#AI Reads Urine# Untargeted urine metabolomics reveals dynamic metabolic differences and key biomarkers across different stages of Alzheimer’s disease

This research, published in *Frontiers in Aging Neuroscience*, was conducted by Xiaoya Feng and Shenglan Zhao. It focuses on Alzheimer's disease (AD), a progressive neurodegenerative disorder with an unclear etiology and a complex pathogenesis that hampers effective treatment development. Mild cognitive impairment (MCI) often serves as a precursor to AD, and early intervention at this stage can delay AD onset.

The study used untargeted urine metabolomics data from the MetaboLights database (MTBLS8662). The 162 participants, aged 50 and above, were divided into AD, MCI, and cognitively normal (CN) groups. Urine samples were processed and analyzed with an LC-MS/MS system. Through methods like partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA), differential metabolites were screened. Pathway enrichment analysis was carried out, and a decision tree approach was used to identify key metabolites for constructing an AD progression prediction model.

The results showed that the OPLS-DA model effectively distinguished metabolic characteristics at different stages. Drug metabolism was significantly enriched across all stages, while retinol metabolism was prominent during transition stages. Key metabolites like theophylline, vanillylmandelic acid (VMA), and adenosine showed significant differences in the early stages. 1,7-Dimethyluric acid, cystathionine, and indole had strong predictive value during the MCI to AD transition. The constructed prediction model demonstrated excellent classification and prediction capabilities.

However, the study has limitations. The sample size was relatively small, urine collection conditions were not well-controlled, the impact of medications was not considered, and the prediction model requires further validation with larger clinical datasets.

In conclusion, this research systematically analyzed the dynamic metabolic differences during AD progression, identified key metabolites and pathways as potential biomarkers, and provided a theoretical basis for monitoring AD progression and improving prevention and intervention strategies.

Front Aging Neurosci. 2025 Jan 27:17:1530046. doi: 10.3389/fnagi.2025.1530046

Youhe Gao

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