Risk Of Diabetic Kidney Disease Research Articles

Association of dietary iron intake with diabetic kidney disease among individuals with diabetes.

The current study investigated the correlation between dietary iron intake and diabetic kidney disease among diabetic adults. This cross-sectional study enrolled 8118 participants who suffered from diabetes from the National Health and Nutrition Examination Survey (NHANES) 1999-2018. Dietary iron intake was obtained from 24 h recall interviews, and diabetic kidney disease was defined as eGFR < 60 mL/min per 1.73 m2 or albumin creatinine ratio (ACR) ≥ 30 mg/g. Three weighted logistic regression models were utilized to investigate odd ratio (OR) and 95% CIs for diabetic kidney disease. Stratified analyses were performed by gender, age, BMI, HbA1c, hypertension status, and smoking status, and diabetes types. Among 8118 participants (51.6% male, mean age 61.3 years), 40.7% of participants suffered from diabetic kidney disease. With the adjustment of potential covariates, we found that ≥ 12.59 mg of dietary iron was related to a lower risk of diabetic kidney disease (OR = 0.78, 95% CI: 0.63 to 0.96; OR = 0.79, 95% CI: 0.63 to 0.98). In stratified analyses, higher iron intake was negatively related to diabetic kidney disease, especially among those who were male, < 60 years, those with hypertension, those with HbA1c < 7.0%, and those who were ex-smokers. The result remained robust in sensitivity analyses. We found that ≥ 12.59 mg of dietary iron is associated with a lower risk of diabetic kidney disease, especially in those who were male, younger, heavier weight, have better blood sugar control, and those who were ex-smokers.

Developing screening tools to estimate the risk of diabetic kidney disease in patients with type 2 diabetes mellitus.

Diabetic kidney disease (DKD) is an important microvascular complication of diabetes mellitus (DM). This study aimed to develop predictive nomograms to estimate the risk of DKD in patients with type 2 diabetes mellitus (T2DM). The medical records of patients with T2DM in our hospital from March 2022 to March 2023 were retrospectively reviewed. The enrolled patients were randomly selected for training and validation sets in a 7:3 ratio. The models for predicting risk of DKD were virtualized by the nomograms using logistic regression analysis. Among the enrolled 597 patients, 418 were assigned to the training set, while 179 were assigned to the validation set. Using the predictors included glycated hemoglobin A1c (HbA1c), high density lipoprotein cholesterol (HDL-C), presence of diabetic retinopathy (DR) and duration of diabetes (DD), we constructed a full model (model 1) for predicting DKD. And using the laboratory indexes of HbA1c, HDL-C, and cystatin C (Cys-C), we developed a laboratory-based model (model 2). The C-indexes were 0.897 for model 1 and 0.867 for model 2, respectively. The calibration curves demonstrated a good agreement between prediction and observation in the two models. The decision curve analysis (DCA) curves showed that the two models achieved a net benefit across all threshold probabilities. We successfully constructed two prediction models to evaluate the risk of DKD in patients with T2DM. The two models exhibited good predictive performance and could be recommended for DKD screening and early detection.

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.

Transient albuminuria in the setting of short-term severe hyperglycemia in type 1 diabetes

In a cohort of 1817 children with type 1 diabetes (T1D), short-term hyperglycemia was associated with transient albuminuria (11 % during new-onset T1D without diabetic ketoacidosis (DKA), 12 % during/after DKA, 6 % during routine screening). Our findings have implications regarding future risk of diabetic kidney disease and further investigation is needed.

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

BackgroundThis 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.MethodsWe 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.ResultsA 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.ConclusionsElevated 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.

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.

Association Between Liver Fibrosis Score and Diabetic Kidney Disease: A Retrospective Cross-Sectional Study of Hospitalized Patients.

