Year: 2026
Type: article
Abstract: Background Recurrent sickle cell leg ulcers (SCLUs) are disabling complications of sickle cell disease (SCD), but their circulating lipidomic correlates remain poorly characterized. To identify candidate plasma lipidomic patterns associated with a recurrent SCLU phenotype in a multicenter SCD cohort. Method This exploratory case–control study included 129 REDS-III Brazil participants with SCD: 72 with documented prior SCLU and at least one subsequent ulcer episode during follow-up, and 57 controls without documented ulcer history or follow-up ulcer episodes. Plasma was analyzed by untargeted LC–HRMS in negative ionization mode. After QC-based filtering, 2,419 features were retained. Exploratory training-set screening used nominal univariate testing; putatively annotated features were interpreted at their supported confidence level. Supervised classification was evaluated secondarily using an internal hold-out set. Results Training-set screening identified 181 nominal candidate features, including 44 putatively annotated features selected for interpretation. Recurrent SCLU status was associated with higher lysophospholipid and oxygenated fatty acid and oxylipin-related signals, whereas controls showed higher FAHFA, sphingolipid, and cholesterol-sulfate-related signals. Random Forest yielded an apparent hold-out AUC of 0.85. Conclusion Untargeted plasma lipidomics identified exploratory lipid-class patterns associated with a recurrent SCLU phenotype. These findings nominate candidates for targeted quantification, covariate-aware analysis, external replication, and longitudinal studies with detailed ulcer phenotyping.
Authors: Sheila O. G. Mateos, Marcos E. S. Abreu, Fábio E. Leal, Felipe C. C. Santos, André R. Belisário, Brian S. Custer, Carla L. Dinardo, Luiz M. A. Filho, Scott A. Bowler, Lishomwa C. Ndhlovu, Paula F. B. Caselli, Hebert Culler, Luís A. P. C. Lage, Juliana Pereira, Cadiele O. Reichertv, Dahra Teles, Monica Conde Tilli, Shannon Kelly, Alex Ap. R. Silva, Ester C. Sabino
Language: English
Subfield: Genetics (clinical)
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7223099924