Prognostic nomogram and score to predict renal survival of patients with biopsy-proven diabetic nephropathy

Representation of multimorbidity and frailty in the development and validation of kidney failure prognostic prediction models: a systematic review

BACKGROUND

Prognostic models that identify individuals with chronic kidney disease (CKD) at greatest risk of developing kidney failure help clinicians to make decisions and deliver precision medicine. It is recognised that people with CKD usually have multiple long-term health conditions (multimorbidity) and often experience frailty. We undertook a systematic review to evaluate the representation and consideration of multimorbidity and frailty within CKD cohorts used to develop and/or validate prognostic models assessing the risk of kidney failure.

METHODS

We identified studies that described derivation, validation or update of kidney failure prognostic models in MEDLINE, CINAHL Plus and the Cochrane Library-CENTRAL. The primary outcome was representation of multimorbidity or frailty. The secondary outcome was predictive accuracy of identified models in relation to presence of multimorbidity or frailty.

RESULTS

Ninety-seven studies reporting 121 different kidney failure prognostic models were identified. Two studies reported prevalence of multimorbidity and a single study reported prevalence of frailty. The rates of specific comorbidities were reported in a greater proportion of studies: 67.0% reported baseline data on diabetes, 54.6% reported hypertension and 39.2% reported cardiovascular disease. No studies included frailty in model development, and only one study considered multimorbidity as a predictor variable. No studies assessed model performance in populations in relation to multimorbidity. A single study assessed associations between frailty and the risks of kidney failure and death.

CONCLUSIONS

There is a paucity of kidney failure risk prediction models that consider the impact of multimorbidity and/or frailty, resulting in a lack of clear evidence-based practice for multimorbid or frail individuals. These knowledge gaps should be explored to help clinicians know whether these models can be used for CKD patients who experience multimorbidity and/or frailty.

SYSTEMATIC REVIEW REGISTRATION

This review has been registered on PROSPERO (CRD42022347295).

Association of the podocyte phenotype with extracapillary hypercellularity in patients with diabetic kidney disease

BACKGROUND

Extracapillary hypercellularity was recently identified as a poor prognostic factor for diabetic kidney disease (DKD), but its nature, pathogenesis, and relationship with glomerular sclerosis are still unclear.

METHODS

We retrospectively studied 107 patients with biopsy-proven DKD, recruited from January 2018 through December 2020. We compared the clinicopathologic characteristics of 25 patients with extracapillary hypercellularity lesions (the extracapillary hypercellularity group) to those of 82 patients without extracapillary hypercellularity (the control group). Multiple cell-specific markers were used for immunohistochemical staining to analyse the types of cells that exhibited extracapillary hypercellularity. Podocyte phenotype changes were evaluated via immunohistochemical staining for Synaptopodin and Nephrin, and foot process width was measured via transmission electron microscopy.

RESULTS

Patients with extracapillary hypercellularity lesions had more severe clinical features than patients without extracapillary hypercellularity in DKD, as indicated by elevated proteinuria and serum creatinine levels, and decreased serum albumin. Pathologically, extracapillary hypercellularity was accompanied by increased mesangial hyperplasia and interstitial fibrosis. Severe obliterative microvascular disease was observed more frequently in the extracapillary hypercellularity group than in the control group. At cell type analysis, 25 patients in the DKD-extracapillary hypercellularity group showed that a mixture of cells expressed either Wilm's tumor-1 or paired box protein 2. Furthermore, DKD-extracapillary hypercellularity patients had significant loss of podocyte phenotype and severe foot process effacement. Cells in extracapillary hypercellularity had increased hypoxia-induced factor-1 alpha expression.

CONCLUSIONS

Extracapillary hypercellularity is associated with severe renal dysfunction and renal sclerosis. Vascular damage is closely related to severe podocyte hypoxia injury and requires additional attention in future research.

