Clinical predictive factors in diabetic kidney disease progression

Identification of mitochondria-related genes as diagnostic biomarkers for diabetic nephropathy and their correlation with immune infiltration: New insights from bioinformatics analysis

BACKGROUND

Diabetic nephropathy (DN) is a common and severe microvascular complication of diabetes. Mitochondrial dysfunction and immune inflammation are important factors in the pathogenesis of DN. However, the specific mechanisms and their intricate interactions in DN remain unclear. Besides, there are no effective specific predictive or diagnostic biomarkers for DN so far. Therefore, this study aims to elucidate the role of mitochondrial-related genes and their possibility as predictive or diagnostic biomarkers, as well as their crosstalk with immune infiltration in the progression of DN.

METHODS

Based on the GEO database and limma R package, the differentially expressed genes (DEGs) of DN were identified. Mitochondrial-related DEGs (MitoDEGs) were then obtained by intersecting these DEGs with mitochondria-related genes from the MitoCarta 3.0 database. Subsequently, the candidate hub genes were further screened by gene co-expression network analysis (WGCNA), and verified mRNA levels of these genes by real-time quantitative PCR (qRT-PCR) in high-glucose-treated human proximal tubular (HK-2) cells. The verified hub genes were utilized to construct a combined diagnostic model for DN, with its diagnostic efficacy assessed across the GSE30122 and GSE96804 datasets. Additionally, the immune infiltration pattern in DN was assessed with the CIBERSORT algorithm, and the Nephroseq v5 database was used to analyze the correlation between hub genes and clinical features of DN.

RESULTS

Seven mitochondria-related candidate hub genes were screened from 56 MitoDEGs. Subsequently, the expression levels of six of them, namely EFHD1, CASP3, AASS, MPC1, NT5DC2, and BCL2A1, exhibited significant inter-group differences in the HK-2 cell model. The diagnostic model based on the six genes demonstrated good diagnostic efficacy in both training and validation sets. Furthermore, correlation analysis indicated that EFHD1 and AASS, downregulated in DN, are positively correlated with eGFR and negatively with serum creatinine. Conversely, CASP3, NT5DC2, and BCL2A1, upregulated in DN, show opposite correlations. In addition, spearman analysis revealed that the six hub genes were significantly associated with the infiltration of immune cells, including M1 and M2 macrophages, mast cells, resting NK cells, gamma delta T cells, and follicular helper T cells.

CONCLUSION

This study elucidated the characteristics of mitochondria-related genes and their correlation with immune cell infiltration in DN, providing new insights for exploring the pathogenesis of DN and facilitating the identification of new potential biomarkers and therapeutic targets.

Application of a validated prognostic plasma protein biomarker test for renal decline in type 2 diabetes to type 1 diabetes: the Fremantle Diabetes Study Phase II

BACKGROUND

There are scant data relating to prognostic biomarkers for chronic kidney disease (CKD) complicating type 1 diabetes. The aim of this study was to assess the performance of the plasma protein biomarker-based PromarkerD test developed and validated for predicting renal decline in type 2 diabetes in the context of type 1 diabetes.

METHODS

The baseline PromarkerD test score was determined in 91 community-based individuals (mean age 46.2 years, 56.5% males) with confirmed type 1 diabetes recruited to the longitudinal observational Fremantle Diabetes Study Phase II. The performance of the PromarkerD test in predicting the risk of incident CKD (estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73m2 in people without CKD at baseline) or an eGFR decline of ≥ 30% over the next four years was determined. The score can range from 0 to 100%, and is categorized as representing low (< 10%), moderate (10% to < 20%) or high (≥ 20%) risk.

RESULTS

The area under the receiver operating characteristic curve was 0.93 (95% confidence interval 0.87-0.99) for the composite renal endpoint, indicating strong predictive accuracy. The positive and negative predictive values at moderate (10% to < 20%) and high (≥ 20%) risk PromarkerD cut-offs were 46.7-50.0% and ≥ 92.0%, respectively.

CONCLUSIONS

These preliminary data suggest that PromarkerD is at least as good a prognostic test for renal decline in type 1 as type 2 diabetes.

Evaluating the incidence of complications among people with diabetes according to age of onset: Findings from the UK Biobank

AIMS

To examine the impact of current age, age at diagnosis, and duration of diabetes on the incidence rate of complications among people with type 2 diabetes.

METHODS

Baseline data from 19,327 individuals with type 2 diabetes in the UK Biobank were analysed. Poisson regression was used to model incidence rates by current age, age at diagnosis, and duration of diabetes for the following outcomes: myocardial infarction (MI), heart failure (HF), stroke, end-stage kidney diseases (ESKD), chronic kidney diseases (CKD), liver diseases, depression, and anxiety.

RESULTS

The mean age at baseline was 60.2 years, and median follow-up was 13.9 years. Diabetes duration was significantly longer among those with younger-onset type 2 diabetes (diagnosed at <40 years) compared to later-onset type 2 diabetes (diagnosed at ≥40 years), 16.2 and 5.3 years, respectively. Incidence rates of MI, HF, stroke, and CKD had strong positive associations with age and duration of diabetes, whereas incidence rates of ESKD liver diseases, and anxiety mainly depended on duration of diabetes. The incidence rates of depression showed minor variation by age and duration of diabetes and were highest among those diagnosed at earlier ages. No clear evidence of an effect of age of onset of diabetes on risk of complications was apparent after accounting for current age and duration of diabetes.

CONCLUSIONS

Our study indicates age at diagnosis of diabetes does not significantly impact the incidence of complications, independently of the duration of diabetes. Instead, complications are primarily influenced by current age and diabetes duration.

Factors associated with patient activation among patients with diabetes on hemodialysis: a multicenter cross-sectional study from a developing country

BACKGROUND

Diabetes mellitus (DM) is a major public health concern with considerable morbidity and mortality. DM affects patients' quality of life and can lead to multiple complications, including chronic kidney disease (CKD) and the need for dialysis. Higher patient activation can improve health outcomes in hemodialysis patients with DM. This study aimed to explore the factors associated with higher patient activation and health-related quality of life (HRQoL) among hemodialysis patients with DM.

METHODS

This was a cross-sectional, questionnaire-based study conducted on hemodialysis patients with DM in Palestine. The quota sampling method was utilized to draw samples from six dialysis centers. The questionnaire consists of three sections. The first section includes demographic, socioeconomic and clinical questions. The second section utilizes the patient activation measure-13 (PAM-13) to measure patient activation, while the third section assesses HRQoL using the EQ-5D-5 L tool and the visual analog scale (VAS). Mann‒Whitney and Kruskal‒Wallis tests were employed to examine the relationships between variables at the bivariate level, and multiple regression analysis was employed at the multivariate level.

RESULTS

Of the 200 patients who were approached, 158 were included. The median PAM, EQ-5D index, and VAS score were low at 51.0, 0.58, and 60.0, respectively. A higher PAM score was independently associated with a higher household income level and taking medications independently. A higher EQ-5D index was associated with taking more than eight medications, taking medications independently, living with fewer than three comorbid conditions, and having a higher PAM. A higher VAS score was associated with being married, and receiving less than 3.5 hours of hemodialysis.

CONCLUSIONS

A higher patient activation level was associated with a higher income level and independence in taking medications. Interventions designed to improve patient activation, such as medication management programs, should address these factors among the target population. Longitudinal studies are needed to assess the time effect and direction of causation between health status and patient activation.

Strategies to address diabetic kidney disease burden in Mexico: a narrative review by the Mexican College of Nephrologists

Chronic kidney disease (CKD) is a growing global public health challenge worldwide. In Mexico, CKD prevalence is alarmingly high and remains a leading cause of morbidity and mortality. Diabetic kidney disease (DKD), a severe complication of diabetes, is a leading determinant of CKD. The escalating diabetes prevalence and the complex regional landscape in Mexico underscore the pressing need for tailored strategies to reduce the burden of CKD. This narrative review, endorsed by the Mexican College of Nephrologists, aims to provide a brief overview and specific strategies for healthcare providers regarding preventing, screening, and treating CKD in patients living with diabetes in all care settings. The key topics covered in this review include the main cardiometabolic contributors of DKD (overweight/obesity, hyperglycemia, arterial hypertension, and dyslipidemia), the identification of kidney-related damage markers, and the benefit of novel pharmacological approaches based on Sodium-Glucose Co-Transporter-2 Inhibitors (SGLT2i) and Glucagon-Like Peptide-1 Receptor Agonists (GLP-1 RA). We also address the potential use of novel therapies based on Mineralocorticoid Receptor Antagonists (MRAs) and their future implications. Emphasizing the importance of multidisciplinary treatment, this narrative review aims to promote strategies that may be useful to alleviate the burden of DKD and its associated complications. It underscores the critical role of healthcare providers and advocates for collaborative efforts to enhance the quality of life for millions of patients affected by DKD.

Inhibition of Interleukin-33 to Reduce Glomerular Endothelial Inflammation in Diabetic Kidney Disease

Introduction

Inflammation is a significant contributor to cardiorenal morbidity and mortality in diabetic kidney disease (DKD). The pathophysiological mechanisms linking systemic, subacute inflammation and local, kidney injury-initiated immune maladaptation is partially understood.

Methods

Here, we explored the expression of proinflammatory cytokines in patients with DKD; investigated mouse models of type 1 and type 2 diabetes (T2D); evaluated glomerular signaling in vitro; performed post hoc analyses of systemic and urinary markers of inflammation; and initiated a phase 2b clinical study (FRONTIER-1; NCT04170543).

Results

Transcriptomic profiling of kidney biopsies from patients with DKD revealed significant glomerular upregulation of interleukin-33 (IL-33). Inhibition of IL-33 signaling reduced glomerular damage and albuminuria in the uninephrectomized db/db mouse model (T2D/DKD). On a cellular level, inhibiting IL-33 improved glomerular endothelial health by decreasing cellular inflammation and reducing release of proinflammatory cytokines. Therefore, FRONTIER-1 was designed to test the safety and efficacy of the IL-33-targeted monoclonal antibody tozorakimab in patients with DKD. So far, 578 patients are enrolled in FRONTIER-1. The baseline inflammation status of participants (N > 146) was assessed in blood and urine. Comparison to independent reference cohorts (N > 200) validated the distribution of urinary tumor necrosis factor receptor 1 (TNFR1) and C-C motif chemokine ligand 2 (CCL2). Treatment with dapagliflozin for 6 weeks did not alter these biomarkers significantly.

