Uncovering the role of the gut microbiome and metabolome in Schistosoma mansoni-induced modulation of cardiovascular disease risk in humans

Uncovering the role of the gut microbiome and metabolome in Schistosoma mansoni-induced modulation of cardiovascular disease risk in humans | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Uncovering the role of the gut microbiome and metabolome in Schistosoma mansoni -induced modulation of cardiovascular disease risk in humans Bridgious Walusimbi, Melissa AE Lawson, Allison J. Bancroft, Jacent Nassuuna, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7053382/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Helminth infections have been consistently associated with reduced cardiovascular disease (CVD) risk, yet the underlying mechanisms remain poorly understood. We hypothesized that Schistosoma mansoni infection alters the gut microbiome and metabolome in ways that modulate CVD risk factors—specifically high total cholesterol, LDL cholesterol, and blood pressure. We further hypothesized that S. mansoni -associated microbes linked with these risk factors would correlate with similarly associated metabolites. Methods We profiled the gut microbiome (using 16S rRNA gene sequencing) and faecal metabolome (using liquid chromatography–mass spectrometry) of 216 individuals from two settings in Uganda with contrasting S. mansoni endemicity. We conducted differential abundance, linear discriminant, linear regression, mediation, pathway enrichment, and integrative multi-omics analyses to investigate associations between S. mansoni infection, microbial and metabolite profiles, and CVD risk factors. Results S. mansoni ( S. m ) infection was associated with increased microbiome alpha diversity (Shannon index p = 0.048; observed richness p = 0.008), though beta diversity separation was observed only in urban communities (PERMANOVA p = 0.011). Linear discriminant analysis (LDA) and differential abundance testing revealed distinct taxa enriched in S. m + individuals, including Lysobacter , Domibacillus , and Treponema , while Prevotella and Streptococcus were consistently depleted. Mediation analysis identified several taxa such as Treponema, Roseburia , Lachnospiraceae_UCG.00 4 and Methanobrevibacter , that significantly mediated the relationship between S. mansoni infection and reduced cardiovascular disease (CVD) risk factors. Metabolomic profiling of faecal samples identified differentially abundant metabolites (p< 0.05) present in each group, although the overall global metabolomic separation between S. m + and S. m – groups was not significant. Integrative analyses revealed coherent microbe–metabolite–CVD networks present; one example identified were those bacterial species belonging to Lysobacter and Arthrobacter were inversely correlated with metabolites such as glycerolipids, steroids, and fatty acyls which are associated with both total and LDL cholesterol levels found in blood. These findings support a putative helminth–microbiome–metabolome axis that may modulate host cardiometabolic risk. Conclusion Our findings reveal a gut microbiome–metabolome pathway induced from S. m infection that may reduce CVD risk. Collectively this research provides novel insight into host-parasite interactions and identifying microbial and metabolic signatures that could offer new strategies to mimic the cardioprotective effects of helminth infections. Bioinformatics Parasitology General Microbiology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Globally, cardiovascular diseases (CVDs) pose a significant threat to public health, consistently ranking as the primary cause of mortality over the last thirty years [ 1 ]. For example, CVDs accounted for approximately 20.5 million deaths (over 31% of global deaths) in 2021 alone [ 2 ]. More than 75% of CVD related deaths have been reported to occur in low and middle income countries, demonstrating a critical need for intensified research to address the risk factors for CVD that can be modulated to reduce the incidence of disease [ 3 ]. These risk factors include dietary risks, high systolic blood pressure, dyslipidaemia (particularly high low-density lipoprotein (LDL) cholesterol) and high fasting plasma glucose [ 1 ]. Several studies have linked cardiovascular risk factors with the immune system response, both in humans and animal models [ 4 ]. Particularly, chronic inflammation has been highlighted as the main immunological feature characterizing cardiovascular or metabolic risk [ 5 , 6 ]. For example, increased circulating tumour necrosis factor (TNF)-⍺ is associated with glucose intolerance, and inhibiting its expression