Gene-Based Network Analysis Reveals Prognostic Biomarkers Implicated in Diabetic Tubulointerstitial Injury

The causal effect of inflammatory proteins and immune cell populations on diabetic nephropathy: evidence from Mendelian randomization

BACKGROUND

Diabetic nephropathy (DN) is one of the diabetic microvascular complications with complex pathophysiology, and exploring the landscape of immune dysregulation in DN is valuable for pathogenesis and disease treatment. We crystallized possible inflammatory exposures into 91 circulating inflammatory proteins and 109 blood immune cells; and assessed the causal relationship between inflammation and DN using Mendelian randomization (MR).

METHODS

Based on publicly available genetic data, we explored causal associations between inflammation and DN risk by two-sample MR analysis. Genome-wide association study (GWAS) summary statistics for 91 circulating inflammatory proteins, 109 immune cells absolute counts, and DN were acquired from the GWAS Catalog. Inverse Variance Weighted (IVW) was the main MR method, while MR-Egger and MR-pleiotropy residuals and outliers (MR-PRESSO) were utilized for sensitivity analysis. Cochrane's Q was used to test for heterogeneity. The leave-one-out method ensured the stability of the MR results.

RESULTS

This study revealed that higher levels of TNF-related activation-induced cytokine and tumor necrosis factor ligand superfamily member 14 were possibly associated with the increased risk of DN according to the IVW approach, with estimated odds ratios (OR) of 1.287 (95% confidence interval [CI] 1.051 to 1.577, P = 0.015) and 1.249 (95% CI 1.018 to 1.532, P = 0.033). Five immune cell traits were identified that might be linked to increased DN risk, including the higher absolute counts of HLA DR+ natural killer cell (OR = 1.248, 95% CI 1.055 to 1.476, P = 0.010), IgD+ CD38+ B cell (OR = 1.148, 95% CI 1.033 to 1.276, P = 0.010), CD25++ CD8+ T cell (OR = 1.159, 95% CI 1.032 to 1.302, P = 0.013), CD4- CD8- T cell (OR = 1.226, 95% CI 1.032 to 1.457, P = 0.020), and IgD- CD38- B cell (OR = 1.182, 95% CI 1.009 to 1.386, P = 0.039). In addition, elevated levels of interleukin-1 alpha (OR = 0.712, 95% CI 0.514 to 0.984, P = 0.040) and unswitched memory B cell (OR = 0.797, 95% CI 0.651 to 0.974, P = 0.027) may reduce the risk of developing DN.

CONCLUSION

We identified inflammation-related exposures that may be associated with the risk of DN at the level of genetic prediction, which contributes to a better understanding of the etiologic of DN and facilitates the development of targeted therapies for DN.

Integrated multiple-microarray analysis and mendelian randomization to identify novel targets involved in diabetic nephropathy

Background

Diabetic nephropathy (DN), which is the main cause of renal failure in end-stage renal disease, is becoming a common chronic renal disease worldwide. Mendelian randomization (MR) is a genetic tool that is widely used to minimize confounding and reverse causation when identifying the causal effects of complex traits. In this study, we conducted an integrated multiple microarray analysis and large-scale plasma proteome MR analysis to identify candidate biomarkers and evaluate the causal effects of prospective therapeutic targets in DN.

Methods

Five DN gene expression datasets were selected from the Gene Expression Omnibus. The robust rank aggregation (RRA) method was used to integrate differentially expressed genes (DEGs) of glomerular samples between patients with DN and controls, followed by functional enrichment analysis. Protein quantitative trait loci were incorporated from seven different proteomic genome-wide association studies, and genetic association data on DN were obtained from FinnGen (3676 cases and 283,456 controls) for two-sample MR analysis. External validation and clinical correlation were also conducted.

Results

A total of 82 DEGs (53 upregulated and 29 downregulated) were identified through RRA integrated analysis. The enriched Gene Ontology annotations and Kyoto Encyclopedia of Genes and Genomes pathways of the DEGs were significantly enriched in neutrophil degranulation, neutrophil activation, proteoglycan binding, collagen binding, secretory granule lumen, gluconeogenesis, tricarboxylic acid cycle, and pentose phosphate pathways. MR analysis revealed that the genetically predicted levels of MHC class I polypeptide-related sequence B (MICB), granzyme A (GZMA), cathepsin S (CTSS), chloride intracellular channel protein 5, and ficolin-1 (FCN1) were causally associated with DN risk. Expression validation and clinical correlation analysis showed that MICB, GZMA, FCN1, and insulin-like growth factor 1 may participate in the development of DN, and carbonic anhydrase 2 and lipoprotein lipase may play protective roles in patients with DN.

Conclusion

Our integrated analysis identified novel biomarkers, including MICB and GZMA, which may help further understand the complicated mechanisms of DN and identify new target pathways for intervention.

Relationship between the red cell distribution width-to-platelet ratio and in-hospital mortality among critically ill patients with acute myocardial infarction: a retrospective analysis of the MIMIC-IV database

OBJECTIVES

We aimed to investigate the association between red cell distribution width-to-platelet ratio (RPR), and in-hospital mortality in critically ill patients with acute myocardial infarction (AMI).

DESIGN

A retrospective cohort study.

SETTING

Data were collected from the Medical Information Mart for Intensive Care database (MIMIC-IV) consisting of critically ill participants between 2008 and 2019 at the Beth Israel Deaconess Medical Centre in Boston.

PARTICIPANTS

A total of 5067 patients with AMI were enrolled from the MIMIC-IV database.

PRIMARY AND SECONDARY OUTCOME

In-hospital mortality.

RESULTS

A total of 4034 patients survived, while 1033 died. In a multiple regression analysis adjusted for age, weight and ethnicity, RPR also showed a positive correlation with in-hospital mortality (HR 1.91, 95% CI 1.42 to 2.56, p<0.0001). Moreover, after adjusting for additional confounding factors, obvious changes were observed (HR 1.63, 95% CI 1.03 to 2.57, p=0.0357). In model 2, the high ratio quartile remained positively associated with hospital mortality compared with the low ratio quartile (HR 1.20, 95% CI 1.01 to 1. 43), with a p-value trend of 0.0177. Subgroup analyses showed no significant effect modifications on the association between RPR and in-hospital mortality in the different AMI groups (p>0.05).

CONCLUSION

RPR is an independent predictor of in-hospital mortality in critically ill patients with AMI.