Development of Serum Lactate Level-Based Nomograms for Predicting Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients.
Chunxia Jiang, Xiumei Ma, Jiao Chen, Yan Zeng, Man Guo, Xiaozhen Tan, Yuping Wang, Peng Wang, Pijun Yan, Yi Lei, Yang Long, Betty Yuen Kwan Law, Yong Xu
Author Information
Chunxia Jiang: 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. ORCID
Xiumei Ma: 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.
Jiao Chen: Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People's Republic of China.
Yan Zeng: 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.
Man Guo: Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People's Republic of China. ORCID
Xiaozhen Tan: Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People's Republic of China.
Yuping Wang: 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.
Peng Wang: 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.
Pijun Yan: Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People's Republic of China.
Yi Lei: 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.
Yang Long: Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People's Republic of China. ORCID
Betty Yuen Kwan Law: 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.
Yong Xu: 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. ORCID
Purpose: To establish nomograms integrating serum lactate levels and traditional risk factors for predicting diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients. Patients and methods: 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). Results: 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. Conclusion: 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.