in adipose tissues affects sensitivity to insulin, and tolerance for glucose, in obese individuals [ 6 ]. Additionally, inflammatory pathways involving the activation of macrophages, dendritic cells, and mast cells have been shown to rely on the availability of dietary lipids such as saturated fats and cholesterol [ 7 ]. Dietary lipids such as omega-3 fatty acids have been linked to production of inflammatory cytokines such as TNF-⍺ and interleukin (IL)-2 [ 8 ]. As such, this lipid-inflammation interplay suggests that inflammation alters lipid profiles and metabolism in hosts. Multiple lines of evidence show that cytokines such as IL-6, IL-1 and TNF-⍺ are associated with increased production of triglycerides and LDL cholesterol levels in serum [ 6 , 9 – 11 ]. Atherosclerosis, a chronic inflammatory condition typified by thickening of arterial walls, mediated by accumulation of lipids and cells such as macrophages in the vascular intima, is central in cardiovascular disease prognosis [ 12 ]. Given the centrality of inflammation in cardiovascular risk, it has been hypothesized that infections that induce immunomodulatory responses protect hosts against metabolic disorders. One such class of infections with immunomodulatory effects are chronic helminth infections [ 13 ]. These are characterized by a polarized T-helper 2 cell response, important in resisting or eliminating helminth infections in the host [ 14 ]. However, helminths can modulate the host’s immune response to prolong their own survival [ 14 – 17 ]. For example, helminths induce production of IL-10, a cytokine known to be pivotal in suppressing inflammation, to enhance their survival [ 16 , 18 – 20 ]. Therefore, owing to their ability to downregulate inflammation in the host, the hypothesis that helminths may be protective against CVD risk is plausible. Several epidemiological studies have shown an inverse association between chronic helminth infection and metabolic risk factors such as LDL cholesterol and high blood pressure. For example, Wiria et al. , reported reduced total cholesterol, LDL cholesterol, body mass index (BMI) and waist-to-hip ratio in people infected with soil transmitted helminth (STH) compared to those without STH infection among Indonesian subjects living in helminth endemic region [ 21 ]. In another study by Magen et al , individuals with chronic Opisthorchis felineus infection had significantly reduced total cholesterol compared to those without the infection [ 22 ]. Furthermore, Shen et al found an inverse association between previous schistosomiasis infection and triglyceride levels, waist-to-hip ratio and BMI [ 23 ]. In the same study, diastolic blood pressure was significantly lower in subjects with previous schistosomiasis than those without infection [ 23 ]. Moreover, in separate cross-sectional studies investigating whether previous schistosome infection protects against development of diabetes and metabolic syndrome, Chen et al found lowered systolic blood pressure (SBP) and diastolic blood pressure (DBP) in people with schistosomiasis infection compared to those without [ 24 ]. The aforementioned evidence is further supported by meta-analyses that have begun to show the importance of the inverse association of helminths with CVD risk. Tracey et al reported an association of lower glucose levels, insulin resistance, metabolic syndrome, and a 50% reduced likelihood of susceptibility to CVD risk factors such as type 2 diabetes (T2D), with helminth infection [ 25 ]. This was reaffirmed by Rennie et al who found reduced fasting glucose, glycated hemoglobin (HbA1c) levels, prevalence of T2D and metabolic syndrome in people with helminth infections compared to those without [ 26 ]. Given that much of the existing evidence linking helmith infections to cardiometabolic protection is informed by studies focusing on STH, it would be informative to investigate how helminths acquired through alternative routes, such as S. mansoni , might affect one’s cardiovascular risk, in a population with a high prevalence of S. mansoni infection, and in case of a protective effect, study the mechanisms by which these parasites bring about this benefit to the host [ 27 ]. Despite the well-reviewed importance of the anti-inflammatory effect of helminths such as S. mansoni in protecting the host against CVD, other possible pathways have been suggested [ 28 ]. Schistosomiasis, caused by parasitic trematodes of the genus Schistosoma , remains one of the most prevalent neglected tropical diseases worldwide [ 29 ], affecting millions of individuals, primarily in low-resource regions. Beyond its direct pathological effects, emerging