Conclusion

We show that blocking the IL-33 pathway may mitigate glomerular endothelial inflammation in DKD. The findings from the FRONTIER-1 study will provide valuable insights into the therapeutic potential of IL-33 inhibition in DKD.

Age- and gender-adjusted estimated glomerular filtration rate definition reveals hyperfiltration as a risk factor for renal function deterioration in type 2 diabetes

AIM

To assess the role of hyperfiltration for diabetic kidney disease (DKD) progression.

MATERIALS AND METHODS

A retrospective observational cohort study enrolled type 2 diabetes (T2D) patients with an initial estimated glomerular filtration rate (eGFR) of 60 mL/min/1.73m2 or higher. Patients were categorized into two groups: hyperfiltration (eGFR exceeding the age- and gender-specific 95th percentile values from a prior national cohort study) and normofiltration. Rapid DKD progression was defined as an eGFR decline of more than 5 mL/min/1.73m2/year. We used a linear mixed effect model and Cox regression with time-varying covariate model to compare eGFR changes and identify factors associated with rapid DKD progression.

RESULTS

Of the enrolled 7563 T2D patients, 7.2% had hyperfiltration. The hyperfiltration group exhibited a higher rate of eGFR decline compared with the normofiltration group (-2.0 ± 0.9 vs. -1.1 ± 0.9 mL/min/1.73m2/year; P < .001). During an average follow-up period of 4.65 ± 3.86 years, 24.7% of patients with hyperfiltration experienced rapid DKD progression, compared with 15.7% of patients with normofiltration (P < .001). Cox regression analyses identified that initial hyperfiltration was a significant determinant of rapid DKD progression, with a hazard ratio of 1.66 (95% confidence interval: 1.41-1.95; P < .001). When combined with albuminuria, the risk of progression was further compounded (hazard ratio 1.76-3.11, all P < .001).

CONCLUSIONS

In addition to using the current Kidney Disease: Improving Global Outcomes CGA classification system, considering glomerular hyperfiltration status can improve the accuracy of predicting DKD progression.

Predictors of chronic kidney disease survival in type 2 diabetes: a 12-year retrospective cohort study utilizing estimated glomerular filtration rate

Predicting the course of kidney disease in individuals with both type 1 and type 2 diabetes mellitus (DM) is a significant clinical and policy challenge. In several regions, DM is now the leading cause of end-stage renal disease. The aim of this study to identify both modifiable and non-modifiable risk factors, along with clinical markers and coexisting conditions, that increase the likelihood of stage 3-5 chronic kidney disease (CKD) development in individuals with type 2 DM in the United Arab Emirates (UAE). This was a single-center retrospective cohort study based on data derived from electronic medical records of UAE patients with DM who were registered at outpatient clinics at Tawam Hospital in Al Ain, UAE, between January 2011 and December 2021. Type 2 DM patients aged ≥ 18 years who had serum HbA1c levels ≥ 6.5% were included in the study. Patients with type 1 DM, who had undergone permanent renal replacement therapy, who had under 1 year of follow-up, or who had missing or incomplete data were excluded from the study. Factors associated with diabetic patients developing stage 3-5 CKD were identified through Cox regression analysis and a fine and gray competing risk model to account for competing events that could potentially hinder the development of CKD. A total of 1003 patients were recruited for the study. The mean age of the study cohort at baseline was 70.6 ± 28.2 years. Several factors were found to increase the risk of developing stage 3-5 CKD: advancing age (HR 1.005, 95% CI 1.002-1.009, p = 0.026), a history of hypertension (HR 1.69, 95% CI 1.032-2.8, p = 0.037), a history of heart disease (HR 1.49, 95% CI 1.16-1.92, p = 0.002), elevated levels of serum creatinine (HR 1.006, 95% CI 1.002-1.010, p = 0.003), decreased levels of estimated glomerular filtration rate (eGFR) (HR 0.943, 95% CI, 0.938-0.947; p < 0.001), and the use of beta-blockers (HR 139, 95% CI 112-173, p = 0.003). Implementing preventative measures, initiating early interventions, and developing personalized care plans tailored to address specific risk factors are imperative for reducing the impact of CKD. Additionally, the unforeseen findings related to eGFR highlight the ongoing need for research to deepen our understanding of the complexities of kidney disease.

Expression of apelin‑13 and its negative correlation with TGF‑β1 in patients with diabetic kidney disease

Diabetic kidney disease (DKD) is a severe microvascular complication of diabetes, one key feature of which includes renal fibrosis. As apelin is an adipokine closely related to diabetes, the present study aimed to evaluate apelin-13 expression levels and the relationship between apelin-13 and disease indicators in patients with diabetic kidney disease (DKD). The present case-control study enrolled 70 patients with diabetes, including 31 with diabetic kidney disease (DKD group), 39 without DKD (non-DKD group) and 30 healthy controls. The levels of serum apelin-13 and TGF-β1, the key driver of renal fibrosis, were determined by ELISA. Additionally, age, mean disease duration, weight, blood pressure, fasting blood glucose, triglyceride, low-density lipoprotein cholesterol, high-density lipoprotein, cholesterol, urea nitrogen, blood creatinine and 24-hour urinary total protein (24-h UTP) were recorded. The results demonstrated that apelin-13 and TGF-β1 expression levels, age, blood pressure, fasting blood glucose, cholesterol and blood urea nitrogen levels were significantly higher in patients with diabetes compared with the healthy controls (P<0.05). Moreover, apelin-13 and TGF-β1 expression levels, mean disease duration, systolic pressure, blood creatinine, blood urea nitrogen and 24-h UTP were significantly higher in the DKD group compared with the non-DKD group (P<0.05). The estimated glomerular filtration rate (eGFR) was significantly reduced in the DKD group compared with the non-DKD group (P<0.05). Correlation analysis demonstrated a negative correlation between apelin-13 and eGFR expression and a positive correlation between apelin-13 expression and 24-h UTP in both the DKD and non-DKD groups (P<0.05). A negative correlation was also demonstrated between apelin-13 and TGF-β1 expression levels in the DKD group and non-DKD groups (both P<0.05). In conclusion, apelin-13 and TGF-β1 expression levels were significantly higher in the DKD group compared with those in the non-DKD group. Additionally, apelin-13 expression was negatively correlated with TGF-β1 expression in the DKD and non-DKD groups. Therefore, apelin-13 could potentially be used in the future as an indicator of renal fibrosis or destruction in patients with DKD. The present trial was retrospectively registered in the Chinese Clinical Trial Registry (trial registration no. ChiCTR2200060945) on 14.06.2022.

Gender Differences in the Incidence of Nephropathy and Changes in Renal Function in Patients with Type 2 Diabetes Mellitus: A Retrospective Cohort Study

Purpose

This research aims to examine and scrutinize gender variations in the incidence of diabetic nephropathy (DN) and the trajectory of renal function in type 2 diabetes mellitus (T2DM) patients.

Patients and Methods

We conducted a retrospective cohort study that enrolled 1549 patients diagnosed with T2DM from May 2015 to July 2023. We separately compared the clinical characteristics of male and female participants with and without DN. We utilized the Kaplan-Meier method to examine the cumulative incidence of DN among T2DM patients of varying genders. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated using univariable and multivariable Cox proportional hazards regression analysis to evaluate the correlation between various factors and the risk of DN incidence. Multiple linear regression was utilized to investigate the relationship between ΔeGFR% and each factor. Logistic regression with cubic spline function and smooth curve fitting was employed to analyze the nonlinear link between ΔeGFR% and the risk of DN among participants of different genders.

Results

The prevalence of DN was higher in female participants (17.31%) than in male participants (12.62%), with a significant cumulative risk ratio (1.33 [1.02-1.73], P = 0.034). Multiple linear regression analysis revealed that creatinine, female gender, blood urea nitrogen, alkaline phosphatase, and total cholesterol had a significant impact on ΔeGFR% in T2DM patients, with standardized β coefficients of -0.325, -0.219, -0.164, -0.084, and 0.071, respectively. The restricted cubic spline analysis demonstrated a strong negative association between ΔeGFR% and the risk of developing DN (P < 0.001).

Conclusion

Both male and female patients with T2DM had a higher prevalence of DN over the 5-year follow-up period. However, women had a greater risk of developing DN and a faster decline in renal function compared to men.

Long-term exposure to ambient air pollutants and increased risk of end-stage renal disease in patients with type 2 diabetes mellitus and chronic kidney disease: a retrospective cohort study in Beijing, China

Limited data have examined the association between air pollution and the risk of end-stage renal disease (ESRD) in patients with type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD). We aimed to investigate whether long-term exposure to air pollutants is related to the development of ESRD among patients with T2DM and CKD. A total of 1,738 patients with T2DM and CKD hospitalized in Peking University Third Hospital from January 1, 2013, to December 31, 2021 were enrolled in this study. The outcome was defined as the occurrence of ESRD. Data on six air pollutants (PM2.5, PM10, CO, NO2, SO2, and O3) from 35 monitoring stations were obtained from the Beijing Municipal Ecological and Environmental Monitoring Center. Long-term exposure to air pollutants during the follow-up period was measured using the ordinary Kriging method. During a mean follow-up of 41 months, 98 patients developed ESRD. Multivariate logistic regression analysis showed that an increase of 10 μg/m3 in PM2.5 (odds ratio [OR] 1.19, 95% confidence interval [CI] 1.03-1.36) and PM10 (OR 1.15, 95% CI 1.02-1.30) concentration were positively associated with ESRD. An increase of 1 mg/m3 in CO (2.80, 1.05-7.48) and an increase of 1 μg/m3 in SO2 (1.06, 1.00-1.13) concentration were also positively associated with ESRD. Apart from O3 and NO2, all the above air pollutants have additional predictive value for ESRD in patients with T2DM and CKD. The results of Bayesian kernel machine regression and the weighted quantile sum regression all showed that PM2.5 was the most important air pollutant. Backward stepwise logistic regression showed that PM2.5 was the only pollutant remaining in the prediction model. In patients with T2DM and CKD, long-term exposure to ambient PM2.5, PM10, CO, and SO2 was positively associated with the development of ESRD.

