Elevated ALOX12 in renal tissue predicts progression in diabetic kidney disease

Diabetic kidney disease (DKD) is one of the major causes of end-stage renal disease and one of the significant complications of diabetes. This study aims to identify the main differentially expressed genes in DKD from transcriptome sequencing results and analyze their diagnostic value. The present study sequenced db/m mouse and db/db mouse to determine the ALOX12 genetic changes related to DKD. After preliminary validation, ALOX12 levels were significantly elevated in the blood of DKD patients, but not during disease progression. Moreover, urine ALOX12 was increased only in macroalbuminuria patients. Therefore, to visualize the diagnostic efficacy of ALOX12 on the onset and progression of renal injury in DKD, we collected kidney tissue from patients for immunohistochemical staining. ALOX12 was increased in the kidneys of patients with DKD and was more elevated in macroalbuminuria patients. Clinical chemical and pathological data analysis indicated a correlation between ALOX12 protein expression and renal tubule injury. Further immunofluorescence double staining showed that ALOX12 was expressed in both proximal tubules and distal tubules. Finally, the diagnostic value of the identified gene in the progression of DKD was assessed using receiver operating characteristic (ROC) curve analysis. The area under the curve (AUC) value for ALOX12 in the diagnosis of DKD entering the macroalbuminuria stage was 0.736, suggesting that ALOX12 has good diagnostic efficacy. During the development of DKD, the expression levels of ALOX12 in renal tubules were significantly increased and can be used as one of the predictors of the progression to macroalbuminuria in patients with DKD.

Determinants of non-diabetic kidney diseases in type 2 diabetic patients: Twenty years of single center experience

BACKGROUND

Diabetic nephropathy is one of the most common complications associated with diabetes. However, non-diabetic kidney disease has been reported in patients with type 2 diabetes at varying incidence rates. The objective of our study is to investigate the occurrence, clinicopathological characteristics, and inflammatory markers linked to diabetic and non-diabetic nephropathy (NDN) in patients with type 2 diabetes mellitus (DM). Additionally, we aimed to explore the possibility of identifying non-diabetic pathology using different biopsy indications.

MATERIALS AND METHODS

A total of 159 patients with type 2 DM who underwent renal biopsy at a tertiary care nephrology clinic between January 2000 and January 2022 were enrolled in the study. We collected comprehensive data, including patient demographics, co-morbidities, diabetes duration, renal biopsy indications and results, serological markers, renal function, diabetic retinopathy (DRP), full blood count, blood biochemistry, urinalysis, and inflammatory markers. Patients were categorized based on their biopsy indications, and their biopsy results were classified into three groups: isolated NDN, isolated diabetic nephropathy (DN), and mixed nephropathy with concurrent NDN. We evaluated the relationship between biopsy indications and accompanying pathologies and statistically assessed the likelihood of each biopsy indication detecting non-diabetic renal pathology. Additionally, differences in other data, including demographic and laboratory results and medical histories, among the three groups were investigated.

RESULTS

The most frequent indication of renal biopsy was atypical presentations of nephrotic syndrome or nephrotic range proteinuria (ANS/ANP) in 25.1% of patients. Other indications included unexplained renal failure (URF) in 22.6%, atypical presentations of non-nephrotic range proteinuria (ANNP) in 18.2%, acute kidney injury or rapidly progressive kidney dysfunction (AKI/RPKD) in 16.9%, microscopic hematuria in 15.7%, URF with ANNP in 11.3%, and severe nephrotic range proteinuria (SNP) in 9.4%. Renal biopsy revealed isolated NDN in 64.8%, DN in 25.1%, and mixed nephropathy in 10.1% of patients. Primary glomerular diseases were the main non-diabetic renal pathology, predominantly focal segmental glomerulosclerosis (FSGS) (36.4%) followed by MN (10.6%) and IgA nephropathy (7.5%). In comparison with the isolated DN and mixed nephropathy groups, patients in the isolated NDN group had significantly shorter diabetes duration, fewer DRP, as well as lower serum creatinine and neutrophil-to-lymphocyte ratio (NLR). Multivariate logistic regression analysis revealed that presence of hematuria (OR 4.40; 95% CI 1.34 - 14.46, p = 0.014), acute nephrotic range proteinuria (OR 11.93; 95% CI 1.56 - 90.77, p = 0.017), and AKI/APKD (OR 41.08; 95% CI 3.40 - 495.39, p = 0.003) were strong predictors of NDN. Lower NLR (OR 0.77; 95% CI 0.60 - 0.98, p = 0.035), shorter duration of diabetes (OR 0.90; 95% CI 0.84 - 0.97, p = 0.010), and absence of DRP (OR 0.35; 95% CI 0.12 - 0.98, p = 0.046) were also found to be independent indicators of NDN. Receiver operating characteristic curve (ROC) analysis revealed a cut-off value of ≤ 3.01 for NLR (sensitivity of 63.1%, specificity of 63.5%) with regards to predicting non-diabetic renal pathology (p = 0.006).

CONCLUSION

Renal biopsy findings in patients with type 2 DM highlight that the prevalence of NDN may be higher than assumed, as presented mainly in the form of primary glomerular disease. The presence of AKI/RPKD, hematuria, and ANS/ANP serves as a reliable indicator of non-diabetic renal pathology. In more ambiguous situations, factors such as a shorter duration of diabetes, absence of DRP, and a lower NLR value may assist clinicians in biopsy decision.

Clinical significance of amphiregulin in patients with chronic kidney disease

BACKGROUND

Amphiregulin (AREG) is a ligand of epidermal growth factor receptor (EGFR), which plays an important role in injury-induced kidney fibrosis. However, the clinical significance of serum soluble AREG in chronic kidney disease (CKD) is unclear. In this study, we elucidated the clinical significance of serum soluble AREG in CKD by analyzing the association of serum soluble AREG levels with renal function and other clinical parameters in patients with CKD.

METHODS

In total, 418 Japanese patients with CKD were enrolled, and serum samples were collected for the determination of soluble AREG and creatinine (Cr) levels, and other clinical parameters. Additionally, these parameters were evaluated after 2 and 3 years. Moreover, immunohistochemical assay was performed ate AREG expression in the kidney tissues of patients with CKD.

RESULTS

Soluble AREG levels were positively correlated with serum Cr (p < 0.0001). Notably, initial AREG levels were positively correlated with changes in renal function (ΔCr) after 2 (p < 0.0001) and 3 years (P = 0.048). Additionally, soluble AREG levels were significantly higher (p < 0.05) in patients with diabetic nephropathy or primary hypertension. Moreover, AREG was highly expressed in renal tubular cells in patients with advanced CKD, but only weakly expressed in patients with preserved renal function.

CONCLUSION

Serum soluble AREG levels were significantly correlated with renal function, and changes in renal function after 2 and 3 years, indicating that serum soluble AREG levels might serve as a biomarker of renal function and renal prognosis in CKD.

Associations between cardiometabolic indices and the risk of diabetic kidney disease in patients with type 2 diabetes

BACKGROUND

This study was designed to assess the associations between emerging cardiometabolic indices-the atherogenic index of plasma (AIP), the stress hyperglycemia ratio (SHR), the triglyceride-glucose (TyG) index, and the homeostasis model assessment of insulin resistance (HOMA-IR)-and the incidence of diabetic kidney disease (DKD) in type 2 diabetes (T2D) patients.

METHODS

We consecutively enrolled 4351 T2D patients. The AIP, SHR, TyG index, and HOMA-IR were calculated from baseline parameters. DKD was defined as a urine albumin/creatinine ratio > 30 mg/g or an eGFR < 60 mL/min per 1.73 m. All participants were categorized into tertiles based on the cardiometabolic indices. Multivariate logistic regression models, restricted cubic splines, and receiver operating characteristic (ROC) curves were used for analysis.

RESULTS

A total of 1371 (31.5%) patients were diagnosed with DKD. A restricted cubic spline showed a J-shaped association of the AIP and TyG index with DKD, a log-shaped association between HOMA-IR and DKD, and a U-shaped association between the SHR and DKD incidence. Multivariate logistic regression revealed that individuals in the highest tertile of the four cardiometabolic indices had a significantly greater risk of DKD than did those in the lowest tertile (AIP: OR = 1.08, 95% CI = 1.02-1.14, P = 0.005; SHR: OR = 1.42, 95% CI = 1.12-1.81, P = 0.004; TyG index: OR = 1.86, 95% CI = 1.42-2.45, P < 0.001; HOMA-IR: OR = 2.24, 95% CI = 1.52-3.30, P < 0.001). The receiver operating characteristic curves showed that the HOMA-IR score was better than other indices at predicting the risk of DKD, with an optimal cutoff of 3.532.

CONCLUSIONS

Elevated AIP, SHR, TyG index and HOMA-IR are associated with a greater risk of DKD in patients with T2D. Among these indices, the HOMA-IR score demonstrated the strongest association with and predictive value for DKD incidence.

Diabetic kidney disease screening status and related factors: a cross-sectional study of patients with type 2 diabetes in six provinces in China

OBJECTIVE

To understand the awareness and practice of diabetic kidney disease (DKD) or nephropathy screening among community-based patients with type 2 diabetes in six provinces and cities in China, and to analyse the related factors affecting screening practices.

METHODS

From December 2021 to March 2022, a cross-sectional survey was conducted using a structured questionnaire in 6230 patients with type 2 diabetes aged 18 years and older. The content of the questionnaire includes three parts: the general situation of diabetic patients (gender, age, ethnicity, marriage, education, occupation, etc.), DKD screening practices, and the evaluation of DKD screening services.

RESULTS

89.70% of the patients had their fasting blood glucose measured every six months, 21.12% of the patients had their glycosylated hemoglobin measured every six months, and only 13.11% and 9.34% of the patients had a urine protein-creatinine ratio test and estimated glomerular filtration rate test every 12 months. The proportions of glycosylated hemoglobin, urine protein-creatinine ratio, and estimated glomerular filtration rate were relatively high in young, northern, highly educated, and long-duration type 2 diabetic patients.

CONCLUSION

The results of this survey found that the proportion of urine protein-creatinine ratio testing, estimated glomerular filtration rate testing, and glycosylated hemoglobin testing in Chinese patients with type 2 diabetes was very low. Patients with type 2 diabetes in rural areas, southern areas, with low education level, and short course of disease have lower detection rates for DKD, and hence lower rates of prevention and treatment.

