bioRxivMicrobiologyPreprint
Plasma metabolomics reveals lipid-predominant metabolic disruptions in virologically suppressed people with HIV
Christopher M. BastingiD, Candace Guerrero, Kevin Escandón-VargasiD, Jodi L. Anderson, Garritt Wieking, Erik Swanson, Ty Schroeder, Charlotte R. HemmilaiD, Ross T. CromartyiD, Fernanda Torres-RuiziD, Maribel Soto-NavaiD, Lady Ruiz-Carbajal, Karla Krystel Ordaz-Candelario, Olivia BriceñoiD, Nicholas FunderburgiD, Santiago Ávila‐RíosiD, Melanie L. GrahamiD, Timothy W. SchackeriD, Gonzalo Salgado Montes de OcaiD, Nichole R. KlattiD
Abstract
People with HIV (PWH) on antiretroviral therapy (ART) experience excess morbidity and mortality from comorbidities including cardiovascular and metabolic disease, yet the biological mechanisms underlying these outcomes in virally suppressed PWH (VS-PWH) remain incompletely understood. We applied high-dimensional targeted plasma metabolomics to quantify 254 small molecules and 750 lipids across 49 classes in viremic PWH (Vi-PWH), VS-PWH, and people without HIV (PWoH), analyzed alongside multiple T cell metrics and plasma cytokine concentrations. Globally, the plasma metabolome of VS-PWH was indistinguishable from PWoH, while Vi-PWH exhibited substantial metabolic disruption characterized by depletion of phosphatidylcholines, sphingomyelins, and hexosylceramides alongside triglyceride accumulation, dysregulation of the tryptophan-kynurenine and arginine-citrulline axes, and elevations in diacetylated polyamines and N4-acetylcytidine. Ordinal trend analysis identified subtle but consistent residual alterations in VS-PWH, particularly within phosphatidylcholine and sphingomyelin classes, that correlated with elevated TNF and reduced CD4+ T cell counts and CD4/CD8 ratio. Together, these findings indicate that residual TNF-associated inflammation and incomplete T cell recovery continue to shape the plasma metabolome in treated HIV, identifying candidate biomarkers for further mechanistic and clinical investigation.
- organism: human
- assay: metabolomics
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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