Protein glycation products associate with progression of kidney disease and incident cardiovascular events in individuals with type 1 diabetes.

BACKGROUND: Despite improved glycemic treatment, the impact of glycation on pathological consequences may persist and contribute to adverse clinical outcomes in diabetes. In the present study we investigated the association between serum protein glycation products and progression of kidney disease as well as incident major adverse cardiovascular events (MACE) in type 1 diabetes. METHODS: Fructosamine, advanced glycation end products (AGEs), and methylglyoxal-modified hydro-imidazolone (MG-H1) were measured from baseline serum samples in the FinnDiane study (n = 575). Kidney disease progression was defined as steep eGFR decline (> 3 mL/min/1.73 m2/year) or progression of albuminuria (from lower to higher stage of albuminuria). MACE was defined as acute myocardial infarction, coronary revascularization, cerebrovascular event (stroke), and cardiovascular death. RESULTS: Fructosamine was independently associated with steep eGFR decline (OR 2.15 [95% CI 1.16-4.01], p = 0.016) in the fully adjusted model (age, sex, baseline eGFR). AGEs were associated with steep eGFR decline (OR 1.58 per 1 unit of SD [95% CI 1.07-2.32], p = 0.02), progression to end-stage kidney disease (ESKD) (HR 2.09 per 1 unit of SD [95% CI 1.43-3.05], p < 0.001), and pooled progression (to any stage of albuminuria) (HR 2.72 per 1 unit of SD [95% CI 2.04-3.62], p < 0.001). AGEs (HR 1.57 per 1 unit of SD [95% CI 1.23-2.00], p < 0.001) and MG-H1 (HR 4.99 [95% CI 0.98-25.55], p = 0.054) were associated with incident MACE. MG-H1 was also associated with pooled progression (HR 4.19 [95% CI 1.11-15.89], p = 0.035). Most AGEs and MG-H1 associations were no more significant after adjusting for baseline eGFR. CONCLUSIONS: Overall, these findings suggest that protein glycation products are an important risk factor for target organ damage in type 1 diabetes. The data provide further support to investigate a potential causal role of serum protein glycation in the progression of diabetes complications.

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

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

Metabolomics in diabetic nephropathy: Unveiling novel biomarkers for diagnosis (Review).

Diabetic nephropathy (DN) also known as diabetic kidney disease, is a major microvascular complication of diabetes and a leading cause of end­stage renal disease (ESRD), which affects the morbidity and mortality of patients with diabetes. Despite advancements in diabetes care, current diagnostic methods, such as the determination of albuminuria and the estimated glomerular filtration rate, are limited in sensitivity and specificity, often only identifying kidney damage after considerable morphological changes. The present review discusses the potential of metabolomics as an approach for the early detection and management of DN. Metabolomics is the study of metabolites, the small molecules produced by cellular processes, and may provide a more sensitive and specific diagnostic tool compared with traditional methods. For the purposes of this review, a systematic search was conducted on PubMed and Google Scholar for recent human studies published between 2011 and 2023 that used metabolomics in the diagnosis of DN. Metabolomics has demonstrated potential in identifying metabolic biomarkers specific to DN. The ability to detect a broad spectrum of metabolites with high sensitivity and specificity may allow for earlier diagnosis and better management of patients with DN, potentially reducing the progression to ESRD. Furthermore, metabolomics pathway analysis assesses the pathophysiological mechanisms underlying DN. On the whole, metabolomics is a potential tool in the diagnosis and management of DN. By providing a more in­depth understanding of metabolic alterations associated with DN, metabolomics could significantly improve early detection, enable timely interventions and reduce the healthcare burdens associated with this condition.

Association of diabetes with cardiovascular calcification and all-cause mortality in end-stage renal disease in the early stages of hemodialysis: a retrospective cohort study.

