Frontiers in cellular and infection microbiology · 2026
Metabolic dysbiosis as a predictor of immunological non-response among people living with HIV.
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Zhao Heping, Da Fenqi, Hou Huijun, Chen Xiejie, Ling Xuemei, Wu Shuizhen, Li Linghua
Аннотация
INTRODUCTION: The molecular mechanisms of immunological non-response in people living with HIV (PLWH) on antiretroviral therapy (ART) remain unclear. This study aimed to identify baseline metabolic and cytokine profiles that differ between immunological responders (IR) and immunological non-responders (INR), thereby exploring early molecular features associated with this discordant outcome.
METHODS: A cohort of PLWH was established at Guangzhou Eighth People's Hospital during November-December 2023. Following a minimum of four years on ART, participants were classified as INR (CD4+ T cell count < 350 cells/μL) or IR (≥350 cells/μL). Baseline plasma metabolites and cytokines were quantified using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS) and the Olink multiplex proximity extension assay (PEA), respectively.
RESULTS: A total of 101 PLWH were enrolled, comprising 46 IR and 55 INR. Baseline CD4+ T cell counts were comparable between the groups (IR: median 116 cells/μL; INR: 82 cells/μL). None of the 92 cytokines showed statistically significant differences between IR and INR. Quantitative analysis of 189 plasma metabolites revealed six with significant differential abundance between the IR and INR groups. Specifically, the INR group exhibited higher concentrations of arginine, glycodeoxycholate acid, glycodeoxycholic acid 3-sulfate, guanidoacetic acid, and tidiacic acid, along with a lower concentration of 2-methylpentanoic acid relative to the IR group. The six differential metabolites were used to construct exploratory classification models. The logistic regression model based on these metabolites yielded an AUC of 0.717 (95% CI: 0.616-0.817). Notably, in both random forest (RF) and support vector machine (SVM) analyses, 2-methylpentanoic acid and guanidoacetic acid were consistently ranked as the top two features for distinguishing INR from IR. Furthermore, correlation analysis indicated that guanidoacetic acid, glycodeoxycholate acid, and glycodeoxycholic acid 3-sulfate levels were inversely associated with the ΔCD4+ T cell count (r = -0.31, -0.38, and -0.35, respectively), whereas 2-methylpentanoic acid showed a positive correlation (r = 0.26).
CONCLUSIONS: Divergent immunological responses in PLWH were associated with distinct baseline metabolic profiles. These findings suggest potential metabolic pathways that may contribute to immune non-response and highlight specific plasma metabolites as candidates for future mechanistic and translational studies.
METHODS: A cohort of PLWH was established at Guangzhou Eighth People's Hospital during November-December 2023. Following a minimum of four years on ART, participants were classified as INR (CD4+ T cell count < 350 cells/μL) or IR (≥350 cells/μL). Baseline plasma metabolites and cytokines were quantified using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS) and the Olink multiplex proximity extension assay (PEA), respectively.
RESULTS: A total of 101 PLWH were enrolled, comprising 46 IR and 55 INR. Baseline CD4+ T cell counts were comparable between the groups (IR: median 116 cells/μL; INR: 82 cells/μL). None of the 92 cytokines showed statistically significant differences between IR and INR. Quantitative analysis of 189 plasma metabolites revealed six with significant differential abundance between the IR and INR groups. Specifically, the INR group exhibited higher concentrations of arginine, glycodeoxycholate acid, glycodeoxycholic acid 3-sulfate, guanidoacetic acid, and tidiacic acid, along with a lower concentration of 2-methylpentanoic acid relative to the IR group. The six differential metabolites were used to construct exploratory classification models. The logistic regression model based on these metabolites yielded an AUC of 0.717 (95% CI: 0.616-0.817). Notably, in both random forest (RF) and support vector machine (SVM) analyses, 2-methylpentanoic acid and guanidoacetic acid were consistently ranked as the top two features for distinguishing INR from IR. Furthermore, correlation analysis indicated that guanidoacetic acid, glycodeoxycholate acid, and glycodeoxycholic acid 3-sulfate levels were inversely associated with the ΔCD4+ T cell count (r = -0.31, -0.38, and -0.35, respectively), whereas 2-methylpentanoic acid showed a positive correlation (r = 0.26).
CONCLUSIONS: Divergent immunological responses in PLWH were associated with distinct baseline metabolic profiles. These findings suggest potential metabolic pathways that may contribute to immune non-response and highlight specific plasma metabolites as candidates for future mechanistic and translational studies.