Xiaohui Luo, Longhao Jia, Jixin Cao, Qihao Guo, Jianfeng Feng, Gunter Schumann, Tianye Jia, Chun‐Yi Zac Lo, Shuqiao Yao, Xiang Wang, Tianhong Zhang, Shenxun Shi, Qiang Luo, Jijun Wang, Jie Zhang, Xin Wang, Jing Ding, D. Liu, G. Yu, Hao Wang, Feng Li, Marina Cao, Chunshui Yu, Guang Yang, Xiao-Yong Zhang, Deniz Vatansever, Jingqi Chen, Xing-Ming Zhao
Metabolomics may reveal non-invasive biomarkers for early diagnosis in Alzheimer's disease (AD) and provide new insights into the disease mechanisms to develop effective treatments. Here, we comprehensively analyzed the blood plasma metabolomes from a Chinese cohort of 447 individuals, including 188 AD, 181 MCI (mild cognitive impairment), and 78 NC (normal control). Differential analysis identified altered metabolites, followed by forward feature selection to prioritize a panel of key metabolites, and construction of a diagnostic model using logistic regression. Key metabolite-enriched pathways were identified and quantified for comparison across different groups, which was then validated through external datasets. We observed extensive metabolic dysregulation in AD compared to age-matched NC, with 25% of the differential metabolites also significantly dysregulated in MCI in the same directions. A panel of 22 key metabolites was prioritized, where triglycerides (TG) and phosphatidylethanolamines (PE) ranked top in importance. With these key metabolites, we trained a diagnostic model that classified AD from NC accurately (Area Under the Curve [AUC] = 0.935 in the replication cohort). Pathway quantification analysis showed significant changes in lipid metabolism in AD, which were validated in two external cohorts. We presented a precise and robust blood metabolic diagnostic model for AD, which may help promote early diagnosis and deepen the understanding of AD mechanisms.