BACKGROUND: The main goal of this study was to examine how diabetes, cardiovascular calcification characteristics and other risk factors affect mortality in end-stage renal disease (ESRD) patients in the early stages of hemodialysis. METHODS: A total of 285 ESRD patients in the early stages of hemodialysis were enrolled in this research, including 101 patients with diabetes. Survival time was monitored, and general data, biochemical results, cardiac ultrasound calcification of valvular tissue, and thoracic CT calcification of the coronary artery and thoracic aorta were recorded. Subgroup analysis and logistic regression were applied to investigate the association between diabetes and calcification. Cox regression analysis and survival between calcification, diabetes, and all-cause mortality. Additionally, the nomogram model was used to estimate the probability of survival for these individuals, and its performance was evaluated using risk stratification, receiver operating characteristic, decision, and calibration curves. RESULTS: Cardiovascular calcification was found in 81.2% of diabetic patients (82/101) and 33.7% of nondiabetic patients (62/184). Diabetic patients had lower phosphorus, calcium, calcium-phosphorus product, plasma PTH levels and lower albumin levels (p < 0.001). People with diabetes were more likely to have calcification than people without diabetes (OR 5.66, 95% CI 1.96-16.36; p < 0.001). The overall mortality rate was 14.7% (42/285). The risk of death was notably greater in patients with both diabetes and calcification (29.27%, 24/82). Diabetes and calcification, along with other factors, collectively predict the risk of death in these patients. The nomogram model demonstrated excellent discriminatory power (area under the curve (AUC) = 0.975 at 5 years), outstanding calibration at low to high-risk levels and provided the greatest net benefit across a wide range of clinical decision thresholds. CONCLUSIONS: In patients with ESRD during the early period of haemodialysis, diabetes significantly increases the risk of cardiovascular calcification, particularly multisite calcification, which is correlated with a higher mortality rate. The risk scores and nomograms developed in this study can assist clinicians in predicting the risk of death and providing individualised treatment plans to lower mortality rates in the early stages of hemodialysis.

Growth Differentiation Factor 15 and Matrix Metalloproteinase 3 in Plasma as Biomarkers for Neuropathy and Nephropathy in Type 1 Diabetes.

Diabetic neuropathy and nephropathy are common complications of type 1 diabetes (T1D). The symptoms are often elusive in the early stages, and available diagnostic methods can be improved using biomarkers. Matrix metalloproteinase 3 (MMP-3) has been identified in the kidneys and is thought to be involved in diabetic nephropathy. Growth differentiation factor 15 (GDF-15) has been suggested to have positive effects in diabetes, but is otherwise associated with adverse effects such as cardiovascular risk, declined kidney function, and neurodegeneration. This study aims to investigate plasma MMP-3 and GDF-15 as systemic biomarkers for diabetic neuropathy and nephropathy in T1D. The study involves patients with childhood-onset T1D (n = 48, age 38 ± 4 years) and a healthy control group (n = 30, age 38 ± 5 years). Neurophysiology tests, evaluations of albuminuria, and measurements of routine biochemical markers were conducted. The neuropathy impairment assessment (NIA) scoring system, where factors such as loss of sensation and weakened reflexes are evaluated, was used to screen for symptoms of neuropathy. MMP-3 and GDF-15 concentrations were determined in heparinized plasma using ELISA kits. In total, 9 patients (19%) had albuminuria, and 25 (52%) had diabetic neuropathy. No significant differences were found in MMP-3 concentrations between the groups. GDF-15 levels were higher in T1D, with median and interquartile range (IQR) of 358 (242) pg/mL in T1D and 295 (59) in controls (p < 0.001). In the merged patient group, a positive correlation was found between MMP-3 and plasma creatinine, a negative correlation was found between MMP-3 and estimated glomerular filtration rate (eGFR; rho = -0.358, p = 0.012), and there was a positive correlation between GDF-15 and NIA (rho = 0.723, p < 0.001) and high-sensitive C-reactive protein (rho = 0.395, p = 0.005). MMP-3 was increased in macroalbuminuria and correlated positively with NIA only in the nine T1D patients with albuminuria (rho = 0.836, p = 0.005). The present study indicates that high MMP-3 is associated with low eGFR, high plasma creatinine, and macroalbuminuria, and that GDF-15 can be a biomarker for diabetic neuropathy in T1D. MMP-3 may be useful as biomarker for neuropathy in T1D with albuminuria.

Risk prediction models for diabetic nephropathy among type 2 diabetes patients in China: a systematic review and meta-analysis.