evidence suggests a complex interplay between schistosomiasis infection and the modulation of host immune responses, metabolic pathways, and disease susceptibility, and more recently the potential of infection to have immune-mediated protective effect against cardiovascular disease risk [ 30 – 32 ]. However, the mechanisms underlying these potential benefits remain poorly understood. One promising area of investigation is the gut microbiota, a complex ecosystem of trillions of microorganisms that profoundly shape host immunity, metabolism, and overall health [ 33 , 34 ]. Dysbiosis, characterized by abnormal changes in the composition and function of gut microbiota, has been implicated in various pathological conditions, including CVDs [ 34 – 36 ]. Helminth infections are known to modulate the gut microbiome, but it remains unclear whether such changes play a mediating role in the relationship between S. mansoni infection and cardiovascular risk. There is growing evidence suggesting that the gut microbiota and the metabolites they produce serve as critical mediators of host-parasite interactions. In the context of schistosomiasis, the parasite-host interaction within the gut environment can influence microbial composition and metabolic activity, leading to systemic effects on host physiology and immune responses [ 37 ]. Moreover, specific microbial metabolites, such as short-chain fatty acids (SCFAs), and trimethylamine N-oxide (TMAO), have been implicated in modulating CVD risk factors such as blood pressure and cholesterol [ 33 ], and may contribute to the observed protective effect of schistosomiasis against CVD risk. Despite the emerging evidence separately implicating the gut microbiome and its metabolites, and S. mansoni , the precise mechanisms underlying this complex helminth-microbiome interplay in driving cardiovascular risk modulation remain poorly understood. Our previous findings from a cluster-randomised trial involving 1,898 participants (the Lake Victoria Island Intervention Trial on Worms and Allergy-related Diseases [LaVIISWA], which was extended to investigate metabolic outcomes) showed that Schistosoma mansoni infection was associated with lower levels of total and LDL cholesterol, while intensive anthelmintic treatment led to an increase in LDL cholesterol [ 27 ]. Further, heavy and moderate S. mansoni infection intensities were associated with lower diastolic blood pressure, triglycerides and LDL cholesterol [ 27 ]. In pursuit of a deeper understanding of how S. mansoni infection could lead to these changes in CVD risk, the current work therefore used samples from our LaVIISWA trial, aiming at deciphering S. mansoni -associated dysbiosis, the mediatory role of gut microbiome in altering CVD risk, and the gut-microbiome and metabolome interaction in the context of chronic schistosomiasis infection and its impact on cardiovascular risk. By using an integrative, multi-omics approach including microbiome and metabolomics, we unravel the molecular pathways and microbial signatures associated with the protective effect of schistosomiasis on CVD risk. Ultimately, this research provides novel insights into host-parasite interactions, microbial dysbiosis, and metabolic influence, with implications for the development of targeted interventions to mitigate cardiovascular disease risk in humans. Methods Firstly, we established a comprehensive framework to investigate how Schistosoma mansoni infection influences cardiovascular disease risk through alterations in the gut microbiome and metabolome (Fig. 1 ). We used samples from rural participants in the LaVIISWA trial [ 27 , 38 ], and a second, well-characterized survey in a nearby urban setting in Uganda [ 39 ]. LaVIISWA was a cluster-randomised trial conducted among Lake Victoria Island fishing communities in Mukono district, Uganda. The study was conducted in 27 fishing villages: one was selected for piloting the study and the remaining 26 were randomised in a 1:1 ratio to standard deworming (single dose praziquantel given once a year and single dose albendazole twice a year) or intensive deworming (single dose praziquantel and triple dose albendazole four times a year). The samples we used were collected during the metabolic survey undertaken after 4 years of intervention. Contemporaneously, the rest of the samples for our study were collected from participants of an Urban Survey that was conducted in Entebbe municipality (an urban setting) found on shores north of Lake Victoria. Entebbe is in Wakiso district approximately 40km southwest of Kampala, the Ugandan capital city. We have previously reported that the rural population showed