Risk Factor For Diabetic Kidney Disease Research Articles

Correlation between triglyceride-glucose index and diabetic kidney disease risk in adults with type 1 diabetes mellitus

BackgroundThe triglyceride-glucose (TyG) index is identified as an alternative indicator of insulin resistance (IR) and is associated with macro- and micro-vascular diseases among patients with type 2 diabetes mellitus. The relationship between the TyG index and IR and its impact on diabetic kidney disease (DKD) remains unclear among adults with type 1 diabetes mellitus(T1DM).MethodsThis study comprised a cross-sectional analysis using data from the Guangdong T1DM Translational Medicine Study (GTT) and a longitudinal analysis using data from the type 1 diabetes (T1D) Exchange registry study. Correlation analysis was used to investigate the association between the TyG index and IR. Logistic regression and Cox proportional hazards regression were performed to explore the impact of the TyG index on DKD risk.ResultsThe GTT Study included 836 adults (216 with DKD and 620 without DKD). A significant correlation existed between the TyG index and the estimated glucose disposal rate (r=-0.64, p < 0.01). The TyG index was a risk factor for DKD after confounder adjustment (OR = 1.34, 95% CI:1.03–1.74). The T1D Exchange registry study included 8,771 adults (2,050 with DKD and 6,721 without DKD). After adjusting confounding factors, the TyG index was identified as an independent risk factor for DKD at enrollment, with the highest risk of DKD incidence observed in the highest TyG tertile group (OR = 1.92, 95%CI:1.67–2.20). During a median follow-up of 44.58(21.84, 67.09) months, the risk of developing DKD was increased by 32% at every 1 SD increase of the TyG index over time among participants without DKD at enrollment.ConclusionsThe TyG index could be used to assess IR and was identified as an independent risk factor of DKD among adults with T1DM.

Interpretable subgroup learning-based modeling framework: Study of diabetic kidney disease prediction.

Complex diseases, like diabetic kidney disease (DKD), often exhibit heterogeneity, challenging accurate risk prediction with machine learning. Traditional global models ignore patient differences, and subgroup learning lacks interpretability and predictive efficiency. This study introduces the Interpretable Subgroup Learning-based Modeling (iSLIM) framework to address these issues. iSLIM integrates expert knowledge with a tree-based recursive partitioning approach to identify DKD subgroups within an EHR dataset of 11,559 patients. It then constructs separate models for each subgroup, enhancing predictive accuracy while preserving interpretability. Five clinically relevant subgroups are identified, achieving an average sensitivity of 0.8074, outperforming a single global model by 0.1104. Post hoc analyses provide pathological and biological evidence supporting subgroup validity and potential DKD risk factors. The iSLIM surpasses traditional global model in predictive performance and subgroup-specific risk factor interpretation, enhancing the understanding of DKD's heterogeneous mechanisms and potentially increasing the adoption of machine learning models in clinical decision-making.

Disruption of branched-chain amino acid homeostasis promotes the progression of DKD via enhancing inflammation and fibrosis-associated epithelial-mesenchymal transition

Background and aimsThe disrupted homeostasis of branched-chain amino acids (BCAAs, including leucine, isoleucine, and valine) has been strongly correlated with diabetes with a potential causal role. However, the relationship between BCAAs and diabetic kidney disease (DKD) remains to be established. Here, we show that the elevated BCAAs from BCAAs homeostatic disruption promote DKD progression unexpectedly as an independent risk factor. Methods and resultsSimilar to other tissues, the suppressed BCAAs catabolic gene expression and elevated BCAAs abundance were detected in the kidneys of type 2 diabetic mice and individuals with DKD. Genetic and nutritional studies demonstrated that the elevated BCAAs from systemic disruption of BCAAs homeostasis promoted the progression of DKD. Of note, the elevated BCAAs promoted DKD progression without exacerbating diabetes in the animal models of type 2 DKD. Mechanistic studies demonstrated that the elevated BCAAs promoted fibrosis-associated epithelial-mesenchymal transition (EMT) by enhancing the activation of proinflammatory macrophages through mTOR signaling. Furthermore, pharmacological enhancement of systemic BCAAs catabolism using small molecule inhibitor attenuated type 2 DKD. Finally, the elevated BCAAs also promoted DKD progression in type 1 diabetic mice without exacerbating diabetes. ConclusionBCAA homeostatic disruption serves as an independent risk factor for DKD and restoring BCAA homeostasis pharmacologically or dietarily represents a promising therapeutic strategy to ameliorate the progression of DKD.

