Simultaneous detection of multiple urinary biomarkers in patients with early-stage diabetic kidney disease using Luminex liquid suspension chip technology.

Background: Several urinary biomarkers have good diagnostic value for diabetic kidney disease (DKD); however, the predictive value is limited with the use of single biomarkers. We investigated the clinical value of Luminex liquid suspension chip detection of several urinary biomarkers simultaneously. Methods: The study included 737 patients: 585 with diabetes mellitus (DM) and 152 with DKD. Propensity score matching (PSM) of demographic and medical characteristics identified a subset of 78 patients (DM = 39, DKD = 39). Two Luminex liquid suspension chips were used to detect 11 urinary biomarkers according to their molecular weight and concentration. The biomarkers, including cystatin C (CysC), nephrin, epidermal growth factor (EGF), kidney injury molecule-1 (KIM-1), retinol-binding protein4 (RBP4), α1-microglobulin (α1-MG), ß2-microglobulin (ß2-MG), vitamin D binding protein (VDBP), tissue inhibitor of metalloproteinases-1 (TIMP-1), tumor necrosis factor receptor-1 (TNFR-1), and tumor necrosis factor receptor-2 (TNFR-2) were compared in the DM and DKD groups. The diagnostic values of single biomarkers and various biomarker combinations for early diagnosis of DKD were assessed using receiver operating characteristic (ROC) curve analysis. Results: Urinary levels of VDBP, RBP4, and KIM-1 were markedly higher in the DKD group than in the DM group (p < 0.05), whereas the TIMP-1, TNFR-1, TNFR-2, α1-MG, ß2-MG, CysC, nephrin, and EGF levels were not significantly different between the groups. RBP4, KIM-1, TNFR-2, and VDBP reached p < 0.01 in univariate analysis and were entered into the final analysis. VDBP had the highest AUC (0.780, p < 0.01), followed by RBP4 (0.711, p < 0.01), KIM-1 (0.640, p = 0.044), and TNFR-2 (0.615, p = 0.081). However, a combination of these four urinary biomarkers had the highest AUC (0.812), with a sensitivity of 0.742 and a specificity of 0.760. Conclusions: The urinary levels of VDBP, RBP4, KIM-1, and TNFR-2 can be detected simultaneously using Luminex liquid suspension chip technology. The combination of these biomarkers, which reflect different mechanisms of kidney damage, had the highest diagnostic value for DKD. However, this finding should be explored further to understand the synergistic effects of these biomarkers.

Practicalities and importance of assessing urine albumin excretion in type 2 diabetes: A cutting-edge update.

Type 2 diabetes (T2D) is associated with increased risk for chronic kidney disease (CKD). It is estimated that 40 % of people with diabetes have CKD, which consequently leads to increase in morbidity and mortality from cardiovascular diseases (CVDs). Diabetic kidney disease (DKD) is leading cause of CKD and end-stage renal disease (ESRD) globally. On the other hand, DKD is independent risk factor for CVDs, stroke and overall mortality. According to the guidelines, using spot urine sample and assessing urine albumin-to-creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) are both mandatory methods for screening of CKD in T2D at diagnosis and at least annually thereafter. Diagnosis of CKD is confirmed by persistent albuminuria followed by a progressive decline in eGFR in two urine samples at an interval of 3 to 6 months. However, many patients with T2D remain underdiagnosed and undertreated, so there is an urgent need to improve the screening by detection of albuminuria at all levels of health care. This review discusses the importance of albuminuria as a marker of CKD and cardiorenal risk and provides insights into the practical aspects of methods for determination of albuminuria in routine clinical care of patients with T2D.

Urinary biomarkers NG AL and beta-2 microglobulin in children with type 1 diabetes mellitus.

