#AI reads Urine# Urine Exposome−Metabolome Network Analysis Identifies Differential Chemical Connectivity Associated with Mild Cognitive Impairment

The study analyzed urine samples from 60 participants aged 60 and above in Changzhi, Shanxi Province, China, including 30 patients with mild cognitive impairment (MCI) and 30 healthy controls. Using a broad-spectrum targeted liquid chromatography-tandem mass spectrometry platform, exogenous chemicals and endogenous metabolites in urine were detected. Through various biodiversity indices, network construction, and statistical methods, it was found that MCI patients had significantly higher chemical richness and exposome-metabolome network connectivity in their urine. There were 16 exogenous chemicals and 97 metabolites with significant differences. A core differential correlation network was constructed, with 1-hydroxypyrene, perfluorooctanoic acid, etc., as core environmental hubs, and acetylcholine, guanine, etc., as key metabolic nodes. These molecules are involved in pathways such as oxidative stress and neuroinflammation and are related to MCI status and cognitive scores. The study emphasizes the pathophysiological significance of chemical-metabolic interactions in early cognitive decline but has limitations such as a cross-sectional design and small sample size.

J Proteome Res. 2025 Jul 30. doi: 10.1021/acs.jproteome.5c00030.

Youhe Gao

Statement: During the preparation of this work the author(s) used Doubao / AI reading for summarizing the content. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

For earlier AI Reads Urine articles：

https://www.keaipublishing.com/en/journals/advances-in-biomarker-sciences-and-technology/ai-reads-urine/