Clinical Outcomes Among High-Risk Primary Care Patients With Diabetic Kidney Disease: Methodological Challenges and Results From the STOP-DKD Study.

Slowing the progression of diabetic kidney disease (DKD) is critical. We conducted a randomized controlled trial to target risk factors for DKD progression. We evaluated the effect of a pharmacist-led intervention focused on supporting healthy behaviors, medication management, and self-monitoring on decline in estimated glomerular filtration rate (eGFR) for 36 months compared with an educational control. We randomized 138 individuals to the intervention group and 143 to control. At baseline, mean (SD) eGFR was 80.7 (21.7) mL/min/1.73m 2 , 56% of participants had chronic kidney disease and a history of uncontrolled hypertension with a baseline SBP of 134.3mm Hg. The mean (SD) decline in eGFR by cystatin C from baseline to 36 months was 5.0 (19.6) and 5.9 (18.6) mL/min/1.73m 2 for the control and intervention groups, respectively, with no significant between-group difference ( P =0.75). We did not observe a significant difference in clinical outcomes by study arm. However, we showed that individuals with DKD will engage in a pharmacist-led intervention. The potential explanations for a lack of change in DKD risk factors can be attributed to 5 broad issues, challenges: (1) associated with enrolling patients with low eGFR and poor BP control; (2) implementing the intervention; (3) limited duration during which to observe any clinical benefit from the intervention; (4) potential co-intervention or contamination; and (5) low statistical power.

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

BackgroundPrevious 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).MethodsThis 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.ResultsThe 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&amp;lt;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 &amp;gt; 9.35, DKD risk increased gradually with rising TyG index. Among patients with TyG index &amp;gt; 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).ConclusionThe 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.

Construction of Risk Prediction Model of Type 2 Diabetic Kidney Disease Based on Deep Learning.

This study aimed to develop a diabetic kidney disease (DKD) prediction model using long short term memory (LSTM) neural network and evaluate its performance using accuracy, precision, recall, and area under the curve (AUC) of the receiver operating characteristic (ROC) curve. The study identified DKD risk factors through literature review and physician focus group, and collected 7 years of data from 6,040 type 2 diabetes mellitus patients based on the risk factors. Pytorch was used to build the LSTM neural network, with 70% of the data used for training and the other 30% for testing. Three models were established to examine the impact of glycosylated hemoglobin (HbA1c), systolic blood pressure (SBP), and pulse pressure (PP) variabilities on the model's performance. The developed model achieved an accuracy of 83% and an AUC of 0.83. When the risk factor of HbA1c variability, SBP variability, or PP variability was removed one by one, the accuracy of each model was significantly lower than that of the optimal model, with an accuracy of 78% (P<0.001), 79% (P<0.001), and 81% (P<0.001), respectively. The AUC of ROC was also significantly lower for each model, with values of 0.72 (P<0.001), 0.75 (P<0.001), and 0.77 (P<0.05). The developed DKD risk predictive model using LSTM neural networks demonstrated high accuracy and AUC value. When HbA1c, SBP, and PP variabilities were added to the model as featured characteristics, the model's performance was greatly improved.

Association between the triglyceride glucose index, triglyceride-glucose body mass index and diabetic kidney disease in adults with newly diagnosed type 2 diabetes.

