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Disturbances in the gut microbiota potentially associated with metabolic syndrome among patients living with HIV-1 and on antiretroviral therapy at Bafoussam Regional Hospital, Cameroon

Diabetology & Metabolic Syndrome volume 17, Article number: 86 (2025) Cite this article

Abstract

Background

This study investigates the gut microbiota components associated with metabolic syndrome in patients living with HIV-1 at Bafoussam Regional Hospital, West Cameroon, it focuses on gastrointestinal mucosal barrier disruption and dysbiosis, and their effects on persistent inflammation and metabolic disorders.

Methods

A pilot study was conducted involving fourteen patients living with HIV-1. The patients were divided into two groups of seven in each group. One group consisted of patients with metabolic syndrome, and the other group included patients without metabolic syndrome. Gut microbiota was characterized using 16 S rRNA gene-targeted sequencing to analyze microbial diversity and composition. Beta diversity and the relative abundance of bacterial taxa were compared between patients with and without metabolic syndrome.

Results

Patients living with HIV-1 and metabolic syndrome showed significantly altered beta diversity compared to those without metabolic syndrome. A higher relative abundance of Firmicutes and increased proliferation of Proteobacteria were observed in patients with metabolic syndrome. Additionally, a decrease in metabolically beneficial bacteria, such as Bifidobacterium sp., Lactobacillus sp., Akkermansia sp., and Faecalibacterium sp., was noted. Several beneficial bacterial species were associated with participants' metadata, suggesting potential links between gut microbiota and metabolic syndrome.

Conclusion

This preliminary study highlights that gut microbial balance, rather than the presence of specific bacteria, plays a crucial role in managing metabolic health in patients living with HIV-1. The altered gut microbiota in participants with metabolic syndrome emphasizes the need for further research into the optimal gut microbial structure. Understanding the interaction between gut microbiota changes and the chemical environment in these patients could guide targeted interventions to improve metabolic outcomes.

Introduction

Human immunodeficiency virus (HIV) is a retrovirus composed of fifteen proteins meticulously organized to infect and persist within the host’s immune cells [1]. A distinctive feature of HIV is its error-prone reverse transcriptase, which enables it to undergo antigenic variations and evade the host’s immune defenses [2]. This infection triggers chronic immune system activation, progressively destroys gut-associated lymphoid tissue, disrupts the gut microbiota, promotes microbial translocation, and results in chronic systemic inflammation by releasing proinflammatory cytokines [3, 4]. Metabolic syndrome (MeS) encompasses conditions such as HDL hypocholesterolemia, hypertriglyceridemia, obesity, hyperglycemia, and high blood pressure, all characterized by a state of chronic inflammation [5]. The inflammation impacts the insulin signaling pathway, involving oxidative stress, activation of c-Jun N-terminal kinase (JNK), inhibition of nuclear factor-κB (NF-κB) kinase-β (IKKβ/NF-κB) pathways, and the production of cytokines like tumour necrosis factor-alpha (TNFα), interleukin 6 (IL-6), and C-reactive protein (CRP). These processes contribute to insulin resistance, promoting the development of MeS components [6, 7].

In Cameroon, there is an increasing burden of MeS among HIV-infected patients [8]. This trend is linked to a variety of factors, including environmental, lifestyle, genetic, ethnic, medication, and HIV-related influences. While antiretroviral therapy (ART), especially protease inhibitors (PIs), effectively controls HIV infection, it often exacerbates inflammation by altering adipocyte differentiation, carbohydrate-lipid metabolism, and reducing mitochondrial proteins [9]. The prevalence of MeS is further influenced by factors affecting gut microbiota dynamics, structure, and functional interactions with the host [10]. Numerous reports have confirmed a higher presence of pro-inflammatory gut pathogens and Proteobacteria proliferation in patients living with HIV-1 [11,12,13,14,15,16]. In a previous study involving 371 HIV-infected patients undergoing HAART, approximately 38% developed metabolic syndrome, with 92% exhibiting HDL-hypocholesterolemia [8]. This indicates that addressing gut microbiota dysbiosis may mitigate the metabolic complications associated with HIV infection.

We hypothesized that gut dysbiosis contributes to the high incidence of metabolic syndrome in people living with HIV/AIDS (PLWHA) on ART. Our objective was to characterize the gut microbiota composition in Cameroonians living with HIV-1 along with and without metabolic syndrome and to analyze changes associated with the status of virological and metabolic syndrome. We selected a subset from our previous study [8], focusing on patients with the highest metabolic syndrome scores and those with the lowest scores as a preliminary investigation for future, larger-scale research.