Poricoic acid A induces mitophagy to ameliorate podocyte injury in diabetic kidney disease via downregulating FUNDC1

Diabetic kidney disease (DKD) is a devastating complication of diabetes mellitus (DM) and is the most prevalent chronic kidney disease (CKD). Poricoic acid A (PAA), a component isolated from Traditional Chinese Medicine (TCM) Poria cocos, has hypoglycaemic and anti-fibrosis effects. However, the role of PAA in DKD remains largely unclear. To mimics an in vitro model of DKD, the mouse podocyte MPC5 cells were treated with high glucose (25 mM; HG) for 24 h. CCK-8 and flow cytometry assays were conducted for assessing MPC5 cell viability and apoptosis. Meanwhile, streptozotocin (STZ) was used to induce experimental DKD in mice by intraperitoneal injection. PAA notably inhibited the apoptosis and inflammation, reduced the generation of ROS, and elevated the MMP level in HG-treated MPC5 cells. Moreover, PAA obviously reduced blood glucose and urine protein levels, inhibited renal fibrosis in DKD mice. Meanwhile, PAA markedly increased LC3 and ATG5 levels and declined p62 and FUNDC1 levels in HG-treated MPC5 cells and in the kidney tissues of DKD mice, leading to the activation of cell mitophagy. Furthermore, the downregulation of FUNDC1 also inhibited apoptosis, inflammation, and promoted mitophagy in HG-treated MPC5 cells. As expected, the knockdown of FUNDC1 further enhanced the protective role of PAA in MPC5 cells following HG treatment, indicating that induction of mitophagy could attenuate podocyte injury. Collectively, PAA could exert beneficial effects on podocyte injury in DKD by promoting mitophagy via downregulating FUNDC1. These findings suggested that PAA may have great potential in alleviating kidney injury in DKD.

Risk prediction of the progression of chronic kidney disease stage 1 based on peripheral blood samples: construction and internal validation of a nomogram

Patients with chronic kidney disease (CKD) have high morbidity and mortality, and the disease progression has a significant impact on their survival and living standards. This research aims to analyze risk factors for CKD stage 1 and provide a reference for clinical decision making. The clinical data and peripheral blood samples of 300 patients with CKD stage 1 were collected retrospectively. Patients were randomly assigned into a training set (n = 210) and a validation set (n = 90). Patients' baseline characteristic levels were subjected to statistical tests for difference. Univariate and multivariate Cox regression analyses were utilized to identify risk factors influencing disease progression. Subsequently, a prediction model for disease progression was developed using a nomogram, and the model's accuracy was assessed using the C-index and calibration curve. The results revealed that hypertension, diabetes, and urinary albumin were essential factors in the progression of CKD stage 1. The nomogram was constructed and then the C-index was calculated. The calibration curve was utilized to assess the risk model. The C-index of the training set was 0.75, and the C-index of the validation set was 0.73, suggesting a good predictive ability of the model. The risk model accurately predicted the progression of CKD stage 1, which is of great significance to developing personalized treatment for patients in clinical practice.

Identification of necroptosis-related features in diabetic nephropathy and analysis of their immune microenvironent and inflammatory response

Background: Diabetic nephropathy (DN) was considered a severe microvascular complication of diabetes, which was recognized as the second leading cause of end-stage renal diseases. Therefore, identifying several effective biomarkers and models to diagnosis and subtype DN is imminent. Necroptosis, a distinct form of programmed cell death, has been established to play a critical role in various inflammatory diseases. Herein, we described the novel landscape of necroptosis in DN and exploit a powerful necroptosis-mediated model for the diagnosis of DN. Methods: We obtained three datasets (GSE96804, GSE30122, and GSE30528) from the Gene Expression Omnibus (GEO) database and necroptosis-related genes (NRGs) from the GeneCards website. Via differential expression analysis and machine learning, significant NRGs were identified. And different necroptosis-related DN subtypes were divided using consensus cluster analysis. The principal component analysis (PCA) algorithm was utilized to calculate the necroptosis score. Finally, the logistic multivariate analysis were performed to construct the necroptosis-mediated diagnostic model for DN. Results: According to several public transcriptomic datasets in GEO, we obtained eight significant necroptosis-related regulators in the occurrence and progress of DN, including CFLAR, FMR1, GSDMD, IKBKB, MAP3K7, NFKBIA, PTGES3, and SFTPA1 via diversified machine learning methods. Subsequently, employing consensus cluster analysis and PCA algorithm, the DN samples in our training set were stratified into two diverse necroptosis-related subtypes based on our eight regulators' expression levels. These subtypes exhibited varying necroptosis scores. Then, we used various functional enrichment analysis and immune infiltration analysis to explore the biological background, immune landscape and inflammatory status of the above subtypes. Finally, a necroptosis-mediated diagnostic model was exploited based on the two subtypes and validated in several external verification datasets. Moreover, the expression level of our eight regulators were verified in the singe-cell level and glomerulus samples. And we further explored the relationship between the expression of eight regulators and the kidney function of DN. Conclusion: In summary, our necroptosis scoring model and necroptosis-mediated diagnostic model fill in the blank of the relationship between necroptosis and DN in the field of bioinformatics, which may provide novel diagnostic insights and therapy strategies for DN.

The triglyceride-glucose index predicts 1-year major adverse cardiovascular events in end-stage renal disease patients with coronary artery disease

BACKGROUND

The triglyceride-glucose (TyG) index has been suggested as a dependable indicator for predicting major adverse cardiovascular events (MACE) in individuals with cardiovascular conditions. Nevertheless, there is insufficient data on the predictive significance of the TyG index in end-stage renal disease (ESRD) patients with coronary artery disease (CAD).

METHODS

This study, conducted at multiple centers in China, included 959 patients diagnosed with dialysis and CAD from January 2015 to June 2021. Based on the TyG index, the participants were categorized into three distinct groups. The study's primary endpoint was the combination of MACE occurring within one year of follow-up, including death from any cause, non-fatal myocardial infarction, and non-fatal stroke. We assessed the association between the TyG index and MACE using Cox proportional hazard models and restricted cubic spline analysis. The TyG index value was evaluated for prediction incrementally using C-statistics, continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

RESULTS

The three groups showed notable variations in the risk of MACE (16.3% in tertile 1, 23.5% in tertile 2, and 27.2% in tertile 3; log-rank P = 0.003). Following complete adjustment, patients with the highest TyG index exhibited a notably elevated risk of MACE in comparison to those in the lowest tertile (hazard ratio [HR] 1.63, 95% confidence interval [CI] 1.14-2.35, P = 0.007). Likewise, each unit increase in the TyG index correlated with a 1.37-fold higher risk of MACE (HR 1.37, 95% CI 1.13-1.66, P = 0.001). Restricted cubic spline analysis revealed a connection between the TyG index and MACE (P for nonlinearity > 0.05). Furthermore, incorporating the TyG index to the Global Registry of Acute Coronary Events risk score or baseline risk model with fully adjusted factors considerably enhanced the forecast of MACE, as demonstrated by the C-statistic, continuous NRI, and IDI.

CONCLUSIONS

The TyG index might serve as a valuable and dependable indicator of MACE risk in individuals with dialysis and CAD, indicating its potential significance in enhancing risk categorization in clinical settings.

Factors associated with chronic kidney disease in patients with diabetes in French Guiana

Introduction

With over half of the population living under the poverty threshold, the social and health context in French Guiana is more difficult than in mainland France. The prevalence of diabetes is twice as great and end-stage renal failure is 45% higher than in mainland France.

Objective

Our objective was to describe the profile of diabetic patients with chronic kidney disease in French Guiana and search for possible risk factors.

Method

We conducted a multicenter cross-sectional observational study based on the CODIAM cohort (Cohort of Diabetes in French Amazonia). We analyzed 1,287 patients followed up between May 2019 and June 2021 at Cayenne Hospital, Saint Laurent Hospital, and delocalized health centers.

Results

In our cohort, chronic kidney disease was present after an average of 12 years of diabetes. Compared with the French population, 41% of diabetic patients had chronic kidney disease (i.e., 12% more), and had an average age of 56 years (i.e., 10 years younger). Forty-eight per cent of these patients were obese (i.e., 7% more). Seventy-four per cent of patients were precarious and 45% were foreigners but neither was associated with chronic kidney disease, contrary to countries where the health system is not universal.

Conclusion

Screening of patients with chronic kidney disease among diabetics in French Guiana remains a real challenge. Patients were younger and more obese than in other French territories. In this cohort, precariousness and immigration were not associated with the presence of chronic kidney disease. However, particular attention should be paid to hypertensive patients and those over 65 years of age, which are, with diabetes itself, the two most obvious risk factors for developing chronic kidney disease among diabetic patients in our territory.

Association of triglyceride-glucose index with coronary severity and mortality in patients on dialysis with coronary artery disease

BACKGROUND

The triglyceride-glucose (TyG) index is validated as a reliable biomarker of insulin resistance and an independent predictor of cardiovascular prognosis. However, the prognostic value of the TyG index in patients on dialysis with coronary artery disease (CAD) remained unexplored. This study aimed to determine the association between the TyG index and CAD severity and mortality in these patients.

METHODS

A total of 1061 dialysis patients with CAD were enrolled in this multi-center cohort study from January 2015 to June 2021. The extent and severity of CAD were evaluated using the multivessel disease and Gensini score (GS). Patients were followed up for all-cause death and cardiovascular death.

RESULTS

The multivariable logistic regression model indicated that the TyG index was significantly associated with multivessel disease (odds ratio [OR] 1.51, 95% confidence interval [CI] 1.18-1.94, P = 0.001), and high GS (OR 1.33, 95% CI 1.10-1.61, P = 0.003). After adjusting for baseline risk factors, the hazards of all-cause death and cardiovascular death were 1.23 (95% CI 1.06-1.43, P = 0.007), and 1.33 (95% CI 1.11-1.59, P = 0.002), independent of CAD severity. Restricted cubic spline analysis identified a dose-response association between the TyG index and both CAD severity and mortality (all P for nonlinearity > 0.05). When modeling the TyG index as a categorical variable, these independent associations remained. Subgroup analyses did not substantially modify the results. Furthermore, incorporating the TyG index into the existing risk prediction model improved the predictive accuracy for all-cause death and cardiovascular death, as evaluated by C-statistic, continuous net reclassification improvement, and integrated discrimination improvement.

CONCLUSIONS

In patients on dialysis with CAD, the TyG index was significantly associated with more severe CAD as well as mortality. These results highlight the clinical importance of the TyG index for assessing CAD severity and risk stratification in patients on dialysis with CAD.

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

OBJECTIVE

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).