The triglyceride glucose (TyG) index has been proved to be a reliable marker of diabetic kidney disease (DKD). We further investigated the association between TyG index, and its derivative, triglyceride-glucose body mass index (TyG-BMI), and the risk of DKD among adults with newly diagnosed type 2 diabetes (T2D). This cross-sectional study was conducted among patients with newly diagnosed T2D. We assessed the correlation between TyG index, TyG-BMI, and the risk of DKD using logistic regression analysis, restricted cubic spline analysis, trend tests, receiver operating characteristic curve, and subgroup analyses. Among the 924 included patients, 199 (21.5%) had DKD. Logistic regression revealed that TyG index (odds ratio [OR] 1.232, 95% confidence interval [CI] 1.064-1.428, p = 0.005) and TyG-BMI (OR 1.003, 95% CI 1.000-1.006, p = 0.021) were risk factors for DKD. The trend test demonstrated a dose-response association between TyG index (p for trend = 0.004), TyG-BMI (p for trend = 0.035), and the risk of DKD. Restricted cubic spline analysis indicated a nonlinear correlation between TyG index and the risk of DKD, with an increase in the risk of DKD when the TyG index was greater than 9.68 (p for nonlinearity = 0.014). In contrast, TyG-BMI and the risk of DKD exhibited a linear dose-response relationship, with an increase in the risk of DKD when the TyG-BMI was greater than 243 (p for nonlinearity = 0.034). According to the receiver operating characteristic curve, the optimal cutoff values for TyG index and TyG-BMI were 10.08 and 221.5, respectively. Among newly diagnosed T2D patients, the risk of DKD increases with the increase of TyG index and TyG-BMI, with their respective cut-off values being 9.68 and 243. Both TyG index and TyG-BMI have poor diagnostic value for the risk of DKD.

Low Levels of Metrnl are Linked to the Deterioration of Diabetic Kidney Disease.

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. Metrnl is a secreted protein that plays an important role in kidney disease. The aim of this study was to investigate DKD-related factors and the correlation between serum Metrnl levels and the severity of DKD. Ninety-six type 2 diabetes mellitus (T2DM) patients and 45 DKD patients were included in the study. A range of parameters were measured simultaneously, including waist-to-hip ratio (WHR), body mass index (BMI), urinary albumin/creatinine ratio (UACR), monocyte-lymphocyte ratio (MLR), albumin/globulin (A/G), liver and kidney function, blood lipid profile, islet function, and others. Subsequently, the related factors and predictive significance of DKD were identified. The correlation between the relevant factors of DKD and serum Metrnl levels with DKD was evaluated. The duration of the disease (OR: 1.12, 95% CI: 1.01-1.24, P=0.031), hypertension (OR: 4.86, 95% CI: 1.16-20.49, P=0.031), fasting blood glucose (OR: 1.23, 95% CI: 1.03-1.48, P=0.025), WHR (OR: 2.53, 95% CI: 1.03-6.22, P=0.044), and MLR (OR: 1.91, 95% CI: 1.18-3.08, P=0.008) are independent risk factors for DKD (P < 0.05). Conversely, A/G (OR: 0.13, 95% CI: 0.02-0.76, P=0.024) and Metrnl (OR: 0.99, 95% CI: 0.98-1.00, P=0.001) have been identified as protective factors against DKD. Furthermore, the level of Metrnl was negatively correlated with the severity of DKD (rs=-0.447, P<0.001). The area under receiver operating characteristic (ROC) curves for the diagnostic accuracy of Metrnl for DKD is 0.765 (95% CI: 0.686-0.844). The duration of the disease, hypertension, fasting blood glucose, WHR, and MLR are major risk factors for DKD. Metrnl and A/G are protective factors for DKD. Serum Metrnl concentrations are inversely correlated with DKD severity.

Association of Systemic Immune-Inflammation Index and Systemic Inflammation Response Index with Diabetic Kidney Disease in Patients with Type 2 Diabetes Mellitus.