Methods

Ethics

This study protocol was reviewed and approved by the National Ethics Committee for Human Health Research, Cameroon, approval number 2017/11/955/CE/CNERSH/SP. Written informed consent was obtained from all study patients enrolled in the study. This study was implemented according to the approved protocol guidelines. The molecular biology work and its analysis were conducted at the ICMR-National AIDS Research Institute (NARI) of India after approval from the Institutional Ethics Committee of ICMR-NARI, approval number NARI/EC/Approval/2019/343.

Study design and sample size justification

Study design

Patients were recruited as previously described [8] at the accredited therapeutic center of the Bafoussam Regional Hospital according to their IDF score of MeS (the score of metabolic syndrome according to the International Diabetes Federation Criteria). Out of the cohort involved in the study from Diesse et al. (2020) [8], 14 patients with and without MeS were selected for their metabolic syndrome score, undetectable or low viral load, and CD4 cell count above the level that exposes the body to infectious risks. All patients were on c-ART with a combination of 2 nucleoside reverse transcriptase inhibitors (NRTIs), 3TC/TDF/AZT, and one non-nucleoside reverse transcriptase inhibitor (NNRTI), EFV/NVP or one protease inhibitor (PI), LPV + r, for at least 12 months and were not on prophylactic cotrimoxazole at the time of recruitment. No patients were on antibiotics, antifungals, or contraceptive hormones during recruitment or within the month before sample collection. The demographic and clinical statuses of the patients were collected using an ethically approved set of questionnaires.

On the recruitment day, stool samples were collected in a dry sterile container and transported to the laboratory directly after collection in a cooler. Blood samples were collected by venipuncture at the collection site; plasma was prepared and transported to the “Saint Vincent de Paul” Hospital of Dschang for analysis.

Sample size justification

Due to the high cost of next-generation sequencing and the lack of preliminary data on microbiota variability in our target population, we designed this study as a pilot investigation with seven samples per group. This sample size aligns with previous pilot studies in microbiome research, which often includes 5 to 10 samples per group to explore microbial diversity and detect preliminary trends.

Despite the small sample size, we employed rigorous analytical approaches to maximize data interpretation. Thes include alpha and beta diversity analyses, rarefaction curves, and PERMANOVA-based statistical tests, which have been shown to be effective in microbiome studies with limited sample sizes. Additionally, the results from this pilot study will provide variance estimates necessary for future power calculations, allowing for a more robust sample size determination in subsequent large-scale studies.

Furthermore, this investigation serves as proof-of-concept to support future funding applications, enabling the expansion of sample collection and enhancing the statistical power of subsequent research. Given the exploratory nature of this study, our primary aim is not to achieve definitive conclusions but to generate hypotheses and identify microbiome-related trends that warrant further investigation.

Sample processing

HIV RNA levels in plasma were measured by quantitative PCR using the Abbott Real-Time HIV-1 m1000/m2000rt platform. The CD4 T-cell count was determined using a BD FACSCOUNT [12, 17].

Additionally, DNA was extracted from fecal samples at the Molecular Parasitology & Entomology Unit at the University of Dschang using the ReliaPrep™ Blood gDNA MiniPrep System according to the manufacturer’s instructions (Promega, France). The extracted DNA was transported under frozen conditions to the Division of Molecular Biology at the ICMR-National AIDS Research Institute, India, for subsequent analysis. The concentration and purity of the extracted DNA were evaluated on a Nanodrop 1000 (Thermo Fisher Scientific, USA) by measuring the A260/A280 ratio and Qubit 2.0 (Thermo Scientific, USA). DNA samples of optimal purity (A260/A280 ratio of 1.8-2.0) were amplified via PCR.

Sequencing of the V3 region of the 16S rRNA gene

16S V3 metagenome libraries was prepared using region-specific primers. Genomic DNA was amplified for 21 cycles with the KAPA Hi-Fi Hot Start PCR Kit. The primer concentration was 5 µM per sample. Amplicons were analyzed on a 1.2% agarose gel. One microliter of diluted PCR products was used for indexing PCR, amplified for ten cycles with Illumina bar-coded adapters (Nextera XT v2 Index Kit). Round 2 amplicons were also analyzed on a 1.2% agarose gel. Libraries were normalized, pooled, and sequenced on an Illumina HiSeq X Ten sequencer for 150 cycles in a paired-end manner at Genotypic Technology Pvt. Ltd., Bangalore, India.

