Diagnosis Of Diabetic Kidney Disease Research Articles

Role Of Urinary Podocalyxin In Early Diagnosis Of Diabetic Kidney Disease

Role Of Urinary Podocalyxin In Early Diagnosis Of Diabetic Kidney Disease

Integration of metabolomics and peptidomics reveals distinct molecular landscape of human diabetic kidney disease.

Diabetic kidney disease (DKD) is the most common microvascular complication of diabetes, and there is an urgent need to discover reliable biomarkers for early diagnosis. Here, we established an effective urine multi-omics platform and integrated metabolomics and peptidomics to investigate the biological changes during DKD pathogenesis. Methods: Totally 766 volunteers (221 HC, 198 T2DM, 175 early DKD, 125 overt DKD, and 47 grey-zone T2DM patients with abnormal urinary mALB concentration) were included in this study. Non-targeted metabolic fingerprints of urine samples were acquired on matrix-free LDI-MS platform by the tip-contact extraction method using fluorinated ethylene propylene coated silicon nanowires chips (FEP@SiNWs), while peptide profiles hidden in urine samples were uncovered by MALDI-TOF MS after capturing urine peptides by porous silicon microparticles. Results: After multivariate analysis, ten metabolites and six peptides were verified to be stepwise regulated in different DKD stages. The altered metabolic pathways and biological processes associated with the DKD pathogenesis were concentrated in amino acid metabolism and cellular protein metabolic process, which were supported by renal transcriptomics. Interestingly, multi-omics significantly increased the diagnostic accuracy for both early DKD diagnosis and DKD status discrimination. Combined with machine learning, a stepwise prediction model was constructed and 89.9% of HC, 75.5% of T2DM, 69.6% of early DKD and 75.7% of overt DKD subjects in the external validation cohort were correctly classified. In addition, 87.5% of grey-zone patients were successfully distinguished from T2DM patients. Conclusion: This multi-omics platform displayed a satisfactory ability to explore molecular information and provided a new insight for establishing effective DKD management.

Oxidized Albumin: Evaluation of Oxidative Stress as a Marker for the Progression of Kidney Disease.

Oxidative stress has been reported to be associated with the progression of renal pathology as well as with the onset of complications associated with this condition. Bardoxolone methyl, a nuclear factor-erythroid 2-related factor 2 (Nrf2) activator with anti-oxidative and inflammatory modulation effects, has been reported to improve renal function in clinical trials. As of this writing, there have been no systems for the quantitative evaluation of oxidative stress that could be applied as a clinical test. We recently reported on post-translational modifications of albumin using electrospray-ionization time-of-flight mass spectrometry (ESI-TOF MS) and the results indicated that oxidized albumin (cysteinylated albumin: a molecule that was oxidatively cysteinylated at Cys34) was found to increase with the progression of renal pathology. A clinical study demonstrated that monitoring the levels of oxidized albumin could be useful for the early diagnosis of diabetic kidney disease and for predicting renal prognosis. Higher levels of serum oxidized albumin were also associated with cardiovascular complications or sarcopenia in patients who were undergoing hemodialysis. The overall findings indicate that oxidized albumin could also be an index of therapeutic efficacy against kidney disease. Oxidized albumin is not only a marker for kidney disease but also a nephrotoxic substance. This suggests that measuring the levels of oxidized albumin could be a biomarker for diagnosing the progression of and complications associated with renal disease in addition to evaluating the efficacy of therapeutic drugs.

Prevalence of non-diabetic kidney disease and inability of clinical predictors to differentiate it from diabetic kidney disease: results from a prospectively performed renal biopsy study

IntroductionRenal involvement in type 2 diabetes mellitus (T2DM) may be due to diabetes (diabetic kidney disease (DKD)), causes other than diabetes (non-diabetic kidney disease (NDKD)) or overlap of DKD and...

