Machine Learning Approach to Select Small Compounds in Plasma as Predictors of Alzheimer's Disease.

This study employs a machine learning approach to identify a small-molecule-based signature capable of predicting Alzheimer's disease (AD). Utilizing metabolomics data from the plasma of a well-characterized cohort of 94 AD patients and 62 healthy controls; metabolite levels were assessed using the platform. Data preprocessing involved removing low-quality samples, selecting relevant biochemical groups, and normalizing metabolite data based on demographic variables such as age, sex, and fasting time. Linear regression models were used to identify concomitant parameters that consisted of the data for a given metabolite within each of the biochemical families that were considered. Detection of these "concomitant" metabolites facilitates normalization and allows sample comparison. Residual analysis revealed distinct metabolite profiles between AD patients and controls across groups, such as amino acid-related compounds, bile acids, biogenic amines, indoles, carboxylic acids, and fatty acids. Correlation heatmaps illustrated significant interdependencies, highlighting specific molecules like carnosine, 5-aminovaleric acid (5-AVA), cholic acid (CA), and indoxyl sulfate (Ind-SO) as promising indicators. Linear Discriminant Analysis (LDA), validated using Leave-One-Out Cross-Validation, demonstrated that combinations of four or five molecules could classify AD with accuracy exceeding 75%, sensitivity up to 80%, and specificity around 79%. Notably, optimal combinations integrated metabolites with both a tendency to increase and a tendency to decrease in AD. A multivariate strategy consistently identified included 5-AVA, carnosine, CA, and hypoxanthine as having predictive potential. Overall, this study supports the utility of combining data of plasma small molecules as predictors for AD, offering a novel diagnostic tool and paving the way for advancements in personalized medicine.