C-peptide, glycaemic control, and diabetic complications in type 2 diabetes mellitus: A real-world study

C-peptide Level in Patients With Uncontrolled Type 2 Diabetes Mellitus on Oral Anti-diabetic Drugs

BACKGROUND

In the development and progression of type 2 diabetes mellitus, β-cell dysfunction occurs after insulin resistance. Despite poor glycaemic control, there is a practice of increasing the dose of oral anti-diabetics or adding more drugs to the regimen due to the common perception that low endogenous insulin secretion is related to type 1 diabetes mellitus only and patient's poor compliance to injectables. Keeping this perspective in mind, this study was conducted to assess the prevalence of beta cell dysfunction by low serum C-peptide levels and its correlation with poor glycaemic control.

MATERIALS AND METHODS

A total of 134 patients with type 2 diabetes mellitus for more than 10 years on oral anti-diabetic drugs fulfilling our eligibility criteria were enrolled in our study. Blood samples for fasting blood sugar and fasting C-peptide level were taken before breakfast and uptake of anti-diabetic drugs. Correlation analysis was performed to evaluate the relationship between fasting C-peptide and glycaemic control with respect to glycated haemoglobin (HbA1c).

RESULTS

Of the patients, 19.40% had insufficient beta cell reserve serum levels (C-peptide < 0.5 ng/ml), of which most of the patients (14/26 = 53.85%) had poor glycaemic control (HbA1c < 8.0%). Overall, there was a significant correlation between poor glycaemic control with respect to HbA1c and low serum C-peptide levels (p < 0.05). We found a significant association of beta cell dysfunction (low fasting C-peptide level) with the use of insulin secretagogue. The proportion of patients with C-peptide levels less than 0.5 ng/ml was lower in patients using sodium-glucose cotransporter-2 (SGLT-2) inhibitors as compared to insulin secretagogue.

CONCLUSION

SGLT-2 inhibitors should be preferred over other anti-diabetic drugs as an add-on to existing metformin therapy. Insulin requirement must be assessed in patients who have long-term type 2 diabetes mellitus.

Inverse association of serum albumin levels with diabetic retinopathy in type 2 diabetic patients: a cross-sectional study

This study aimed to explore the association between serum albumin (ALB) levels and diabetic retinopathy in patients with type 2 diabetes. In this cross-sectional study, we retrospectively collected clinical data from patients with type 2 diabetes who were admitted to the Endocrinology Department of the Affiliated Hospital of Qingdao University between January 1, 2021, and December 1, 2022. All included patients underwent measurements of serum albumin levels and screening for diabetes-related complications. The association between serum albumin levels and retinopathy was assessed using logistic regression after adjusting for potential confounders. Further, stratified analyses and curve fitting were conducted to delve deeper into the relationship. After inclusion and exclusion criteria were applied, a total of 1947 patients were analyzed. Among these, 982 were male and 965 were female. The mean serum albumin level was 39.86 ± 3.27 g/L. Diabetic retinopathy was present in 41.24% of the patients. After adjusting for potential confounders, we observed a significant inverse association between serum albumin levels and the incidence of retinopathy. Specifically, for every 10 g/L increase in albumin level, the odds of retinopathy decreased (odds ratio [OR] = 0.67; 95% confidence interval [CI] = 0.48-0.94; P = 0.0209).The curve fitting validated the inverse relationship between serum albumin and retinopathy without evidence of non-linearity or threshold saturation effects. Stratified analyses consistently indicated no interaction effects across subgroups. This cross-sectional study identified a significant inverse relationship between serum albumin levels and diabetic retinopathy in patients with type 2 diabetes. However, due to the cross-sectional nature of this study, further prospective studies are warranted to confirm these findings.

Different associations of anthropometric indices with diabetic retinopathy and diabetic kidney disease in chinese patients with type 2 diabetes mellitus

AIMS

To investigate the associations of anthropometric indices, including body mass index (BMI), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), waist circumference (WC) and hip circumference (HC), with diabetic retinopathy (DR) and diabetic kidney disease (DKD) in Chinese patients with type 2 diabetes mellitus (T2DM).