OBJECTIVES: To study the urinary neutrophil gelatinase-associated lipocalin (NGAL) and beta-2-microglobulin (ß2M) levels as markers of tubular damage in children with type 1 diabetes (T1DM). METHODS: Forty T1DM children and 40 age-matched controls were enrolled. Subjects with coexisting kidney disorder, intake of oral glucose lowering drugs and syndromic diabetes mellitus were excluded. Fasting plasma glucose, glycated hemoglobin (HbA1c), kidney function, urinary albumin-creatinine ratio (UACR), NGAL and ß2M were measured and compared in cases and controls. RESULTS: The median (IQR) age of cases and controls was 10.6 (8, 14.2) and 10.7 (8.4, 13.7) years, respectively. Cases had disease duration of 4 (3, 6.8) years and HbA1c 10.9 (9, 13.1) %. Microalbuminuria was seen in 14 (35 %). Median (IQR) levels of UACR were higher in cases than controls [19.38 (10.27, 35.26) and 6.49 (3.10, 11.65) µg/mg; p<0.001], similarly NGAL/creatinine [352.21 (191.49, 572.45) and 190.54 (125.91, 322.83) ng/mg; p=0.006], unlike ß2M/creatinine [1.7 (0.43, 6.02) and 2.12 (1.05, 4.47) µg/mg; p=0.637]. Children with higher HbA1c (≥10 %) had higher urinary ACR and tubular biomarkers than HbA1c<10 % (p>0.05). Urinary ACR showed positive correlation with NGAL/creatinine (r=0.38, p=0.019) and ß2M/creatinine (r=0.42, p=0.009). CONCLUSIONS: Urinary biomarkers NGAL and ß2M were elevated in the presence of normal urinary microalbumin levels suggestive of early tubular damage in T1DM.

Panel miRNAs are potential diagnostic markers for chronic kidney diseases: a systematic review and meta-analysis.

BACKGROUND: Accurate detection of kidney damage is key to preventing renal failure, and identifying biomarkers is essential for this purpose. We aimed to assess the accuracy of miRNAs as diagnostic tools for chronic kidney disease (CKD). METHODS: We thoroughly searched five databases (MEDLINE, Web of Science, Embase, Scopus, and CENTRAL) and performed a meta-analysis using R software. We assessed the overall diagnostic potential using the pooled area under the curve (pAUC), sensitivity (SEN), and specificity (SPE) values and the risk of bias by using the QUADAS-2 tool. The study protocol was registered on PROSPERO (CRD42021282785). RESULTS: We analyzed data from 8351 CKD patients, 2989 healthy individuals, and 4331 people with chronic diseases. Among the single miRNAs, the pooled SEN was 0.82, and the SPE was 0.81 for diabetic nephropathy (DN) vs. diabetes mellitus (DM). The SEN and SPE were 0.91 and 0.89 for DN and healthy controls, respectively. miR-192 was the most frequently reported miRNA in DN patients, with a pAUC of 0.91 and SEN and SPE of 0.89 and 0.89, respectively, compared to those in healthy controls. The panel of miRNAs outperformed the single miRNAs (pAUC of 0.86 vs. 0.79, p < 0.05). The SEN and SPE of the panel miRNAs were 0.89 and 0.73, respectively, for DN vs. DM. In the lupus nephritis (LN) vs. systemic lupus erythematosus (SLE) cohorts, the SEN and SPE were 0.84 and 0.81, respectively. Urinary miRNAs tended to be more effective than blood miRNAs (p = 0.06). CONCLUSION: MiRNAs show promise as effective diagnostic markers for CKD. The detection of miRNAs in urine and the use of a panel of miRNAs allows more accurate diagnosis.

Plasma type IV collagen levels as a potential biomarker for early detection of nephropathy in diabetes mellitus patients.

