A nomogram for predicting the risk of new-onset albuminuria based on baseline urinary ACR, orosomucoid, and HbA1c in patients with type 2 diabetes

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.

Risk prediction models for diabetic nephropathy among type 2 diabetes patients in China: a systematic review and meta-analysis

Objective

This study systematically reviews and meta-analyzes existing risk prediction models for diabetic kidney disease (DKD) among patients with type 2 diabetes, aiming to provide references for scholars in China to develop higher-quality risk prediction models.

Methods

We searched databases including China National Knowledge Infrastructure (CNKI), Wanfang Data, VIP Chinese Science and Technology Journal Database, Chinese Biomedical Literature Database (CBM), PubMed, Web of Science, Embase, and the Cochrane Library for studies on the construction of DKD risk prediction models among type 2 diabetes patients, up until 28 December 2023. Two researchers independently screened the literature and extracted and evaluated information according to a data extraction form and bias risk assessment tool for prediction model studies. The area under the curve (AUC) values of the models were meta-analyzed using STATA 14.0 software.

Results

A total of 32 studies were included, with 31 performing internal validation and 22 reporting calibration. The incidence rate of DKD among patients with type 2 diabetes ranged from 6.0% to 62.3%. The AUC ranged from 0.713 to 0.949, indicating the prediction models have fair to excellent prediction accuracy. The overall applicability of the included studies was good; however, there was a high overall risk of bias, mainly due to the retrospective nature of most studies, unreasonable sample sizes, and studies conducted in a single center. Meta-analysis of the models yielded a combined AUC of 0.810 (95% CI: 0.780-0.840), indicating good predictive performance.

Conclusion

Research on DKD risk prediction models for patients with type 2 diabetes in China is still in its initial stages, with a high overall risk of bias and a lack of clinical application. Future efforts could focus on constructing high-performance, easy-to-use prediction models based on interpretable machine learning methods and applying them in clinical settings.

Registration

This systematic review and meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a recognized guideline for such research.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42024498015.

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.

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

BACKGROUND

Previous studies had showed divergent findings on the associations of C-peptide and/or uric acid (UA) with renal dysfunction odds in patients with type 2 diabetes mellitus (T2DM). We hypothesized that there were non-linear relationships between C-peptide, UA and renal dysfunction odds. This study aimed to further investigate the relationships of different stratification of C-peptide and UA with renal dysfunction in patients with T2DM.

METHOD

We conducted a cross-sectional real-world observational study of 411 patients with T2DM. The levels of fasting C-peptide, 2h postprandial C-peptide, the ratio of fasting C-peptide to 2h postprandial C-peptide (C0/C2 ratio), UA and other characteristics were recorded. Restricted cubic spline (RCS) curves was performed to evaluated the associations of stratified C-peptide and UA with renal dysfunction odds.

RESULTS

Fasting C-peptide, C0/C2 ratio and UA were independently and significantly associated with renal dysfunction in patients with T2DM as assessed by multivariate analyses (p < 0.05). In especial, non-linear relationships with threshold effects were observed among fasting C-peptide, UA and renal dysfunction according to RCS analyses. Compared with patients with 0.28 ≤ fasting C-peptide ≤ 0.56 nmol/L, patients with fasting C-peptide < 0.28 nmol/L (OR = 1.38, p = 0.246) or fasting C-peptide > 0.56 nmol/L (OR = 1.85, p = 0.021) had relatively higher renal dysfunction odds after adjusting for confounding factors. Similarly, compared with patients with 276 ≤ UA ≤ 409 μmol/L, patients with UA < 276 μmol/L (OR = 1.32, p = 0.262) or UA > 409 μmol/L (OR = 6.24, p < 0.001) had relatively higher odds of renal dysfunction.

CONCLUSION

The renal dysfunction odds in patients with T2DM was non-linearly associated with the levels of serum fasting C-peptide and UA. Fasting C-peptide and UA might have the potential role in odds stratification of renal dysfunction.

Prediction of the Short-Term Risk of New-Onset Renal Dysfunction in Patients with Type 2 Diabetes: A Longitudinal Observational Study

Background

Studies in the past decade have reported many novel biomarkers for predicting the new-onset or progression risk of renal dysfunction in patients with type 2 diabetes (T2D) based on the genomic, metabolomic, and proteomic technologies. These novel predictive markers, however, are difficult to be widely used in clinical practice over the short term due to their high technology content, instability, and high cost. This study was aimed at evaluating the associations of clinical features and six traditional renal markers with the short-term risk of new-onset renal dysfunction in patients with T2D.

Methods

This study involved 213 participants with T2D and normal renal function at baseline. The baseline levels of the albumin-to-creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), alpha-1-microglobulin-to-creatinine ratio (A1MCR), neutrophil gelatinase-associated lipocalin-to-creatinine ratio, transferrin-to-creatinine ratio (UTRF/Cr), and retinol-binding protein-to-creatinine ratio (URBP/Cr) were analyzed. Multivariate logistic models were established and validated.

Results

During the two-year follow-up period, 23.01% participants progressed to renal dysfunction. The basal levels of ACR, A1MCR, UTRF/Cr, and URBP/Cr were the independent risk factors of new-onset renal dysfunction (P < 0.05). Several logistic models incorporating clinical characteristics and these renal markers were constructed for predicting the short-term risk of new-onset renal dysfunction. Comparatively, the model including age, glycated hemoglobin (HbA1c), hypertension, ACR, A1MCR, UTRF/Cr, and URBP/Cr levels at baseline had the highest potential (C − index = 0.785, P < 0.001). This model was validated using the K-fold cross-validation method; the accuracy was 0.815 ± 0.013 in training sets and 0.784 ± 0.019 in validation sets, indicating a good consistency for predicting the new-onset renal dysfunction risk. Finally, a nomogram based on this model was constructed to provide a quantitative tool to assess the individualized risk of short-term new-onset renal dysfunction.

Conclusion

The model incorporating these markers and clinical features may have a high potential to predict the short-term risk of new-onset renal dysfunction.