Data analysis

Initially, the software cutadapt 2.3 [18] removed the primers from the raw sequences. The sequences were subsequently analyzed using the Dada2 pipeline [19], and the SILVA reference database (version 132) was used for taxonomy assignment. A total of 8,144,102 sequences, including 2350 amplicon sequence variants (ASVs), were obtained after the Dada2 processing step, and 8,117,096 sequences, including 933 ASVs, were obtained after abundance filtering (< 0.05%). The final data were subjected to downstream analyses using the R package phyloseq [20]. Alpha diversity within microbial communities was described using the Chao1, Shannon, and inverse Simpson indices from an unfiltered dataset with bacterial reads. Beta diversity was analyzed using principal coordinate analysis (PCoA) based on Aitchison distance with CLR-transformed data or Euclidean distance with phILR transformation, accounting for microbiome data compositionality. Since beta diversity is a method for analyzing the differences between microbial communities. This data is of specific type known as compositional data while centered log ratio (CLR) transformation is a mathematical transformation method applied to compositional data to make it suitable for standard statistical analysis. phILR is another such method which considers the phylogenetic relationships between microbes. Aitchison distance & Euclidean distance are types of distance metrics used for CLR and phILR transformations respectively. In Mathematics and Statistics, ordination techniques are used to simplify complex data by using dimension reduction techniques. Principal Coordinate Analysis (PCoA) is one of such ordination techniques used for multivariate analysis. It maps the relative similarities or differences between various species of samples onto a two-dimensional plane for visualization. A scatter plot is generated using appropriate distance metric. The method of calculating sample similarity distance has an impact on the results. The PCoA analysis is based on the concept of dimensionality reduction to project sample relationships onto a low-dimensional plane. Additionally, group diversity comparisons were made using the Wilcoxon rank sum test and the Kruskal-Wallis test with the R package ggpubr [21].

The differential abundance of taxa between groups was tested with the R package DESeq2 [22] at the species level. For a given taxon, differential abundance was significant if the false discovery rate (FDR) adjusted p-value was less than 0.5. Microbial associations with patient clinical parameters were determined based on Pearson correlation analysis performed on relative-transformed data using the functional taxa.env. After correlation with the microbiome, the Seq R package [23]p-values associated with the correlation test were FDR-adjusted with a cutoff of 0.05.

Results

Demographic and clinical characteristics of study patients

The demographic characteristics of the enrolled patients are shown in Table 1, and their clinical parameters are shown in Table 2. The cohort consisted of 7 patients living with HIV-1 and MeS (50%) and 7 patients living with HIV-1 and without MeS (50%). Patients with MeS were between 30 and 52 years old, with a mean age of 43 (SD = 6.97). Those without MeS were between 23 and 54 years old, with a mean age of 39 (SD = 10.53). The sex ratio was also similar between the two groups. The duration of HIV infection and duration of ART were calculated according to the median and interquartile ranges (IQRs) for both groups. The delay in ART initiation was calculated based on the difference between the mean duration in years of HIV and duration of ART (0.875 years in both groups); however, the IQR calculated for age in years for the HIV + MeS- group (11.5) was more than double that for the HIV + MeS + group (5.5) shown in Table 1.

Table 1 Demographic characteristics of the patients
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All the patients without MeS were under an NNRTI-based regimen (3TC-TDF-EFV), while patients with MeS were under an NNRTI-based regimen (3TC-TDF/AZT-EFV/NVP) or a PI-based regimen (3TC-TDF-LPV/r). The IDF score of MeS for patients with MeS was between 4 and 5, while for those without MeS, this score was between 0 and 2. Viroimmunological status, namely, CD4 T-cell count and viral load, was also measured: none of the patients had elevated HIV viremia or a CD4 T-cell count < 200 cells/mm3, except for participant 5P, who exhibited a plasma viral load > 1000 copies/mL. Central tendency and variability measures were calculated, depicting the median and interquartile ranges (IQRs) of all laboratory parameters shown in Table 2.

Table 2 Clinical parameters of the patients
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Additionally, the results suggest no significant difference in median CD4 T cells between the groups with and without metabolic syndrome when considering only the CD4 T cells. However, there was a notable difference between the groups when considering the delay in initiating ART. There was no significant difference in the median CD4 T-cell density between the delayed and immediate ART arms (HIV + MeS + p = 0.121, HIV + MeS - p = 0.245). The patients with metabolic syndrome for whom the ART was initiated immediately after the test results were tested had a greater median CD4 T-cell density than any other group shown in Fig. 1A& B.