MATERIALS AND METHODS

This cross-sectional study evaluated 5226 Chinese participants with T2DM at three hospitals between 2005 and 2016. Logistic regression models and restricted cubic spline analysis were used to assess the associations of anthropometric indices with DR and DKD.

RESULTS

A BMI of around 25 kg/m2 was related to a low risk of DR (OR based on the third fifth: 0.752, 95%CI: 0.615-0.920). Besides, HC had an inverse association with DR in men independently of BMI (OR based on the highest fifth: 0.495, 95%CI: 0.350-0.697). In the restricted cubic spline models, BMI, WHtR, WC, and HC showed J-shaped associations with DKD, while WHR showed an S-shaped association with DKD. Compared to the lowest fifth, the odds ratios (OR) based on the highest fifth of BMI, WHR, WHtR, WC and HC for DKD were 1.927 (1.572-2.366), 1.566 (1.277-1.923), 1.910 (1.554-2.351), 1.624 (1.312-2.012) and 1.585 (1.300-1.937) respectively in multivariable models.

CONCLUSIONS

A median BMI and a large hip might be related to a low risk of DR, while lower levels of all the anthropometric indices were associated with a lower risk of DKD. Our findings suggested maintain a median BMI, a low WHR, a low WHtR and a large hip for prevention of DR and DKD.

Development and evaluation of a risk prediction model for diabetes mellitus type 2 patients with vision-threatening diabetic retinopathy

Objective

This study aims to develop and evaluate a non-imaging clinical data-based nomogram for predicting the risk of vision-threatening diabetic retinopathy (VTDR) in diabetes mellitus type 2 (T2DM) patients.

Methods

Based on the baseline data of the Guangdong Shaoguan Diabetes Cohort Study conducted by the Zhongshan Ophthalmic Center (ZOC) in 2019, 2294 complete data of T2DM patients were randomly divided into a training set (n=1605) and a testing set (n=689). Independent risk factors were selected through univariate and multivariate logistic regression analysis on the training dataset, and a nomogram was constructed for predicting the risk of VTDR in T2DM patients. The model was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC) in the training and testing datasets to assess discrimination, and Hosmer-Lemeshow test and calibration curves to assess calibration.

Results

The results of the multivariate logistic regression analysis showed that Age (OR = 0.954, 95% CI: 0.940-0.969, p = 0.000), BMI (OR = 0.942, 95% CI: 0.902-0.984, p = 0.007), systolic blood pressure (SBP) (OR =1.014, 95% CI: 1.007-1.022, p = 0.000), diabetes duration (10-15y: OR =3.126, 95% CI: 2.087-4.682, p = 0.000; >15y: OR =3.750, 95% CI: 2.362-5.954, p = 0.000), and glycated hemoglobin (HbA1C) (OR = 1.325, 95% CI: 1.221-1.438, p = 0.000) were independent risk factors for T2DM patients with VTDR. A nomogram was constructed using these variables. The model discrimination results showed an AUC of 0.7193 for the training set and 0.6897 for the testing set. The Hosmer-Lemeshow test results showed a high consistency between the predicted and observed probabilities for both the training set (Chi-square=2.2029, P=0.9742) and the testing set (Chi-square=7.6628, P=0.4671).

Conclusion

The introduction of Age, BMI, SBP, Duration, and HbA1C as variables helps to stratify the risk of T2DM patients with VTDR.

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.

Quantitatively Evaluating the Relationships between Insulin Resistance and Retinal Neurodegeneration with Optical Coherence Tomography in Early Type 2 Diabetes Mellitus

INTRODUCTION

The aim of this study was to quantitatively assess retinal neurodegenerative changes with optical coherence tomography (Cirrus HD-OCT) in type 2 diabetes mellitus (T2DM) patients without diabetic retinopathy (DR) and evaluate their relationships with insulin resistance (IR) and associated systemic indicators.