Machine learning techniques to predict the risk of developing diabetic nephropathy: a literature review

Purpose

Diabetes is a major public health challenge with widespread prevalence, often leading to complications such as Diabetic Nephropathy (DN)-a chronic condition that progressively impairs kidney function. In this context, it is important to evaluate if Machine learning models can exploit the inherent temporal factor in clinical data to predict the risk of developing DN faster and more accurately than current clinical models.

Methods

Three different databases were used for this literature review: Scopus, Web of Science, and PubMed. Only articles written in English and published between January 2015 and December 2022 were included.

Results

We included 11 studies, from which we discuss a number of algorithms capable of extracting knowledge from clinical data, incorporating dynamic aspects in patient assessment, and exploring their evolution over time. We also present a comparison of the different approaches, their performance, advantages, disadvantages, interpretation, and the value that the time factor can bring to a more successful prediction of diabetic nephropathy.

Conclusion

Our analysis showed that some studies ignored the temporal factor, while others partially exploited it. Greater use of the temporal aspect inherent in Electronic Health Records (EHR) data, together with the integration of omics data, could lead to the development of more reliable and powerful predictive models.

Glomerular crescents are associated with the risk of type 2 diabetic kidney disease progression: a retrospective cohort study

BACKGROUND

Diabetic kidney disease (DKD) stands as the predominant cause of chronic kidney disease and end-stage kidney disease. Its diverse range of manifestations complicates the treatment approach for patients. Although kidney biopsy is considered the gold standard for diagnosis, it lacks precision in predicting the progression of kidney dysfunction. Herein, we addressed whether the presence of glomerular crescents is linked to the outcomes in patients with biopsy-confirmed type 2 DKD.

METHODS

We performed a retrospective evaluation, involving 327 patients diagnosed with biopsy-confirmed DKD in the context of type 2 diabetes, excluding cases with other glomerular diseases, from nine tertiary hospitals. Hazard ratios (HRs) were calculated using a Cox regression model to assess the risk of kidney disease progression, defined as either ≥ 50% decrease in estimated glomerular filtration rates or the development of end-stage kidney disease, based on the presence of glomerular crescents.

RESULTS

Out of the 327 patients selected, ten patients had glomerular crescents observed in their biopsied tissues. Over the follow-up period (median of 19 months, with a maximum of 18 years), the crescent group exhibited a higher risk of kidney disease progression than the no crescent group, with an adjusted HR of 2.82 (1.32-6.06) (P = 0.008). The presence of heavy proteinuria was associated with an increased risk of developing glomerular crescents.

CONCLUSION

The presence of glomerular crescents is indeed linked to the progression of type 2 DKD. Therefore, it is important to determine whether there is an additional immune-mediated glomerulonephritis requiring immunomodulation, and it may be prudent to monitor the histology and repeat a biopsy.

Navigating the future of diabetes: innovative nomogram models for predicting all-cause mortality risk in diabetic nephropathy

OBJECTIVE

This study aims to establish and validate a nomogram model for the all-cause mortality rate in patients with diabetic nephropathy (DN).

METHODS

We analyzed data from the National Health and Nutrition Examination Survey (NHANES) spanning from 2007 to 2016. A random split of 7:3 was performed between the training and validation sets. Utilizing follow-up data until December 31, 2019, we examined the all-cause mortality rate. Cox regression models and Least Absolute Shrinkage and Selection Operator (LASSO) regression models were employed in the training cohort to develop a nomogram for predicting all-cause mortality in the studied population. Finally, various validation methods were employed to assess the predictive performance of the nomogram, and Decision Curve Analysis (DCA) was conducted to evaluate the clinical utility of the nomogram.

RESULTS

After the results of LASSO regression models and Cox multivariate analyses, a total of 8 variables were selected, gender, age, poverty income ratio, heart failure, body mass index, albumin, blood urea nitrogen and serum uric acid. A nomogram model was built based on these predictors. The C-index values in training cohort of 3-year, 5-year, 10-year mortality rates were 0.820, 0.807, and 0.798. In the validation cohort, the C-index values of 3-year, 5-year, 10-year mortality rates were 0.773, 0.788, and 0.817, respectively. The calibration curve demonstrates satisfactory consistency between the two cohorts.