Genetic Risk Scores Identify People at High Risk of Developing Diabetic Kidney Disease: A Systematic Review

CONTEXT

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. Measures to prevent and treat DKD require better identification of patients most at risk. In this systematic review, we summarize the existing evidence of genetic risk scores (GRSs) and their utility for predicting DKD in people with type 1 or type 2 diabetes.

EVIDENCE ACQUISITION

We searched MEDLINE, Embase, Web of Science, and Cochrane Reviews in June 2022 to identify all existing and relevant literature. Main data items sought were study design, sample size, population, single nucleotide polymorphisms of interest, DKD-related outcomes, and relevant summary measures of result. The Critical Appraisal Skills Programme checklist was used to evaluate the methodological quality of studies.

EVIDENCE SYNTHESIS

We identified 400 citations of which 15 are included in this review. Overall, 7 studies had positive results, 5 had mixed results, and 3 had negative results. Most studies with the strongest methodological quality (n = 9) reported statistically significant and favourable findings of a GRS's association with at least 1 measure of DKD.

CONCLUSION

This systematic review presents evidence of the utility of GRSs to identify people with diabetes that are at high risk of developing DKD. In practice, a robust GRS could be used at the first clinical encounter with a person living with diabetes in order to stratify their risk of complications. Further prospective research is needed.

Association of the systemic immuno-inflammation index, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio with diabetic microvascular complications

Background

The systemic immuno-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) are widely used and have been shown to be predictive indicators of various diseases. Diabetic nephropathy (DN), retinopathy (DR), and peripheral neuropathy (DPN) are the most prominent and common microvascular complications, which have seriously negative impacts on patients, families, and society. Exploring the associations with these three indicators and diabetic microvascular complications are the main purpose.

Methods

There were 1058 individuals with type 2 diabetes mellitus (T2DM) in this retrospective cross-sectional study. SII, NLR, and PLR were calculated. The diseases were diagnosed by endocrinologists. Logistic regression and subgroup analysis were applied to evaluate the association between SII, NLP, and PLR and diabetic microvascular complications.

Results

SII, NLR, and PLR were significantly associated with the risk of DN [odds ratios (ORs): 1.52, 1.71, and 1.60, respectively] and DR [ORs: 1.57, 1.79, and 1.55, respectively] by multivariate logistic regression. When NLR ≥2.66, the OR was significantly higher for the risk of DPN (OR: 1.985, 95% confidence interval: 1.29-3.05). Subgroup analysis showed no significant positive associations across different demographics and comorbidities, including sex, age, hypertension, HbA1c (glycated hemoglobin), and dyslipidemia.

Conclusion

This study found a positive relationship between NLR and DN, DR, and DPN. In contrast, SII and PLR were found to be only associated with DN and DR. Therefore, for the diagnosis of diabetic microvascular complications, SII, NLR and PLR are highly valuable.

Differential impact of glomerular and tubule-interstitial histological changes on kidney outcome between non-proteinuric and proteinuric diabetic nephropathy

BACKGROUND

Studies on kidney function and histological findings in diabetic nephropathy (DN) with low urinary protein (UP) are few. We examined the differential impact of histological changes on kidney outcomes between non-proteinuric and proteinuric DN.

METHODS

Patients diagnosed with DN by renal biopsy during 1981-2014 were divided into non-proteinuric (UP ≤ 0.5 g/day) and proteinuric (UP > 0.5 g/day) DN. The Cox proportional hazard model was used to examine the association of glomerular lesions (GLs) and interstitial fibrosis and tubular atrophy (IFTA) with end-stage kidney disease (ESKD) development after adjusting for relevant confounders.

RESULTS

The non-proteinuric and proteinuric DN groups included 197 and 199 patients, respectively. During the 10.7-year median follow-up period, 16 and 83 patients developed ESKD in the non-proteinuric and proteinuric DN groups, respectively. In the multivariable Cox hazard model, hazard ratios (HRs) [95% confidence intervals (CIs)] of GL and IFTA for ESKD in proteinuric DN were 2.94 [1.67-5.36] and 3.82 [2.06-7.53], respectively. Meanwhile, HRs [95% CIs] of GL and IFTA in non-proteinuric DN were < 0.01 [0-2.48] and 4.98 [1.33-18.0], respectively. IFTA was consistently associated with higher incidences of ESKD regardless of proteinuria levels (P for interaction = 0.49). The prognostic impact of GLs on ESKD was significantly decreased as proteinuria levels decreased (P for interaction < 0.01).

CONCLUSIONS

IFTA is consistently a useful predictor of kidney prognosis in both non-proteinuric and proteinuric DN, while GLs are a significant predictor of kidney prognosis only in proteinuric DN.

Anti-factor H antibody-positive C3 glomerulonephritis secondary to poststreptococcal acute glomerulonephritis with diabetic nephropathy

Poststreptococcal acute kidney glomerulonephritis (PSAGN) has been seen in adults in recent years, especially in patients with type 2 diabetes mellitus, and the renal prognosis has not always been good. There have been cases of PSAGN in which complete remission was not achieved and hematuria and proteinuria persisted, leading to end-stage renal disease. Previous reports showed that the patients subjected to PSAGN have an underlying defect in regulating the alternative pathway of complement, and they identified that antibodies to the C3 convertase, C3 nephritic factors (C3NeF), are involved. C3NeF stabilizes C3 convertase, sustains C3 activation, and causes C3 glomerulonephritis (C3GN). On the other hand, factor H is a glycoprotein that suppresses the overactivation of the alternative pathway by decaying the C3 convertase. Anti-factor H (aFH) antibodies interfere with factor H and cause the same activation of the alternative pathway as C3NeF. However, a limited number of reports describe the clinical course of C3GN with aFH antibodies. We encountered a 49-year-old Japanese man with type 2 diabetes mellitus. He was referred to our hospital because of his elevated serum creatinine, proteinuria, hematuria, and developed edema on both legs. He was diagnosed as PSAGN at the first kidney biopsy, and his renal function improved and edema and hematuria disappeared, but proteinuria persisted after 5 months. He was diagnosed as C3GN at the second kidney biopsy. In our case, no C3NeF was detected. However, a high titer of aFH antibodies was detected in stored serum from the initial presentation, providing a unified diagnosis of aFH antibody-positive C3GN secondary to PSAGN. He progressed to end-stage renal disease (ESRD) and hemodialysis was started. The persistence of high levels of aFH autoantibodies may have caused C3GN secondary to PSAGN due to activating the alternative complement pathway, which eventually worsened the nephropathy and led to ESRD.

Association between polyunsaturated fatty acids and progression among patients with diabetic kidney disease

AIMS

Diabetic kidney disease (DKD) is the major complication of diabetes mellitus (DM) and one of the leading causes of end-stage renal disease. Early detection and treatment are contributing to delay the progression of DKD. Dietary management has potential benefits for DKD, especially the intake of polyunsaturated fatty acids (PUFAs). However, there is a lack of sufficient evidence, so we aimed to explore the association between PUFAs intake and DKD progression.

METHODS

In the National Heath and Nutrition Examination Survey (NHANES) between 2011-2018, a cross-sectional study was conducted among adults with T2DM. DKD was diagnosed with urine albumin to creatinine ratio (ACR) ≥ 30 mg/g or estimated glomerular filtration rate (eGFR) ＜60 ml/min/1.73 m2. Using Survey package of R to arrange the collected PUFAs intake data in order from small to large and divide them into four equal parts, which were expressed as Q1, Q2, Q3 and Q4 respectively. To investigate the association between PUFAs intake and DKD, a weighted univariate logistic regression analysis was performed and the odds ratio (OR) and 95% confidence interval (CI) were calculated for the association with DKD and PUFAs quartiles.

RESULTS

The study involved 3287 participants with T2DM, including 2043 non-DKD and 1244 DKD patients. The results showed that the intake of PUFAs was a protective factor for DKD (p = 0.022), and with the increase of the PUFAs, renal function improved in DKD patients, the adjusted mean of eGFR and Scr changing from 57 (41, 86) in Q1 to 71 (55, 101) ml/min in Q4 (p 0.001), 103 (73, 131) in Q1 to 90 (68, 117) in Q4 (p = 0.031), respectively.

CONCLUSION

Our study indicated that intake of more PUFAs may contribute to delay DKD progression, while different n-6/n-3 ratios need to be explored to protect the kidney.

Establishment and validation of a diagnostic model for diabetic nephropathy in type 2 diabetes mellitus

PURPOSE

There are few studies on the establishment of diagnostic models for diabetic nephropathy (DN) in in type 2 diabetes mellitus (T2DM) patients based on biomarkers. This study was to establish a model for diagnosing DN in T2DM.

METHODS

In this cross-sectional study, data were collected from the Second Hospital of Shijiazhuang between August 2018 to March 2021. Totally, 359 eligible participants were included. Clinical characteristics and laboratory data were collected. LASSO regression analysis was used to screen out diagnostic factors, and the selected factors were input into the decision tree for fivefold cross validation; then a diagnostic model was established. The performances of the diagnosis model were evaluated by the area under the receiver operator characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. The diagnostic performance of the model was also validated through risk stratifications.

RESULTS

Totally, 199 patients (55.43%) were diagnosed with DN. Age, diastolic blood pressure (DBP), fasting blood glucose, insulin treatment, mean corpuscular hemoglobin concentration (MCHC), platelet distribution width (PDW), uric acid (UA), serum creatinine (SCR), fibrinogen (FIB), international normalized ratio (INR), and low-density lipoprotein cholesterol (LDL-C) were the diagnostic factors for DN in T2DM. The diagnostic model presented good performances, with the sensitivity, specificity, PPV, NPV, AUC, and accuracy being 0.849, 0.969, 0.971, 0.838, 0.965, and 0.903, respectively. The diagnostic model based on the stratifications also showed excellent diagnostic performance for diagnosing DN in T2DM patients.

CONCLUSION

Our diagnostic model with simple and accessible factors provides a noninvasive method for the diagnosis of DN.

