颈动脉超声联合血清miR-34a检测对动脉粥样硬化脑梗死发生及梗死体积的关系分析

Correlation analysis of carotid ultrasound combined with serum miR-34a on occurrence and volume of atherosclerotic cerebral

目的 研究颈动脉超声联合血清miR-34a检测对动脉粥样硬化脑梗死发生及梗死体积的关系。方法 选择2018年11月至2020年10月邢台市第一医院治疗的152例脑梗死患者(脑梗死组),其中男性85例,女性67例;年龄49~71岁,平均年龄60.23岁;病程4~12 h,平均病程8.06 h。选择同期在医院行健康体检的60例(对照组),其中男性39例,女性21例;年龄49~71岁,平均年龄60.09岁。行颈动脉超声检查,采用实时荧光定量聚合酶链式反应检测血清miR-34a水平。比较两组颈动脉超声和miR-34a水平,Pearman法分析颈动脉超声和miR-34a水平与相关性,单因素分析临床资料,Logistic回归分析确定动脉粥样硬化脑梗死发生的独立影响因素,通过受试者工作特性(ROC)曲线分析各指标及回归分析诊断效能。结果 脑梗死组患者颈动脉内膜中膜厚度(IMT)和miR-34a明显高于对照组(1.57±0.18 vs0.67±0.11,2.34±0.58 vs 1.05±0.30。P=0.000、0.000)。脑梗死体积与IMT和miR-34a均呈正相关(r=0.888、0.788,P=0.000)。Logistic回归分析表明,IMT [比值比(OR)=2.726,95%可信区间(CI)1.587~4.683]和血清miR-34a (OR=2.199,95%CI 1.275~3.792)等指标是脑梗死发生的独立影响因素(P <0.05)。根据ROC曲线可得,IMT诊断的临界值为1.23 mm,其灵敏度为61.84%,特异度为76.67%,ROC曲线下面积(AUC)为0.801(95%CI 0.750~0.852);血清miR-34a诊断的临界值为1.55,其对应的灵敏度为63.16%,特异度为76.67%,AUC为0.760(95%CI 0.707~0.812)。将回归预测方程作为新变量P,在最佳临界切点时,回归分析灵敏度度为89.47%,特异度为80.00%,AUC为0.869(95%CI 0.831~0.900),回归分析灵敏度和AUC显著高于各指标单独检测(P <0.05)。结论 梗死体积与颈动脉超声和血清miR-34a成正相关,颈动脉超声和血清miR-34a可应用于单独预测动脉粥样硬化脑梗死的发生,且联合诊断预测脑梗死诊断效能更高,值得临床进一步研究和推广。

Objective To explore the correlation between carotid ultrasound combined with serum miR-34a and occurrence as well as volume of atherosclerotic cerebral infarction. Methods From November 2018 to October 2020, a total of 152 patients with cerebral infarction were enrolled, which included 85 males and 67 females, aged 49-71 years old with mean age of 60.23 years old;course of disease was 4-12 days with mean course of 8.06-hour. Sixty healthy were set as control group,which included 39 males and 21 females, aged 49-71 years old with mean age of 60.09 years old. The carotid ultrasonography was performed, and serum miR-34a level was detected by real-time fluorescence quantitative polymerase chain reaction.The carotid ultrasound and miR-34a level between 2 groups was compared, and Pearman method was used to analyze correlation between carotid ultrasound and serum level of miR-34a. The clinical data were analyzed by univariate analysis. Logistic regression analysis was used to determine the independent influencing factors of atherosclerotic cerebral infarction. The receiver operating characteristic(ROC) curve was used to analyze the diagnostic efficacy of each index and regression analysis. Results The carotid intima-media thickness(IMT) and miR-34a in cerebral infarction group were significantly higher than those in control group(1.57 ± 0.18 vs 0.67 ± 0.11, 2.34 ± 0.58 vs 1.05 ± 0.30. P = 0.000, 0.000). The volume of cerebral infarction was positively correlated with IMT and miR-34a(r = 0.888, 0.788, P = 0.000). Logistic regression analysis showed that IMT[odds ratio(OR) = 2.726, 95 % confidence interval(CI) 1.587-4.683] and serum miR-34a(OR = 2.199, 95 % CI 1.275-3.792) were independent influencing factors of cerebral infarction(P<0.05). According to ROC curve, the critical value of IMT diagnosis was 1.23 mm,the sensitivity was 61.84 %, the specificity was 76.67 %, and area under ROC curve(AUC) was 0.801(95 % CI 0.750-0.852).The critical value of serum miR-34a diagnosis was 1.55, with corresponding sensitivity of 63.16 %, specificity of 76.67 %, and AUC of 0.760(95 % CI 0.707-0.812). The regression prediction equation was used as novel variable P, the sensitivity of regression analysis was 89.47 %, specificity was 80.00 %, and AUC was 0. 869(95 % CI 0. 831-0. 900) at optimal cri tical point. The sensitivity and AUC of regression analysis were significantly higher than those of each index(P<0.05). Conclusion It is demonstrated that infarction volume is positively correlated with carotid ultrasound and serum miR-34a, carotid ultrasound and serum miR-34a could be used to predict occurrence of atherosclerotic cerebral infarction alone, combined diagnosis showed higher diagnostic efficiency in predicting cerebral infarction, which is worthy of further clinical research and promotion.