Extracellular vesicles metabolic changes reveals plasma signature in stage-dependent diabetic kidney disease

Objective Early diagnosis of diabetic kidney disease (DKD) has long been a complex problem. This study aimed to analyze the metabolomic characteristics of plasma extracellular vesicles (EVs) at different stages of DKD in order to evaluate the metabolites of plasma EVs and select new biomarkers for the early diagnosis of DKD. Patients and methods A total of 78 plasma samples were collected, including samples from 20 healthy controls, 20 patients with type 2 diabetes mellitus (T2DM), 18 patients with DKD stage III, and 20 patients with DKD stage IV. In addition, EVs were isolated for metabolomics analysis. Results The results identified differences in EV metabolomic characteristics in DKD patients at different stages, as well as significant differences in EV metabolomics between T2DM patients without DKD and patients with DKD. Ten Significantly differential metabolites were associated with the occurrence and progression of DKD. Uracil, LPC(O-18:1/0:0), sphingosine 1-phosphate, and 4-acetamidobutyric acid were identified as potential early biomarkers for DKD, showing excellent predictive performance. Conclusion Uracil, LPC(O-18:1/0:0), sphingosine 1-phosphate, and 4-acetamidobutyric acid exhibited potential as suitable biomarkers for early DKD diagnosis. Unexpectedly, combining these four candidate metabolites resulted in enhanced predictive ability for DKD.

Continuous Positive Airway Pressure Effect on Albuminuria Progression in Patients with Obstructive Sleep Apnea and Diabetic Kidney Disease: A Randomized Clinical Trial.

Rationale: Obstructive sleep apnea (OSA) is associated with impaired glycemic control and a higher risk of vascular complications, such as diabetic kidney disease (DKD). However, the effect of apnea-hypopnea suppression on DKD progression is unclear. Objectives: To assess the effect of continuous positive airway pressure (CPAP) on the urinary albumin-to-creatinine ratio (UACR) in patients with DKD and OSA. Methods: In a 52-week, multicentric, open-label, parallel, and randomized clinical trial, 185 patients with OSA and DKD were randomized to CPAP and usual care (n = 93) or usual care alone (n = 92). Measurements and Main Results: UACR, estimated glomerular filtration rate, serum concentrations of creatinine and glycated hemoglobin, insulin resistance, lipid concentrations, sleepiness, and quality of life. A 52-week change in UACR from baseline did not differ significantly between the CPAP group and the usual-care group. However, in per-protocol analyses that included 125 participants who met prespecified criteria for adherence, CPAP treatment was associated with a great reduction in UACR (mean difference, -10.56% [95% confidence interval, -19.06 to -2.06]; P = 0.015). CPAP effect on UACR was higher in nonsleepy patients with more severe OSA, worse renal function, and a more recent diagnosis of DKD. CPAP treatment also improved glycemic control and insulin resistance, as well as sleepiness and health-related quality of life. Conclusions: In patients with OSA and DKD, the prescription of CPAP did not result in a statistically significant reduction in albuminuria. However, good adherence to CPAP treatment in addition to usual care may result in long-term albuminuria reduction compared with usual care alone. Clinical trial registered with www.clinicaltrials.gov (NCT02816762).

Novel Urinary Metabolite Biomarkers in Diagnosis of Diabetic Kidney Disease

Novel Urinary Metabolite Biomarkers in Diagnosis of Diabetic Kidney Disease

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.

Ultrasonography Combined with Blood Biochemistry on the Early Diagnosis of Diabetic Kidney Disease.

