Combining glomerular basement membrane and tubular basement membrane assessment improves the prediction of diabetic end-stage renal disease

ITGAM-mediated macrophages contribute to basement membrane damage in diabetic nephropathy and atherosclerosis

BACKGROUND

Diabetic nephropathy (DN) and atherosclerosis (AS) are prevalent and severe complications associated with diabetes, exhibiting lesions in the basement membrane, an essential component found within the glomerulus, tubules, and arteries. These lesions contribute significantly to the progression of both diseases, however, the precise underlying mechanisms, as well as any potential shared pathogenic processes between them, remain elusive.

METHODS

Our study analyzed transcriptomic profiles from DN and AS patients, sourced from the Gene Expression Omnibus database. A combination of integrated bioinformatics approaches and machine learning models were deployed to identify crucial genes connected to basement membrane lesions in both conditions. The role of integrin subunit alpha M (ITGAM) was further explored using immune infiltration analysis and genetic correlation studies. Single-cell sequencing analysis was employed to delineate the expression of ITGAM across different cell types within DN and AS tissues.

RESULTS

Our analyses identified ITGAM as a key gene involved in basement membrane alterations and revealed its primary expression within macrophages in both DN and AS. ITGAM was significantly correlated with tissue immune infiltration within these diseases. Furthermore, the expression of genes encoding core components of the basement membrane was influenced by the expression level of ITGAM.

CONCLUSION

Our findings suggest that macrophages may contribute to basement membrane lesions in DN and AS through the action of ITGAM. Moreover, therapeutic strategies that target ITGAM may offer potential avenues to mitigate basement membrane lesions in these two diabetes-related complications.

Integration of basement membrane-related genes in a risk signature for prognosis in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is characterized by high heterogeneity and recurrence rates, posing significant challenges for stratification and treatment. Basement membrane-related genes (BMGs) play a crucial role in tumor initiation and progression. Clinical and transcriptomic data of ccRCC patients were extracted from TCGA and GEO databases. We employed univariate regression and LASSO-Cox stepwise regression analysis to construct a BMscore model based on BMGs expression level. A nomogram combining clinical features and BMscore was constructed to predict individual survival probabilities. Further enrichment analysis and immune-related analysis were conducted to explore the enriched pathways and immune features associated with BMGs. High-risk individuals predicted by BMscore exhibited poorer overall survival, which was consistent with the validation dataset. BMscore was identified as an independent risk factor for ccRCC. Functional analysis revealed that BMGs were related to cell-matrix and tumor-associated signaling pathways. Immune profiling suggests that BMGs play a key role in immune interactions and the tumor microenvironment. BMGs serve as a novel prognostic predictor for ccRCC and play a role in the immune microenvironment and treatment response. Targeting the BM may represent an alternative therapeutic approach for ccRCC.

Comparison of conventional mathematical model and machine learning model based on recent advances in mathematical models for predicting diabetic kidney disease

Previous research suggests that mathematical models could serve as valuable tools for diagnosing or predicting diseases like diabetic kidney disease, which often necessitate invasive examinations for conclusive diagnosis. In the big-data era, there are several mathematical modeling methods, but generally, two types are recognized: conventional mathematical model and machine learning model. Each modeling method has its advantages and disadvantages, but a thorough comparison of the two models is lacking. In this article, we describe and briefly compare the conventional mathematical model and machine learning model, and provide research prospects in this field.

Identification of basement membrane markers in diabetic kidney disease and immune infiltration by using bioinformatics analysis and experimental verification

Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease worldwide. Basement membranes (BMs) are ubiquitous extracellular matrices which are affected in many diseases including DKD. Here, the authors aimed to identify BM-related markers in DKD and explored the immune cell infiltration in this process. The expression profiles of three datasets were downloaded from the Gene Expression Omnibus database. BM-related differentially expression genes (DEGs) were identified and Kyoto encyclopaedia of genes and genomes pathway enrichment analysis were applied to biological functions. Immune cell infiltration and immune function in the kidneys of patients with DKD and healthy controls were evaluated and compared using the ssGSEA algorithm. The association of hub genes and immune cells and immune function were explored. A total of 30 BM-related DEGs were identified. The functional analysis showed that BM-related DEGs were notably associated with basement membrane alterations. Crucially, BM-related hub genes in DKD were finally identified, which were able to distinguish patients with DKD from controls. Moreover, the authors observed that laminin subunit gamma 1(LAMC1) expression was significantly high in HK2 cells treated with high glucose. Immunohistochemistry results showed that, compared with those in db/m mouse kidneys, the levels of LAMC1 in db/db mouse kidneys were significantly increased. The biomarkers genes may prove crucial for DKD treatment as they could be targeted in future DKD treatment protocols.