Objective: This study systematically reviews and meta-analyzes existing risk prediction models for diabetic kidney disease (DKD) among patients with type 2 diabetes, aiming to provide references for scholars in China to develop higher-quality risk prediction models. Methods: We searched databases including China National Knowledge Infrastructure (CNKI), Wanfang Data, VIP Chinese Science and Technology Journal Database, Chinese Biomedical Literature Database (CBM), PubMed, Web of Science, Embase, and the Cochrane Library for studies on the construction of DKD risk prediction models among type 2 diabetes patients, up until 28 December 2023. Two researchers independently screened the literature and extracted and evaluated information according to a data extraction form and bias risk assessment tool for prediction model studies. The area under the curve (AUC) values of the models were meta-analyzed using STATA 14.0 software. Results: A total of 32 studies were included, with 31 performing internal validation and 22 reporting calibration. The incidence rate of DKD among patients with type 2 diabetes ranged from 6.0% to 62.3%. The AUC ranged from 0.713 to 0.949, indicating the prediction models have fair to excellent prediction accuracy. The overall applicability of the included studies was good; however, there was a high overall risk of bias, mainly due to the retrospective nature of most studies, unreasonable sample sizes, and studies conducted in a single center. Meta-analysis of the models yielded a combined AUC of 0.810 (95% CI: 0.780-0.840), indicating good predictive performance. Conclusion: Research on DKD risk prediction models for patients with type 2 diabetes in China is still in its initial stages, with a high overall risk of bias and a lack of clinical application. Future efforts could focus on constructing high-performance, easy-to-use prediction models based on interpretable machine learning methods and applying them in clinical settings. Registration: This systematic review and meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a recognized guideline for such research. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42024498015.

Glucocorticoid does not improve the renal prognosis of patients with diabetic nephropathy combined with acute tubulointerstitial nephritis: a retrospective analysis.

BACKGROUND AND OBJECTIVES: In clinical practice, some patients are diagnosed with diabetic nephropathy (DN) combined with acute tubulointerstitial nephritis (ATIN) through renal biopsy. There is relatively little research on the treatment and prognosis of such patients, and no consensus exists on the use of glucocorticoid for treatment. Therefore, our study explores the progression of DN combined with ATIN and the renal outcomes after treatment with glucocorticoid. METHODS: This study retrospectively analyzed patients diagnosed with DN combined with ATIN through renal biopsy at our center from January 1, 2015, to December 31, 2021. We collected general patient information, laboratory indicators, renal pathology indicators, and the glucocorticoid usage after kidney biopsy. Follow-up data were collected from medical records. Statistical analysis methods included t-tests, non-parametric tests, and chi-square tests. Univariate and multivariate Cox regression analyses were used to evaluate the risk factors for renal endpoint events in patients. Statistical significance was defined as p-values < 0.05. RESULTS: In this study, a total of 67 patients were included. The subjects were divided into two groups based on whether they received glucocorticoid treatment: 33 patients in the steroid group and 34 in the non-steroid group. In the steroid group, 19 patients reached the renal endpoint event, which was significantly higher than in the non-steroid group (57.58% vs. 29.41%, p = 0.038). Univariate Cox regression analysis showed that serum creatinine (HR = 1.008, p < 0.001), albumin (HR = 0.919, p < 0.001), 24-h urinary protein (HR = 1.093, p = 0.002), hemoglobin (HR = 0.964, p = 0.001), triglycerides (HR = 1.12, p = 0.04), and the use of glucocorticoid (HR = 2.507, p = 0.019) were influencing factors for renal endpoint events in patients with DN combined with ATIN. Multivariate Cox regression analysis showed that albumin (HR = 0.863, p = 0.003) was an independent risk factor for renal endpoint events in patients with DN combined with ATIN. CONCLUSIONS: The use of glucocorticoid in treatment does not improve renal prognosis in patients with DN combined with ATIN. Lower levels of albumin are associated with a worse renal prognosis.

Incident heart failure and the subsequent risk of progression to end stage kidney disease in individuals with type 2 diabetes.

