Network-based multi-omics integration reveals metabolic at-risk profile within treated HIV-infection.
HIV
aging
computational biology
infectious disease
metabolomics
microbiology
microbiome
systems biology
viruses
Journal
eLife
ISSN: 2050-084X
Titre abrégé: Elife
Pays: England
ID NLM: 101579614
Informations de publication
Date de publication:
16 02 2023
16 02 2023
Historique:
received:
17
08
2022
accepted:
15
02
2023
pubmed:
17
2
2023
medline:
17
3
2023
entrez:
16
2
2023
Statut:
epublish
Résumé
Multiomics technologies improve the biological understanding of health status in people living with HIV on antiretroviral therapy (PWH). 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, metabolomic, and fecal 16 S microbiome) data-driven stratification and characterization to identify the metabolic at-risk profile within PWH. Through network analysis and similarity network fusion (SNF), we identified three groups of PWH (SNF-1-3): healthy (HC)-like (SNF-1), mild at-risk (SNF-3), and severe at-risk (SNF-2). The PWH in the SNF-2 (45%) had 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
Identifiants
pubmed: 36794912
doi: 10.7554/eLife.82785
pii: 82785
pmc: PMC10017104
doi:
pii:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2023, Mikaeloff et al.
Déclaration de conflit d'intérêts
FM, MG, RB, AK, BV, JH, JH, TB, DM, CG, AM, MT, SN, UN No competing interests declared
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