Incidence Of Diabetic Kidney Disease Research Articles

Targeting RAC1 might be a potential therapeutic strategy for diabetic kidney disease: a Mendelian randomization study.

This study aimed to ascertain the causal association between Ras-related C3 botulinum toxin substrate 1 (RAC1) and the incidence and progression of diabetic kidney disease (DKD) through Mendelian randomization analysis. RAC1 expression, evaluated using expression quantitative trait loci data from the eQTLGen Consortium, was served as the exposure variable. Outcomes encompassed the risk of DKD, end-stage renal disease (ESRD), albuminuria assessed by the urinary albumin-to-creatinine ratio (ACR), and estimated glomerular filtration rate (eGFR) among individuals with diabetes. Causal associations were computed using the inverse variance weighted (IVW), weighted median, and MR-PRESSO models. Additionally, we conducted analyses for heterogeneity, horizontal pleiotropy, and sensitivity. This study revealed a causal association between the genetic activation of RAC1 and an elevated risk of DKD among individuals with diabetes [IVW, odds ratio (OR) = 1.28, 95% confidence intervals (CI) 1.08-1.51, P = 0.004]. Furthermore, increased expression of RAC1 was linked to a higher risk of ESRD (IVW, OR = 1.20, 95% CI 1.02-1.43, P = 0.032). Excessive RAC1 expression was causally associated with elevated ACR (IVW, β = 0.052, 95% CI 0.003-0.100, P = 0.036). However, the analysis regarding RAC1 and eGFR showed significant heterogeneity and pleiotropy, with no discernible causal relationship. These findings suggested a positive correlation between the genetic activation of RAC1 and the incidence of DKD, the risk of ESRD, and exacerbated albuminuria among individuals with diabetes. Targeting RAC1 might potentially serve as a therapeutic strategy for DKD.

Type 1 Diabetes as a Risk Factor for Chronic Kidney Diseases and Renal Failure in Adolescents: A Systematic Review

Objectives: The purpose of this systematic review is to investigate the risk factors and association of the incidence of diabetic kidney disease in adolescents with type 1 diabetes (T1D) patients. Methods: We conducted a thorough search of PubMed, SCOPUS, Web of Science, Google Scholar, and Science Direct to find pertinent literature. Rayyan QRCI was utilized during the entire process. Results: We included eight studies with a total of 11,468 T2D patients and 4966 (43.3%) were females. The available literature on the association between T1D and CKD among adolescents lacks epidemiological data on the prevalence and sex differences. Higher eGFR, diabetes duration, low C-peptide levels, glycemic control, age at a clinic visit, advanced glycation end products, and BMI were reported as significant risk factors for developing renal impairment in adolescents with T1D. Conclusion: The results of this research point to the necessity of a standardized screening procedure for the early identification and appropriate treatment of DKD. In order to provide an accurate assessment of this illness, methodological approaches should be taken into account. Furthermore, it's imperative to educate teenagers with T1D about the possibility of DKD, which can result in renal failure and even death. Future improvements in the quality of life for teenagers with T1D are anticipated as a result of this awareness.

Elucidating the Mechanism of Jisheng Shenqi Pills in the Treatment of Diabetic Kidney Disease: Network Pharmacology Combined with Experimental Verification.

While the annual incidence of diabetic kidney disease (DKD) has been soaring, the exact mechanisms underlying its onset and progression remain partially understood. The present study delved into the underlying mechanisms of Jisheng Shenqi Pill (JSP) in the treatment of DKD. The active constituents and prospective targets of JSP were identified from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), while DKD-associated disease targets were obtained from the GeneCards database. Subsequently, Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to assess the overlapping segment of drugs and disease targets. Meanwhile, a component-target-pathway network was constructed to identify pivotal components, targets, and pathways. Molecular docking and molecular dynamics simulation were also carried out to validate the binding efficacy of the pivotal components with the targets. Finally, animal experiments were conducted to corroborate the efficacy of the aforementioned targets and pathways. According to bioinformatics analysis, the primary targets included JUN, TNF, and BAX, while the pivotal pathways involved were AGE/RAGE and PI3K/AKT signaling cascades. In vivo experiments demonstrated that JSP effectively mitigated renal impairment in DKD by reducing renal inflammation and apoptosis. This effect was presumably achieved by modulating the AGERAGE axis and the PI3K/AKT signaling pathway. Our findings imply that JSP could ameliorate renal inflammation and apoptosis in DKD mice by modulating the AGE/RAGE axis and the PI3K/AKT signaling pathway. These findings provide valuable insights into traditional Chinese medicine-based treatments for DKD.

