Network-based multi-omics integration reveals metabolic at-risk profile within treated HIV-infection

This article has 4 evaluations Published on
Read the full article Related papers
This article on Sciety

Abstract

Multiomics technologies improve the biological understanding of health status in people living with HIV on antiretroviral therapy (PLWHART). Still, a systematic and in-depth characterization of metabolic risk profile during successful long-term treatment is lacking. Here, we used multi-omics (plasma lipidomic and metabolomic, and fecal 16s microbiome) data-driven stratification and characterization to identify the metabolic at-risk profile within PLWHART. Through network analysis and similarity network fusion (SNF), we identified three groups of PLWHART (SNF-1 to 3). The PLWHART at SNF-2 (45%) was a severe at-risk metabolic profile with increased visceral adipose tissue, BMI, higher incidence of metabolic syndrome (MetS), and increased di- and triglycerides despite having higher CD4+ T-cell counts than the other two clusters. However, the healthy-like and severe at-risk group had a similar metabolic profile differing from HC, with dysregulation of amino acid metabolism. At the microbiome profile, the healthy-like group had a lower α-diversity, a lower proportion of MSM, and was enriched in Bacteroides. In contrast, in at-risk groups, there was an increase in Prevotella, with a high proportion of men who have sex with men (MSM) confirming the influence of sexual orientation on the microbiome profile The multi-omics integrative analysis reveals a complex microbial interplay by microbiome-derived metabolites in PLWHART. PLWHART those are severely at-risk clusters may benefit from personalized medicine and lifestyle intervention to improve their metabolic profile.

Significance

The network and factorization-based integrative analysis of plasma metabolomics, lipidomics, and microbiome profile identified three different diseases’ state -omics phenotypes within PLWHART driven by metabolomics, lipidomics, and microbiome that a single omics or clinical feature could not explain. The severe at-risk group has a dysregulated metabolic profile that potentiates metabolic diseases that could be barriers to healthy aging. The at-risk group may benefit from personalized medicine and lifestyle intervention to improve their metabolic profile.

Related articles

Related articles are currently not available for this article.