Circulating miR-129-3p in combination with clinical factors predicts vascular calcification in hemodialysis patients

Abstract

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.

Keywords: clinical variables; hemodialysis; miR-129-3p; nomogram; vascular calcification.

Graphical Abstract

Figure 1:

MiR-129–3p was screened and identified to target osteogenic differentiation of VSMCs under calcified conditions. (A) Four different miRNAs were predicted to potentially interact with Runx2 mRNA through the miRWalk program. (B) The relative expression of the four miRNAs in VSMCs under the calcified medium for 0, 7, and 14 days, suggesting that only miR-129–3p was reduced in a time-dependent manner. **P < 0.01 versus VSMCs treaded with high phosphorus for 0 or 7 days. (C) The expression levels of Runx2 mRNA were increased in VSMCs under the calcified medium for 7 and 14 days. ***P < 0.001 and **P < 0.01 versus VSMCs cultured by high phosphorus for 0 or 7 days. (D) Negative correlation analysis was revealed between the expression of miR-129–3p and Runx2 in β-GP-induced VSMCs (r = −0.883, P = 0.002). (E) The sequence of miR-129–3p was predicted to bind Runx2 mRNA. (F) Dual-luciferase assay showed that miR‑129–3p mimics significantly decreased the relative luciferase activity in Runx2 3′UTR WT co-transfected cells compared to mimic NC, **P < 0.01. (G) Both miR-129–3p and Runx2 mRNA levels were significantly elevated in the precipitant of anti-Ago2 group compared to the anti-IgG group in VSMCs by RIP assays, **P < 0.01.

Figure 2:

Overexpression of miR-129–3p prevented osteogenic differentiation of VSMCs to alleviate vascular calcification. (A) Protein expression levels of Runx2 were determined by western blotting in normal or high phosphorus induced VSMCs transfected with miR‐129–3p mimics or mimic NC, *P < 0.05. (B) Immunohistochemical staining of Runx2 was performed in each group. (C) VSMCs were cultured and stained with Alizarin Red S for calcium deposition. (D) Calcium contents of VSMCs with same treatment as stated above were assessed using a calcium assay kit, *P < 0.05. (E) ALP activity of VSMCs was measured in each group, *P < 0.05.

Figure 3:

The association between circulating miR-129–3p and vascular calcification in HD patients. (A–C) Radiological quantification of coronary artery calcification using dual-source computed tomographic scanner in healthy controls, HD patients without VC and HD patients with VC from training cohort, respectively. (D) Serum levels of miR-129–3p in healthy controls, HD patients without VC and HD patients with VC from training cohort. (E) Serum levels of miR-129–3p in HD participants with mild VC, moderate VC, and severe VC from training cohort. (F) The negative correlation analysis between serum miR-129–3p and coronary artery calcification scores in HD patients with VC from the training cohort.

Figure 4:

Development of the prediction model and nomogram for vascular calcification in HD patients in the training cohort. (A) LASSO regression by ten-fold cross-validation method showed the optimal lambda was 0.0318, and five variables were selected as candidate predictors for further logistic regression analysis. (B) Results of the logistic regression and ablation analysis, showing that model 4 with the best satisfactory predictive discrimination was identified as the final predict model. (C) ROC curves for models; the solid line denotes the final prediction model. (D) Nomogram for the final prediction model of vascular calcification in HD patients.

Figure 5:

Predictive accuracy and net benefit of the model. (A) Calibration curve for the prediction model in training cohort. (B) The decision curve analysis of the prediction model in training cohort. (C) The internal validation calibration curve by bootstrap process in training dataset.

Figure 6:

Validation of the prediction model by another independent cohort. (A) Serum levels of miR-129–3p in HD patients without VC were higher than that in HD patients with VC from the validation cohort, **P < 0.01. (B) The negative correlation analysis between serum miR-129–3p and coronary artery calcification scores in HD patients with VC from the validation cohort. (C) ROC for the prediction model in the validation cohort. (D) The calibration curve of the prediction model in the validation cohort. (E) The decision curve analysis of the prediction model in the validation cohort.