Serum C-Peptide’s Role in Predicting Renal Dysfunction in Type 2 Diabetes

Diabetes, particularly type 2 (T2D), is a major cause of reduced life expectancy and premature death globally. Its complications, notably diabetic kidney disease (DKD), significantly impact patient mortality. DKD, leading to end-stage renal disease, poses a substantial public health challenge. Early detection and intervention in DKD are crucial, and the search for clinical risk factors or biomarkers is ongoing. Previous studies have identified various clinical risk factors associated with DKD, including age, gender, hypertension, dyslipidemia, glycated hemoglobin levels, and use of specific medications. Urinary biomarkers like the albumin-to-creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR) are commonly used in clinical practice.

To establish a novel and practical approach to predict the risk of renal dysfunction in T2D patients, potentially enabling earlier interventions and improved patient management strategies, a new study published in the Journal Frontiers in Endocrinology by  Dr. Dongmei Sun, Dr. Yifei Hu, Dr. Yongjun Ma and led by Dr. Huabin Wang from the  Department of Clinical Laboratory- Affiliated Jinhua Hospital at Zhejiang University School of Medicine, embarked on a mission to investigate the predictive role of serum C-peptide in forecasting new-onset renal dysfunction among patients with T2D. They aimed to develop a model that could effectively use serum C-peptide, among other clinical parameters, to predict this risk. The study focused on patients with T2D who initially had normal renal function. The researchers collected various baseline variables from these patients, including demographic information, clinical data, and laboratory test results. To identify the most relevant predictors of renal dysfunction, the researchers utilized the LASSO (Least Absolute Shrinkage and Selection Operator) algorithm. This method is effective in handling data with numerous variables, as it can shrink less important predictor coefficients to zero, effectively selecting the most significant predictors. Moreover, using the predictors identified through LASSO regression, the researchers then constructed a logistic regression model. This model was designed to predict the risk of new-onset renal dysfunction in the study population. The performance of the logistic regression model was evaluated using various statistical metrics, such as the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. These metrics provided insights into how well the model could distinguish between patients who would develop renal dysfunction and those who would not.  To assess the reliability and effectiveness of their model, the researchers conducted a power analysis. This step is crucial in determining the ability of a study to detect an effect when there is one, thereby confirming the robustness of their findings.  They observed that 21.08% of subjects progressed to renal dysfunction over a 2-year follow-up period. Six predictors were identified using LASSO regression: baseline ACR, 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. The LR model achieved an AUC of 0.83, with 75.80% accuracy, 88.60% sensitivity, and 70.80% specificity. The statistical power of the LR model was 0.81, indicating a high level of reliability.

The study highlighted the potential role of serum C-peptide in predicting new-onset renal dysfunction in T2D patients. The LR model, validated for its efficiency, could guide individualized risk assessments in clinical practice. The inclusion of serum C-peptide, particularly in the forms of fasting C-peptide quartiles and C0/C2 quartiles, was a novel approach in this context. Serum C-peptide, traditionally viewed as an inert molecule, has emerged as a significant indicator with multiple functions, including signaling pathways activation and protection against complications of diabetes.

In conclusion, the new study by Dr. Huabin Wang  and colleagues highlights the importance of considering serum C-peptide as a predictive feature for renal dysfunction risk in T2D patients. This approach is a step forward in personalized medicine, allowing for more targeted interventions based on individual risk profiles. The development of a nomogram based on the LR model further enhances its practical utility, providing an accessible tool for healthcare professionals. The findings of this study pave the way for more nuanced and effective management strategies for T2D patients, potentially reducing the burden of DKD and improving patient outcomes.