AI Article Synopsis

  • - The study explores the differentiation between neurodegenerative diseases, emphasizing the challenge of distinguishing early stages (prodromal) from later dementia, primarily due to a lack of suitable biomarkers.
  • - Researchers applied a machine learning classifier (Disease State Index) to analyze metabolic data from serum and cerebrospinal fluid samples of patients diagnosed with Alzheimer’s, mild cognitive impairment, and control groups.
  • - Findings indicate that traditional cerebrospinal fluid biomarkers are effective for identifying Alzheimer’s disease but struggle to differentiate between mild cognitive impairment and dementia, whereas metabolic profiling is more effective in making this distinction.

Article Abstract

Accurate differentiation between neurodegenerative diseases is developing quickly and has reached an effective level in disease recognition. However, there has been less focus on effectively distinguishing the prodromal state from later dementia stages due to a lack of suitable biomarkers. We utilized the Disease State Index (DSI) machine learning classifier to see how well quantified metabolomics data compares to clinically used cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD). The metabolic profiles were quantified for 498 serum and CSF samples using proton nuclear magnetic resonance spectroscopy. The patient cohorts in this study were dementia (with a clinical AD diagnosis) (N = 359), mild cognitive impairment (MCI) (N = 96), and control patients with subjective memory complaints (N = 43). DSI classification was conducted for MCI (N = 51) and dementia (N = 214) patients with low CSF amyloid-β levels indicating AD pathology and controls without such amyloid pathology (N = 36). We saw that the conventional CSF markers of AD were better at classifying controls from both dementia and MCI patients. However, quantified metabolic subclasses were more effective in classifying MCI from dementia. Our results show the consistent effectiveness of traditional CSF biomarkers in AD diagnostics. However, these markers are relatively ineffective in differentiating between MCI and the dementia stage, where the quantified metabolomics data provided significant benefit.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7175942PMC
http://dx.doi.org/10.3233/JAD-191226DOI Listing

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