To investigate the association between liver fibrosis score and diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM). A total of 897 hospitalized patients with T2DM were included in this study. Each patient completed DKD screening. Logistic regression analysis was used to assess the predictive value of non-alcoholic fatty liver disease fibrosis score (NAFLD-FS) and fibrosis-4 (FIB-4) for the occurrence of DKD and risk for DKD progression, respectively. The prevalence of DKD and risk for its progression significantly increased with increasing NAFLD-FS risk category. DKD prevalence also increased with increasing FIB-4 risk category. Multivariate logistic regression analysis showed that the "high-risk" NAFLD-FS had a significantly higher risk of DKD (odds ratio [OR]: 1.89, 95% confidence interval [CI]: 1.16-3.08) and risk for DKD progression (OR: 2.88, 95% CI: 1.23-6.78), and the "intermediate-risk" FIB-4 had a significantly higher risk of DKD (OR: 1.41, 95% CI: 1.00-1.98). Subgroup analysis showed that the association between NAFLD-FS and FIB-4 and DKD was significant in the female subgroup, whereas the association between the "high-risk" NAFLD-FS and risk for DKD progression was significant in the male subgroup. NAFLD-FS and FIB-4 are strongly associated with DKD and risk for DKD progression in patients with T2DM. Additionally, sexual dimorphism exists in this association.

Serum carnosinase 1, an early indicator for incident microalbuminuria in type 1 diabetes

Abstract Aims Carnosinase (CN1) polymorphisms have been linked to diabetic kidney disease (DKD), as CN1 degrades dipeptides which scavenge oxidative metabolites and prevent the formation of advanced glycation end-products. In this work, we studied the association between serum CN1, the systemic redox status and long-term renal outcome in type 1 diabetes. Methods Serum CN1 was measured in a prospective type 1 diabetes cohort (n = 218) with a 16-year follow-up. A total of 218 patients treated at the Diabetes Outpatient Clinic of the Weezenlanden Hospital (nowadays Isala Hospital, Zwolle, The Netherlands) were included in this analysis. We assessed whether serum CN1 was associated with renal function and development of DKD as well as other diabetic complications. Results At baseline, age, systemic redox status and N-terminal pro brain-natriuretic peptide (NT-proBNP) were associated with serum CN1 concentration (p &lt; 0.05). During follow-up, CN1 concentration in the middle tertile was associated with less incident microalbuminuria (odds ratio = 0.194, 95% C.I.: 0.049—0.772, p = 0.02) after adjustment for age, systemic redox status, NT-proBNP and sex. Discussion Serum CN1 could predict incident microalbuminuria and may be used as a novel parameter to identify patients at risk for DKD.

Distributions and Trends of the Global Burden of DKD Attributable to Lead Exposure: A Systematic Analysis of GBD from 1990 to 2019.

Long-term exposure to lead is associated with an increased risk of diabetic kidney disease (DKD). However, limited data exist on global trends in DKD burden attributable to lead exposure, especially across diverse regions categorized by socioeconomic level. We aimed to assess the spatiotemporal changes in DKD burden attributable to lead exposure from 1990 to 2019 across 204 countries and regions with varying socio-demographic index (SDI) metrics. This retrospective analysis utilized data from the Global Burden of Disease Study 2019 (GBD2019) database. We estimated the burden of DKD attributable to lead exposure using the age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life year rate (ASDR), accounting for sex, age, nationality, and SDI. The annual percentage change (APC) and average annual percentage change (AAPC) were calculated using the Joinpoint model to evaluate trends in the ASMR and ASDR attributable to lead exposure from 1990 to 2019. Gaussian process regression was used to model the relationship between the SDI and ASMR/ASDR. Globally, the burden of DKD attributable to lead exposure has significantly increased since 1990, especially among elderly men and in regions such as Asia, Central Latin America, North Africa, the Middle East, and low-SDI regions. In 2019, the ASMR and ASDR of DKD attributable to lead exposure were 0.68 (95% CI: 0.40, 0.98) per 100,000 people and 15.02 (95% CI: 8.68, 22.26) per 100,000 people, respectively. From 1990 to 2019, the global ASMR and ASDR attributable to lead-associated DKD changed by 15.45% and -1.78%, respectively. The global AAPCs of the ASMR and ASDR were 0.55 (95% CI: 0.45, 0.65) and -0.01 (95% CI: -0.12, 0.1), respectively. Significant declining trends were observed in the high-income Asia Pacific region, eastern sub-Saharan Africa, North Africa, the Middle East, and other regions with high SDIs. Over this 30-year study period, the global burden of DKD attributable to lead exposure has increased, particularly in regions with low SDI. Lead exposure remains a significant concern in the global burden of diabetic kidney disease.