Exosomal microRNAs: potential nanotherapeutic targets for diabetic kidney disease

Diabetic kidney disease (DKD) is a primary cause for end-stage renal disease, but no specific therapeutic approaches exist. Exosomal miRNAs, a key functional cargo of nanovesicles, play crucial roles in the pathophysiological processes of DKD. Exosomal miRNAs are involved in cell-to-cell transfer of biological information, mediating nephritic inflammation, oxidative stress, apoptosis, autophagy, epithelial-mesenchymal transition and fibrosis. Circulating exosomal miRNAs derived from urine or serum might function as noninvasive prognostic biomarkers for DKD. Exosomal miRNAs from stem cells have been reported to exert beneficial effects on diabetic kidneys, which suggests that these exosomes might function as potential nanotherapy tools for treating DKD. In this review, we have summarized recent studies based on the association between exosomal miRNAs and DKD.

Metabolic phenotypes and risk of end-stage kidney disease in patients with type 2 diabetes

Background

Obesity often initiates or coexists with metabolic abnormalities. This study aimed to investigate the pathological characteristics and the independent or mutual relations of obesity and metabolic abnormalities with end-stage kidney disease (ESKD) in patients with type 2 diabetes (T2D) and associated diabetic kidney disease (DKD).

Methods

A total of 495 Chinese patients with T2D and biopsy-confirmed DKD between 2003 and 2020 were enrolled in this retrospective study. The metabolic phenotypes were based on the body weight index (BMI)-based categories (obesity, BMI ≥ 25.0 kg/m²) and metabolic status (metabolically unhealthy status, ≥ 1 criterion National Cholesterol Education Program Adult Treatment Panel III (NCEP/ATP III) excluding waist circumference and hyperglycemia) and were categorized into four types: metabolically healthy non-obesity (MHNO), metabolically healthy obesity (MHO), metabolically unhealthy non-obesity (MUNO), and metabolically unhealthy obesity (MUO). The pathological findings were defined by the Renal Pathology Society classification. Cox proportional hazards models were used to estimate hazard ratios (HRs) for ESKD.

Results

There are 56 (11.3%) MHNO patients, 28 (5.7%) MHO patients, 176 (35.6%) MUNO patients, and 235 (47.5%) MUO patients. The high prevalence of the Kimmelstiel-Wilson nodule and severe mesangial expansion were associated with obesity, whereas severe IFTA was related to metabolically unhealthy status. In the multivariate analysis, the adjusted HR (aHR) was 2.09 [95% confidence interval (CI) 0.99-4.88] in the MHO group, 2.16 (95% CI 1.20-3.88) in the MUNO group, and 2.31 (95% CI 1.27-4.20) in the MUO group compared with the MHNO group. Furthermore, the presence of obesity was insignificantly associated with ESKD compared with non-obese patients (aHR 1.22, 95% CI 0.88-1.68), while the metabolically unhealthy status was significantly associated with ESKD compared to the metabolically healthy status in the multivariate analysis (aHR 1.69, 95% CI 1.10-2.60).

Conclusion

Obesity itself was insignificantly associated with ESKD; however, adding a metabolically unhealthy status to obesity increased the risk for progression to ESKD in T2D and biopsy-proven DKD.