Objective Diabetic kidney disease (DKD) has been well recognized as a microvascular complication of diabetes mellitus. Perfusion of intrarenal arteries is closely related with development of DKD. The aim of the present study was to investigate relation of ultrasonography performance of intrarenal arteries and grade of DKD. Methods From May to December at 2021, a total of 54 DKD patients and 36 non-DKD cases were recruited. Ultrasonography performance of intrarenal and arteries at lower extremity was examined by high-resolution ultrasound diagnostic equipment; maximum (Vsmax) as well as minimum (Vdmin) blood velocity of arteries were recorded, and resistance index (RI) of arteries were calculated. Blood routine and biochemical parameters were determined from clinical laboratory of our hospital. Results According to eGFR grading, 42.50% of the 54 DKD cases are at Grade 1, and 18.52%, 11.11%, 9.26%, and 18.52% cases were at Grade 2, 3a, 3b, and 4-5, respectively. Blood urea and creatinine were significantly positively related with progress of DKD, while level of Hb was negatively related with DKD. By ultrasonography; we found that Vsmax and Vdmin of main renal artery (MRA), segmental renal artery (SRA), and interlobular renal artery (IRA) were significantly reduced compared with healthy cases; IR of the above arteries was dramatically elevated, and changes of the above data were more obvious than that of lower extremity. Vdmin of MRA, SRA, and IRA was negatively related with grading of DKD, while RI was positively related with the grading. Converging from RI and level of Hb, we found that the level of Hb is positively related with healthy status of the kidney, while RI of the arteries is negatively with that. Conclusions Resistance index (RI) of intrarenal arteries, obtained from ultrasonography combining with level of hemoglobin (Hb), is the predictor of progress of DKD.

Interpretation of Urine Routine Report of a Diabetic Patient

Introduction: Diabetic patients are at an increased risk of multiple complications, among which one of the most dreaded complications is diabetic nephropathy. Early diagnosis and appropriate management will at least prolong the life of the kidney and prevent the development of end-stage renal failure. Materials and Methods: Simple urinalysis is a useful tool for early diagnosis of diabetic kidney disease and helps differentiate between diabetic and nondiabetic kidney diseases. In this review, we have discussed the usefulness of urinalysis for a diabetic patient. Results: Early detection of an abnormality can be an alarming sign, and we can diagnose treatable causes of renal failure in diabetic patients. Conclusions: History, examination, and urinalysis are sufficient in most of the cases for diagnosing diabetic kidney disease. Renal biopsy is indicated in special situations where diagnosis cannot be made even after appropriate non-invasive investigations. Few limitations of the study are resource settings and knowledge of this simple test to predict an alarming sign of the kidney disease.

Circulating expression and clinical significance of LncRNA ANRIL in diabetic kidney disease

BackgroundLong noncoding RNA ANRIL has been found to be involved in the pathogenesis of diabetic kidney disease (DKD) and is expected to be a new target for prevention of DKD. However, the circulating expression and clinical significance of ANRIL in DKD patients is uncertain. This study aims to explore this issue.MethodsThe study consisted of 20 healthy controls, 22 T2DM patients (normalbuminuria) and 66 DKD patients (grouped as follows: microalbuminuria, n = 23; macroalbuminuria, n = 22 and renal dysfunction, n = 21). The expressions of ANRIL in peripheral whole blood of all participants were measured by RT-qPCR.ResultsThe expression of ANRIL was significantly up-regulated in DKD patients (microalbuminuria, macroalbuminuria and renal dysfunction groups) than that in healthy control group. ANRIL was also over-expressed in macroalbuminuria and renal dysfunction groups in comparison with normalbuminuria group. ANRIL expression was positively correlated with Scr, BUN, CysC, urine β2-MG and urine α1-MG; while negatively correlated with eGFR in DKD patients. In addition, ANRIL was the risk factor for DKD with OR value of 1.681. The AUC of ANRIL in identifying DKD was 0.922, and the sensitivity and specificity of DKD diagnosis were 83.3% and 90.5%, respectively.ConclusionOur results indicated that highly expressed ANRIL in peripheral blood is associated with progression of DKD. Circulating ANRIL is an independent risk factor of DKD and has a highly predictive value in identifying DKD.

Identification and validation of key biomarkers for the early diagnosis of diabetic kidney disease.