BACKGROUND: Diabetic kidney disease is an established risk factor for heart failure. However, the impact of incident heart failure on the subsequent risk of renal failure has not been systematically assessed in diabetic population. We sought to study the risk of progression to end stage kidney disease (ESKD) after incident heart failure in Asian patients with type 2 diabetes. METHODS: In this prospective cohort study, 1985 outpatients with type 2 diabetes from a regional hospital and a primary care facility in Singapore were followed for a median of 8.6 (interquartile range 6.2-9.6) years. ESKD was defined as a composite of progression to sustained eGFR below 15 ml/min/1.73m2, maintenance dialysis or renal death, whichever occurred first. RESULTS: 180 incident heart failure events and 181 incident ESKD events were identified during follow-up. Of 181 ESKD events, 38 (21%) occurred after incident heart failure. Compared to those did not progress to ESKD after incident heart failure (n = 142), participants who progressed to ESKD after heart failure occurrence were younger, had higher HbA1c and higher urine albumin-to-creatinine ratio at baseline. The excess risk of ESKD manifested immediately after heart failure occurrence, persisted for two years and was moderated thereafter. Cox regression suggested that, compared to counterparts with no heart failure event, participants with heart failure occurrence had 9.6 (95% CI 5.0- 18.3) fold increased risk for incident ESKD after adjustment for baseline cardio-renal risk factors including eGFR and albuminuria. It appeared that heart failure with preserved ejection fraction had a higher risk for ESKD as compared to those with reduced ejection fraction (adjusted HR 13.7 [6.3-29.5] versus 6.5 [2.3-18.6]). CONCLUSION: Incident heart failure impinges a high risk for progression to ESKD in individuals with type 2 diabetes. Our data highlight the need for intensive surveillance of kidney function after incident heart failure, especially within the first two years after heart failure diagnosis.

The threshold effect of triglyceride glucose index on diabetic kidney disease risk in patients with type 2 diabetes: unveiling a non-linear association.

Background: Previous studies have confirmed that the triglyceride glucose (TyG) index, recognized as a reliable marker of insulin resistance, is an important risk factor for diabetic kidney disease (DKD). However, it is still unclear whether the DKD risk continues to increase linearly with the elevation of TyG index. This study aimed to thoroughly investigated the intrinsic relationship between TyG index and DKD risk in type 2 diabetes (T2D). Methods: This cross-sectional study included 933 patients with T2D in China, who were categorized into DKD and non-DKD groups and stratified by TyG index levels. Logistic regression analysis identified the independent risk factors for DKD. The association between DKD risk and TyG index was evaluated using the restricted cubic spline (RCS) curves analysis. The R package 'CatPredi' was utilized to determine the optimal cut-off point for the relationship between DKD risk and TyG index, followed by threshold effect analysis. Results: The prevalence of DKD was 33.01%. After adjusting for confounding factors, TyG index was identified as a prominent clinical risk factor for DKD, showing the highest odds ratio (OR 1.57 (1.26 - 1.94), P<0.001). RCS analysis revealed a non-linear relationship with a threshold interval effect between the TyG index and DKD risk. When TyG index ≤ 9.35, DKD risk plateaued at a low level; however, when TyG index > 9.35, DKD risk increased gradually with rising TyG index. Among patients with TyG index > 9.35, each 1-unit increase was associated with a 1.94-fold increased DKD risk (OR=1.94 (1.10 - 3.43), P=0.022). Conclusion: The DKD risk presented a threshold effect with the increase of TyG index, initially stable at a low level, and then gradually rising when the TyG index is above 9.35.

Association between red cell distribution width/serum albumin ratio and diabetic kidney disease.

BACKGROUND: Previous studies have shown that the red cell distribution width (RDW)/serum albumin ratio (RA) is an integrative and new inflammatory marker. RA is associated with clinical outcomes in a variety of diseases, but the clinical value of RDW/RA in the assessment of diabetic kidney disease (DKD) has not been elucidated. We examined the link between diabetic RA and DKD while controlling for a wide variety of possible confounders. METHODS: Retrospective cohort analysis of the National Health and Nutrition Examination Survey (NHANES: 2009-2018) database from the Second Affiliated Hospital and Yuying Children's Hospital and the Wenzhou Medical University (WMU) database was conducted. Multivariate logistic regression analysis was used to assess the association between RA and DKD. RESULTS: Overall, 4513 diabetic patients from the NHANES database (n = 2839) and the WMU (n = 1412) were included in this study; 974 patients were diagnosed with DKD in NHANES and 462 in WMU. In the NHANES cohort, diabetes mellitus (DM) patients with higher RA level had a higher risk of DKD (odds ratio = 1.461, 95% confidence interval: 1.250-1.707, p < 0.00001). After adjusting for confounders and propensity score-matched (PSM) analysis, both shown RA levels were independently linked to DKD (pAdjust = 0.00994, pPSM = 0.02889). Similar results were also observed in the WMU cohort (p < 0.00001). CONCLUSIONS: The study observes that the RA was an independent predictor of DKD in DM patients. The RA, a biomarker that is cost-effective and easy-to-access, may have potential for risk stratification of DKD.

