| AIP联合PNI及内脏脂肪面积对非肥胖T2DM合并MAFLD的预测价值 |
| 投稿时间：2025-07-13 修订日期：2025-09-10 点此下载全文 |
| 引用本文：吕昭迪,崔俊莹,张磊,霍雨,金子玉.AIP联合PNI及内脏脂肪面积对非肥胖T2DM合并MAFLD的预测价值[J].医学研究杂志,2026,55(1):103-109 |
| DOI：
10.11969/j.issn.1673-548X.2026.01.018 |
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| 基金项目:河南省重点研发与推广专项(科技攻关)项目(232102310247) |
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| 中文摘要:目的 探讨血浆致动脉粥样硬化指数(atherogenic index of plasma,AIP)联合预后营养指数(prognostic nutritional index,PNI)及内脏脂肪面积(visceral fat area,VFA)对非肥胖2型糖尿病(type 2 diabetes mellitus,T2DM)患者合并代谢相关脂肪性肝病(metabolic-associated fatty liver disease,MAFLD)的预测价值,构建列线图模型并进行验证。方法 纳入2023年1月~2024年11月就诊于郑州大学附属郑州中心医院的非肥胖T2DM患者共799例,以7∶3的比例随机划分为训练集(n=559)和验证集(n=240)。采用LASSO回归和多因素Logistic回归筛选非肥胖T2DM患者合并MAFLD的独立危险因素,并构建列线图预测模型。采用受试者工作特征曲线、校准曲线、决策曲线分析(decision curve analysis,DCA)对联合模型进行评价。结果 体重指数(body mass index,BMI)、γ-谷氨酰转移酶(γ-glutamyl transferase,GGT)、AIP、PNI、VFA为非肥胖T2DM患者合并MAFLD的独立危险因素。纳入上述5项危险因素建立的列线图风险模型在训练集、验证集的曲线下面积分别为0.872(95%CI:0.844~0.900)、0.864(95%CI:0.818~0.909),敏感度分别为77.9%、72.6%,特异性分别为80.0%、83.7%,校准曲线显示该模型拟合良好,DCA曲线显示该预测模型具有良好的临床实用价值。联合模型的预测能力显著优于AIP、PNI、VFA的单独预测能力。结论 AIP、PNI、VFA是非肥胖T2DM患者合并MAFLD的独立危险因素。整合BMI、GGT、AIP、PNI和VFA构建的列线图模型表现出良好的预测能力,可为早期识别非肥胖T2DM合并MAFLD患者提供参考。 |
| 中文关键词:非肥胖 代谢相关脂肪性肝病 2型糖尿病 血浆致动脉粥样硬化指数 预后营养指数 内脏脂肪面积 |
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| Predictive Value of AIP Combined with PNI and Visceral Fat Area for Non-obese T2DM with MAFLD. |
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| Abstract:Objective To investigate the predictive value of atherogenic index of plasma (AIP) combined with prognostic nutritional index (PNI) and visceral fat area (VFA) for metabolic-associated fatty liver disease (MAFLD) in non-obese patients with type 2diabetes mellitus (T2DM), and to construct and validate a nomogram model. Methods A total of 799 non-obese T2DM patients admitted to Zhengzhou Central Hospital Affiliated to Zhengzhou University from January 2023 to November 2024 were enrolled and randomly divided into training set (n =559) and validation set (n =240) according to the ratio of 7∶3. LASSO regression and multivariate Logistic regression were used to screen the independent risk factors of MAFLD in non-obese T2DM patients, and a nomogram prediction model was constructed. Receiver operating characteristic curve, calibration curve and decision curve analysis (DCA) were used to evaluate the combined model. Results Body mass index (BMI), γ-glutamyl transferase (GGT), AIP,PNI, and VFA were independent risk factors for the development of MAFLD. The nomogram model established using the above five risk factors had an AUC of 0.872 (95%CI:0.844-0.900) and 0.864 (95%CI:0.818-0.909) in the training set and validation set, respectively, with sensitivities of 77.9% and 72.6%, and specificities of 80.0% and 83.7%, respectively. The calibration curve indicated that the model fits well, and the DCA curve demonstrated that the model has good clinical utility. The predictive ability of the combined model was significantly superior to that of AIP, PNI, and VFA alone. Conclusion AIP, PNI, and VFA are independent risk factors for MAFLD in non-obese T2DM patients. The nomogram model integrating BMI, GGT, AIP, PNI, and VFA shows good predictive ability, which can provide reference for early identification of non-obese T2DM patients with MAFLD. |
| keywords:Non-obese Metabolic-associated fatty liver disease Type 2diabetes mellitus Atherogenic index of plasma Prognostic nutritional index Visceral fat area |
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