Non-linear association of fasting C-peptide and uric acid levels with renal dysfunction based on restricted cubic spline in patients with type 2 diabetes: A real-world study

A non-linear association between low-density lipoprotein cholesterol and the risk of diabetic kidney disease in patients with type 2 diabetes in China

Objective

To explore the intrinsic relationship between low-density lipoprotein cholesterol (LDL-C) and diabetic kidney disease (DKD) in patients with type 2 diabetes (T2D) in China.

Methods

This cross-sectional study included 1,313 patients with type 2 diabetes treated at the Affiliated Jinhua Hospital of Zhejiang University School of Medicine, located in Jinhua, China. The data were combined from two periods, 2017 and 2020-2021. Participants were categorized into groups with and without DKD. The relationship between LDL-C levels and DKD was evaluated employing logistics regression analysis and restricted cubic spline (RCS) curves.

Results

Generally, there was no statistical difference in LDL-C levels between DKD and non-DKD groups, however, a significantly non-linear relationship (Pnon-linear = 0.011) was observed between LDL-C levels and DKD prevalence after adjusting for confounding factors according to the RCS analysis. Two optimal cut-points of 2.97 and 3.61 mmol/L were selected out using random forest algorithm. With the middle LDL-C concentration (2.97-3.61 mmol/L) as the reference, the odds ratios for low (<2.97 mmol/L) and high (>3.61 mmol/L) concentrations were 1.45 (1.08-1.96) and 1.47 (1.01-2.15) respectively, after adjusting for confounding factors in the multivariate analyses. Notably, this association was more pronounced among female participants in the subgroup analyses.

Conclusion

A non-linear association was observed between LDL-C levels and the risk of DKD in patients with T2D in China. LDL-C levels below 2.97 mmol/L may elevate the risk of DKD, particularly in female patients with T2D.

High levels of high-density lipoprotein cholesterol may increase the risk of diabetic kidney disease in patients with type 2 diabetes

Studies have indicated that low high-density lipoprotein cholesterol (HDL-C) level is an important risk factor for diabetic kidney disease (DKD) in patients with type 2 diabetes (T2D). However, whether higher HDL-C levels decrease the risk of developing DKD remains unclear. This study aimed to clarify the relationship between HDL-C levels and DKD risk in individuals with T2D in China. In total, 936 patients with T2D were divided into DKD and non-DKD groups. The association between HDL-C levels and DKD risk was evaluated using logistic regression analysis and restricted cubic spline curves adjusted for potential confounders. Threshold effect analysis of HDL-C for DKD risk was also performed. Higher HDL-C levels did not consistently decrease the DKD risk. Furthermore, a nonlinear association with threshold interval effects between HDL-C levels and the incidence of DKD was observed. Patients with HDL-C ≤ 0.94 mmol/L or HDL-C > 1.54 mmol/L had significantly higher DKD risk after adjusting for confounding factors. Interestingly, the association between high HDL-C levels and increased DKD risk was more significant in women. A U-shaped association between HDL-C levels and DKD risk was observed; therefore, low and high HDL-C levels may increase the DKD risk in patients with T2D.

The threshold effect of triglyceride glucose index on diabetic kidney disease risk in patients with type 2 diabetes: unveiling a non-linear association

Background

Previous studies have confirmed that the triglyceride glucose (TyG) index, recognized as a reliable marker of insulin resistance, is an important risk factor for diabetic kidney disease (DKD). However, it is still unclear whether the DKD risk continues to increase linearly with the elevation of TyG index. This study aimed to thoroughly investigated the intrinsic relationship between TyG index and DKD risk in type 2 diabetes (T2D).

Methods

This cross-sectional study included 933 patients with T2D in China, who were categorized into DKD and non-DKD groups and stratified by TyG index levels. Logistic regression analysis identified the independent risk factors for DKD. The association between DKD risk and TyG index was evaluated using the restricted cubic spline (RCS) curves analysis. The R package 'CatPredi' was utilized to determine the optimal cut-off point for the relationship between DKD risk and TyG index, followed by threshold effect analysis.

Results

The prevalence of DKD was 33.01%. After adjusting for confounding factors, TyG index was identified as a prominent clinical risk factor for DKD, showing the highest odds ratio (OR 1.57 (1.26 - 1.94), P<0.001). RCS analysis revealed a non-linear relationship with a threshold interval effect between the TyG index and DKD risk. When TyG index ≤ 9.35, DKD risk plateaued at a low level; however, when TyG index > 9.35, DKD risk increased gradually with rising TyG index. Among patients with TyG index > 9.35, each 1-unit increase was associated with a 1.94-fold increased DKD risk (OR=1.94 (1.10 - 3.43), P=0.022).

Conclusion

The DKD risk presented a threshold effect with the increase of TyG index, initially stable at a low level, and then gradually rising when the TyG index is above 9.35.

Interpretable machine learning for predicting chronic kidney disease progression risk

Objective

Chronic kidney disease (CKD) poses a major global health burden. Early CKD risk prediction enables timely interventions, but conventional models have limited accuracy. Machine learning (ML) enhances prediction, but interpretability is needed to support clinical usage with both in diagnostic and decision-making.

Methods

A cohort of 491 patients with clinical data was collected for this study. The dataset was randomly split into an 80% training set and a 20% testing set. To achieve the first objective, we developed four ML algorithms (logistic regression, random forests, neural networks, and eXtreme Gradient Boosting (XGBoost)) to classify patients into two classes-those who progressed to CKD stages 3-5 during follow-up (positive class) and those who did not (negative class). For the classification task, the area under the receiver operating characteristic curve (AUC-ROC) was used to evaluate model performance in discriminating between the two classes. For survival analysis, Cox proportional hazards regression (COX) and random survival forests (RSFs) were employed to predict CKD progression, and the concordance index (C-index) and integrated Brier score were used for model evaluation. Furthermore, variable importance, partial dependence plots, and restrict cubic splines were used to interpret the models' results.

Results

XGBOOST demonstrated the best predictive performance for CKD progression in the classification task, with an AUC-ROC of 0.867 (95% confidence interval (CI): 0.728-0.100), outperforming the other ML algorithms. In survival analysis, RSF showed slightly better discrimination and calibration on the test set compared to COX, indicating better generalization to new data. Variable importance analysis identified estimated glomerular filtration rate, age, and creatinine as the most important predictors for CKD survival analysis. Further analysis revealed non-linear associations between age and CKD progression, suggesting higher risks in patients aged 52-55 and 65-66 years. The association between cholesterol levels and CKD progression was also non-linear, with lower risks observed when cholesterol levels were in the range of 5.8-6.4 mmol/L.

Conclusions

Our study demonstrated the effectiveness of interpretable ML models for predicting CKD progression. The comparison between COX and RSF highlighted the advantages of ML in survival analysis, particularly in handling non-linearity and high-dimensional data. By leveraging interpretable ML for unraveling risk factor relationships, contrasting predictive techniques, and exposing non-linear associations, this study significantly advances CKD risk prediction to enable enhanced clinical decision-making.

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

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.