Role of serum β2-microglobulin, glycosylated hemoglobin, and vascular endothelial growth factor levels in diabetic nephropathy

Serum VEGF as a predictive marker of glycemic control and diabetic nephropathy in Chinese older adults with type 2 diabetes mellitus

Objectives

Recent researches have demonstrated good correlation between vascular endothelial growth factor (VEGF) and diabetic nephropathy (DN); however, this relationship seems less clear-cut when VEGF was measured in blood samples. We tended to explore the possible association between serum VEGF and glycemic control and diabetic nephropathy severity in Chinese older adults with type 2 diabetes mellitus (T2DM).

Materials and methods

This study retrospectively enrolled 595 older T2DM adults at random. Participants were clinically grouped across the urine albumin-to-creatinine ratio (UACR) and the HbA1c tertiles by genders. Linear regressions were performed for the correlation of VEGF with HbA1c and UACR and binary logistic regressions for the odds of DN after adjusting for confounders. The receiver operating characteristic (ROC) curves were conducted for the predictive value of VEGF for DN.

Results

Both males and females with DN exhibited higher VEGF levels than non-DN (P < 0.001). Furthermore, a positive correlation of VEGF with UACR and HbA1c was presented regardless of adjusting confounding factors (P < 0.001). Serum VEGF level and fasting plasma glucose (FPG) were independent risk factors of DN in older adults of both genders (P < 0.05), while the risk prediction of DN by HbA1c only reflected in female patients (P < 0.05). The ROC curve of VEGF for DN had the area under curve (AUC) of 0.819 for males and 0.793 for females, indicating the clinical value of serum VEGF as a predictive biomarker.

Conclusions

Serum VEGF was strongly associated with UACR and HbA1c in both genders, and could be regarded as a predictive biomarker for glycemic control and diabetic nephropathy in older adults with T2DM.

Bioinformatics led discovery of biomarkers related to immune infiltration in diabetes nephropathy

BACKGROUND

The leading cause of end-stage renal disease is diabetic nephropathy (DN). A key factor in DN is immune cell infiltration (ICI). It has been shown that immune-related genes play a significant role in inflammation and immune cell recruitment. However, neither the underlying mechanisms nor immune-related biomarkers have been identified in DNs. Using bioinformatics, this study investigated biomarkers associated with immunity in DN.

METHODS

Using bioinformatic methods, this study aimed to identify biomarkers and immune infiltration associated with DN. Gene expression profiles (GSE30528, GSE47183, and GSE104948) were selected from the Gene Expression Omnibus database. First, we identified 23 differentially expressed immune-related genes and 7 signature genes, LYZ, CCL5, ALB, IGF1, CXCL2, NR4A2, and RBP4. Subsequently, protein-protein interaction networks were created, and functional enrichment analysis and genome enrichment analysis were performed using the gene ontology and Kyoto Encyclopedia of Genes and Genome databases. In the R software, the ConsensusClusterPlus package identified 2 different immune modes (cluster A and cluster B) following the consistent clustering method. The infiltration of immune cells between the 2 clusters was analyzed by applying the CIBERSORT method. And preliminarily verified the characteristic genes through in vitro experiments.

RESULTS

In this study, the samples of diabetes nephropathy were classified based on immune related genes, and the Hub genes LYZ, CCL5, ALB, IGF1, CXCL2, NR4A2 and RBP4 related to immune infiltration of diabetes nephropathy were obtained through the analysis of gene expression differences between different subtypes.

CONCLUSIONS

This study was based on bioinformatics technology to analyze the biomarkers of immune related genes in diabetes nephropathy. To analyze the pathogenesis of diabetes nephropathy at the RNA level, and ultimately provide guidance for disease diagnosis, treatment, and prognosis.