实用医学杂志 ›› 2026, Vol. 42 ›› Issue (1): 87-93.doi: 10.3969/j.issn.1006-5725.2026.01.011

• 慢性病防治专栏 • 上一篇    下一篇

早发2型糖尿病肾脏损伤的危险因素分析

李佳,刘心悦,时佳,樊晓轩,刘苏()   

  1. 南京中医药大学附属医院内分泌科 (江苏 南京 210029 )
  • 收稿日期:2025-09-27 出版日期:2026-01-10 发布日期:2026-01-14
  • 通讯作者: 刘苏 E-mail:nfmkls@163.com
  • 基金资助:
    国家自然科学基金青年基金项目(81804027);江苏省卫生健康委科研项目(M2022053);江苏省研究生科研与实践创新计划项目(SJCX25_0996)

Analysis of risk factors associated with kidney injury in early-onset type 2 diabetes mellitus

Jia LI,Xinyue LIU,Jia SHI,Xiaoxuan FAN,Su LIU()   

  1. Department of Endocrinology,The First Clinical Medical College of Nanjing University of Chinese Medicine,Nanjing 210029,Jiangsu,China
  • Received:2025-09-27 Online:2026-01-10 Published:2026-01-14
  • Contact: Su LIU E-mail:nfmkls@163.com

摘要:

目的 通过多维度评估,识别早发2型糖尿病(EOT2DM)相关肾脏损伤的危险因素,为建立早期预警体系、优化临床干预策略提供科学依据。 方法 回顾性收集2024年8月至2025年8月于南京中医药大学附属医院内分泌科住院的EOT2DM患者541例,按是否出现肾脏损伤相关指标异常分为EOT2DM伴有肾损组(n = 241)和EOT2DM不伴有肾损组(n = 300)。收集患者临床资料,进行单因素分析,针对P < 0.05的变量以及结合既往文献研究具有明确临床意义的单因素分析P ≥ 0.05的变量,构建Lasso logistic回归模型,剔除冗余变量后进行多因素logistic回归分析。计算约登指数确定各变量的最佳cutoff值,根据最佳cutoff值将连续型变量分为高风险组(≥ cutoff值)与低风险组(< cutoff值),进行协方差矫正后计算校正后的OR值及其95% CI,以精确量化各风险变量高低水平与EOT2DM肾脏损伤风险的独立关联强度。绘制ROC曲线,结合ROC曲线下面积判断各风险因素的区分能力。 结果 多因素logistic回归分析后筛选出6个风险因素:血栓调节蛋白(sTM)(OR = 1.789,AUC = 0.702)、甘油三酯(TG)(OR = 2.647,AUC = 0.602)、血尿酸(UA)(OR = 1.693,AUC = 0.637)、低密度脂蛋白(LDL-C)(OR = 1.942,AUC = 0.562)、腰臀比WHR(OR = 2.364,AUC = 0.566)、颈动脉斑块(OR = 1.872,AUC = 0.607);结合cutoff值进一步精确关联强度后得出以下四个风险因素:sTM、TG、WHR、颈动脉斑块。 结论 高sTM、TG、WHR、颈动脉斑块是EOT2DM患者发生肾脏损伤的独立危险因素,其中sTM对EOT2DM肾脏损伤状态的判断准确性更优,可能更适合作为临床中评估肾脏损伤的工具。

关键词: 早发2型糖尿病, 肾脏损伤, 危险因素

Abstract:

Objective To identify risk factors associated with renal impairment in early-onset type 2 diabetes mellitus (EOT2DM) through a multidimensional assessment, so as to provide a scientific basis for establishing an early warning system and optimizing clinical intervention strategies. Methods This retrospective study recruited 541 patients diagnosed with EOT2DM who were admitted to the Department of Endocrinology at the Jiangsu Provincial Hospital of Traditional Chinese Medicine from August 2024 to August 2025. The participants were stratified into two groups according to the presence of abnormal renal impairment indicators: an EOT2DM group with renal impairment (n = 241) and an EOT2DM group without renal impairment (n = 300). Clinical data were collected and underwent univariate analysis. Variables with a P-value < 0.05 in the univariate analysis, as well as variables with established clinical significance according to previous literature (even if P ≥ 0.05), were included in a least absolute shrinkage and selection operator (LASSO) logistic regression model to remove redundant variables. Subsequently, multivariate logistic regression analysis was carried out. The optimal cut-off values for significant continuous variables were identified by calculating the Youden's index. Based on these cut-offs, continuous variables were dichotomized into high-risk (≥ cutoff) and low-risk (< cutoff) groups. After covariance adjustment, the adjusted odds ratios (ORs) and their 95% confidence intervals (CIs) were computed to accurately quantify the independent association between the level of each risk variable and the risk of renal impairment in EOT2DM. Receiver Operating Characteristic (ROC) curves were drawn, and the discriminatory ability of each risk factor was evaluated by the area under the ROC curve. Results Multivariate logistic regression analysis has identified six risk factors: sTM (OR = 1.789, AUC = 0.702), TG (OR = 2.647, AUC = 0.602), UA (OR = 1.693, AUC = 0.637), LDL-C (OR = 1.942, AUC = 0.562), WHR (OR = 2.364, AUC = 0.566), and carotid plaque (OR = 1.872, AUC = 0.607). After further refining the strength of the association by using cutoff values, the following four risk factors were determined: sTM, TG, WHR, and carotid plaque. Conclusions Elevated sTM, TG, WHR, and the presence of carotid plaque are independent risk factors for renal impairment in patients with EOT2DM. Among these, sTM demonstrated superior accuracy for renal impairment in EOT2DM and may be more suitable as a clinical assessment tool.

Key words: early-onset type 2 diabetes mellitus, renal impairment, risk factor

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