Association between age at diagnosis of diabetes and ocular disease:Insights from a recent article

In this article,we discuss Ye et al's recent article on the association between age at diabetes diagnosis and subsequent risk of age-related ocular diseases.The study,which utilized United Kingdom Biobank data,highlighted a strong link between early diabetes onset and major eye conditions,such as cataracts,glaucoma,agerelated macular degeneration,and vision loss,independent of glycemic control and disease duration.This finding challenges the previous belief that diabetic eye disease primarily correlates with hyperglycemia.As lifestyles evolve and the age of diabetes diagnosis decreases,understanding this relationship may reveal the complex pathogenesis underlying diabetes-related complications.This editorial summarizes potential mechanisms connecting the age of diabetes onset with four types of ocular diseases,emphasizing the significance of early diagnosis.

Association of autoimmune thyroid disease with type 1 diabetes mellitus and its ultrasonic diagnosis and management

As a common hyperglycemic disease,type 1 diabetes mellitus(T1DM)is a complicated disorder that requires a lifelong insulin supply due to the immunemediated destruction of pancreaticβcells.Although it is an organ-specific autoimmune disorder,T1DM is often associated with multiple other autoimmune disorders.The most prevalent concomitant autoimmune disorder occurring in T1DM is autoimmune thyroid disease(AITD),which mainly exhibits two extremes of phenotypes:hyperthyroidism[Graves'disease(GD)]and hypothyroidism[Hashimoto's thyroiditis,(HT)].However,the presence of comorbid AITD may negatively affect metabolic management in T1DM patients and thereby may increase the risk for potential diabetes-related complications.Thus,routine screening of thyroid function has been recommended when T1DM is diagnosed.Here,first,we summarize current knowledge regarding the etiology and pathogenesis mechanisms of both diseases.Subsequently,an updated review of the association between T1DM and AITD is offered.Finally,we provide a relatively detailed review focusing on the application of thyroid ultrasonography in diagnosing and managing HT and GD,suggesting its critical role in the timely and accurate diagnosis of AITD in T1DM.

Association of age at diagnosis of diabetes with subsequent risk of age-related ocular diseases and vision acuity

BACKGROUND The importance of age on the development of ocular conditions has been reported by numerous studies.Diabetes may have different associations with different stages of ocular conditions,and the duration of diabetes may affect the development of diabetic eye disease.While there is a dose-response relationship between the age at diagnosis of diabetes and the risk of cardiovascular disease and mortality,whether the age at diagnosis of diabetes is associated with incident ocular conditions remains to be explored.It is unclear which types of diabetes are more predictive of ocular conditions.AIM To examine associations between the age of diabetes diagnosis and the incidence of cataract,glaucoma,age-related macular degeneration(AMD),and vision acuity.METHODS Our analysis was using the UK Biobank.The cohort included 8709 diabetic participants and 17418 controls for ocular condition analysis,and 6689 diabetic participants and 13378 controls for vision analysis.Ocular diseases were identified using inpatient records until January 2021.Vision acuity was assessed using a chart.RESULTS During a median follow-up of 11.0 years,3874,665,and 616 new cases of cataract,glaucoma,and AMD,respectively,were identified.A stronger association between diabetes and incident ocular conditions was observed where diabetes was diagnosed at a younger age.Individuals with type 2 diabetes(T2D)diagnosed at<45 years[HR(95%CI):2.71(1.49-4.93)],45-49 years[2.57(1.17-5.65)],50-54 years[1.85(1.13-3.04)],or 50-59 years of age[1.53(1.00-2.34)]had a higher risk of AMD independent of glycated haemoglobin.T2D diagnosed<45 years[HR(95%CI):2.18(1.71-2.79)],45-49 years[1.54(1.19-2.01)],50-54 years[1.60(1.31-1.96)],or 55-59 years of age[1.21(1.02-1.43)]was associated with an increased cataract risk.T2D diagnosed<45 years of age only was associated with an increased risk of glaucoma[HR(95%CI):1.76(1.00-3.12)].HRs(95%CIs)for AMD,cataract,and glaucoma associated with type 1 diabetes(T1D)were 4.12(1.99-8.53),2.95(2.17-4.02),and 2.40(1.09-5.31),respectively.In multivariable-adjusted analysis,individuals with T2D diagnosed<45 years of age[β95%CI:0.025(0.009,0.040)]had a larger increase in LogMAR.Theβ(95%CI)for LogMAR associated with T1D was 0.044(0.014,0.073).CONCLUSION The younger age at the diagnosis of diabetes is associated with a larger relative risk of incident ocular diseases and greater vision loss.

