DIABETIC RETINOPATHY, CLASSIFIED USING THE LESION-AWARE DEEP LEARNING SYSTEM, PREDICTS DIABETIC END-STAGE RENAL DISEASE IN CHINESE PATIENTS

Integrated machine learning and deep learning for predicting diabetic nephropathy model construction, validation, and interpretability

OBJECTIVE

To construct a risk prediction model for assisted diagnosis of Diabetic Nephropathy (DN) using machine learning algorithms, and to validate it internally and externally.

METHODS

Firstly, the data was cleaned and enhanced, and was divided into training and test sets according to the 7:3 ratio. Then, the metrics related to DN were filtered by difference analysis, Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination (RFE), and Max-relevance and Min-redundancy (MRMR) algorithms. Ten machine learning models were constructed based on the key variables. The best model was filtered by Receiver Operating Characteristic (ROC), Precision-Recall (PR), Accuracy, Matthews Correlation Coefficient (MCC), and Kappa, and was internally and externally validated. Based on the best model, an online platform had been constructed.

RESULTS

15 key variables were selected, and among the 10 machine learning models, the Random Forest model achieved the best predictive performance. In the test set, the area under the ROC curve was 0.912, and in two external validation cohorts, the area under the ROC curve was 0.828 and 0.863, indicating excellent predictive and generalization abilities.

CONCLUSION

The model has a good predictive value and is expected to help in the early diagnosis and screening of clinical DN.

Correlation between diabetic retinopathy and diabetic nephropathy: a two-sample Mendelian randomization study

Rationale & objective

A causal relationship concerning diabetic retinopathy (DR) and diabetic nephropathy (DN) has been studied in many epidemiological observational studies. We conducted a two-sample mendelian randomization study from the perspective of genetics to assess these associations.

Methods

20 independent single nucleotide polymorphisms (SNPs) associated with diabetic retinopathy were selected from the FinnGen consortium. Summary-level data for diabetic nephropathy were obtained from the publicly available genome-wide association studies (GWAS) database, FinnGen and CKDGen consortium. Inverse variance weighted (IVW) was selected as the primary analysis. MR-Egger, weighted median (WM), simple mode and weighted mode were used as complementary methods to examine causality. Additionally, sensitivity analyses including Cochran's Q test, MR-Egger, MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO), and leave-one-out analyses were conducted to guarantee the accuracy and robustness of our MR analysis.

Results

Our current study demonstrated positive associations of genetically predicted diabetic retinopathy with diabetic nephropathy (OR=1.32; P=3.72E-11), type 1 diabetes with renal complications (OR=1.96; P= 7.11E-11), and type 2 diabetes with renal complications (OR=1.26, P=3.58E-04). Further subtype analysis and multivariate mendelian randomization (MVMR) also reached the same conclusion. A significant casualty with DN was demonstrated both in non-proliferative DR (OR=1.07, P=0.000396) and proliferative DR (OR=1.67, P=3.699068E-14). All the findings were robust across several sensitivity analyses.

Conclusion

Consistent with previous clinical studies, our findings revealed a positive correlation between DR and DN, providing genetic evidence for the non-invasive nature of DR in predicting DN.

The automatic detection of diabetic kidney disease from retinal vascular parameters combined with clinical variables using artificial intelligence in type-2 diabetes patients

BACKGROUND

Diabetic kidney disease (DKD) has become the largest cause of end-stage kidney disease. Early and accurate detection of DKD is beneficial for patients. The present detection depends on the measurement of albuminuria or the estimated glomerular filtration rate, which is invasive and not optimal; therefore, new detection tools are urgently needed. Meanwhile, a close relationship between diabetic retinopathy and DKD has been reported; thus, we aimed to develop a novel detection algorithm for DKD using artificial intelligence technology based on retinal vascular parameters combined with several easily available clinical parameters in patients with type-2 diabetes.

METHODS

A total of 515 consecutive patients with type-2 diabetes mellitus from Xiangyang Central Hospital were included. Patients were stratified by DKD diagnosis and split randomly into either the training set (70%, N = 360) or the testing set (30%, N = 155) (random seed = 1). Data from the training set were used to develop the machine learning algorithm (MLA), while those from the testing set were used to validate the MLA. Model performances were evaluated.

RESULTS

The MLA using the random forest classifier presented optimal performance compared with other classifiers. When validated, the accuracy, sensitivity, specificity, F1 score, and AUC for the optimal model were 84.5%(95% CI 83.3-85.7), 84.5%(82.3-86.7), 84.5%(82.7-86.3), 0.845(0.831-0.859), and 0.914(0.903-0.925), respectively.

CONCLUSIONS

A new machine learning algorithm for DKD diagnosis based on fundus images and 8 easily available clinical parameters was developed, which indicated that retinal vascular changes can assist in DKD screening and detection.

Lesion-aware attention network for diabetic nephropathy diagnosis with optical coherence tomography images

Purpose

For early screening of diabetic nephropathy patients, we propose a deep learning algorithm to screen high-risk patients with diabetic nephropathy from retinal images of diabetic patients.

Methods

We propose the use of attentional mechanisms to improve the model's focus on lesion-prone regions of retinal OCT images. First, the data is trained using the base network and the Grad-CAM algorithm locates image regions that have a large impact on the model output and generates a rough mask localization map. The mask is used as a auxiliary region to realize the auxiliary attention module. We then inserted the region-guided attention module into the baseline model and trained the CNN model to guide the model to better focus on relevant lesion features. The proposed model improves the recognition of the lesion region.

