Combined Predictive Value of miR-155 and miR-146a in Peripheral Blood Mononuclear Cells for Assessing Poor Prognosis Risk in Patients with Uremia Undergoing Maintenance Hemodialysis
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摘要:
目的 探讨外周血单个核细胞（peripheral blood mononuclear cells，PBMC）微小核糖核酸-155（microRNA-155，miR-155）与微小核糖核酸-146a（microRNA-146a，miR-146a）联合预测尿毒症维持性血液透析（maintenance hemodialysis，MHD）患者预后不良风险的价值。 方法 前瞻性选取2021年1月至2023年11月214例尿毒症患者作为研究对象，均行MHD治疗，随访1年，统计预后情况。检测所有患者首次血液透析前PBMC中miR-155、miR-146a水平，比较不同预后患者PBMC中miR-155、miR-146a水平，以两者水平的中位数为界分为高miR-155和低miR-155，高miR-146a和低miR-146a，据此分为低miR-155低miR-146a组（Q1）、低miR-155高miR-146a组（Q2）、高miR-155低miR-146a组（Q3）、高miR-155高miR-146a组（Q4），比较4组临床资料、预后不良累计发生率，Logistic回归分析PBMC中miR-155、miR-146a对预后不良的影响及交互效应，受试者工作特征（receiver operating characteristic，ROC）曲线分析PBMC中miR-155、miR-146a预测预后不良的价值。 结果 预后不良患者PBMC中miR-155、miR-146a水平高于预后良好患者（P < 0.05）；4组收缩压（systolic blood pressure，SBP）、舒张压（diastolic blood pressure，DBP）、C反应蛋白（c-reactive protein，CRP）、中性粒细胞与淋巴细胞比值（neutrophil-to-lymphocyte ratio，NLR）、全段甲状旁腺激素（intact parathyroid hormone，iPTH）、钙磷乘积比较，差异有统计学意义（P < 0.05），且Q4组SBP、DBP、CRP、NLR、iPTH、钙磷乘积高于Q1组、Q2组、Q3组，Q2组、Q3组高于Q1组（P < 0.05），Q3组CRP、NLR高于Q2组（P < 0.05）；Q4组预后不良累计发生率51.43%高于Q1组9.09%、Q2组30.00%、Q3组25.49%，Q2组、Q3组预后不良累计发生率高于Q1组（P < 0.05）；Lasso-Logistic回归分析显示，校正混杂因素因素后，与Q1相比，Q2、Q3、Q4发生预后不良风险的OR分别为2.397、2.735、5.033；交互作用分析显示，校正其他因素前后PBMC中miR-155与miR-146a的交互作用指数RERI = 0.901，AP = 0.179，S = 1.288，高miR-155、高miR-146a在预后不良风险中协同增强效应（P < 0.05）；ROC分析显示，PBMC中miR-155、miR-146a联合预测预后不良风险的曲线下面积（area under the curve，AUC）为0.870，优于单一预测价值（Z = 2.240、2.057，P = 0.025、0.040）。 结论 PBMC中miR-155、miR-146a均是尿毒症MHD患者预后不良的独立影响因素，二者在预后不良风险中呈正向交互作用，且联合检测对预后不良具有一定预测价值。 Abstract:Objective To explore the value of microRNA-155 （miR-155） and microRNA-146a （miR-146a） in peripheral blood mononuclear cells （PBMC） in combined prediction of poor prognosis risk in uremic patients undergoing maintenance hemodialysis （MHD）. Methods A prospective study enrolled 214 uremic patients from January 2021 to November 2023 as research subjects. All patients received MHD treatment with 1-year follow-up, and prognostic outcomes were recorded. Levels of miR-155 and miR-146a in PBMC before the first hemodialysis were detected in all patients. PBMC miR-155 and miR-146a levels were compared between patients with different prognoses. Using the median levels as cutoff values, patients were stratified into high miR-155/low miR-155 and high miR-146a/low miR-146a groups, which were further divided into four subgroups: low miR-155/low miR-146a （Q1）, low miR-155/high miR-146a （Q2）, high miR-155/low miR-146a （Q3）, and high miR-155/high miR-146a （Q4）. Clinical data and cumulative incidence of poor prognosis were compared among the four groups. Logistic regression was used to analyze the effects of miR-155 and miR-146a in PBMC on poor prognosis and their interactive efficacy. Receiver operating characteristic （ROC） curve