RESUMEN
Abdominal aortic aneurysm (AAA) can be fatal if ruptured, but there is no predictive biomarker. Our aim was to evaluate the prognostic potential of microRNAs (miRNAs/miRs) in an AAA mouse model and patients with unruptured AAA (URAAA) and ruptured AAA (RAAA). Among the 64 miRNAs differentially expressed in mice with AAA compared to control, miR-30c-1-3p, miR-432-3p, miR-3154, and miR-379-5p had high homology with human miRNAs. MiR-30c-1-3p plasma levels were significantly lower in patients with RAAA than in those with URAAA or control and tended to negatively correlate with the maximum aortic diameter (r = -0.3153, P = 0.06109). MiR-30c-1-3p targeted matrix metalloproteinase (MMP)-9 mRNA through the coding region and downregulated its expression in vitro. MMP-9 plasma concentrations were significantly higher in the RAAA group than in the URAAA group (P < 0.001) and were negatively associated with miR-30c-1-3p levels (r = -0.3671, P = 0.01981) and positively-with the maximal aortic diameter (r = 0.6251, P < 0.0001). The optimal cutoff values for MMP-9 expression and the maximal aortic diameter were 461.08 ng/ml and 55.95 mm, with areas under the curve of 0.816 and 0.844, respectively. Our results indicate that plasma levels of miR-30c-1-3p and MMP-9 may be candidate biomarkers of AAA progression. KEY MESSAGES: Downregulation of miR-30c-1-3p expression and upregulation of its potential target MMP-9 are predictors of the devastation of AAA. Downregulation of miR-30c-1-3p expression and its downstream impact on MMP-9 have a potential on predicting the development and rupture of AAA.
Asunto(s)
Aneurisma de la Aorta Abdominal , MicroARNs , Animales , Aneurisma de la Aorta Abdominal/genética , Biomarcadores , Regulación hacia Abajo , Humanos , Metaloproteinasa 9 de la Matriz/genética , Metaloproteinasa 9 de la Matriz/metabolismo , Ratones , MicroARNs/metabolismo , Regulación hacia ArribaRESUMEN
Background: This study aimed to identify the risk factors for in-hospital mortality in patients with Stanford type B aortic dissection (TBAD) and develop and validate a prognostic dynamic nomogram for in-hospital mortality in these patients. Methods: This retrospective study involved patients with TBAD treated from April 2002 to December 2020 at the General Hospital of Northern Theater Command. The patients with TBAD were divided into survival and non-survival groups. The data were analyzed by univariate and multivariate logistic regression analyses. To identify independent risk factors for in-hospital mortality, multivariate logistic regression analysis, least absolute shrinkage, and selection operator regression were used. A prediction model was constructed using a nomogram based on these factors and validated using the original data set. To assess its discriminative ability, the area under the receiver operating characteristic curve (AUC) was calculated, and the calibration ability was tested using a calibration curve and the Hosmer-Lemeshow test. Clinical utility was evaluated using decision curve analysis (DCA) and clinical impact curves (CIC). Results: Of the 978 included patients, 52 (5.3%) died in hospital. The following variables helped predict in-hospital mortality: pleural effusion, systolic blood pressure ≥160 mmHg, heart rate >100 bpm, anemia, ischemic cerebrovascular disease, abnormal cTnT level, and estimated glomerular filtration rate <60 ml/min. The prediction model demonstrated good discrimination [AUC = 0.894; 95% confidence interval (CI), 0.850-0.938]. The predicted probabilities of in-hospital death corresponded well to the actual prevalence rate [calibration curve: via 1,000 bootstrap resamples, a bootstrap-corrected Harrell's concordance index of 0.905 (95% CI, 0.865-0.945), and the Hosmer-Lemeshow test (χ2 = 8.3334, P = 0.4016)]. DCA indicated that when the risk threshold was set between 0.04 and 0.88, the predictive model could achieve larger clinical net benefits than "no intervention" or "intervention for all" options. Moreover, CIC showed good predictive ability and clinical utility for the model. Conclusion: We developed and validated prediction nomograms, including a simple bed nomogram and online dynamic nomogram, that could be used to identify patients with TBAD at higher risk of in-hospital mortality, thereby better enabling clinicians to provide individualized patient management and timely and effective interventions.