Diabetic nephropathy represents a microvascular complication related to type 2 diabetes mellitus (T2DM) that ultimately causes end-stage renal disease. Our study aimed to evaluate the association of plasma type IV collagen with albuminuria status and to assess the clinical significance of plasma type IV collagen as a potential biomarker in the early stage of diabetic nephropathy. The study comprised 75 participants diagnosed with T2DM allocated equally (n=25) into three groups: (A) normal albuminuria levels, (B) microalbuminuria, and (C) macroalbuminuria, depending on their urine albumin-to-creatinine ratio. A comparative analysis was conducted between these groups and a control group (D, n=15). The enzyme-linked immunosorbent assay (ELISA) method was employed for measuring plasma type IV collagen levels. The results revealed that plasma type IV collagen levels were significantly higher in T2DM groups than in the control group. Moreover, diabetic patients without albuminuria had significantly higher plasma type IV collagen levels than the control group (p < 0.001). Furthermore, albuminuria levels among diabetic patient groups were significantly increased as albuminuria categories increased (p < 0.001). A significant positive correlation existed between plasma type IV collagen and glycated hemoglobin (HbA1c) levels in the macroalbuminuric diabetic group. Our study employed the receiver operating characteristic (ROC) curve analysis to determine plasma type IV collagen diagnostic utility in macroalbuminuria prediction. The ROC curve analysis revealed that type IV collagen can significantly determine macroalbuminuric patients at a cutoff value of 2.25 with sensitivity, specificity, positive predictive value, and negative predictive value of 68%, 100%, 100%, and 75.8%, respectively (p < 0.001). In conclusion, plasma type IV collagen levels might serve as a valuable predictor of albuminuria onset in patients with T2DM.

Urine biomarkers in type 2 diabetes mellitus with or without microvascular complications.

OBJECTIVE: To investigate the distribution of nine (9) urine biomarkers in people living with type 2 diabetes mellitus (T2DM), with or without microvascular complications. METHODS: In total, 407 people with T2DM were enrolled from 2021 to 2022. According to diabetic retinopathy (DR) and urinary albumin-creatinine ratio (UACR), the 407 people were divided into four (4) groups, DR(-)UACR(-), DR(+)UACR(-), DR(-)UACR(+), and DR( + )UACR(+). In addition, 112 healthy volunteers were enrolled during the same period. The nine (9) urine markers included α1-microglobulin (u-α1MG), immunoglobulin G (u-IgG), neutrophil gelatinase-associated lipid carrier protein (u-NGAL), cystatin C (u-CysC), retinol-binding protein (u-RBP), ß2-microglobulin (u-ß2MG), N-acetyl-ß-D-glucosaminidase (u-NAG), transferrin (u-Trf), and collagen type IV (u-Col). For each marker, the respective level of 97.5 percentile in healthy volunteers was taken as an upper reference limit. RESULTS: Among the 407 people, 248 individuals (61%) were DR(-)UACR(-), 100 (25%) were DR(-)UACR(+), 37 (9%) were DR(+)UACR(-), and 22 (5%) were DR(+)UACR(+). The u-NAG/Cr biomarker level showed a significant difference between healthy participants and people with T2DM. In the DR(-)UACR(-)group, u-Trf/Cr showed the highest positive rate (21.37%), followed by u-IgG/Cr (14.52%); u-NAG/Cr (10.48%); u-ß2MG/Cr (4.44%); u-CysC/Cr (4.03%); u-NGAL/Cr (4.03%); u-RBP/Cr (2.82%); u-α1MG/Cr (2.42%); 17.34% of people with T2DM showed multiple biomarkers positive (≥2 biomarkers). The positive rates of one biomarker (21.33%) and two biomarkers (18.67%) in people who have less than five (5) years of T2DM were almost close to those of the DR(-)UACR(-) group (21.37%, and 12.10%, respectively). CONCLUSION: Renal tubule biomarkers may be used as an indicator in the early detection and monitoring of renal injury in diabetes mellitus. The u-NAG biomarker should be measured for the people with T2DM of the first-time diagnosis.

Comparative Analysis of Protein Quantification by the SomaScan Assay versus Orthogonal Methods in Urine from People with Diabetic Kidney Disease.

To our knowledge, calibration curves or other validations for thousands of SomaScan aptamers are not publicly available. Moreover, the abundance of urine proteins obtained from these assays is not routinely validated with orthogonal methods (OMs). We report an in-depth comparison of SomaScan readout for 23 proteins in urine samples from patients with diabetic kidney disease (n = 118) vs OMs, including liquid chromatography-targeted mass spectrometry (LC-MS), ELISA, and nephelometry. Pearson correlation between urine abundance of the 23 proteins from SomaScan 3.2 vs OMs ranged from -0.58 to 0.86, with a median (interquartile ratio, [IQR]) of 0.49 (0.18, 0.53). In multivariable linear regression, the SomaScan readout for 6 of the 23 examined proteins (26%) was most strongly associated with the OM-derived abundance of the same (target) protein. For 3 of 23 (13%), the SomaScan and OM-derived abundance of each protein were significantly associated, but the SomaScan readout was more strongly associated with OM-derived abundance of one or more "off-target" proteins. For the remaining 14 proteins (61%), the SomaScan readouts were not significantly associated with the OM-derived abundance of the targeted proteins. In 6 of the latest group, the SomaScan readout was not associated with urine abundance of any of the 23 quantified proteins. To sum, over half of the SomaScan results could not be confirmed by independent orthogonal methods.