Association between visceral adiposity index and incidence of diabetic kidney disease in adults with diabetes in the United States

Visceral adiposity index (VAI) is a reliable indicator of visceral adiposity. However, no stu-dies have evaluated the association between VAI and DKD in US adults with diabetes. Theref-ore, this study aimed to explore the relationship between them and whether VAI is a good pr-edictor of DKD in US adults with diabetes. Our cross-sectional study included 2508 participan-ts with diabetes who were eligible for the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018. Univariate and multivariate logistic regression were used to an-alyze the association between VAI level and DKD. Three models were used to control for pot-ential confounding factors, and subgroup analysis was performed for further verification. A tot-al of 2508 diabetic patients were enrolled, of whom 945 (37.68%) were diagnosed with DKD. Overall, the VAI was 3.36 ± 0.18 in the DKD group and 2.76 ± 0.11 in the control group. VAI was positively correlated with DKD (OR = 1.050, 95% CI 1.049, 1.050) after fully adjusting for co-nfounding factors. Compared with participants in the lowest tertile of VAI, participants in the highest tertile of VAI had a significantly increased risk of DKD by 35.9% (OR = 1.359, 95% CI 1.355, 1.362). Through subgroup analysis, we found that VAI was positively correlated with the occurrence of DKD in all age subgroups, male(OR = 1.043, 95% CI 1.010, 1.080), participants wit-hout cardiovascular disease(OR = 1.038, 95% CI 1.011, 1.069), hypertension (OR = 1.054, 95% CI 1.021, 1.090), unmarried participants (OR = 1.153, 95% CI 1.036, 1.294), PIR < 1.30(OR = 1.049, 95% CI 1.010, 1.094), PIR ≧ 3 (OR = 1.085, 95% CI 1.021, 1.160), BMI ≧ 30 kg/m2 (OR = 1.050, 95% CI 1.016, 1.091), former smokers (OR = 1.060, 95% CI 1.011, 1.117), never exercised (OR = 1.033, 95% CI 1.004, 1.067), non-Hispanic white population (OR = 1.055, 95% CI 1.010, 1.106) and non-Hipanic black population (OR = 1.129, 95% CI 1.033, 1.258). Our results suggest that elevated VAI levels are closely associated with the development of DKD in diabetic patients. VAI may be a simpl-e and cost-effective index to predict the occurrence of DKD. This needs to be verified in furt-her prospective investigations.

High levels of high-density lipoprotein cholesterol may increase the risk of diabetic kidney disease in patients with type 2 diabetes

Studies have indicated that low high-density lipoprotein cholesterol (HDL-C) level is an important risk factor for diabetic kidney disease (DKD) in patients with type 2 diabetes (T2D). However, whether higher HDL-C levels decrease the risk of developing DKD remains unclear. This study aimed to clarify the relationship between HDL-C levels and DKD risk in individuals with T2D in China. In total, 936 patients with T2D were divided into DKD and non-DKD groups. The association between HDL-C levels and DKD risk was evaluated using logistic regression analysis and restricted cubic spline curves adjusted for potential confounders. Threshold effect analysis of HDL-C for DKD risk was also performed. Higher HDL-C levels did not consistently decrease the DKD risk. Furthermore, a nonlinear association with threshold interval effects between HDL-C levels and the incidence of DKD was observed. Patients with HDL-C ≤ 0.94 mmol/L or HDL-C > 1.54 mmol/L had significantly higher DKD risk after adjusting for confounding factors. Interestingly, the association between high HDL-C levels and increased DKD risk was more significant in women. A U-shaped association between HDL-C levels and DKD risk was observed; therefore, low and high HDL-C levels may increase the DKD risk in patients with T2D.

