| 王月,杜君兰,刘淅檬,等.预后营养指数和系统免疫炎症指数在糖尿病肾病预后评估中的应用价值分析[J].安徽医药,2026,30(7):1382-1389. |
| 预后营养指数和系统免疫炎症指数在糖尿病肾病预后评估中的应用价值分析 |
| The value of prognostic nutritional index and systemic immune-inflammation index in the prognostic assessment of diabetic nephropathy |
| |
| DOI：10.3969/j.issn.1009-6469.2026.07.022 |
| 中文关键词: 糖尿病肾病 预后营养指数 系统免疫炎症指数 预后 终点事件 |
| 英文关键词: Diabetic nephropathy Prognostic nutritional index Systemic immune-inflammation index Prognosis Endpoint event |
| 基金项目:贵州省科学技术基金资助项目(黔科合基础 -ZK〔2021〕一般 379) |
|
| 摘要点击次数: 237 |
| 全文下载次数: 80 |
| 中文摘要: |
| 目的分析预后营养指数( PNI)与系统免疫炎症指数( SII)与糖尿病肾病( DN)病人预后的相关性，探讨 PNI和 SII作为预测 DN病人发生终点事件的潜在价值。方法回顾性收集 2014年 11月至 2023年 12月在贵州医科大学附属医院确诊 DN病人的资料，基线临床资料包括年龄、性别、身高、身体质量指数( BMI)、吸烟史、糖尿病病程、收缩压、舒张压、血糖、血尿素氮(BUN)、血清肌酐( Scr)、尿酸、白蛋白、血红蛋白( Hb)、中性粒细胞计数( NEUT)、淋巴细胞计数( LY)、血小板计数( PLT)、甘油三酯( TG)、胆固醇、低密度脂蛋白胆固醇( LDL-C)、高密度脂蛋白胆固醇( HDL-C)、血钙、血磷、免疫球蛋白( IgG、IgA、IgM)、补体成分( C3、C4)、估算肾小球滤过率( eGFR)以及 24 h尿蛋白总量( 24 h UTP)并根据血常规指标计算 PNI和 SII。随访至发生终点事件或研究结束，终点事件定义为开始肾脏替代治疗、肌酐值翻倍或死亡，。通过受试者操作特征曲线( ROC曲线)确定 PNI和 SII预测终点事件的最佳截断值，据此分组并比较组间差异。使用 Spearman和 Pearson相关性分析评估 PNI和 SII与其他指标的相关性， ROC曲线评估风险预测效能。结果 PNI与性别、 eGFR、白蛋白、血钙、 IgG、IgA、Hb、LY呈正相关，与收缩压、舒张压、血糖、胆固醇、 LDL-C、HDL-C、IgM、24 h UTP呈负相关。 SII与糖尿病视网膜病变、收缩压、血糖、 NEUT、PLT、24 h UTP呈正相关，与白蛋白、 LY呈负相关。终点事件组中，收缩压、舒张压、 BUN、Scr、SII、24 h UTP均高于无终点事件组( P<0.05)，白蛋白、血钙、 Hb、PNI、eGFR、IgG、C3均低于无终点事件组( P<0.05)。其中，终点事件组白蛋白为( 31.36±6.28)g/L，低于无终点事件组的( 36.48±7.50)g/L；终点事件组 Hb为( 106.65±21.46)g/L，低于无终点事件组的( 125.04±21.50)g/L。PNI在预测 DN病人发生终点事件方面表现出较高的效能，其 ROC曲线下面积( AUC)为 0.73(P<0.001)，特异度和灵敏度分别为 0.67和 0.73，约登指数为 0.40，最佳截断值为 42.77。相比之下， SII的预测效能较低，其 AUC为 0.63(P=0.008)特异度为 0.64，灵敏度为 0.66，约登指数为 0.30，最佳截断值为 590.72。预测效能相对较低。 PNI和 SII联合预测发生终点事件，的 AUC为 0.73(P<0.001)特异度为 0.81，灵敏度为 0.62，约登指数为 0.43，联合预测模型具有较好的预测效能。结论 PNI具有较好地预测发生终点的能力，可以作为评估 DN病人发生终点事件风险的一个有效指标。 SII虽然也能用于风险预测，但其 AUC值较低( 0.63)，预测效能不如 PNI；PNI和 SII的联合预测模型(AUC=0.73)比单独使用其中任一指标有更高的预测效能，在临床实践中，结合多个指标进行事件，风险评估可能更为准确。 |
| 英文摘要: |
| Objective To analyze the correlation between prognostic nutritional index (PNI) and systemic immune-inflammation in-dex (SII) and the prognosis of patients with diabetic nephropathy (DN), and to explore the potential value of PNI and SII in predictingendpoint events in patients with DN.Methods Retrospective collection of data on DN patients diagnosed at the Affiliated Hospital ofGuizhou Medical University from November 2014 to December 2023. Baseline clinical data included age, gender, height, body mass in-dex (BMI), smoking history, duration of diabetes mellitus, systolic blood pressure (SBP) and diastolic blood pressure (DBP), glucose(Glu), blood urea nitrogen (BUN), serum creatinine (Scr), uric acid (UA), albumin (ALB), hemoglobin (Hb), neutrophil count (NEUT),lymphocyte count (LY), platelet count (PLT), triglycerides (TG), cholesterol (Chol), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), calcium (Ca), phosphorus (P), immunoglobulins (IgG, IgA, IgM), complement