AUTHOR=Zhao Yinjiao , Song Peiyu , Zhang Hui , Chen Xiaoyu , Han Peipei , Yu Xing , Fang Chenghu , Xie Fandi , Guo Qi 

TITLE=Alteration of plasma metabolic profile and physical performance combined with metabolites is more sensitive to early screening for mild cognitive impairment

JOURNAL=Frontiers in Aging Neuroscience

VOLUME=Volume 14 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2022.951146

DOI=10.3389/fnagi.2022.951146

ISSN=1663-4365

ABSTRACT=Objective: Unbiased metabolic profiling has been initiated to identify novel metabolites. However, it remains a challenge to define reliable biomarkers for rapid and accurate diagnosis of mild cognitive impairment (MCI). Our study aimed to evaluate the association of serum metabolites with MCI, trying to find new biomarkers and combination models that are distinguishing for MCI.
Methods: A total of 380 participants were recruited (mean age:72.5±5.19 years). We performed an untargeted metabolomics analysis from older adults who underwent the Mini-Mental State Examination (MMSE), Instrumental Activities of Daily Living (IADL) and physical performance including hand grip, Timed Up and Go Test (TUGT), and walking speed. Orthogonal Partial Least-Squares-Discriminant Analysis (OPLS-DA) and heat map were utilized to distinguish the metabolites that differ between groups.
Results: For all subjects, 47 were diagnosed with MCI and methods based on the propensity score are used to match the MCI group with the normal ctrl (NC) group(n=47). The final analytic sample comprised 94 participants (mean age 75.2). The data process from the metabolic profiles identified 1008 metabolites. A cluster and pathway enrichment analysis showed that sphingolipid metabolism involved in MCI development. Combination of metabolite panel and physical performance were significantly increased discriminating abilities on MCI than a single physical performance test (Model1: AUC=0.863; Model2: AUC=0.886; Model3: AUC=0.870, P<0.001).
Conclusions: In our study, untargeted metabolomics was used to detect disturbance of metabolism occurs in MCI. Physical performance test combined with phosphatidylcholines (PCs) showed good utility in discriminating between NC and MCI, which is meaningful for the early diagnosis of MCI.