Results

To evaluate the lesion-aware attention network, we trained and tested it using OCT volumetric data collected from 66 patients with diabetic retinal microangiopathy (89 eyes, male = 43, female = 23). There were 45 patients (60 eyes, male=27, female = 18) in DR group and 21 patients (29 eyes, male = 16, female = 5) in DN group. Our proposed model performs even better in disease classification, specifically, the accuracy of the proposed model was 91.68%, the sensitivity was 89.99%, and the specificity was 92.18%.

Conclusion

The proposed lesion-aware attention model can provide reliable screening of high-risk patients with diabetic nephropathy.

Establishment and validation of a prognostic nomogram for long-term low vision after diabetic vitrectomy

Purpose

We aimed to evaluate the risk factors and develop a prognostic nomogram of long-term low vision after diabetic vitrectomy.

Methods

This retrospective study included 186 patients (250 eyes) that underwent primary vitrectomy for proliferative diabetic retinopathy with a minimum follow-up period of one year. Patients were assigned to the training cohort (200 eyes) or validation cohort (50 eyes) at a 4:1 ratio randomly. Based on a cutoff value of 0.3 in best-corrected visual acuity (BCVA) measurement, the training cohort was separated into groups with or without low vision. Univariate and multivariate logistic regression analyses were performed on preoperative systemic and ocular characteristics to develop a risk prediction model and nomogram. The calibration curve and the area under the receiver operating characteristic curves (AUC) were used to evaluate the calibration and discrimination of the model. The nomogram was internally validated using the bootstrapping method, and it was further verified in an external cohort.

Results

Four independent risk factors were selected by stepwise forward regression, including tractional retinal detachment (β=1.443, OR=4.235, P<0.001), symptom duration ≥6 months (β=0.954, OR=2.595, P=0.004), preoperative BCVA measurement (β=0.540, OR=1.716, P=0.033), and hypertension (β=0.645, OR=1.905, P=0.044). AUC values of 0.764 (95% CI: 0.699-0.829) in the training cohort and 0.755 (95% CI: 0.619-0.891) in the validation cohort indicated the good predictive ability of the model.

Conclusion

The prognostic nomogram established in this study is useful for predicting long-term low vision after diabetic vitrectomy.

Metabolic phenotypes and risk of end-stage kidney disease in patients with type 2 diabetes

Background

Obesity often initiates or coexists with metabolic abnormalities. This study aimed to investigate the pathological characteristics and the independent or mutual relations of obesity and metabolic abnormalities with end-stage kidney disease (ESKD) in patients with type 2 diabetes (T2D) and associated diabetic kidney disease (DKD).

Methods

A total of 495 Chinese patients with T2D and biopsy-confirmed DKD between 2003 and 2020 were enrolled in this retrospective study. The metabolic phenotypes were based on the body weight index (BMI)-based categories (obesity, BMI ≥ 25.0 kg/m²) and metabolic status (metabolically unhealthy status, ≥ 1 criterion National Cholesterol Education Program Adult Treatment Panel III (NCEP/ATP III) excluding waist circumference and hyperglycemia) and were categorized into four types: metabolically healthy non-obesity (MHNO), metabolically healthy obesity (MHO), metabolically unhealthy non-obesity (MUNO), and metabolically unhealthy obesity (MUO). The pathological findings were defined by the Renal Pathology Society classification. Cox proportional hazards models were used to estimate hazard ratios (HRs) for ESKD.

Results

There are 56 (11.3%) MHNO patients, 28 (5.7%) MHO patients, 176 (35.6%) MUNO patients, and 235 (47.5%) MUO patients. The high prevalence of the Kimmelstiel-Wilson nodule and severe mesangial expansion were associated with obesity, whereas severe IFTA was related to metabolically unhealthy status. In the multivariate analysis, the adjusted HR (aHR) was 2.09 [95% confidence interval (CI) 0.99-4.88] in the MHO group, 2.16 (95% CI 1.20-3.88) in the MUNO group, and 2.31 (95% CI 1.27-4.20) in the MUO group compared with the MHNO group. Furthermore, the presence of obesity was insignificantly associated with ESKD compared with non-obese patients (aHR 1.22, 95% CI 0.88-1.68), while the metabolically unhealthy status was significantly associated with ESKD compared to the metabolically healthy status in the multivariate analysis (aHR 1.69, 95% CI 1.10-2.60).

Conclusion

Obesity itself was insignificantly associated with ESKD; however, adding a metabolically unhealthy status to obesity increased the risk for progression to ESKD in T2D and biopsy-proven DKD.

A cross-sectional study to correlate antioxidant enzymes, oxidative stress and inflammation with prevalence of hypertension

AIMS

Hypertension a multifactorial consequence of environmental factors, life style and genetics is the well-recognized risk factor contributing to coronary heart diseases. The antioxidant imbalance, excessive reactive oxygen species (ROS) leads to oxidative stress which is pivotal in progression of hypertension. The present study aims to understand the complex interaction between oxidative stress, inflammation and antioxidant system which is crucial to maintain cellular homeostasis which further can exaggerate hypertension pathophysiology.

MATERIALS AND METHODS

The metabolic profile of hypertensive and normotensive subjects from Malwa region, Punjab was compared by estimating lipid profile, cardiac, hepatic and renal markers. The oxidative stress markers (protein carbonyls and lipid peroxidation), inflammatory markers (Nitric oxide, Myeloperoxidase and advanced oxygen protein products), and antioxidant enzymes (Superoxide Dismutase, Catalase, and Total Antioxidant Capacity) were analyzed.