Fig. 1
figure 1

Association of CD4 counts in HIV-positive patients with and without metabolic syndrome (A) and CD4 counts in HIV-positive patients with the involvement of delayed and immediate ART arms in both groups (B)

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Furthermore, we also observed an association between BMI and delay or immediate use of ART in both groups. A statistically significant difference was observed in the median BMI between patients with and without metabolic syndrome (p = 0.002). At the same time, a delay in ART initiation did not show any association with BMI in either group as demonstrated in Fig. 2A & B).

Fig. 2
figure 2

Association of BMI with metabolic syndrome in HIV-positive patients (A) and BMI with the involvement of delayed and immediate ART arms (B) in both groups

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Description of the gut microbiota of patients

The microbiomes of the study patients were relatively rich in 12 phyla. Firmicutes and Proteobacteria predominated, followed by Bacteroidetes and Actinobacteria, whose relative abundances were similar in patients with and without MeS. In contrast, Cyanobacteria, Verrucomicrobia, and Fusobacteria were present in patients without MeS but appeared rarer in patients with MeS. The other phyla observed had a very low relative abundance and seemed similar between the groups shown in Fig. 3.

Fig. 3
figure 3

Taxonomic composition of the gut microbiota in patients living with HIV-1 and without metabolic syndrome (HIV + MeS-); patients living with HIV-1 and metabolic syndrome (HIV + MeS+), shown at the phylum level (A) and for the most prevalent bacterial lineages (B)

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Alpha and beta diversity

No significant difference in median alpha diversity was observed between the two groups of patients, although patients with MeS had more excellent dispersion than patients without MeS. Indeed, the variation in alpha diversity between the groups was highly overlapping as shown in Fig. 4, and the Wilcoxon rank sum test did not allow us to reject the null hypothesis of no difference in location between the groups (p = 0.53; 0.8 and 1 for Chao1, Shannon and Inverse Simpson, respectively). On the other hand, there was an alteration in beta diversity between the patients’ samples, with a significant difference in the gut microbiota structures (p = 0.01 and 0.003 using CLR and PhILR, respectively).

Fig. 4
figure 4

Changes in alpha diversity measured by the Chao1, Shannon, and inverse Simpson indices by metabolic syndrome (MeS) (A) and alterations in beta diversity evaluated based on the Aitchison distance using central log-ratio (CLR) transformation with Euclidean distance (B) and phylogenetic isometric log-ratio (phILR) transformation (C)

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Differential abundance

To determine which members of the bacterial communities drove the compositional shifts, we identified the species that were differentially abundant according to the MeS status. The abundance of twenty-one bacterial species increased in patients with MeS (12 Firmicutes: Ruminococcus gnavus, Anaerostipes hadrus, Butyricococcus sp., Erysipelatoclostridium ramosum, GCA-900066575 sp., Intestinibacter sp., Ligilacto bacillus sp., Limosilactobacillus sp., Parvimonas micra, Sarcina sp., Streptococcus mutans, and un_Clostridiaceae; 4 Proteobacteria: Escherichia/Shigella sp., Haemophilus parainfluenzae, Haemophilus sp., and Sutterella sp.; 3 Actinobacteria: Atopobium parvulum, Collinsella sp., and Senegalimassilia anaerobia; and 2 Bacteroidetes: Prevotellaceae and Sedimini bacterium salmoneum). On the other hand, 12 bacterial species had increased abundance in patients without MeS (6 Firmicutes: Family XIII AD3011 group sp., Hungatella sp., LachnospiraceaeUCG-009 sp., Oribacterium sp., Phascolarctobacterium succinatutens and Ruminococcus flavefaciens; 5 Bacteroidetes: Bacteroides pectinophilus, Alistipes putredinis, Alistipes sp., Bacteroides ovatus and Bacteroides uniformis; and 1 Actinobacteria: Coriobacteriales Incertaesedis). Thus, there appears to be an increased abundance of Firmicutes and proliferation of Proteobacteria in patients with MeS. In contrast, there is an increased abundance of Bacteroidetes in patients without MeS, as shown in Fig. 5.

