Antiretroviral therapy (ART) has improved the immune profile by suppressing viral replication and reducing the morbidity and mortality of people living with HIV (PWH). Yet living with HIV under ART induces a strong metabolic perturbation in the body due to virus persistence, immune activation, chronic low-grade inflammation, and treatment toxicity, mostly with older antiretrovirals (Yoshimura, 2017). The biological shifts due to a mixed effect of drugs and viruses are also highly personalized depending on the patient genetic background, age, sex, immunological, and lifestyle factors (Pelchen-Matthews et al., 2018). Long-term HIV infection, even with viral suppression, is associated with an accentuated onset of non-AIDS-related comorbidities (Deeks, 2011). Consequently, diseases of the aged population appear in relatively young HIV patients, including cardiovascular disease, liver-kidney disease, and neurocognitive and metabolic disorders (Nasi et al., 2017).

Systems biological analyses are valuable methodologies for systematically understanding pathology and identifying potential novel treatment strategies (Karahalil, 2016). Microbiome studies have provided enormous knowledge about the microbial association with HIV status, sexual practice, and gender (Zhou et al., 2020; Gelpi et al., 2020; Noguera-Julian et al., 2016), and the possible interplay between HIV-related gut microbiota, immune dysfunction, and comorbidities like metabolic syndrome (MetS), and visceral adipose tissue (VAT) accumulation (Gelpi et al., 2020). Our extensive metabolomics studies from three different cohorts from India (Babu et al., 2019), Cameroon (Mikaeloff et al., 2022), and Denmark (Gelpi et al., 2021) with more than 500 PWH have indicated disrupted amino acid (AA) metabolism in PWH with ART (PWH) following prolonged treatment that plays the central role in the comorbidities such as MetS (Gelpi et al., 2021).

The application of integrative omics to understand the disease pathogenesis in PWH under suppressive ART is lacking. To our knowledge, no integrative omics studies have been performed to understand complex biological phenotypes in PWH during prolonged suppressive ART. Multi-omic characterizations may offer insights into understanding the mechanisms underlying biological processes in a specific disease condition. A recent longitudinal study integrating metabolomics, plasma protein biomarkers, and transcriptomics in patients' samples identified potential lipid and amino acid metabolism perturbations in PWH with immune reconstitution inflammatory syndrome (IRIS) (Pei et al., 2021). Our recent network-based integrative plasma lipidomics, metabolic biomarker, and clinical data indicated a coordinated role of clinical parameters like accumulation of visceral adipose tissue (VAT) and exposure to earlier generations of antiretrovirals with glycerolipids and glutamate metabolism in the pathogenesis of PWH with MetS (Olund Villumsen et al., 2021).

The present study aimed to identify a molecular data-driven phenotypic patient stratification using network-based integration of plasma metabolomics/lipidomics and fecal microbiota within a cohort of PWH with prolonged suppressive therapy who were at-risk of metabolic complications. We further investigated the underlying factors differing from these profiles and the link to their clinical phenotype to clarify the risk factors for metabolic disease.

In this study, we used network and factorization-based integrative analysis of plasma metabolomics, lipidomics, and microbiome profile to characterize clinical phenotypes in the PWH. We identified three different diseases' state-omics phenotypes (HC-like, mild, and severe at-risk) within PWH driven by metabolomics, lipidomics, and microbiome that a single omics or clinical feature could not explain. The integrative omics highlighted the importance of highly intercorrelated microbiome-derived metabolites and their association with the clinical parameters in PWH cluster separation, shaping their systemic health profile. The severe at-risk group (SNF-2) has the at-risk metabolic profile characterized by an increase in TAG and DAG, highest median BMI, MetS incidence, VAT, and SAT, but had a higher CD4 T-cell count at sample collection compared to HC-like and mild at-risk group, which displayed an HC like lipidomic profile. However, the HC-like and severe at-risk group had a similar metabolic profile differing from HC, with dysregulation of AA metabolism. At the microbiome profile, the HC-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 MSM confirming the influence of sexual orientation on the microbiome profile (Noguera-Julian et al., 2016). Our study thus identified a risk group of PWH with successful treatment with a dysregulated metabolic profile potentiate metabolic diseases that could be barriers to healthy aging.

