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. Nevertheless, the underlying mechanisms linking air pollution to DKD are still not well understood. This study seeks to investigate the molecular pathways connecting air pollution to DKD by utilizing network toxicology, machine learning, and mendelian randomization (MR) methods.

Multiple databases were integrated to obtain potential target genes of 10 common air pollutants. The GEO database was employed to acquire datasets related to DKD. Differential expression analysis and WGCNA analysis were performed to identify DKD-related genes. The intersection of the target genes of air pollutants and DKD-related genes was used as potential targets. 12 machine learning algorithms were utilized to generate 113 unique predictive models, which were employed to select hub genes. Subsequently, MR, single-cell, GSEA, and immune infiltration analyses were performed, followed by molecular docking of hub genes with air pollutants.

Multiple databases were integrated to obtain potential target genes of 10 common air pollutants. The GEO database was employed to acquire datasets related to DKD. Differential expression analysis and WGCNA analysis were performed to identify DKD-related genes. The intersection of the target genes of air pollutants and DKD-related genes was used as potential targets. 12 machine learning algorithms were utilized to generate 113 unique predictive models, which were employed to select hub genes. Subsequently, MR, single-cell, GSEA, and immune infiltration analyses were performed, followed by molecular docking of hub genes with air pollutants.

Air pollutants may affect various biological processes through key genes such as ADH5, CASP3, NOS3, PTGS2, and SDHB, including metabolic reprogramming, inflammatory activation, and immune microenvironment alterations, thereby promoting the development and progression of DKD. These results offer a novel theoretical foundation for exploring the molecular mechanisms that connect air pollution to DKD, while also offering clues for identifying potential intervention targets for DKD.