KEY FINDINGS

It is observed that the metabolic markers are altered in hypertensive subjects which further these subjects showed increased oxidative, inflammatory profile and compromised antioxidant status when compared with normotensive subjects. Co-relation analysis validated the involvement of inflammation and oxidative stress in impaired endothelial function and vital organ damage.

SIGNIFICANCE OF STUDY

These markers may act as early indicators of hypertension which usually do not show any physical symptoms, thus can be diagnosed and treated at the earliest. The current study suggests that disturbed homeostasis, a consequence of altered interaction between antioxidant system and inflammatory events raises the oxidative stress levels which eventually leads to hypertension and associated complications. These indicators can serve as early indicators of future chronic complications of hypertension.

Association of Obesity Indices with Diabetic Kidney Disease and Diabetic Retinopathy in Type 2 Diabetes: A Real-World Study

Background

Diabetic microvascular complications mainly include diabetic kidney disease (DKD) and diabetic retinopathy (DR). Obesity was recognized as a risk factor for DKD, while the reported relationship between obesity and DR was inconsistent. Moreover, whether the associations can be attributed to C-peptide levels is unknown.

Methods

Data from 1142 sequential inpatients with T2DM at Xiangyang Central Hospital between June 2019 and March 2022 were extracted retrospectively from the electronic medical record system. The associations between four obesity indices (body mass index (BMI), waist-hip circumference ratio (WHR), visceral fat tissue area (VFA), and subcutaneous fat tissue area (SFA)) and DKD and DR were evaluated. Whether the associations can be attributed to C-peptide levels was also explored.

Results

Obesity was a risk factor for DKD after adjusting for sex, HbA1c, TG, TC, HDL, LDL, smoking history, education, duration of diabetes, and insulin use (obesity indices: BMI (OR 1.050: 95% CI: 1.008-1.094; P = 0.020); WHR (OR 10.97; 95% CI: 1.250-92.267; P = 0.031); VFA (OR 1.005; 95% CI: 1.001-1.008; P = 0.008)), but it became insignificant after further adjusting for fasting C-peptide. The associations between BMI, WHR, VFA, and DKD might be U-shaped. Obesity and FCP tended to protect against DR; however, they became insignificant after adjusting for multiple potential confounders. C2/C0 (the ratio of the postprandial serum C-peptide to fasting C-peptide) was a protective factor for both DKD (OR 0.894, 95% CI: 0.833-0.959, P < 0.05) and DR (OR 0.851, 95% CI: 0.787-0.919; P < 0.05).

Conclusions

Obesity was a risk factor for DKD, and the effect may be attributable to C-peptide, which represents insulin resistance. The protective effect of obesity or C-peptide on DR was not independent and could be confounded by multiple factors. Higher C2/C0 was associated with both decreased DKD and DR.

Gene Biomarkers Related to Th17 Cells in Macular Edema of Diabetic Retinopathy: Cutting-Edge Comprehensive Bioinformatics Analysis and In Vivo Validation

Background

Previous studies have shown that T-helper 17 (Th17) cell-related cytokines are significantly increased in the vitreous of proliferative diabetic retinopathy (PDR), suggesting that Th17 cells play an important role in the inflammatory response of diabetic retinopathy (DR), but its cell infiltration and gene correlation in the retina of DR, especially in diabetic macular edema (DME), have not been studied.

Methods

The dataset GSE160306 was downloaded from the Gene Expression Omnibus (GEO) database, which contains 9 NPDR samples and 10 DME samples. ImmuCellAI algorithm was used to estimate the abundance of Th17 cells in 24 kinds of infiltrating immune cells. The differentially expressed Th17 related genes (DETh17RGs) between NPDR and DME were documented by difference analysis and correlation analysis. Through aggregate analyses such as gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analysis, a protein-protein interaction (PPI) network was constructed to analyze the potential function of DETh17RGs. CytoHubba plug-in algorithm, Lasso regression analysis and support vector machine recursive feature elimination (SVM-RFE) were implemented to comprehensively identify Hub DETh17RGs. The expression archetypes of Hub DETh17RGs were further verified in several other independent datasets related to DR. The Th17RG score was defined as the genetic characterization of six Hub DETh17RGs using the GSVA sample score method, which was used to distinguish early and advanced diabetic nephropathy (DN) as well as normal and diabetic nephropathy. Finally, real-time quantitative PCR (qPCR) was implemented to verify the transcription levels of Hub DETh17RGs in the STZ-induced DR model mice (C57BL/6J).

Results

238 DETh17RGs were identified, of which 212 genes were positively correlated while only 26 genes were negatively correlated. Six genes (CD44, CDC42, TIMP1, BMP7, RHOC, FLT1) were identified as Hub DETh17RGs. Because DR and DN have a strong correlation in clinical practice, the verification of multiple independent datasets related to DR and DN proved that Hub DETh17RGs can not only distinguish PDR patients from normal people, but also distinguish DN patients from normal people. It can also identify the initial and advanced stages of the two diseases (NPDR vs DME, Early DN vs Advanced DN). Except for CDC42 and TIMP1, the qPCR transcription levels and trends of other Hub DETh17RGs in STZ-induced DR model mice were consistent with the human transcriptome level in this study.