METHODS

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.

RESULTS

(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).

CONCLUSION

A decrease in the FT3/FT4 is an independent predictor of DKD occurrence in patients with T2DM.

Microalbuminuria as the Tip of Iceberg in Type 2 Diabetes Mellitus: Prevalence, Risk Factors, and Associated Diabetic Complications

Background Microalbuminuria (MA) is an important clinical marker for the early detection of kidney damage in patients with type 2 diabetes (T2DM). Urine albumin-to-creatinine ratio (ACR), also known as urine microalbumin, is a sign of diabetic nephropathy (DN), which is a prevalent complication of diabetes and can result in end-stage renal disease (ESRD) if not managed. The prevalence of MA in T2DM has been steadily increasing worldwide, making it a significant public health concern. The goal of this study was to estimate the prevalence of MA and its relationship to hypertension and other diabetic complications among people with T2DM. Methodology This descriptive cross-sectional study was conducted from February 5, 2022, to February 10, 2023, to analyse data from T2DM patients who visited the outpatient diabetic clinic of Sheikh Zayed Medical College and Hospital, Rahim Yar Khan, Pakistan. This study included a total of 640 patients, aged 35-60 years, who had been diagnosed with T2DM for at least five years and fulfilled the inclusion criteria. Data on demographic and clinical characteristics, blood pressure (BP) measurements, and laboratory investigations were collected. MA was assessed based on the ACR in a spot urine sample of more than 30 mg/l. Blood pressure greater than 140/90 or already taking anti-hypertensives was taken to constitute hypertension. Factors associated with MA like hypertension, gender, mode of diabetes treatment, duration of diabetes, glycosylated haemoglobin (HbA1c), dyslipidemia, and other diabetic complications such as retinopathy and neuropathy were also recorded. Results The prevalence of MA in this study of T2DM patients study was 39.1%. The mean age of the participants with MA was 53.9 with a standard deviation (SD) of 6.1 years, and the mean duration of diabetes was 10.1 years (SD 6.2 years); 101 (33.4%) males (n=302) and 103 (30.5%) females (n=338) had MA.  There was a statistically significant correlation between MA > 30mg/d and hypertension (p = <0.001), diabetes duration since diagnosis (p=0.04), HbA1C level (p = <0.001), dyslipidemia (p=0.001), therapy type (p = <0.001), triglyceridemia (p = 0.03), history of diabetes retinopathy (p= <0.002), and peripheral neuropathy (p= <0.001). However, there was no statistically significant correlation between MA and age (p = 0.56), female gender (p = 0.08), low- and high-density lipids, or statin use (p = 0.06). Conclusion The prevalence of microalbuminuria among T2DM patients is significantly high (39.1%) and is positively correlated with various factors such as male gender, hypertension, suboptimal control of diabetes mellitus, high HbA1c levels, longer disease duration, dyslipidemia with high triglycerides, treatment modalities of T2DM, and other diabetic complications like neuropathy and retinopathy. As diabetes is very prevalent in our country, the number of patients with diabetic kidney disease will rise significantly in the near future, leading to ESRD and other diabetic complications, and immediate intervention is needed to prevent this. Further research is warranted to explore potential interventions and evaluate their impact on patient outcomes.

Predicting diabetic kidney disease for type 2 diabetes mellitus by machine learning in the real world: a multicenter retrospective study

Objective

Diabetic kidney disease (DKD) has been reported as a main microvascular complication of diabetes mellitus. Although renal biopsy is capable of distinguishing DKD from Non Diabetic kidney disease(NDKD), no gold standard has been validated to assess the development of DKD.This study aimed to build an auxiliary diagnosis model for type 2 Diabetic kidney disease (T2DKD) based on machine learning algorithms.

Methods

Clinical data on 3624 individuals with type 2 diabetes (T2DM) was gathered from January 1, 2019 to December 31, 2019 using a multi-center retrospective database. The data fell into a training set and a validation set at random at a ratio of 8:2. To identify critical clinical variables, the absolute shrinkage and selection operator with the lowest number was employed. Fifteen machine learning models were built to support the diagnosis of T2DKD, and the optimal model was selected in accordance with the area under the receiver operating characteristic curve (AUC) and accuracy. The model was improved with the use of Bayesian Optimization methods. The Shapley Additive explanations (SHAP) approach was used to illustrate prediction findings.

Results

DKD was diagnosed in 1856 (51.2 percent) of the 3624 individuals within the final cohort. As revealed by the SHAP findings, the Categorical Boosting (CatBoost) model achieved the optimal performance 1in the prediction of the risk of T2DKD, with an AUC of 0.86 based on the top 38 characteristics. The SHAP findings suggested that a simplified CatBoost model with an AUC of 0.84 was built in accordance with the top 12 characteristics. The more basic model features consisted of systolic blood pressure (SBP), creatinine (CREA), length of stay (LOS), thrombin time (TT), Age, prothrombin time (PT), platelet large cell ratio (P-LCR), albumin (ALB), glucose (GLU), fibrinogen (FIB-C), red blood cell distribution width-standard deviation (RDW-SD), as well as hemoglobin A1C(HbA1C).

Conclusion

A machine learning-based model for the prediction of the risk of developing T2DKD was built, and its effectiveness was verified. The CatBoost model can contribute to the diagnosis of T2DKD. Clinicians could gain more insights into the outcomes if the ML model is made interpretable.

miR-223-3p mediates the diabetic kidney disease progression by targeting IL6ST/STAT3 pathway

Diabetic kidney disease (DKD), the most pervasive complication in diabetic patients, has become a major health threat to the aging population. Our previous miRNA profiling identified hsa-miR-223-3p as a dysregulated miRNA in the DKD samples, which may serve as a biomarker for DKD diagnosis. However, the specific mechanism of miR-223-3p in the pathogenesis of DKD remains to be elucidated. In this study, we first verified that miR-223-3p level was significantly decreased in the in vitro cell model and in vivo db/db DKD model, accompanied with endothelial cell damage. Importantly, inhibiting the expression of miR-223-3p exacerbated high-glucose induced damages in Human Umbilical Vein Endothelial Cells (HUVECs) and Human Renal Glomerular Endothelial Cells (HRGECs), while miR-223-3p overexpression showed the opposite effect. We further demonstrated that miR-223-3p associated with IL6T mRNA and attenuated the progression of DKD by suppressing the downstream STAT3 activation, indicative of the implication of miR-223-3p/IL6T/STAT3 axis in the pathogenesis of DKD.

A prediction model of CKD progression among individuals with type 2 diabetes in the United States

BACKGROUND

CKD progression among individuals with T2D is associated with poor health outcomes and high healthcare costs, which have not been fully studied. This study aimed to predict CKD progression among individuals with diabetes.

METHOD

Using ACCORD trial data, a time-varying Cox model was developed to predict the risk of CKD progression among patients with CKD and T2D. CKD progression was defined as a 50 % decline, or 25 mL/min/1.73 m² decline in eGFR from baseline, doubling of the serum creatinine, or onset of ESKD. A list of candidate variables included demographic characteristics, physical exam results, laboratory results, medical history, drug use, and healthcare utilization. A stepwise algorithm was used for variable selection. Model performance was evaluated by Brier score and C-statistics. Confidence intervals (CI) were calculated using a bootstrap method. Decomposition analysis was conducted to assess the predictor contribution. Generalizability was assessed on patient-level data of the Harmony Outcome trial and CRIC study.

RESULTS

A total of 6982 diabetes patients with CKD were used for model development, with a median follow-up of 4 years and 3346 events. The predictors for CKD progression included female sex, age at T2D diagnosis, smoking status, SBP, DBP, HR, HbA1c, alanine aminotransferase (ALT), eGFR, UACR, retinopathy event, hospitalization. The model demonstrated good discrimination (C-statistics 0.745 [95 % CI 0.723-0.763]) and calibration (Brier Score 0.0923 [95 % CI 0.0873-0.0965]) performance in the ACCORD data. The most contributing predictors for CKD progression were eGFR, HbA1c, and SBP. The model demonstrated acceptable discrimination and calibration performance in the two external data.

CONCLUSION

For high-risk patients with both diabetes and CKD, the tool as a dynamic risk prediction of CKD progression may help develop novel strategies to lower the risk of CKD progression.

Urine Immunoglobin G Greater Than 2.45 mg/L Has a Correlation with the Onset and Progression of Diabetic Kidney Disease: A Retrospective Cohort Study

Aim: To further assess the correlation between urine immunoglobin G (IgG) greater than 2.45 mg/L and the onset and progression of diabetic kidney disease (DKD). Methods: One thousand and thirty-five patients with type 2 diabetes mellitus (T2DM) were divided into two groups based on the baseline levels of 24 h urinary albumin excretion (24 h UAE): one group with 24 h UAE < 30 mg/24 h and one with 24 h UAE ≥ 30 mg/24 h. The groups were subdivided using baseline levels of urine IgG (≤2.45 mg/L and >2.45 mg/L; hereafter, the Low and High groups, respectively). We used logistic regression to assess the risk of urine IgG and it exceeding 2.45 mg/L. Kaplan–Meier curves were used to compare the onset and progression time of DKD. The receiver operating characteristic curve was used to test the predictive value of urine IgG exceeding 2.45 mg/L. Results: Urine IgG was an independent risk factor for the onset and progression of DKD. The rate and risk of DKD onset and progression at the end of follow-up increased significantly in the High group. The onset and progression time of DKD was earlier in the High group. Urine IgG exceeding 2.45 mg/L has a certain predictive value for DKD onset. Conclusions: Urine IgG exceeding 2.45 mg/L has a correlation with the onset and progression of DKD, and it also has a certain predictive value for DKD onset.

Progesterone levels associated with proteinuria in male diabetes mellitus patients: A cross-sectional retrospective study

INTRODUCTION

The relationship between progesterone (P) and diabetic nephropathy (DKD) is unclear. Herein, we investigated the relationship between progesterone and DKD in men and postmenopausal women with type 2 diabetes mellitus. MATERIALS AND METHODS: We recruited 3,556 male and postmenopausal female patients and obtained the dominance ratio (OR) and corresponding 95% confidence intervals (CIs) associated with progesterone by logistic regression analysis after adjusting for potentially confounding variants.