CONCLUSION

The newly developed nomogram proves to be effective in predicting the all-cause mortality risk in patients with diabetic nephropathy, and it has undergone robust internal validation.

A dynamic nomogram for predicting survival among diabetic patients on maintenance hemodialysis

INTRODUCTION

Among maintenance hemodialysis (MHD) patients, ones with diabetes mellitus (DM) are known to have the worst outcome.

METHODS

A total of 263 MHD patients were included, a dynamic nomogram was established based on multivariable Cox regression analysis.

RESULTS

The median overall survival (OS) time was 46 months. The 1-, 3-, and 5-year OS rates were 90.9%, 70.5% and 53.9%, respectively. The multivariable Cox regression analysis indicated that DM duration, cardiovascular complication, baseline values before starting MHD for estimated glomerular filtration rate and serum phosphate were independent risk factors. The C-index of the dynamic nomogram was 0.745 and the calibration curves showed optimal agreement between the model prediction and actual observation for predicting survival probabilities.

CONCLUSIONS

Our study was the first to establish dynamic nomogram among diabetic MHD patients, the fast and convenient online tool can be used for individual risk estimation at the point of prognosis prediction.

Crescents, an Independent Risk Factor for the Progression of Type 2 Diabetic Kidney Disease

CONTEXT

Crescents have been noticed in pathologic changes in patients with diabetic kidney disease (DKD). However, the clinical significance of crescents is still not well recognized.

OBJECTIVE

The main objective was to investigate the association between crescents and the prognoses of type 2 DKD (T2DKD) patients, and, secondly, to analyze the relationship between crescents and clinicopathologic features.

METHODS

A retrospective cohort study of 155 patients with T2DKD diagnosed by renal biopsy was carried out in a single center. Clinicopathologic features of patients with or without crescents were analyzed. Cox regression models and meta-analysis were used to determine the prognostic values of crescents for T2DKD. A nomogram was constructed to provide a simple estimation method of 1, 3, and 5-year renal survival for patients with T2DKD.

RESULTS

Compared with T2DKD patients without crescents, patients with crescents had higher 24-hour proteinuria and serum creatinine levels, as well as more severe Kimmelstiel-Wilson (K-W) nodules, segmental sclerosis (SS), and mesangiolysis (all P < .05). Furthermore, the crescents were positively correlated with serum creatinine, 24-hour proteinuria, K-W nodules, SS, mesangiolysis, and complement 3 deposition. Multivariate Cox models showed that crescents were an independent prognostic risk factor for renal survival (hazard ratio [HR] 2.68, 95% CI 1.27-5.64). The meta-analyzed results of 4 studies on crescents in T2DKD confirmed that patients with crescents had a significantly higher HR for renal progression.

CONCLUSION

Patients with crescents in T2DKD have more severe clinicopathologic changes and worse prognoses. The crescent can serve as an independent risk factor for T2DKD progression.

Construction of a Prediction Model for the Mortality of Elderly Patients with Diabetic Nephropathy