Conclusion

This study will improve our understanding of Th17 cell-related molecular mechanisms in the progression of DME. At the same time, it also provides an updated basis for the molecular mechanism of Th17 cell crosstalk in the eye and kidney in diabetes.

Diabetic Retinopathy Is a Predictor of Progression of Diabetic Kidney Disease: A Systematic Review and Meta-Analysis

Methods

A systematic search was conducted on PubMed, Embase, and the Google scholar for eligible studies through September 2021. The quality of selected articles was assessed using JBI checklist. Higgins and Thompson's I² statistic was used to see the degree of heterogeneity. Based on degree of heterogeneity, fixed or random effects model was used to estimate pooled effect using inverse variance method. Results were expressed as hazard ratios and odds ratios with 95% CIs.

Results

After scrutinizing 18017 articles, data from ten relevant studies (seven prospective and three retrospective) was extracted. DR was significantly associated with DKD progression with a pooled HR of 2.42 (95% CI: 1.70–3.45) and a pooled OR of 2.62 (95% CI: 1.76–3.89). There was also a significant association between the severity of DR and risk of progression of DKD with a pooled OR of 2.13 (95% CI: 1.82–2.50) for nonproliferative DR and 2.56 (95% CI: 2.93–.33) for proliferative DR.

Conclusion

Our study suggests that presence of DR is a strong predictor of risk of kidney disease progression in DKD patients. Furthermore, the risk of DKD progression increases with DR severity. Screening for retinal vascular changes could potentially help in prognostication and risk-stratification of patients with DKD.

Integration of Metabolomics and Proteomics in Exploring the Endothelial Dysfunction Mechanism Induced by Serum Exosomes From Diabetic Retinopathy and Diabetic Nephropathy Patients

Background

The prevalence of diabetic microvascular diseases has increased significantly worldwide, the most common of which are diabetic nephropathy (DN) and diabetic retinopathy (DR). Microvascular endothelial cells are thought to be major targets of hyperglycemic damage, while the underlying mechanism of diffuse endothelial dysfunction in multiple organs needs to be further investigated.

Aim

The aim of this study is to explore the endothelial dysfunction mechanisms of serum exosomes (SExos) extracted from DR and DN (DRDN) patients.

Methods

In this study, human glomerular endothelial cells (HGECs) were used as the cell model. Metabolomics ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and proteomics tandem mass tag (TMT)-based liquid chromatography-tandem mass spectrometry (LC-MS/MS) together with bioinformatics, the correlation analysis, and the joint pathway analysis were employed to discover the underlying mechanisms of endothelial dysfunction caused by patient's SExos.

Results

It can be assumed that serum exosomes extracted by DRDN patients might cause endothelial dysfunction mainly by upregulating alpha subunit of the coagulation factor fibrinogen (FIBA) and downregulating 1-methylhistidine (1-MH). Bioinformatics analysis pointed to an important role in reducing excess cysteine and methionine metabolism.

Conclusion

FIBA overexpression and 1-MH loss may be linked to the pathogenicity of diabetic endothelial dysfunction in DR/DN, implying that a cohort study is needed to further investigate the role of FIBA and 1-MH in the development of DN and DR, as well as the related pathways between the two proteins.

Untangling Computer-Aided Diagnostic System for Screening Diabetic Retinopathy Based on Deep Learning Techniques

Diabetic Retinopathy (DR) is a predominant cause of visual impairment and loss. Approximately 285 million worldwide population is affected with diabetes, and one-third of these patients have symptoms of DR. Specifically, it tends to affect the patients with 20 years or more with diabetes, but it can be reduced by early detection and proper treatment. Diagnosis of DR by using manual methods is a time-consuming and expensive task which involves trained ophthalmologists to observe and evaluate DR using digital fundus images of the retina. This study aims to systematically find and analyze high-quality research work for the diagnosis of DR using deep learning approaches. This research comprehends the DR grading, staging protocols and also presents the DR taxonomy. Furthermore, identifies, compares, and investigates the deep learning-based algorithms, techniques, and, methods for classifying DR stages. Various publicly available dataset used for deep learning have also been analyzed and dispensed for descriptive and empirical understanding for real-time DR applications. Our in-depth study shows that in the last few years there has been an increasing inclination towards deep learning approaches. 35% of the studies have used Convolutional Neural Networks (CNNs), 26% implemented the Ensemble CNN (ECNN) and, 13% Deep Neural Networks (DNN) are amongst the most used algorithms for the DR classification. Thus using the deep learning algorithms for DR diagnostics have future research potential for DR early detection and prevention based solution.

Addition of glomerular lesion severity improves the value of anemia status for the prediction of renal outcomes in Chinese patients with type 2 diabetes

We aimed to determine the utility of biopsy data and anemia for the prediction of renal outcomes in Chinese patients with type 2 diabetes. In total, 441 Chinese patients with type 2 diabetes and biopsy-confirmed diabetic nephropathy (DN) were enrolled in a retrospective study. Their renal pathology was assessed using the Renal Pathology Society system. Cox proportional hazards models were used to estimate hazard ratios (HRs) for end-stage renal disease (ESRD), and immunofluorescence staining was used to assess the expression of hypoxia-inducible factor (HIF)-α in patients’ kidneys. We found that glomerular pathology classification was an independent pathological predictor of low hemoglobin concentration, according to linear and logistic regression analyses. Each 1 g/dL decrease in baseline hemoglobin concentration was associated with a 42% higher risk of an adverse renal outcome, after adjustment for clinical and pathologic covariates. In patients with severe glomerular lesions, the risk of progression to ESRD was significantly higher if mild or moderate/severe anemia was present, but in patients with mild glomerular lesions, the risk was only significantly higher in those with moderate or severe anemia than in the absence of anemia. Harrell’s C Concordance was improved, but the Akaike information criterion was worsened by adding the glomerular pathology classification to the use of anemia status and clinical data. Immunofluorescence staining revealed that renal HIF-1α and HIF-2α expression was significantly higher in classes II–IV than class I. Thus, the addition of glomerular pathology classification increases the value of anemia status for the prediction of the progression to ESRD.

