Memory loss is the most common clinical sign in Alzheimer's disease (AD); thus, searching for peripheral biomarkers to predict cognitive decline is promising for early diagnosis of AD. As platelets share similarities to neuron biology, it may serve as a peripheral matrix for biomarkers of neurological disorders. Here, we conducted a comprehensive and in-depth platelet proteomic analysis using TMT-LC-MS/MS in the populations with mild cognitive impairment (MCI, MMSE = 18-23), severe cognitive impairments (AD, MMSE = 2-17), and the age-/sex-matched normal cognition controls (MMSE = 29-30). A total of 360 differential proteins were detected in MCI and AD patients compared with the controls. These differential proteins were involved in multiple KEGG pathways, including AD, AMP-activated protein kinase (AMPK) pathway, telomerase RNA localization, platelet activation, and complement activation. By correlation analysis with MMSE score, three positively correlated pathways and two negatively correlated pathways were identified to be closely related to cognitive decline in MCI and AD patients. Partial least squares discriminant analysis (PLS-DA) showed that changes of nine proteins, including PHB, UQCRH, CD63, GP1BA, FINC, RAP1A, ITPR1/2, and ADAM10 could effectively distinguish the cognitively impaired patients from the controls. Further machine learning analysis revealed that a combination of four decreased platelet proteins, that is, PHB, UQCRH, GP1BA, and FINC, was most promising for predicting cognitive decline in MCI and AD patients. Taken together, our data provide a set of platelet biomarkers for predicting cognitive decline which may be applied for the early screening of AD.
Keywords: Alzheimer’s disease; machine learning; peripheral biomarkers; platelet; proteomics.
© 2021 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.