Establishment of a potent weighted risk model for determining the progression of diabetic kidney disease

The association between vitamin D receptor gene polymorphism FokI and type 2 diabetic kidney disease and its molecular mechanism: a case control study

BACKGROUND

The role of the vitamin D receptor single nucleotide polymorphism FOKI (VDR-FOKI) (rs2228570) in genetic susceptibility to type 2 diabetic kidney disease (T2DKD) remains uncertain. This study investigated the relationship between VDR-FOKI and T2DKD within the Chinese Plateau Han population and analyzed the underlying mechanisms.

METHODS

A total of 316 subjects were enrolled, including 44 healthy adults, 114 individuals with type 2 diabetes mellitus (T2DM), and 158 patients with T2DKD. According to the 2023 American Diabetes Association Diabetes Guidelines, patients with T2DKD were categorized into low-medium-risk and high-risk groups based on estimates of glomerular filtration rate and urinary albumin-to-creatinine ratio. The VDR-FokI genotypes of all participants were identified using the Taqman probe and classified as homozygous mutant genotypes (C/C or FF), heterozygous mutant genotypes (C/T or Ff), and homozygous wild genotypes (T/T or ff). Plasma levels of malondialdehyde (MDA), glutathione (GSH), and superoxide dismutase activity (SOD) were assessed in T2DKD patients with FF and ff genotypes. Additionally, the levels of plasma VDR, GPX4, and P53 were determined using ELISA, while the relative expressions of VDR mRNA, GPX4 mRNA, and TP53 mRNA in whole blood were measured by RT-qPCR.

RESULTS

The T2DM patients with the ff genotype exhibited a 2.93-fold increased likelihood of developing T2DKD compared to those with the FF genotype (ORadjusted = 2.93; 95% CI: 1.142-7.513). Additionally, they were 2.01 times more likely to develop T2DKD than individuals with the FF and Ff genotypes (ORadjusted = 2.01; 95% CI: 1.008-4.006). However, no significant differences in VDR-FokI genotype distribution were observed between the healthy control group and the T2DM group, as well as between the low-medium-risk and high-risk groups of T2DKD. Furthermore, T2DKD patients with the ff genotype had significantly higher plasma levels of MDA compared to those with the FF genotype. In contrast, plasma GSH and SOD content was significantly lower in the ff genotype patients (P < 0.05). Additionally, the GPX4 concentration in ff genotype patients was significantly lower than in FF genotype patients [14.88 (11.32,22.39) vs. 12.76 (8.55,13.75), P = 0.037]. Nevertheless, no statistically significant difference was observed in the expression of VDRmRNA, GPX4mRNA, TP53mRNA, plasma VDR, and plasma P53.

CONCLUSIONS

The ff genotype of VDR-FokI is a risk factor for T2DKD, and the potential mechanism may be related to ferroptosis. However, It is not associated with T2DM or the progression of T2DKD.

Development of a machine learning-based model for the prediction and progression of diabetic kidney disease: A single centred retrospective study

BACKGROUND

Diabetic kidney disease (DKD) is a diabetic microvascular complication often characterized by an unpredictable progression. Hence, early detection and recognition of patients vulnerable to progression is crucial.

OBJECTIVE

To develop a prediction model to identify the stages of DKD and the factors contributing to progression to each stage using machine learning.

METHODOLOGY

A retrospective study was conducted in a South Indian tertiary care hospital and collected the details of patients diagnosed with DKD from January 2017 to January 2022. Bayesian optimization-based machine learning techniques such as classification and regression were employed. The model was developed with the help of an optimization framework that effectively balances classification, prediction accuracy, and explainability.

RESULTS

Of the 311 patients diagnosed with DKD, 227 were selected for the study. A system for predicting DKD has been created for a patient dataset utilizing a variety of machine-learning approaches. The eXtreme gradient (XG) Boost method excelled, achieving 88.75% accuracy, 88.57% precision, 91.4% sensitivity,100% specificity, and 89.49% F1-score. An interpretable data-driven method highlights significant features for early DKD diagnosis. The best explainable prediction model uses the XG Boost classifier, revealing serum uric acid, urea, phosphorous, red blood cells, calcium, and absolute eosinophil count as the major predictors influencing the progression of DKD. In the case of regression models, the gradient boost regressor performed the best, with an R2 score of 0.97.

CONCLUSION

Machine learning algorithms can effectively predict the stages of DKD and thus help physicians in providing patients with personalized care at the right time.

Targeting senescence to prevent diabetic kidney disease: Exploring molecular mechanisms and potential therapeutic targets for disease management

BACKGROUND/AIMS

As a microvascular complication, diabetic kidney disease is the leading cause of chronic kidney disease and end-stage renal disease worldwide. While the underlying pathophysiology driving transition of diabetic kidney disease to renal failure is yet to be fully understood, recent studies suggest that cellular senescence is central in disease development and progression. Consequently, understanding the molecular mechanisms which initiate and drive senescence in response to the diabetic milieu is crucial in developing targeted therapies that halt progression of renal disease.