Preanalytical considerations in quantifying circulating miRNAs that predict end-stage kidney disease in diabetes.

Our previous study identified 8 risk and 9 protective plasma miRNAs associated with progression to end-stage kidney disease (ESKD) in diabetes. This study aimed to elucidate preanalytical factors that influence the quantification of circulating miRNAs. Using the EdgeSeq platform, which quantifies 2,002 miRNAs in plasma, including ESKD-associated miRNAs, we compared miRNA profiles in whole plasma versus miRNA profiles in RNA extracted from the same plasma specimens. Less than half of the miRNAs were detected in standard RNA extraction from plasma. Detection of individual and concentrations of miRNAs were much lower when RNA extracted from plasma was quantified by RNA sequencing (RNA-Seq) or quantitative reverse transcription PCR (qRT-PCR) platforms compared with EdgeSeq. Plasma profiles of miRNAs determined by the EdgeSeq platform had excellent reproducibility in assessment and had no variation with age, sex, hemoglobin A1c, BMI, and cryostorage time. The risk ESKD-associated miRNAs were detected and measured accurately only in whole plasma and using the EdgeSeq platform. Protective ESKD-associated miRNAs were detected by all platforms except qRT-PCR; however, correlations among concentrations obtained with different platforms were weak or nonexistent. In conclusion, preanalytical factors have a profound effect on detection and quantification of circulating miRNAs in ESKD in diabetes. Quantification of miRNAs in whole plasma and using the EdgeSeq platform may be the preferable method to study profiles of circulating cell-free miRNAs associated with ESKD and possibly other diseases.

Urinary biomarkers in diabetic nephropathy.

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

Urinary SIRT2 Reflects Kidney Injury in Type 2 Diabetes.

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

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

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

Interpretable machine learning identifies metabolites associated with glomerular filtration rate in type 2 diabetes patients.

Objective: The co-occurrence of kidney disease in patients with type 2 diabetes (T2D) is a major public health challenge. Although early detection and intervention can prevent or slow down the progression, the commonly used estimated glomerular filtration rate (eGFR) based on serum creatinine may be influenced by factors unrelated to kidney function. Therefore, there is a need to identify novel biomarkers that can more accurately assess renal function in T2D patients. In this study, we employed an interpretable machine-learning framework to identify plasma metabolomic features associated with GFR in T2D patients. Methods: We retrieved 1626 patients with type 2 diabetes (T2D) in Liaoning Medical University First Affiliated Hospital (LMUFAH) as a development cohort and 716 T2D patients in Second Affiliated Hospital of Dalian Medical University (SAHDMU) as an external validation cohort. The metabolite features were screened by the orthogonal partial least squares discriminant analysis (OPLS-DA). We compared machine learning prediction methods, including logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost). The Shapley Additive exPlanations (SHAP) were used to explain the optimal model. Results: For T2D patients, compared with the normal or elevated eGFR group, glutarylcarnitine (C5DC) and decanoylcarnitine (C10) were significantly elevated in GFR mild reduction group, and citrulline and 9 acylcarnitines were also elevated significantly (FDR<0.05, FC > 1.2 and VIP > 1) in moderate or severe reduction group. The XGBoost model with metabolites had the best performance: in the internal validate dataset (AUROC=0.90, AUPRC=0.65, BS=0.064) and external validate cohort (AUROC=0.970, AUPRC=0.857, BS=0.046). Through the SHAP method, we found that C5DC higher than 0.1µmol/L, Cit higher than 26 µmol/L, triglyceride higher than 2 mmol/L, age greater than 65 years old, and duration of T2D more than 10 years were associated with reduced GFR. Conclusion: Elevated plasma levels of citrulline and a panel of acylcarnitines were associated with reduced GFR in T2D patients, independent of other conventional risk factors.