Integrative Analysis by Mendelian Randomization and Large-Scale Single-Cell Transcriptomics Reveals Causal Links between B Cell Subtypes and Diabetic Kidney Disease

Introduction: The increasing incidence of diabetic kidney disease (DKD) and the challenges in its management highlight the necessity for a deeper understanding of its pathogenesis. While recent studies have underscored the substantial impact of circulating immunity on the development of diabetic microvascular complications such as retinopathy and neuropathy, research on circulating immunity in DKD remains limited. Methods: This study utilized Mendelian randomization analysis to explore the potential independent causal relationships between circulating immune cells and DKD pathogenesis. Additionally, a combination of single-cell disease relevance score (scDRS) and immune cell infiltration analysis was employed to map the circulating immunity landscape in DKD patients. Results: Ten immune traits, including 5 of B cells, 2 of T cells, 2 of granulocytes, and one of monocytes, were defined to be associated with the pathogenesis of DKD. Notably, IgD-CD27-B cell Absolute Count (IVW: OR, 1.102 [1.023–1.189], p = 0.011) and IgD-CD24-B cell Absolute Count (IVW: OR, 1.106 [1.030–1.188], p = 0.005) were associated with promoting DKD pathogenesis, while CD24+CD27+B cell %B cell (IVW: OR, 0.943 [0.898–0.989], p = 0.016) demonstrated a protective effect against DKD onset. The presence of B cell-activating factor receptor (BAFF-R) on CD20−CD38−B cell (IVW: OR, 0.946 [0.904–0.989], p = 0.015) and BAFF-R on IgD-CD38+B cell (IVW: OR, 0.902 [0.834–0.975], p = 0.009) also indicated a potential role in preventing DKD. scDRS analysis revealed that two main subsets of B cells, naïve B and memory B cells, had a higher proportion of DKD-related cells or a higher scDRS score of DKD phenotype, indicating their strong association with DKD. Furthermore, immune infiltrate deconvolution analysis showed a notable decrease in the circulating memory B cells and class-switched memory B cells in DKD patients compared to those of DM patients without DKD. Conclusion: Our study revealed the causal relations between circulating immunity and DKD susceptibility, particularly highlighted the potential roles of B cell subtypes in DKD development. Further studies addressing the related mechanisms would broaden the current understanding of DKD pathogenesis.

#635 Effect of multi-biomarker panels based on urinary N-terminal osteopontin on prediction of diabetic kidney disease in patients with diabetes mellitus

