Urine metabolomics phenotyping and urinary biomarker exploratory in mild cognitive impairment and Alzheimer’s disease

• Published in: Frontiers in aging neuroscience Vol. 15; p. 1273807
• Main Authors: Wang, Yuye, Sun, Yu, Wang, Yu, Jia, Shuhong, Qiao, Yanan, Zhou, Zhi, Shao, Wen, Zhang, Xiangfei, Guo, Jing, Song, Xincheng, Niu, Xiaoqian, Peng, Dantao
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 22.12.2023; Frontiers Media S.A
• Subjects: Alzheimer's disease; Apolipoprotein E; Atropine; Bioinformatics; Biomarkers; Brain diseases; Chemical elements; Cognitive ability; Dementia; Diabetes; Diagnosis; diagnostic biomarker; Discriminant analysis; Down-regulation; Environmental effects; Gas flow; machine learning; Mannose; Metabolic disorders; Metabolism; Metabolites; Metabolomics; Methionine; mild cognitive impairment; N-Acetyl-L-methionine; Neurodegenerative diseases; Neuropsychology; Patients; Phenotypes; Phenotyping; Prostaglandin D2; Risk factors; Statistical analysis; Urine; urine metabolomics; Variance analysis; China; Japan; Germany; mild cognitive impairment; Alzheimer’s disease; machine learning; diagnostic biomarker; urine metabolomics
• ISSN: 1663-4365; 1663-4365
• DOI: 10.3389/fnagi.2023.1273807

Alzheimer's disease is a prevalent disease with a heavy global burden and is suggested to be a metabolic disease in the brain in recent years. The metabolome is considered to be the most promising phenotype which reflects changes in genetic, transcript, and protein profiles as well as environmental effects. Aiming to obtain a comprehensive understanding and convenient diagnosis of MCI and AD from another perspective, researchers are working on AD metabolomics. Urine is more convenient which could reflect the change of disease at an earlier stage. Thus, we conducted a cross-sectional study to investigate novel diagnostic panels. We first enrolled participants from China-Japan Friendship Hospital from April 2022 to November 2022, collected urine samples and conducted an LC-MS/MS analysis. In parallel, clinical data were collected and clinical examinations were performed. After statistical and bioinformatics analyzes, significant risk factors and differential urinary metabolites were determined. We attempt to investigate diagnostic panels based on machine learning including LASSO and SVM. Fifty-seven AD patients, 43 MCI patients and 62 CN subjects were enrolled. A total of 2,140 metabolites were identified among which 125 significantly differed between the AD and CN groups, including 46 upregulated ones and 79 downregulated ones. In parallel, there were 93 significant differential metabolites between the MCI and CN groups, including 23 upregulated ones and 70 downregulated ones. AD diagnostic panel (30 metabolites+ age + APOE) achieved an AUC of 0.9575 in the test set while MCI diagnostic panel (45 metabolites+ age + APOE) achieved an AUC of 0.7333 in the test set. Atropine, S-Methyl-L-cysteine-S-oxide, D-Mannose 6-phosphate (M6P), Spiculisporic Acid, N-Acetyl-L-methionine, 13,14-dihydro-15-keto-tetranor Prostaglandin D2, Pyridoxal 5'-Phosphate (PLP) and 17(S)-HpDHA were considered valuable for both AD and MCI diagnosis and defined as hub metabolites. Besides, diagnostic metabolites were weakly correlated with cognitive functions. In conclusion, the procedure is convenient, non-invasive, and useful for diagnosis, which could assist physicians in differentiating AD and MCI from CN. Atropine, M6P and PLP were evidence-based hub metabolites in AD.