Development of Serum Lactate Level-Based Nomograms for Predicting Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients

Abstract

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 (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: diabetic kidney disease; nomograms; prediction model; risk factors; serum lactate.

Figure 1

Serum lactate levels in subjects with healthy persons, T2DM patients without DKD, and DKD patients. Serum lactate levels were higher in T2DM patients with DKD and those without than in healthy controls. ***p<0.001.

Figure 2

The forest plots of predictors of DKD in T2DM patients classified by lactate levels. The OR and a p value with 95% CI of the selected risk factors for DKD risk in T2DM patients with normal (A) and high (B) serum lactate levels are shown.

Figure 3

The nomograms to quantitatively predict probability of DKD in T2DM patients classified by lactate levels. (A): the nomogram for DKD prediction in T2DM patients with normal lactate levels. (B): the nomogram for DKD prediction in T2DM patients with high lactate levels. Each variable’s influence is quantified by assigning scores, which are then summed to calculate the total score, the score corresponds to the value on the linear predictor, so as to provides the probability of DKD.

Figure 4

The performance of predictive models for DKD in T2DM patients classified by lactate levels. (A): ROC curves of DKD prediction model in T2DM patients with normal lactate levels. (B): ROC curves of DKD prediction model in T2DM patients with high lactate levels. The y-axis and x-axis represent the true and false-positive rate of the risk prediction, respectively. The blue line represents the performance of the nomogram of DKD risk in patients with T2DM with normal (A) and high (B) levels of serum lactate. (C): calibration curves of DKD prediction model in T2DM patients with normal lactate levels. (D): calibration curves of DKD prediction model in T2DM patients with high lactate levels. The y-axis represents actual diagnosed cases of DKD, the x-axis represents the predicted risk of DKD. The diagonal dotted line represents a perfect prediction by an ideal model, the red line represents the performance of the nomogram, of which a closer fit to the diagonal dotted line represents a better prediction. (E): the DCA of DKD prediction model in T2DM patients with normal lactate levels. (F): the DCA of DKD prediction model in T2DM patients with high lactate levels. The y-axis measures the net benefit and the x-axis measures the risk threshold. The blue line represents the DKD incidence risk nomogram. The red line represents the assumption that all patients are diagnosed as DKD. The green line represents the assumption that no patients are diagnosed as DKD. The DCA showed that if the threshold probability of a patient is from 17 to 99% in T2DM patients with normal levels of serum lactate and from 24 to 99% in high levels of serum lactate, using the nomogram to predict DKD incidence risk adds more benefit than the diagnosing-all-patients scheme.