To construct a prediction model for all-cause mortality in elderly diabetic nephropathy (DN) patients, in this cohort study, the data of 511 DN patients aged ≥65 years were collected and the participants were divided into the training set (n = 358) and the testing set (n = 153). The median survival time of all participants was 2 years. The data in the training set were grouped into the survival group (n = 203) or the death group (n = 155). Variables with P ≤ 0.1 between the two groups were selected as preliminary predictors and involved into the multivariable logistic regression model and the covariables were gradually adjusted. The receiver operator characteristic (ROC), Kolmogorov-Smirnov (KS), and calibration curves were plotted for evaluating the predictive performance of the model. Internal validation of the performance of the model was verified in the testing set. The predictive values of the model were also conducted in terms of people with different genders and ages or accompanied with chronic kidney disease (CKD) or cardiovascular diseases (CVD), respectively. In total, 216 (42.27%) elderly DN patients were dead within 2 years. The prediction model for the 2-year mortality of elderly patients with DN was established based on length of stay (LOS), temperature, heart rate, peripheral oxygen saturation (SpO₂), serum creatinine (Scr), red cell distribution width (RDW), the simplified acute physiology score-II (SAPS-II), hyperlipidemia, and the Chronic Kidney Disease Epidemiology Collaboration equation for estimated glomerular filtration rate (eGFR-CKD-EPI). The AUC of the model was 0.78 (95% CI: 0.73–0.83) in the training set and 0.72 (95% CI: 0.63–0.80) in the testing set. The AUC of the model was 0.78 (95% CI: 0.65–0.91) in females and 0.78 (95%CI: 0.68–0.88) in patients ≤75 years. The AUC of the model was 0.74 (95% CI: 0.64–0.84) in patients accompanied with CKD. The model had good predictive value for the mortality of elderly patients with DN within 2 years. In addition, the model showed good predictive values for female DN patients, DN patients ≤75 years, and DN patients accompanied with CKD.

Prediction model for the risk of ESKD in patients with primary FSGS

The purpose of this study is to build a prediction model for accurate assessment of the risk of end-stage kidney disease (ESKD) in individuals with primary focal segmental glomerulosclerosis (FSGS) by integrating clinical and pathological features at biopsy. The prediction model was created based on a retrospective study of 99 patients with biopsy-proven primary FSGS diagnosed at our hospital between December 2012 and December 2019. We assessed discriminative ability and predictive accuracy of the model by C-index and calibration plot. Internal validation of the prediction model was performed with 1000-bootstrap procedure. Eight patients (8.1%) progressed to ESKD before 31 March 2021. Univariate analysis revealed that disease duration before biopsy, hematuria, hemoglobin, eGFR, and percentages of sclerosis and global sclerosis were associated with renal outcome. In multivariate analysis, three predictors were included in final prediction model: eGFR, hematuria, and percentage of sclerosis. The C-index of the model was 0.811 and 5-year calibration plot showed good agreement between predicted renal survival probability and actual observation. A nomogram and an online risk calculator were built on the basis of the prediction model. In conclusion, we constructed and internally validated the first prediction model for risk of ESKD in primary FSGS, which showed good discriminative ability and calibration performance. The prediction model provides an accurate and simple strategy to predict renal prognosis which may help to identify patients at high risk of ESKD and guide the management for patients with primary FSGS in clinical practice.

MUC1 Promotes Mesangial Cell Proliferation and Kidney Fibrosis in Diabetic Nephropathy Through Activating STAT and β-Catenin Signal Pathway

Diabetic nephropathy (DN) is a complication of diabetes, which leads to most end-stage kidney diseases and threatens health of patients. Mucin 1 (MUC1) is a heterodimeric oncoprotein, which is abnormally expressed in tumors and hematologic diseases. The aim of this study is to clarify the mechanism and role of MUC1 in DN. The mesangial cells (MCs) suffered from high glucose (HG) treatment to mimic DN in vitro. The cell proliferation was detected by Cell Counting Kit-8 assay and 5-ethynyl-2-deoxyuridine (EdU) staining assay. The expression of MUC1 and fibrosis markers: fibronectin, collagen I, and collagen IV were assessed by western blot. In this study, we demonstrated that HG treatment induced MUC1 expression in MCs. With knockdown of MUC1 or overexpressed MUC1 in MCs, the results indicated that knockdown of MUC1 inhibited MCs proliferation and reduced kidney fibrosis markers expression, including fibronectin, collagen I, and collagen IV, whereas overexpression of MUC1 led to opposite results. Mechanically, MUC1 activated signal transducers and activators of transcription (STAT) and β-catenin signal pathway. After added AG490 (STAT inhibitor) or FH535 (β-catenin inhibitor), blocking STAT3 and β-catenin signal pathway attenuated MUC1-induced cell proliferation and fibronectin production in MCs. Finally, knockdown of MUC1 attenuated DN-induced kidney fibrosis in db/db mice. Therapeutic target for DN. In conclusion, MUC1 promotes MCs proliferation and kidney fibrosis in DN through activating STAT and β-catenin signal pathway, which can help to provide a novel therapeutic target for DN.