Background: Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. This study explored the core genes and pathways associated with DKD to identify potential diagnostic and therapeutic targets. Methods: We downloaded microarray datasets GSE96804 and GSE104948 from the Gene Expression Omnibus (GEO) database. The dataset includes a total of 53 DKD samples and 41 normal samples. Differentially expressed genes (DEGs) were identified using the R package “limma”. The Metascape database was subjected to Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to identify the pathway and functional annotations of DEGs. A WGCAN network was constructed, the hub genes in the turquoise module were screened, and the core genes were selected using LASSO regression to construct a diagnostic model that was then validated in an independent dataset. The core genes were verified by in vitro and in vivo experiments. Results: A total of 430 DEGs were identified in the GSE96804 dataset, including 285 upregulated and 145 downregulated DEGs. WGCNA screened out 128 modeled candidate gene sets. A total of eight genes characteristic of DKD were identified by LASSO regression to build a prediction model. The results showed accuracies of 99.15% in the training set (GSE96804) and 94.44% and 100%, respectively, in the test (GSE104948-GPL22945 and GSE104948-GPL24120). Three core genes (OAS1, SECTM1, and SNW1) with high connectivity were selected among the modeled genes. In vitro and in vivo experiments confirmed the upregulation of these genes. Conclusion: Bioinformatics analysis combined with experimental validation identified three novel DKD-specific genes. These findings may advance our understanding of the molecular basis of DKD and provide potential therapeutic targets for its clinical management.

Critical shear stress of red blood cells as a novel integrated biomarker for screening chronic kidney diseases in cases of type 2 diabetes.

BACKGROUND:The glomerular filtration rate (GFR) and albumin-to-creatinine ratio (ACR) have been widely used to identify and manage diabetic kidney disease (DKD). However, classifications based on these two indices do not always concur in terms of DKD diagnosis; for example, cases of high ACR with normal GFR or normal ACR with low GFR may occur. A recent study suggested that critical shear stress (CSS), a hemorheological parameter to represent aggregating force of red blood cells (RBCs), is a potential screening index for DKD. In the present study, we investigated the diagnostic potential of CSS for DKD according to the KDIGO 2012 Guideline.METHODS:A total of 378 patients with type 2 diabetes who visited Yeungnam University Hospital between 2014 and 2017 were included. CSS was measured using a transient microfluidic hemorheometer, Rheoscan-D300® (Rheomeditech, Seoul, Republic of Korea) with whole blood. Patients who were DKD negative (green zone) were compared with patients who were DKD positive (red zone) as Model 1 and patients at risk for (orange zone) and red zones as Model 2, respectively.RESULTS:After exclusion criteria such as eGFR < 30 mL/min/1.73 m2, alcoholism, and macrovascular complications were applied, the sensitivity and specificity were 100% and 77.8% for Model 1 and 75.0% and 72.0% for Model 2, respectively. The diagnostic accuracy measures of the CSS for Model 1 were found to be highly accurate or have the potential to alter clinical decisions. Similarly, the diagnostic accuracy measures of CSS for Model 2 were found to provide useful information, despite them expanding to the orange and red zones.CONCLUSION:DKD was successfully identified using a novel integrated hemorheological index of CSS that satisfied both ACR and GFR criteria. Therefore, CSS may be useful for the additive diagnosis of DKD with GFR and uACR.

REG1A and RUNX3 Are Potential Biomarkers for Predicting the Risk of Diabetic Kidney Disease.