Guidelines and standards for comprehensive clinical diagnosis and interventional treatment for diabetic foot in China(Issue 7.0)

Diabetic foot(DF)is one of the most common complications of diabetes and is associated with high morbidity,disability,lethality and low cure-rate.The clinical diagnosis and treatment of DF need to be standardized.The Chinese Diabetic Foot Cell and Interventional Therapy Technology Alliance has released six editions of guidelines and standards for clinical diagnosis and interventional treatment of DF,which filled the gap in the domestic DF treatment standard and played an important role in improving the level of diagnosis and treatment in China.In line with the latest developments in diagnosis and treatment,the Alliance,along with other 89 institutions,developed and issued the new edition based on the sixth edition to help standardize the clinical diagnosis and treatment of DF in China.

Research progress in artificial intelligence assisted diabetic retinopathy diagnosis

Diabetic retinopathy(DR)is one of the most common retinal vascular diseases and one of the main causes of blindness worldwide.Early detection and treatment can effectively delay vision decline and even blindness in patients with DR.In recent years,artificial intelligence(AI)models constructed by machine learning and deep learning(DL)algorithms have been widely used in ophthalmology research,especially in diagnosing and treating ophthalmic diseases,particularly DR.Regarding DR,AI has mainly been used in its diagnosis,grading,and lesion recognition and segmentation,and good research and application results have been achieved.This study summarizes the research progress in AI models based on machine learning and DL algorithms for DR diagnosis and discusses some limitations and challenges in AI research.

An Optimal Deep Learning Based Computer-Aided Diagnosis System for Diabetic Retinopathy

Diabetic Retinopathy(DR)is a significant blinding disease that poses serious threat to human vision rapidly.Classification and severity grading of DR are difficult processes to accomplish.Traditionally,it depends on ophthalmoscopically-visible symptoms of growing severity,which is then ranked in a stepwise scale from no retinopathy to various levels of DR severity.This paper presents an ensemble of Orthogonal Learning Particle Swarm Optimization(OPSO)algorithm-based Convolutional Neural Network(CNN)Model EOPSO-CNN in order to perform DR detection and grading.The proposed EOPSO-CNN model involves three main processes such as preprocessing,feature extraction,and classification.The proposed model initially involves preprocessing stage which removes the presence of noise in the input image.Then,the watershed algorithm is applied to segment the preprocessed images.Followed by,feature extraction takes place by leveraging EOPSO-CNN model.Finally,the extracted feature vectors are provided to a Decision Tree(DT)classifier to classify the DR images.The study experiments were carried out using Messidor DR Dataset and the results showed an extraordinary performance by the proposed method over compared methods in a considerable way.The simulation outcome offered the maximum classification with accuracy,sensitivity,and specificity values being 98.47%,96.43%,and 99.02%respectively.

Diabetic Retinopathy Diagnosis Using ResNet with Fuzzy Rough C-Means Clustering

Diabetic Retinopathy(DR)is a vision disease due to the long-term prevalenceof Diabetes Mellitus.It affects the retina of the eye and causes severedamage to the vision.If not treated on time it may lead to permanent vision lossin diabetic patients.Today’s development in science has no medication to cureDiabetic Retinopathy.However,if diagnosed at an early stage it can be controlledand permanent vision loss can be avoided.Compared to the diabetic population,experts to diagnose Diabetic Retinopathy are very less in particular to local areas.Hence an automatic computer-aided diagnosis for DR detection is necessary.Inthis paper,we propose an unsupervised clustering technique to automatically clusterthe DR into one of its five development stages.The deep learning based unsupervisedclustering is made to improve itself with the help of fuzzy rough c-meansclustering where cluster centers are updated by fuzzy rough c-means clusteringalgorithm during the forward pass and the deep learning model representationsare updated by Stochastic Gradient Descent during the backward pass of training.The proposed method was implemented using python and the results were takenon DGX server with Tesla V100 GPU cards.An experimental result on the publicallyavailable Kaggle dataset shows an overall accuracy of 88.7%.The proposedmodel improves the accuracy of DR diagnosis compared to the existingunsupervised algorithms like k-means,FCM,auto-encoder,and FRCM withalexnet.