RESULTS

We found that progesterone levels were significantly lower in the massive proteinuria and microproteinuria groups compared with the non-DKD group for male patients. Also, microproteinuria and massive proteinuria prevalence were higher in the first (lowest) progesterone quartile than in the second to fourth quartiles. After adjusting for confounders, compared with the first (lowest) progesterone quartile group, the OR for the second to fourth quartiles in the male microproteinuria subgroup, were: Q2: 0.846 (95% CI: 0.581-1.233, P = 0.385); Q3: 0.667 (95% CI: 0.45-0988, P = 0.044); Q4: 0.597 (95% CI: 0.393-0.907, P = 0.016). In the male massive proteinuria subgroup, the OR for the third quartile group was 0.418 (95% CI: 0.201-0.867, P = 0.019). In contrast, no significant association was detected between progesterone and DKD prevalence in the female group.

CONCLUSIONS

Progesterone levels were negatively associated with DKD incidence in hospitalized male patients with type 2 diabetes mellitus.

Triglyceride content of lipoprotein subclasses and kidney hemodynamic function and injury in adolescents with type 1 diabetes

AIMS

Elevated triglycerides (TG) are associated with development and progression of kidney disease, and TG distributions across lipoprotein subclasses predict kidney dysfunction in adults with type 1 diabetes (T1D). Little is known regarding these relationships in youth.

METHODS

In this single center study conducted from October 2018-2019, lipid constituents from lipoprotein subclasses were quantified by targeted nuclear magnetic resonance spectroscopy. Glomerular filtration rate (GFR), renal plasma flow (RPF), afferent arteriolar resistance (RA), efferent arteriolar resistance (RE), intraglomerular pressure (PGLO), urine albumin-to-creatinine ratio (UACR), and chitinase-3-like protein 1 (YKL-40), a marker of kidney tubule injury, were assessed. Cross-sectional relationships were assessed by correlation and multivariable linear regression (adjusted for age, sex, HbA1c) models.

RESULTS

Fifty youth with T1D (age 16 ± 3 years, 50 % female, HbA1c 8.7 ± 1.3 %, T1D duration 5.7 ± 2.6 years) were included. Very-low-density lipoprotein (VLDL)-TG concentrations correlated and associated with intraglomerular hemodynamic function markers including GFR, PGLO, UACR, as did small low-density lipoprotein (LDL)-TG and small high-density lipoprotein (HDL)-TG. YKL-40 correlated with all lipoprotein subclasses.

CONCLUSION

TG within lipoprotein subclasses, particularly VLDL, associated with PGLO, GFR, albuminuria, and YKL-40. Lipid perturbations may serve as novel targets to mitigate early kidney disease.

[Clinical Characteristics and Prognosis of Diabetic Foot Ulcers Patients of Different Renal Function Statuses]

Objective

To explore the clinical characteristics and the prognosis of diabetic foot ulcers (DFU) inpatients of different renal function statuses.

Methods

A retrospective analysis of 962 inpatients with DFU was conducted. The patients were divided into three groups according to their renal function statuses, and the clinical characteristics of the three groups were compared to identify differences. In addition, the patients were followed up in outpatient clinics or by telephone and their prognostic status and risk factors for death were analyzed.

Results

Analysis of the clinical characteristics showed that, compared with diabetic patients with normal renal function or mild renal function impairment, diabetic patients with moderate and severe renal function impairment had a longer course of disease ( P<0.001). Patients with foot ulcers of Wagner grade 4 predominates the moderate and severe renal function impairment groups ( P<0.05). Patients in the moderate and severe renal function impairment groups had a relatively higher proportion of comorbidities, including hypertension, coronary heart disease, and peripheral arterial disease ( P<0.05). These patients had relatively lower levels of glycosylated hemoglobin and hemoglobin (all P<0.05) and relatively higher levels of neutrophil ratio and procalcitonin (all P<0.05). Of the two groups, patients in the moderate renal function impairment group were older ( P<0.001) and had lower ankle-brachial index ( P<0.001). The severe renal function impairment group had a higher proportion of patients with foot ulcers of Wagner grades 3 and 5 (all P<0.05). For the purpose of conducting prognostic analysis, 748 patients were followed up in outpatient clinics or by telephone for a median length of 41 months. Among them, 239 died. The all-cause mortality was 31.9%, and the mortality in the three groups was 25.8%, 46.2% ( P<0.001), and 59.4% ( P<0.001), respectively. The survival rate of patients in the moderate and severe renal function impairment groups was significantly lower than those in the normal renal function and mild renal function impairment groups ( P<0.001). Univariate Cox regression analysis showed that age, concomitant coronary heart disease and peripheral arterial disease, degree of renal function impairment, and foot ulcers of Wagner grade 4 and 5 were associated with all-cause deaths. Furthermore, multivariate Cox regression analysis showed that moderate and severe renal function impairment was an independent risk factor for all-cause deaths in DFU patients ( P<0.001).

Conclusions

As renal function impairment worsens, patients with DFU present clinical characteristics of greater complexity, higher risks of cardiovascular events, and higher mortality. It is essential to prevent kidney damage and foot ulcers, to pay attention to the cardiovascular risks of DFU patients with moderate and severe renal function impairment, and to reduce mortality.

The concordance and discordance of diabetic kidney disease and retinopathy in patients with type 2 diabetes mellitus: A cross-sectional study of 26,809 patients from 5 primary hospitals in China

INTRODUCTION

Diabetic kidney disease (DKD) and diabetic retinopathy (DR) share similar pathophysiological mechanisms. However, signs of DKD may be present at diagnosis of diabetes without retinopathy. Risk factors for the development of DKD and DR may not be identical.

METHODS

This study aimed to evaluate the concordance and discordance between DKD and DR by investigating the distribution of DKD and DR in patients with type 2 diabetes mellitus from 5 Chinese cities. A total of 26,809 patients were involved in this study. The clinical characteristics were compared among patients based on the presence of DKD and DR. Logistic regression models were used to analyze the independent risk factors of DKD and DR.

RESULTS

The prevalence of DKD and DR was 32.3% and 34.6%, respectively. Among eligible patients, 1,752 patients without DR had an increased urinary albumin-to-creatinine ratio (ACR) or reduced estimated glomerular filtration rate (eGFR), and 1,483 patients with DR had no DKD. The positive predictive value of DR for DKD was 47.4% and negative predictive value was 67.1%. Elder age, male gender, a longer duration of disease, higher values of waist circumference and HbA1c were associated with both DR and DKD. A lower educational level was associated with DR. Higher BP and TG would predict increased prevalence of DKD.

CONCLUSIONS

DKD and DR shared many risk factors, but a significant discordance was present in patients with type 2 diabetes mellitus. DKD was more strongly associated with blood pressure and triglycerides than DR.

A Multifactorial Risk Score System for the Prediction of Diabetic Kidney Disease in Patients with Type 2 Diabetes Mellitus

PURPOSE

In-depth investigations of risk factors for the identification of diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) are rare. We aimed to investigate the risk factors for developing DKD from multiple types of clinical data and conduct a comprehensive risk assessment for individuals with diabetes.

METHODS

We carried out a case-control study, enrolling 958 patients to identify the risk factors for developing DKD in T2DM patients from a database established from inpatient electronic medical records. Multivariable logistic regression was applied to develop a prediction model and the performance of the model was evaluated using the area under the curve (AUC) and calibration curve. A multifactorial risk score system was established according to the Framingham Study risk score.

RESULTS

DKD accounted for 34.03% of eligible patients in total. Twelve risk factors were selected in the final prediction model, including age, duration of diabetes, duration of hypertension, fasting blood glucose, fasting C-peptide, insulin use, systolic blood pressure, low-density lipoprotein, γ-glutamyl transpeptidase, platelet, uric acid, and thyroid stimulating hormone; and one protective factor, serum albumin. The prediction model showed an AUC of 0.862 (95% Confidence Interval (CI) 0.834-0.890) with an accuracy of 81.5% in the derivation dataset and an AUC of 0.876 (95% CI 0.825-0.928) in the validation dataset. The calibration curves were excellent and the estimated probability of DKD was more than 80% when the cumulative score for risk factors reached 17 points.

CONCLUSION

Newly recognized risk factors were applied to assess the development of DKD in T2DM patients and the established risk score system was a reliable and feasible tool for assisting clinicians to identify patients at high risk of DKD.

Association between triglyceride-glucose index and risk of end-stage renal disease in patients with type 2 diabetes mellitus and chronic kidney disease

Aims

It has been suggested that the triglyceride-glucose (TyG) index is a novel and reliable surrogate marker of insulin resistance (IR). However, its relationship with the risk of end-stage renal disease (ESRD) in patients with type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD) remains uncertain. Accordingly, we sought to examine the relationship between the TyG index and ESRD risk in patients with T2DM and CKD.

Methods

From January 2013 to December 2021, 1,936 patients with T2DM and CKD hospitalized at Peking University Third Hospital (Beijing, China) were enrolled into the study. The formula for calculating the TyG index was ln[fasting triglyceride (mg/dL) × fasting blood glucose (mg/dL)/2]. ESRD was defined as an estimated glomerular filtration rate of less than 15 mL/min/1.73 m² or the commencement of dialysis or renal transplantation. The relationship between the TyG index and ESRD risk was analyzed using Cox proportional hazard regression.

Results

105 (5.42%) participants developed ESRD over a mean follow-up of 41 months. The unadjusted analysis revealed a 1.50-fold (95% confidence interval [CI] 1.17-1.93; P = 0.001) increased risk for ESRD per one unit rise in the TyG index, and the positive association remained stable in the fully adjusted model (hazard ratio, 1.49; 95% CI, 1.12-1.99; P = 0.006). Analysis using restricted cubic spline revealed a significant positive association between the TyG index and ESRD risk. In addition, Kaplan-Meier analysis revealed significant risk stratification with a TyG index cutoff value of 9.5 (P = 0.003).

Conclusion

In individuals with T2DM and CKD, a significant and positive association was shown between an elevated TyG index and the risk of ESRD. This conclusion provides evidence for the clinical importance of the TyG index for evaluating renal function decline in individuals with T2DM and CKD.

Renoprotective effects of the ginger (Zingiber officinale) on Diabetic kidney disease, current knowledge and future direction: a systematic review of animal studies

OBJECTIVE

Diabetic kidney disease affects approximately 40% of diabetic patients and is the leading cause of chronic kidney disease (CKD) worldwide. As a result, preventing renal complications in diabetic patients is critical. Ginger (Zingiber Officinale Rosco) is a popular spice and natral medicine. The present study was a systematic review focused on the existing evidence of the renoprotective effect of ginger extract on some features of diabetic kidney disease.