Abstract Background and Aims Diabetic kidney disease (DKD), occurs in 20–40% of patients with diabetes mellitus (DM), is the leading cause of end-stage renal disease (ESRD). DKD is a clinical diagnosis mainly based on the persistent albuminuria and reduced estimated glomerular filtration rate (eGFR) [1]. However, increased UACR and reduced eGFR are the final consequences due to DKD, novel biomarkers are critical for predicting DKD development. Osteopontin (OPN) is a profibrotic adhesion phosphoprotein that participates in cell chemotaxis, adhesion, migration, and proliferation, as well as extracellular matrix (ECM) hyperplasia. Hyperglycemia could enhance OPN gene expression through the activation of the renin-angiotensin system (RAS), mTOR pathway, NF-κB, and TGF-β pathway and then cause podocyte injury and ECM hyperplasia. Therefore, OPN upregulation is not only the result of various pathophysiological processes in DKD, but also results in kidney injury. Osteopontin (OPN) could predict incident DKD in DM patients [2], N-terminal OPN (ntOPN) has a stronger profibrotic adhesion effect than full-length OPN. This study aims to reveal the clinical benefit of ntOPN as a potential marker to identify DM patients at high risk of DKD, and establish ntOPN-based diagnostic and forecast models for renal outcomes in DM patients. Method We performed a cross-sectional study of 316 adults with Type 1 DM≥ 5 years or Type 2 DM, then followed by a prospective observational cohort study of 143 adult DM patients without renal involvement at baseline and follow-up for at least one year. During the follow-up period, the primary endpoint was “DKD occurrence”, defined as the presence of one of the following conditions in DM patients [3]: (1) repeat UACR ≥30 mg/g at least 2 of 3 measurements within 3 to 6 months; (2) eGFR &amp;lt;60 mL/min/1.73 m2 for more than three months; (3) renal pathological findings were consistent with DKD. The secondary endpoint was “DKD progression”, which included: (1) eGFR sustained decreased by at least 25%; (2) development of ESRD, and/or need for renal replacement therapy; (3) death from the renal cause. Logistic regression analysis was performed to analyze the relationship between parameters and the events of DKD occurrence and progression. Receiver operator characteristic (ROC) analysis was used to assess the predictive ability of established models for clinical endpoints. Results The median value of urinary ntOPN (UntOPN) was 44.15 ng/ml in the cross-sectional cohort, DKD prevalence was significantly higher in the high UntOPN group than in the low UntOPN group. The ROC curves of UntOPN, urinary neutrophil gelatinase-associated lipocalin (UNGAL), and their combination indicated that both combination and ntOPN alone perform better than UNGAL for DKD diagnosis (Fig. 1A). In the prospective cohort, UntOPN was an independent risk factor and further improved the predictive ability for DKD occurrence and DKD progression than UNGAL (Figs. 1B and 1C). Based on the parameters detected as risk factors for DKD occurrence and progression, we set up a series of multi-biomarker panels for DKD prediction using UntOPN, UNGAL, serum cystatin C, serum creatinine (Scr), UACR, and TCH/HDL-C ratio. Compared with the model of Scr + UACR, the area under ROC curve (AUC) of the six-biomarker model was higher, and also ranked the highest among the six ROC curves in predicting 1-year risk of DKD occurrence and DKD progression (Figs. 1B and 1C). Conclusion Our results showed that urinary ntOPN is associated with DKD development, and elevated urinary ntOPN is an independent predictor for DKD occurrence and progression. Compared with the traditional biomarkers of Scr + UACR, our multi-biomarker models based on urinary ntOPN performed better in predicting DKD development, which could provide more accurate tools for DKD risk prediction, thereby improving the renal prognosis in DM patients.

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.

Endothelial CXCR2 deficiency attenuates renal inflammation and glycocalyx shedding through NF-κB signaling in diabetic kidney disease

BackgroundThe incidence of diabetic kidney disease (DKD) continues to rapidly increase, with limited available treatment options. One of the hallmarks of DKD is persistent inflammation, but the underlying molecular mechanisms of early diabetic kidney injury remain poorly understood. C-X-C chemokine receptor 2 (CXCR2), plays an important role in the progression of inflammation-related vascular diseases and may bridge between glomerular endothelium and persistent inflammation in DKD.MethodsMultiple methods were employed to assess the expression levels of CXCR2 and its ligands, as well as renal inflammatory response and endothelial glycocalyx shedding in patients with DKD. The effects of CXCR2 on glycocalyx shedding, and persistent renal inflammation was examined in a type 2 diabetic mouse model with Cxcr2 knockout specifically in endothelial cells (DKD-Cxcr2eCKO mice), as well as in glomerular endothelial cells (GECs), cultured in high glucose conditions.ResultsCXCR2 was associated with early renal decline in DKD patients, and endothelial-specific knockout of CXCR2 significantly improved renal function in DKD mice, reduced inflammatory cell infiltration, and simultaneously decreased the expression of proinflammatory factors and chemokines in renal tissue. In DKD conditions, glycocalyx shedding was suppressed in endothelial Cxcr2 knockout mice compared to Cxcr2L/L mice. Modulating CXCR2 expression also affected high glucose-induced inflammation and glycocalyx shedding in GECs. Mechanistically, CXCR2 deficiency inhibited the activation of NF-κB signaling, thereby regulating inflammation, restoring the endothelial glycocalyx, and alleviating DKD.ConclusionsTaken together, under DKD conditions, activation of CXCR2 exacerbates inflammation through regulation of the NF-κB pathway, leading to endothelial glycocalyx shedding and deteriorating renal function. Endothelial CXCR2 deficiency has a protective role in inflammation and glycocalyx dysfunction, suggesting its potential as a promising therapeutic target for DKD treatment.

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.

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.

Association between machine learning-assisted heavy metal exposures and diabetic kidney disease: a cross-sectional survey and Mendelian randomization analysis.