Modified arteriosclerosis score predicts the outcomes of diabetic kidney disease

BACKGROUND

The significance of renal arteriosclerosis in the prediction of the renal outcomes of diabetic kidney disease (DKD) remains undetermined.

METHODS

We enrolled 174 patients with DKD from three centres from January 2010 to July 2017. The severity and extent of arteriosclerosis were analysed on sections based on dual immunohistochemical staining of CD31 and α-smooth muscle actin. An X-tile plot was used to determine the optimal cut-off value. The primary endpoint was renal survival (RS), defined as the duration from renal biopsy to end-stage renal disease or death.

RESULTS

The baseline estimated glomerular filtration rate (eGFR) of 135 qualified patients was 45 (29 ~ 70) ml/min per 1.73 m², and the average 24-h urine protein was 4.52 (2.45 ~ 7.66) g/24 h. The number of glomeruli in the biopsy specimens was 21.07 ± 9.7. The proportion of severe arteriosclerosis in the kidney positively correlated with the Renal Pathology Society glomerular classification (r = 0.28, P < 0.012), interstitial fibrosis and tubular atrophy (IFTA) (r = 0.39, P < 0.001), urine protein (r = 0.213, P = 0.013), systolic BP (r = 0.305, P = 0.000), and age (r = 0.220, P = 0.010) and significantly negatively correlated with baseline eGFR (r = - 0.285, P = 0.001). In the multivariable model, the primary outcomes were significantly correlated with glomerular class (HR: 1.72, CI: 1.15 ~ 2.57), IFTA (HR: 1.96, CI: 1.26 ~ 3.06) and the modified arteriosclerosis score (HR: 2.21, CI: 1.18 ~ 4.13). After risk adjustment, RS was independently associated with the baseline eGFR (HR: 0.97, CI: 0.96 ~ 0.98), urine proteinuria (HR: 1.10, CI: 1.04 ~ 1.17) and the modified arteriosclerosis score (HR: 2.01, CI: 1.10 ~ 3.67), and the nomogram exhibited good calibration and acceptable discrimination (C-index = 0.82, CI: 0.75 ~ 0.87).

CONCLUSIONS

The severity and proportion of arteriosclerosis may be helpful prognostic indicators for DKD.

A Nomogram Model that Predicts the Risk of Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Retrospective Study

OBJECTIVE

To construct a novel nomogram model that predicts the risk of diabetic nephropathy (DN) incidence in Chinese patients with type 2 diabetes mellitus (T2DM).

METHODS

Questionnaire surveys, physical examinations, routine blood tests, and biochemical index evaluations were conducted on 1095 patients with T2DM from Guilin. A least absolute contraction selection operator (LASSO) regression and multivariable logistic regression analysis were used to screen out DN risk factors. A logistic regression analysis incorporating the screened risk factors was used to establish a predictive nomogram model. The performance of the nomogram model was evaluated using the C-index, an area under the receiver operating characteristic curve (AUC), calibration plots, and a decision curve analysis. Bootstrapping was applied for internal validation.

RESULTS

Independent predictors for DN incidence risk included gender, age, hypertension, medicine use, duration of diabetes, body mass index, blood urea nitrogen level, serum creatinine level, neutrophil to lymphocyte ratio, and red blood cell distribution width. The nomogram model exhibited moderate prediction ability with a C-index of 0.819 (95% confidence interval (CI): 0.783-0.853) and an AUC of 0.813 (95%CI: 0.778-0.848). The C-index from internal validation reached 0.796 (95%CI: 0.763-0.829). The decision curve analysis displayed that the DN risk nomogram was clinically applicable when the risk threshold was between 1 and 83%.

CONCLUSION

Our novel and simple nomogram containing 10 factors may be useful in predicting DN incidence risk in T2DM patients.