Application of Proteomics and Machine Learning Methods to Study the Pathogenesis of Diabetic Nephropathy and Screen Urinary Biomarkers.

Diabetic nephropathy (DN) has become the main cause of end-stage renal disease worldwide, causing significant health problems. Early diagnosis of the disease is quite inadequate. To screen urine biomarkers of DN and explore its potential mechanism, this study collected urine from 87 patients with type 2 diabetes mellitus (which will be classified into normal albuminuria, microalbuminuria, and macroalbuminuria groups) and 38 healthy subjects. Twelve individuals from each group were then randomly selected as the screening cohort for proteomics analysis and the rest as the validation cohort. The results showed that humoral immune response, complement activation, complement and coagulation cascades, renin-angiotensin system, and cell adhesion molecules were closely related to the progression of DN. Five overlapping proteins (KLK1, CSPG4, PLAU, SERPINA3, and ALB) were identified as potential biomarkers by machine learning methods. Among them, KLK1 and CSPG4 were positively correlated with the urinary albumin to creatinine ratio (UACR), and SERPINA3 was negatively correlated with the UACR, which were validated by enzyme-linked immunosorbent assay (ELISA). This study provides new insights into disease mechanisms and biomarkers for early diagnosis of DN.

Urinary biomarkers in diabetic nephropathy.

Diabetic nephropathy (DN), a significant consequence of diabetes, is associated with adverse cardiovascular and renal disease as well as mortality. Although microalbuminuria is considered the best non-invasive marker for DN, better predictive markers are needed of sufficient sensitivity and specificity to detect disease in general and in early disease specifically. Even prior to appearance of microalbuminuria, urinary biomarkers increase in diabetics and can serve as accurate nephropathy biomarkers even in normoalbuminuria. In this review, a number of novel urine biomarkers including those reflecting kidney damage caused by glomerular/podocyte damage, tubular damage, oxidative stress, inflammation, and intrarenal renin-angiotensin system activation are discussed. Our review also includes emerging biomarkers such as urinary microRNAs. These short noncoding miRNAs regulate gene expression and could be utilized to identify potential novel biomarkers in DN development and progression. .

Is the urinary neutrophil gelatinase-associated lipocalin concentration in children and adolescents with type 1 diabetes mellitus different from that in healthy children?

Introduction: Diabetic kidney disease (DKD) is one of the major microvascular complications of type 1 diabetes mellitus (T1DM). Some studies suggest that changes of renal tubular components emerge before the glomerular lesions thus introducing the concept of diabetic tubulopathy with urinary neutrophil gelatinase-associated lipocalin (uNGAL) as a potential marker of DKD. This concept was not confirmed in all studies. Materials and methods: In 198 T1DM patients with median age 15 years and diabetes duration over one year, an albumin/creatinine ratio (ACR) was determined and uNGAL measured in spot urine sample. Urine samples for ACR and uNGAL were also collected in the control group of 100 healthy children of similar age. Results: There was no significant difference in uNGAL concentration or uNGAL/creatinine between T1DM children and healthy subjects (6.9 (2.8-20.1) ng/mL vs 7.9 (2.9-21.0) ng/mL, P = 0.969 and 6.8 (2.2-18.4) ng/mg vs 6.5 (1.9-13.4) ng/mg, P = 0.448, respectively) or between T1DM subjects with albuminuria A2 and albuminuria A1 (P = 0.573 and 0.595, respectively). Among T1DM patients 168 (85%) had normal uNGAL concentrations, while in 30 (15%) patients uNGAL was above the defined cut-off value of 30.9 ng/mL. There was no difference in BMI, HbA1c and diabetes duration between patients with elevated uNGAL compared to those with normal uNGAL. Conclusions: We found no significant difference in uNGAL concentration or uNGAL/creatinine between T1DM children and healthy subjects or between albuminuria A2 and albuminuria A1 T1DM subjects. Therefore, uNGAL should not be recommended as a single marker for detecting diabetic kidney disease in children and adolescents.