a markedly higher burden of helminth infections, with Schistosoma mansoni detected significantly more frequently than in the urban group, as illustrated by both stool Kato-Katz microscopy (31.7% vs 9.9%, p < 0.001) and stool PCR analysis (47.6% vs 22.2%, p < 0.001) [ 39 ]. In both studies, following overnight fasting, stool and blood samples were collected from the participants. Metabolic outcomes measured are: fasting blood sugar, insulin levels, serum lipid levels, body mass index (BMI), waist and hip circumference and blood pressure (systolic and diastolic). Exposure and Outcome Assessment Sociodemographic and Anthropometric Data Age and sex were documented using a structured survey tool. Body weight was recorded to the nearest 0.1 kg using a digital scale (SECA model 875), with participants lightly clothed and barefoot. Standing height was measured to the nearest 0.1 cm using a portable stadiometer (SECA model 213). Waist circumference was measured midway between the lower rib and iliac crest, while hip circumference was taken at the level of the greater trochanters, both using a non-elastic measuring tape. Body mass index (BMI) was calculated as weight (kg) divided by height (m²), and waist-to-hip ratio was derived accordingly. Parasitological Assessment Stool samples were analysed for helminth infections using both microscopy and molecular techniques. The Kato-Katz method was used to quantify S. mansoni and T. trichiura eggs as desribed by Sanya et al [ 27 ]. Real-time PCR assays were employed to detect DNA of S. mansoni , hookworm ( N. americanus ), and S. stercoralis , the latter being exclusively detected by PCR. PCR data were prioritized for diagnostic confirmation of hookworm due to slide timing variability. Dietary Intake Dietary patterns were evaluated using a semi-quantitative food frequency questionnaire (FFQ) tailored to reflect local Ugandan food consumption habits. The FFQ assessed usual intake over the past month, covering major food categories such as cereals, legumes, animal proteins, dairy, fruits, vegetables, oils, and sugary drinks. Responses were used to compute dietary diversity scores to adjust for potential confounding in downstream microbiome and metabolome analyses. Blood Pressure Blood pressure was measured three times at five-minute intervals in a seated, rested position using a validated automatic sphygmomanometer (OMRON M2, HEM-7121-E), with the average of the last two readings used for analysis. Blood pressure monitors were routinely calibrated through the Uganda National Bureau of Standards to ensure accuracy. Cardiometabolic Biomarkers Fasting venous blood samples were collected into EDTA, fluoride oxalate, and serum separator tubes following an overnight fast (≥ 8 hours). Participants were advised to avoid physical exertion and tobacco use prior to sampling. Plasma and serum were separated within one hour of collection and cryopreserved in liquid nitrogen. All biochemical analyses were performed at the MRC/UVRI & LSHTM Uganda Research Unit laboratory (Entebbe) using a Roche Cobas 6000 platform (c 501 module). Fasting plasma glucose and serum lipids—total cholesterol, triglycerides, HDL-c, and LDL-c were quantified using enzymatic colorimetric methods. HbA1c was assessed in whole blood using a turbidimetric inhibition immunoassay, and fasting insulin via electrochemiluminescence immunoassay (ECLIA). Insulin resistance was calculated using the Homeostasis Model Assessment (HOMA-IR) [ 40 ]. Microbiome profiling Selected samples were prepared from MRC/UVRI and LSHTM-Uganda Research Unit and shipped to Novogene for 16S rRNA sequencing. From stool samples collected from these participants at the time the CVD measurements were done, we profiled gut microbial diversity and performed untargeted metabolomics. Genomic microbial DNA was extracted from 150 mg of faecal sample of every selected individual, using the QIAamp DNA Stool kits (Qiagen, Hilden, Germany) according to the manufacturer's instructions. Following extraction, DNA concentration and purity were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific), and integrity was evaluated via 2% agarose gel electrophoresis. DNA samples (5 µL) were mixed with 1 µL of 6× loading dye and loaded alongside a 1 kb DNA ladder (Thermo Scientific) into a 2% agarose gel prepared with Tris-Acetate-EDTA (1 xTAE) buffer and stained with ethidium bromide (0.5 µg/mL). Electrophoresis was performed at 100 volts for approximately 45 minutes. Gels were visualized using a UV transilluminator, and high molecular weight DNA was confirmed by the presence of a distinct, unsmeared band above 10 kb. Only samples with high-quality, intact