Yuchi Meng Murong Cheng Hongyan Qu Zhenxue Song Ling Zhang Yuanjun Zeng Suyun Li ^* Dongfeng Zhang ^*

• Qingdao University, Qingdao, China

Previous research has suggested a link between the onset of Alzheimer's disease (AD) and metabolic disorder; however, the findings have been inconsistent. To date, the majority of metabolomics studies have focused on AD, resulting in a relative paucity of research on early-stage conditions such as mild cognitive impairment (MCI) underexplored. In this study, we employed a comprehensive platform for the early screening of individuals with MCI using high-throughput targeted metabolomics. We included 171 participants including 124 individuals with MCI and 47 healthy subjects. Univariate statistical analysis was conducted using t-tests or Wilcoxon rank-sum tests, with p-values corrected by the Benjamini-Hochberg method. The screening criteria were set at FDR < 0.05 and fold change (FC) > 1.5 or < 0.67. Multivariate analysis was performed using orthogonal partial least squares discriminant analysis (OPLS-DA), where differential metabolites were identified based on variable influence on projection (VIP) scores (VIP > 1 and FDR < 0.05). Random forest analysis was used to further evaluate the ability of the metabolic data to distinguish effectively between the two groups. A total of 14 differential metabolites were identified, leading to the discovery of a biomarker panel consisting of three plasma metabolites-uric acid, pyruvic acid and isolithocholic acid-that effectively distinguished MCI patients from healthy subjects. These findings have provided a comprehensive metabolic profile, offering valuable insights into the early prediction and understanding of the pathogenic processes underlying MCI. This study holds the potential for advancing early detection and intervention strategies for MCI.