Incorporation of Urinary Neutrophil Gelatinase-Associated Lipocalin and Computed Tomography Quantification to Predict Acute Kidney Injury and In-Hospital Death in COVID-19 Patients

Background

The prevalence of acute kidney injury (AKI) in COVID-19 patients is high, with poor prognosis. Early identification of COVID-19 patients who are at risk for AKI and may develop critical illness and death is of great importance.

Objective

The aim of this study was to develop and validate a prognostic model of AKI and in-hospital death in patients with COVID-19, incorporating the new tubular injury biomarker urinary neutrophil gelatinase-associated lipocalin (u-NGAL) and artificial intelligence (AI)-based chest computed tomography (CT) analysis.

Methods

A single-center cohort of patients with COVID-19 from Wuhan Leishenshan Hospital were included in this study. Demographic characteristics, laboratory findings, and AI-assisted chest CT imaging variables identified on hospital admission were screened using least absolute shrinkage and selection operator (LASSO) and logistic regression to develop a model for predicting the AKI risk. The accuracy of the AKI prediction model was measured using the concordance index (C-index), and the internal validity of the model was assessed by bootstrap resampling. A multivariate Cox regression model and Kaplan-Meier curves were analyzed for survival analysis in COVID-19 patients.

Results

One hundred seventy-four patients were included. The median (±SD) age of the patients was 63.59 ± 13.79 years, and 83 (47.7%) were men.u-NGAL, serum creatinine, serum uric acid, and CT ground-glass opacity (GGO) volume were independent predictors of AKI, and all were selected in the nomogram. The prediction model was validated by internal bootstrapping resampling, showing results similar to those obtained from the original samples (i.e., 0.958; 95% CI 0.9097–0.9864). The C-index for predicting AKI was 0.955 (95% CI 0.916–0.995). Multivariate Cox proportional hazards regression confirmed that a high u-NGAL level, an increased GGO volume, and lymphopenia are strong predictors of a poor prognosis and a high risk of in-hospital death.

Conclusions

This model provides a useful individualized risk estimate of AKI in patients with COVID-19. Measurement of u-NGAL and AI-based chest CT quantification are worthy of application and may help clinicians to identify patients with a poor prognosis in COVID-19 at an early stage.

Diagnostic value of VDBP and miR-155-5p in diabetic nephropathy and the correlation with urinary microalbumin

This study explored the diagnostic and therapeutic significance of vitamin D binding protein (VDBP) and miR-155-5p for diabetic nephropathy and the correlation with urinary microalbumin. A total of 145 patients with type 2 diabetes who attended the Hwamei hospital were selected as research objects and assigned to diabetic nephropathy group (DN group) and diabetes group according to whether they suffered from diabetic nephropathy (DN). The expression levels of urine VDBP and serum miR-155-5p in the two groups were detected, and the correlation between urinary microalbumin (mAlb), serum cystatin C (Cys C) and 24-h urinary protein was analyzed. The predictive value of single and joint detection of urinary VDBP and serum miR-155-5p for DN onset and poor prognosis was analyzed. In DN patients, urine VDBP and serum miR-155-5p were highly expressed, and urine VDBP, serum miR-155-5p and mAlb, Cys C and 24-h urine protein were positively correlated (P<0.05). Moreover, the joint detection of urine VDBP and serum miR-155-5p was more valuable in diagnosis and poor prognosis prediction of DN patients than its single detection. Urine VDBP and serum miR-155-5p have good diagnostic value for DN, but their joint diagnostic value is higher, and their expression levels are all related to mAlb of DN patients, which may be used as new biological indicators for diagnosis and disease assessment.