Insulin sensitivity estimates and their longitudinal association with coronary artery disease in type 1 diabetes. Does it matter?

BACKGROUND: Insulin resistance and chronic kidney disease are both associated with increased coronary artery disease risk. Many formulae estimating glucose disposal rate in type 1 diabetes infer insulin sensitivity from clinical data. We compare associations and performance relative to traditional risk factors and kidney disease severity between three formulae estimating the glucose disposal rate and coronary artery disease in people with type 1 diabetes. METHODS: The baseline glucose disposal rate was estimated by three (Williams, Duca, and Januszewski) formulae in FinnDiane Study participants and related to subsequent incidence of coronary artery disease, by baseline kidney status. RESULTS: In 3517 adults with type 1 diabetes, during median (IQR) 19.3 (14.6, 21.4) years, 539 (15.3%) experienced a coronary artery disease event, with higher rates with worsening baseline kidney status. Correlations between the three formulae estimating the glucose disposal rate were weak, but the lowest quartile of each formula was associated with higher incidence of coronary artery disease. Importantly, only the glucose disposal rate estimation by Williams showed a linear association with coronary artery disease risk in all analyses. Of the three formulae, Williams was the strongest predictor of coronary artery disease. Only age and diabetes duration were stronger predictors. The strength of associations between estimated glucose disposal rate and CAD incidence varied by formula and kidney status. CONCLUSIONS: In type 1 diabetes, estimated glucose disposal rates are associated with subsequent coronary artery disease, modulated by kidney disease severity. Future research is merited regarding the clinical usefulness of estimating the glucose disposal rate as a coronary artery disease risk factor and potential therapeutic target.

Research on disease diagnosis based on teacher-student network and Raman spectroscopy.

Diabetic nephropathy is a serious complication of diabetes, and primary Sjögren's syndrome is a disease that poses a major threat to women's health. Therefore, studying these two diseases is of practical significance. In the field of spectral analysis, although common Raman spectral feature selection models can effectively extract features, they have the problem of changing the characteristics of the original data. The teacher-student network combined with Raman spectroscopy can perform feature selection while retaining the original features, and transfer the performance of the complex deep neural network structure to another lightweight network structure model. This study selects five flow learning models as the teacher network, builds a neural network as the student network, uses multi-layer perceptron for classification, and selects the optimal features based on the evaluation indicators accuracy, precision, recall, and F1-score. After five-fold cross-validation, the research results show that in the diagnosis of diabetic nephropathy, the optimal accuracy rate can reach 98.3%, which is 14.02% higher than the existing research; in the diagnosis of primary Sjögren's syndrome, the optimal accuracy rate can be reached 100%, which is 10.48% higher than the existing research. This study proved the feasibility of Raman spectroscopy combined with teacher-student network in the field of disease diagnosis by producing good experimental results in the diagnosis of diabetic nephropathy and primary Sjögren's syndrome.

Characteristics and quality of clinical practice guidelines for diabetic kidney disease: a systematic review.

OBJECTIVE: To assess the quality of Clinical practice guidelines (CPGs) in the context of diabetic kidney disease (DKD) and determine whether any factors affect the quality. METHODS: We searched eight databases along with five international and national organizations to develop or archive guidelines from their inception to July 2023, with an additional search of medlive.cn. And the authoritative organizations related to nephrology. CPGs and consensus statements created using direct differential diagnosis or therapy for DKD were included without language restrictions. Their quality was evaluated by four reviewers using the Appraisal of Guidelines for Research and Evaluation Ⅱ (AGREE Ⅱ) instrument. Along with the item and domain scores, the guideline was also allocated an overall quality score, which ranged from 1 (lowest possible quality) to 7 (highest possible quality). Moreover, an overall recommendation for use was also assigned ("recommended", "recommended with modifications" or "not recommended"). RESULTS: A total of 16 CPGs were included, of which 14 were from Asia and the remaining two from Europe. These two CPGs were updated in the third version. Six CPGs were recommended for use because their primary domains scored in the medium or high category. Furthermore, five CPGs were recommended with modifications as the stakeholder involvement, applicability, and editorial independence domains were evaluated as low categories. In all domains, the lowest average score was for rigour of development (33%), followed by application (36%), and stakeholder involvement (51%). The highest average score was for scope and purpose (79%), followed by clarity of presentation (75%). None of the CPGs considered the patient's viewpoint, and six of 16 CPGs did not use any grading system to translate the evidence into recommendations. Additionally, only three of 16 CPGs shared search strategy, and eight of 16 CPGs did not declare a funding source. CONCLUSIONS: According to the AGREE II evaluation, more than one in four CPGs for DKD had poor methodological quality. Enhanced efforts are needed to advance the rigour of development, application, and editorial independence of DKD guideline panels for most guidelines. Stakeholders, CPG developers, and CPG users should consider methodological quality while choosing CPGs, and interpret and implement their issued suggestions.