METHODS

The literature was searched in online databases such as PubMed, Scopus, EMBASE, ProQuest databases, and Google Scholar from inception to July 2022.

RESULTS

This review included 41 articles that met the eligibility criteria. Ginger supplementation was found to be associated with a significant decrease in blood glucose in 28 studies. Nine studies showed a significant reduction in malondialdehyde (MDA) after supplementation. Also, seventeen studies showed decreased serum levels of creatinine. Fifteen studies reported a decrease in total cholesterol (TC) and fourteen studies showed a lowered triglycerides (TG) concentrations. In twenty-six studies, ginger reduced renal injuries due to diabetes.

CONCLUSION

Ginger may improve blood sugar indices, lipid profile, some inflammatory markers, oxidative stress, and pathologic injuries in diabetic kidney disease. However, future well-designed clinical trials and meta-analyses are required for a solid consensus.

Prevalence of diabetic nephropathy in the diabetes mellitus population: A protocol for systematic review and meta-analysis

BACKGROUND

Diabetic nephropathy (DN) is the leading cause of end-stage renal disease worldwide, placing enormous pressure on healthcare systems and creating a heavy socioeconomic burden. It is urgent to comprehensively study the epidemiological characteristics of DN in diabetic patients and to analyze the related factors to its incidence in order to implement effective prevention and control measures.

METHODS AND ANALYSIS

Computer-aided searches of the MEDLINE, EMBASE, Web of Science, PsycINFO, and CINAHL databases will be performed for prospective cohort studies reporting the prevalence of DN in diabetic populations. Studies will be pooled using a generalized linear mixed model, and a single proportion of included studies will be calculated to derive the overall incidence of DN in the diabetic population, and to analyze the effect of different factors on the incidence of DN. Publication bias will be assessed using a funnel plot combined with Begg test. Sensitivity analyses will be performed using the separation method, the exclusion of low-quality studies, and the trim and fill method.

RESULTS

The primary outcome will be the prevalence of DN in the diabetic population; secondary outcomes will be the influence of factors such as age, gender, region, ethnicity, duration of diabetes, type of diabetes, baseline body mass index, baseline glycated hemoglobin level, baseline blood pressure, quality of included studies, and follow-up time on the prevalence of DN in diabetic patients.

CONCLUSION

Through this systematic review and meta-analysis, the study will more comprehensively obtain the prevalence of DN in diabetic populations worldwide, and gain a deeper understanding of the differences in the prevalence of DN in diabetic populations with different characteristics, so as to provide evidence for the management of diabetes and the prevention of DN.

Crescents, an Independent Risk Factor for the Progression of Type 2 Diabetic Kidney Disease

CONTEXT

Crescents have been noticed in pathologic changes in patients with diabetic kidney disease (DKD). However, the clinical significance of crescents is still not well recognized.

OBJECTIVE

The main objective was to investigate the association between crescents and the prognoses of type 2 DKD (T2DKD) patients, and, secondly, to analyze the relationship between crescents and clinicopathologic features.

METHODS

A retrospective cohort study of 155 patients with T2DKD diagnosed by renal biopsy was carried out in a single center. Clinicopathologic features of patients with or without crescents were analyzed. Cox regression models and meta-analysis were used to determine the prognostic values of crescents for T2DKD. A nomogram was constructed to provide a simple estimation method of 1, 3, and 5-year renal survival for patients with T2DKD.

RESULTS

Compared with T2DKD patients without crescents, patients with crescents had higher 24-hour proteinuria and serum creatinine levels, as well as more severe Kimmelstiel-Wilson (K-W) nodules, segmental sclerosis (SS), and mesangiolysis (all P < .05). Furthermore, the crescents were positively correlated with serum creatinine, 24-hour proteinuria, K-W nodules, SS, mesangiolysis, and complement 3 deposition. Multivariate Cox models showed that crescents were an independent prognostic risk factor for renal survival (hazard ratio [HR] 2.68, 95% CI 1.27-5.64). The meta-analyzed results of 4 studies on crescents in T2DKD confirmed that patients with crescents had a significantly higher HR for renal progression.

CONCLUSION

Patients with crescents in T2DKD have more severe clinicopathologic changes and worse prognoses. The crescent can serve as an independent risk factor for T2DKD progression.

Nomogram-Based Chronic Kidney Disease Prediction Model for Type 1 Diabetes Mellitus Patients Using Routine Pathological Data

Type 1 diabetes mellitus (T1DM) patients are a significant threat to chronic kidney disease (CKD) development during their life. However, there is always a high chance of delay in CKD detection because CKD can be asymptomatic, and T1DM patients bypass traditional CKD tests during their routine checkups. This study aims to develop and validate a prediction model and nomogram of CKD in T1DM patients using readily available routine checkup data for early CKD detection. This research utilized 1375 T1DM patients’ sixteen years of longitudinal data from multi-center Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials conducted at 28 sites in the USA and Canada and considered 17 routinely available features. Three feature ranking algorithms, extreme gradient boosting (XGB), random forest (RF), and extremely randomized trees classifier (ERT), were applied to create three feature ranking lists, and logistic regression analyses were performed to develop CKD prediction models using these ranked feature lists to identify the best performing top-ranked features combination. Finally, the most significant features were selected to develop a multivariate logistic regression-based CKD prediction model for T1DM patients. This model was evaluated using sensitivity, specificity, accuracy, precision, and F1 score on train and test data. A nomogram of the final model was further generated for easy application in clinical practices. Hypertension, duration of diabetes, drinking habit, triglycerides, ACE inhibitors, low-density lipoprotein (LDL) cholesterol, age, and smoking habit were the top-8 features ranked by the XGB model and identified as the most important features for predicting CKD in T1DM patients. These eight features were selected to develop the final prediction model using multivariate logistic regression, which showed 90.04% and 88.59% accuracy in internal and test data validation. The proposed model showed excellent performance and can be used for CKD identification in T1DM patients during routine checkups.

Review of potential biomarkers of inflammation and kidney injury in diabetic kidney disease

Diabetic kidney disease is expected to increase rapidly over the coming decades with rising prevalence of diabetes worldwide. Current measures of kidney function based on albuminuria and estimated glomerular filtration rate do not accurately stratify and predict individuals at risk of declining kidney function in diabetes. As a result, recent attention has turned towards identifying and assessing the utility of biomarkers in diabetic kidney disease. This review explores the current literature on biomarkers of inflammation and kidney injury focussing on studies of single or multiple biomarkers between January 2014 and February 2020. Multiple serum and urine biomarkers of inflammation and kidney injury have demonstrated significant association with the development and progression of diabetic kidney disease. Of the inflammatory biomarkers, tumour necrosis factor receptor-1 and -2 were frequently studied and appear to hold most promise as markers of diabetic kidney disease. With regards to kidney injury biomarkers, studies have largely targeted markers of tubular injury of which kidney injury molecule-1, beta-2-microglobulin and neutrophil gelatinase-associated lipocalin emerged as potential candidates. Finally, the use of a small panel of selective biomarkers appears to perform just as well as a panel of multiple biomarkers for predicting kidney function decline.

Influence of Gender in Diabetes Mellitus and Its Complication

In medicine, there is growing evidence that gender differences are important and lead to variations in the pathophysiology and treatment of many diseases with traits that appear to be particularly relevant in influencing the outcomes of many morbid forms. Today, the inclusion of gender in biomedical research, to improve the scientific quality and scientific relevance of knowledge, of technology is an increasingly present element precisely due to the practical implications that derive from it. Gender differences describe the biological variability between women and men, which is, in turn, related to differences in the information contained in sex chromosomes, the specific gene expression of autosomes linked to sex, the different number and quality of sex hormones, and their different effects on systems and organs, without neglecting the fact that each of the sexes has different target organs on which these hormones act. Additionally, both genders undergo metabolic changes throughout their lives, and this is especially true for women who show more dramatic changes due to their role in reproduction. Gender differences are not only the result of our genetic makeup but are also mixed with socio-cultural habits, behaviors, and lifestyles, differences between women and men, exposure to specific environmental influences, different food and lifestyle styles or stress, or different attitude in compliance with treatments and disease prevention campaigns. Gender differences also affect behavior throughout life, and physical changes can have implications for lifestyle, social roles, and mental health. Therefore, determinism and therapeutic outcome in chronic diseases are influenced by a complex combination of biological and environmental factors, not forgetting that there are many interactions of social and biological factors in women and men. This review will address the role of gender differences in the management of various forms of diabetes and its complications considering the different biological functions of hormones, the difference in body composition, physiological differences in glucose and fat metabolism, also considering the role of the microbiota. intestinal, as well as the description of gestational diabetes linked to possible pathophysiological events typical of reproduction.

Predicting diabetic nephropathy in type 2 diabetic patients using machine learning algorithms

Background

Global healthcare centers today are challenged by the dramatic increase in the prevalence of diabetes. Also, complications from diabetes are a major cause of deaths worldwide. One of the most frequent microvascular complications in diabetic patients is diabetic nephropathy (DN) which is the leading cause of death and end-stage renal disease (ESRD). Despite the different risk factors for DN identified in previous research, machine learning (ML) methods can help determine the importance of the predictors and prioritize them.

Objective

The main focus of this investigation is on predicting the incidence of DN in type 2 diabetic mellitus (T2DM) patients using ML algorithms.

Methods

Demographic information, laboratory results, and examinations on 6235 patients with T2DM covering a period of 10 years (2011-2020) were extracted from the electronic database of the Diabetes Clinic of the Imam Khomeini Hospital Complex (IKHC) in Iran. Recursive feature elimination using the cross-validation (RFECV) technique was then used with the three classification algorithms to select the important risk factors. Next, five ML algorithms were used to construct a predictive model for DN in T2DM patients. Finally, the results of the algorithms were evaluated according to the AUC criteria and the one with the best performance in terms of prediction and classification was selected.

Results

The 18 DN risk factors selected by RFECV were age, diabetes duration, BMI, SBP, hypertension, retinopathy, ALT, CVD, 2HPP, uric acid, HbA1c, waist-to-hip ratio, cholesterol, LDL, HDL, FBS, triglyceride, and serum insulin. Based on a 10-fold cross-validation, the best performance among the five classification algorithms was that of the random forest with 85% AUC.