Prognostic value of metabolic syndrome in renal structural changes in type 2 diabetes

PURPOSE

To investigate the prognostic value of metabolic syndrome (MetS) and its relationship with renal structure changes in patients with type 2 diabetes and associated diabetic nephropathy (DN).

METHODS

411 Chinese patients with type 2 diabetes and biopsy-confirmed DN were enrolled in this retrospective study. MetS was defined according to the modified criteria of the 2005 International Diabetes Federation. Baseline demographics and clinical information at the time of renal biopsy were extracted from the hospital's electronic medical records system. Renal pathological findings were assessed according to Renal Pathology Society system. Univariate and multivariate logistic regression analyses were performed to define the pathological covariates associated with MetS. A competing risk model, with death as the competing risk, was used to estimate the sub-distribution hazard ratio (SHR) of MetS for end-stage kidney disease (ESKD).

RESULTS

224 (55%) patients had MetS. Patients with MetS had poor renal function and more severe interstitial fibrosis tubular atrophy scores (IFTA) than those without MetS. Multivariate logistic regression analysis revealed that IFTA was significantly associated with MetS (odds ratio per score increase 1.45, 95% confidence interval [CI] 1.02-2.05). Of the patients with DN at risk, 40% of patients progressed to ESKD. After adjusting for renal function and pathological parameters, the presence of MetS was an independent predictor for progression to ESKD (SHR 1.93, 95% CI 1.34-2.79). The SHRs for progression to ESKD also increased as the number of MetS components increased. Additionally, adding the IFTA scores improved the prognostic power of a model that only contained MetS and clinical covariates for predicting future ESKD.

CONCLUSION

MetS is an independent prognostic predictor of ESKD in patients with T2D and DN, while adding the IFTA scores increased the prognostic value of MetS for renal outcome.

Clinical and Pathological Features of Chinese Patients with Type 2 Diabetes, Biopsy-Proven Diabetic Kidney Disease, and Rapid eGFR Decline

OBJECTIVE

The rate of kidney function decline in patients with diabetic kidney disease (DKD) is known to differ. This study analyzed the clinicopathologic features and related risk factors affecting long-term renal survival in Chinese type 2 diabetic patients with rapid estimated glomerular filtration rate (eGFR) decline.

METHODS

In this retrospective descriptive study, 191 DKD patients were first classified as rapid eGFR decliners and slow eGFR decliners on the basis of the median eGFR slope value (-8.0 mL/min/1.73 m²/year). In total, 96 patients with rapid eGFR decline were included in the analyses and subsequently allocated to end-stage renal disease (ESRD) and non-ESRD groups. Baseline clinicopathological data of rapid eGFR decliners were collected. Cox proportional hazard analysis was performed to calculate the hazard ratios (HRs) for progression to ESRD.

RESULTS

During a median follow-up of 25 months, 52 (54.2%) rapid eGFR decliners progressed to ESRD. These 52 rapid eGFR decliners had poorer renal function, lower hemoglobin and albumin concentrations, higher total cholesterol and baseline proteinuria levels, and more severe interstitial inflammation than those who did not progress to ESRD. After adjustment for age, gender, baseline eGFR, proteinuria, hemoglobin level, serum albumin concentration, and histopathologic parameters, multivariate Cox proportional hazard analysis revealed that eGFR (HR 0.973, 95% CI 0.956-0.989) and proteinuria (HR 1.125, 95% CI 1.030-1.228) were associated with the increased risk of progression to ESRD.

CONCLUSION

Higher proteinuria and lower eGFR were independent risk factors for renal progression in Chinese patients with type 2 diabetes and rapid eGFR decline.

Proteomic profiling and correlations with clinical features reveal biomarkers indicative of diabetic retinopathy with diabetic kidney disease

Diabetic retinopathy (DR) and diabetic kidney disease (DKD) are complications of diabetes and place serious health and economic burdens on society. However, the identification and characterization of early biomarkers for DKD, especially for nonproliferative DR (NPDR) patients with DKD, are still needed. This study aimed to demonstrate the plasma proteomic profiles of NPDR+DKD and NPDR patients and identify potential biomarkers for early diagnosis of DKD. Fifteen plasma samples from the NPDR group and nine from the NPDR+DKD group were analyzed by LC-MS/MS to identify the differentially expressed proteins between the two groups. Functional enrichment, protein-protein interaction and clinical feature correlation analyses revealed the target protein candidates, which were verified using ELISA and receiver operating characteristic (ROC) analysis. In total, 410 proteins were detected in plasma; 15 were significantly upregulated and 7 were downregulated in the NPDR+DKD group. Bioinformatics analysis suggested that DKD is closely related to cell adhesion and immunity pathways. β-2-Microglobulin (B2M) and vimentin (VIM) were upregulated in NPDR+DKD, enriched as hub proteins and strongly correlated with clinical features. ELISA showed that B2M (p<0.001) and VIM (p<0.0001) were significantly upregulated in NPDR+DKD compared with NPDR. In ROC analysis, B2M and VIM could distinguish DKD from NPDR with area under the curve values of 0.9000 (p < 0.0001) and 0.9950. Our proteomic study revealed alterations in the proteomic profile and identified VIM and B2M as early biomarkers of DKD, laying the foundation for the prevention, diagnosis and treatment of DKD.