A late eating midpoint is associated with increased risk of diabetic kidney disease: a cross-sectional study based on NHANES 2013–2020

BackgroundModifying diet is crucial for diabetes and complication management. Numerous studies have shown that adjusting eating habits to align with the circadian rhythm may positively affect metabolic health. However, eating midpoint, eating duration, and their associations with diabetic kidney disease (DKD) are poorly understood.MethodsThe National Health and Nutrition Examination Survey (2013–2020) was examined for information on diabetes and dietary habits. From the beginning and ending times of each meal, we calculated the eating midpoint and eating duration. Urinary albumin-to-creatinine ratio (UACR) ≥ 30 mg/g and/or estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 were the specific diagnostic criteria for DKD.ResultsIn total, details of 2194 subjects with diabetes were collected for analysis. The overall population were divided into four subgroups based on the eating midpoint quartiles. The prevalence of DKD varied noticeably (P = 0.037) across the four categories. When comparing subjects in the second and fourth quartiles of eating midpoint to those in the first one, the odds ratios (ORs) of DKD were 1.31 (95% CI, 1.03 to 1.67) and 1.33 (95% CI, 1.05 to 1.70), respectively. And after controlling for potential confounders, the corresponding ORs of DKD in the second and fourth quartiles were 1.42 (95% CI, 1.07 to 1.90) and 1.39 (95% CI, 1.04 to 1.85), respectively.ConclusionsA strong correlation was found between an earlier eating midpoint and a reduced incidence of DKD. Eating early in the day may potentially improve renal outcomes in patients with diabetes.

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

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). 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. 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. 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.

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. 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. 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. 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.

Correlation between weight variability and the risk of diabetic nephropathy in patients with type 2 diabetes mellitus

Objective: To evaluate the relationship between different indexes of weight variability and the risk of diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM). Methods: A retrospective cohort study. The clinical data of 2 180 T2DM patients without DKD who underwent case management at Lee's United Clinic in Taiwan, China from 2002 to 2018 were retrospectively analyzed, including 1 103 females and 1 077 males, with an average age of (64.8±12.4) years. Regular follow-up was conducted for patients for at least 2 years, and their metabolic indexes were monitored annually. BMI variability independent of the mean (BMI-VIM), average yearly mean square successive difference (BMI-ASV), coefficient of variation (BMI-CV) and standard deviation (BMI-SD) were calculated,based on the body mass index (BMI) recorded annually by the patients. Patients were divided into four groups (Q1-Q4) based on the quartiles of the four weight variability indexes. DKD group and non-DKN group(NDKD group) were defined based on the occurrence of DKD at the end of the follow-up. Cox proportional hazards regression models were used to analyze the relationship between the four weight variability indicators and the incidence of DKD. Subgroup analysis was performed by categorizing patients into non-obesity (BMI<28 kg/m2) and obesity groups (BMI≥28 kg/m2) to investigate the impact of the four weight variability indicators on the risk of DKD. Results: After a follow-up of (4.55±2.13) years, 904 patients developed DKD. Compared with the NDKD group, patients in the DKD group had a higher proportion of females, older age, longer duration of diabetes, more insulin users, higher waist-to-hip ratio, higher levels of BMI-VIM, BMI-ASV, BMI-CV, BMI-SD, systolic blood pressure, diastolic blood pressure, and urine albumin-creatinine ratio, a lower proportion of hypoglycemic drugs, estimated glomerular filtration rate, and high-density lipoprotein cholesterol level, with statistically significant differences between the two groups(all P<0.05). Cox proportional hazards regression analysis results revealed that the risk of DKD in T2DM patients increased with the increase in BMI-SD, BMI-CV, BMI-VIM, and BMI-ASV after correcting a series of influencing factors. In the BMI-VIM subgroup, compared with the Q1 group, the risk of DKD in the Q4 group increased by 22.4% [HR=1.224 (95%CI:1.008-1.487), P=0.041]. In the BMI-ASV group, compared with the Q1 group, the risk of DKD in the Q4 group increased by 51.1% [HR=1.511 (95%CI:1.240-1.841), P<0.01]. In the BMI-CV group, compared with the Q1 group, the risk of DKD in the Q4 group increased by 22.2% [HR=1.222 (95%CI:1.006-1.485), P=0.044]. In the BMI-SD subgroup, compared with the Q1 group, the risk of DKD in the Q4 group increased by 22.2% [HR=1.222 (95%CI:1.002-1.490), P=0.048]. Sub-group analysis showed that when the non-obesity group was grouped by BMI-ASV, after correcting a series of influencing factors, compared with the Q1 group, the highest risk of DKD occurred in the Q4 group [HR=1.551 (95%CI:1.228-1.958), P<0.001];when the obesity group was grouped by BMI-ASV, after correcting a series of influencing factors, compared with the Q1 group, the highest risk of DKD occurred in the Q4 group [HR=1.703 (95%CI:1.168-2.485), P=0.006]. Conclusion: Increases in BMI-VIM, BMI-ASV, BMI-CV, and BMI-SD are associated with an increased risk of DKD in T2DM patients.