Association between lactate dehydrogenase and the risk of diabetic kidney disease in patients with type 2 diabetes

Objective

This study aims to investigate the association between lactate dehydrogenase (LDH) and the risk of diabetic kidney disease (DKD) in patients with type 2 diabetes (T2D).

Methods

The study enrolled patients with diagnosis of T2D between 2009 and 2018 from the National Nutrition and Health Examination Survey (NHANES) database. Demographic information, laboratory test, and diagnostic data were collected. Restricted cubic spline (RCS) plots were used to assess the dose-effect relationship between LDH levels and the risk of DKD in patients with T2D. Based on LDH levels, individuals were divided into higher and lower groups using dichotomy, and multivariate logistic regression analysis was conducted to explore the relationship between different LDH levels and the risk of DKD in T2D patients. Stratified analysis was performed to assess the consistency of the result.

Results

A total of 4888 patients were included in the study, with 2976 (60.9%) patients without DKD and 1912 (39.1%) patients with DKD. RCS plots showed that the risk of DKD increased with increasing LDH levels. Multifactorial logistic regression analysis revealed that T2D patients with higher LDH levels had a 45% increased risk of DKD compared to those with lower LDH levels (OR=1.45; 95% CI: 1.11-1.89). Furthermore, each standard deviation increase in LDH level was associated with a 24% increase in DKD incidence among T2D patients (OR=1.24; 95% CI: 1.07-1.44). Stratified analysis consistently supported these findings.

Conclusions

LDH can serve as a valuable biomarker for screening DKD in patients with T2D.

The relationship between weight-adjusted-waist index and diabetic kidney disease in patients with type 2 diabetes mellitus

Purpose

Obesity, particularly abdominal obesity, is seen as a risk factor for diabetic complications. The weight-adjusted-waist index (WWI) is a recently developed index for measuring adiposity. Our goal was to uncover the potential correlation between the WWI index and diabetic kidney disease (DKD) risk.

Methods

This cross-sectional study included adults with type 2 diabetes mellitus (T2DM) who participated in the NHANES database (2007-2018). The WWI index was calculated as waist circumference (WC, cm) divided by the square root of weight (kg). DKD was diagnosed based on impaired estimated glomerular filtration rate (eGFR<60 mL/min/1.73m2), albuminuria (urinary albumin to urinary creatinine ratio>30 mg/g), or both in T2DM patients. The independent relationship between WWI index and DKD risk was evaluated.

Results

A total of 5,028 participants with T2DM were included, with an average WWI index of 11.61 ± 0.02. As the quartile range of the WWI index increased, the prevalence of DKD gradually increased (26.76% vs. 32.63% vs. 39.06% vs. 42.96%, P<0.001). After adjusting for various confounding factors, the WWI index was independently associated with DKD risk (OR=1.32, 95%CI:1.12-1.56, P<0.001). The area under the ROC curve (AUC) of the WWI index was higher than that of body mass index (BMI, kg/m2) and WC. Subgroup analysis suggested that the relationship between the WWI index and DKD risk was of greater concern in patients over 60 years old and those with cardiovascular disease.

Conclusions

Our findings suggest that higher WWI levels are linked to DKD in T2DM patients. The WWI index could be a cost-effective and simple way to detect DKD, but further prospective studies are needed to confirm this.

Secretory leukocyte protease inhibitor as a novel predictive biomarker in patients with diabetic kidney disease

Background

Secretory leukocyte protease inhibitor (SLPI) is a multifunctional protein involved in the chronic inflammatory process, implicated in the pathogenesis of diabetic kidney disease (DKD). However, its potential as a diagnostic and prognostic biomarker of DKD has yet to be evaluated. This study explored the clinical utility of SLPI in the diagnosis and prognosis of renal endpoint events in patients with DKD.

Methods

A multi-center cross-sectional study comprised of 266 patients with DKD and a predictive cohort study comprised of 120 patients with stage IV DKD conducted between December 2016 and January 2022. The clinical parameters were collected for statistical analysis, a multivariate Cox proportional hazards model was used to evaluate the independent risk factors for renal endpoints.

Results

Serum SLPI levels gradually increased with DKD progression (p<0.01). A significant correlation was observed between serum SLPI levels and renal function in patients with DKD. The mean follow-up duration in this cohort study was 2.32 ± 1.30 years. Multivariate Cox regression analysis showed SLPI levels≥51.61ng/mL (HR=2.95, 95% CI[1.55, 5.60], p<0.01), 24h urinary protein levels≥3500 mg/24h (HR=3.02, 95% CI[1.66, 5.52], p<0.01), Alb levels<30g/l (HR=2.19, 95% CI[1.12, 4.28], p<0.05), HGB levels<13g/dl (HR=3.18, 95% CI[1.49, 6.80], p<0.01), and urea levels≥7.1 mmol/L (HR=8.27, 95% CI[1.96, 34.93], p<0.01) were the independent risk factors for renal endpoint events in DKD patients.

Conclusions

Serum SLPI levels increased with DKD progression and were associated with clinical parameters of DKD. Moreover, elevated SLPI levels showed potential prognostic value for renal endpoint events in individuals with DKD. These findings validate the results of previous studies on SLPI in patients with DKD and provide new insights into the role of SLPI as a biomarker for the diagnosis and prognosis of DKD that require validation.

Clinical metabolomics characteristics of diabetic kidney disease: A meta-analysis of 1875 cases with diabetic kidney disease and 4503 controls

AIMS

Diabetic Kidney Disease (DKD), one of the major complications of diabetes, is also a major cause of end-stage renal disease. Metabolomics can provide a unique metabolic profile of the disease and thus predict or diagnose the development of the disease. Therefore, this study summarises a more comprehensive set of clinical biomarkers related to DKD to identify functional metabolites significantly associated with the development of DKD and reveal their driving mechanisms for DKD.

MATERIALS AND METHODS

We searched PubMed, Embase, the Cochrane Library and Web of Science databases through October 2022. A meta-analysis was conducted on untargeted or targeted metabolomics research data based on the strategy of standardized mean differences and the process of ratio of means as the effect size, respectively. We compared the changes in metabolite levels between the DKD patients and the controls and explored the source of heterogeneity through subgroup analyses, sensitivity analysis and meta-regression analysis.

RESULTS

The 34 clinical-based metabolomics studies clarified the differential metabolites between DKD and controls, containing 4503 control subjects and 1875 patients with DKD. The results showed that a total of 60 common differential metabolites were found in both meta-analyses, of which 5 metabolites (p < 0.05) were identified as essential metabolites. Compared with the control group, metabolites glycine, aconitic acid, glycolic acid and uracil decreased significantly in DKD patients; cysteine was significantly higher. This indicates that amino acid metabolism, lipid metabolism and pyrimidine metabolism in DKD patients are disordered.

CONCLUSIONS

We have identified 5 metabolites and metabolic pathways related to DKD which can serve as biomarkers or targets for disease prevention and drug therapy.

Machine Learning Models for Prediction of Diabetic Microvascular Complications

IMPORTANCE AND AIMS

Diabetic microvascular complications significantly impact morbidity and mortality. This review focuses on machine learning/artificial intelligence (ML/AI) in predicting diabetic retinopathy (DR), diabetic kidney disease (DKD), and diabetic neuropathy (DN).

METHODS

A comprehensive PubMed search from 1990 to 2023 identified studies on ML/AI models for diabetic microvascular complications. The review analyzed study design, cohorts, predictors, ML techniques, prediction horizon, and performance metrics.

RESULTS

Among the 74 identified studies, 256 featured internally validated ML models and 124 had externally validated models, with about half being retrospective. Since 2010, there has been a rise in the use of ML for predicting microvascular complications, mainly driven by DKD research across 27 countries. A more modest increase in ML research on DR and DN was observed, with publications from fewer countries. For all microvascular complications, predictive models achieved a mean (standard deviation) c-statistic of 0.79 (0.09) on internal validation and 0.72 (0.12) on external validation. Diabetic kidney disease models had the highest discrimination, with c-statistics of 0.81 (0.09) on internal validation and 0.74 (0.13) on external validation, respectively. Few studies externally validated prediction of DN. The prediction horizon, outcome definitions, number and type of predictors, and ML technique significantly influenced model performance.

CONCLUSIONS AND RELEVANCE

There is growing global interest in using ML for predicting diabetic microvascular complications. Research on DKD is the most advanced in terms of publication volume and overall prediction performance. Both DR and DN require more research. External validation and adherence to recommended guidelines are crucial.

Continuous glucose monitoring in people with diabetes and end-stage kidney disease-review of association studies and Evidence-Based discussion

Diabetic nephropathy is currently the leading cause of end-stage kidney disease. The present methods of assessing diabetes control, such as glycated hemoglobin or self-monitoring of blood glucose, have limitations. Over the past decade, the field of continuous glucose monitoring has been greatly improved and expanded. This review examines the use of continuous glucose monitoring in people with end-stage kidney disease treated with hemodialysis (HD), peritoneal dialysis (PD), or kidney transplantation. We assessed the use of both real-time continuous glucose monitoring and flash glucose monitoring technology in terms of hypoglycemia detection, glycemic variability, and efficacy, defined as an improvement in clinical outcomes and diabetes control. Overall, the use of continuous glucose monitoring in individuals with end-stage kidney disease may improve glycemic control and detection of hypoglycemia. However, most of the published studies were observational with no control group. Moreover, not all studies used the same assessment parameters. There are very few studies involving subjects on peritoneal dialysis. The small number of studies with limited numbers of participants, short follow-up period, and small number of manufacturers of continuous glucose monitoring systems are limitations of the review. More studies need to be performed to obtain more reliable results.

Elevated plasma myoglobin level is closely associated with type 2 diabetic kidney disease

BACKGROUND

Diabetic kidney disease (DKD) is the most frequent complication in patients with type 2 diabetes mellitus (T2DM). It causes a chronic and progressive decline in kidney function, and ultimately patients require renal replacement therapy. To date, an increasing number of clinical studies have been conducted to explore the potential and novel biomarkers, which can advance the diagnosis, estimate the prognosis, and optimize the therapeutic strategies at the early stage of DKD. In the current study, we sought to investigate the association of plasma myoglobin with DKD.

