A Community-Based Study Identifying Metabolic Biomarkers of Mild Cognitive Impairment and Alzheimer's Disease Using Artificial Intelligence and Machine Learning
| Autor: | Michael Maddens, William T. Hu, Sumeyya Akyol, Ilyas Ustun, Ali Yilmaz, Massimo S. Fiandaca, Stewart F. Graham, Mark Mapstone, Howard J. Federoff, Zafer Ugur |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Male Proton Magnetic Resonance Spectroscopy Disease Community based study Machine learning computer.software_genre Mass Spectrometry Machine Learning 03 medical and health sciences 0302 clinical medicine Metabolomics Alzheimer Disease Artificial Intelligence Tandem Mass Spectrometry medicine Dementia Humans Cognitive Dysfunction Cognitive impairment Aged Aged 80 and over Metabolic biomarkers business.industry General Neuroscience General Medicine medicine.disease Psychiatry and Mental health Clinical Psychology 030104 developmental biology Targeted mass spectrometry Cohort Metabolome Female Artificial intelligence Geriatrics and Gerontology business computer 030217 neurology & neurosurgery Chromatography Liquid |
| Zdroj: | Journal of Alzheimer's disease : JAD. 78(4) |
| ISSN: | 1875-8908 |
| Popis: | Background: Currently, there is no objective, clinically available tool for the accurate diagnosis of Alzheimer’s disease (AD). There is a pressing need for a novel, minimally invasive, cost friendly, and easily accessible tool to diagnose AD, assess disease severity, and prognosticate course. Metabolomics is a promising tool for discovery of new, biologically, and clinically relevant biomarkers for AD detection and classification. Objective: Utilizing artificial intelligence and machine learning, we aim to assess whether a panel of metabolites as detected in plasma can be used as an objective and clinically feasible tool for the diagnosis of mild cognitive impairment (MCI) and AD. Methods: Using a community-based sample cohort acquired from different sites across the US, we adopted an approach combining Proton Nuclear Magnetic Resonance Spectroscopy (1H NMR), Liquid Chromatography coupled with Mass Spectrometry (LC-MS) and various machine learning statistical approaches to identify a biomarker panel capable of identifying those patients with AD and MCI from healthy controls. Results: Of the 212 measured metabolites, 5 were identified as optimal to discriminate between controls, and individuals with MCI or AD. Our models performed with AUC values in the range of 0.72–0.76, with the sensitivity and specificity values ranging from 0.75–0.85 and 0.69–0.81, respectively. Univariate and pathway analysis identified lipid metabolism as the most perturbed biochemical pathway in MCI and AD. Conclusion: A comprehensive method of acquiring metabolomics data, coupled with machine learning techniques, has identified a strong panel of diagnostic biomarkers capable of identifying individuals with MCI and AD. Further, our data confirm what other groups have reported, that lipid metabolism is significantly perturbed in those individuals suffering with dementia. This work may provide additional insight into AD pathogenesis and encourage more in-depth analysis of the AD lipidome. |
| Databáze: | OpenAIRE |
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