To evaluate the association of the systemic immune-inflammatory index (SII) and systemic inflammatory response index (SIRI) with the clinical and pathological features and progression of diabetic kidney disease (DKD). We analyzed 303 patients with type 2 diabetes mellitus (T2DM), classifying them into distinct groups: T2DM, early DKD (EDKD), and clinical DKD (Cli-DKD). Variations in SII and SIRI levels across these groups and their association with renal function were assessed. Logistic regression analysis was used to identify independent risk factors for DKD. Additionally, in 43 patients with biopsy-confirmed DKD, we analyzed the relationship between SII, SIRI, and pathological changes. Kaplan-Meier survival analysis and the Cox proportional hazards model were used to assess the influence of SII and SIRI levels on outcomes in patients with DKD. SII and SIRI were significantly higher in the Cli-DKD group than in the T2DM and EDKD groups, and were positively correlated with the urinary albumin-creatinine ratio and negatively correlated with estimated glomerular filtration rate. Notably, SIRI was identified as an independent risk factor for DKD development. Additionally, a lower SII score was associated with a higher cumulative survival rate. This study demonstrates an association between SII, SIRI, and renal function in patients with T2DM. A high SIRI was an independent risk factor for DKD, while an elevated SII was associated with an increased risk of kidney disease progression in biopsy-confirmed DKD cases. Our findings underscore the potential implications of utilizing SII and SIRI as cost-effective and readily available inflammatory indicators for monitoring DKD in primary care settings.

Plasma thrombin-activatable fibrinolysis inhibitor and the 1040C/T polymorphism are risk factors for diabetic kidney disease in Chinese patients with type 2 diabetes.

Inflammatory and hemostatic disorders in diabetic microangiopathy (DMA) can be linked to thrombin-activatable fibrinolysis inhibitor (TAFI) and its own gene polymorphisms. Thus, the study aimed to investigate the associations of plasma TAFI and gene polymorphisms with DMA in Chinese patients with type 2 diabetes (T2D). Plasma TAFI of 223 patients with T2D was measured, and the genotypes and alleles of the 1040C/T, 438G/A, and 505G/A polymorphisms of the TAFI gene were analyzed. A ROC curve was constructed to evaluate the identifying power of TAFI between patients with T2D and DMA, and logistic regression analysis was used to observe the correlation of plasma TAFI and gene polymorphisms with the risk for DMA. Plasma TAFI was higher in patients with DMA than in patients with only T2D (p < 0.05). TAFI exhibited the largest area under ROC in identifying diabetic kidney disease (DKD) from only T2D (0.763, 95% CI [0.674-0.853], p < 0.01), and adjusted multivariate analysis showed a high odds ratio (OR: 15.72, 95% CI [4.573-53.987], p < 0.001) for DKD. Higher frequencies of the CT genotype and T allele of the 1040C/T polymorphism were found in DKD compared with only T2D (respectively p < 0.05), and the CT genotype exhibited a high OR (1.623, 95% CI [1.173-2.710], p < 0.05) for DKD. DKD patients with the CT genotype had higher plasma TAFI levels, while T2D and DKD patients with CC/TT genotypes had lower plasma TAFI levels. Plasma TAFI and the CT genotype and T allele of the 1040C/T polymorphism are independent risk factors for DKD in Chinese T2D patients.

Blood Lipid Indicators and Different Clinical Classifications of Dyslipidemia and Diabetic Kidney Disease: Correlation and Predictive Value