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. Clinical features are traditionally used to predict DKD, yet with low diagnostic efficacy. Most of the recent biomarkers used to predict DKD are based on transcriptomics and metabolomics; however, they also should be used in combination with many other predictive indicators. The purpose of this study was thus to identify a simplified class of blood biomarkers capable of predicting the risk of developing DKD. The Gene Expression Omnibus database was screened for DKD biomarkers, and differentially expressed genes (DEGs) in human blood and kidney were identified via gene expression analysis and the Least Absolute Shrinkage and Selection Operator regression. A comparison of the area under the curve (AUC) profiles on multiple receiver operating characteristic curves of the DEGs in DKD and other renal diseases revealed that REG1A and RUNX3 had the highest specificity for DKD diagnosis. The AUCs of the combined expression of REG1A and RUNX3 in kidney (AUC = 0.929) and blood samples (AUC = 0.917) of DKD patients were similar to each other. The AUC of blood samples from DKD patients and healthy individuals obtained for external validation further demonstrated that REG1A combined with RUNX3 had significant diagnostic efficacy (AUC=0.948). REG1A and RUNX3 expression levels were found to be positively and negatively correlated with urinary albumin creatinine ratio and estimated glomerular filtration rate, respectively. Kaplan-Meier curves also revealed the potential of REG1A and RUNX3 for predicting the risk of DKD. In conclusion, REG1A and RUNX3 may serve as biomarkers for predicting the risk of developing DKD.

In-depth profiling of urinary N-glycome in diabetic kidney disease by ultrafast glycoprotein immobilization for glycan extraction (UltraGIG)

Diabetic kidney disease (DKD) is a common and serious kidney-related complication of diabetic mellitus. Although albuminuria and estimated glomerular filtration rate (eGFR) are commonly used diagnostic biomarkers, they have limitations in the diagnosis of DKD due to the lack of specificity and sensitivity. Urinary N-glycans are emerging as novel biomarkers for predicting renal prognosis in DKD. However, most of the current N-glycan profiling methods for DKD were based on lectin affinity enrichment or hydrophilic interaction chromatography (HILIC) with optical detection, which provided limited N-glycome coverage in low throughput. Herein, we developed a novel N-glycan purification method, termed Ultrafast Glycoprotein Immobilization for Glycan extraction (UltraGIG), for in-depth profiling of urinary N-glycome in DKD using mass spectrometry (MS). In UltraGIG, proteins were rapidly (within 40 min) immobilized to resin with high efficiency (over 98%) through NHS-reactive chemistry, and then N-glycans were enzymatically released from the resin by PNGase F. Owing to good efficiency of immobilization of proteins to resin, the subsequent washing steps to remove proteins and other impurities could be easily performed. The UltraGIG showed good selectivity towards glycans (extracting N-glycans from the mixture of IgG and BSA at a 1:100 M ratio) and samples loss was minimized (detection sensitivity at fmol level). A total of 237 N-glycan compositions were identified from urine samples with DKD and healthy controls, which representing the largest data set of N-glycome from DKD. Compared to healthy controls, 3 N-glycans were up-regulated and 14 N-glycans were down-regulated in DKD patients. Collectively, UltraGIG offers a competitive sample purification method for in-depth analysis of urinary N-glycome by MS and provide new insights into biomarker study for the diagnosis of DKD.

Chronic Kidney Disease in Patients with Diabetes: Diabetic vs. Non-Diabetic Kidney Etiologies

Diabetes mellitus is a growing burden on global healthcare. Although the definition of Diabetic Kidney Disease (DKD) is that the kidney disease due to type 1 or 2 diabetes, the clinical diagnosis of DKD remains controversial. The diagnosis of DKD is highly subjective, depending on the doctor’s judgment and experience. It is necessary to distinguish the non-diabetic kidney disease from DKD at an early time, however, it is also seems difficult and inefficient. Up to date, there is lack of the diagnostic flow diagram for the disease. In this mini-review, we for the first time focused to create a diagnostic workflow for the disease

12(S)-hydroxyeicosatetraenoic acid is significantly increased in diabetic kidney disease and associated with renal function decline.

