Metabolic syndrome (MetS) is a significant factor for cardiometabolic comorbidities in people living with HIV (PLWH) and a barrier to healthy aging. The long-term consequences of HIV-infection and combination antiretroviral therapy (cART) in metabolic reprogramming are unknown. In this study, we investigated metabolic alterations in well-treated PLWH with MetS to identify potential mechanisms behind the MetS phenotype using advanced statistical and machine learning algorithms.

We included 200 PLWH from the Copenhagen Comorbidity in HIV-infection (COCOMO) study. PLWH were grouped into PLWH with MetS (n = 100) defined according to the International Diabetes Federation (IDF) consensus worldwide definition of the MetS or without MetS (n = 100). The untargeted plasma metabolomics was performed using ultra-high-performance liquid chromatography/mass spectrometry (UHPLC/MS/MS) and immune-phenotyping of Glut1 (glucose transporter), xCT (glutamate/cysteine transporter) and MCT1 (pyruvate/lactate transporter) by flow cytometry. We applied several conventional approaches, machine learning algorithms, and linear classification models to identify the biologically relevant metabolites associated with MetS in PLWH.

Of the 877 identified biochemicals, 9% (76/877) differed significantly between PLWH with and without MetS (false discovery rate < 0.05). The majority belonged to amino acid metabolism (43%). A consensus identification by combining supervised and unsupervised methods indicated 11 biomarkers of MetS phenotype in PLWH. A weighted co-expression network identified seven communities of positively intercorrelated metabolites. A single community contained six of the potential biomarkers mainly related to glutamate metabolism. Transporter expression identified altered xCT and MCT in both lymphocytic and monocytic cells. Combining metabolomics and immune-phenotyping indicated altered glutamate metabolism associated with MetS in PLWH, which has clinical significance.