Conclusions

This investigation validates the known risk factors for DN and emphasizes the importance of controlling the blood pressure, weight, cholesterol, and blood sugar of T2DM patients. In addition, as an example of the application of ML approaches in medical predictions, the findings of this study demonstrate the advantages of using these techniques.

Relationship between periodontal status and dyslipidemia in patients with type 2 diabetic nephropathy and chronic periodontitis: A cross-sectional study

OBJECTIVE

The aim of this study was to investigate the association between periodontitis and total serum cholesterol level in patients with type 2 diabetic nephropathy (T2DN).

BACKGROUND

Periodontitis is now recognized as the sixth complication of diabetes and can also affect other complications of diabetes, including nephropathy and coronary artery diseases. Studies have considered dyslipidemia as a risk factor for exacerbation of periodontitis.

METHODS

A total of 119 T2DN patients with chronic periodontitis were included in this observational study. Participants were stratified into the Normal (serum total cholesterol <5.17 mmol/L, n = 89) and the Dyslipidemia groups (serum total cholesterol ≥5.17 mmol/L, n = 30). Participants completed a validated questionnaire that collected information on oral hygiene behaviors and knowledge of oral health and underwent a clinical oral examination. The number of remaining teeth, probing depth (PD), clinical attachment level (CAL), and bleeding index (BI) was recorded. Physical examination and laboratory tests (fasting plasma glucose, serum glycosylated hemoglobin (HbA1c), total cholesterol, high-density lipoprotein-cholesterol (HDL-C), low-density lipoprotein-cholesterol (LDL-C), triglyceride, and high-sensitivity C-reactive protein levels) were performed.

RESULTS

Means of CAL and BI were significantly higher in the Dyslipidemia group compared with the Normal group. In the Dyslipidemia group, PD and percent of sites with PD ≥4 mm were positively correlated with urinary albumin/creatinine ratios; PD and percent of sites with PD ≥4 and PD ≥5 mm were positively correlated with HbA1c level; a number of remaining teeth were negatively correlated with serum LDL-C level. After adjusting for age, gender, body mass index, smoking, FPG, and serum HbA1c and triglyceride levels, BI was found to be positively associated with dyslipidemia in T2DN patients with periodontitis.

CONCLUSION

T2DN patients with chronic periodontitis had a 2.355-fold higher risk of developing dyslipidemia, implying an important relationship between periodontitis and blood lipid control among T2DN patients.

Clinical predictors for nondiabetic kidney diseases in patients with type 2 diabetes mellitus: a retrospective study from 2017 to 2021

BACKGROUND

Nondiabetic kidney disease (NDKD), which is prevalent among patients with diabetes mellitus (DM), is considerably different from diabetic kidney disease (DKD) in terms of the pathological features, treatment strategy and prognosis. Although renal biopsy is the current gold-standard diagnostic method, it cannot be routinely performed due to a range of risks. The aim of this study was to explore the predictors for differentiating NDKD from DKD to meet the urgent medical needs of patients who cannot afford kidney biopsy.

METHODS

This is a retrospective study conducted by reviewing the medical records of patients with type 2 DM who underwent percutaneous renal biopsy at the Affiliated Hospital of Guizhou Medical University between January 2017 and May 2021. The demographic data, clinical data, blood test results, and pathological examination results of the patients were obtained from their medical records. Multivariate regression analysis was performed to evaluate the predictive factors for NDKD.

RESULTS

A total of 244 patients were analyzed. The median age at biopsy was 55 (46, 62) years. Patients diagnosed with true DKD, those diagnosed with NDKD and those diagnosed with NDKD superimposed DKD represented 48.36% (118/244), 45.9% (112/244) and 5.74% (14/244), respectively, of the patient population. Immunoglobulin A nephropathy was the most common type of lesion in those with NDKD (59, 52.68%) and NDKD superimposed DKD (10, 71.43%). Independent predictive indicators for diagnosing NDKD included a DM duration of less than 5 years (odds ratio [OR] = 4.476; 95% confidence interval [CI]: 2.257-8.877; P < 0.001), an absence of diabetic retinopathy (OR = 4.174; 95% CI: 2.049-8.502; P < 0.001), a high RBC count (OR = 1.901; 95% CI: 1.251-2.889; P = 0.003), and a negative of urinary glucose excretion test result (OR = 2.985; 95% CI: 1.474-6.044; P = 0.002)..

CONCLUSIONS

A DM duration less than 5 years, an absence of retinopathy, a high RBC count and an absence of urinary glucose excretion were independent indicators for the diagnosis of NDKD, suggesting that patients with NDKD may require a different treatment regimen than those with DKD.

The analysis of risk factors for diabetic kidney disease progression: a single-centre and cross-sectional experiment in Shanghai

OBJECTIVE

To identify the risk factors for diabetic kidney disease (DKD) development, especially the difference between patients with different courses.

PATIENTS AND METHODS

791 patients were considered to be eligible and were enrolled in the cross-sectional study from Shanghai Tongren Hospital Inpatient Department. 36 variables were initially screened by univariate analysis. The risk factors affecting progression of DKD were determined by logistics regression analysis. Subgroups were grouped according to the course of diabetes disease, and multivariate logistics regression analysis was performed to find out the different risk factors in two subgroups. Finally, the receiver operating characteristics curve is used to verify the result.

RESULTS

The logistic regression model indicated age (OR=1.020, p=0.017, 95% CI 1.004 to 1.040), systolic blood pressure (OR=1.013, p=0.006, 95% CI 1.004 to 1.022), waist circumference (OR=1.021, p=0.015, 95% CI 1.004 to 1.038), white blood cells (WBC, OR=1.185, p=0.001, 95% CI 1.085 to 1.295) and triglycerides (TG, OR=1.110, p=0.047, 95% CI 1.001 to 1.230) were risk factors for DKD, while free triiodothyronine (fT3, OR=0.711, p=0.011, 95% CI 0.547 to 0.926) was a protective factor for DKD in patients with type 2 diabetes mellitus (T2DM). Subgroup analysis revealed that in patients with a short duration of diabetes (<8 years), WBC (OR=1.306, p<0.001, 95% CI 1.157 to 1.475) and TG (OR=1.188, p=0.033, 95% CI 1.014 to 1.393) were risk factors for DKD,fT3 (OR=0.544, p=0.002, 95% CI 0.367 to 0.804) was a protective factor for DKD; whereas for patients with disease course more than 8 years, age (OR=1.026, Pp=0.012, 95%CI=95% CI[ 1.006- to 1.048]) was identified as the only risk factor for DKD and fT3 (OR=0.036, Pp=0.017, 95%CI=95% CI[ 0.439- to 0.922]) was a protective factor for DKD.

CONCLUSION

The focus of attention should especially be on patients with a prolonged course of T2DM, and those with comorbid hypertension and hypertriglyceridaemia waist phenotype. More potential clinical indexes such as thyroid function and inflammatory indicators might be considered as early warning factors for DKD in T2DM. Women should pay attention to controlling inflammation and TGs, and men should strictly control blood pressure. Avoiding abdominal obesity in both men and women will bring great benefits.

Development and internal validation of machine learning algorithms for end-stage renal disease risk prediction model of people with type 2 diabetes mellitus and diabetic kidney disease

Aims

Diabetic kidney disease (DKD) is the most common cause of end-stage renal disease (ESRD) and is associated with increased morbidity and mortality in patients with diabetes. Identification of risk factors involved in the progression of DKD to ESRD is expected to result in early detection and appropriate intervention and improve prognosis. Therefore, this study aimed to establish a risk prediction model for ESRD resulting from DKD in patients with type 2 diabetes mellitus (T2DM).

Methods

Between January 2008 and July 2019, a total of 390 Chinese patients with T2DM and DKD confirmed by percutaneous renal biopsy were enrolled and followed up for at least 1 year. Four machine learning algorithms (gradient boosting machine, support vector machine, logistic regression, and random forest (RF)) were used to identify the critical clinical and pathological features and to build a risk prediction model for ESRD.

Results

There were 158 renal outcome events (ESRD) (40.51%) during the 3-year median follow up. The RF algorithm showed the best performance at predicting progression to ESRD, showing the highest AUC (0.90) and ACC (82.65%). The RF algorithm identified five major factors: Cystatin-C, serum albumin (sAlb), hemoglobin (Hb), 24-hour urine urinary total protein, and estimated glomerular filtration rate. A nomogram according to the aforementioned five predictive factors was constructed to predict the incidence of ESRD.

Conclusion

Machine learning algorithms can efficiently predict the incident ESRD in DKD participants. Compared with the previous models, the importance of sAlb and Hb were highlighted in the current model.

Highlights

What is already known? Identification of risk factors for the progression of DKD to ESRD is expected to improve the prognosis by early detection and appropriate intervention.

What this study has found? Machine learning algorithms were used to construct a risk prediction model of ESRD in patients with T2DM and DKD. The major predictive factors were found to be CysC, sAlb, Hb, eGFR, and UTP.

What are the implications of the study? In contrast with the treatment of participants with early-phase T2DM with or without mild kidney damage, major emphasis should be placed on indicators of kidney function, nutrition, anemia, and proteinuria for participants with T2DM and advanced DKD to delay ESRD, rather than age, sex, and control of hypertension and glycemia.

Role of Zinc in Diabetic Kidney Disease

Diabetic Kidney Disease (DKD) represents the most common cause of Chronic Kidney Disease (CKD) in developed countries. Approximately 30% to 40% of diabetes mellitus (DM) subjects develop DKD, and its presence significantly increases the risk for morbidity and mortality. In this context, Zinc seems to have a potential role in kidney and body homeostasis in diabetic individuals as well as in patients at a high risk of developing this condition. This essential element has functions that may counteract diabetes-related risk factors and complications, which include stabilization of insulin hexamers and pancreatic insulin storage and improved glycemic control. In our review, we analyzed the current knowledge on the role of zinc in the management of renal impairment in course of DM. Several studies underline the critical role of zinc in reducing oxidative stress levels, which is considered the common denominator of the mechanisms responsible for the progression of kidney disease. Reaching and maintaining a proper serum zinc level could represent a valuable target to reduce symptoms related to DM complications and contrast the progression of kidney impairment in patients with the high risk of developing end-stage renal disease. In conclusion, analyzing the beneficial role of zinc in this review would advance our knowledge on the possible strategies of DM and DKD treatment.

