Comprehensive Lipidomic Profiling Combined With Machine Learning Reveals Baseline Signatures Predictive of First Leg Ulcer Development in Sickle Cell Disease
Year: 2026
Type: article
Study design: Observational Study
Abstract: Introduction Human T-cell lymphotropic virus type 1 (HTLV-1) infection can lead to different clinical outcomes, such as asymptomatic infection, HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP), and adult T-cell leukemia/lymphoma (ATLL). The intracellular metabolic differences associated with these phenotypes remain poorly characterized. Aim This study aimed to characterize and compare intracellular CD4+ T-cell metabolic profiles across HTLV-1 clinical groups. Methodology We analyzed 63 leg ulcer–naïve participants from the REDS-III Brazil cohort with longitudinal follow-up (median 2.21-years). Baseline plasma lipidomics was acquired by untargeted LC-MS. Supervised classifiers for incident first leg ulcer were developed using leakage-resistant preprocessing and strict separation of model development and evaluation; performance was estimated by stratified five-fold cross-validation within the training set, with a stratified 20% hold-out split used as an internal validation. Results During follow-up, 18 participants developed a first leg ulcer. Baseline clinical characteristics did not show clear discrimination between individuals who developed leg ulcers and those who remained ulcer-free. Training-set feature selection identified a panel of 17 putatively annotated lipids (MSI level 2), enriched in sphingomyelins and glycerophospholipid-derived lysophospholipids, reflecting biologically coherent lipid remodeling patterns. Models based on this panel achieved consistent discrimination (cross-validated AUC 0.80–0.88) and reproduced a high-specificity, moderate-sensitivity operating profile in the hold-out split (AUC up to 0.89). Discussion The high-specificity predictive performance observed across machine learning models was accompanied by biologically coherent lipid alterations involving sphingomyelins, lysophospholipids, and fatty acids. These metabolites converge on pathways related to endothelial dysfunction, chronic inflammation, oxidative stress, and vascular homeostasis, indicating that systemic metabolic differences are already present at baseline before the first clinical manifestation of leg ulcers. Conclusion Baseline plasma lipidomic profiling identified a reproducible molecular signature associated with future first leg ulcer development in sickle cell disease, supporting its evaluation as a prognostic approach for early risk stratification. Validation in larger prospective cohorts is warranted. Financial Support This research was supported by Financiadora de Estudos e Projetos (FINEP), grant number 01.22.0589.01, by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), grant number 112969/2025-9, by National Institutes of Health, grant number HHSN2682011, and by funding from the Universidade Municipal de São Caetano do Sul (USCS).
Authors: Felipe Custódio Carvalho DOS SANTOS, Álex Aparecido Rosini SILVA, Marcos Eduardo Souza ABREU, Fábio Eudes LEAL, André Rolim BELISáRIO, Brian Scott CUSTER, Carla Luana DINARDO, Luiz De Melo Amori FILHO, Scott BOWLER, Lishomwa NDHLOVU, Paula Fraiman Blatyta CASELLI, Dahra TELES, Shannon KELLY, Ester Cerdeira SABINO, Sheila De Oliveira Garcia MATEOS
Language: English
Subfield: Genetics (clinical)
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7223090190