DNA were retained for downstream amplification and sequencing. The 16S library preparation protocol (Reference No: GHFS-LH-039) from Institute of Food Research was used to amplify the V3-V4 hypervariable regions of the bacterial 16S rRNA genes to profile the gut microbiota. The same amount of PCR products from each sample was pooled, end-repaired, A-tailed and further ligated with Illumina adapters. Libraries were sequenced on a paired-end Illumina platform to generate 250bp paired-end raw reads. The library was checked with Qubit and real-time PCR for quantification and bioanalyzer for size distribution detection. Quantified libraries were pooled and sequenced on Illumina platforms, according to effective library concentration and data amount required. Paired-end reads were assigned to samples based on their unique barcodes and were truncated by cutting off the barcodes and primer sequences. Paired-end reads were merged using FLASH (Version 1.2.11) [ 41 ], a fast and accurate analysis tool designed to merge paired-end reads when at least some of the reads overlap with the reads generated from the opposite end of the same DNA fragment, and the splicing sequences were called Raw Tags. Quality filtering on the raw tags was performed using the fastp (Version 0.20.0) software to obtain high-quality Clean Tags. The Clean Tags were compared with the reference database (Silva database https://www.arb-silva.de ) using Vsearch (Version 2.15.0) to detect the chimera sequences, and then the chimera sequences were removed to obtain the EffectiveTags [ 42 ]. For the Effective Tags obtained previously, denoise was performed with DADA2 to obtain initial Amplicon Sequence Variants (ASVs) and then ASVs with abundance less than 5 were filtered out[ 43 ]. Species annotation was performed using QIIME2 software. The annotation database used was Silva Database. To study phylogenetic relationship of each ASV and the differences of the dominant species among different samples(groups), multiple sequence alignment was performed using QIIME2 software. The absolute abundance of ASVs was normalized using a standard of sequence number corresponding to the sample with the least sequences. Subsequent analyses of alpha diversity and beta diversity were performed based on the output normalized data. Metabolite extraction and profiling from stool samples Metabolites were extracted from stool samples using a solid-phase extraction (SPE) approach. Briefly, 1.25 mL of 80:20 methanol:water solution was added to each fecal sample, followed by vortexing and addition of 1 mL of the resulting mixture to 9 mL of molecular-grade water in a 15 mL conical tube. The mixture was centrifuged at 4,000 rpm for 1 minute, and 5 mL of the resulting supernatant was loaded onto SPE cartridges via a syringe. The cartridges were dried by pushing air through them twice using the same syringe and then sealed for shipment to the analytical laboratory in Manchester. A blank control sample without faecal material was prepared alongside the experimental samples. Upon receipt, metabolite elution was performed using 1.5 mL of 85:15 acetonitrile:methanol, drawn into a 2 mL luer-lock syringe and passed through the cartridge into a sterile 1.5 mL microcentrifuge tube. After allowing 1 minute of solvent equilibration to re-solvate the stationary phase, the eluate was collected at a rate of approximately one drop per second. Samples were then subjected to nitrogen blowdown drying in batches of up to 50, using a 60-position dryer platform. Dried samples as provided were resuspended in 100 µl 5:95 acetonitrile/water and centrifuged at 20,000 x g for 3 min. The top 80 µl supernatant was transferred to a glass autosampler vial with 300 µl insert and capped. Quality control samples were made by pooling 5 µl from each sample. Liquid chromatography-mass spectrometry analysis was performed using a Thermo-Fisher Ultimate 3000 HPLC system consisting of an HPG-3400RS high pressure gradient pump, TCC 3000SD column compartment and WPS 3000 Autosampler, coupled to a SCIEX 6600 TripleTOF Q-TOF mass spectrometer with TurboV ion source. The system was controlled by SCIEX Analyst 1.7.1, DCMS Link and Chromeleon Xpress software. A sample volume of 5 µL was injected by pulled loop onto a 5 µL sample loop with 150 µl post-injection needle wash with 5:95 acetonitrile and water. Injection cycle time was 1 minute per sample. Separations were performed using a Thermo Accucore C18 column with dimensions of 150 mm length, 2.1 mm diameter and 2.6 µm particle size equipped with a guard column of the same phase. Mobile phase A was water with 0.1% formic acid; mobile phase B was acetonitrile