Early renal structural changes and potential biomarkers in diabetic nephropathy

Diabetic nephropathy is one of the most serious microvascular complications of diabetes mellitus, with increasing prevalence and mortality. Currently, renal function is assessed clinically using albumin excretion rate and glomerular filtration rate. But before the appearance of micro-albumin, the glomerular structure has been severely damaged. Glomerular filtration rate based on serum creatinine is a certain underestimate of renal status. Early diagnosis of diabetic nephropathy has an important role in improving kidney function and delaying disease progression with drugs. There is an urgent need for biomarkers that can characterize the structural changes associated with the kidney. In this review, we focus on the early glomerular and tubular structural alterations, with a detailed description of the glomerular injury markers SMAD1 and Podocalyxin, and the tubular injury markers NGAL, Netrin-1, and L-FABP in the context of diabetic nephropathy. We have summarized the currently studied protein markers and performed bioprocess analysis. Also, a brief review of proteomic and scRNA-seq method in the search of diabetic nephropathy.

Prognostic value of metabolic syndrome in renal structural changes in type 2 diabetes

PURPOSE

To investigate the prognostic value of metabolic syndrome (MetS) and its relationship with renal structure changes in patients with type 2 diabetes and associated diabetic nephropathy (DN).

METHODS

411 Chinese patients with type 2 diabetes and biopsy-confirmed DN were enrolled in this retrospective study. MetS was defined according to the modified criteria of the 2005 International Diabetes Federation. Baseline demographics and clinical information at the time of renal biopsy were extracted from the hospital's electronic medical records system. Renal pathological findings were assessed according to Renal Pathology Society system. Univariate and multivariate logistic regression analyses were performed to define the pathological covariates associated with MetS. A competing risk model, with death as the competing risk, was used to estimate the sub-distribution hazard ratio (SHR) of MetS for end-stage kidney disease (ESKD).

RESULTS

224 (55%) patients had MetS. Patients with MetS had poor renal function and more severe interstitial fibrosis tubular atrophy scores (IFTA) than those without MetS. Multivariate logistic regression analysis revealed that IFTA was significantly associated with MetS (odds ratio per score increase 1.45, 95% confidence interval [CI] 1.02-2.05). Of the patients with DN at risk, 40% of patients progressed to ESKD. After adjusting for renal function and pathological parameters, the presence of MetS was an independent predictor for progression to ESKD (SHR 1.93, 95% CI 1.34-2.79). The SHRs for progression to ESKD also increased as the number of MetS components increased. Additionally, adding the IFTA scores improved the prognostic power of a model that only contained MetS and clinical covariates for predicting future ESKD.

CONCLUSION

MetS is an independent prognostic predictor of ESKD in patients with T2D and DN, while adding the IFTA scores increased the prognostic value of MetS for renal outcome.

Combining glomerular basement membrane and tubular basement membrane assessment improves the prediction of diabetic end-stage renal disease

BACKGROUND

To address the prognostic value of combining tubular basement membrane (TBM) and glomerular basement membrane (GBM) thickness in diabetic nephropathy (DN).

METHODS

This retrospective study enrolled 110 patients with type 2 diabetes and biopsy-proven DN from 2011 to 2018. The pathological findings were confirmed according to the Renal Pathology Society classifications. GBM and TBM thicknesses were determined using the Haas' direct measurement/arithmetic mean method and orthogonal intercept method, respectively. Cox proportional hazard models were used to investigate the hazard ratios (HRs) for the influence of combined GBM and TBM thickness for predicting end-stage renal disease (ESRD).

RESULTS

Patients were assigned to three groups according to the median GBM and TBM thickness: GBM^lo TBM^lo (GBM < 681 nm and TBM < 1200 nm), GBM^hi TBM^lo /GBM^lo TBM^hi (GBM ≥ 681 nm and TBM < 1200 nm, or GBM < 681 nm and TBM ≥ 1200 nm), and GBM^hi TBM^hi (GBM ≥ 681 nm and TBM ≥ 1200 nm). The GBM^hi TBM^lo /GBM^lo TBM^hi and GBM^hi TBM^hi groups displayed poorer renal function, a more severe glomerular classification and interstitial inflammation, and poorer renal survival rates than the GBM^lo TBM^lo group The GBM^hi TBM^lo /GBM^lo TBM^hi and GBM^hi TBM^hi groups had adjusted HRs of 1.49 (95% confidence interval [CI], 1.21-9.75) and 3.07 (95% CI, 2.87-12.78), respectively, compared with the GBM^lo TBM^lo group.

CONCLUSIONS

TBM thickness enhanced GBM thickness for renal prognosis in patients with type 2 diabetes.