Relationship between renal function and prognosis of Chinese proliferative diabetic retinopathy patients undergoing the first vitrectomy: protocol for a prospective cohort study

INTRODUCTION

China has the largest number of adults with diabetes aged 20-79 years (116.4 million) in 2019. Due to the socioeconomic condition or a lack of awareness of diabetic complications, many adults with diabetes have proliferative diabetic retinopathy (PDR) or renal function impairment at their first visit to the clinic for a sudden loss of vision, and pars plana vitrectomy (PPV) is required for their treatment. Risk factors for the outcomes and complications of PPV surgery in PDR patients have been widely explored in many epidemiological studies and clinical trials. However, few prospective studies have analysed the association between renal function and surgical outcomes in PDR.

METHODS AND ANALYSIS

This is a single-centre, prospective cohort study of PDR patients with type 2 diabetes mellitus who have definite indications for PPV surgery with or without renal function impairment. We will consecutively enrol PDR patients who meet the inclusion and exclusion criteria from November 2020 to December 2023. Each participant will be followed up for at least 6 months after surgery. Clinical data from medical records and vitreous fluid will be collected.Demographic characteristics and study outcomes will be summarised using descriptive statistics. The variation will be described and evaluated using the χ² test or Kruskal-Wallis test. Generalise additive mixed models will be used to explore the association between the renal profile and surgical outcomes including BCVA, and retinal and choroidal microvasculature/microstructure. Multivariate ordinal regression analysis will be used to detect the independent association between renal profile and BCVA changes, and smooth curve fitting will be employed to briefly present the tendency.

ETHICS AND DISSEMINATION

The trial has received ethical approval from the West China Hospital of Sichuan University. Results of this trial will be disseminated through publication in peer-reviewed journals and presentations at local and international meetings.

TRIAL REGISTRATION NUMBER

ChiCTR2000039698.

Urinary complement proteins and risk of end-stage renal disease: quantitative urinary proteomics in patients with type 2 diabetes and biopsy-proven diabetic nephropathy

PURPOSE

To investigate the association between urinary complement proteins and renal outcome in biopsy-proven diabetic nephropathy (DN).

METHODS

Untargeted proteomic and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses and targeted proteomic analysis using parallel reaction-monitoring (PRM)-mass spectrometry was performed to determine the abundance of urinary complement proteins in healthy controls, type 2 diabetes mellitus (T2DM) patients, and patients with T2DM and biopsy-proven DN. The abundance of each urinary complement protein was individually included in Cox proportional hazards models for predicting progression to end-stage renal disease (ESRD).

RESULTS

Untargeted proteomic and functional analysis using the KEGG showed that differentially expressed urinary proteins were primarily associated with the complement and coagulation cascades. Subsequent urinary complement proteins quantification using PRM showed that urinary abundances of C3, C9, and complement factor H (CFAH) correlated negatively with annual estimated glomerular filtration rate (eGFR) decline, while urinary abundances of C5, decay-accelerating factor (DAF), and CD59 correlated positively with annual rate of eGFR decline. Furthermore, higher urinary abundance of CFAH and lower urinary abundance of DAF were independently associated with greater risk of progression to ESRD. Urinary abundance of CFAH and DAF had a larger area under the curve (AUC) than that of eGFR, proteinuria, or any pathological parameter. Moreover, the model that included CFAH or DAF had a larger AUC than that with only clinical or pathological parameters.

CONCLUSION

Urinary abundance of complement proteins was significantly associated with ESRD in patients with T2DM and biopsy-proven DN, indicating that therapeutically targeting the complement pathway may alleviate progression of DN.

Correlations Between Different Angiogenic and Inflammatory Factors in Vitreous Fluid of Eyes With Proliferative Diabetic Retinopathy

Purpose: To investigate the expression of various angiogenesis and inflammation mediators in the vitreous fluid of eyes with proliferative diabetic retinopathy (PDR).

Methods: A total of 38 eyes with PDR and 37 control eyes were included. Vitreous fluid was collected during vitrectomy. Vitreous levels of colony stimulating factor-1 receptor (CSF-1R), syndecan-1, placental growth factor (PIGF), and angiopoietin-like protein 4 (ANGPTL-4) were measured by multiplex immunoassay. Vitreous levels of vascular endothelial growth factor (VEGF), interleukin-6 (IL-6), interleukin-8 (IL-8), monocyte chemotactic protein-1 (MCP-1), tumor necrosis factor-α (TNF-α), and intercellular adhesion molecule-1 (ICAM-1) were measured by cytometric beads array. Levels of these mediators were compared between the PDR and control eyes. Correlations between levels of different mediators and between these mediators and kidney function metrics in the PDR group were also analyzed.