The Difference Between Cystatin C- and Creatinine-Based Estimated Glomerular Filtration Rate and Risk of Diabetic Microvascular Complications Among Adults With Diabetes: A Population-Based Cohort Study.

The impact of the difference between cystatin C- and creatinine-based estimated glomerular filtration rate (eGFRdiff) on diabetic microvascular complications (DMCs) remains unknown. We investigated the associations of eGFRdiff with overall DMCs and subtypes, including diabetic retinopathy (DR), diabetic kidney disease (DKD), and diabetic neuropathy (DN). This prospective cohort study included 25,825 participants with diabetes free of DMCs at baseline (2006 to 2010) from the UK Biobank. eGFRdiff was calculated using both absolute difference (eGFRabdiff) and the ratio (eGFRrediff) between cystatin C- and creatinine-based calculations. Incidence of DMCs was ascertained using electronic health records. Cox proportional hazards regression models were used to evaluate the associations of eGFRdiff with overall DMCs and subtypes. During a median follow-up of 13.6 years, DMCs developed in 5,753 participants, including 2,752 cases of DR, 3,203 of DKD, and 1,149 of DN. Each SD decrease of eGFRabdiff was associated with a 28% higher risk of overall DMCs, 14% higher risk of DR, 56% higher risk of DKD, and 29% higher risk of DN. For each 10% decrease in eGFRrediff, the corresponding hazard ratios (95% CIs) were 1.16 (1.14, 1.18) for overall DMCs, 1.08 (1.05, 1.11) for DR, 1.29 (1.26, 1.33) for DKD, and 1.17 (1.12, 1.22) for DN. The magnitude of associations was not materially altered in any of the sensitivity analyses. Large eGFRdiff was independently associated with risk of DMCs and its subtypes. Our findings suggested monitoring eGFRdiff in the diabetes population has potential benefit for identification of high-risk patients.

Utilization of the corticomedullary difference in magnetic resonance imaging-derived apparent diffusion coefficient for noninvasive assessment of chronic kidney disease in type 2 diabetes

BackgroundsAccumulating data demonstrated that the cortico-medullary difference in apparent diffusion coefficient (ΔADC) of diffusion-weighted magnetic resonance imaging (DWI) was a better correlation with kidney fibrosis, tubular atrophy progression, and a predictor of kidney function evolution in chronic kidney disease (CKD). ObjectivesWe aimed to assess the value of ΔADC in evaluating disease severity, differential diagnosis, and the prognostic risk stratification for patients with type 2 diabetes (T2D) and CKD. MethodsTotal 119 patients with T2D and CKD who underwent renal MRI were prospectively enrolled. Of them, 89 patients had performed kidney biopsy for pathological examination, including 38 patients with biopsy-proven diabetic kidney disease (DKD) and 51 patients with biopsy-proven non-diabetic kidney disease (NDKD) and Mix (DKD + NDKD). Clinicopathological characteristics were compared according to different ΔADC levels. Moreover, univariate and multivariate-linear regression analyses were performed to explore whether ΔADC was independently associated with estimated glomerular filtration rate (eGFR) and urinary albumin creatinine ratio (UACR). The diagnostic performance of ΔADC for discriminating DKD from NDKD + Mix was evaluated by receiver operating characteristic (ROC) analysis. In addition, an individual's 2- or 5-year risk probability of progressing to end-stage kidney disease (ESKD) was calculated by the kidney failure risk equation (KFRE). The effect of ΔADC on prognostic risk stratification was assessed. Additionally, net reclassification improvement (NRI) was used to evaluate the model performance. ResultsAll enrolled patients had a median ΔADC level of 86 (IQR 28, 155) × 10−6 mm2/s. ΔADC significantly decreased across the increasing staging of CKD (P < 0.001). Moreover, those with pathological-confirmed DKD has a significantly lower level of ΔADC than those with NDKD and Mix (P < 0.001). It showed that ΔADC was independently associated with eGFR (β = 1.058, 95% CI = [1.002,1.118], P = 0.042) and UACR (β = −3.862, 95% CI = [-7.360, -0.365], P = 0.031) at multivariate linear regression analyses. Besides, ΔADC achieved an AUC of 0.707 (71% sensitivity and 75% specificity) and AUC of 0.823 (94% sensitivity and 67% specificity) for discriminating DKD from NDKD + Mix and higher ESKD risk categories (≥50% at 5 years; ≥10% at 2 years) from lower risk categories (<50% at 5 years; <10% at 2 years). Accordingly, the optimal cutoff value of ΔADC for higher ESKD risk categories was 66 × 10−6 mm2/s, and the group with the low-cutoff level of ΔADC group was associated with 1.232 -fold (95% CI 1.086, 1.398) likelihood of higher ESKD risk categories as compared to the high-cutoff level of ΔADC group in the fully-adjusted model. Reclassification analyses confirmed that the final adjusted model improved NRI. ConclusionsΔADC was strongly associated with eGFR and UACR in patients with T2D and CKD. More importantly, baseline ΔADC was predictive of higher ESKD risk, independently of significant clinical confounding. Specifically, ΔADC <78 × 10−6 mm2/s and <66 × 10−6 mm2/s would help to identify T2D patients with the diagnosis of DKD and higher ESKD risk categories, respectively.