METHODS

102 T2DM patients without DR and 48 healthy controls were included in this observational cross-sectional study. The OCT parameters of macular retinal thickness (MRT) and ganglion cell-inner plexiform layer (GCIPL) thicknesses were evaluated between diabetic and normal eyes. The receiver operating characteristics (ROC) curve was generated to evaluate the discrimination power of early diabetes. Correlation and multiple regression analysis were performed between ophthalmological parameters and T2DM-related demographic and anthropometric variables, and serum biomarkers and homeostasis model assessment of insulin resistance (HOMA-IR) scores.

RESULTS

MRT and GCIPL thicknesses showed significant thinning in patients, especially in inferotemporal area. High body mass index (BMI) correlated with decreased GCIPL thicknesses and elevated intraocular pressure (IOP). A negative correlation between waist-to-hip circumference ratio (WHR) and GCIPL thicknesses was also found. High-density lipoprotein (HDL) and fasting C-peptide (CP0) were associated with GCIPL thickness but only in inferotemporal region (r = 0.20, p = 0.04; r = -0.20, p = 0.05, respectively). Multiple regression analysis showed that increased HOMA-IR scores independently predicted both average (β = -0.30, p = 0.05) and inferotemporal (β = -0.34, p = 0.03) GCIPL thinning.

CONCLUSION

Retinal thinning in early T2DM was associated with obesity-related metabolic disorders. IR as an independent risk factor for retinal neurodegeneration may increase the risk of developing glaucoma.

Systemic C-peptide supplementation ameliorates retinal neurodegeneration by inhibiting VEGF-induced pathological events in diabetes

Diabetic retinopathy (DR) is caused by retinal vascular dysfunction and neurodegeneration. Intraocular delivery of C-peptide has been shown to be beneficial against hyperglycemia-induced microvascular leakage in the retina of diabetes; however, the effect of C-peptide on diabetes-induced retinal neurodegeneration remains unknown. Moreover, extraocular C-peptide replacement therapy against DR to avoid various adverse effects caused by intravitreal injections has not been studied. Here, we demonstrate that systemic C-peptide supplementation using osmotic pumps or biopolymer-conjugated C-peptide hydrogels ameliorates neurodegeneration by inhibiting vascular endothelial growth factor-induced pathological events, but not hyperglycemia-induced vascular endothelial growth factor expression, in the retinas of diabetic mice. C-peptide inhibited hyperglycemia-induced activation of macroglial and microglial cells, downregulation of glutamate aspartate transporter 1 expression, neuronal apoptosis, and histopathological changes by a mechanism involving reactive oxygen species generation in the retinas of diabetic mice, but transglutaminase 2, which is involved in retinal vascular leakage, is not associated with these pathological events. Overall, our findings suggest that systemic C-peptide supplementation alleviates hyperglycemia-induced retinal neurodegeneration by inhibiting a pathological mechanism, involving reactive oxygen species, but not transglutaminase 2, in diabetes.

A Multifactorial Risk Score System for the Prediction of Diabetic Kidney Disease in Patients with Type 2 Diabetes Mellitus

PURPOSE

In-depth investigations of risk factors for the identification of diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) are rare. We aimed to investigate the risk factors for developing DKD from multiple types of clinical data and conduct a comprehensive risk assessment for individuals with diabetes.

METHODS

We carried out a case-control study, enrolling 958 patients to identify the risk factors for developing DKD in T2DM patients from a database established from inpatient electronic medical records. Multivariable logistic regression was applied to develop a prediction model and the performance of the model was evaluated using the area under the curve (AUC) and calibration curve. A multifactorial risk score system was established according to the Framingham Study risk score.

