Genomic and transcriptomic analysis reveals particulate matter exposure causally impairs renal function in diabetes, highlighting inflammatory and oxidative stress pathways.
Key Points
To evaluate the causal link between particulate matter exposure and renal dysfunction while uncovering shared molecular mechanisms in diabetic kidney disease.
Conducted bidirectional Mendelian randomization to evaluate causal associations between particulate matter (PM2.5–10) exposure and estimated glomerular filtration rate (eGFR).
Integrated multi-cohort transcriptomic datasets with machine learning to identify shared PM-diabetic kidney disease feature genes and build predictive nomograms.
Characterized single-cell and spatial expression patterns across kidney zones alongside in silico knockout and drug-target binding analyses.
PM2.5–10 exposure was causally associated with decreased eGFR, particularly among individuals with diabetes, without evidence of reverse causality.
Identified 168 shared PM-diabetic kidney disease genes enriched in AGE-RAGE, IL-17, TNF, and PI3K-Akt signaling, alongside a validated 7-feature gene diagnostic signature (AVPI1, DUSP1, FOSB, JUNB, PDK2, TPPP3, VIM).
Single-cell and spatial profiling localized VIM to fibrotic regions and TPPP3 to podocytes, identifying VIM as a potential candidate target showing binding affinity with sanguinarine.