Development and validation of a predictive model for end-stage renal disease risk in patients with diabetic nephropathy confirmed by renal biopsy

This study was performed to develop and validate a predictive model for the risk of end-stage renal disease (ESRD) inpatients with diabetic nephropathy (DN) confirmed by renal biopsy. We conducted a retrospective study with 968 patients with T2DM who underwentrenal biopsy for the pathological confirmation of DNat the First Affiliated Hospital of Zhengzhou University from February 2012 to January 2015; the patients were followed until December 2018. The outcome was defined as a fatal or nonfatal ESRD event (peritoneal dialysis or hemodialysis for ESRD, renal transplantation, or death due to chronic renal failure or ESRD). The dataset was randomly split into development (75%) and validation (25%) cohorts. We used stepwise multivariablelogistic regression to identify baseline predictors for model development. The model’s performance in the two cohorts, including discrimination and calibration, was evaluated by the C-statistic and the P value of the Hosmer-Lemeshow test. During the 3-year follow-up period, there were 225 outcome events (47.1%) during follow-up. Outcomes occurred in 187 patients (52.2%) in the derivation cohort and 38 patients (31.7%) in the validation cohort. The variables selected in the final multivariable logistic regression after backward selection were pathological grade, Log Urinary Albumin-to-creatinine ratio (Log ACR), cystatin C, estimated glomerular filtration rate (eGFR) and B-type natriuretic peptide (BNP). 4 prediction models were created in a derivation cohort of 478 patients: a clinical model that included cystatin C, eGFR, BNP, Log ACR; a clinical-pathological model and a clinical-medication model, respectively, also contained pathological grade and renin-angiotensin system blocker (RASB) use; and a full model that also contained the pathological grade, RASB use and age. Compared with the clinical model, the clinical-pathological model and the full model had better C statistics (0.865 and 0.866, respectively, vs. 0.864) in the derivation cohort and better C statistics (0.876 and 0.875, respectively, vs. 0.870) in the validation cohort. Among the four models, the clinical-pathological model had the lowest AIC of 332.53 and the best P value of 0.909 of the Hosmer-Lemeshow test. We constructed a nomogram which was a simple calculator to predict the risk ratio of progression to ESRD for patients with DN within 3 years. The clinical-pathological model using routinely available clinical measurements was shown to be accurate and validated method for predicting disease progression in patients with DN. The risk model can be used in clinical practice to improve the quality of risk management and early intervention.

Uncovering the Mechanism of Astragalus membranaceus in the Treatment of Diabetic Nephropathy Based on Network Pharmacology

BACKGROUND

Diabetic nephropathy (DN), characterized by hyperglycemia, hypertension, proteinuria, and edema, is a unique microvascular complication of diabetes. Traditional Chinese medicine (TCM) Astragalus membranaceus (AM) has been widely used for DN in China while the pharmacological mechanisms are still unclear. This work is aimed at undertaking a network pharmacology analysis to reveal the mechanism of the effects of AM in DN. Materials and Methods. In this study, chemical constituents of AM were obtained via Traditional Chinese Medicine Systems Pharmacology Database (TCMSP), and the potential targets of AM were identified using the Therapeutic Target Database (TTD). DisGeNET and GeneCards databases were used to collect DN-related target genes. DN-AM common target protein interaction network was established by using the STRING database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to further explore the DN mechanism and therapeutic effect of AM. The network diagrams of the active component-action target and protein-protein interaction (PPI) networks were constructed using Cytoscape software.

RESULTS

A total of 16 active ingredients contained and 78 putative identified target genes were screened from AM, of which 42 overlapped with the targets of DN and were considered potential therapeutic targets. The analysis of the network results showed that the AM activity of component quercetin, formononetin, calycosin, 7-O-methylisomucronulatol, and quercetin have a good binding activity with top ten screened targets, such as VEGFA, TNF, IL-6, MAPK, CCL3, NOS3, PTGS2, IL-1β, JUN, and EGFR. GO and KEGG analyses revealed that these targets were associated with inflammatory response, angiogenesis, oxidative stress reaction, rheumatoid arthritis, and other biological process.

CONCLUSIONS

This study demonstrated the multicomponent, multitarget, and multichannel characteristics of AM, which provided a novel approach for further research of the mechanism of AM in the treatment of DN.