Relation of serum heart type fatty acid binding protein to left ventricular diastolic dysfunction in patients with type 2 diabetes and early diabetic kidney disease

AIMS

We aimed to investigate the serum level of heart type fatty acid binding protein (H-FABP) and its relation to left ventricular (LV) diastolic dysfunction in patients with type 2 diabetes (T2DM) and early diabetic kidney disease (DKD).

METHODS

This study was conducted on 100 T2DM patients divided into 50 patients with early DKD and 50 patients without DKD. Doppler echocardiography was used to assess LV function and serum H-FABP levels were measured using ELISA technique.

RESULTS

78% of patients with DKD and 12% of patients without DKD had LV diastolic dysfunction. Among patients with DKD, those with diastolic dysfunction had significantly higher urinary albumin to creatinine ratio (UACR) (p = 0.041). H-FABP levels were significantly higher in patients with DKD (p˂0.001) and it had significant positive correlation with UACR (p = 0.009). No significant difference was found regarding serum H-FABP levels between patients with normal LV function and those with diastolic dysfunction in both study groups.

CONCLUSION

Diastolic dysfunction is a common finding among patients with T2DM. UACR, but not serum H-FABP, is significantly associated with diastolic dysfunction in patients with early DKD. Serum H-FABP level is significantly higher in early DKD and positively correlated with the level of albuminuria.

Sex and Gender Related Differences in Diabetic Kidney Disease

Diversity in sex and gender are important considerations in the pathogenesis, prognostication, research, and management of diabetic kidney disease (DKD). Sex and gender differences in the disease risk, disease-specific mechanisms, and outcomes in DKD may be attributed to biological differences between males and females at the cellular and tissue level, inconsistencies in the diagnostic and assessment tools used in chronic kidney disease and DKD, as well societal differences in the way men, women, and gender-diverse individuals self-manage and interact with health care systems. This review outlines key considerations related to the impact of sex on DKD, specifically elaborating on how they contribute to observed differences in disease epidemiology, pathogenesis, and treatment strategies. We also highlight the effect of gender on DKD progression and care.

Blood DNA Methylation Predicts Diabetic Kidney Disease Progression in High Fat Diet-Fed Mice

Diabetic kidney disease (DKD) progresses at different rates among patients with type 2 diabetes mellitus (T2D). Early identification of patients with a higher risk of DKD progression is essential to improve prognosis. Epigenetic modifications, particularly DNA methylation, have been independently implicated in T2D and chronic kidney disease. The current study aimed to determine changes in blood DNA methylation that reflects and predicts DKD progression. C57BL/6 mice were fed a high-fat diet (HFD) from weaning and subclassified into two groups, HFD-1 and HFD-2, according to urinary kidney injury marker KIM-1/creatinine ratios (low vs. high) and histological abnormalities (mild–moderate vs. advanced). DNA methylation profiles were determined by reduced representative bisulfide sequencing (RRBS). Our results confirmed early and established DKD at week 9 and week 32, respectively. At week 32, advanced kidney injury was associated with dysregulation of methylation and demethylation enzymes in the kidney. Blood RRBS revealed 579 and 203 differentially methylated sites (DMS) between HFD-1 and HFD-2 animals at week 32 and week 9, respectively, among which 11 were common. The DMS in blood and kidney at week 32 were both related to organ development, neurogenesis, cell junction, and Wnt signalling, while the DMS in blood at week 9 suggested a specific enrichment of kidney development processes. In conclusion, our data strongly support the implication of early blood DNA methylation modifications and DKD progression in T2D that could be used to improve the disease’s prognostication.

Diabetic kidney disease progression is associated with decreased lower-limb muscle mass and increased visceral fat area in T2DM patients

AIM

This study aimed to explore the relationship between lower-limb muscle mass/visceral fat area and diabetic kidney disease (DKD) progression in patients with type 2 diabetes mellitus (T2DM).

METHODS

A total of 879 participants with T2DM were divided into 4 groups according to the prognosis of CKD classification from Kidney Disease: Improving Global Outcomes (KDIGO). Rectus femoris cross-sectional area (RFCSA) was measured through ultrasound, and visceral fat area (VFA) was evaluated with bioelectric impedance analysis (BIA).

RESULTS

T2DM patients with high to very high prognostic risk of DKD showed a reduced RFCSA (male P < 0.001; female P < 0.05), and an enlarged VFA (male P < 0.05; female P < 0.05). The prognostic risk of DKD was negatively correlated with RFCSA (P < 0.05), but positively correlated with VFA (P < 0.05). Receiver-operating characteristic analysis revealed that the cutoff points of T2DM duration combined with RFCSA and VFA were as follows: (male: 7 years, 6.60 cm2, and 111 cm2; AUC = 0.82; 95% CI: 0.78-0.88; sensitivity, 78.0%; specificity, 68.6%, P < 0.001) (female: 9 years, 5.05 cm2, and 91 cm2; AUC = 0.73; 95% CI: 0.66-0.81; sensitivity, 73.9%; specificity, 63.3%, P < 0.001).

CONCLUSION

A significant association was demonstrated between reduced RFCSA/increased VFA and high- to very high-prognostic risk of DKD. T2DM duration, RFCSA, and VFA may be valuable markers of DKD progression in patients with T2DM.

CLINICAL TRIAL REGISTRATION

http://www.chictr.org.cn, identifier ChiCTR2100042214.

Development and External Validation of a Nomogram and a Risk Table for Prediction of Type 2 Diabetic Kidney Disease Progression Based on a Retrospective Cohort Study in China

PURPOSE

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease worldwide. Risk assessment provides information about patient prognosis, contributing to the risk stratification of patients and the rational allocation of medical resources. We aimed to develop a model for individualized prediction of renal function decline in patients with type 2 DKD (T2DKD).

PATIENTS AND METHODS

In a retrospective observational study, we followed 307 T2DKD patients and evaluated the determinants of 1) risk of doubling in serum creatinine (Scr), 2) risk of eGFR<15 mL/min/1.73m² using potential risk factors at baseline. A prediction model represented by a nomogram and a risk table was developed using Cox regression and externally validated in another cohort with 206 T2DKD patients. The discrimination and calibration of the prediction model were evaluated by the concordance index (C-index) and calibration curve, respectively.

RESULTS

Four predictors were selected to establish the final model: Scr, urinary albumin/creatinine ratio, plasma albumin, and insulin treatment. The nomogram achieved satisfactory prediction performance, with a C-index of 0.791 [95% confidence interval (CI) 0.762-0.820] in the derivation cohort and 0.793 (95% CI 0.746-0.840) in the external validation cohort. Then, all predictors were scored according to their weightings. A risk table with the highest score of 11.5 was developed. The C-index of the risk table was 0.764 (95% CI: 0.731-0.797), which was similar to the external validation cohort (0.763; 95% CI: 0.714-0.812). Additionally, the patients were divided into two groups based on the risk table, and significant differences in the probability of outcome events were observed between the high-risk (score >2) and low-risk (score ≤2) groups in the derivation and external validation cohorts (P < 0.001).

CONCLUSION

The nomogram and the risk table using readily available clinical parameters could be new tools for bedside prediction of renal function decline in T2DKD patients.

Bioinformatics analysis identifies immune-related gene signatures and subtypes in diabetic nephropathy

BACKGROUND

Systemic inflammation and immune response are involved in the pathogenesis of diabetic nephropathy (DN). However, the specific immune-associated signature during DN development is unclear. Our study aimed to reveal the roles of immune-related genes during DN progression.

METHODS

The GSE30529 and GSE30528 datasets were acquired from the Gene Expression Omnibus (GEO) database. Then, the intersection between differentially expressed genes (DEGs) and immune score-related genes (ISRGs) was screened. Subsequently, functional enrichment analyses were performed. The different immune phenotype-related subgroups were finally divided using unsupervised clustering. The core genes were identified by WGCNA and the protein-protein interaction (PPI) network. xCell algorithm was applied to assess the proportion of immune cell infiltration.

RESULTS

92 immune score-related DEGs (ISRDEGs) were identified, and these genes were enriched in inflammation- and immune-associated pathways. Furthermore, two distinct immune-associated subgroups (C1 and C2) were identified, and the C1 subgroup exhibited activated immune pathways and a higher percentage of immune cells compared to the C2 subgroup. Two core genes (LCK and HCK) were identified and all up-regulated in DN, and the expressions were verified using GSE30122, GSE142025, and GSE104954 datasets. GSEA indicated the core genes were mainly enriched in immune-related pathways. Correlation analysis indicated LCK and HCK expressions were positively correlated with aDC, CD4+ Tem, CD8+T cells, CD8+ Tem, and mast cells.

CONCLUSIONS

We identified two immune-related genes and two immune-associated subgroups, which might help to design more precise tailored immunotherapy for DN patients.

The Utility of Perirenal Fat in Determining the Risk of Onset and Progression of Diabetic Kidney Disease

BACKGROUND

Perirenal fat (PRF) has multiple effects on the kidney through its physical structure and adipocytokine-secreting ability. The present study explored the relationship between PRF thickness and the onset and progression of albuminuria in patients with diabetes.

METHODS

In the cross-sectional analysis, we screened 959 patients from 8764 subjects with type 2 diabetes mellitus (T2DM) who met the inclusion criteria and measured their perirenal fat thickness (PFT) using color Doppler ultrasound. A group of laboratory indexes were included in the analysis models. In a longitudinal study, a total of 218 patients with a baseline UACR <30 mg/g were included in the follow-up study.

RESULTS

In a cross-sectional analysis, patients with diabetes and higher PFT presented with higher albuminuria. Multiple logistic regression analysis indicated that PFT was an independent risk factor for the degree of albuminuria in patients with T2DM (odds ratio = 4.186, 95%CI: 2.290-7.653, P < 0.001). In a longitudinal study, 218 albuminuria-free patients with T2DM at the baseline were followed up for a mean of 12.3 months. Based on the cutoff value from the ROC diagnostic test in the cross-sectional study, patients were divided into two groups: higher PFT (H-PFT) and lower PFT (L-PFT). Kaplan-Meier survival curve analysis showed that H-PFT was associated with a higher incidence of albuminuria than L-PFT (log-rank test, χ2 = 4.522, P = 0.033). Cox regression analysis showed that PFT was a risk factor for the earlier onset of albuminuria (hazard ratio 2.83, 95% CI: 1.34-4.88, P < 0.001).

CONCLUSIONS

PRF evaluated by color Doppler ultrasound is an easy and reliable tool for predicting the onset and progression of albuminuria in patients with T2DM.