Heavy metals, ubiquitous in the environment, pose a global public health concern. The correlation between these and diabetic kidney disease (DKD) remains unclear. Our objective was to explore the correlation between heavy metal exposures and the incidence of DKD. We analyzed data from the NHANES (2005-2020), using machine learning, and cross-sectional survey. Our study also involved a bidirectional two-sample Mendelian randomization (MR) analysis. Machine learning reveals correlation coefficients of -0.5059 and - 0.6510 for urinary Ba and urinary Tl with DKD, respectively. Multifactorial logistic regression implicates urinary Ba, urinary Pb, blood Cd, and blood Pb as potential associates of DKD. When adjusted for all covariates, the odds ratios and 95% confidence intervals are 0.87 (0.78, 0.98) (p = 0.023), 0.70 (0.53, 0.92) (p = 0.012), 0.53 (0.34, 0.82) (p = 0.005), and 0.76 (0.64, 0.90) (p = 0.002) in order. Furthermore, multiplicative interactions between urinary Ba and urinary Sb, urinary Cd and urinary Co, urinary Cd and urinary Pb, and blood Cd and blood Hg might be present. Among the diabetic population, the OR of urinary Tl with DKD is a mere 0.10, with a 95%CI of (0.01, 0.74), urinary Co 0.73 (0.54, 0.98) in Model 3, and urinary Pb 0.72 (0.55, 0.95) in Model 2. Restricted Cubic Splines (RCS) indicate a linear linkage between blood Cd in the general population and urinary Co, urinary Pb, and urinary Tl with DKD among diabetics. An observable trend effect is present between urinary Pb and urinary Tl with DKD. MR analysis reveals odds ratios and 95% confidence intervals of 1.16 (1.03, 1.32) (p = 0.018) and 1.17 (1.00, 1.36) (p = 0.044) for blood Cd and blood Mn, respectively. In the general population, urinary Ba demonstrates a nonlinear inverse association with DKD, whereas in the diabetic population, urinary Tl displays a linear inverse relationship with DKD.

Prediction of diabetic kidney disease risk using machine learning models: A population-based cohort study of Asian adults.

Machine learning (ML) techniques improve disease prediction by identifying the most relevant features in multidimensional data. We compared the accuracy of ML algorithms for predicting incident diabetic kidney disease (DKD). We utilized longitudinal data from 1365 Chinese, Malay, and Indian participants aged 40-80 y with diabetes but free of DKD who participated in the baseline and 6-year follow-up visit of the Singapore Epidemiology of Eye Diseases Study (2004-2017). Incident DKD (11.9%) was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 with at least 25% decrease in eGFR at follow-up from baseline. A total of 339 features, including participant characteristics, retinal imaging, and genetic and blood metabolites, were used as predictors. Performances of several ML models were compared to each other and to logistic regression (LR) model based on established features of DKD (age, sex, ethnicity, duration of diabetes, systolic blood pressure, HbA1c, and body mass index) using area under the receiver operating characteristic curve (AUC). ML model Elastic Net (EN) had the best AUC (95% CI) of 0.851 (0.847-0.856), which was 7.0% relatively higher than by LR 0.795 (0.790-0.801). Sensitivity and specificity of EN were 88.2 and 65.9% vs. 73.0 and 72.8% by LR. The top 15 predictors included age, ethnicity, antidiabetic medication, hypertension, diabetic retinopathy, systolic blood pressure, HbA1c, eGFR, and metabolites related to lipids, lipoproteins, fatty acids, and ketone bodies. Our results showed that ML, together with feature selection, improves prediction accuracy of DKD risk in an asymptomatic stable population and identifies novel risk factors, including metabolites. This study was supported by the National Medical Research Council, NMRC/OFLCG/001/2017 and NMRC/HCSAINV/MOH-001019-00. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

1288-P: Predictive Modeling of Type 2 Diabetes and Complications Using Machine Learning