To explore the relationship between blood lipid indicators and different clinical classifications of dyslipidemia and diabetic kidney disease (DKD) and to compare the value of different clinical classifications of dyslipidemia for predicting DKD. Continuously enrollment of subjects was conducted at the First Affiliated Hospital of Chongqing Medical University and the Yongchuan Hospital of Chongqing Medical University between October 2020 and October 2021. A total of 356 type 2 diabetes mellitus (T2DM) patients admitted to the two hospitals were enrolled. They were divided into DKD group ( n=126) and simple T2DM group ( n=230) according to whether their T2DM was combined with DKD. In addition, 250 healthy individuals undergoing physical examination during the same period were enrolled for the control group. The blood pressure, blood lipid, blood glucose, and the kidney function indicators of the three groups were measured. The effects of different classifications of dyslipidemia on DKD were analyzed with unconditional logistic regression models, the receiver operating characteristic (ROC) curve was constructed, the area under the curve ( AUC) of ROC was calculated, and the value of different classifications of dyslipidemia for predicting DKD was analyzed. The diastolic blood pressure (DBP), systolic blood pressure (SBP), total cholesterol (TC), triacylglycerol (TG), low-density lipoprotein cholesterol (LDL-C), serum creatinine (Scr), uric acid (UA), and glycosylated hemoglobin A1c (HbA1c) of the DKD group and the simple T2DM group were significantly higher than those of the control group, while the high-density lipoprotein cholesterol (HDL-C) levels of the DKD group and the simple T2DM group were lower than that of the control group (all P<0.05). The disease course of T2DM, DBP, SBP, TC, TG, Scr, UA and HbA1c of the DKD group were significantly higher than those of the T2DM group (all P<0.05). After adjusting for the effects of T2DM disease course, DBP, SBP, Scr, UA and HbA1c, the results showed that TC ( OR=1.426, 95% CI: 1.088-1.868) and TG ( OR=1.404, 95% CI: 1.075-1.833) were independent risk factors for DKD, and that hypercholesterolemia ( OR=1.817, 95% CI: 1.040-3.177) and mixed hyperlipidemia ( OR=2.148, 95% CI: 1.110-4.159) were independent risk factors for DKD (all P<0.05). The AUC (95% CI) of hypercholesterolemia was 0.789 (0.729-0.871). The AUC (95% CI) of mixed hyperlipidemia was 0.671 (0.579-0.760). Hypercholesterolemia showed better predictive value for the diagnosis and prediction of DKD. Among the blood lipid indicators, TC and TG are independent risk factors of DKD. In the clinical classifications of dyslipidemia, hypercholesterolemia and mixed hyperlipidemia are independent risk factors of DKD. Hypercholesterolemia can be used as a predictor to screen for DKD among T2DM patients and is well suited for extensive application in outpatient screening.

Correlation Between the Variability of Different Obesity Indices and Diabetic Kidney Disease: A Retrospective Cohort Study Based on Populations in Taiwan.

To investigate the association of five obesity indices and the variability of these indices with diabetic kidney disease (DKD) in patients with type 2 diabetes and compare the predictive validity of these markers for the risk of DKD in this large longitudinal cohort study. A total of 2659 patients with type 2 diabetes who did not have DKD were enrolled between 2006 and 2019 at Lee's United Clinic in Taiwan. Data were collected for each subject, including demographic data, personal medical history, clinical parameters and calculated Body mass index (BMI), visceral adiposity index (VAI), lipid accumulation product (LAP), body roundness index (BRI) and variability of five obesity indices. Cox regression analysis was performed to determine the relationship between different obesity indicators and DKD risk. Cox's proportional hazards model was evaluated the predictive effect of obesity indices on DKD. The risk of developing DKD increased with an increase in the BRI, LAP, VAI, WC and BMI (all P trend<0.05), and the variability of VAI was significantly associated with DKD [HR=1.132, 95% CI (1.001, 1.281)] after adjusting for corresponding variables. BRI had the strongest predictive effect on DKD. BRI had the best predictive performance, with AUC of 0.807, 0.663 and 0.673 at 1, 3 and 5 years, respectively. Cox regression analysis of risk factors for DKD in patients stratified by BRI quartiles showed that patients in the Q4 group had the highest risk of developing DKD [HR=1.356, 95% CI (1.131, 1.626)]. BMI, WC, VAI, LAP, BRI and VAI variability were associated with a significant increase in the risk of DKD events, and BRI was superior and alternative obesity index for predicting DKD.

Predictive Effects of FT3/FT4 on Diabetic Kidney Disease: An Exploratory Study on Hospitalized Euthyroid Patients with T2DM in China.