Urinary SIRT2 Reflects Kidney Injury in Type 2 Diabetes.

INTRODUCTION: The early diagnosis of kidney injury in type 2 diabetes (T2DM) is important to prevent the long-term damaging effects of kidney loss and is decisive for patient outcomes. While SIRT2 is implicated in diabetes pathogenesis, its correlation with diabetic nephropathy remains unexplored. This study was designed to evaluate the association of urine SIRT2 levels with diabetic kidney injury, as well as potential underlying mechanisms. METHODS: In T2DM patients, db/db mice, and high glucose plus palmitic acid treated HK2 cell models, ELISA, Immunoturbidimetry, Immunohistochemistry, Western blot, and Quantitative real-time polymerase chain reaction were used to detect SIRT2 levels and kidney damage. According to urinary albumin/creatinine ratio (UACR), 163 T2DM patients were divided into three groups. Spearman correlation analysis was used to investigate the relationship between urinary sirtuin2/creatinine ratio (USCR) and biomarkers of kidney injury. The influencing factors of albuminuria in T2DM patients were analyzed by logistic regression model. RESULTS: In our findings, the Macro group exhibited the highest USCR levels as UACR increased. There was a positive association between USCR and UACR, α1-microglobulin/creatinine ratio (UαCR), ß2-microglobulin/creatinine ratio (UßCR), and retinol-binding protein/creatinine ratio (URCR), with a negative correlation observed with eGFR. Logistic ordered multiclassification regression analysis, adjusting for confounding variables, confirmed that USCR remained a significant risk factor for the severity of albuminuria in T2DM patients. In the db/db mice kidney SIRT2 protein level increased significantly. Increased SIRT2 protein levels were also observed in renal tubular epithelial cells treated with high glucose plus palmitic acid. Moreover, SIRT2 promotes the expression of proinflammatory factors TNF-α and IL-6 by modulating the phosphorylation of p38 MAPK and p-JNK in renal tubular cells induced by high glucose and palmitic acid. CONCLUSION: Urinary SIRT2 is closely related to eGFR, renal tubule injury, and urinary albumin excretion in T2DM patients, which is expected to be an important indicator to comprehensively reflect renal injury.

Machine learning for prediction of chronic kidney disease progression: Validation of the Klinrisk model in the CANVAS Program and CREDENCE trial.

AIM: To validate the Klinrisk machine learning model for prediction of chronic kidney disease (CKD) progression in patients with type 2 diabetes in the pooled CANVAS/CREDENCE trials. MATERIALS AND METHODS: We externally validated the Klinrisk model for prediction of CKD progression, defined as 40% or higher decline in estimated glomerular filtration rate (eGFR) or kidney failure. Model performance was assessed for prediction up to 3 years with the area under the receiver operating characteristic curve (AUC), Brier scores and calibration plots of observed and predicted risks. We compared performance of the model with standard of care using eGFR (G1-G4) and urine albumin-creatinine ratio (A1-A3) Kidney Disease Improving Global Outcomes (KDIGO) heatmap categories. RESULTS: The Klinrisk model achieved an AUC of 0.81 (95% confidence interval [CI] 0.78-0.83) at 1 year, and 0.88 (95% CI 0.86-0.89) at 3 years. The Brier scores were 0.020 (0.018-0.022) and 0.056 (0.052-0.059) at 1 and 3 years, respectively. Compared with the KDIGO heatmap, the Klinrisk model had improved performance at every interval (P < .01). CONCLUSIONS: The Klinrisk machine learning model, using routinely collected laboratory data, was highly accurate in its prediction of CKD progression in the CANVAS/CREDENCE trials. Integration of the model in electronic medical records or laboratory information systems can facilitate risk-based care.

