Diabetes, Metabolic Syndrome and Obesity (Mar 2024)
Development of Serum Lactate Level-Based Nomograms for Predicting Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients
Abstract
Chunxia Jiang,1– 4,* Xiumei Ma,1– 4,* Jiao Chen,2– 5,* Yan Zeng,1– 4 Man Guo,2– 4 Xiaozhen Tan,2– 4 Yuping Wang,1,6 Peng Wang,1 Pijun Yan,2– 4 Yi Lei,1– 4 Yang Long,2– 4 Betty Yuen Kwan Law,1 Yong Xu1– 4 1Dr. 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; 2Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China; 3Metabolic Vascular Disease Key Laboratory of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China; 4Sichuan Clinical Research Center for Nephropathy, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China; 5Department of Endocrinology, The Third’s Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, Sichuan, People’s Republic of China; 6Department of Breast, Thyroid and Vascular Surgery, Traditional Chinese Medicine Hospital Affiliated to Southwest Medical University, Luzhou, Sichuan, People’s Republic of China*These authors contributed equally to this workCorrespondence: 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, 999078, People’s Republic of China, Email [email protected] Yong Xu, Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, People’s Republic of China, Email [email protected]: 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 (P< 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.Keywords: serum lactate, diabetic kidney disease, nomograms, prediction model, risk factors
