Abstract
The lack of sensitive and specific biomarkers for the early detection of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) is a major hurdle to improving patient management. A targeted, quantitative metabolomics approach using both 1H NMR and mass spectrometry was employed to investigate the performance of urine metabolites as potential biomarkers for MCI and AD. Correlation-based feature selection (CFS) and least absolute shrinkage and selection operator (LASSO) methods were used to develop biomarker panels tested using support vector machine (SVM) and logistic regression models for diagnosis of each disease state. Metabolic changes were investigated to identify which biochemical pathways were perturbed as a direct result of MCI and AD in urine. Using SVM, we developed a model with 94% sensitivity, 78% specificity, and 78% AUC to distinguish healthy controls from AD sufferers. Using logistic regression, we developed a model with 85% sensitivity, 86% specificity, and an AUC of 82% for AD diagnosis as compared to cognitively healthy controls. Further, we identified 11 urinary metabolites that were significantly altered to include glucose, guanidinoacetate, urocanate, hippuric acid, cytosine, 2- and 3-hydroxyisovalerate, 2-ketoisovalerate, tryptophan, trimethylamine N oxide, and malonate in AD patients, which are also capable of diagnosing MCI, with a sensitivity value of 76%, specificity of 75%, and accuracy of 81% as compared to healthy controls. This pilot study suggests that urine metabolomics may be useful for developing a test capable of diagnosing and distinguishing MCI and AD from cognitively healthy controls.
Highlights
Alzheimer’s disease (AD) is the most common neurodegenerative disease and currently lacks robust, non-invasive, diagnostic biomarkers [1]
For the first time we present a targeted, quantitative metabolomics approach that combines targeted liquid chromatography–tandem mass spectrometry (LC-MS) and 1 H NMR to biochemically profile urine from mild cognitive impairment (MCI) and AD sufferers and compare them with cognitively healthy age- and gender-matched controls
While we provide only a mere snapshot of urine metabolomics and AD, to the authors’ knowledge this is the first study to employ quantitative and global metabolomics approaches to profile urine obtained from patients with AD and individuals suffering from MCI and to compare them with the age- and gender-matched cognitively healthy controls
Summary
Alzheimer’s disease (AD) is the most common neurodegenerative disease and currently lacks robust, non-invasive, diagnostic biomarkers [1]. Metabolites 2020, 10, 357 accumulation of β-amyloid plaques and tau tangles, which cause neuronal damage or loss of function [2]; the actual biochemical basis for neurodegeneration is poorly understood [3]. The etiopathogenesis of AD is thought to begin decades before symptoms become apparent, and once symptoms such as memory loss, language problems, and other cognitive problems arise it is too late to treat the disease as the damage has already occurred [5]. The identification of early diagnostic biomarkers capable of identifying those people with AD years before irreversible brain damage has occurred is the number one priority for most grant-awarding institutions
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