This study aims to explore the correlation between the free-triiodothyronine (FT3)-to-free-thyroxine (FT4) ratio (FT3/FT4) and diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM). This study retrospectively analyzed 1729 patients with T2DM hospitalized in the Department of Endocrinology, Peking University International Hospital, from January 2017 to August 2021, including 1075 males and 654 females. In accordance with the FT3/FT4, the patients were divided into three groups. (1) The levels of glycosylated hemoglobin (HbA1c), fasting blood glucose (FBG) and postprandial blood glucose (PBG) among the three groups were significantly different, with the low FT3/FT4 group having the highest HbA1c, FBG and PBG among the three groups (F = 39.39, p < 0.01; F = 27.04, p < 0.01; F = 5.76, p = 0.03; respectively). (2) The proportion of DKD is the highest in the low FT3/FT4 group and the lowest in the high FT3/FT4 group (χ2 = 25.83, p < 0.01). (3) Logistic regression showed that low FT3/FT4 were independent risk factors for DKD (OR = 2.36, 95 CI% 1.63, 3.43; p = 0.01). A decrease in the FT3/FT4 is an independent predictor of DKD occurrence in patients with T2DM.

Changes in urinary exosomal protein CALM1 may serve as an early noninvasive biomarker for diagnosing diabetic kidney disease

BackgroundThe risk of the development and progression of diabetic kidney disease (DKD) was increased by abnormal calcium release. However, it is still unknown whether calcium signal pathway-related proteins are changed in urinary exosomes. This study aims to explore the changes in urinary exosomal proteins, which may provide novel biomarkers for diagnosing DKD. MethodsUrinary exosomes were isolated from 132 participants by size exclusion chromatography method and 72 participants were tested by LC-MS/MS (Discovery phase). Correlation and multivariate logistics analysis were applied to evaluate selected urinary proteins. Western blot and ELISA were used to validate the selected protein (Validation phase: n = 60). The diagnostic performance of the selected biomarker was evaluated by receiver operating characteristic curve analyses between the discovery and validation phases. ResultsSixteen calcium signal pathway-related proteins were identified, however, only Calmodulin-1(CALM1) was continuously increased. Different expression of CALM1 was found in patients with different level of estimated glomerular filtration rate (eGFR) in two cohorts. The level of CALM1 was correlated with eGFR and serum creatinine levels in two cohorts. Multivariate analysis revealed that serum albumin (ALB) levels and CALM1 were independent risk factors for DKD. A diagnostic model based on CALM1 and serum ALB levels that could significantly distinguish DKD was established and validated. ConclusionsSignificant changes in calcium signal pathway-related urinary exosomal proteins were observed. The CALM1 may serve as an early noninvasive biomarker for diagnosing DKD.

427-P: Comparison of Diabetic Kidney Disease Risk Factors between Normoalbuminuric Reduced and Albuminuric Preserved Kidney Function Groups

The prevalence of normoalbuminuric diabetic kidney disease (NA-DKD) in the U.S. has increased over the past 20 years, and it is higher among patients with good glycemic control. However, the factors associated with NA-DKD remained to be elucidated. Studies have compared NA-DKD with albuminuric DKD (ALB-DKD: ACR ≥ 30 mg/g and eGFR &amp;lt; 60 mL/min/1.73m2); however, ALB-DKD may include cases who have progressed from NA-DKD. Therefore, we aimed to compare DKD risk factors between patients with NA-DKD and those with only albuminuria (only-ALB) status. Among NHANES (1999-2016) adult participants with diagnosed diabetes, we compared DKD risk factors (including demographics) between patients with NA-DKD (ACR &amp;lt; 30 mg/g and eGFR &amp;lt; 60 mL/min/1.73m2) and only-ALB (ACR ≥ 30 mg/g and eGFR ≥ 60 mL/min/1.73m2) using univariate and multivariable analyses. In our diabetes cohort, there were 827 (17.9%) NA-DKD and 923 (16.6%) only-ALB cases. In the multivariable model, diabetes diagnosis age, total cholesterol, HOMA-IR, CRP, and serum uric acid were not significantly associated with albuminuric status, while blood pressure control was the strongest predictor followed by gender [Table]. Future studies should characterize pathways separate from hyperglycemia and elevated BP which contribute to NA-DKD. Disclosure M.Mosslemi: None. T.Costacou: None.