Fig. 5
figure 5

Differentially represented species in patients living with HIV-1 and with MeS and patients living with HIV-1 without MeS using deseq2. The log ratio of the mean abundances is plotted on the Y-axis, with species more abundant in the patients living with HIV-1 and MeS having a ratio less than 0

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Associations between microbiota diversity and HIV-related factors

To address the problem of potential critical confounding factors, Pearson correlation analysis was used to determine the relationships between bacterial populations, biochemical parameters (individual components of MeS, total cholesterol, and LDL), and virological-immunological parameters such as duration of HIV infection, CD4 + T-cell count, and viral load (Fig. 6). The most significant positive correlations appeared between the duration of HIV and Fusobacterium sp. (p < 0.01), Faecalibacterium prausnitzii (p < 0.05), Eubacterium ruminantium (p < 0.001); viral load and Lachnospiraceae sp. (p < 0.001), Gastranaerophilales sp. (p < 0.001), Anaerovibrio sp. (p < 0.001) and Chistensenellaceae sp. (p < 0.05); HDL cholesterol and Fusobacterium sp. (p < 0.05), Eubacterium ruminantium (p < 0.05); body mass index and Clostridia UCG-14 (p < 0.05), Eubacterium coprostanoligenes (p < 0.05), Rumnococus sp. (p < 0.05), Roseburia sp. (p < 0.05), Holdemanella sp. (p < 0.05), Chistensenellaceae sp. (p < 0.05), Eubacterium ruminantium (p < 0.05); diastolic blood pressure and Clostridia UCG-14 (p < 0.05), UCG-005 (p < 0.01), Rumnococus bromii (p < 0.01), Alloprevotella sp (p < 0.01) and Eubacterium siraeum (p < 0.01). Meaning higher levels of these parameters are associated with increased levels of these bacteria species. No very strong correlations were evident with CD4 + T-cell count and triglyceride, but some bacterial species shown slight positive or negative associations with these parameters. Moreover, although not reaching statistical significance, moderate to very strong positive (deep red) or negative (deep blue) correlations were evident with other species such as Subdoligranulum sp., Megamonas sp., Blautia sp., Akkermansia muciniphila, Veillonella sp., Streptococcus sp., Sarcina sp., Pseudomonas sp., Prevotella sp., Monoglobus sp., Janthinobacterium sp., Escherichia/Shigella sp., Erysipelotrichaceae UCG-003, Dorea sp., Collinsella sp., Catenlibacterium sp., Bifidobacterium sp. Pseudobutyrivibrio sp., Klebsiella sp., Butyricicoccus sp., Paeniclostridium sp., Clostridium sensu stricto 2. Bacteria species were correlated with biological parameters more strongly in patients living with HIV-1 and MeS (Fig. 6).

Fig. 6
figure 6

Pearson correlation of bacterial populations at the species level with HIV age (duration of HIV infection), WC (waist circumference), CD4 (CD4 T-cell count), TC (total cholesterol), VL (viral load), Gly (glycemia), HDL-c (high-density lipoprotein cholesterol), BMI (body mass index), LDL-c(low-density lipoprotein cholesterol), DBP (diastolic blood pressure), SBP (systolic blood pressure) and TG (triglycerides) among HIV + MeS- (patients living with HIV-1 and without metabolic syndrome) and HIV + MeS+ (patients living with HIV-1 and with metabolic syndrome) performed on relative-transformed data using the function taxa.env.correlation of the microbiomeSeq R package. The color is consistent with the distribution of the Pearson correlation coefficient: Red shades (positive correlation) Blue shades (negative correlation), White/neutral shades (no correlation) and Stars (*) indicate statistical significance (P < 0.05)

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Discussion

This work aimed to describe the gut microbiota composition of Cameroonians living with HIV-1 along with both with and without MeS and to relate changes in microbiota structure to viroimmunological status and MeS status. Indeed, it has already been demonstrated by different groups of researchers beyond the Cameroonian border that obesity and diabetes, among many other metabolic conditions, are linked to disturbances in the gut microbiota [10]; therefore, we wanted to determine the beginnings of the extent of these dysbiosis in a Cameroonian context of predisposed patients, notably PLWHA, suffering from at least three of the metabolic conditions defined by the IDF. In this restrictive economic context, although the small size of our sample considerably limits our interpretations, it was observed that the major phyla of the human gut microbiota were represented in the patients. However, there was an unusual predominance of specific bacterial groups. The human gut is a complex ecosystem with up to 1014 bacterial cells, of which the colon alone can harbor up to 1012 bacteria grouped into more than 100 species and four predominant phyla that coexist in competitive inhibition [24, 25]. The gut microbiota of patients without MeS was more diversified with bacteria from the phyla Cyanobacteria, Verrucomicrobia, and Fusobacteria than was that of patients with MeS, which may reflect sociodemographic and clinical differences between patients [26, 27]. However, to our knowledge, sex has not yet been associated with gut microbiota diversity, and the patients in this study were relatively young. On the other hand, patients without MeS had a shorter duration of HIV infection and a shorter duration of ART.