Isocitrate dehydrogenase 1 upregulation in urinary extracellular vesicles from proximal tubules of type 2 diabetic rats.

Diabetic nephropathy (DN) is a major cause of chronic kidney disease. Microalbuminuria is currently the most common non-invasive biomarker for the early diagnosis of DN. However, renal structural damage may have advanced when albuminuria is detected. In this study, we sought biomarkers for early DN diagnosis through proteomic analysis of urinary extracellular vesicles (uEVs) from type 2 diabetic model rats and normal controls. Isocitrate dehydrogenase 1 (IDH1) was significantly increased in uEVs from diabetic model rats at the early stage despite minimal differences in albuminuria between the groups. Calorie restriction significantly suppressed the increase in IDH1 in uEVs and 24-hour urinary albumin excretion, suggesting that the increase in IDH1 in uEVs was associated with the progression of DN. Additionally, we investigated the origin of IDH1-containing uEVs based on their surface sugar chains. Lectin affinity enrichment and immunohistochemical staining showed that IDH1-containing uEVs were derived from proximal tubules. These findings suggest that the increase in IDH1 in uEVs reflects pathophysiological alterations in the proximal tubules and that IDH1 in uEVs may serve as a potential biomarker of DN in the proximal tubules.

URINARY EXCRETION OF ALPHA-ACTININ-4 AND TIGHT JUNCTION PROTEIN 1 IN PATIENTS WITH TYPE 2 DIABETES AND DIFFERENT PATTERNS OF CHRONIC KIDNEY DISEASE.

Abnormalities of the cytoskeleton and the slit diaphragm of podocytes have been attributed to diabetic nephropathy. In this study, we assessed urinary excretion of alpha-actinin-4 (ACTN-4), a cytoskeleton protein and a component of the slit diaphragm, and tight junction protein 1 (TJP-1, or ZO-1), a peripheral membrane protein that forms molecular complexes with actin filaments, in patients with type 2 diabetes (T2D) and albuminuric or non-albuminuric chronic kidney disease (CKD). The study included 140 patients with long-term T2D (≥10 years) and 20 healthy subjects as control. Patterns of CKD were identified based on the estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (UACR). Urinary ACTN-4 and TJP-1 were assessed by ELISA. Patients with T2D had increased urinary excretion of ACTN-4 (p=0.03) and TJP-1 (p=0.006). In logistic regression models, both ACTN-4 and TJP-1 demonstrated associations with albuminuric CKD (UACR ≥3.0 mg/mmol and eGFR <60 mL/min×1.73 m2) after adjusting to age, sex, diabetes duration, HbA1c, and smoking. In ROC-analysis, TJP-1 excretion ≥70 pg/mmol was associated with albuminuric CKD (OR 5.45, 95% CI 1.96-15.18, p=0.001). The results demonstrate that elevated urinary ACTN-4 and TJP-1 are associated specifically with albuminuric CKD, but not with non-albuminuric CKD, in T2D patients.

A candidate panel of eight urinary proteins shows potential of early diagnosis and risk assessment for diabetic kidney disease in type 1 diabetes.

Diabetic kidney disease (DKD) poses a significant health challenge for individuals with diabetes. At its initial stages, DKD often presents asymptomatically, and the standard for non-invasive diagnosis, the albumin-creatinine ratio (ACR), employs discrete categorizations (normal, microalbuminuria, macroalbuminuria) with limitations in sensitivity and specificity across diverse population cohorts. Single biomarker reliance further restricts the predictive value in clinical settings. Given the escalating prevalence of diabetes, our study uses proteomic technologies to identify novel urinary proteins as supplementary DKD biomarkers. A total of 158 T1D subjects provided urine samples, with 28 (15 DKD; 13 non-DKD) used in the discovery stage and 131 (45 DKD; 40 pDKD; 46 non-DKD) used in the confirmation. We identified eight proteins (A1BG, AMBP, AZGP1, BTD, RBP4, ORM2, GM2A, and PGCP), all of which demonstrated excellent area-under-the-curve (AUC) values (0.959 to 0.995) in distinguishing DKD from non-DKD. Furthermore, this multi-marker panel successfully segregated the most ambiguous group (microalbuminuria) into three distinct clusters, with 80% of subjects aligning either as DKD or non-DKD. The remaining 20% exhibited continued uncertainty. Overall, the use of these candidate urinary proteins allowed for the better classification of DKD and offered potential for significant improvements in the early identification of DKD in T1D populations.

