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<p>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.</p>

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Identification of plasma biomarkers in lipid metabolism for accurate prediction of Alzheimer’s disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月18日

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  • 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,
  • Dezhi Liu,
  • Bo Yu,
  • He Wang,
  • Fei Li,
  • Miao Cao,
  • Chunshui Yu,
  • Guang Yang,
  • Xiao-Yong Zhang,
  • Deniz Vatansever,
  • Jingqi Chen,
  • Xing-Ming Zhao

摘要

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.