Anicia Thirion, Du Toit Loots, Monray E Williams, Regan Solomons, Shayne Mason
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引用次数: 0
Abstract
Introduction: Because cerebrospinal fluid (CSF) samples are difficult to obtain for paediatric HIV, few studies have attempted to profile neurometabolic dysregulation.
Aim and objective: The aim of this exploratory study was to profile the neurometabolic state of CSF from a South African paediatric cohort using GCxGC-TOF/MS. The study included 54 paediatric cases (< 12 years), 42 HIV-negative controls and 12 HIV-positive individuals.
Results: The results revealed distinct metabolic alterations in the HIV-infected cohort. In the PLS-DA model, 18 metabolites significantly discriminated between HIV-infected and control groups. In addition, fold-change analysis, Mann-Whitney U tests, and effect size measurements verified these findings. Notably, lactose, myo-inositol, and glycerol, although not significant by p-value alone, demonstrated practical significance based on the effect size.
Conclusions: This study provided valuable insights on the impact of HIV on metabolic pathways, including damage to the gut and blood-brain barrier, disruption of bioenergetics processes, gliosis, and a potential marker for antiretroviral therapy. Nevertheless, the study recognized certain constraints, notably a limited sample size and the absence of a validation cohort. Despite these limitations, the rarity of the study's focus on paediatric HIV research underscores the significance and unique contributions of its findings.
期刊介绍:
Metabolomics publishes current research regarding the development of technology platforms for metabolomics. This includes, but is not limited to:
metabolomic applications within man, including pre-clinical and clinical
pharmacometabolomics for precision medicine
metabolic profiling and fingerprinting
metabolite target analysis
metabolomic applications within animals, plants and microbes
transcriptomics and proteomics in systems biology
Metabolomics is an indispensable platform for researchers using new post-genomics approaches, to discover networks and interactions between metabolites, pharmaceuticals, SNPs, proteins and more. Its articles go beyond the genome and metabolome, by including original clinical study material together with big data from new emerging technologies.