METHODS

A total of 355 T2DM patients with DKD and 710 T2DM patients without DKD were enrolled in this study. Laboratory parameters including blood cell count, hemoglobin A1c, biochemical parameters, and plasma myoglobin were recorded. Patients were classified on admission according to the tertile of myoglobin and clinical parameters were compared between the groups. Pearson correlation analysis, linear regression, logistic regression, receiver operating characteristics (ROC) analysis, and spline regression were performed.

RESULTS

Plasma myoglobin significantly increased in patients with DKD and was associated with renal function and inflammatory parameters. Plasma myoglobin was an independent risk factor for the development of DKD. The area under ROC curve of myoglobin was 0.831. Spline regression showed that there was a significant linear association between DKD incidence and a high level of plasma myoglobin when it exceeded 36.4 mg/mL.

CONCLUSIONS

This study shows that elevated plasma myoglobin level is closely associated with the development of kidney injury in patients with T2DM.

The predictive value of miR-377 and phospholipase A2 in the early diagnosis of diabetic kidney disease and their relationship with inflammatory factors

OBJECTIVE

The value of novel biomarkers for DKD has received increasing attention, and there is an urgent need for novel biomarkers with sensitivity, specificity and ability to detect kidney damage.miR-377 regulates many basic biological processes, plays a key role in tumor cell proliferation, migration and inflammation, and can also increase the expression of matrix proteins and fibronectin, leading to renal tubulointerstitial inflammation and renal fibrosis. Lipoprotein-associated phospholipase A2, as an inflammatory marker, is involved in the pathological process of microalbuminuria production and renal function decline, and is a predictive factor of microalbuminuria production and renal function decline, and can be used as an indicator to evaluate the progression of DKD.The aim of this study was to investigate the effects of miR-377 and phospholipase A2 on the development of diabetic kidney disease through regulation of inflammatory factors and the mechanism of action.

METHODS

80 diabetic patients were divided into two groups according to urinary albumin-to-creatinine ratio (UACR): diabetic normal proteinuria group (n = 42) and diabetic proteinuria group (n = 38). Forty-three healthy people were selected as the normal control group. The serum levels of TGF-β, IL-6, and IL-18 were measured by ELISA, miR-377 was detected by qPCR, and the serum levels of phospholipase A2 were detected by electrochemiluminescence. Analyze the correlation of study group indicators, ROC curve was used to evaluate the diagnostic efficacy of miR-377 and phospholipase A2 in diabetic kidney disease.

RESULTS

The average levels of serum TGF-β, IL-6, IL-18, miR-377 and phospholipase A2 in diabetic proteinuria group were significantly higher than those in normal control group and diabetic proteinuria normal group(P < 0.05). miR-377, phospholipase A2 were significantly correlated with inflammatory factors such as glomerular filtration rate and TGF-β. miR-377 and phospholipase A2 are independent predictors of diabetic kidney disease. The area under the curve of miR-377 and phospholipase A2 in the normal diabetic proteinuria group and the diabetic proteinuria group were 0.731 and 0.744, respectively.

CONCLUSION

miR-377 and phospholipase A2 have good diagnostic efficiency for the early diagnosis of diabetic kidney disease. They can be used as early biomarkers.miR-377 and phospholipase A2 were positively correlated with inflammatory factors and involved in the occurrence and development of diabetic kidney disease.

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.

Co-differential genes between DKD and aging: implications for a diagnostic model of DKD

Objective

Diabetic kidney disease (DKD) is a serious complication of diabetes mellitus (DM) that is closely related to aging. In this study, we found co-differential genes between DKD and aging and established a diagnostic model of DKD based on these genes.

Methods

Differentially expressed genes (DEGs) in DKD were screened using GEO datasets. The intersection of the DEGs of DKD and aging-related genes revealed DKD and aging co-differential genes. Based on this, a genetic diagnostic model for DKD was constructed using LASSO regression. The characteristics of these genes were investigated using consensus clustering, WGCNA, functional enrichment, and immune cell infiltration. Finally, the expression of diagnostic model genes was analyzed using single-cell RNA sequencing (scRNA-seq) in DKD mice (model constructed by streptozotocin (STZ) injection and confirmed by tissue section staining).

Results

First, there were 159 common differential genes between DKD and aging, 15 of which were significant. These co-differential genes were involved in stress, glucolipid metabolism, and immunological functions. Second, a genetic diagnostic model (including IGF1, CETP, PCK1, FOS, and HSPA1A) was developed based on these genes. Validation of these model genes in scRNA-seq data revealed statistically significant variations in FOS, HSPA1A, and PCK1 gene expression between the early DKD and control groups. Validation of these model genes in the kidneys of DKD mice revealed that Igf1, Fos, Pck1, and Hspa1a had lower expression in DKD mice, with Igf1 expression being statistically significant.

Conclusion

Our findings suggest that DKD and aging co-differential genes are significant in DKD diagnosis, providing a theoretical basis for novel research directions on DKD.

Serum high mobility group box 1 as a potential biomarker for the progression of kidney disease in patients with type 2 diabetes

Background

As a damage-associated molecular pattern protein, high mobility group box 1 (HMGB1) is associated with kidney and systemic inflammation. The predictive and therapeutic value of HMGB1 as a biomarker has been confirmed in various diseases. However, its value in diabetic kidney disease (DKD) remains unclear. Therefore, this study aimed to investigate the correlation between serum and urine HMGB1 levels and DKD progression.

Methods

We recruited 196 patients with type 2 diabetes mellitus (T2DM), including 109 with DKD and 87 T2DM patients without DKD. Additionally, 60 healthy participants without T2DM were also recruited as controls. Serum and urine samples were collected for HMGB1 analysis. Simultaneously, tumor necrosis factor receptor superfamily member 1A (TNFR-1) in serum and kidney injury molecule (KIM-1) in urine samples were evaluated for comparison.

Results

Serum and urine HMGB1 levels were significantly higher in patients with DKD than in patients with T2DM and healthy controls. Additionally, serum HMGB1 levels significantly and positively correlated with serum TNFR-1 (R 2 = 0.567, p<0.001) and urine KIM-1 levels (R 2 = 0.440, p<0.001), and urine HMGB1 has a similar correlation. In the population with T2DM, the risk of DKD progression increased with an increase in serum HMGB1 levels. Multivariate logistic regression analysis showed that elevated serum HMGB1 level was an independent risk factor for renal function progression in patients with DKD, and regression analysis did not change in the model corrected for multiple variables. The restricted cubic spline depicted a nonlinear relationship between serum HMGB1 and renal function progression in patients with DKD (p-nonlinear=0.007, p<0.001), and this positive effect remained consistent across subgroups.

Conclusion

Serum HMGB1 was significantly correlated with DKD and disease severity. When the HMGB1 level was ≥27 ng/ml, the risk of renal progression increased sharply, indicating that serum HMGB1 can be used as a potential biomarker for the diagnosis of DKD progression.

Prevalence of chronic kidney disease in Tunisian diabetics: the TUN-CKDD survey

BACKGROUND

In Tunisia, the prevalence of diabetes mellitus increased from 15.5% on 2016 to 23% by 2023. While Chronic Kidney Disease (CKD) stills the most dreaded complications of diabetes, studies on the prevalence of chronic kidney disease non-dialysis diet are scarce. The aim of this study was to assess the prevalence of chronic kidney disease among the Tunisian diabetic population based on investigators' specialty, demographic criteria (gender, age, duration of diabetes and geographic distribution) and diagnosis criteria (albuminuria and/or eGFR).

METHODS

This observational, multicentric, and cross-sectional study enrolled all diabetic subjects from all regions of Tunisia with at least 3 months of follow-up before the inclusion date, from 09 January to 08 February 2023. CKD diagnosis was established based on the KDIGO guidelines. The study was carried out at medical departments and ambulatory clinics of different healthcare providers. Baseline data were collected by investigators using an electronic case report form (eCRF). Continuous variables were described by means, median, standard deviation, and quartiles. Categorical data were tabulated in frequencies and percentages.

RESULTS

The overall prevalence of CKD among the 10,145 enrolled patients with diabetes mellitus was 38.7% with a 95%CI [37.8-39.6%]. 50.9% were male, with a mean age of 67.5 (± 11.3) years. The mean diabetes duration was 16.1 years (± 8.9). The highest CKD prevalence was noted among nephrologists (82.2%), while it was similar between the cardiologists and the primary care physicians (30.0%). CKD prevalence was highest among males (43.0% versus 35.1%) and increased proportionally with patients' age and diabetes duration. CKD was more frequent in the Mid-East Area when compared to other regions (49.9% versus 25.3 to 40.1% in other regions). Albuminuria was present within 6.6% of subjects with CKD, and it was found an estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m² within 13.3% of subjects wit h CKD. 18.9% had both criteria.

CONCLUSIONS

In Tunisia, CKD among diabetics had a prevalence of 38.7%, approaching European prevalence. The prevalence discrepancy worldwide of CKD can be improved with a larger population size and by implementing standardized practices.

Early choroidal and retinal changes detected by swept-source oct in type 2 diabetes and their association with diabetic kidney disease: a longitudinal prospective study

BACKGROUND

To evaluate structural changes in retina and choroid in patients with type 2 diabetes (T2D) and their association with diabetic kidney disease (DKD).

METHODS

T2D patients with mild or no diabetic retinopathy (DR) were followed for 3 years using structural SS-OCT and OCT angiography (OCT-A) taken every 6 months. Parameters were compared longitudinally and according to the DKD status on baseline.

RESULTS

One hundred and sixty eyes from 80 patients were followed for 3 years, 72 with no DKD (nDKD) at baseline and 88 with DKD. Trend analysis of T2D showed significant thinning in GCL + and circumpapillary retinal fiber neural layer (cRFNL), choroid, and decreased vascular density (VD) in superficial plexus and central choriocapillaris with foveal avascular zone (FAZ) enlargement. Patients with no DKD on baseline presented more significant declines in retinal center and choroidal thickness, increased FAZ and loss of nasal and temporal choriocapillaris volume. In addition, the nDKD group had worse glycemic control and renal parameters at the end of the study.

CONCLUSION

Our data suggests the potential existence of early and progressive neurovascular damage in the retina and choroid of patients with Type 2 Diabetes (T2D) who have either no or mild Diabetic Retinopathy (DR). The progression of neurovascular damage appears to be correlated with parameters related to glycemic control and renal damage.