Transfer Learning-based Computer-aided Diagnosis System for Predicting Grades of Diabetic Retinopathy

Diabetic retinopathy(DR)diagnosis through digital fundus images requires clinical experts to recognize the presence and importance of many intricate features.This task is very difficult for ophthalmologists and timeconsuming.Therefore,many computer-aided diagnosis(CAD)systems were developed to automate this screening process ofDR.In this paper,aCAD-DR system is proposed based on preprocessing and a pre-train transfer learningbased convolutional neural network(PCNN)to recognize the five stages of DR through retinal fundus images.To develop this CAD-DR system,a preprocessing step is performed in a perceptual-oriented color space to enhance the DR-related lesions and then a standard pre-train PCNN model is improved to get high classification results.The architecture of the PCNN model is based on three main phases.Firstly,the training process of the proposed PCNN is accomplished by using the expected gradient length(EGL)to decrease the image labeling efforts during the training of the CNN model.Secondly,themost informative patches and images were automatically selected using a few pieces of training labeled samples.Thirdly,the PCNN method generated useful masks for prognostication and identified regions of interest.Fourthly,the DR-related lesions involved in the classification task such as micro-aneurysms,hemorrhages,and exudates were detected and then used for recognition of DR.The PCNN model is pre-trained using a high-end graphical processor unit(GPU)on the publicly available Kaggle benchmark.The obtained results demonstrate that the CAD-DR system outperforms compared to other state-of-the-art in terms of sensitivity(SE),specificity(SP),and accuracy(ACC).On the test set of 30,000 images,the CAD-DR system achieved an average SE of 93.20%,SP of 96.10%,and ACC of 98%.This result indicates that the proposed CAD-DR system is appropriate for the screening of the severity-level of DR.

New approaches in the diagnosis and management of diabetic retinopathy

Diabetic retinopathy(DR)presents one of the greatest challenges currently faced in ophthalmology by both patients and clinicians.Simply by virtue of the number of people impacted and the acceleration in the development of diabetes in larger populations throughout the world,the problem of diabetic complications has taken on new urgency in recent years.Because of the immediate impact on quality of life,activities of daily living,and a person’s ability to work and live independently,DR tops the list of concerns about the damage wrought by the explosive growth of diabetes.

Convolutional Neural Networks for Automated Diagnosis of Diabetic Retinopathy in Fundus Images

Diabetic retinopathy(DR),a long-term complication of diabetes,is notoriously hard to detect in its early stages due to the fact that it only shows a subset of symptoms.Standard diagnostic procedures for DR now include optical coherence tomography and digital fundus imaging.If digital fundus images alone could provide a reliable diagnosis,then eliminating the costly optical coherence tomography would be beneficial for all parties involved.Optometrists and their patients will find this useful.Using deep convolutional neural networks(DCNNs),we provide a novel approach to this problem.Our approach deviates from standard DCNN methods by exchanging typical max-pooling layers with fractional max-pooling ones.In order to collect more subtle information for categorization,two such DCNNs,each with a different number of layers,are trained.To establish these limits,we use DCNNs and features extracted from picture metadata to train a support vector machine classifier.In our experiments,we used information from Kaggle’s open DR detection database.We fed our model 34,124 training images,1,000 validation examples,and 53,572 test images to train and test it.Each of the five classes in the proposed DR classifier corresponds to one of the steps in the DR process and is given a numeric value between 0 and 4.Experimental results show a higher identification rate(86.17%)than those found in the existing literature,indicating the suggested strategy may be effective.We have jointly developed an algorithm for machine learning and accompanying software,and we’ve named it deep retina.Images of the fundus acquired by the typical person using a portable ophthalmoscope may be instantly analyzed using our technology.This technology might be used for self-diagnosis,at-home care,and telemedicine.

Non-alcoholic fatty liver disease and diabetes: From physiopathological interplay to diagnosis and treatment

Non-alcoholic fatty liver disease(NAFLD)is highly prevalent in patients with diabetes mellitus and increasing evidence suggests that patients with type 2diabetes are at a particularly high risk for developing the progressive forms of NAFLD,non-alcoholic steatohepatitis and associated advanced liver fibrosis.Moreover,diabetes is an independent risk factor for NAFLD progression,and for hepatocellular carcinoma development and liver-related mortality in prospective studies.Notwithstanding,patients with NAFLD have an elevated prevalence of prediabetes.Recent studies have shown that NAFLD presence predicts the development of type2 diabetes.Diabetes and NAFLD have mutual pathogenetic mechanisms and it is possible that genetic and environmental factors interact with metabolic derangements to accelerate NAFLD progression in diabetic patients.The diagnosis of the more advanced stages of NAFLD in diabetic patients shares the same challenges as in non-diabetic patients and it includes imaging and serological methods,although histopathological evaluation is still considered the gold standard diagnostic method.An effective established treatment is not yet available for patients with steatohepatitis and fibrosis and randomized clinical trials including only diabetic patients are lacking.We sought to outline the published data including epidemiology,pathogenesis,diagnosis and treatment of NAFLD in diabetic patients,in order to better understand the interplay between these two prevalent diseases and identify the gaps that still need to be fulfilled in the management of NAFLD in patients with diabetes mellitus.

