Mapping Thrombosis Serum Markers by H-NMR Allied with Machine Learning Tools

Overview

Journal Molecules

Publisher MDPI

Specialty Biology

Date 2025 Jan 8

PMID 39769984

Authors

Lucas G Martins

Bruna M Manzini

Silmara Montalvao

Millene A Honorato

Marina P Colella

Gabriela G Y Hayakawa

Erich V de Paula

Fernanda A Orsi

Erik S Braga

Natasa Avramovic

Folurunsho Bright Omage

Ljubica Tasic

Joyce M Annichino-Bizzacchi

Affiliations

Soon will be listed here.

Abstract

Machine learning and artificial intelligence tools were used to investigate the discriminatory potential of blood serum metabolites for thromboembolism and antiphospholipid syndrome (APS). H-NMR-based metabonomics data of the serum samples of patients with arterial or venous thromboembolism (VTE) without APS (n = 32), thrombotic primary APS patients (APS, n = 32), and healthy controls (HCs) (n = 32) were investigated. Unique metabolic profiles between VTE and HCs, APS and HCs, and between VTE and triple-positive APS groups were indicative of the significant alterations in the metabolic pathways of glycolysis, the TCA cycle, lipid metabolism, and branched-chain amino acid (BCAA) metabolism, and pointed to the complex pathogenesis mechanisms of APS and VTE. Histidine, 3-hydroxybutyrate, and threonine were shown to be the top three metabolites with the most substantial impact on model predictions, suggesting that these metabolites play a pivotal role in distinguishing among APS, VTE, and HCs. These metabolites might be potential biomarkers to differentiate APS and VTE patients.

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