Ultrasensitive CCL2 Detection in Urine for Diabetic Nephropathy Diagnosis Using a WS2-Based Plasmonic Biosensor.

The accurate diagnosis of diabetic nephropathy relies on achieving ultrasensitive biosensing for biomarker detection. However, existing biosensors face challenges such as poor sensitivity, complexity, time-consuming procedures, and high assay costs. To address these limitations, we report a WS2-based plasmonic biosensor for the ultrasensitive detection of biomarker candidates in clinical human urine samples associated with diabetic nephropathy. Leveraging plasmonic-based electrochemical impedance microscopy (P-EIM) imaging, we observed a remarkable charge sensitivity in monolayer WS2 single crystals. Our biosensor exhibits an exceptionally low detection limit (0.201 ag/mL) and remarkable selectivity in detecting CC chemokine ligand 2 (CCL2) protein biomarkers, outperforming conventional techniques such as ELISA. This work represents a breakthrough in traditional protein sensors, providing a direction and materials foundation for developing ultrasensitive sensors tailored to clinical applications for biomarker sensing.

Urine Albumin-Creatinine Ratio Variability in People With Type 2 Diabetes: Clinical and Research Implications.

RATIONALE & OBJECTIVE: Evidence has demonstrated that albuminuria is a key diagnostic and prognostic marker of diabetic chronic kidney disease, but the impact of its day-to-day variability has not been adequately considered. This study quantified within-individual variability of albuminuria in people with type 2 diabetes to inform clinical albuminuria monitoring. STUDY DESIGN: Descriptive cross-sectional analysis. SETTING & PARTICIPANTS: People with type 2 diabetes (n=826, 67.1 [IQR, 60.3-72.4] years, 64.9% male) participating in the Progression of Diabetic Complications (PREDICT) cohort study. EXPOSURE: Four spot urine collections for measurement of urinary albumin-creatinine ratio (UACR) within 4 weeks. OUTCOME: Variability of UACR. ANALYTICAL APPROACH: We characterized within-individual variability (coefficient of variation [CV], 95% limits of random variation, intraclass correlation coefficient), developed a calculator displaying probabilities that any observed difference between a pair of UACR values truly exceeded a 30% difference, and estimated the ranges of diagnostic uncertainty to inform a need for additional UACR collections to exclude or confirm albuminuria. Multiple linear regression examined factors influencing UACR variability. RESULTS: We observed high within-individual variability (CV 48.8%; 95% limits of random variation showed a repeated UACR to be as high/low as 3.78/0.26 times the first). If a single-collection UACR increased from 2 to 5mg/mmol, the probability that UACR actually increased by at least 30% was only 50%, rising to 97% when 2 collections were obtained at each time point. The ranges of diagnostic uncertainty were 2.0-4.0mg/mmol after an initial UACR test, narrowing to 2.4-3.2 and 2.7-2.9mg/mmol for the mean of 2 and 3 collections, respectively. Some factors correlated with higher (female sex; moderately increased albuminuria) or lower (reduced estimated glomerular filtration rate and sodium-glucose cotransporter 2 inhibitor/angiotensin-converting enzyme inhibitor/angiotensin receptor blocker treatment) within-individual UACR variability. LIMITATIONS: Reliance on the mean of 4 UACR collections as the reference standard for albuminuria. CONCLUSIONS: UACR demonstrates a high degree of within-individual variability among individuals with type 2 diabetes. Multiple urine collections for UACR may improve capacity to monitor changes over time in clinical and research settings but may not be necessary for the diagnosis of albuminuria. PLAIN-LANGUAGE SUMMARY: Albuminuria (albumin in urine) is a diagnostic and prognostic marker of diabetic chronic kidney disease. However, albuminuria can vary within an individual from day to day. We compared 4 random spot urinary albumin-creatinine ratio (UACR) samples from 826 participants. We found that a second UACR collection may be as small as a fourth or as large as almost 4 times the first sample's UACR level. This high degree of variability presents a challenge to our ability to interpret changes in albuminuria. Multiple collections have been suggested as a solution. We have constructed tools that may aid clinicians in deciding how many urine collections are required to monitor and diagnose albuminuria. Multiple urine collections may be required for individual monitoring but not necessarily for diagnosis.