The Triglyceride Glucose (TyG) Index as a Sensible Marker for Identifying Insulin Resistance and Predicting Diabetic Kidney Disease.

BACKGROUND Patients with insulin-resistant diabetes have the highest risk of kidney disease. The triglyceride glucose (TyG) index is considered a reliable and simple marker of insulin resistance. We studied the relationship between the TyG index, diabetic kidney disease (DKD), and related metabolic disorders in patients with type 2 diabetes. MATERIAL AND METHODS This retrospective study included a consecutive case series from January 2021 to October 2022 in the Department of Endocrinology at Hebei Yiling Hospital. In total, 673 patients with type 2 diabetes met the inclusion criteria. The TyG index was calculated by napierian logarithmic (ln) (fasting triglyceride×fasting glucose /2). Patient demographic and clinical indicators were obtained from medical records, and statistical analysis was conducted using SPSS version 23. RESULTS The TyG index was significantly related to metabolic indicators (low-density lipoprotein, high-density lipoprotein, alanine aminotransferase, plasma albumin, serum uric acid, triglyceride, and fasting glucose) and urine albumin (P<0.01) but not with serum creatinine and estimated glomerular filtration rate. In multiple regression analysis, an increase in the TyG index was revealed to be an independent risk factor for DKD (OR: 1.699, P<0.001). CONCLUSIONS The TyG index was independently related to DKD and related metabolic disorders; therefore, the TyG index can be used as an early sensitive target for clinical guidance in the treatment of DKD with insulin resistance.

Genetic and epigenetic background of diabetic kidney disease.

Diabetic kidney disease (DKD) is a severe diabetic complication that affects up to half of the individuals with diabetes. Elevated blood glucose levels are a key underlying cause of DKD, but DKD is a complex multifactorial disease, which takes years to develop. Family studies have shown that inherited factors also contribute to the risk of the disease. During the last decade, genome-wide association studies (GWASs) have emerged as a powerful tool to identify genetic risk factors for DKD. In recent years, the GWASs have acquired larger number of participants, leading to increased statistical power to detect more genetic risk factors. In addition, whole-exome and whole-genome sequencing studies are emerging, aiming to identify rare genetic risk factors for DKD, as well as epigenome-wide association studies, investigating DNA methylation in relation to DKD. This article aims to review the identified genetic and epigenetic risk factors for DKD.

Diagnostic value of serum cathepsin S in type 2 diabetic kidney disease.

Identification of risk factors that have causal effects on the occurrence of diabetic kidney disease (DKD), is of great significance in early screening and intervening for DKD, and in delaying the progression of DKD to end-stage renal disease. Cathepsin S (Cat-S), a novel non-invasive diagnostic marker, mediates vascular endothelial dysfunction. The diagnostic value of Cat-S for DKD has rarely been reported in clinical studies. To analyze whether Cat-S is a risk factor for DKD and evaluate the diagnostic value of serum Cat-S for DKD. Forty-three healthy subjects and 200 type 2 diabetes mellitus (T2DM) patients were enrolled. T2DM patients were divided into subgroups according to various criteria. Enzyme-linked immunosorbent assay was used to detect serum Cat-S levels among different subgroups. Spearman correlation analysis was used to analyze correlations between serum Cat-S and clinical indicators. Multivariate logistic regression analysis was performed to analyze risk factors for the occurrence of DKD and decreased renal function in T2DM patients. Spearman analysis showed that serum Cat-S level was positively correlated with urine albumin creatinine ratio (r=0.76, P<0.05) and negatively correlated with estimated glomerular filtration rate (r=-0.54, P<0.01). Logistic regression analysis showed that increased serum Cat-S and cystatin C(CysC) were independent risk factors for DKD and decreased renal function in T2DM patients (P<0.05). The area under the receiver operating characteristic (ROC) curve was 0.900 of serum Cat-S for diagnosing DKD, and when the best cut-off value was 827.42 pg/mL the sensitivity and specificity were 71.6% and 98.8%, respectively. Thus, serum Cat-S was better than CysC for diagnosing DKD (for CysC, the area under the ROC curve was 0.791, and when the cut-off value was 1.16 mg/L the sensitivity and specificity of CysC were 47.4% and 98.8%, respectively). Increased serum Cat-S were associated with the progression of albuminuria and decreased renal function in T2DM patients. The diagnostic value of serum Cat-S was better than that of CysC for DKD. Monitoring of serum Cat-S levels could be helpful for early screening DKD and assessing the severity of DKD and could provide a new strategy for diagnosing DKD.