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

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

Association between ELMO1 gene polymorphisms and diabetic kidney disease: A systematic review and meta-analysis.

Previous research has suggested that the ELMO1 gene may play a role in the development of diabetic kidney disease. Diabetic kidney disease (DKD) is a serious complication of diabetes and the leading cause of chronic kidney disease and end-stage renal disease (ESRD). This study aim was to systematically review and explore the association between ELMO1 gene polymorphisms and diabetic kidney disease. A comprehensive systematic review provides a clear conclusion and high-level evidence for the association between ELMO1 gene and DKD for future application in personalized medicine. A comprehensive search of electronic databases, per PRISMA instructions, was conducted in Scopus, EMBASE, Web of Science, and PubMed databases from 1980 to January 2023. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using appropriate models. Subgroup and sensitivity analyses were performed to explore potential sources of heterogeneity and assess the robustness of the findings. A total of 5794 diabetes patients with DKD, 4886 diabetes patients without DKD, and 2023 healthy controls were included in the 17 studies that made up this systematic review. In the investigation of DM (Diabetes Mellitus) with DKD vs. DM without DKD, the susceptibility for DKD for the EMLO1 rs741301 polymorphism indicated a significant difference under the dominant, homozygote, and recessive genetic models. The susceptibility for DKD for the EMLO1 rs1345365, rs10255208, and rs7782979 polymorphisms demonstrated a significant difference under the allele genetic models in the analysis of DM with DKD vs. DM without DKD groups. There was a considerable increase in DKD risk in the Middle East when the population was stratified by the region. The findings of the meta-analysis show that there are a significant connection between the EMLO1 rs741301 polymorphism and DKD susceptibility in overall analyses; as well as rs1345365, rs10255208, and rs7782979 polymorphisms; especially in the Middle East region.

Impact of baseline and trajectory of the atherogenic index of plasma on incident diabetic kidney disease and retinopathy in participants with type 2 diabetes: a longitudinal cohort study

BackgroundSome studies have assessed the predictive role of the atherogenic index of plasma (AIP) for macrovascular diseases. This prospective investigation aimed to elucidate whether AIP is associated with diabetic kidney disease (DKD) and diabetic retinopathy (DR) incidence.MethodsThe data were extracted from 4831 participants, of whom 2943 and 3360 participants with type 2 diabetes (T2D) were included in the DKD and DR follow-up analyses, respectively. Cox regression models were performed to test the relationships of AIP value at baseline with the risk of incident DKD and DR. Group-based trajectory modelling was utilized to discern AIP trajectories during the follow-up period. Subsequently, logistic regressions were applied to ascertain the influence of AIP trajectories on the incidence of DKD and DR.ResultsDuring the follow-up period, 709 (24.1%) and 193 (5.7%) participants developed DKD and DR, respectively. The median (interquartile range) follow-up time was 24.2 (26.3) months for DKD and 25.7 (27.0) months for DR. According to the multivariate Cox regression models, baseline AIP was positively and linearly related to the occurrence of DKD, with a hazard ratio of 1.75 (95% confidence interval [CI] 1.36–2.26). Three distinct trajectories of AIP were identified throughout the follow-up time: Low (31.4%), Median (50.2%), and High (18.3%). Compared to participants with the Low AIP trajectory, those with High and Median AIP trajectories presented 117% (95% CI: 1.62–2.91) and 84% (95% CI 1.46–2.32) greater odds of developing DKD, respectively. However, neither baseline levels nor trajectories of AIP were shown to be related to DR after adjusting for confounding factors.ConclusionsBaseline levels and trajectories of AIP were independently related to elevated DKD risk, indicating that AIP could be used as a predictor for identifying T2D participants at higher risk of DKD. No association between AIP and DR was detected.