analysis was used to evaluate the predictive value of PBMC miR-155 and miR-146a for poor prognosis. Results Levels of miR-155 and miR-146a in PBMC were higher in patients with poor prognosis compared to those with good prognosis （P < 0.05）. Significant differences were observed among the four groups regarding systolic blood pressure （SBP）, diastolic blood pressure （DBP）, C-reactive protein （CRP）, neutrophil-to-lymphocyte ratio （NLR）, intact parathyroid hormone （iPTH）, and calcium-phosphorus product （P < 0.05）. Group Q4 had higher SBP, DBP, CRP, NLR, iPTH, and calcium-phosphorus product compared to Q1, Q2, and Q3 groups, while Q2 and Q3 groups were higher than Q1 （P < 0.05）. CRP and NLR in Q3 were higher than in Q2 （P < 0.05）. The cumulative incidence of poor prognosis in Q4 was 51.43%, significantly higher than Q1 （9.09%）, Q2 （30.00%）, and Q3 （25.49%）, with Q2 and Q3 higher than Q1 （P < 0.05）. Lasso-Logistic regression analysis showed that after adjusting for confounding factors, the odds ratios （OR） for poor prognosis in Q2, Q3, and Q4 compared to Q1 were 2.397, 2.735, and 5.033, respectively. Interaction analysis showed that the relative excess risk due to interaction （RERI）, attributable proportion （AP）, and synergy index （S） for miR-155 and miR-146a were 0.901, 0.179, and 1.288, respectively, indicating synergistic enhancement of poor prognosis risk with high miR-155 and high miR-146a （P < 0.05）. ROC analysis demonstrated that the area under the curve （AUC） for combined prediction of poor prognosis using both miR-155 and miR-146a was 0.870, superior to individual predictions （Z = 2.240, 2.057; P = 0.025, 0.040）. Conclusion Both miR-155 and miR-146a in PBMC are independent factors influencing poor prognosis in uremic patients undergoing MHD. These two microRNAs exhibit positive interactive effects on poor prognosis risk, and their combined detection has certain predictive value for poor prognosis. -
Key words:
- PBMC /
- miR-155 /
- miR-146a /
- Uremia /
- Maintenance hemodialysis /
- Prognosis /
- Prediction
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图 1 4组预后不良发生曲线比较
Figure 1. Comparison of the curves of poor prognosis in the four groups
图 2 PBMC中miR-155、miR-146a对预后不良风险的预测价值
Figure 2. Predictive value of miR-155 and miR-146a in PBMC for risk of poor prognosis
表 1 不同预后患者PBMC中miR-155、miR-146a水平比较（$ \bar x \pm s $）
Table 1. Comparison of miR-155 and miR-146a levels in PBMCs of patients with different prognoses （$ \bar x \pm s $）
组别 n miR-155 miR-146a 预后不良患者 57 2.66 ± 0.63 5.41 ± 1.12 预后良好患者 154 1.87 ± 0.57 4.33 ± 0.96 t 8.685 6.929 P <0.001* <0.001* *P < 0.05。 
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表 2 4组基线资料比较[n（%）/（$ \bar x \pm s $）]
Table 2. Comparison of baseline information among the four groups [n （%）/（$ \bar x \pm s $）]