Diagnosis,treatment and prevention of type 2 diabetes mellitus in children and adolescents

During the last two decades,there have been several reports of an increasing incidence of type 2 diabetes mellitus(T2 DM) in children and adolescents,especially among those belonging to minority ethnic groups.This trend,which parallels the increases in prevalence and degree of pediatric obesity,has caused great concern,even though T2 DM remains a relatively rare disease in children.Youth T2 DM differs not only from type 1 diabetes in children,from which it is sometimes difficult to differentiate,but also from T2 DM in adults,since it appears to be an aggressive disease with rapidly progressive β-cell decline,high treatment failure rate,and accelerated development of complications.Despite the recent research,many aspects of youth T2 DM still remain unknown,regarding both its pathophysiology and risk factor contribution,and its optimal management and prevention.Current management approaches include lifestyle changes,such as improved diet and increased physical activity,together with pharmacological interventions,including metformin,insulin,and the recently approved glucagonlike peptide-1 analog liraglutide.What is more important for everyone to realize though,from patients,families and physicians to schools,health services and policy-makers alike,is that T2 DM is a largely preventable disease that will be addressed effectively only if its major contributor(i.e.,pediatric obesity) is confronted and prevented at every possible stage of life,from conception until adulthood.Therefore,relevant comprehensive,coordinated,and innovative strategies are urgently needed.

Acetone sensors for non-invasive diagnosis of diabetes based on metal-oxide-semiconductor materials

In recent years, clinical studies have found that acetone concentration in exhaled breath can be taken as a characteristic marker of diabetes. Metal-oxide-semiconductor (MOS) materials are widely used in acetone gas sensors due to their low cost, high sensitivity, fast response/recovery time, and easy integration. This paper reviews recent progress in acetone sensors based on MOS materials for diabetes diagnosis. The methods of improving the performance of acetone sensor have been explored for comparison, especially in high humidity conditions. We summarize the current excellent methods of preparations of sensors based on MOSs and hope to provide some help for the progress of acetone sensors in the diagnosis of diabetes.

Algorithm of automatic identification of diabetic retinopathy foci based on ultra-widefield scanning laser ophthalmoscopy

AIM:To propose an algorithm for automatic detection of diabetic retinopathy(DR)lesions based on ultra-widefield scanning laser ophthalmoscopy(SLO).METHODS:The algorithm utilized the FasterRCNN(Faster Regions with CNN features)+ResNet50(Residua Network 50)+FPN(Feature Pyramid Networks)method for detecting hemorrhagic spots,cotton wool spots,exudates,and microaneurysms in DR ultra-widefield SLO.Subimage segmentation combined with a deeper residual network FasterRCNN+ResNet50 was employed for feature extraction to enhance intelligent learning rate.Feature fusion was carried out by the feature pyramid network FPN,which significantly improved lesion detection rates in SLO fundus images.RESULTS:By analyzing 1076 ultra-widefield SLO images provided by our hospital,with a resolution of 2600×2048 dpi,the accuracy rates for hemorrhagic spots,cotton wool spots,exudates,and microaneurysms were found to be 87.23%,83.57%,86.75%,and 54.94%,respectively.CONCLUSION:The proposed algorithm demonstrates intelligent detection of DR lesions in ultra-widefield SLO,providing significant advantages over traditional fundus color imaging intelligent diagnosis algorithms.