Results: Vitreous levels of syndecan-1, PIGF, ANGPTL-4, VEGF, and IL-8 were significantly higher in the PDR group compared to the control group (all p < 0.05). Levels of VEGF were significantly correlated with levels of syndecan-1, PIGF, and ANGPTL-4 (r = 0.370 to 0.497, all p < 0.05). Significant positive correlations were detected between levels of any two of the following mediators including syndecan-1, PIGF, ANGPTL-4, and IL-8 (r = 0.370 to 0.906, all p < 0.05). Apart from VEGF, levels of these mediators were positively correlated with serum creatinine and blood urea nitrogen (r = 0.328 to 0.638, all p < 0.05), and negatively correlated with fasting blood glucose and estimated glomerular filtration rate (r = −0.325 to −0.603, all p < 0.05).

Conclusions: Correlations between different angiogenesis and inflammation mediators were observed in eyes with PDR, suggesting cross-talks of different angiogenesis and inflammation pathways in the pathogenesis of PDR. The levels of angiogenesis and inflammation in PDR are correlated with kidney damage, indicating possible common pathways in diabetic retinopathy and nephropathy.

Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes

Prediction models for population-based screening need, for global usage, to be resource-driven, involving predictors that are affordably resourced. Here, we report the development and validation of three resource-driven risk models to identify people with type 2 diabetes (T2DM) at risk of stage 3 CKD defined by a decline in estimated glomerular filtration rate (eGFR) to below 60 mL/min/1.73m2. The observational study cohort used for model development consisted of data from a primary care dataset of 20,510 multi-ethnic individuals with T2DM from London, UK (2007-2018). Discrimination and calibration of the resulting prediction models developed using cox regression were assessed using the c-statistic and calibration slope, respectively. Models were internally validated using tenfold cross-validation and externally validated on 13,346 primary care individuals from Wales, UK. The simplest model was simplified into a risk score to enable implementation in community-based medicine. The derived full model included demographic, laboratory parameters, medication-use, cardiovascular disease history (CVD) and sight threatening retinopathy status (STDR). Two less resource-intense models were developed by excluding CVD and STDR in the second model and HbA1c and HDL in the third model. All three 5-year risk models had good internal discrimination and calibration (optimism adjusted C-statistics were each 0.85 and calibration slopes 0.999-1.002). In Wales, models achieved excellent discrimination(c-statistics ranged 0.82-0.83). Calibration slopes at 5-years suggested models over-predicted risks, however were successfully updated to accommodate reduced incidence of stage 3 CKD in Wales, which improved their alignment with the observed rates in Wales (E/O ratios near to 1). The risk score demonstrated similar model performance compared to direct evaluation of the cox model. These resource-driven risk prediction models may enable universal screening for Stage 3 CKD to enable targeted early optimisation of risk factors for CKD.

Combining glomerular basement membrane and tubular basement membrane assessment improves the prediction of diabetic end-stage renal disease

BACKGROUND

To address the prognostic value of combining tubular basement membrane (TBM) and glomerular basement membrane (GBM) thickness in diabetic nephropathy (DN).

METHODS

This retrospective study enrolled 110 patients with type 2 diabetes and biopsy-proven DN from 2011 to 2018. The pathological findings were confirmed according to the Renal Pathology Society classifications. GBM and TBM thicknesses were determined using the Haas' direct measurement/arithmetic mean method and orthogonal intercept method, respectively. Cox proportional hazard models were used to investigate the hazard ratios (HRs) for the influence of combined GBM and TBM thickness for predicting end-stage renal disease (ESRD).

RESULTS

Patients were assigned to three groups according to the median GBM and TBM thickness: GBM^lo TBM^lo (GBM < 681 nm and TBM < 1200 nm), GBM^hi TBM^lo /GBM^lo TBM^hi (GBM ≥ 681 nm and TBM < 1200 nm, or GBM < 681 nm and TBM ≥ 1200 nm), and GBM^hi TBM^hi (GBM ≥ 681 nm and TBM ≥ 1200 nm). The GBM^hi TBM^lo /GBM^lo TBM^hi and GBM^hi TBM^hi groups displayed poorer renal function, a more severe glomerular classification and interstitial inflammation, and poorer renal survival rates than the GBM^lo TBM^lo group The GBM^hi TBM^lo /GBM^lo TBM^hi and GBM^hi TBM^hi groups had adjusted HRs of 1.49 (95% confidence interval [CI], 1.21-9.75) and 3.07 (95% CI, 2.87-12.78), respectively, compared with the GBM^lo TBM^lo group.

CONCLUSIONS

TBM thickness enhanced GBM thickness for renal prognosis in patients with type 2 diabetes.

Association between serum alkaline phosphatase and renal outcome in patients with type 2 diabetes mellitus

This retrospective study included 299 patients with type 2 diabetes mellitus and biopsy-confirmed diabetic nephropathy (DN) to investigate the prognostic value of alkaline phosphatase (ALP) for renal outcome. Cox proportional hazards models were used to estimate the hazard ratios (HRs) for the serum ALP level on renal outcome, which was defined as end-stage renal disease (ESRD) or a 50% decline in estimated glomerular filtration rate (eGFR) from baseline. The median baseline ALP was 80 IU/L with an interquartile range of 64–97 IU/L. Serum ALP was negatively associated with eGFR but positively associated with proteinuria and renal interstitial fibrosis. During a median follow-up period of 23 months, ESRD or a 50% declined in the eGFR occurred in 156 (52.2%) patients. The highest quartile of ALP was significantly associated with poor renal outcome, as defined above (HR 2.38, 95% confidence interval [CI] 1.09–5.17), when adjusted for sociodemographics, baseline eGFR, proteinuria, liver function parameters, parathyroid hormone levels, and renal pathological findings. Each standard deviation higher in the natural log-transformed ALP was associated with a 25% increased risk for poor renal outcome. Additionally, there was a graded increase in the risk for poor renal outcome with higher ALP in patients with nephrotic-range proteinuria. However, no significant associations were observed between serum ALP levels and renal outcome in patients with non-nephrotic-range proteinuria. In conclusion, an elevated ALP level was independently associated with poor renal outcome in patients with type 2 diabetes mellitus and nephrotic-range proteinuria after multivariate adjustment.

