Author
Listed:
- Yu Guo
(Fudan University)
- Shi-Dong Chen
(Fudan University)
- Jia You
(Fudan University
Fudan University)
- Shu-Yi Huang
(Fudan University)
- Yi-Lin Chen
(Fudan University)
- Yi Zhang
(Fudan University)
- Lin-Bo Wang
(Fudan University)
- Xiao-Yu He
(Fudan University)
- Yue-Ting Deng
(Fudan University)
- Ya-Ru Zhang
(Fudan University)
- Yu-Yuan Huang
(Fudan University)
- Qiang Dong
(Fudan University)
- Jian-Feng Feng
(Fudan University
Ministry of Education)
- Wei Cheng
(Fudan University
Fudan University
Ministry of Education)
- Jin-Tai Yu
(Fudan University)
Abstract
Recent expansion of proteomic coverage opens unparalleled avenues to unveil new biomarkers of Alzheimer’s disease (AD). Among 6,361 cerebrospinal fluid (CSF) proteins analysed from the ADNI database, YWHAG performed best in diagnosing both biologically (AUC = 0.969) and clinically (AUC = 0.857) defined AD. Four- (YWHAG, SMOC1, PIGR and TMOD2) and five- (ACHE, YWHAG, PCSK1, MMP10 and IRF1) protein panels greatly improved the accuracy to 0.987 and 0.975, respectively. Their superior performance was validated in an independent external cohort and in discriminating autopsy-confirmed AD versus non-AD, rivalling even canonical CSF ATN biomarkers. Moreover, they effectively predicted the clinical progression to AD dementia and were strongly associated with AD core biomarkers and cognitive decline. Synaptic, neurogenic and infectious pathways were enriched in distinct AD stages. Mendelian randomization did not support the significant genetic link between CSF proteins and AD. Our findings revealed promising high-performance biomarkers for AD diagnosis and prediction, with implications for clinical trials targeting different pathomechanisms.
Suggested Citation
Yu Guo & Shi-Dong Chen & Jia You & Shu-Yi Huang & Yi-Lin Chen & Yi Zhang & Lin-Bo Wang & Xiao-Yu He & Yue-Ting Deng & Ya-Ru Zhang & Yu-Yuan Huang & Qiang Dong & Jian-Feng Feng & Wei Cheng & Jin-Tai Yu, 2024.
"Multiplex cerebrospinal fluid proteomics identifies biomarkers for diagnosis and prediction of Alzheimer’s disease,"
Nature Human Behaviour, Nature, vol. 8(10), pages 2047-2066, October.
Handle:
RePEc:nat:nathum:v:8:y:2024:i:10:d:10.1038_s41562-024-01924-6
DOI: 10.1038/s41562-024-01924-6
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