Study of Neutrophil Gelatinase Associated Lipocalin and Kidney Injury Molecule-1 in Patients with Type 2 Diabetes Mellitus Nephropathy.

BACKGROUND: There is a need for accurate and rapid biomarkers for the early diagnosis of diabetic nephropathy (DN). We aimed to study the accuracy of urinary neutrophil gelatinase-associated lipocalin (uNGAL), urinary kidney injury molecule-1 (uKIM-1), and blood NGAL (bNGAL) in type 2 diabetics as biomarkers for diagnosis of DN. METHODS: The study was a retrospective case-control study that included 30 control subjects, 40 diabetics with normo-albuminuria < 30 mg/g and eGFR > 60 mL/minute/1.73 m2, and 30 diabetics with albuminuria > 30mg/g and eGFR < 60mL/minute/1.73 m2. Blood and urine samples were obtained to determine levels of bNGAL, uNAGAL, and uKIM1. RESULTS: There was a significant increase in bNGAL, uNGAL, uKIM 1, uNGAL/creatinine and uKIM 1/creatinine among diabetics with albuminuria compared to diabetics with normoalbuminuria and normal control (p < 0.001 for all markers). For diagnosis of early DN, both bNGAL and uKIM 1 had sensitivity and specificity of 100% for each at cutoff values of 322.5 pg/mL and 74.25 ng/mL, respectively. uNGAL had a sensitivity of 97.5% and a spec-ificity of 100% at a cutoff point of 565 ng/mL. uKIM1/creatinine at a cutoff of 51.2 had a sensitivity of 100% and specificity of 100%. CONCLUSIONS: The present study highlights the accuracy of urinary KIM1 and NGAL and blood NGAL as biomarkers for the diagnosis of nephropathy in the early stage of diabetic nephropathy. There were positive correlations with kidney function tests creatinine, blood urea nitrogen, and the presence of albuminuria.

Soluble tumor necrosis factor receptor type 1 is an alternative marker of urinary albumin-creatinine ratio and estimated glomerular filtration rate for predicting the decline of renal function in subjects with type 2 diabetes mellitus.

BACKGROUND: Urinary albumin-creatinine ratio (UACR) and estimated glomerular filtration rates (eGFR) help predict worsening diabetic kidney disease (DKD) but have their limitations. Soluble tumor necrosis factor receptor type 1 (sTNFR1) is a biomarker of DKD. The predictive abilities of sTNFR1 and UACR plus eGFR have not been compared. METHODS: This prospective cohort study included patients with type 2 diabetes (T2D) to identify the risk factors of worsening DKD. Renal events were defined as > 30 % loss in eGFR based on consecutive tests after 6 months. The associations of sTNFR1, UACR, and eGFR levels and the risks of renal events were tested using a Cox regression model and the area under the curve (AUC) was compared between sTNFR1 levels and UACR plus eGFR using receiver-operating characteristic (ROC) analysis. The accuracy of stratification was evaluated using Kaplan-Meier analysis. RESULTS: Levels of sTNFR1 and UACR were associated with risks of > 30 % decline in eGFR after adjusting for relevant factors. The association between sTNFR1 levels and renal outcomes was independent of UACR and eGFR at baseline. The AUC of sTNFR1 level was comparable with that of combined UACR and eGFR (0.73 vs. 0.71, respectively, p = 0.72) and the results persisted for quartile groups of sTNFR1 and risk categories of Kidney Disease: Improving Global Outcomes (KDIGO) (0.70 vs. 0.71, respectively, p = 0.84). Both stratifications by sTNFR1 levels and KDIGO were accurate. CONCLUSION: sTNFR1 could be an alternative marker for identifying patients with diabetes at risk of declining renal function.