资料 Q1组（n = 55） Q2组（n = 70） Q3组（n = 51） Q4组（n = 35） F/χ2 P 性别 0.592 0.898 男 33（60.00） 43（61.43） 28（54.90） 20（57.14） 女 22（40.00） 27（38.57） 23（45.10） 15（42.86） 年龄（岁） 58.72 ± 4.57 58.26 ± 4.69 59.11 ± 5.03 58.49 ± 4.41 0.339 0.797 BMI（kg/m2） 22.30 ± 1.71 22.18 ± 1.56 21.95 ± 1.84 22.06 ± 1.50 0.432 0.730 原发疾病 4.152 0.981 慢性肾小球肾炎 28（50.91） 39（55.71） 27（52.94） 16（45.71） 糖尿病肾病 12（21.82） 14（20.00） 11（21.57） 9（25.71） 高血压肾病 10（18.18） 11（15.71） 7（13.73） 6（17.14） 狼疮性肾炎 3（5.45） 4（5.71） 5（9.80） 4（11.43） 其他 2（3.64） 2（2.86） 1（1.96） 0（0.00） 血管通路 0.843 0.839 内瘘 37（67.27） 44（62.86） 33（64.71） 25（71.43） 导管 18（32.73） 26（37.14） 18（35.29） 10（28.57） 冠心病 0.667 0.881 有 12（21.82） 16（22.86） 13（25.49） 9（25.71） 无 43（78.18） 54（77.14） 38（74.51） 26（74.29） 透析频率 0.625 0.891 2次/周 21（38.18） 24（34.29） 16（31.37） 13（37.14） 3次/周 34（61.82） 46（65.71） 35（68.63） 22（62.86） 血压 SBP（mmHg） 126.14 ± 5.39 135.27 ± 5.74 134.40 ± 6.32 142.17 ± 6.08 56.870 <0.001* DBP（mmHg） 80.61 ± 5.02 85.36 ± 4.69 85.57 ± 5.19 93.21 ± 5.74 43.851 <0.001* 实验室指标 CRP（mg/L） 8.86 ± 1.31 12.47 ± 1.78 18.21 ± 2.46 25.09 ± 3.06 489.269 <0.001* NLR 2.18 ± 0.64 3.72 ± 0.81 5.53 ± 0.97 8.16 ± 1.32 342.291 <0.001* Hb（g/L） 86.69 ± 10.13 87.21 ± 11.46 86.91 ± 10.68 86.24 ± 12.03 0.065 0.978 Alb（g/L） 37.28 ± 3.44 37.51 ± 3.82 36.95 ± 3.69 37.05 ± 4.01 0.252 0.860 Scr（μmmol/L） 711.80 ± 33.52 713.49 ± 40.05 709.68 ± 37.43 715.30 ± 42.17 0.177 0.912 BUN（mmol/L） 24.66 ± 2.11 24.81 ± 2.04 25.02 ± 2.33 25.17 ± 2.28 0.491 0.689 eGFR[mL·min−1·（1.73 m2）−1] 86.44 ± 10.32 84.57 ± 12.15 84.13 ± 11.49 83.65 ± 12.29 0.551 0.648 iPTH（pg/mL） 540.41 ± 64.62 617.50 ± 70.03 620.29 ± 68.48 688.73 ± 76.15 34.145 <0.001* 钙磷乘积（mmol/L）2 4.07 ± 0.26 4.38 ± 0.31 4.41 ± 0.34 4.82 ± 0.40 38.854 <0.001* spKt/V 1.48 ± 0.15 1.46 ± 0.17 1.47 ± 0.16 1.46 ± 0.16 0.134 0.940 *P < 0.05。
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表 3 PBMC中miR-155、miR-146a对预后不良风险的影响
Table 3. Effect of miR-155 and miR-146a in PBMC on the risk of poor prognosis
变量 β S.E. Waldχ2 OR 95%CI P 模型1 CRP 0.533 0.154 11.970 1.704 1.216～2.387 <0.001* NLR 0.478 0.150 10.175 1.614 1.137～2.290 <0.001* iPTH 0.674 0.171 15.531 1.962 1.528～2.519 <0.001* 钙磷乘积 0.622 0.163 14.555 1.862 1.441～2.407 <0.001* miR-155 0.557 0.148 14.145 1.745 1.227～2.481 <0.001* miR-146a 0.494 0.142 12.114 1.639 1.205～2.230 <0.001* 常数项 −0.406 0.127 15.081 − − <0.001* 模型2 <0.001* miR-155 0.465 0.135 11.849 1.592 1.154～2.195 <0.001* miR-146a 0.419 0.131 10.249 1.521 1.098～2.107 <0.001* 常数项 −0.215 0.080 12.746 − − <0.001* 注：模型1为校正CRP、NLR、iPTH、钙磷乘积前，模型2为校正CRP、NLR、iPTH、钙磷乘积后；*P < 0.05。
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表 4 PBMC中miR-155、miR-146a对预后不良风险的交互效应
Table 4. Interaction effect of miR-155，miR-146a in PBMC on the risk of poor prognosis
变量 β S.E. Waldχ2 OR 95%CI P Q1 1.000 Q2 0.874 0.166 27.735 2.397 1.731～3.319 <0.001* Q3 1.006 0.224 20.180 2.735 1.763～4.243 <0.001* Q4 1.166 0.315 26.308 5.033 1.611～15.539 <0.001* 常数项 −0.522 0.100 12.564 − − <0.001* 注：模型1为校正CRP、NLR、iPTH、钙磷乘积前，模型2为校正CRP、NLR、iPTH、钙磷乘积后。
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