Utilizing machine learning algorithms to identify biomarkers associated with diabetic nephropathy: A review

Diabetic nephropathy (DN), a multifaceted disease with various contributing factors, presents challenges in understanding its underlying causes. Uncovering biomarkers linked to this condition can shed light on its pathogenesis and support the creation of new diagnostic and treatment methods. Gene expression data were sourced from accessible public databases, and Weighted Gene Co-expression Network Analysis (WGCNA)was employed to pinpoint gene co-expression modules relevant to DN. Subsequently, various machine learning techniques, such as random forest, lasso regression algorithm (LASSO), and support vector machine-recursive feature elimination (SVM-REF), were utilized for distinguishing DN cases from controls using the identified gene modules. Additionally, functional enrichment analyses were conducted to explore the biological roles of these genes. Our analysis revealed 131 genes showing distinct expression patterns between controlled and uncontrolled groups. During the integrated WCGNA, we identified 61 co-expressed genes encompassing both categories. The enrichment analysis highlighted involvement in various immune responses and complex activities. Techniques like Random Forest, LASSO, and SVM-REF were applied to pinpoint key hub genes, leading to the identification of VWF and DNASE1L3. In the context of DN, they demonstrated significant consistency in both expression and function. Our research uncovered potential biomarkers for DN through the application of WGCNA and various machine learning methods. The results indicate that 2 central genes could serve as innovative diagnostic indicators and therapeutic targets for this disease. This discovery offers fresh perspectives on the development of DN and could contribute to the advancement of new diagnostic and treatment approaches.

The role of endoplasmic reticulum-mitochondria-associated membranes in diabetic kidney disease

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease worldwide. The pathomechanisms of DKD are multifactorial, yet haemodynamic and metabolic changes in the early stages of the disease appear to predispose towards irreversible functional loss and histopathological changes. Recent studies highlight the importance of endoplasmic reticulum-mitochondria-associated membranes (ER-MAMs), structures conveying important cellular homeostatic and metabolic effects, in the pathology of DKD. Disruption of ER-MAM integrity in diabetic kidneys is associated with DKD progression, but the regulation of ER-MAMs and their pathogenic contribution remain largely unknown. Exploring the cell-specific components and dynamic changes of ER-MAMs in diabetic kidneys may lead to the identification of new approaches to detect and stratify diabetic patients with DKD. In addition, these insights may lead to novel therapeutic approaches to target and/or reverse disease progression. In this review, we discuss the association of ER-MAMs with key pathomechanisms driving DKD such as insulin resistance, dyslipidaemia, ER stress, and inflammasome activation and the importance of further exploration of ER-MAMs as diagnostic and therapeutic targets in DKD.

Identification and validation of immune and cuproptosis - related genes for diabetic nephropathy by WGCNA and machine learning

Background

As the leading cause of chronic kidney disease, diabetic kidney disease (DKD) is an enormous burden for all healthcare systems around the world. However, its early diagnosis has no effective methods.

Methods

First, gene expression data in GEO database were extracted, and the differential genes of diabetic tubulopathy were obtained. Immune-related genesets were generated by WGCNA and immune cell infiltration analyses. Then, differentially expressed immune-related cuproptosis genes (DEICGs) were derived by the intersection of differential genes and genes related to cuproptosis and immune. To investigate the functions of DEICGs, volcano plots and GO term enrichment analysis was performed. Machine learning and protein-protein interaction (PPI) network analysis helped to finally screen out hub genes. The diagnostic efficacy of them was evaluated by GSEA analysis, receiver operating characteristic (ROC) curve, single-cell RNA sequencing and the Nephroseq website. The expression of hub genes at the animal level by STZ -induced and db/db DKD mouse models was further verified.

Results

Finally, three hub genes, including FSTL1, CX3CR1 and AGR2 that were up-regulated in both the test set GSE30122 and the validation set GSE30529, were screened. The areas under the curve (AUCs) of ROC curves of hub genes were 0.911, 0.935 and 0.922, respectively, and 0.946 when taking as a whole. Correlation analysis showed that the expression level of three hub genes demonstrated their negative relationship with GFR, while those of FSTL1 displayed a positive correlation with the level of serum creatinine. GSEA was enriched in inflammatory and immune-related pathways. Single-nucleus RNA sequencing indicated the main distribution of FSTL1 in podocyte and mesangial cells, the high expression of CX3CR1 in leukocytes and the main localization of AGR2 in the loop of Henle. In mouse models, all three hub genes were increased in both STZ-induced and db/db DKD models.

Conclusion

Machine learning was combined with WGCNA, immune cell infiltration and PPI analyses to identify three hub genes associated with cuproptosis, immunity and diabetic nephropathy, which all have great potential as diagnostic markers for DKD and even predict disease progression.

Utility of serum β2-microglobulin for prediction of kidney outcome among patients with biopsy-proven diabetic nephropathy

AIM

To examine whether serum β2-microglobulin (β2-MG) could improve the prediction performance for kidney failure with replacement therapy (KFRT) among patients with diabetic nephropathy (DN).

METHODS

Patients with biopsy-proven DN at Nara Medical University Hospital were included. The exposure of interest was log-transformed serum β2-MG levels measured at kidney biopsy. The outcome variable was KFRT. Multivariable Cox regression models and competing-risk regression models, with all-cause mortality as a competing event, were performed. Model fit by adding serum β2-MG levels was calculated using the Akaike information criterion (AIC). The net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indexes were used to evaluate the improvement of predictive performance for 5-year cumulative incidence of KFRT by serum β2-MG levels.

RESULTS

Among 408 patients, 99 developed KFRT during a median follow-up period of 6.7 years. A higher serum β2-MG level (1-unit increase in log-transformed serum β2-MG level) was associated with a higher incidence of KFRT, even after adjustments for previously known clinical and histological risk factors (hazard ratio [95% confidence interval {CI}]: 3.30 [1.57-6.94] and subdistribution hazard ratio [95% CI]: 3.07 [1.55-6.06]). The addition of log-transformed serum β2-MG level reduced AIC and improved the prediction of KFRT (NRI and IDI: 0.32 [0.09-0.54] and 0.03 [0.01-0.56], respectively).

CONCLUSIONS

Among patients with biopsy-proven DN, serum β2-MG was an independent predictor of KFRT and improved prediction performance. In addition to serum creatinine, serum β2-MG should probably be measured for DN.

Role of novel biomarker monocyte chemo-attractant protein-1 in early diagnosis & predicting progression of diabetic kidney disease: A comprehensive review

Diabetic kidney disease (DKD) is the most devastating complication of diabetes mellitus. Identification of patients at the early stages of progression may reduce the disease burden. The limitation of conventional markers such as serum creatinine and proteinuria intensify the need for novel biomarkers. The traditional paradigm of DKD pathogenesis has expanded to the activation of the immune system and inflammatory pathways. Monocyte chemo-attractant protein-1 (MCP-1) is extensively studied, as a key inflammatory mediator that modulates the development of DKD. Recent evidence supports the diagnostic role of MCP-1 in patients with or without proteinuria in DKD, as well as a significant role in the early prediction and risk stratification of DKD. In this review, we will summarize and update present evidence for MCP-1 for diagnostic ability and predicting the progression of DKD.

Baseline Urine Albumin-to-Creatinine Ratio is Associated With Decline of Estimated Glomerular Filtration Rate in Patients Newly Diagnosed With Type 2 Diabetes Mellitus: An Observational 5-year Cohort Study

OBJECTIVE

This study aimed to investigate the association between baseline albuminuria and the progression of diabetic kidney disease (DKD) in patients newly diagnosed with type 2 diabetes mellitus (DM).

METHODS

A retrospective cohort study was conducted among 604 patients aged ≥18 years who were newly diagnosed with type 2 DM between January 2014 and 31 December 2017 at an outpatient clinic in a tertiary hospital in China. The incidence of albuminuria was determined and the associations between albuminuria at baseline and the progression of DKD estimated by estimated glomerular filtration rate slope were evaluated using binary logistic regression analysis.

RESULTS

At diagnosis of type 2 DM, 18.8% of patients had albuminuria, with 17.4% having microalbuminuria and the other 1.4% having macroalbuminuria. During the 5-year follow-up period, patients with albuminuria at the baseline experienced a more rapid decline of estimated glomerular filtration rate over time than patients with normoalbuminuria at baseline (-2.6 vs -1.5 mL/min/1.73 m2 per year, P =.01). Albuminuria at baseline is independently associated with the progression of DKD.

CONCLUSIONS

The prevalence of albuminuria is 18.8% in patients newly diagnosed with type 2 diabetes and the occurrence of albuminuria can predict steeper annual decline in kidney function.

A Prospective, Multicentered, Randomized, Double-Blind, Placebo-Controlled Clinical Trial of Keluoxin Capsules in the Treatment of Microalbuminuria in Patients with Type 2 Early Diabetic Kidney Disease

Background: To evaluate the efficacy and safety of Keluoxin (KLX) capsules and provide validated evidence for the application of KLX in the treatment of diabetic kidney disease (DKD). Methods: A multicenter, randomized, double-blind, placebo-controlled trial design was used to screen 129 patients with DKD (urinary albumin-to-creatinine ratio [UACR]: male, 2.5-30 mg/mmol; female, 3.5-30 mg/mmol) and with Qi and Yin deficiency and blood stasis symptoms. Written informed consent was obtained from all patients. The patients were randomly divided into KLX and control groups. The KLX group was orally administered KLX (6 g/day) and irbesartan tablets (150 mg/day), whereas the control group was administered KLX placebo (6 g/day) and irbesartan tablets (150 mg/day). Patients were observed for 24 weeks to evaluate the natural logarithm of the UACR (log-UACR), the odds ratio (OR) for a sustained increase in the UACR of at least 30% and 40%, estimated glomerular filtration rate (eGFR), changes in symptoms and quality-of-life scores, and adverse events. Results: The changes of the natural log-UACR during the 24 weeks compared with baseline in the KLX group were better than those in the control group (LS mean ± standard error, -0.26 ± 0.10 vs. 0.01 ± 0.09, p = 0.0292). The incidence of a sustained increase in the UACR of at least 30% and 40% was found to be significantly lower in the KLX group (OR, 0.26; 95% confidence interval [CI], 0.09-0.75; OR, 0.29; 95% CI, 0.10-0.82). Changes in symptoms and quality-of-life scores in the KLX group were better than those in the control group. There was no statistically significant difference in eGFR or the incidence of adverse events between the groups. Conclusions: Overall, these results suggest that KLX capsules combined with irbesartan can reduce microalbuminuria, relieve the symptoms, and improve the quality of life for patients with type 2 early DKD compared with the use of irbesartan alone. Trial registration: Chinese Clinical Trial Registry, registration number: ChiCTR2100052764.

