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bioRxiv · 10.1101/2022.06.08.495246

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

Mikaeloff, F.·Gelpi, M.·Benfeitas, R.·Knudsen, A. D.·Vestad, B.·Hogh, J.·Hov, J.·Benfield, T.·Murray, D.·Giske, C. G.·Mardinoglu, A.·Troseid, M.·Poulsen, S. D.·Neogi, U.
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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 -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. SignificanceThe 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.

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Mikaeloff, F., Gelpi, M., Benfeitas, R., Knudsen, A. D., Vestad, B., Hogh, J., Hov, J., Benfield, T., Murray, D., Giske, C. G., Mardinoglu, A., Troseid, M., Poulsen, S. D., Neogi, U.. 2022-06-13. Network-based multi-omics integration reveals metabolic risk profile within treated HIV-infection. https://doi.org/10.1101/2022.06.08.495246

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