12(S)-hydroxyeicosatetraenoic (12(S)-HETE), an alternate arachidonic acid metabolite, has been recently examined in metabolic disease. However, the role of 12(S)-HETE in diabetic kidney disease (DKD) remains unclear. We studied for the first time the relationship of serum 12(S)-HETE and DKD and renal function parameters in a Chinese population. We recruited 275 subjects who were diagnosed with type 2 diabetes (T2DM) for more than 10years, including 149 DKD patients and 126 T2DM patients without DKD. Serum 12(S)-HETE was measured using the enzyme-linked immunosorbent assay. Serum 12(S)-HETE was significantly higher in DKD patients than controls [384.69 (77.54, 1003.05) pg/ml and 17.77 (8.11, 75.13) pg/ml, respectively, p<0.0001]. Compared to controls, 12(S)-HETE was significantly increased in both macroalbuminuria and microalbuminuria groups (p<0.0001). Further, the macroalbuminuria group also had a higher serum 12(S)-HETE level compared to the microalbuminuria group (p=0.0063). Moreover, serum 12(S)-HETE was positively correlated with the albuminuria level (r=0.5833, p<0.0001), serum creatinine (r=0.2725, p<0.0001), and was negatively associated with the estimated glomerular filtration rate (r=-0.2085, p=0.0005). Further, receiver operating characteristic analysis (ROC) revealed that 12(S)-HETE had a good performance of distinguishing DKD from controls (AUC 0.828) with a sensitivity of 0.913 and a specificity of 0.711. Our findings revealed that serum 12(S)-HETE significantly associated with DKD and disease severity, suggesting that serum 12(S)-HETE may be used as a potential biomarker for the early diagnosis of DKD.

The 2021–2022 position of Brazilian Diabetes Society on diabetic kidney disease (DKD) management: an evidence-based guideline to clinical practice. Screening and treatment of hyperglycemia, arterial hypertension, and dyslipidemia in the patient with DKD

BackgroundDiabetic kidney disease is the leading cause of end-stage renal disease and is associated with increased morbidity and mortality. This review is an authorized literal translation of part of the Brazilian Diabetes Society (SBD) Guidelines 2021–2022. This evidence-based guideline provides guidance on the correct management of Diabetic Kidney Disease (DKD) in clinical practice.MethodsThe methodology was published elsewhere in previous SBD guidelines and was approved by the internal institutional Steering Committee for publication. Briefly, the Brazilian Diabetes Society indicated 14 experts to constitute the Central Committee, designed to regulate methodology, review the manuscripts, and make judgments on degrees of recommendations and levels of evidence. SBD Renal Disease Department drafted the manuscript selecting key clinical questions to make a narrative review using MEDLINE via PubMed, with the best evidence available including high-quality clinical trials, metanalysis, and large observational studies related to DKD diagnosis and treatment, by using the MeSH terms [diabetes], [type 2 diabetes], [type 1 diabetes] and [chronic kidney disease].ResultsThe extensive review of the literature made by the 14 members of the Central Committee defined 24 recommendations. Three levels of evidence were considered: A. Data from more than 1 randomized clinical trial or 1 metanalysis of randomized clinical trials with low heterogeneity (I2 < 40%). B. Data from metanalysis, including large observational studies, a single randomized clinical trial, or a pre-specified subgroup analysis. C: Data from small or non-randomized studies, exploratory analyses, or consensus of expert opinion. The degree of recommendation was obtained based on a poll sent to the panelists, using the following criteria: Grade I: when more than 90% of agreement; Grade IIa 75–89% of agreement; IIb 50–74% of agreement, and III, when most of the panelist recommends against a defined treatment.ConclusionsTo prevent or at least postpone the advanced stages of DKD with the associated cardiovascular complications, intensive glycemic and blood pressure control are required, as well as the use of renin–angiotensin–aldosterone system blocker agents such as ARB, ACEI, and MRA. Recently, SGLT2 inhibitors and GLP1 receptor agonists have been added to the therapeutic arsenal, with well-proven benefits regarding kidney protection and patients’ survival.

Novel Markers in Diabetic Kidney Disease-Current State and Perspectives.

Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease. Along with the increasing prevalence of diabetes, DKD is expected to affect a higher number of patients. Despite the major progress in the therapy of DKD and diabetes mellitus (DM), the classic clinical diagnostic tools in DKD remain insufficient, delaying proper diagnosis and therapeutic interventions. We put forward a thesis that there is a need for novel markers that will be early, specific, and non-invasively obtained. The ongoing investigations uncover new molecules that may potentially become new markers of DKD—among those are: soluble α-Klotho and proteases (ADAM10, ADAM17, cathepsin, dipeptidyl peptidase 4, caspase, thrombin, and circulating microRNAs). This review summarizes the current clinical state-of-the-art in the diagnosis of DKD and a selection of potential novel markers, based on up-to-date literature.

IDF21-0460 How many urine samples are required for diagnosis and monitoring of diabetic kidney disease?

Background: Measurement of the albumin/creatinine ratio (UACR) from a random spot urine sample is commonly used for diabetic kidney disease (DKD) assessment. High day-to-day variability presents a challenge for both monitoring and diagnosis. Guidelines recommend performing multiple UACR tests for diagnosis, but consensus on the number of tests required has not been reached. Aim: To determine the within-individual variability of random spot UACR in people with type 2 diabetes and its implications for the number of samples required for monitoring and diagnosis of DKD. Method: People with type 2 diabetes (n=383; 66±9 years, 69% male) participating in the Progression of Diabetic Complications (PREDICT) prospective cohort study provided four random spot urine samples within a four-week period. UACR was measured according to standard hospital pathology laboratory protocols. Participants were classified as having normo- (69%; UACR <3mg/mmol), micro- (26%; UACR 3-30mg/mmol) or macroalbuminuria (5%; UACR>30mg/mmol), per the geometric mean of four samples. Within-individual variability was characterised by 95% limits of agreement (range encompassing 95% of differences between one/two/three samples vs. the mean of four). Relative reliability (the degree to which an individual maintains their rank in a population over repeated measures) was based on the intraclass correlation coefficient (ICC). We also determined the diagnostic accuracy of classifying albuminuria (UACR ≥3mg/mmol) based on a randomly selected subset of one/two/three samples (vs. mean of four using area under receiver operating characteristic [AUROC] curve analyses). Results: The 95% limits of agreement were derived for log-transformed UACR data (due to skewness), and then back-transformed to produce 95% limits as a ratio. These 95% limits (Table 1) showed that a single sample could be expected to be between 0.44 and 2.34 times the four-sample mean. This variability narrowed to 0.64/1.57 for the two-sample mean, and 0.75/1.28 for the three-sample mean. Relative reliability was more favourable (ICC=0.88 [0.87–0.90]).Table 1Agreement between UACR assessed using one/two/three urine samples, compared with the mean of four samplesOne sampleTwo samplesThree samples95% limits of agreement of one/two/three sample mean (X), with the four-sample mean (Y)Upper limit of ratio (X/Y)2.341.571.28Lower limit of ratio (X/Y)0.440.640.75Diagnostic metrics using classification of four-sample mean as the reference standardAUROC0.9540.9890.995Sensitivity82.4%91.6%92.4%Specificity90.9%95.5%98.5%PPV80.3%90.1%96.5%NPV92.0%96.2%96.7%Correctly classified88.3%94.3%96.6% Open table in a new tab AUROC curve analyses and corresponding diagnostic test metrics were calculated (Table 1). The AUROC was 0.95 for a single sample compared with the four-sample classification, rising to 0.99 for when two/three samples were averaged, respectively. Similarly, sensitivity was >80% for one sample, rising to >90% for two-sample and three-sample means. Discussion: The wide limits of agreement between results from one/two/three urine samples compared with four, suggests multiple samples are necessary to monitor UACR on a continuous scale. However, the high ICC and AUROC values for even a single UACR measurement suggest that this variability has only modest impact on diagnosis/classification. A higher number of repeated UACR measurements may be required for DKD monitoring, than for diagnosis.