with 0.1% formic acid. Separation was performed by gradient chromatography at a flow rate of 0.3 ml/min, starting at 5% B for 1 minute, ramping to 100% B over 7 minutes, hold at 100% B for 2 minutes, then back to 5% B. Re-equilibration time was 4 min. Total run time including 1 minute injection cycle was 15 minutes. The mass spectrometer was run in positive mode under the following source conditions: curtain gas pressure, 50 psi; ionspray voltage, 5500 V; temperature, 400°C; ESI nebulizer gas pressure, 50 psi; heater gas pressure, -70 psi; declustering potential, -80 V. Data were acquired in an information dependent manner across 10 high sensitivity product ion scans, each with an accumulation time of 100 ms and a TOF survey scan with accumulation time of 250 ms. Total cycle time was 1.3 s. Collision energy was determined using the formula CE (V) = 0.084 x m/z + 12 up to a maximum of 55 V. Isotopes within 4 Da were excluded from the scan. Acquired data were checked in PeakView 2.2 and imported into Progenesis Qi 2.4 for metabolomics, where they were aligned, peaks were picked, normalised to all compounds and deconvoluted according to standard Progenesis workflows. Annotations were made by searching the accurate mass, MS/MS spectrum and isotope distribution ratios of acquired data against the NIST MS/MS metabolite library. Metabolites were identified by searching retention times and accurate masses against an in-house chemical standard library. A validated identification is only given if identical hits are made against both the NIST MS/MS and in-house chemical standard libraries. Statistical and Computational Analysis Microbiome data analysis To analyze the diversity, richness and uniformity of the communities in the sample, alpha diversity was calculated from indices, including Shannon, observed richness and Pielou_e. Statistical comparisons between infected and uninfected groups was done using the Krusal-Wallis test. Beta diversity was evaluated using Bray–Curtis dissimilarity to compare community structure between samples. Principal coordinates analysis (PCoA) was used to visualize ordination, and PERMANOVA (Adonis) implemented in the `vegan` R package, was used to assess statistical differences in beta diversity across infection groups. Differential abundance analysis To identify microbial taxa differentially abundant between Schistosoma mansoni –infected and uninfected individuals, we performed differential abundance (DA) testing using the DESeq2 method implemented within the phyloseq R package. A Wald test was applied to estimate log₂ fold changes in microbial abundance between the two groups. Resulting p -values were adjusted for multiple testing using the Benjamini–Hochberg false discovery rate (FDR) procedure. Taxa with an FDR-adjusted p < 0.05 were considered statistically significant. A volcano plot was generated to visualize the results, displaying the log₂ fold change on the x-axis and the –log₁₀ FDR-adjusted p -value on the y-axis. Linear discriminant analysis (LDA) effect size (LEfSe) To complement DESeq2-based DA testing, we additionally applied linear discriminant analysis (LDA) using the LEfSe algorithm to identify microbial features that consistently discriminate between S. mansoni –infected and uninfected individuals. While DESeq2 provides robust statistical inference and effect size estimates for individual taxa across groups, LDA ranks taxa based on their ability to explain group differences by combining statistical significance with biological consistency and effect relevance. This approach helps prioritize taxa most likely to contribute to distinguishing phenotypic states. Taxa with a logarithmic LDA score > 2.0 and p < 0.05 were considered significantly enriched. Analyses were stratified by community type (rural vs. urban) to account for environmental and lifestyle heterogeneity. Visualization of LEfSe results was done via bar plots showing LDA scores. By integrating both DA and LDA approaches, we capture a broader perspective on microbiome differences—identifying statistically robust changes (via DESeq2) while also highlighting microbial signatures with high discriminatory power (via LEfSe) that may serve as candidate biomarkers. Microbiome–CVD risk associations To evaluate the direct associations between microbial taxa and specific CVD risk factors (e.g., blood pressure, total cholesterol, LDL cholesterol), multivariate linear regression models were fitted separately for infected and uninfected groups within rural and urban settings. Models were adjusted for age, sex, BMI, and diet (in the rural population). Significance was set at p < 0.05, and false discovery rate (FDR) correction was applied. The top 50 most abundant taxa were prioritized for analysis. Results were visualized with heatmaps and annotated by significance level (p < 0.05, p < 0.01). Mediation analysis To investigate potential microbial mediation of the relationship between S. mansoni infection and CVD risk, non-parametric bootstrap-based mediation analysis was conducted using the `pingouin.mediation_analysis` package in R. This approach estimated the indirect effect of differentially abundant taxa (from differential abundance analysis (FDR-adjusted p < 0.05)) on CVD risk scores, with 5,000 bootstrap iterations and 97.5% confidence intervals. Microbes demonstrating significant negative or positive mediation effects (p < 0.05) were visualized using alluvial plots to capture the pathway from infection status to cardiovascular outcome through the microbiome. Metabolite–CVD risk associations Similar linear regression models as described above were used to assess the associations between individual metabolites and specific CVD risk factors (blood pressure, total cholesterol, LDL cholesterol). A p-value < 0.01 was used to identify significantly associated metabolites that were taken forward for integrative omics analysis with CVD-associated microbes. Metabolomic Profiling and Pathway Enrichment Analysis Untargeted metabolomics was conducted using liquid chromatography–mass spectrometry (LC-MS) on faecal samples. Differential metabolite abundance between S. m + and S. m - individuals was assessed using volcano plots with thresholds set at p ≤ 0.05 and log 2-fold change (FC) ≥ 1.0 (for upregulation in infected) or FC < 1.0 (for upregulation in uninfected). To evaluate whether the metabolomic profiles could discriminate between groups, we employed Partial Least Squares Discriminant Analysis (PLS-DA) using the caret package in R. The dataset was split into training and test sets using stratified sampling to maintain class balance. Model training was performed using 5-fold cross-validation, repeated three times, to optimize model parameters and assess classification performance. Model accuracy, sensitivity, and specificity were calculated on the test set, and the discriminative capacity was further evaluated by constructing Receiver Operating Characteristic (ROC) curve s and calculating the area under the curve (AUC) with 95% confidence intervals using the pROC package. We assessed group-level differences in metabolomic profiles using PERMANOVA on scaled Euclidean distances. Feature importance was derived to identify the most discriminative metabolites. Pathway enrichment analysis was conducted using Integrated Molecular Pathway Level Analysis (IMPaLA), incorporating Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and other curated databases. Samples were grouped according to specified criteria provided. For statistical analysis, a minimum fold change between sample groups of at least 1.5-fold, ANOVA p values of < 0.05 were used in IMPaLA. Metabolite annotation and functional classification Detected features were matched against reference libraries using mass-to-charge ratio (m/z) and retention time (RT) as primary identifiers. To improve matching precision, m/z values were rounded to five decimal places and RTs to one decimal place, generating a unique combined feature ID for each metabolite. These IDs were used to merge the detected features with annotation outputs from the xMSannotator platform, which provides multi-parameter chemical identification including adduct patterns, isotope distributions, and pathway associations. Annotation confidence was further refined by cross-referencing putative matches with the Human Metabolome Database. When available, we prioritized annotations with the highest confidence scores as assigned by the xMSannotator workflow. Significantly altered metabolites associated with insulin resistance and other cardiometabolic risk factors were annotated into functional classes using LIPID MAPS and CLASSYFIRE The annotated metabolites were grouped by chemical class and displayed via bar plots (for CLASSYFIRE categories) and pathway clusters (via RaMP-DB). The significance of pathway enrichment was determined using Fisher’s exact test with multiple testing correction. Results were visualized via bar plots and redundancy-aware “lollipop” plots generated using RaMP-DB, clustering functionally overlapping pathways shown in supplementary Figs. 7–11. Multimodal integration Where relevant, co-association networks and integrative heatmaps were generated to explore relationships between S. mansoni infection, microbial taxa, metabolites, and CVD phenotypes. Correlation networks were