The patients in this study (100% in both groups) exhibited a high relative abundance of bacterial species from the phylum Proteobacteria, as described previously [15]. The proliferation of this phylum of bacteria (which is composed mainly of potentially pathogenic gram-negative species) in HIV-infected patients is thought to be induced by the disruption of intestinal homeostasis [14], mainly through the depletion of intestinal CD4 + T cells and the disruption of the balance between effector T cells and regulatory T cells [28], leading to oxidative stress, which is likely to cause dysbiosis in HIV-infected patients [13, 14, 26, 28]. Dysbiosis in HIV-infected patients has consequences for the gastrointestinal mucosal barrier and the mucosal immune system, where disruption leads to impaired digestive function, increased intestinal permeability, and, consequently, microbial translocation [14, 28]. All these factors contribute to the activation of the immune system and the persistence of inflammation, which leads to an increased frequency of metabolic diseases in HIV-infected patients. The increased proliferation of Proteobacteria in patients with MeS supports this position. Although the alpha diversity differed significantly within samples, the beta diversity was substantially different between the groups. Furthermore, differential abundance analysis revealed the proliferation of proinflammatory members of the phylum Proteobacteria (Escherichia/Shigella sp., Haemophilus parainfluenzae, Haemophilus sp., and Sutterella sp.) in the patients with MeS, highlighting the difference in microbiota structure between the groups.

Furthermore, an increased abundance of Firmicutes and a decreased abundance of Bacteroidetes were also observed in the gut microbiota of patients with MeS. Many bacteria belonging to these phyla (e.g., some species of Ruminococcus, Lachnospiraceae, Bacteroides, Prevotella, Hungatella, and Phascolarctobacterium) are known for their metabolic functions as producers of n-butyrate, which is essential for maintaining homeostasis [10, 29, 30]. Indeed, disturbances in their equilibrium are associated with metabolic disorders in guinea pig models [29]. Recently, many reports have confirmed the importance of these bacterial species in the pathogenesis of obesity, type 2 diabetes, hypertension, and dyslipidemia in humans [10, 29, 31,32,33,34,35].

In addition, although patients with MeS showed a greater abundance of some Actinobacteria (functionally beneficial) than did the other patients, there was an overrepresentation of the potentially pathogenic or poorly understood metabolic functions of Firmicutes and Bacteroidetes, which are abundant in the latter. These include abundant mucin-degrading and non-butyrate-producing bacteria (such as Ruminococcus gnavus) and some bacteria phylogenetically related to Clostridium (Butyricicoccus, Erysipelatoclostridium ramosum, GCA-900066575 sp, Intestinibacter sp, and Sarcina sp), which have emerged as markers of dysbiosis and systemic inflammation [10]. Anaerostipes sp and Parvimonas micra are proinflammatory bacteria reportedly increasing in obese microbiota. Streptococcus populations are standard members of the gut and oral microbiota and atherosclerotic plaques that have been reported to be correlated with total cholesterol and LDL cholesterol (common risk indicators for atherosclerosis) [10]. Ligilactobacillus sp and Limosilactobacillus sp are not well-known homo or heteroreceptors. Sediminibacterium salmoneum is a newly identified species whose metabolic functions have not been fully elucidated. In contrast, Alistipes sp, which has an increased abundance in patients without MeS, has been reported by other authors to be a standard member of the human gut microbiota and is capable of producing short-chain fatty acids (SCFAs), which are essential for maintaining gut balance and protecting against cardiometabolic risk [29]. It should also be noted that many bacteria of the phyla Firmicutes and Proteobacteria have been shown to influence the initial conversion of l-carnitine to gamma-butyrobetaine (γ-BB) and choline to trimethylamine (TMA), which are the direct and indirect pathways, respectively, for the formation of TMA oxide, an important cardiovascular risk factor [10, 36].