Methylglyoxal-Derived Nucleoside Adducts Drive Vascular Dysfunction in a RAGE-Dependent Manner.

Diabetic kidney disease (DKD) is a leading cause of death in patients with diabetes. An early precursor to DKD is endothelial cell dysfunction (ECD), which often precedes and exacerbates vascular disease progression. We previously discovered that covalent adducts formed on DNA, RNA, and proteins by the reactive metabolic by-product methylglyoxal (MG) predict DKD risk in patients with type 1 diabetes up to 16 years pre-diagnosis. However, the mechanisms by which MG adducts contribute to vascular disease onset and progression remain unclear. Here, we report that the most predominant MG-induced nucleoside adducts, N2-(1-carboxyethyl)-deoxyguanosine (CEdG) and N2-(1-carboxyethyl)-guanosine (CEG), drive endothelial dysfunction. Following CEdG or CEG exposure, primary human umbilical vein endothelial cells (HUVECs) undergo endothelial dysfunction, resulting in enhanced monocyte adhesion, increased reactive oxygen species production, endothelial permeability, impaired endothelial homeostasis, and exhibit a dysfunctional transcriptomic signature. These effects were discovered to be mediated through the receptor for advanced glycation end products (RAGE), as an inhibitor for intracellular RAGE signaling diminished these dysfunctional phenotypes. Therefore, we found that not only are MG adducts biomarkers for DKD, but that they may also have a role as potential drivers of vascular disease onset and progression and a new therapeutic modality.

Association between neutrophil-to-lymphocyte ratio and diabetic kidney disease in type 2 diabetes mellitus patients: a cross-sectional study.

This investigation examined the possibility of a relationship between neutrophil-to-lymphocyte ratio (NLR) and diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients. Adults with T2DM who were included in the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2020 were the subjects of the current cross-sectional investigation. Low estimated glomerular filtration rate (eGFR) (< 60 mL/min/1.73 m2) or albuminuria (urinary albumin-to-creatinine ratio (ACR) ≥ 30 mg/g) in T2DM patients were the diagnostic criteria for DKD. Weighted multivariable logistic regression models and generalized additive models were used to investigate the independent relationships between NLR levels with DKD, albuminuria, and low-eGFR. Additionally, we examined the relationships between DKD, albuminuria, and low-eGFR with other inflammatory markers, such as the aggregate index of systemic inflammation (AISI), systemic immune-inflammation index (SII), system inflammation response index (SIRI), and platelet-to-lymphocyte ratio (PLR) and monocyte-to-lymphocyte ratio (MLR). Their diagnostic capabilities were evaluated and contrasted using receiver operating characteristic (ROC) curves. 44.65% of the 7,153 participants who were recruited for this study were males. DKD, albuminuria, and low-eGFR were prevalent in 31.76%, 23.08%, and 14.55% of cases, respectively. Positive correlations were seen between the NLR with the prevalences of DKD, albuminuria, and low-eGFR. Subgroup analysis and interaction tests revealed that the associations of NLR with DKD, albuminuria, and low-eGFR were not significantly different across populations. In addition, MLR, SII and SIRI showed positive associations with the prevalence of DKD. ROC analysis discovered that when compared to other inflammatory markers (MLR, PLR, SII, SIRI, and AISI), NLR may demonstrate more discriminatory power and accuracy in assessing the risk of DKD, albuminuria, and low-eGFR. Compared to other inflammatory markers (MLR, PLR, SII, SIRI, and AISI), NLR may serve as the more effective potential inflammatory marker for identifying the risk of DKD, albuminuria, and low-eGFR in US T2DM patients. T2DM patients with elevated levels of NLR, MLR, SII, and SIRI should be closely monitored for their potential risk to renal function.