Glycated albumin as a biomarker for diagnosis of diabetes mellitus:A systematic review and meta-analysis

BACKGROUND Glycated albumin(GA),the non-enzymatic glycation product of albumin in plasma,became a glycemic marker in the beginning of the 21st century.The assay is not affected by hemoglobin levels and reflects the glycemic status over a shorter period as compared to HbA1c measurements.Thus,GA may contributes as an intermediate glucose index in the current diabetes mellitus(DM)diagnostic system.AIM To search and summarize the available data on glycated albumin measurements required for the diagnosis of diabetes mellitus.METHODS Databases,including PubMed,Embase,Web of Science,and Cochrane Central Register of Controlled Trials(CENTRAL),among others,were systematically searched.The Quality Assessment of Diagnostic Accuracy Studies-2 tool was applied for the assessment of quality,and the bivariate model was used to pool the sensitivity and specificity.The hierarchical summary receiver operator characteristic curves(HSROC)model was utilized to estimate the summary receiver operating characteristics curve(SROC).Sensitivity analysis was performed to investigate the association of the study design and patient characteristics with the test accuracy and meta-regression to find the source of heterogeneity.RESULTS Three studies regarding gestational diabetes mellitus(GDM)and a meta-analysis of 16 non-GDM studies,comprising a total sample size of 12876,were included in the work.Results reveal that the average cut-off values of GA reported for the diagnosis of GDM diagnosis was much lower than those for non-GDM.For non-GDM cases,diagnosing DM with a circulating GA cut-off of 14.0%had a sensitivity of 0.766(95%CI:0.539,0.901),specificity of 0.687(95%CI:0.364,0.894),and area under the curve of 0.80(95%CI:0.76,0.83)for the SROC.The estimated SROC at different GA cut-off values for non-GDM exhibited that the average location parameter lambda of 16 non-GDM studies was 2.354(95%CI:2.002,2.707),and the scale parameter beta was-0.163(95%CI:-0.614,0.288).These non-GDM studies with various thresholds had substantial heterogeneity,which may be attributed to the type of DM,age,and body mass index as possible sources.CONCLUSION Glycated albumin in non-DM exhibits a moderate diagnostic accuracy.Further research on the diagnostic accuracy of GA for GDM and combinational measurements of GA and other assays is suggested.

Serum von Willebrand factor for early diagnosis of lung adenocarcinoma in patients with type 2 diabetes mellitus

BACKGROUND The elevation of plasma von Willebrand factor(vWF)has been proposed to be a predictor of lung cancer.Type 2 diabetes mellitus(T2DM)causes endothelial activation,resulting in the secretion of vWF.However,the role of vWF in patients with T2DM complicated with lung cancer remains unclear.AIM To investigate the clinical value of serum vWF as a tumor marker in patients with T2DM combined with lung adenocarcinoma in situ(AIS).METHODS This study enrolled 43 patients with T2DM combined with lung AIS(T2DM+AIS group),43 patients with T2DM alone(T2DM group),43 patients with lung AIS alone(AIS group),and 43 healthy volunteers(control group).The serum levels of vWF,insulin-like growth factor 1,and insulin-like growth factor binding protein 3 were determined.Multiple linear stepwise regression was performed to determine the correlations among variables.RESULTS Serum concentration of vWF in the T2DM+AIS group was significantly higher than those in the T2DM,AIS,and control groups(P<0.05).Serum vWF levels in the T2DM and AIS groups were significantly higher than that in the control group(P<0.05).There was no significant difference in serum vWF level between the T2DM and AIS groups.In the T2DM+AIS group,serum vWF was independently associated and positively correlated with serum levels of insulinlike growth factor 1 and insulin-like growth factor binding protein 3(P<0.05).CONCLUSION Serum vWF level may represent a novel biomarker for the early diagnosis of lung AIS.

THE VALUE OF URINE PROTEIN EXCRETION IN DIAGNOSING EARLY DIABETIC NEPHROPATHY

We tested urine albumin excretion rate (UAER), urine transrerrin (TRF ), retinolulnding protein (RBP), N --acetyl--aD-gi ucosamlnldase (N AG ), P, - mi c rogl obu I in (P, -- M G ) and lgGIn 45 cases of NIDDM. Thirty cases with UAER<300 mg/d were divided into two groups. Data wereshown with urine protein index (urine protein/urine creatlne). ResultS showed that urine transferrinwas more seusltlve than albumlu, and the combined test or urine Protein is nontraumatic, which hadsteatncance to diagnose early diabetic nepkropatby.