Targeted and untargeted serum NMR metabolomics to reveal initial kidney disease in diabetes mellitus.

Serum 1H NMR metabolomics has been used as a diagnostic tool for screening type 2 diabetes (T2D) with chronic kidney disease (CKD) as comorbidity. This work aimed to evaluate 1H NMR data to detect the initial kidney damage and CKD in T2D subjects, through multivariate statistical analysis. Clinical data and biochemical parameters were obtained for classifying five experimental groups using KDIGO guidelines: Control (healthy subjects), T2D, T2D-CKD-mild, T2D-CKD-moderate, and T2D-CKD-severe. Serum 1H NMR spectra were recorded to follow two strategies: one based on metabolite-to-creatinine (Met/Cr) ratios as targeted metabolomics, and the second one based on untargeted metabolomics from the 1H NMR profile. A prospective biomarkers panel of the early stage of T2D-CKD based in metabolite-to-creatinine ratio (ornithine/Cr, serine/Cr, mannose/Cr, acetate/Cr, acetoacetate/Cr, formate/Cr, and glutamate/Cr) was proposed. Later, a statistical model based on non-targeted metabolomics was used to predict initial CKD, and its metabolic pathway analysis allowed identifying the most affected pathways: phenylalanine, tyrosine, and tryptophan biosynthesis; valine, leucine, and isoleucine degradation; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; and histidine metabolism. Nonetheless, further studies with a larger cohort are advised to precise ranges in metabolite-to-creatinine ratios and evaluate the prediction pertinency to detect initial CKD in T2D patients in both statistical models proposed.

25(OH)D level and vascular lesion scores in kidney histopathology as risk-stratification tool for predicting renal progression in people with type 2 diabetes.

AIM: To evaluate the potential of the combined individual vascular histopathological lesion and serum 25-hydroxy vitamin D [25(OH)D] level as predictors of outcomes in patients with diabetes and chronic kidney disease. METHODS: A total of 190 patients with type 2 diabetes and kidney disease stages 1-4 were retrospectively included. Kaplan-Meier analysis and the log-rank test were performed to assess renal survival differences. And the time-dependent receiver operating characteristic analyses were used to characterize the predictive accuracy. Hazard ratios for vascular lesion scores and 25(OH)D levels with renal outcomes were estimated using Cox proportional hazards regression models with follow-up time. RESULTS: Over a median follow-up of 23.78 (12.61, 37.14) months, 71 patients (37.4 %) experienced the renal outcomes. Enrolled patients with more severe vascular lesions had worse kidney function, heavier proteinuria, lower serum 25(OH)D levels, and higher prevalence of composite kidney outcomes. Baseline serum 25(OH)D was a significant independent risk factor for vascular lesion scores. The effect of serum 25(OH)D level on kidney prognosis was more pronounced in males and those with more exacerbated vascular lesions (score 2). The severity of vascular lesions and serum 25(OH)D levels were associated with unfavorable kidney outcomes. Accordingly, further time-dependent receiver operating characteristic curves confirmed that combined 25(OH)D level and vascular lesion score had a stable and reliable performance in renal outcomes prediction at short and long-term follow-up times. CONCLUSIONS: 25(OH)D level and vascular lesion scores in kidney histopathology could serve as a useful risk-stratification tool for predicting renal progression in patients with type 2 diabetes.