Solidified glomerulosclerosis, identified using single glomerular proteomics, predicts end-stage renal disease in Chinese patients with type 2 diabetes

Few histological prognostic indicators for end-stage renal disease (ESRD) have been validated in diabetic patients. This biopsy-based study aimed to identify nephropathological risk factors for ESRD in Chinese patients with type 2 diabetes. Histological features of 322 Chinese type 2 diabetic patients with biopsy-confirmed diabetic nephropathy (DN) were retrospectively analysed. Cox proportional hazards analysis was used to estimate the hazard ratio (HR) for ESRD. Single glomerular proteomics and immunohistochemistry were used to identify differentially expressed proteins and enriched pathways in glomeruli. During the median follow-up period of 24 months, 144 (45%) patients progressed to ESRD. In multivariable models, the Renal Pathology Society classification failed to predict ESRD, although the solidified glomerulosclerosis (score 1: HR 1.65, 95% confidence interval [CI] 1.04-2.60; score 2: HR 2.48, 95% CI 1.40-4.37) and extracapillary hypercellularity (HR 2.68, 95% CI 1.55-4.62) were identified as independent risk factors. Additionally, single glomerular proteomics, combined with immunohistochemistry, revealed that complement C9 and apolipoprotein E were highly expressed in solidified glomerulosclerosis. Therefore, solidified glomerulosclerosis and extracapillary hypercellularity predict diabetic ESRD in Chinese patients. Single glomerular proteomics identified solidified glomerulosclerosis as a unique pathological change that may be associated with complement overactivation and abnormal lipid metabolism.

Clinicopathologic features and prognostic factors in older patients with biopsy-proven diabetic nephropathy

PURPOSE

The older population has increased sharply in China. However, renal clinical and histopathological data in this population are lacking. This study investigated the clinicopathologic features and the related risk factors for long-term renal survival in older patients with diabetic nephropathy (DN).

METHODS

In this retrospective observational study, 74 older patients (≥ 60 years old) with type 2 diabetes mellitus and biopsy-proven DN from 2007 to 2019 were included. Clinical data were extracted from electronic records. Renal biopsy specimens were semiquantitatively evaluated using the Renal Pathology Society (RPS) classification system. Cox proportional hazard analysis was used to estimate hazard ratios (HRs) for progression to end-stage renal disease (ESRD).

RESULTS

During the median follow-up period of 22 months, 24 (32%) older patients progressed to ESRD. Older patients who progressed to ESRD had poorer renal function, lower hemoglobin and albumin concentrations, more severe glomerular lesions, and higher percentages of Kimmelstiel-Wilson lesions than those who did not progress to ESRD. After adjusting for age, sex, baseline renal function, and pathological parameters, multivariate Cox proportional hazard analysis showed that RPS glomerular classification (HR 2.49, 95% confidence interval [CI] 1.03-6.04), estimated glomerular filtration rate (eGFR) (HR 0.76, 95% CI 0.58-0.99), and proteinuria (HR 3.85, 95% CI 1.44-10.27) were independent risk factors for progression to ESRD.

CONCLUSION

Lower eGFR, heavier proteinuria, and more severe RPS glomerular lesions were associated with ESRD in older patients with type 2 diabetes mellitus and DN.

Associations Between High-Altitude Residence and End-Stage Kidney Disease in Chinese Patients with Type 2 Diabetes

Zhao, Lijun, Xi Wang, Tingli Wang, Wenxin Fan, Honghong Ren, Rui Zhang, Yutong Zou, Huan Xu, Jie Zhang, Yunhong Wu, and Fang Liu. Associations between high-altitude residence and end-stage kidney disease in Chinese patients with type 2 diabetes. High Alt Med Biol. 21:396-405, 2020. Background: This study investigated whether living at high altitude was associated with progression to end-stage kidney disease (ESKD) in Chinese patients with diabetic nephropathy (DN). Materials and Methods: This retrospective study included 369 patients with type 2 diabetes mellitus (T2DM) and biopsy-confirmed DN. Cox proportional hazards models were used to estimate hazard ratios (HRs) for the influence of living at high altitude on ESKD. Results: Patients living at ≥2,000 m above sea level were more likely to be Tibetan, and they had higher mean body mass indexes, glycosylated hemoglobin, hemoglobin concentrations, and baseline estimated glomerular filtration rates than those living at lower altitudes. During a median follow-up period of 20 months, 141 (38%) patients progressed to ESKD. In multivariable Cox analysis adjusted for age, sex, ethnicity, and clinical and pathological parameters, living at high altitude was independently associated with progression to ESKD in Chinese DN patients [HR 2.83, 95% confidence interval (CI) 1.05-7.58]. Compared with Han Chinese, Tibetans were at a lower risk of progression to ESKD (HR 0.15, 95% CI 0.04-0.59). Conclusions: Living at high altitude was independently associated with renal outcome in Han Chinese patients with T2DM and DN, but not native Tibetans.