Similarity network analysis reduces the high-dimensional nature and different variances of multi-omics data to group patients based on the most similar profile (Wang et al., 2014). One of the main advantages of this method is the possibility to compare the networks' similarities to find out which layer has the most similarity with the final network. The similarity network fusion-based patient stratification has been used primarily in non-communicable diseases like cancer [to identify cancer subtypes (Wang et al., 2014; Chierici et al., 2020) and prognosis (Wang et al., 2021)], respiratory diseases (Narayana et al., 2021), and to study the influence of diet on human health (Burton-Pimentel et al., 2021). Recently we developed SNF-based patient stratification by integrating transcriptomics and metabolomics to define disease severity in COVID-19 that are predictive of the most robust biological features (Ambikan et al., 2022). We also reported the influence of gut microbiota on the systemic metabolic profile associated with disease severity (Albrich et al., 2022). However, no data were presented to stratify the PWH to fingerprint their disease status. The SNF has shown that the most crucial omics layer in cluster separation was lipids (NMI = 0.6), supported by the MOFA analysis. A study reported that ART and HIV reservoirs are responsible for changes in adipose tissue and lipids metabolism in PWH (Lagathu et al., 2019). Dyslipidemia represents the increase in triglycerides, low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and decrease of high-density lipoprotein cholesterol (HDL-C) cholesterol in the blood is a well-recognized complication observed in PWH; both naïve (Wang et al., 2016) and after ART initiation leading to cardiovascular diseases and mortality (Bowman and Funderburg, 2019; Fiseha et al., 2021). We found that the severe at-risk individuals (44/97) had most lipids classes upregulated, especially TAG, DAG, and CER, compared to the other groups, while HC-like and mild at-risk groups had no difference with HC. The severe at-risk group also has more patients with high BMI, VAT, SAT, and incidence of MetS. DAG and TAG high levels have been linked to cardiovascular events (Bowman and Funderburg, 2019; Stegemann et al., 2014). The TAG levels have been linked to insulin resistance and increased diabetes risk (Bowman and Funderburg, 2019), confirming this cluster group’s qualification as patients with dysregulated lipid profiles and metabolic disease risk. The association of lipid profiles with CD4 counts is still debated. It is positively associated with (Fiseha et al., 2021; Ji et al., 2019), and negatively (Ombeni and Kamuhabwa, 2016) associated with the highly abundant lipid profile. Interestingly, we found the severe at-risk group to have the highest CD4 count and suppressed viremia but have dysregulated lipid profiles that could be reasoned for unhealthy aging and adverse cardio-metabolic health. Therefore, we propose using a holistic view to define the clinical and immunological treatment success of PWH beyond viral suppression and immune reconstitution.

The second omics-defining clusters were metabolites (NMI = 0.4). Interestingly, the metabolic profile did not completely overlap with the lipid profile showing the complexity associated with the disease. PWH in the HC-like group differed most from the HC regarding their HC-like clinical parameter, with the lowest BMI, VAT, and SAT. Nevertheless, 32% of PWH in the HC-like group had MetS, half of the severe at-risk group (70%) but double the mild at-risk group (17%), indicating a possible lipid-independent metabolic dysregulation. Still, the mild at-risk group had the profile of the most HC-like, similar to the lipids, despite having a significantly higher number of patients with hypertension than the HC-like group. The HC-like and severe at-risk groups showed an up-regulation of the metabolites in the xenobiotics, nucleotides, and AA metabolism, indicating a potential role of diet. We previously showed that the glutamate metabolism was highly disrupted in PWH with MetS in the same COCOMO cohort (Gelpi et al., 2021), which can be responsible for late immune recovery in short-term ART patients (Rosado-Sánchez et al., 2019). Also, short-chain dicarboxylacylcarnitines (SCDA) and glutamine/valine were higher in PWH with coronary artery disease than in controls (Okeke et al., 2018). In our cohort, we observed glutamate, N-acetyl-glutamate, phenyl-acetyl-glutamate, gamma-glutamylglutamate, and 4-hydroxyglutamate part of the glutamine/glutamate metabolism had higher abundance in severe at-risk groups than the mild at-risk group. N-acetyl-glutamate was increased in the mild at-risk group compared to the HC-like group.

