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Updated: Jun 18, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Omics signature of new-onset mild cognitive impairment and dementia in a population-based study
Yanxia Lu1, Crystal Tze Ying Tan2, Xinyi Gwee3
1Department of Medical Psychology and Ethics, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, 44 Wenhua Xi Road, Jinan, 250012, Shandong, P.R. China. A0068932@u.nus.edu.
Abstract:
Plasma proteomics and metabolomics snapshots reveal a molecular signature in circulation delineating pathophysiology of major and minor neurocognitive disorder. To identify new cues to disease aetiology and diagnostic approach, we applied plasma proteomics and metabolomics profiling platforms to samples collected in a population-based study of the Singapore Longitudinal Ageing Studies Wave 2 (SLAS-2). In this longitudinal study, blood samples were analysed with standard clinical chemistry, plasma proteomics (Sengenics) and metabolomics (Nightingale) panels. Participants were followed up for the development of mild cognitive impairment (MCI) and dementia for 3-5 years. Of the total 1,892 molecules in all assay types, 463 demonstrated significant associations with baseline prevalent MCI and dementia. We trained an automatic linear modelling of predictors for follow-up new-onset MCI and dementia. The best model consists of 10 variables including ZSCAN18, PRKD3, SPANXN4, DDX43, saturated fatty acids, PPP3CA, NFATC4, IL-8, PAK6, and PDGFB. In terms of molecular function, these molecular markers are involved in immunological dysfunction and inflammatory reaction, protein coding, lipids, DNA-binding transcription factor activity, and nervous system development. In conclusion, our current research has identified an omics signature linked to new-onset mild cognitive disorder and dementia, which we hope can help enhance the accuracy of their diagnosis using circulating blood samples.
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