Unveiling the role of air pollution in diabetic kidney disease: an integrated study combining network toxicology, machine learning, and Mendelian randomization

Clinical Snapshot

Key Findings

• P Value: Statistically significant for NOS3 causal association (specific p-value not provided)

• Effect Size: Not specified in abstract

• Primary Outcome: Identification of 5 hub genes (ADH5, CASP3, NOS3, PTGS2, SDHB) linking air pollution to DKD

• Nnt Or Sensitivity: 714 air pollution targets identified, 80 DKD-related genes, 5 hub genes selected

• Confidence Interval: Not reported

Clinical Application

Findings require translation to clinical biomarkers and therapeutic targets before practical application Highly relevant given Australia's air quality challenges from bushfires and urban pollution; could inform TGA considerations for environmental health interventions Patients with diabetes at risk of kidney disease, particularly those in high air pollution environments

Abstract

Diabetic kidney disease (DKD) is a major and severe complication associated with diabetes. Air pollution is not only an independent risk factor for metabolic disorders but also an "accelerator" of DKD progression. This study seeks to investigate the molecular pathways connecting air pollution to DKD. Multiple databases were integrated to obtain potential target genes of 10 common air pollutants. The gene expression omnibus (GEO) database was employed to acquire DKD datasets. Differential expression analysis and weighted correlation network analysis (WGCNA) were performed to identify DKD-related genes. 12 machine learning algorithms were utilized to generate 113 unique predictive models, which were employed to select hub genes. Subsequently, MR, single-cell, gene set enrichment analysis (GSEA), and immune infiltration analyses were performed, followed by molecular docking of hub genes with air pollutants. A total of 714 targets were identified from 10 air pollutants, and 80 potential targets were identified from DKD transcriptomic data. Machine learning methods identified 5 hub genes that are closely associated with DKD. Mendelian randomization (MR) analysis indicated that, among the five hub genes, only NOS3 demonstrated a statistically significant causal association with DKD. Immune infiltration analysis found that hub genes were closely related to immune cells. Molecular docking validation indicated that certain air pollutants can stably bind with hub genes such as NOS3 and PTGS2. Air pollutants may be linked to alterations in various biological processes, potentially involving key genes such as ADH5, CASP3, NOS3, PTGS2, and SDHB, including potential metabolic reprogramming, inflammatory processes, and immune microenvironment changes.

References

1. 1.Kang, Y., Jin, Q., Li, S., Lv, Y., Wang, X., Jin, X., Zhou, M., Li, D., Lv, J., Zheng, H., & Wang, Y. (2026). Unveiling the role of air pollution in diabetic kidney disease: an integrated study combining network toxicology, machine learning, and Mendelian randomization. Renal Failure. https://doi.org/10.1080/0886022X.2026.2668270