The microbiome network had a modest similarity with the final SNF network (NMI = 0.3), and the PCA plot did not observe apparent clustering of patients. Metabolism and immunity of the host are affected by bacteria and disrupted microbiomes linked to illness (Sun et al., 2016). More importantly, there is a high variability of microbiota among individuals based on lifestyle, diet, medication, and physiology (Knight et al., 2018). Increased α-diversity is associated with good health and decreased diversity in several diseases, including HIV (Zhou et al., 2020). A meta-analysis reported that HIV status was not associated with decreased a-diversity in MSM, perhaps due to sexual behaviors, but was decreased in PWH with heterosexual transmission (Tuddenham et al., 2020). Despite having healthy clinical and metabolic profiles, we observed a higher α-diversity in the severe at-risk group compared to the HC-like group, probably driven by a higher prevalence of MSM. In terms of bacterial composition, early studies reported that PWH had a higher abundance of Prevotella and a lower abundance of Bacteroides (Neff et al., 2018), which in subsequent studies were found to be more related to MSM behaviors than HIV status (Zhou et al., 2020; Gelpi et al., 2020; Noguera-Julian et al., 2016; Vujkovic-Cvijin et al., 2020). Our study observed that the severe at-risk group was enriched in Prevotella and depleted in Bacteriodes compared to the HC-like group. Interestingly, the decrease of Bacteroides in obese patients was inversely correlated with serum glutamate (Wu et al., 2021), which was also observed in severe at-risk group patients. On the other hand, some Prevotella species have pro-inflammatory effects, leading to intestinal inflammation, bacterial translocation, and microbiome dysbiosis (Iljazovic et al., 2021). In general, the complete cohort is mainly composed of MSM (65%, 63/97). As described above, it confirmed that the difference in the microbiome is driven by MSM status in severe at-risk groups, as there was 81% of MSM in that group. The mild at-risk group, even if there is no difference from the severe at-risk group according to PERMANOVA, has the same proportion of MSM as the HC-like group. It has been proposed that early regulation of the MSM-related microbiome could help prevent HIV infection (Zhou et al., 2020). However, the question remains whether the MSM-related microbiome is a potential driving force of metabolic comorbidities or whether MSM is a confounding factor disturbing a potentially clinical signal from a disturbed microbiome. Moreover, an increase in Prevotella could potentially aggravate intestinal and systemic inflammation leading to an increased cardiometabolic risk profile (Iljazovic et al., 2021; Littlefield et al., 2022).

Microbial compositions have implications for metabolism and metabolic diseases, notably through the production of MAMs (Agus et al., 2021). Secondary bile acids transformed from primary bile acids by bacteria have a role in lipid digestion. It regulates host metabolism through signaling and can inhibit the production of pro-inflammatory cytokines by immune cells (Postler and Ghosh, 2017). Lipid metabolism, including triglyceride trafficking, is influenced by bile acids through the interaction with the Farnesoid X receptor (FXR) receptor and has been implicated in mice’s metabolic disorders (Schoeler and Caesar, 2019). A bile acid, glycolithocholate was downregulated in PWH compared to controls previously associated with insulin resistance (Diboun et al., 2021). It was negatively associated with food elements such as vegetable intake and choice of fat for cooking, alcohol, and HIV-related parameters such as CD4 levels (nadir and at ART initiation) and HIV duration. High glycodeoxycholate was observed in the at-risk group compared to controls, while glycodeoxycholic acid is negatively associated with insulin resistance (Wu et al., 2021). Glycocholenate sulfate was downregulated in the three clusters compared to controls. All secondary bile acids were shown to be highly intercorrelated in co-expression analysis. Three other bile acids, lithocholate sulfate, glycousodesoxycholic acid sulfate, and deoxycholic acid 12-sulfate, were negatively associated with metabolic perturbations, including MetS, VAT, and central obesity. Acetate, propionates, and butyrate are part of short-chain fatty acids (SCFAs) and are obtained from the fiber bacterial fermentation in the colon that the host’s enzymes cannot digest (Alwin and Karst, 2021). Proprionate derivates were upregulated in HC-like and severe at-risk groups. Acetate and butyrate derivates had a more variable profile. Imidazole propionate (IMP) and 3-ureidopropionate were linked to the SNF clusters. In our study, the IMP was also linked to vegetable intake, reportedly involved in insulin resistance (Agus et al., 2021). The Bacteroides metabolize most of the acetate and propionate from polysaccharides, and Firmicutes produce butyrate (Postler and Ghosh, 2017), which does not explain the relationship within the SNF clusters indicating a more complex interplay between the MAMs and bacterial community in a diseased condition. Tryptophan is converted by bacterial tryptophanase into indole, and indole derivates are involved in the host-microbiota homeostasis (Krautkramer et al., 2021). Indoles derivates were mainly upregulated in the HC-like and severe at-risk groups. Our data thus suggested the role of MDMs in shaping the clinical phenotype and systemic health profile in PWH, which could be a therapeutic target for improving health.

Although our study is the first to demonstrate an integrative multi-omics approach to the role of MAMs in systemic alterations in PWH, our study has limitations that merit comments. First, the study is cross-sectional and therefore restricted to predicting dynamic interactions of different omics layers. Second, the microbiome data analysis was done through 16 S methodologies and has a high level of missing data at the genus and species level. Third, although the network-based analysis and the observational data suggest a potential causal association of altered metabolic profile with clinical features, other factors may drive observed effects. Fourth, although this is the largest study to date to perform integrative omics in PWH, the number of samples was relatively low. Finally, microbiome and metabolomics are highly dependent upon an individual’s genetics, environment, and diet. The interaction noted may characterize the epiphenomena of a personalized immune system that can be an avenue for future studies to develop a more personalized model for integrative omics to phenotype the disease states we recently reported (Ambikan et al., 2022).

