AUTHOR=Liu Yan , Kang Dongmei , Lei Yuan 
  
TITLE=Transcriptomics-based identification of shared biomarkers across type 2 diabetes, mild cognitive impairment, and uric acid metabolism
  
JOURNAL=Experimental Biology and Medicine
  
VOLUME=Volume 251 - 2026
  
YEAR=2026
  
URL=https://www.ebm-journal.org/journals/experimental-biology-and-medicine/articles/10.3389/ebm.2026.11060
  
DOI=10.3389/ebm.2026.11060
  
ISSN=1535-3699
  
ABSTRACT=Uric acid metabolism is associated with the development of type 2 diabetes mellitus (T2DM), cardiometabolic, and cardiovascular diseases. Additionally, T2DM patients often exhibit mild cognitive impairment (MCI). However, the underlying mechanisms remain unclear. This study aims to identify and validate biomarkers associated with uric acid metabolism in T2DM and MCI, with the goal of discovering potential diagnostic and therapeutic targets to improve the quality of life for T2DM patients. Transcriptomic data for T2DM, MCI and uric acid metabolism-related genes were sourced from public databases. Biomarkers were screened using machine learning and validated for expression. Subsequent analyses included functional enrichment, immune infiltration, subcellular localization, and drug prediction. Three biomarkers—HP, ITGB3, and SELP—were identified. All showed significantly elevated expression in the T2DM group (p < 0.05). HP and ITGB3 were primarily enriched in ribosome-related pathways, primary immunodeficiency, and adherens junction processes. Immune infiltration analysis revealed that immature B cells and plasmacytoid dendritic cells were significantly enriched in T2DM. HP showed the strongest positive correlation with plasmacytoid dendritic cells (cor = 0.65, FDR <0.05), while ITGB3 exhibited the strongest positive correlation with immature B cells (cor = 0.76, FDR <0.05). Several potential therapeutic drugs were predicted, including calcifediol (score = −99.93) and meclofenamic acid (score = −99.89). This study identified three candidate biomarkers co-dysregulated across T2DM and MCI transcriptomes and associated with uric acid metabolism. Given the exploratory sample sizes, these findings are considered hypothesis-generating and require validation in larger independent cohorts.