC. Logistic regression (LR) models were used to examine the risk factors associated with VC. Additionally, we developed an miR-15a-based nomogram based on the results of the multivariate LR analysis. A total of 138 patients under hemodialysis were investigated (age: 58.41 ± 13.22 years; 54 males). VC occurred in 79 (57.2%) patients. Multivariate LR analysis indicated that serum miR-15a, age, and WBC count were independent risk factors for VC. A miR-15a-based nomogram was developed by incorporating the following five predictors: age, dialysis vintage, predialysis nitrogen, WBC count, and miR-15a. The receiver operating characteristic (ROC) curve had an area under the curve of 0.921, diagnostic threshold of 0.396, sensitivity of 0.722, and specificity of 0.932, indicating that this model had good discrimination. This study concluded that serum miR-15a levels, age, and white blood cell (WBC) count are independent risk factors for VC. A nomogram constructed by integrating these risk factors can be used to predict the risk of VC in patients undergoing hemodialysis. |
| format | Article |
| id | doaj-art-603432334ee548509eaa4f0455c64b02 |
| institution | Kabale University |
| issn | 0886-022X 1525-6049 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj-art-603432334ee548509eaa4f0455c64b022025-01-23T04:17:48ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492024-12-0146110.1080/0886022X.2024.2313175Construction of a miR-15a-based risk prediction model for vascular calcification detection in patients undergoing hemodialysisChen Fu0Yingjie Liu1Huayu Yang2Qiaojing Liang3Wenhu Liu4Weikang Guo5Department of Nephrology, Faculty of Kidney Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, PR ChinaDepartment of Nephrology, Faculty of Kidney Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, PR ChinaDivision of Geriatrics, Medical and Health Care Center, Beijing Friendship Hospital, Capital Medical University, Beijing, PR ChinaDivision of Geriatrics, Medical and Health Care Center, Beijing Friendship Hospital, Capital Medical University, Beijing, PR ChinaDepartment of Nephrology, Faculty of Kidney Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, PR ChinaDepartment of Nephrology, Faculty of Kidney Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, PR ChinaVascular calcification (VC) is highly prevalent in patients undergoing hemodialysis, and is a significant contributor to the mortality rate. Therefore, biomarkers that can accurately predict the onset of VC are urgently required. Our study aimed to investigate serum miR-15a levels in relation to VC and to develop a predictive model for VC in patients undergoing hemodialysis at the Beijing Friendship Hospital hemodialysis center between 1 January 2019 and 31 December 2020. The patients were categorized into two groups: VC and non-VC. Logistic regression (LR) models were used to examine the risk factors associated with VC. Additionally, we developed an miR-15a-based nomogram based on the results of the multivariate LR analysis. A total of 138 patients under hemodialysis were investigated (age: 58.41 ± 13.22 years; 54 males). VC occurred in 79 (57.2%) patients. Multivariate LR analysis indicated that serum miR-15a, age, and WBC count were independent risk factors for VC. A miR-15a-based nomogram was developed by incorporating the following five predictors: age, dialysis vintage, predialysis nitrogen, WBC count, and miR-15a. The receiver operating characteristic (ROC) curve had an area under the curve of 0.921, diagnostic threshold of 0.396, sensitivity of 0.722, and specificity of 0.932, indicating that this model had good discrimination. This study concluded that serum miR-15a levels, age, and white blood cell (WBC) count are independent risk factors for VC. A nomogram constructed by integrating these risk factors can be used to predict the risk of VC in patients undergoing hemodialysis.https://www.tandfonline.com/doi/10.1080/0886022X.2024.2313175HemodialysismiR-15avascular calcificationabdominal aortic calcification score (AACS)nomogram model |
| spellingShingle | Chen Fu Yingjie Liu Huayu Yang Qiaojing Liang Wenhu Liu Weikang Guo Construction of a miR-15a-based risk prediction model for vascular calcification detection in patients undergoing hemodialysis Renal Failure Hemodialysis miR-15a vascular calcification abdominal aortic calcification score (AACS) nomogram model |
| title | Construction of a miR-15a-based risk prediction model for vascular calcification detection in patients undergoing hemodialysis |
| title_full | Construction of a miR-15a-based risk prediction model for vascular calcification detection in patients undergoing hemodialysis |
| title_fullStr | Construction of a miR-15a-based risk prediction model for vascular calcification detection in patients undergoing hemodialysis |
| title_full_unstemmed | Construction of a miR-15a-based risk prediction model for vascular calcification detection in patients undergoing hemodialysis |
| title_short | Construction of a miR-15a-based risk prediction model for vascular calcification detection in patients undergoing hemodialysis |
| title_sort | construction of a mir 15a based risk prediction model for vascular calcification detection in patients undergoing hemodialysis |
| topic | Hemodialysis miR-15a vascular calcification abdominal aortic calcification score (AACS) nomogram model |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2024.2313175 |
| work_keys_str_mv | AT chenfu constructionofamir15abasedriskpredictionmodelforvascularcalcificationdetectioninpatientsundergoinghemodialysis AT yingjieliu constructionofamir15abasedriskpredictionmodelforvascularcalcificationdetectioninpatientsundergoinghemodialysis AT huayuyang constructionofamir15abasedriskpredictionmodelforvascularcalcificationdetectioninpatientsundergoinghemodialysis AT qiaojingliang constructionofamir15abasedriskpredictionmodelforvascularcalcificationdetectioninpatientsundergoinghemodialysis AT wenhuliu constructionofamir15abasedriskpredictionmodelforvascularcalcificationdetectioninpatientsundergoinghemodialysis AT weikangguo constructionofamir15abasedriskpredictionmodelforvascularcalcificationdetectioninpatientsundergoinghemodialysis |