MicroRNAs as biomarkers of diabetic retinopathy and disease progression

Diabetes mellitus, together with its complications, has been increasing in prevalence worldwide. Its complications include cardiovascular disease(e.g., myocardial infarction, stroke), neuropathy, nephropathy, and eye complications(e.g., glaucoma, cataracts, retinopathy, and macular edema). In patients with either type 1 or type 2 diabetes mellitus, diabetic retinopathy is the leading cause of visual impairment or blindness. It is characterized by progressive changes in the retinal microvasculature. The progression from nonproliferative diabetic retinopathy to a more advanced stage of moderate to severe nonproliferative diabetic retinopathy and proliferative diabetic retinopathy occurs very quickly after diagnosis of mild nonproliferative diabetic retinopathy. The etiology of diabetic retinopathy is unclear, and present treatments have limited effectiveness. Currently diabetic retinopathy can only be diagnosed by a trained specialist, which reduces the population that can be examined. A screening biomarker of diabetic retinopathy with high sensitivity and specificity would aid considerably in identifying those individuals in need of clinical assessment and treatment. The majority of the studies reviewed identified specific microRNAs in blood serum/plasma able to distinguish diabetic patients with retinopathy from those without retinopathy and for the progresion of the disease from nonproliferative diabetic retinopathy to proliferative diabetic retinopathy. In addition,certain microRNAs in vitreous humor were dysregulated in proliferative diabetic retinopathy compared to controls. A very high percentage of patients with diabetic retinopathy develop Alzheimer’s disease. Thus, identifying diabetic retinopathy by measurement of suitable biomarkers would also enable better screening and treatment of those individuals at risk of Alzheimer’s disease.

Early detection of diabetic kidney disease: Present limitations and future perspectives

Diabetic kidney disease(DKD) is one of the most common diabetic complications, as well as the leading cause of chronic kidney disease and end-stage renal disease around the world. To prevent the dreadful consequence, development of new assays for diagnostic of DKD has always been the priority in the research field of diabetic complications. At present, urinary albumin-to-creatinine ratio and estimated glomerular filtration rate(eG FR) are the standard methods for assessing glomerular damage and renal function changes in clinical practice. However, due to diverse tissue involvement in different individuals, the so-called "non-albuminuric renal impairment" is not uncommon, especially in patients with type 2 diabetes. On the other hand, the precision of creatinine-based GFR estimates is limited in hyperfiltration status. These facts make albuminuria and eG FR less reliable indicators for early-stage DKD. In recent years, considerable progress has been made in the understanding of the pathogenesis of DKD, along with the elucidation of its genetic profiles and phenotypic expression of different molecules. With the help of ever-evolving technologies, it has gradually become plausible to apply the thriving information in clinical practice. The strength and weakness of several novel biomarkers, genomic, proteomic and metabolomic signatures in assisting the early diagnosis of DKD will be discussed in this article.

Value of elastography point quantification in improving the diagnostic accuracy of early diabetic kidney disease

BACKGROUND Diabetic kidney disease(DKD)is a common complication of diabetes.The patient’s prognosis is poor once DKD progresses to advanced stage.Accurate diagnosis and timely treatment of early DKD are important for improving patient’s prognosis and reducing mortality.AIM To explore the value of elastography point quantification(ElastPQ)in improving the accuracy of early DKD diagnosis.METHODS A total of 69 patients with type 2 diabetes were recruited from Naval Military Medical University Affiliated Gongli Hospital.Patients were divided into early DKD group and medium DKD group according to pathological results and urinary albumin excretion rate(UAER).Another 40 patients with simple diabetes were included as the diabetes group.The baseline data,laboratory diagnostic indicators,and ultrasound indicators for each patient were recorded.The differences of the indicators in the three groups were compared.Multivariate logistic regression was used to analyze the influencing factors of the development from simple diabetes into early DKD and from early DKD into medium DKD.Receiver operating characteristic analyses of potential indicators in identifying early DKD and medium DKD,and early DKD and simple diabetes were established.RESULTS Multivariate logistic regression analysis showed that UAER(P<0.001),renocortical Young's Modulus(YM)(P<0.001),and renal parenchymal thickness(P=0.013)were the independent influencing factors of the development from early DKD into medium DKD.Diabetes duration(P=0.041),UAER(P=0.034),and renocortical YM(P=0.017)were the independent influencing factors of the development from simple diabetes into early DKD.Receiver operating characteristic analysis indicated that UAER,renocortical YM,and renal parenchymal thickness were accurate in identifying early DKD and medium DKD[all area under curve(AUC)>0.9].The accuracy of UAER(AUC=0.744),diabetes duration(AUC=0.757),and renocortical YM(AUC=0.782)for the diagnosis of early DKD and simple diabetes were limited.However,the combined diagnosis of UAER,diabetes duration,and renocortical YM was accurate in identifying early DKD and simple diabetes(AUC=0.906),which was significantly higher than any of the three indicators(all P<0.05).CONCLUSION ElastPQ is of great value in the diagnosis of early DKD.When combined with the diabetes duration and UAER,it is expected to diagnose accurately early DKD.