Jiang Chunxia, Ma Xiumei, Chen Jiao, Zeng Yan, Guo Man, Tan Xiaozhen, Wang Yuping, Wang Peng, Yan Pijun, Lei Yi, Long Yang, Law Betty Yuen Kwan, Xu Yong
Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Faculty of Chinese Medicine, Macau University of Science and Technology, Macao, People's Republic of China.
Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People's Republic of China.
Diabetes Metab Syndr Obes. 2024 Mar 1;17:1051-1068. doi: 10.2147/DMSO.S453543. eCollection 2024.
To establish nomograms integrating serum lactate levels and traditional risk factors for predicting diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients.
A total of 570 T2DM patients and 100 healthy subjects were enrolled. T2DM patients were categorized into normal and high lactate groups. Univariate and multivariate logistic regression analyses were employed to identify independent predictors for DKD. Then, nomograms for predicting DKD were established, and the model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis (DCA).
T2DM patients exhibited higher lactate levels compared to those in healthy subjects. Glucose, platelet, uric acid, creatinine, and hypertension were independent factors for DKD in T2DM patients with normal lactate levels, while diabetes duration, creatinine, total cholesterol, and hypertension were indicators in high lactate levels group (<0.05). The AUC values were 0.834 (95% CI, 0.776 to 0.891) and 0.741 (95% CI, 0.688 to 0.795) for nomograms in both normal lactate and high lactate groups, respectively. The calibration curve demonstrated excellent agreement of fit. Furthermore, the DCA revealed that the threshold probability and highest Net Yield were 17-99% and 0.36, and 24-99% and 0.24 for the models in normal lactate and high lactate groups, respectively.
The serum lactate level-based nomogram models, combined with traditional risk factors, offer an effective tool for predicting DKD probability in T2DM patients. This approach holds promise for early risk assessment and tailored intervention strategies.
建立整合血清乳酸水平和传统危险因素的列线图，用于预测2型糖尿病（T2DM）患者的糖尿病肾病（DKD）。
共纳入570例T2DM患者和100例健康受试者。T2DM患者分为正常乳酸组和高乳酸组。采用单因素和多因素logistic回归分析确定DKD的独立预测因素。然后，建立预测DKD的列线图，并使用受试者操作特征曲线下面积（AUC）、校准和决策曲线分析（DCA）评估模型性能。
与健康受试者相比，T2DM患者的乳酸水平更高。在乳酸水平正常的T2DM患者中，血糖、血小板、尿酸、肌酐和高血压是DKD的独立因素，而糖尿病病程、肌酐、总胆固醇和高血压是高乳酸水平组的指标（P<0.05）。正常乳酸组和高乳酸组列线图的AUC值分别为0.834（95%CI，0.776至0.891）和0.741（95%CI，0.688至0.795）。校准曲线显示拟合度极佳。此外，DCA显示，正常乳酸组和高乳酸组模型的阈值概率和最高净收益分别为17 - 99%和0.36，以及24 - 99%和0.24。
基于血清乳酸水平的列线图模型结合传统危险因素，为预测T2DM患者的DKD概率提供了一种有效工具。这种方法有望用于早期风险评估和制定个性化干预策略。
