Mei Cui1, Yanfeng Jiang2,3,4, Qianhua Zhao1,5, Zhen Zhu4,6, Xiaoniu Liang1,5, Kexun Zhang4,6, Wanqing Wu1,5, Qiang Dong1, Yanpeng An7, Huiru Tang7, Ding Ding1,5, Xingdong Chen2,3,4. 1. Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China. 2. State Key Laboratory of Genetic Engineering, Human Phenome Institute, Fudan University, Shanghai, China. 3. School of Life Sciences, Fudan University, Shanghai, China. 4. Fudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China. 5. National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China. 6. Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China. 7. Metabonomics and Systems Biology Laboratory, School of Life Sciences, Fudan University, Shanghai, China.
Abstract
INTRODUCTION: Metabolomics provide a promising tool to understand the pathogenesis and to identify novel biomarkers of dementia. This study aimed to determine circulating metabolites associated with incident dementia in a Chinese cohort, and whether a selected metabolite panel could predict dementia. METHODS: Thirty-eight metabolites in baseline serum were profiled by nuclear magnetic resonance in 1440 dementia-free participants followed 5 years in the Shanghai Aging Study. RESULTS: Higher serum levels of glutamine and O-acetyl-glycoproteins were associated with increased risk of dementia, whereas glutamate, tyrosine, acetate, glycine, and phenylalanine were negatively related to incident dementia. A panel of five metabolites selected by least absolute shrinkage and selection operator within cross-validation regression analysis could predict incident dementia with an area under the receiver-operating characteristic curve of 0.72. DISCUSSION: We identified seven candidate serum metabolic biomarkers for dementia. These findings and the underlying biological mechanisms need to be further replicated and elucidated in future studies.
INTRODUCTION: Metabolomics provide a promising tool to understand the pathogenesis and to identify novel biomarkers of dementia. This study aimed to determine circulating metabolites associated with incident dementia in a Chinese cohort, and whether a selected metabolite panel could predict dementia. METHODS: Thirty-eight metabolites in baseline serum were profiled by nuclear magnetic resonance in 1440 dementia-freeparticipants followed 5 years in the Shanghai Aging Study. RESULTS: Higher serum levels of glutamine and O-acetyl-glycoproteins were associated with increased risk of dementia, whereas glutamate, tyrosine, acetate, glycine, and phenylalanine were negatively related to incident dementia. A panel of five metabolites selected by least absolute shrinkage and selection operator within cross-validation regression analysis could predict incident dementia with an area under the receiver-operating characteristic curve of 0.72. DISCUSSION: We identified seven candidate serum metabolic biomarkers for dementia. These findings and the underlying biological mechanisms need to be further replicated and elucidated in future studies.