METHODS

To understand the mechanistic pathways underpinning cellular senescence in the context of diabetic kidney disease, we reviewed the literature using PubMed for English language articles that contained key words related to senescence, inflammation, fibrosis, senescence-associated secretory phenotype (SASP), autophagy, and diabetes.

RESULTS

Aberrant accumulation of metabolically active senescent cells is a notable event in the progression of diabetic kidney disease. Through autocrine- and paracrine-mediated mechanisms, resident senescent cells potentiate inflammation and fibrosis through increased expression and secretion of pro-inflammatory cytokines, chemoattractants, recruitment of immune cells, myofibroblast activation, and extracellular matrix remodelling. Compounds that eliminate senescent cells and/or target the SASP - including senolytic and senomorphics drugs - demonstrate promising results in reducing the senescent cell burden and associated pro-inflammatory effect.

CONCLUSIONS

Here we evidence the link between senescence and diabetic kidney disease and highlight underlying molecular mechanisms and potential therapeutic targets that could be exploited to delay disease progression and improve outcomes for individuals with the disease. Trials are now required to translate their therapeutic potential to a clinical setting.

Current updates on metabolites and its interlinked pathways as biomarkers for diabetic kidney disease: A systematic review

Diabetic kidney disease (DKD) is a major microvascular complication of diabetes mellitus (DM) that poses a serious risk as it can lead to end-stage renal disease (ESRD). DKD is linked to changes in the diversity, composition, and functionality of the microbiota present in the gastrointestinal tract. The interplay between the gut microbiota and the host organism is primarily facilitated by metabolites generated by microbial metabolic processes from both dietary substrates and endogenous host compounds. The production of numerous metabolites by the gut microbiota is a crucial factor in the pathogenesis of DKD. However, a comprehensive understanding of the precise mechanisms by which gut microbiota and its metabolites contribute to the onset and progression of DKD remains incomplete. This review will provide a summary of the current scenario of metabolites in DKD and the impact of these metabolites on DKD progression. We will discuss in detail the primary and gut-derived metabolites in DKD, and the mechanisms of the metabolites involved in DKD progression. Further, we will address the importance of metabolomics in helping identify potential DKD markers. Furthermore, the possible therapeutic interventions and research gaps will be highlighted.

Association of systemic immune-inflammation index with diabetic kidney disease in patients with type 2 diabetes: a cross-sectional study in Chinese population

Objective

Systemic immune-inflammation index (SII), a novel inflammatory marker, has been reported to be associated with diabetic kidney disease (DKD) in the U.S., however, such a close relationship with DKD in other countries, including China, has not been never determined. We aimed to explore the association between SII and DKD in Chinese population.

Methods

A total of 1922 hospitalized patients with type 2 diabetes mellitus (T2DM) included in this cross-sectional study were divided into three groups based on estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (ACR): non-DKD group, DKD stages 1-2 Alb group, and DKD-non-Alb+DKD stage 3 Alb group. The possible association of SII with DKD was investigated by correlation and multivariate logistic regression analysis, and receiver-operating characteristic (ROC) curves analysis.

Results

Moving from the non-DKD group to the DKD-non-Alb+DKD stage 3 Alb group, SII level was gradually increased (P for trend <0.01). Partial correlation analysis revealed that SII was positively associated with urinary ACR and prevalence of DKD, and negatively with eGFR (all P<0.01). Multivariate logistic regression analysis showed that SII remained independently significantly associated with the presence of DKD after adjustment for all confounding factors [(odds ratio (OR), 2.735; 95% confidence interval (CI), 1.840-4.063; P < 0.01)]. Moreover, compared with subjects in the lowest quartile of SII (Q1), the fully adjusted OR for presence of DKD was 1.060 (95% CI 0.773-1.455) in Q2, 1.167 (95% CI 0.995-1.368) in Q3, 1.266 (95% CI 1.129-1.420) in the highest quartile (Q4) (P for trend <0.01). Similar results were observed in presence of DKD stages 1-2 Alb or presence of DKD-non- Alb+DKD stage 3 Alb among SII quartiles. Last, the analysis of ROC curves revealed that the best cutoff values for SII to predict DKD, Alb DKD stages 1- 2, and DKD-non-Alb+ DKD stage 3 Alb were 609.85 (sensitivity: 48.3%; specificity: 72.8%), 601.71 (sensitivity: 43.9%; specificity: 72.3%), and 589.27 (sensitivity: 61.1%; specificity: 71.1%), respectively.

Conclusion

Higher SII is independently associated with an increased risk of the presence and severity of DKD, and SII might be a promising biomarker for DKD and its distinct phenotypes in Chinese population.