Development of a clinical prediction model for diabetic kidney disease with glucose and lipid metabolism disorders based on machine learning and bioinformatics technology

OBJECTIVE

In this study, we investigated the internal relationship between the pathogenesis of diabetic kidney disease (DKD) and abnormal glucose and lipid metabolism to identify potential biomarkers for diagnosis and treatment and investigated the role of the immune microenvironment of glucose and lipid metabolism disorders in the occurrence and progression of DKD.

MATERIALS AND METHODS

The chip datasets GSE104948 and GSE96804 from the Gene Expression Common Database (GEO) were merged using the "lima" and "sva" software packages in R Software (4.2.3), and the merged dataset was used as the validation set. The intersection between the differential genes of DKD and the glucose and lipid metabolism genes in the MSigDB database was identified, and a nomogram of the incidence risk of DKD was built using three machine learning methods, namely LASSO regression, support vector machine (SVM), and random forest (RF), to validate the accuracy of the prediction model. Immune scores were conducted using the unsupervised clustering method, and patients were divided into two subgroups. The two subgroups were screened for differential genes for enrichment analysis. The differential genes of patients diagnosed with DKD were clustered into two gene subgroups for co-expression analysis. In this study, we utilized the Cytoscape software to construct a network of interactions among key genes.

RESULTS

Using machine learning, a diagnostic model was developed with G6PC and HSD17B14 as key factors. Enrichment analysis and immune scoring demonstrated that the development of DKD was related to the imbalance in the microenvironment brought about by glucose lipid metabolism disorders.

CONCLUSIONS

G6PC and HSD17B14 may be potential biomarkers for DKD, and the established predictive model is more helpful in predicting the incidence of DKD.

The relationship between diabetic retinopathy and diabetic nephropathy in type 2 diabetes

Context

Diabetic retinopathy (DR) and diabetic nephropathy (DN), are major microvascular complications of diabetes. DR is an important predictor of DN, but the relationship between the severity of DR and the pathological severity of diabetic glomerulopathy remains unclear.

Objective

To investigate the relationship between severity of diabetic retinopathy (DR) and histological changes and clinical indicators of diabetic nephropathy (DN) in patients with type 2 diabetes mellitus (T2DM).

Methods

Patients with T2DM (n=272) who underwent a renal biopsy were eligible. Severity of DR was classified as non-diabetic retinopathy, non-proliferative retinopathy, and proliferative retinopathy (PDR). Relationship between DN and DR and the diagnostic efficacy of DR for DN were explored.

Results

DN had a higher prevalence of DR (86.4%) and DR was more severe. The sensitivity and specificity of DR in DN were 86.4% and 78.8%, while PDR was 26.4% and 98.5%, respectively. In DN patients, the severity of glomerular lesions (p=0.001) and prevalence of KW nodules (p<0.001) significantly increased with increasing severity of DR. The presence of KW nodules, lower hemoglobin levels, and younger age were independent risk factors associated with more severe DR in patients with DN.

Conclusion

DR was a good predictor of DN. In DN patients, the severity of DR was associated with glomerular injury, and presence of KW nodules, lower hemoglobin levels and younger age were independent risk factors associated with more severe DR.

Trial registration

ClinicalTrails.gov, NCT03865914.

Presentation of glomerulocystic disease in a young onset diabetes: A case report

RATIONALE

This case report presents a challenging medical scenario involving a young adult male who exhibited an unusual combination of symptoms, including abrupt weight loss, declining renal function, proteinuria, and concurrent onset of diabetes mellitus. Remarkably, the patient had no previous medical history or family history of similar conditions, necessitating a comprehensive investigation.

PATIENT CONCERNS

On March 10, 2021, a 25-year-old male sought medical attention due to the aforementioned symptoms. Initial assessments revealed stage 5 chronic kidney disease, with elevated blood urea nitrogen (BUN) and serum creatinine (Cr) levels, as well as significant proteinuria. The only notable physical finding was obesity, and renal ultrasound showed normal-sized kidneys without cysts.

DIAGNOSIS

A treatment plan was initiated to stabilize creatinine levels, including medications such as Glimepiride, Glyxambi, Bisoprolol, Amlodipine, and Valsartan. However, despite diligent medication management, proteinuria persisted, prompting further evaluation. A renal biopsy was performed on April 12th, 2023, leading to the diagnosis of glomerulocystic kidney disease with early-stage changes indicative of diabetic nephropathy.

INTERVENTIONS

The patient continues to receive ongoing care and follow-up at our outpatient clinic to optimize therapeutic interventions and elucidate the underlying etiology of this complex clinical scenario.

OUTCOMES

Ongoing investigations and therapeutic interventions are crucial to understand the underlying cause and optimize patient care in this intricate clinical scenario.

LESSONS

This case underscores the complexity of diagnosing and managing a young adult presenting with concurrent renal dysfunction, proteinuria, and diabetes mellitus in the absence of prior underlying conditions. It highlights the importance of comprehensive evaluation and ongoing care in such challenging cases.

Urinary podocyte stress marker as a prognostic indicator for diabetic kidney disease

BACKGROUND

Diabetic kidney diseases (DKD) is a the most common cause of end-stage kidney disease (ESKD) around the world. Previous studies suggest that urinary podocyte stress biomarker, e.g. podocin:nephrin mRNA ratio, is a surrogate marker of podocyte injury in non-diabetic kidney diseases.

METHOD

We studied 118 patients with biopsy-proved DKD and 13 non-diabetic controls. Their urinary mRNA levels of nephrin, podocin, and aquaporin-2 (AQP2) were quantified. Renal events, defined as death, dialysis, or 40% reduction in glomerular filtration rate, were determined at 12 months.

RESULTS

Urinary podocin:nephrin mRNA ratio of DKD was significantly higher than the control group (p = 0.0019), while urinary nephrin:AQP2 or podocin:AQP2 ratios were not different between groups. In DKD, urinary podocin:nephrin mRNA ratio correlated with the severity of tubulointerstitial fibrosis (r = 0.254, p = 0.006). and was associated with the renal event-free survival in 12 months (unadjusted hazard ratio [HR], 1.523; 95% confidence interval [CI] 1.157-2.006; p = 0.003). After adjusting for clinical and pathological factors, urinary podocin:nephrin mRNA ratio have a trend to predict renal event-free survival (adjusted HR, 1.327; 95%CI 0.980-1.797; p = 0.067), but the result did not reach statistical significance.

CONCLUSION

Urinary podocin:nephrin mRNA ratio has a marginal prognostic value in biopsy-proven DKD. Further validation is required for DKD patients without kidney biopsy.

Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy

The aim of this study is to analyze the effect of serum metabolites on diabetic nephropathy (DN) and predict the prevalence of DN through a machine learning approach. The dataset consists of 548 patients from April 2018 to April 2019 in the Second Affiliated Hospital of Dalian Medical University (SAHDMU). We select the optimal 38 features through a least absolute shrinkage and selection operator (LASSO) regression model and a 10-fold cross-validation. We compare four machine learning algorithms, including extreme gradient boosting (XGB), random forest, decision tree, and logistic regression, by AUC-ROC curves, decision curves, and calibration curves. We quantify feature importance and interaction effects in the optimal predictive model by Shapley additive explanation (SHAP) method. The XGB model has the best performance to screen for DN with the highest AUC value of 0.966. The XGB model also gains more clinical net benefits than others, and the fitting degree is better. In addition, there are significant interactions between serum metabolites and duration of diabetes. We develop a predictive model by XGB algorithm to screen for DN. C2, C5DC, Tyr, Ser, Met, C24, C4DC, and Cys have great contribution in the model and can possibly be biomarkers for DN.

Integrative analysis of potential diagnostic markers and therapeutic targets for glomerulus-associated diabetic nephropathy based on cellular senescence

Introduction

Diabetic nephropathy (DN), distinguished by detrimental changes in the renal glomeruli, is regarded as the leading cause of death from end-stage renal disease among diabetics. Cellular senescence plays a paramount role, profoundly affecting the onset and progression of chronic kidney disease (CKD) and acute kidney injuries. This study was designed to delve deeply into the pathological mechanisms between glomerulus-associated DN and cellular senescence.

Methods

Glomerulus-associated DN datasets and cellular senescence-related genes were acquired from the Gene Expression Omnibus (GEO) and CellAge database respectively. By integrating bioinformatics and machine learning methodologies including the LASSO regression analysis and Random Forest, we screened out four signature genes. The receiver operating characteristic (ROC) curve was performed to evaluate the diagnostic performance of the selected genes. Rigorous experimental validations were subsequently conducted in the mouse model to corroborate the identification of three signature genes, namely LOX, FOXD1 and GJA1. Molecular docking with chlorogenic acids (CGA) was further established not only to validate LOX, FOXD1 and GJA1 as diagnostic markers but also reveal their potential therapeutic effects.

Results and discussion

In conclusion, our findings pinpointed three diagnostic markers of glomerulus-associated DN on the basis of cellular senescence. These markers could not only predict an increased risk of DN progression but also present promising therapeutic targets, potentially ushering in innovative treatments for DN in the elderly population.

Liquid Biopsy: A New Avenue for the Diagnosis of Kidney Disease: Diabetic Kidney Disease, Renal Cancer, and IgA Nephropathy

Kidney diseases are some of the most common healthcare problems. As the population of elderly individuals with concurrent health conditions continues to rise, there will be a heightened occurrence of these diseases. Due to the renal condition being one of the longevity predictors, early diagnosis of kidney dysfunction plays a crucial role. Currently, prevalent diagnostic tools include laboratory tests and kidney tissue biopsies. New technologies, particularly liquid biopsy and new detection biomarkers, hold promise for diagnosing kidney disorders. The aim of this review is to present modern diagnostic methods for kidney diseases. The paper focuses on the advances in diagnosing three common renal disorders: diabetic kidney disease, renal cancer, and immunoglobulin A nephropathy. We highlight the significance of liquid biopsy and epigenetic changes, such as DNA methylation, microRNA, piRNAs, and lncRNAs expression, or single-cell transcriptome sequencing in the assessment of kidney diseases. This review underscores the importance of early diagnosis for the effective management of kidney diseases and investigates liquid biopsy as a promising approach.

