The Systems Virology Lab, Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, Stockholm, Sweden
Marco Gelpi
Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
Rui Benfeitas
National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
Andreas D Knudsen
Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
Beate Vestad
Research Institute of Internal Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway; Norwegian PSC Research Center, Oslo University Hospital Rikshospitalet, Oslo, Norway
Julie Høgh
Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
Johannes R Hov
Research Institute of Internal Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway; Norwegian PSC Research Center, Oslo University Hospital Rikshospitalet, Oslo, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway
Department of Infectious Diseases, Copenhagen University Hospital – Amager and Hvidovre, Hvidovre, Denmark
Daniel Murray
Centre of Excellence for Health, Immunity and Infections (CHIP), Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
Christian G Giske
Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
Adil Mardinoglu
Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, United Kingdom
Marius Trøseid
Research Institute of Internal Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway; Institute of Clinical Medicine, Oslo, Norway
Susanne D Nielsen
Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
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+ T-cell counts than the other two clusters. However, the HC-like and the severe at-risk group had a similar metabolic profile differing from HIV-negative controls (HNC), with dysregulation of amino acid metabolism. At the microbiome profile, the HC-like group had a lower α-diversity, a lower proportion of men having sex with men (MSM) and was enriched in Bacteroides. In contrast, in at-risk groups, there was an increase in Prevotella, with a high proportion of MSM, which could potentially lead to higher systemic inflammation and increased cardiometabolic risk profile. The multi-omics integrative analysis also revealed a complex microbial interplay of the microbiome-associated metabolites in PWH. Those severely at-risk clusters may benefit from personalized medicine and lifestyle intervention to improve their dysregulated metabolic traits, aiming to achieve healthier aging.