components (C3,C4), estimated glomerular filtration rate (eGFR), and 24-hour urinary total protein (24 h UTP). PNI and SII were calculated based on routine blood markers. Follow-up was performed until the occurrence of an endpoint event or the end of the study. The endpoint eventswere defined as the initiation of renal replacement therapy, a doubling of creatinine values, or death. The optimal cut-off values for the PNI and SII predicted endpoint events were determined by receiver operating characteristic curves (ROC curves), according to whichthe subjects were grouped and differences between groups were compared. Spearman and Pearson correlation analyses were used to as-sess the correlation of SII and PNI with other indicators, and ROC curves were used to assess risk prediction efficacy. P<0.05 was con-sidered statistically significant.Results PNI was positively correlated with gender, eGFR, ALB, Ca, IgG, IgA, Hb and LY, while nega-tively correlated with SBP, DBP, Glu, Chol, LDL-C, HDL-C, IgM and 24 h UTP. SII was positively correlated with diabetic retinopathy,SBP, Glu, NEUT, PLT and 24 h UTP, while negatively correlated with ALB and LY. In the endpoint event group, SBP, DBP, BUN, Scr,SII and 24 h UTP were higher than those in the non-endpoint event group (P<0.05), while ALB, Ca, Hb, PNI, eGFR, IgG and C3 were lower than those in the non-endpoint event group (P<0.05). Specifically, the endpoint event group had lower ALB [(31.36±6.28) g/L vs. (36.48±7.50) g/L] and Hb [(106.65±21.46) g/L vs. (125.04±21.50) g/L] compared with the non-endpoint event group. PNI showed highefficacy in predicting the occurrence of endpoint events in DN patients with an area under the ROC curve (AUC) of 0.73 (P<0.001), specificity and sensitivity of 0.67 and 0.73, respectively, and Youden's index of 0.40, with an optimal cut-off value of 42.77. In con-trast, SII showed lower predictive efficacy with an AUC of 0.63 (P=0.008), specificity of 0.64, sensitivity of 0.66, Youden's index of 0.30, and optimal cut-off value of 590.72. The AUC for the joint prediction of the occurrence of endpoint events by PNI and SII was 0.73 (P<0.001), with specificity of 0.81, the sensitivity of 0.62, and the Youden's index of 0.43, which showed that the joint predictionmodel had a better predictive efficacy.Conclusions PNI has a better ability to predict the occurrence of endpoint events and can beused as an effective indicator for assessing the risk of endpoint events in DN patients. SII, although it can also be used for risk predic-tion, has a lower AUC value (0.63), and its predictive efficacy is not as good as that of PNI. The joint prediction model of PNI and SII(AUC=0.73) has a higher predictive efficacy than either indicator alone, and thus risk assessment may be more accurate in clinical prac-tice when combining multiple indicators. |
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |
|
|
|