The rarity of some endogenous bacteria of the gut microbiota, namely, Bacteroides thetaiotaomicron, Bifidobacterium sp, Lactobacillus sp, and Akkermansia sp, which are very abundant in the human gut microbiota in normobiosis and have mainly beneficial metabolic activities, was observed in all patients in this study [11, 24, 37]. These bacterial groups are primarily responsible for the hydrolysis, oxidation, reduction, and hydroxylation of colonic lipids [35]. Their metabolic action produces conjugated intermediates and beneficial SCFAs in several metabolic diseases, including obesity and diabetes. Indeed, some biosynthesized SCFAs can enter host cells to act as intracellular messengers, particularly within signaling pathways that control gene expression [38]. They also induce the expression of enzymes involved in gluconeogenesis [30]. They negatively regulate the peroxisome proliferator-activated receptor (PPARγ), which stimulates fatty acid oxidation and reduces lipogenesis in the liver and adipose tissue [32]. Via G protein-coupled receptors, they also regulate the release of intestinal hormones (GLP-1, peptide YY), which are involved in postprandial glucose clearance and a reduced gastrointestinal transit time and appetite [39,40,41]. Finally, they are involved in signaling pathways inhibiting NF-kB activity, producing proinflammatory cytokines (TNF-α and IL-6), and stimulating the secretion of the anti-inflammatory cytokine IL-10 by activating GPR43 [42,43,44].

However, the underrepresentation of functionally beneficial bacteria may be a consequence of the overgrowth of other potentially harmful bacteria, such as Klebsiella sp, Escherichia coli, Prevotella sp, Streptococcus sp, Shigella sp, and bacteria phylogenetically related to Clostridium. These bacterial groups, which have poorly understood metabolic functions and are sometimes associated with intestinal inflammation and enteric diseases, can frequently or occasionally accentuate intestinal dysbiosis [45, 46]. Indeed, microorganisms constantly compete for available substrates in their ecosystem and attempt to inhibit the growth of other organisms through biofilm formation, mucin production, and bacteriocin secretion [47, 48]. Therefore, the overgrowth of transitory or pathogenic bacteria in HIV-infected patients may explain the inhibition of beneficial bacteria for colonic metabolism and the high prevalence of metabolic disorders in these patients.

Finally, the variations in the gut microbiota observed among the patients in this study demonstrated that although intestinal dysbiosis explains the occurrence of MeS in patients living with HIV-1, individual colonic functional capacity and other environmental parameters play a role. Resident bacteria of the gut microbiota are thought to be functionally beneficial at first and to lose their function under certain conditions; however, these bacteria still need to be better understood due to current technological challenges. Indeed, gut inhabitants, such as E. coli, which potentially produce N-acyl phosphatidyl ethanolamine (NAPE), which functions as an anorexigenic bacterium (Shigella) and is sometimes implicated in decreasing global adiposity, rapidly improving glucose metabolism and reversing obesity comorbidities, are beneficial for manipulating the microbiota to counteract the progression of atherosclerotic disease and obesity [10, 29]. Likewise, Metanosarcina transforms TMA into biologically inert molecules. Ruminococcus gnavus has been reported among the protective species against pathogens and is associated with increased Lachnospiraceae in the intestine.

In contrast, cardiovascular protectors such as linoleic acid (LA) and α-linolenic acid have been reported to be negatively associated with genera such as Hungatella [29]. All these observations suggest that the gut microbial balance is more critical to host health than the individual involvement of specific bacteria. One more factor essential for the normal functioning of each bacterial species will depend on the available chemical environment for their survival in symbiosis and in the presence of interacting dominating species. This is particularly relevant because the analysis of correlation between microbiota and clinical-biological parameters yielded complex results, indicating associations that were sometimes positive, negative, or nonexistent with potentially beneficial or harmful bacteria in both groups of patients. However, Correlations between bacterial species (particularly Fusobacterium sp, Faecalibacterium sp and Eubacterium sp) and viral load, makers of obesity (body mass index and waist circumference), lipid profile and glycemia were more evident in patients living with HIV-1 and with metabolic syndrome, reinforcing the link between gut microbiota and cholesterol/glucose metabolism disturbances, in metabolic syndrome in our study cohort. These results seem to be consistent with no significant associations observed between CD4 T-cell count and microbiota components, suggesting that CD4 T-cell count may not be a primary factor influencing gut microbiota composition. Indeed, many bacteria positively associated with viral load were negatively associated with CD4 T-cell count and vice versa. Certain bacteria positively correlated with the duration of HIV infection in patients without MeS were negatively correlated with those with MeS, and certain bacteria inclined to increase with the duration of HIV infection in patients with MeS were decreased in those without MeS. The likely overall interpretation of these observations is that HIV and/or C-Art induce a progressive structural change in the intestinal environment. This suggest that the increase in metabolic disorders in PLWHA is multifactorial, with one of the likely mechanisms involving HIV, which causes progressive disturbances in the microbiome. In addition, c-ART can slow the spread of the virus but ultimately leads to disturbances, exacerbating inflammation leading to metabolic syndrome. Finally, significant correlations with blood pressure, more pronounced in patients living with HIV-1 and without metabolic syndrome, may reflect primarily that inflammation and the role of hypertension/cardiovascular-related complications which can settle early during HIV infection. Future studies should include a larger number of patients and account for as many confounding factors as possible to unequivocally validate our claims.