Association of systemic immune-inflammation index with diabetic kidney disease in patients with type 2 diabetes: a cross-sectional study in Chinese population

Objective

Systemic immune-inflammation index (SII), a novel inflammatory marker, has been reported to be associated with diabetic kidney disease (DKD) in the U.S., however, such a close relationship with DKD in other countries, including China, has not been never determined. We aimed to explore the association between SII and DKD in Chinese population.

Methods

A total of 1922 hospitalized patients with type 2 diabetes mellitus (T2DM) included in this cross-sectional study were divided into three groups based on estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (ACR): non-DKD group, DKD stages 1-2 Alb group, and DKD-non-Alb+DKD stage 3 Alb group. The possible association of SII with DKD was investigated by correlation and multivariate logistic regression analysis, and receiver-operating characteristic (ROC) curves analysis.

Results

Moving from the non-DKD group to the DKD-non-Alb+DKD stage 3 Alb group, SII level was gradually increased (P for trend <0.01). Partial correlation analysis revealed that SII was positively associated with urinary ACR and prevalence of DKD, and negatively with eGFR (all P<0.01). Multivariate logistic regression analysis showed that SII remained independently significantly associated with the presence of DKD after adjustment for all confounding factors [(odds ratio (OR), 2.735; 95% confidence interval (CI), 1.840-4.063; P < 0.01)]. Moreover, compared with subjects in the lowest quartile of SII (Q1), the fully adjusted OR for presence of DKD was 1.060 (95% CI 0.773-1.455) in Q2, 1.167 (95% CI 0.995-1.368) in Q3, 1.266 (95% CI 1.129-1.420) in the highest quartile (Q4) (P for trend <0.01). Similar results were observed in presence of DKD stages 1-2 Alb or presence of DKD-non- Alb+DKD stage 3 Alb among SII quartiles. Last, the analysis of ROC curves revealed that the best cutoff values for SII to predict DKD, Alb DKD stages 1- 2, and DKD-non-Alb+ DKD stage 3 Alb were 609.85 (sensitivity: 48.3%; specificity: 72.8%), 601.71 (sensitivity: 43.9%; specificity: 72.3%), and 589.27 (sensitivity: 61.1%; specificity: 71.1%), respectively.

Conclusion

Higher SII is independently associated with an increased risk of the presence and severity of DKD, and SII might be a promising biomarker for DKD and its distinct phenotypes in Chinese population.

Biomarkers for Diabetic Nephropathy with a Focus on Kidney Injury Molecule-1 (KIM-1)

Diabetic Nephropathy (DN), with an increasing rate of mortality and morbidity, is considered the main cause of End-Stage Renal Disease (ESRD). A wide spectrum of biomarkers exist for early detection of DN, but they suffer from low specificity and sensitivity, indicating the urgent demand for finding more effective biomarkers. Also, the pathophysiology of tubular damage and its relation to DN are not yet completely understood. Kidney Injury Molecule-1 (KIM-1) is a protein that is expressed at substantially low contents in the kidney under physiological conditions. A number of reports have demonstrated the close relationship between urine and tissue KIM-1 levels and kidney disorders. KIM-1 is known as a biomarker for diabetic nephropathy and renal injury. In this study, we aim to review the potential clinical and pathological roles of KIM-1 in diabetic nephropathy.

Diabetic Kidney Disease Without Albuminuria: A New Entity in Diabetic Nephropathy

Non-albuminuric diabetic kidney disease (NA-DKD) is characterized by progressive loss of kidney function with an annual loss of estimated glomerular filtration rate (eGFR) more than 3 mL/ min/ 1.73m2 per year. NA-DKD is also associated with the late manifestation of diabetic kidney disease, characterized by reduced eGFR (< 60 mL/min/ 1.73m2), in the absence of albuminuria (urine albumin-to-creatinine ratio [UACR] less than 30 mg/g. The typical glomerular changes seen in diabetic nephropathy are less frequently observed in normoalbuminuric patients, while they predominantly show mesangial expansion and tubulointerstitial and vascular changes. The prevalence of NA-DKD has been increasing during the past decade, with a wide range of prevalence in different studies. It seems that patients with NA-DKD are more likely to be female and have better metabolic profile including a lower Hb A1c, lower triglyceride, lower cholesterol, lower BMI and systolic blood pressure, and lower rate of retinopathy. Compared to patients with albuminuria, those with NA-DKD show a lower risk for progression to end-stage kidney disease (ESKD), or rapid decline in eGFR. They also have increased risks of death and hospitalization for heart failure compared with non-DKD diabetic patients, but a lower risk in comparison with albuminuric DKD, regardless of GFR. There is no effective treatment for this phenotype of the disease, but limited data support the use of SGLT2 inhibitors to slow chronic kidney disease progression along with appropriate metabolic risk factor control. More clinical research and pathologic studies are needed for a better understanding of the phenotype, prevention, and treatment methods of the disease.  DOI: 10.52547/ijkd.7966.

Development of an automated estimation of foot process width using deep learning in kidney biopsies from patients with Fabry, minimal change, and diabetic kidney diseases

Podocyte injury plays a key role in pathogenesis of many kidney diseases with increased podocyte foot process width (FPW), an important measure of podocyte injury. Unfortunately, there is no consensus on the best way to estimate FPW and unbiased stereology, the current gold standard, is time consuming and not widely available. To address this, we developed an automated FPW estimation technique using deep learning. A U-Net architecture variant model was trained to semantically segment the podocyte-glomerular basement membrane interface and filtration slits. Additionally, we employed a post-processing computer vision approach to accurately estimate FPW. A custom segmentation utility was also created to manually classify these structures on digital electron microscopy (EM) images and to prepare a training dataset. The model was applied to EM images of kidney biopsies from 56 patients with Fabry disease, 15 with type 2 diabetes, 10 with minimal change disease, and 17 normal individuals. The results were compared with unbiased stereology measurements performed by expert technicians unaware of the clinical information. FPW measured by deep learning and by the expert technicians were highly correlated and not statistically different in any of the studied groups. A Bland-Altman plot confirmed interchangeability of the methods. FPW measurement time per biopsy was substantially reduced by deep learning. Thus, we have developed a novel validated deep learning model for FPW measurement on EM images. The model is accessible through a cloud-based application making calculation of this important biomarker more widely accessible for research and clinical applications.

Effect of Repeated Structured Diabetes Education on Lifestyle Knowledge and Self-Care Diabetes Management in Kidney Transplant Patients With Posttransplant Diabetes

OBJECTIVES

Diabetes knowledge among kidney transplant recipients with posttransplant diabetes has not been clearly assessed. We evaluated whether diabetes education in kidney transplant recipients with posttransplant diabetes affected self-care, metabolic control variables, and reversibility of early diabetic microangiopathies.

MATERIALS AND METHODS

In this prospective randomized controlled study, we enrolled 210 renal transplant recipients with posttransplant diabetes. Group 1 patients (n = 140) received structured diabetes education, and group 2 patients (n = 70) received conventional education. Patient data were collected through patient identification and metabolic control parameter forms and a diabetes self-care scale questionnaire (scores between 0 and 7).

RESULTS

Diet knowledge improved and waist circumference was reduced with mild to moderate exercise in group 1 (P < .001), despite no differences between the 2 groups in mean body weight or body mass index. Patients in group 1 (structured diabetes education with repeated reinforcement) showed significant improvement in healthy lifestyle parameter scores versus group 2 (P < .05) and versus values before education (P < .05). At end of study, these achievements were translated into proper blood sugar monitoring, management of both hypoand hyperglycemia, improvements in logbook use and healthy sharp disposal, Ramadan fasting, sick day management, and knowledge on the importance of HbA1c (P < .05), which translated to decrease of HbA1c in group 1 by 1.35%. In group 1, proteinuria decreased significantly compared with before education and compared with group 2 values (P = .016). Diabetic retinopathy and neuropathy remained comparable between groups (P > .05).

CONCLUSIONS

Structured diabetes education improved lifestyle knowledge, self-care diabetes management, and metabolic control variables among kidney transplant recipients with posttransplant diabetes. Structured diabetes education also resulted in partial reversibility of the present early diabetic nephropathy. We recommended such education to be delivered to all kidney transplant recipients with diabetes.

DBAN: An improved dual branch attention network combined with serum Raman spectroscopy for diagnosis of diabetic kidney disease

Diabetic kidney disease (DKD) is one of the most common kidney diseases worldwide. It is estimated that approximately 537 million adults worldwide have diabetes, and up to 30%-40% of diabetic patients are at risk of developing nephropathy. The pathogenesis of DKD is complex, and its onset is insidious. Currently, the clinical diagnosis of DKD primarily relies on the increase of urinary albumin and the decrease in glomerular filtration rate in diabetic patients. However, the excretion of urinary albumin is influenced by various factors, such as physical activity, infections, fever, and high blood glucose, making it challenging to achieve an objective and accurate diagnosis. Therefore, there is an urgent need to develop an efficient, fast, and low-cost auxiliary diagnostic technology for DKD. In this study, an improved Dual Branch Attention Network (DBAN) was developed to quickly identify DKD. Serum Raman spectroscopy samples were collected from 32 DKD patients and 32 healthy volunteers. The collected data were preprocessed using the adaptive iteratively reweighted penalized least squares (airPLS) algorithm, and the DBAN was used to classify the serum Raman spectroscopy data of DKD. The model consists of a dual branch structure that extracts features using Convolutional Neural Network (CNN) and bottleneck layer modules. The attention module allows the model to learn features specifically, and lateral connections are added between the dual branches to achieve multi-level and multi-scale fusion of shallow and deep features, as well as local and global features, improving the classification accuracy of the experiment. The results of the study showed that compared to traditional deep learning algorithms such as Artificial Neural Network (ANN), CNN, GoogleNet, ResNet, and AlexNet, our proposed DBAN classification model achieved 95.4% accuracy, 98.0% precision, 96.5% sensitivity, and 97.2% specificity, demonstrating the best classification performance. This is the best method for identifying DKD, and has important reference value for the diagnosis of DKD patients, as well as improving the accuracy of medical auxiliary diagnosis.