Background: Given the prevalence of T2D worldwide, early detection and prevention of both T2D and its complications are of utmost importance. We propose comprehensive person-centered machine learning (ML) risk prediction models for T2D and complications. Methods: From the UK biobank (UKBB), we identified a prevalent T2D group at baseline (9,136 cases and 108,615 controls), an incident T2D group over 10 years of follow-up (29,845 cases and 354,818 controls), and an incident diabetic kidney disease (DKD) group (4435 cases and 33083 controls). We included 3 types of risk factors: clinical (biomarkers and diseases with known diabetes risk like CVD), genetic in the form of polygenic risk scores (PRS) for the pathogenesis of T2D and other clinical risks, and lifestyle. For each group, we trained a model using decision-tree-based ensemble ML algorithm. We then used SHAP values to determine feature importance and chose the top 10 predictors to build a final model. Results: The T2D prevalence model included 74 features and had an AUC of 0.97, and area under the precision-recall curve (PR AUC) of 0.83. The final model with the top features (HbA1c, T2D PRS, primary HTN, LDL, major dietary changes in the last 5 years, age, plasma glucose, waist circumference, and total cholesterol) had an AUC of 0.98, and PR AUC of 0.88. The T2D incidence model included the same 74 features and had an AUC of 0.92, and PR AUC of 0.57. The final model with the top features had an AUC of 0.93, and PR AUC of 0.65. The DKD risk prediction model comprised 85 features, and it had an AUC of 0.81, and PR AUC of 0.37. The final model with the top features (Cystatin C, primary HTN, eGFR, age, HbA1c, T2D PRS, urea, BMI, BMI PRS, NAFLD PRS) had an AUC of 0.80, and PR AUC of 0.39. Conclusions: This study is an ongoing work; our interim results show the capability of ML to identify relevant risk factors and that comprehensive ML models could help clinicians decide on more precise and individualized T2D screening and treatment approaches to prevent T2D and complications. Disclosure A.Khattab: None. S.Chen: None. H.J.Sadaei: None. A.Torkamani: None.

1003-P: Cost of Care for Patients with Diabetic Kidney Disease in Veterans Health Administration

Introduction: Diabetic kidney disease (DKD) represents a substantial burden on the Veterans Health Administration (VHA). We calculated the cost of DKD care for US Veterans from the healthcare system perspective. Methods: Veterans with type 2 diabetes (T2D) in 2015-2021 were identified using diagnosis codes, prescription records, and problem lists. We identified prevalent and incident DKD stages based on urine albumin, urine protein, and eGFR, and following the Kidney Disease Improving Global Outcome definitions. Aggregate all-cause monthly healthcare costs per patient for managing each DKD stage (ie, stages 1 through 5) were estimated by summing patient-level VHA costs in incident DKD stages from January 2018 until December 2021. Summary statistics include descriptive median and interquartile ranges of per-patient per-month [PPPM] cost. All costs were inflated to 2022 US dollars (USD) based on the Consumer Price Index of all items for urban consumers. Results and Conclusion: Out of two million US Veterans with T2D, we identified 401,214 DKD patients with 1+ incident stage progression in the follow-up period. Median PPPM all-cause healthcare cost increased exponentially from $748 at Stage 1 to $2,661 at Stage 5 (Figure). Interventions targeting patients with rapid DKD progression could markedly decrease the burden of DKD in US Veterans. Disclosure K.Kim: Research Support; Renalytix, GRAIL (acquired by Illumina in 2021), AstraZeneca. J.L.Crook: None. C.S.Lu: None. R.E.Nelson: None. H.Nyman: None. J.Lafleur: Research Support; Renalytix, Genentech, Inc., Novartis Pharmaceuticals Corporation.

