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Calcification of Vascular Smooth Muscle Cells and Imaging of Aortic Calcification and Inflammation
Published on: May 31, 2016
Circulating miR-129-3p in combination with clinical factors predicts vascular calcification in hemodialysis patients
Jingjing Jin1,2,3, Meijuan Cheng1,2,3, Xueying Wu1,2,3
1Departments of Nephrology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, PR China.
Insights
A new prediction model using miR-129-3p and clinical factors can assess vascular calcification (VC) risk in hemodialysis patients. This tool aids in stratifying risk and improving cardiovascular event outcomes.
Area of Science:
- Biomedical research
- Cardiovascular science
- Nephrology
Background:
- Vascular calcification (VC) is a prevalent complication in hemodialysis patients, significantly increasing cardiovascular event risk and mortality.
- Optimizing individual patient management requires accurate diagnostic tools for predicting VC probability.
Purpose of the Study:
- To develop and validate a multivariable prediction model for assessing the probability of vascular calcification in hemodialysis patients.
- To identify key predictors, including microRNAs and clinical indicators, for VC risk stratification.
Main Methods:
- Identification of microRNAs (miRNAs) regulating vascular smooth muscle cell osteogenic differentiation.
- In vitro and in vivo assessment of miR-129-3p's role in VC and its association with circulating levels in hemodialysis patients.
- Development of a prediction model using logistic regression and Lasso screening, presented as a nomogram, with validation in training and external cohorts.
Main Results:
- miR-129-3p was found to attenuate VC, with serum miR-129-3p negatively correlating with VC in patients.
- A prediction model incorporating miR-129-3p, age, dialysis duration, and smoking demonstrated strong predictive performance (AUC=0.8698) and good calibration.
- Internal and external validation confirmed the model's stability and reliability for predicting VC.
Conclusions:
- A diagnostic prediction model based on miR-129-3p and clinical indicators was developed for evaluating VC probability in hemodialysis patients.
- This intuitive tool facilitates risk stratification and informed decision-making, potentially reducing serious cardiovascular event risks.
- The model's validation underscores its utility in managing VC and improving patient outcomes in hemodialysis settings.
Background:
Vascular calcification (VC) commonly occurs and seriously increases the risk of cardiovascular events and mortality in patients with hemodialysis. For optimizing individual management, we will develop a diagnostic multivariable prediction model for evaluating the probability of VC.
Methods:
The study was conducted in four steps. First, identification of miRNAs regulating osteogenic differentiation of vascular smooth muscle cells (VSMCs) in calcified condition. Second, observing the role of miR-129-3p on VC in vitro and the association between circulating miR-129-3p and VC in hemodialysis patients. Third, collecting all indicators related to VC as candidate variables, screening predictors from the candidate variables by Lasso regression, developing the prediction model by logistic regression and showing it as a nomogram in training cohort. Last, verifying predictive performance of the model in validation cohort.
Results:
In cell experiments, miR-129-3p was found to attenuate vascular calcification, and in human, serum miR-129-3p exhibited a negative correlation with vascular calcification, suggesting that miR-129-3p could be one of the candidate predictor variables. Regression analysis demonstrated that miR-129-3p, age, dialysis duration and smoking were valid factors to establish the prediction model and nomogram for VC. The area under receiver operating characteristic curve of the model was 0.8698. The calibration curve showed that predicted probability of the model was in good agreement with actual probability and decision curve analysis indicated better net benefit of the model. Furthermore, internal validation through bootstrap process and external validation by another independent cohort confirmed the stability of the model.
Conclusion:
We build a diagnostic prediction model and present it as an intuitive tool based on miR-129-3p and clinical indicators to evaluate the probability of VC in hemodialysis patients, facilitating risk stratification and effective decision, which may be of great importance for reducing the risk of serious cardiovascular events.
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