[Status Quo and Research Progress in Diagnosis and Treatment of Patients With Diabetes Mellitus and Chronic Kidney Disease]

As the incidence of diabetes mellitus is rapidly increasing worldwide,that of related complications,such as diabetic kidney disease(DKD),also increases,conferring a heavy economic burden on the patients,families,society,and government.Diabetes mellitus complicated with chronic kidney disease(CKD)includes DKD and the CKD caused by other reasons.Because of the insufficient knowledge about CKD,the assessment of diabetes mellitus complicated with CKD remains to be improved.The therapies for diabetes mellitus complicated with CKD focus on reducing the risk factors.In clinical practice,DKD may not be the CKD caused by diabetes.According to clinical criteria,some non-diabetic kidney disease may be misdiagnosed as DKD and not be treated accurately.This review summarizes the status quo and research progress in the assessment,diagnosis,and treatment of diabetes mellitus complicated with CKD and predicts the directions of future research in this field.

Clinical value of noninvasive lens advanced glycation end product detection in early screening and severity evaluation of patients with diabetic kidney disease

BACKGROUND

Advanced glycation end products (AGEs) deposited in the lens are correlated with those in the kidneys, indicating a possible value in evaluating diabetic kidney disease (DKD). This study explored the value of noninvasively measuring lens AGEs to diagnose and evaluate the severity of diabetic nephropathy in patients with type 2 diabetes mellitus (T2DM).

METHODOLOGY

A total of 134 T2DM patients admitted to the Fifth People's Hospital of Shanghai from March 2020 to May 2021 were selected randomly. Patients were divided into low-, medium-and high-risk groups according to the risk assessment criteria for DKD progression and into DKD and non-DKD (non-DKD) groups according to the Guidelines for the Prevention and Treatment of Diabetic Nephropathy in China. The concentrations of noninvasive AGEs in the lens in all the groups were retrospectively analyzed.

RESULTS

The concentration of noninvasive lens AGEs in the high-risk patients, according to the 2012 guidelines of the Global Organization for Improving the Prognosis of Kidney Diseases, was significantly higher than that in the remaining groups. Regression analysis suggested the value of lens AGEs in diagnosing DKD and evaluating DKD severity. Cox regression analysis indicated that the noninvasive lens AGE concentration was positive correlated with the course of disease.

CONCLUSION

The receiver operating characteristic (ROC) curve suggested that using noninvasive lens AGE measurements has clinical value in the diagnosis of DKD (area under the curve 62.4%,95% confidence interval (CI) 52.4%-73.9%, p = 0.014) and in assessing the severity of DKD (area under the curve 83.2%, 95% CI 74.1%-92.3%, P < 0.001). Noninvasive lens AGE testing helps screen T2DM patients for DKD and evaluate the severity of DKD.

Cardiovascular risk and renal injury profile in subjects with type 2 diabetes and non-albuminuric diabetic kidney disease

BACKGROUND

In the last years, the classical pattern of diabetic kidney disease (DKD) has been partially overcome, because of the uncovering of a new DKD phenotype with significant renal dysfunction without presence of albuminuria: the non-albuminuric DKD (NA-DKD). To date, the cardiovascular risk associated with this phenotype is still debated. We investigated the cardiovascular risk and renal injury profile of NA-DKD subjects in comparison with other DKD phenotypes.

METHODS

Pulse wave velocity (PWV), intima-media thickness, presence of carotid atherosclerotic plaque, renal resistive index (RRI), and a panel of urinary biomarkers of kidney injury were evaluated in 160 subjects with type 2 diabetes, stratified according to estimated glomerular filtration rate (eGFR) and urinary albumin to creatinine ratio (UACR) into four groups: controls (UACR < 30 mg/g and eGFR ≥ 60 mL/min/1.73 m2), A-DKD (Albuminuric-DKD, UACR ≥ 30 mg/g and eGFR ≥ 60 mL/min/1.73 m2), NA-DKD (UACR < 30 mg/g and eGFR < 60 mL/min/1.73 m2), AL-DKD (Albuminuric and Low eGFR-DKD; UACR ≥ 30 mg/g and eGFR < 60 mL/min/1.73 m2).

RESULTS

Subjects with NA-DKD showed a higher PWV (11.83 ± 3.74 m/s vs. 10.24 ± 2.67 m/s, P = 0.045), RRI (0.76 ± 0.11 vs. 0.71 ± 0.09, P = 0.04), and prevalence of carotid atherosclerotic plaque (59% vs. 31%, P = 0.009) compared with controls. These characteristics were similar to those of subjects with AL-DKD, whereas the profile of A-DKD subjects was closer to controls. After multiple regression analyses, we found that RRI, that is in turn influenced by eGFR (β = - 0.01, P = 0.01), was one of the major determinants of PWV (β = 9.4, P = 0.02). Urinary TreFoil Factor 3, a marker of tubular damage, was higher in NA-DKD subjects vs. controls (1533.14 ± 878.31 ng/mL vs. 1253.84 ± 682.17 ng/mL, P = 0.047). Furthermore, after multiple regression analyses, we found that urinary osteopontin was independently associated with PWV (β = 2.6, P = 0.049) and RRI (β = 0.09, P = 0.006).

CONCLUSIONS

Our data showed a worse cardiovascular and renal injury profile in NA-DKD subjects. This finding emphasizes the central role of eGFR in the definition of cardiovascular risk profile of diabetic subjects together with albuminuria.

Resting energy expenditure based on equation estimation can predict renal outcomes in patients with type 2 diabetes mellitus and biopsy-proven diabetic kidney disease

AIMS

The aim of this study was to investigate the relationship between resting energy expenditure (REE) based on equation estimation and renal outcomes in patients with diabetes kidney disease (DKD).

METHODS

A total of 124 patients were enrolled from a retrospective cohort of Type 2 Diabetes mellitus (T2DM) patients with biopsy-proven DKD. Renal outcome defined as End-Stage Renal Disease (ESRD). To compare the predictive ability of different REE estimation equations on ESRD. Patients' REE was assessed according to the estimating equation with the best predictive power, and then the relationship between REE and ESRD risk was fitted using a restricted cubic spline curve (RCS) plot and REE cutoff values were obtained. Grouping using cutoff values, and ultimately evaluate the relationship between REE and the risk of ESRD using a Multivariate Cox regression model.

RESULTS

The strongest predictive validity for renal outcomes was the NDCKD-equation. The patients were divided into the higher-REE group (n = 78) and the lower-REE group (n = 46), based on the cutoff value. During the follow-up, 30 of 124 patients (24.2%) proceeded to ESRD. Multivariate Cox regression models showed that the risk of ESRD in patients with lower REE was 6.08 times increased compared with that in those with higher REE (HR = 6.08; 95% CI, 1.28-28.80, p = 0.023).

CONCLUSION

These findings suggested that the lower REE was an independent risk factor for unfavorable renal outcomes in patients with DKD.

Trajectories of eGFR and risk of albuminuria in youth with type 2 diabetes: results from the TODAY cohort study

BACKGROUND

We conducted exploratory analyses to identify distinct trajectories of estimated glomerular filtration rate (eGFR) and their relationship with hyperfiltration, subsequent rapid eGFR decline, and albuminuria in participants with youth-onset type 2 diabetes enrolled in the Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY) study.

METHODS

Annual serum creatinine, cystatin C, urine albumin, and creatinine measurements were obtained from 377 participants followed for ≥ 10 years. Albuminuria and eGFR were calculated. Hyperfiltration peak is the greatest eGFR inflection point during follow-up. Latent class modeling was applied to identify distinct eGFR trajectories.

RESULTS

At baseline, participants' mean age was 14 years, type 2 diabetes duration was 6 months, mean HbA1c was 6%, and mean eGFR was 120 ml/min/1.73 m2. Five eGFR trajectories associated with different rates of albuminuria were identified, including a "progressive increasing eGFR" group (10%), three "stable eGFR" groups with varying starting mean eGFR, and an "eGFR steady decline" group (1%). Participants who exhibited the greatest peak eGFR also had the highest levels of elevated albuminuria at year 10. This group membership was characterized by a greater proportion of female and Hispanic participants.

CONCLUSIONS

Distinct eGFR trajectories that associate with albuminuria risk were identified, with the eGFR trajectory characterized by increasing eGFR over time associating with the highest level of albuminuria. These descriptive data support the current recommendations to estimate GFR annually in young persons with type 2 diabetes and provide insight into eGFR-related factors which may contribute to predictive risk strategies for kidney disease therapies in youth with type 2 diabetes.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT00081328, date registered 2002. A higher resolution version of the Graphical abstract is available as Supplementary information.

Quantitative profiling of carboxylic compounds by gas chromatography-mass spectrometry for revealing biomarkers of diabetic kidney disease

Diabetic kidney disease (DKD), a common microvascular complication of diabetes, currently lacks specific diagnostic indicators and therapeutic targets, resulting in miss of early intervention. To profile metabolic conditions in complex and precious biological samples and screen potential biomarkers for DKD diagnosis and prognosis, a rapid, convenient and reliable quantification method for carboxyl compounds by gas chromatography-mass spectrometry (GC-MS) was established with isobutyl chloroformate derivatization. The derivatives were extracted with hexane, injected into GC-MS and quantified with selected ion monitoring mode. This method showed excellent linearity(R2 > 0.99), good recoveries (81.1%-115.5%), good repeatability (RSD < 20%) and sensitivity (LODs: 0.20-499.90 pg, LOQs: 2.00-1007.00 pg). Among the 37 carboxyl compounds analyzed, 12 metabolites in short-chain fatty acids (SCFAs) metabolism pathway and amino acid metabolism pathway were linked with DKD development and among them, 6 metabolites were associated with both development and prognosis of DKD in mice. In conclusion, a reliable, convenient and sensitive method based on isobutyl chloroformate derivatization and GC-MS analysis is established and successfully applied to quantify 37 carboxyl compounds in biological samples of mice and 12 potential biomarkers for DKD development and prognosis are screened.