Network-based multi-omics integration reveals metabolic at-risk profile within treated HIV-infection
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Curated by eLife
eLife assessment
This study systematically integrates multi-omics data to identify the metabolic at-risk profiles within people living with HIV on antiretroviral therapy and presents findings that have focused importance and scope. The methods, data, and analyses as described now only partially support the primary claims.
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Abstract
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.
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Author Response
Reviewer #1 (Public Review):
This study intended to identify the metabolic at-risk profile within PLWH on ART, by integrating and analyzing the multiomics data from multi-omics including untargeted plasma metabolomic, lipidomic, and fecal 16s microbiome. The overall strength of the study is the long-term treatment (~15 years) of the study subjects with well-recovered CD4 cell count and viral suppression. The integration and analysis of multi-omics data using similarity network fusion and factor analysis, etc. to group or differentiate HIV patients are informative and useful. The weakness of the study is the lack of presentation of comparability between patients and healthy controls and the use of multiple regression analysis for controlling potential confounders.
We are thankful to the reviewer for the critical …
Author Response
Reviewer #1 (Public Review):
This study intended to identify the metabolic at-risk profile within PLWH on ART, by integrating and analyzing the multiomics data from multi-omics including untargeted plasma metabolomic, lipidomic, and fecal 16s microbiome. The overall strength of the study is the long-term treatment (~15 years) of the study subjects with well-recovered CD4 cell count and viral suppression. The integration and analysis of multi-omics data using similarity network fusion and factor analysis, etc. to group or differentiate HIV patients are informative and useful. The weakness of the study is the lack of presentation of comparability between patients and healthy controls and the use of multiple regression analysis for controlling potential confounders.
We are thankful to the reviewer for the critical reading of our manuscript. The primary aim of our study was to identify the molecular data-driven phenotypic patient stratification in a cohort of PLWHART with prolonged suppressive therapy to identify the at-risk metabolic profile following long-term successful therapy. We and others have reported in several studies (e.g., Ref#9 and 10) that there were distinct systemic patterns in multi-omics data. However, as suggested, we have now provided Table 1-source data 1. We have kept HC in the analysis to define which group is presenting an HC-like profile among HIV, but we are not using them to perform statistics and draw conclusions.
Reviewer #2 (Public Review):
This study systematically integrates multi-omics (plasma lipidomic and metabolomic, and fecal 16s microbiome) data to identify the metabolic at-risk profiles within people living with HIV on antiretroviral therapy (PLWHART). As a result, three groups of PLWHART (SNF-1 to 3) were identified, which showed distinct phenotypes. Such insights cannot be obtained by a single type of omics data or clinical data, and have implications in personalized medicine and lifestyle intervention. Connecting the findings in this study with specific medical/clinical insights is the next challenge.
We are thankful to the reviewer for the suggestion. System biology's application in identifying a disease state's biological mechanism in HIV-infected individuals is a relatively new field. We agree with the reviewer that connecting the findings in this study with specific medical/clinical insights is the next challenge. However, the first proof-of-concept study on 108 patients showed that multi-omics studies could generate a correlation network of communities of related analytes associated with physiology and disease. More importantly, the behavioral coaching informed by personal data helped participants to improve clinical biomarkers [PMID: 28714965]. The applications of multi-omics data are more and more valuable in non-communicable diseases [PMID: 35528975, PMID: 36503356 etc.]. As suggested by the reviewer, we have now elaborated on the medical/clinical value in identifying metabolic at-risk profiles, in particular the potential to improve individual risk stratification and to personalize lifestyle interventions. Still, as our study is an association study, data should be regarded as exploratory, and not sufficient to suggest any changes in clinical practice.
We have concluded the manuscript as follows:
“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 PLWH 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 PLWH 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.”
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eLife assessment
This study systematically integrates multi-omics data to identify the metabolic at-risk profiles within people living with HIV on antiretroviral therapy and presents findings that have focused importance and scope. The methods, data, and analyses as described now only partially support the primary claims.
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Reviewer #1 (Public Review):
This study intended to identify the metabolic at-risk profile within PLWH on ART, by integrating and analyzing the multiomics data from multi-omics including untargeted plasma metabolomic, lipidomic, and fecal 16s microbiome. The overall strength of the study is the long-term treatment (~15 years) of the study subjects with well-recovered CD4 cell count and viral suppression. The integration and analysis of multi-omics data using similarity network fusion and factor analysis, etc. to group or differentiate HIV patients are informative and useful. The weakness of the study is the lack of presentation of comparability between patients and healthy controls and the use of multiple regression analysis for controlling potential confounders.
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Reviewer #2 (Public Review):
This study systematically integrates multi-omics (plasma lipidomic and metabolomic, and fecal 16s microbiome) data to identify the metabolic at-risk profiles within people living with HIV on antiretroviral therapy (PLWHART). As a result, three groups of PLWHART (SNF-1 to 3) were identified, which showed distinct phenotypes. Such insights cannot be obtained by a single type of omics data or clinical data, and have implications in personalized medicine and lifestyle intervention. Connecting the findings in this study with specific medical/clinical insights is the next challenge.
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