Systems Analysis of Gut Microbiome Influence on Metabolic Disease in HIV-Positive and High-Risk Populations

Abstract

Poor metabolic health, characterized by insulin resistance and dyslipidemia, is higher in people living with HIV and has been linked with inflammation, antiretroviral therapy (ART) drugs, and ART-associated lipodystrophy (LD). Metabolic disease is associated with gut microbiome composition outside the context of HIV but has not been deeply explored in HIV infection or in high-risk men who have sex with men (HR-MSM), who have a highly altered gut microbiome composition. Furthermore, the contribution of increased bacterial translocation and associated systemic inflammation that has been described in HIV-positive and HR-MSM individuals has not been explored. We used a multiomic approach to explore relationships between impaired metabolic health, defined using fasting blood markers, gut microbes, immune phenotypes, and diet. Our cohort included ART-treated HIV-positive MSM with or without LD, untreated HIV-positive MSM, and HR-MSM. For HIV-positive MSM on ART, we further explored associations with the plasma metabolome. We found that elevated plasma lipopolysaccharide binding protein (LBP) was the most important predictor of impaired metabolic health and network analysis showed that LBP formed a hub joining correlated microbial and immune predictors of metabolic disease. Taken together, our results suggest the role of inflammatory processes linked with bacterial translocation and interaction with the gut microbiome in metabolic disease among HIV-positive and -negative MSM.IMPORTANCE The gut microbiome in people living with HIV (PLWH) is of interest since chronic infection often results in long-term comorbidities. Metabolic disease is prevalent in PLWH even in well-controlled infection and has been linked with the gut microbiome in previous studies, but little attention has been given to PLWH. Furthermore, integrated analyses that consider gut microbiome, together with diet, systemic immune activation, metabolites, and demographics, have been lacking. In a systems-level analysis of predictors of metabolic disease in PLWH and men who are at high risk of acquiring HIV, we found that increased lipopolysaccharide-binding protein, an inflammatory marker indicative of compromised intestinal barrier function, was associated with worse metabolic health. We also found impaired metabolic health associated with specific dietary components, gut microbes, and host and microbial metabolites. This study lays the framework for mechanistic studies aimed at targeting the microbiome to prevent or treat metabolic endotoxemia in HIV-infected individuals.

Keywords: HIV; MSM; endotoxemia; human immunodeficiency virus; men who have sex with men; metabolic disease; microbiome.

FIG 1

Study design schematic. (A) Measures were collected from four compartments: gut microbiome, peripheral immune, diet questionnaire, and plasma metabolome. These separate compartments can all influence each other and can be influenced by other clinical and demographic characteristics such as HIV and treatment status. (B) Analysis pipeline for the study. First, a metabolic health outcome was determined. Second, informative variables were selected using random forest analysis. Lastly, the relationships between these informative variables and the metabolic health outcome were examined.

FIG 2

Calculation of the metabolic disease score. (A) PCA of metabolic markers in fasting blood of 164 men and women, including 113 participants described in this report, along with 51 individuals recruited at the same time and under the same exclusion criteria as study participants. The metabolic disease score is calculated as the PC1 coordinates shifted to a minimum of 1 and log transformed. (B) Metabolic disease scores broken up by cohort. The percentages noted above the groups are the percentages of individuals with a score above our metabolic impairment cutoff (see Fig. S1). There is no significant difference between the proportions in each group (Fisher exact test, P = 0.11) or between mean ranks in each group (Kruskal-Wallis test, P = 0.13). (C) Relationships between metabolic disease score and age stratified by cohort. Statistical significances of slopes are indicated and were calculated according to the following linear model: score ∼ age + cohort + age × cohort. **, P < 0.01; *, P < 0.05.

FIG 3

Networks of selected measures reveal several strong associations with metabolic disease score and between measures. (A and B) Correlation subnetworks of all the non-microbe-selected measures (A) and the nearest neighbors of LBP (B). All Spearman rank correlations with an FDR P < 0.25 are shown. Subnetworks were pulled from a larger network of all VSURF selected measures (see Fig. S3 and Table S2). (C) Network of interactions between measures calculated using iRF. All edges represent an interaction (i.e., proximity in a decision tree) that occurred in 30% or more of the decision trees.

FIG 4

Microbiome-associated metabolite workflow. A two-pronged approach for identifying microbiome-associated metabolites is depicted. Numbers in boldface indicate metabolite counts.