Predictive role of serum C-peptide in new-onset renal dysfunction in type 2 diabetes: a longitudinal observational study

Abstract

Background: Our previous cross-sectional study has demonstrated the independently non-linear relationship between fasting C-peptide with renal dysfunction odds in patients with type 2 diabetes (T2D) in China. This longitudinal observational study aims to explore the role of serum C-peptide in risk prediction of new-onset renal dysfunction, then construct a predictive model based on serum C-peptide and other clinical parameters.

Methods: The patients with T2D and normal renal function at baseline were recruited in this study. The LASSO algorithm was performed to filter potential predictors from the baseline variables. Logistic regression (LR) was performed to construct the predictive model for new-onset renal dysfunction risk. Power analysis was performed to assess the statistical power of the model.

Results: During a 2-year follow-up period, 21.08% (35/166) of subjects with T2D and normal renal function at baseline progressed to renal dysfunction. Six predictors were determined using LASSO regression, including baseline albumin-to-creatinine ratio, glycated hemoglobin, hypertension, retinol-binding protein-to-creatinine ratio, quartiles of fasting C-peptide, and quartiles of fasting C-peptide to 2h postprandial C-peptide ratio. These 6 predictors were incorporated to develop model for renal dysfunction risk prediction using LR. Finally, the LR model achieved a high efficiency, with an AUC of 0.83 (0.76 - 0.91), an accuracy of 75.80%, a sensitivity of 88.60%, and a specificity of 70.80%. According to the power analysis, the statistical power of the LR model was found to be 0.81, which was at a relatively high level. Finally, a nomogram was developed to make the model more available for individualized prediction in clinical practice.

Conclusion: Our results indicated that the baseline level of serum C-peptide had the potential role in the risk prediction of new-onset renal dysfunction. The LR model demonstrated high efficiency and had the potential to guide individualized risk assessments for renal dysfunction in clinical practice.

Keywords: new-onset; predictive role; renal dysfunction; serum C-peptide; type 2 diabetes.

Figure 1

The correlations between the potential continuous variables for the univariate analysis (p ≤ 0.1). HbA1c, glycated hemoglobin; UA, uric acid; ACR, albumin to creatinine ratio; URBP/Cr, retinol-binding protein to creatinine ratio; UTRF/Cr, transferrin to creatinine ratio; UORM/Cr, alpha-1-acid-glycoprotein to creatinine ratio; A1MCR, alpha-1-microglobulin to creatinine ratio.

Figure 2

Potential predictor selection using the LASSO regression method. (A) Tuning parameter selection in the LASSO regression used 10-fold cross-validation. (B) LASSO regression coefficient profiles of variables. In the present study, predictors were chosen according to the lambda value with the minimum mean square error (seed = 1).

Figure 3

The receiver operating characteristic curve (A) and calibration curve (B) of the established model. The blue area represents 95% confidence interval.

Figure 4

SHAP summary plots of the six predictors for new onset renal dysfunction risk in LR model. SHAP, Shapley additive explanations; ACR, albumin to creatinine ratio; HbA1c, glycated hemoglobin; C0/C2, fasting C-peptide to 2h C-peptide ratio; URBP/Cr, retinol-binding protein to creatinine ratio.

Figure 5

Nomogram for prediction of new-onset renal dysfunction risk based on the LR model. ACR, albumin to creatinine ratio; HbA1c, glycated hemoglobin; C0/C2, fasting C-peptide to 2h C-peptide ratio; URBP/Cr, retinol-binding protein to creatinine ratio. Hypertension 0: patients without hypertension; Hypertension 1: patients with hypertension; C-peptide quartiles 0-3: quartile 1, 2, 3 and 4 of fasting C-peptide; C0/C2 quartiles 0-3: quartile 1, 2, 3 and 4 of C0/C2.