RESULTS

DKD accounted for 34.03% of eligible patients in total. Twelve risk factors were selected in the final prediction model, including age, duration of diabetes, duration of hypertension, fasting blood glucose, fasting C-peptide, insulin use, systolic blood pressure, low-density lipoprotein, γ-glutamyl transpeptidase, platelet, uric acid, and thyroid stimulating hormone; and one protective factor, serum albumin. The prediction model showed an AUC of 0.862 (95% Confidence Interval (CI) 0.834-0.890) with an accuracy of 81.5% in the derivation dataset and an AUC of 0.876 (95% CI 0.825-0.928) in the validation dataset. The calibration curves were excellent and the estimated probability of DKD was more than 80% when the cumulative score for risk factors reached 17 points.

CONCLUSION

Newly recognized risk factors were applied to assess the development of DKD in T2DM patients and the established risk score system was a reliable and feasible tool for assisting clinicians to identify patients at high risk of DKD.

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 models for risk of diabetic kidney disease in Chinese patients with type 2 diabetes mellitus

BACKGROUND

Diabetic kidney disease (DKD) is a common and serious complication in patients with diabetic mellitus (DM), the risk of cardiovascular events and all-cause mortality also increases in DKD patients. This study aimed to detect the influencing factors of DKD in type 2 DM (T2DM) patients, and construct DKD prediction models and nomogram for clinical decision-making.

METHODS

A total of 14,628 patients with T2DM were included. These patients were divided into pre-DKD and non-DKD groups, depending on the occurrence of DKD during a 3-year follow-up from first clinic attendance. The influencing indicators of DKD were analyzed, the prediction models were established by multivariable logistic regression, and a nomogram was drawn for DKD risk assessment.

RESULTS

Two prediction models for DKD were built by multivariate logistic regression analysis. Model 1 was created based on 17 variables using the forward selection method, Model 2 was established by 19 variables using the backward elimination method. The Somers' D values of both models were 0.789. Four independent predictors were selected to build the nomogram, including age, UACR, eGFR, and neutrophil percentages. The C-index of the nomogram reached 0.864, suggesting a good predictive accuracy for DKD development.

CONCLUSIONS

Our prediction models had strong predictive powers, and our nomogram provided visual aids to DKD risk calculation, which was simple and fast. These algorithms can provide early DKD risk prediction, which might help to improve the medical care for early detection and intervention in T2DM patients, and then consequently improve the prognosis of DM patients.

Assessment of QRISK3 as a predictor of cardiovascular disease events in type 2 diabetes mellitus

BACKGROUND

The risk of cardiovascular disease (CVD) in diabetes mellitus (DM) patients is two- to three-fold higher than in the general population. We designed a 10-year cohort trial in T2DM patients to explore the performance of QRESEARCH risk estimator version 3 (QRISK3) as a CVD risk assessment tool and compared to Framingham Risk Score (FRS).

METHOD

This is a single-center analysis of prospective data collected from 566 newly-diagnosed patients with type 2 DM (T2DM). The risk scores were compared to CVD development in patients with and without CVD. The risk variables of CVD were identified using univariate analysis and multivariate cox regression analysis. The number of patients classified as low risk (<10%), intermediate risk (10%-20%), and high risk (>20%) for two tools were identified and compared, as well as their sensitivity, specificity, positive and negative predictive values, and consistency (C) statistics analysis.

RESULTS

Among the 566 individuals identified in our cohort, there were 138 (24.4%) CVD episodes. QRISK3 classified most CVD patients as high risk, with 91 (65.9%) patients. QRISK3 had a high sensitivity of 91.3% on a 10% cut-off dichotomy, but a higher specificity of 90.7% on a 20% cut-off dichotomy. With a 10% cut-off dichotomy, FRS had a higher specificity of 89.1%, but a higher sensitivity of 80.1% on a 20% cut-off dichotomy. Regardless of the cut-off dichotomy approach, the C-statistics of QRISK3 were higher than those of FRS.

CONCLUSION

QRISK3 comprehensively and accurately predicted the risk of CVD events in T2DM patients, superior to FRS. In the future, we need to conduct a large-scale T2DM cohort study to verify further the ability of QRISK3 to predict CVD events.