#4326 STATIN INITIATION AND RISK OF INCIDENT KIDNEY DISEASE IN PATIENTS WITH DIABETES

Abstract Background and Aims The role of statin therapy in the development of kidney disease in patients with type 2 diabetes mellitus (T2DM) remains uncertain. We aim to determine the relationships between statin initiation and kidney outcomes in patients with T2DM. Method Using deidentified electronic health record data and new-user design, we conducted a nationwide retrospective cohort of patients with T2DM aged 40 years or older across China. The study outcomes included the development of diabetic kidney disease (DKD) and a sustained ≥40% decline in estimated glomerular filtration rate (eGFR). Cox proportional hazards regression was used to evaluate the relationships between statin initiation and kidney outcomes Analyses were conducted using propensity overlap weighting to balance the characteristics. Results Among 7272 statin initiators and 12 586 noninitiators in the weighted cohort, statin initiation was associated with lower risks of incident DKD (hazard ratio [HR], 0.72; 95% confidence interval [CI] 0.62-0.83), new-onset eGFR &amp;lt;60 ml/min/1.73 m2 (HR 0.63, 95% CI 0.50-0.79), new-onset proteinuria (HR, 0.70; 95% CI 0.59-0.84) and kidney function decline (HR 0.60, 95% CI 0.44-0.81). Among statin initiators, intensive control of LDL-C (&amp;lt;1.8 mmol/L) had a lower risk of incident DKD (HR 0.51, 95% CI 0.32-0.81) than that in non-control group (LDL-C ≥ 3.4 mmol/L). Similar results were obtained for participants with differing patterns of dyslipidemia, those using different statins, and after stratification according to the characteristics of the participants. Conclusion Statin initiation is significantly associated with a lower risk of kidney disease development in patients with diabetes. This reno-protective effect was more prominent in patients with intensive control of LDL-C.

Time to Diabetic Nephropathy and its Predictors Among Diabetic Patients Treated in Wolaita and Dawuro Zone Hospitals, Ethiopia: A Retrospective Cohort Study.

Diabetic kidney disease (DKD) develops in nearly half of patients with type 2 diabetes mellitus (DM) and one-third of those with type 1 DM during their lifetime. The incidence of DKD as a cause of end stage renal disease is increasing each year. So this study aimed to assess the time to develop diabetic nephropathy and predictors among diabetic patients treated in Wolaita zone hospitals. A ten-year retrospective cohort study had conducted among 614 diabetic patients using systematic random sampling in Wolaita and Dawuro zone hospitals. Bivariable and multivariable Cox proportional hazards regression had used to identify the possible associations between variables. Those variables with a p-value of less than 0.25 in bivariable analysis exported to multivariable Cox regression analysis. Finally, variables with p-value less than 0.05 at multivariable Cox regression were considered significantly significant. The Cox-proportional hazard model assumption had checked using the Schoenfeld residual test. Of the total participants, 93 (15.3%; 95% CI = 12.45-18.14) patients had developed nephropathy in 820,048 people year observation. A mean time to diabetic nephropathy in this study was 189.63 (95% CI, 185.01, 194.25) months. Being illiterate (AHR: 2.21, 95% CI: 1.34-3.66), being hypertensive (AHR: 5.76, 95% CI: 3.39-9.59), and being urban dwellers (AHR: 2.25, 95% CI: 1.34-3.77) increases the hazard of nephropathy. According to this follow-up study, the overall incidence rate is substantially high over ten year follow-up period. The mean time to develop diabetic nephropathy was sixteen years. Educational status, place of residence, and being hypertensive were the predictors. So stakeholders should work on complication reduction measures and awareness creation of the impact of comorbidities.

Statin initiation and risk of incident kidney disease in patients with diabetes.