Conclusion

In conclusion, the profiling of the gut microbiota of our cohort of Cameroonian living with HIV-1 revealed significant gut dysbiosis involving depletion of bacteria with functional capacities beneficial to health and enrichment of potentially pathogenic bacteria. Although the small size of our sample considerably limits our interpretations, it was clearly observed that gut microbiota patterns differ between patients living with HIV-1 and with metabolic syndrome and those without metabolic syndrome. Fusobacterium sp, Eubacterium sp, and Faecalibacterium sp were key bacteria involving. The results of this study provide pilot data on gut dysbiosis, which is potentially involved in the high prevalence of metabolic disorders observed among patients living with HIV-1 at Bafoussam Regional Hospital in Cameroon, making it a potential target for future therapeutic interventions, notably for the modification of dietary intake as well as the inclusion of probiotic and prebiotic supplements to improve the gut microbiota in PLWHA. Being pilot study, we can definitely come to a conclusion to get a proper orientation or clue to study an association of patients living with HIV-1 infection along with metabolic syndrome and plan future study based on these findings.

Data availability

The Comprehensive data about patient demographics that support the findings of this study are available from J-R Kuiate, email Id. jrkuiate@yahoo.com, Mob 237699679135, while data on metagenomic analysis are available from BioProject no. PRJNA1081185. Accession no. from SAMN40152144 to SAMN40152168 https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA1081185.

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Acknowledgements

The authors are grateful for the support of the HIV Care Center of BRH and the participation of patients in this study. We would also like to thank the Indian government for financial support through a Research Training Fellowship for Developing Country Scientists awarded to Dr. Diesse from the National AIDS Research Institute-Indian Council of Medical Research DCS/2018/000079, Division of Molecular Biology, which allowed us to perform high-performance computing.

Funding

No funding is available for further research or paying publication charges with any authors or their corresponding institutes.

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Authors and Affiliations

  1. Department of Biochemistry, Faculty of Science, University of Dschang, Dschang, Cameroon

    Joël Martial Diesse, Stephen Lacmata Tamekou, Gustave Simo, Jean Paul Dzoyem & Jules-Roger Kuiate

  2. Division of Molecular Biology, Indian Council of Medical Research - National Institute of Translational Virology and AIDS Research , 73 G MIDC Bhosari, Pune, 411026, India

    Sushama Jadhav & Vijay Nema

  3. Laboratory of Embryology & Biotechnology DBM-IBMM, “Université Libre de Bruxelles”, Brussels, Belgium

    Jacob Souopgui

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Contributions

Data acquisition and manuscript draft: JMD, SJ. Data interpretation: all authors. Manuscript drafting: JMD, SJ. Manuscript revision and approval: All authors. Study design, Manuscript review, revision, and approval: J-RK, SJ and VN. Accountability: All authors.

Corresponding author

Correspondence to Vijay Nema.

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Ethical approval

This study protocol was reviewed and approved by the National Ethics Committee for Human Health Research, Cameroon, approval number 2017/11/955/CE/CNERSH/SP. Written informed consent was obtained from all study patients enrolled in the study. This study was implemented according to the approved protocol guidelines. The molecular biology work and its analysis were conducted at the ICMR-National AIDS Research Institute (NARI) of India after approval from the Institutional Ethics Committee of ICMR-NARI, approval number NARI/EC/Approval/2019/343. Consent to Participate, and Consent to Publish declarations were the part of the consent form, and the manuscript was reviewed and approved by the research integrity unit of ICMR-NARI after checking for the contributions of all the authors and plagiarism, if any.

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Diesse, J.M., Jadhav, S., Tamekou, S.L. et al. Disturbances in the gut microbiota potentially associated with metabolic syndrome among patients living with HIV-1 and on antiretroviral therapy at Bafoussam Regional Hospital, Cameroon. Diabetol Metab Syndr 17, 86 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-025-01653-4

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Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

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