In conclusion, we performed a multi-omics analysis of PWH with different clinical features. We identified the diversity of PWH in HIV-related biological alterations regardless of immunological recovery and virological suppression. A proportion of PWH (severe at-risk group around 45% in the present cohort) showed highly dysregulated lipidomics (increased TAG and DAG) and clinical profile (increased BMI and obesity-related features) with increased Prevotella and decreased Bacteroides, the latter being related to MSM transmission. However, alterations in the metabolomics profile and higher CD4 T-cell count at the time of sample collection indicate a complex systemic interplay between host immunity and metabolic health. It can lead to an aggravated higher inflammation profile leading to a cardiometabolic risk profile among the MSM that might affect healthy aging in this population. Integrative analytical approaches that reflect the overall systemic health profile of PWH may improve patient stratification and individual therapeutic and preventive strategies. Given the complex interplay between the clinical and molecular metabolic profile, the application of the multi-omics data for much larger cohorts of PWH might facilitate a better identification of network perturbations and molecular network connections to detect early disease transition toward metabolic complications at an earlier stage. Developing a more personalized model or targeting the interaction networks rather than individual clinical or omics features may provide novel treatment strategies in countering dysregulated metabolic traits, aiming to achieve healthier aging.

All of the data generated or analyzed during this study are included in this published article and/or the supplementary materials. Created datasets and code are publicly available. The metabolomics and lipidomics data are available from https://doi.org/10.6084/m9.figshare.14356754.v1 and https://doi.org/10.6084/m9.figshare.14509452.v1. All the codes are available at github: https://github.com/neogilab/HIV_multiomics, (copy archived at swh:1:rev:86aae862497b7dbb3dae4ce2e5a44b0369e0dec0).

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Author details

1. Flora Mikaeloff

   The Systems Virology Lab, Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, Stockholm, Sweden

   Contribution

   Data curation, Formal analysis, Visualization, Writing - original draft

   For correspondence

   flora.mikaeloff@ki.se

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6171-7043

2. Marco Gelpi

   Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark

   Contribution

   Data curation, Formal analysis, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

3. Rui Benfeitas

   National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden

   Contribution

   Software, Supervision, Investigation, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

4. Andreas D Knudsen

   Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark

   Contribution

   Data curation, Investigation, Methodology

   Competing interests

   No competing interests declared

5. Beate Vestad

   1. Research Institute of Internal Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway
   2. Norwegian PSC Research Center, Oslo University Hospital Rikshospitalet, Oslo, Norway

   Contribution

   Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

6. Julie Høgh

   Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark

   Contribution

   Data curation, Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

7. Johannes R Hov

   1. Research Institute of Internal Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway
   2. Norwegian PSC Research Center, Oslo University Hospital Rikshospitalet, Oslo, Norway
   3. Institute of Clinical Medicine, University of Oslo, Oslo, Norway

   Contribution

   Data curation, Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

8. Thomas Benfield

   Department of Infectious Diseases, Copenhagen University Hospital – Amager and Hvidovre, Hvidovre, Denmark

   Contribution

   Data curation, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0698-9385

9. Daniel Murray

   Centre of Excellence for Health, Immunity and Infections (CHIP), Rigshospitalet, University of Copenhagen, Copenhagen, Denmark

   Contribution

   Data curation, Funding acquisition, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

10. Christian G Giske

    Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden

    Contribution

    Supervision, Writing – review and editing

    Competing interests

    No competing interests declared

11. Adil Mardinoglu

    1. Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
    2. Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, United Kingdom

    Contribution

    Supervision, Funding acquisition, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

12. Marius Trøseid

    1. Research Institute of Internal Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway
    2. Institute of Clinical Medicine, Oslo, Norway

    Contribution

    Data curation, Formal analysis, Supervision, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

13. Susanne D Nielsen

    Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark

    Contribution

    Conceptualization, Resources, Investigation, Methodology, Project administration, Writing – review and editing

    Contributed equally with

    Ujjwal Neogi

    Competing interests

    No competing interests declared

14. Ujjwal Neogi

    The Systems Virology Lab, Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, Stockholm, Sweden

    Contribution

    Conceptualization, Resources, Supervision, Validation, Visualization, Methodology, Writing - original draft, Project administration

    Contributed equally with

    Susanne D Nielsen

    For correspondence

    ujjwal.neogi@ki.se

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0844-3338

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