Association of Obesity Indices with Diabetic Kidney Disease and Diabetic Retinopathy in Type 2 Diabetes: A Real-World Study.

Diabetic microvascular complications mainly include diabetic kidney disease (DKD) and diabetic retinopathy (DR). Obesity was recognized as a risk factor for DKD, while the reported relationship between obesity and DR was inconsistent. Moreover, whether the associations can be attributed to C-peptide levels is unknown. Data from 1142 sequential inpatients with T2DM at Xiangyang Central Hospital between June 2019 and March 2022 were extracted retrospectively from the electronic medical record system. The associations between four obesity indices (body mass index (BMI), waist-hip circumference ratio (WHR), visceral fat tissue area (VFA), and subcutaneous fat tissue area (SFA)) and DKD and DR were evaluated. Whether the associations can be attributed to C-peptide levels was also explored. Obesity was a risk factor for DKD after adjusting for sex, HbA1c, TG, TC, HDL, LDL, smoking history, education, duration of diabetes, and insulin use (obesity indices: BMI (OR 1.050: 95% CI: 1.008-1.094; P = 0.020); WHR (OR 10.97; 95% CI: 1.250-92.267; P = 0.031); VFA (OR 1.005; 95% CI: 1.001-1.008; P = 0.008)), but it became insignificant after further adjusting for fasting C-peptide. The associations between BMI, WHR, VFA, and DKD might be U-shaped. Obesity and FCP tended to protect against DR; however, they became insignificant after adjusting for multiple potential confounders. C2/C0 (the ratio of the postprandial serum C-peptide to fasting C-peptide) was a protective factor for both DKD (OR 0.894, 95% CI: 0.833-0.959, P < 0.05) and DR (OR 0.851, 95% CI: 0.787-0.919; P < 0.05). Obesity was a risk factor for DKD, and the effect may be attributable to C-peptide, which represents insulin resistance. The protective effect of obesity or C-peptide on DR was not independent and could be confounded by multiple factors. Higher C2/C0 was associated with both decreased DKD and DR.

Diagnostic Value of the Combined Measurement of Serum HCY and NRG4 in Type 2 Diabetes Mellitus with Early Complicating Diabetic Nephropathy

This study aimed to investigate the value of combined detection of HCY and NRG4 in the diagnosis of early diabetic kidney disease (DKD) and to explore the association between the ratio of HCY/NRG4 and DKD. A total of 140 diabetic patients and 43 healthy people were prospectively enrolled. The plasma HCY level, NRG4 level and HCY/NRG4 of them were measured to compare their differences and analyze the correlation with DKD. The independent influencing factors of patients with DKD were screened, and the nomograph of DKD occurrence was constructed. The levels of HCY and HCY/NRG4 in diabetic patients were significantly increased, while the level of NRG4 was significantly decreased (p < 0.01). The AUCs of HCY/NRG4 predicted for DKD were 0.961. HCY/NRG4 and the course of DM were independent risk factors for DKD. A predictive nomograph of DKD was constructed, and decision curve analysis (DCA) showed good clinical application value. HCY/NRG4 was positively correlated with Scr, UACR, TG, UA, BUN, TCHOL and LDL and negatively correlated with eGFR and HDL (p < 0.05). The level of HCY and NRG4 is closely related to the severity of DM, and combined detection of HCY/NRG4 can identify patients with DKD at an early stage.