Type 2 diabetes mellitus (T2DM) is an independent risk factor of
Alzheimer's disease (AD), and thus identifying who among the increasing T2DM populations may develop into AD is important for early intervention. By using TMT-labeling coupled high-throughput mass spectrometry, we conducted a comprehensive plasma proteomic analysis in none-T2DM people (Ctrl,
n = 30), and the age-/sex-matched T2DM patients with
mild cognitive impairment (T2DM-MCI,
n = 30) or T2DM without MCI (T2DM-nMCI, n = 25). The candidate
biomarkers identified by proteomics and bioinformatics analyses were verified by ELISA, and their diagnostic capabilities were evaluated with machine learning. A total of 53 differentially expressed
proteins (DEPs) were identified in T2DM-MCI compared with T2DM-nMCI patients. These DEPs were significantly enriched in multiple biological processes, such as
amyloid neuropathies, CNS disorders, and
metabolic acidosis. Among the DEPs, alpha-1-antitrypsin (SERPINA1), major
viral protein (PRNP), and
valosin-containing protein (VCP) showed strong correlation with AD high-risk genes APP, MAPT,
APOE, PSEN1, and PSEN2. Also, the levels of PP2A
cancer inhibitor (CIP2A), PRNP,
corticotropin-releasing factor-binding protein (CRHBP) were significantly increased, while the level of VCP was decreased in T2DM-MCI patients compared with that of the T2DM-nMCI, and these changes were correlated with the Mini-Mental State Examination (MMSE) score. Further machine learning data showed that increases in PRNP, CRHBP, VCP, and rGSK-3β(T/S9) (ratio of total to
serine-9-phosphorylated
glycogen synthase kinase-3β) had the greatest power to identify mild
cognitive decline in T2DM patients.