The role of statin therapy in the development of kidney disease in patients with type 2 diabetes mellitus (DM) remains uncertain. We aimed to determine the relationships between statin initiation and kidney outcomes in patients with type 2 DM. Through a new-user design, we conducted a multicentre retrospective cohort study using the China Renal Data System database (which includes inpatient and outpatient data from 19 urban academic centres across China). We included patients with type 2 DM who were aged 40 years or older and admitted to hospital between Jan. 1, 2000, and May 26, 2021, and excluded those with pre-existing chronic kidney disease and those who were already on statins or without follow-up at an affiliated outpatient clinic within 90 days after discharge. The primary exposure was initiation of a statin. The primary outcome was the development of diabetic kidney disease (DKD), defined as a composite of the occurrence of kidney dysfunction (estimated glomerular filtration rate [eGFR] < 60 mL/min/1.73 m2 and > 25% decline from baseline) and proteinuria (a urinary albumin-to-creatinine ratio ≥ 30 mg/g and > 50% increase from baseline), sustained for at least 90 days; secondary outcomes included development of kidney function decline (a sustained > 40% decline in eGFR). We used Cox proportional hazards regression to evaluate the relationships between statin initiation and kidney outcomes, as well as to conduct subgroup analyses according to patient characteristics, presence or absence of dyslipidemia, and pattern of dyslipidemia. For statin initiators, we explored the association between different levels of lipid control and outcomes. We conducted analyses using propensity overlap weighting to balance the participant characteristics. Among 7272 statin initiators and 12 586 noninitiators in the weighted cohort, statin initiation was associated with lower risks of incident DKD (hazard ratio [HR] 0.72, 95% confidence interval [CI] 0.62-0.83) and kidney function decline (HR 0.60, 95% CI 0.44-0.81). We obtained similar results to the primary analyses for participants with differing patterns of dyslipidemia, those prescribed different statins, and after stratification according to participant characteristics. Among statin initiators, those with intensive control of high-density lipoprotein cholesterol (LDL-C) (< 1.8 mmol/L) had a lower risk of incident DKD (HR 0.51, 95% CI 0.32-0.81) than those with inadequate lipid control (LDL-C ≥ 3.4 mmol/L). For patients with type 2 DM admitted to and followed up in academic centres, statin initiation was associated with a lower risk of kidney disease development, particularly in those with intensive control of LDL-C. These findings suggest that statin initiation may be an effective and reasonable approach for preventing kidney disease in patients with type 2 DM.

Early diabetic kidney disease: Focus on the glycocalyx

The incidence of diabetic kidney disease (DKD) is sharply increasing worldwide. Microalbuminuria is the primary clinical marker used to identify DKD, and its initiating step in diabetes is glomerular endothelial cell dysfunction, particularly glycocalyx impairment. The glycocalyx found on the surface of glomerular endothelial cells, is a dynamic hydrated layer structure composed of pro-teoglycans, glycoproteins, and some adsorbed soluble components. It reinforces the negative charge barrier, transduces the shear stress, and mediates the interaction of blood corpuscles and podocytes with endothelial cells. In the high-glucose environment of diabetes, excessive reactive oxygen species and proinflammatory cytokines can damage the endothelial glycocalyx (EG) both directly and indirectly, which induces the production of microalbuminuria. Further research is required to elucidate the role of the podocyte glycocalyx, which may, together with endothelial cells, form a line of defense against albumin filtration. Interestingly, recent research has confirmed that the negative charge barrier function of the glycocalyx found in the glomerular basement membrane and its repulsion effect on albumin is limited. Therefore, to improve the early diagnosis and treatment of DKD, the potential mechanisms of EG degradation must be analyzed and more responsive and controllable targets must be explored. The content of this review will provide insights for future research.

Early detection methods of chronic diabetic nephropathy in diabetes mellitus patients in primary health services

Introduction: The prevalence of diabetic kidney disease increases along with the increase in the number of patients with diabetes mellitus. Early detection of diabetic nephropathy in DM patients is an essential step in preventing the incidence of diabetic kidney disease. The study aimed to obtain a method of early detection of diabetic kidney disease in DM patients in primary health care via identifying factors associated with decreased glomerular filtration rate (GFR). Methods: We conducted a case-controlled study at the public health center in Bantul. The research subjects were patients at Kasihan 1 public health center aged 18-75 years. In-depth interviews will be conducted to obtain clinical and social risk factors for GFR decreasing as an early detection tool for diabetic nephropathy (DN) incidence. The IGF-1 levels were checked using ELISA. The Pearson correlation test observed a significant association between patient risk factors and GFR. Results: This study is expected to obtain a model of the method of early detection of chronic diabetic kidney disease in diabetes mellitus patients in primary health services. The results showed that the GFR was related to systolic blood pressure, diastolic blood pressure, hyperglycemia, nicotine levels, and IGF-1 levels (P&lt;0.00). Increased nicotine levels, increased blood pressure, hyperglycemia, and decreased levels of IGF-1 are associated with decreased GFR. Identifying factors related to the glomerular filtration rate can be used to predict impaired renal function. Conclusion: Reduced glomerular filtration rates are linked to higher nicotine levels, higher blood pressure, hyperglycemia, and lower IGF-1 levels.