built using the Spearman’s rank correlation in the mixomics package. Results As summarised in Table 1 , this study included 216 participants selected based on the availability of lipid profiles (LDL, HDL, total cholesterol, triglycerides) and blood pressure data. Of these, 135 participants (62.5%) were classified as Schistosoma mansoni positive, defined by positive results on both Kato-Katz microscopy and PCR, while 81 participants (37.5%) were negative on both tests. Table 1 Characteristics of the study participants from rural and urban communities. Characteristics Infected n = 135 Uninfected n = 81 Total n = 216 Females/ Males 43/92 60/21 103/113 Age group (years) • 10–19 37 23 60 • 20–29 32 25 57 • 30–39 32 17 49 • 40 + years 33 17 50 Setting • Rural I. Intensive treatment II. Standard treatment 89 47 42 44 33 11 133 80 53 • Urban 46 37 83 The cohort comprised 113 males and 103 females. Among females, 43 were S. mansoni positive and 60 were negative. Participants were distributed across age groups: 10–19 years, 20–29 years, 30–39 years, and 40 years and above. From the rural survey (LaVIISWA trial), 89 S. mansoni positive participants were included, with 33 of these from the intensive treatment arm. Among the negative individuals, 44 were from the rural setting and 37 from the urban setting. This distribution provides a balanced comparison across infection status, sex, age, and environmental exposure. A diet distribution analysis of the participants in rural communities showed that the majority (63%) were predominantly fish eaters, while 27.7% consumed a mixed diet. The other diet types were vegetarians and meat eaters that accounted for 5.9% and 3.4%, respectively, of the rural participants. The algorithm used for the diet distribution analysis is shown in supplementary Fig. 4. S. mansoni infection is associated with altered gut microbial diversity and composition We first performed 16S rRNA amplicon sequencing on faecal samples from all the individuals used in this study. We found that sample gut microbial diversity (alpha diversity) was significantly higher in S. m + compared to S. m - (p = 0.048 and p = 0.008, for Shannon index and observed richness, respectively; Fig. 2 A). On the other hand, Bray-Curtis-based beta diversity analysis did not show significant separation (PERMANOVA p = 0.175; Fig. 2 B) between S. m + and S. m - individuals living in the rural setting, although a difference in the overall microbial community structure was observed between the S. m + and S. m - individuals living in the urban setting (PERMANOVA p = 0.011; Fig. 2 C). In addition, we compared the beta diversity of participants that were under intensive anthelminthic treatment to those under standard treatment and we observed no difference in clustering (see supplementary Fig. 1B). We next asked if there were specific bacteria genera that could be used to discriminate between S. m + and S. m − individuals. Linear discriminant analysis (LDA) using LEfSe identified bacterial genera that best discriminated between S. m + and S. m − individuals based on relative abundance patterns (Fig. 2 D–E). Genera with LDA scores > 2 were considered to have a meaningful effect size in separating the groups. Among rural participants, taxa such as Acinetobacter, Methanosphaera, Jeotgalibaca , and Ruminococcus were enriched in S. m + individuals, while Streptococcus, Prevotella , and Roseburia were enriched in the S. m − group. In the urban cohort, LDA highlighted Romboutsia, Succinivibrio, Clostridium_sensu_stricto_1, Treponema, Pseudomonas, Butyrivibrio , and Gastranaerophilales as enriched in S. m+ , whereas Prevotella, Streptococcus, Dialister, Facalibacterium , and Agathobacter were enriched in S. m − individuals. Notably, Prevotella and Streptococcus were consistently enriched in the S. m − group across both settings. To further investigate the impact of S. mansoni infection on the gut microbiome, we compared microbial taxonomic abundance profiles between S. m + and S. m -individuals. Differential abundance analysis revealed a set of taxa significantly enriched in S. m + individuals (FDR adjusted p < 0.05), including Altererythrobacter, Arthrobacter Devosia, Domibacillus , and Lysobacter (Fig. 3 and supplementary Fig. 2). Listeria , Enterobacter and Cetobacterium were significantly depleted in the S. m + individuals. Specific microbial taxa mediate the relationship between S. mansoni infection and cardiovascular risk Regression analyses revealed distinct microbe–CVD risk factor associations present in rural (Fig. 4 A) and urban (Fig. 4 B) sample populations after adjusting for confounding factors including age, sex, BMI, and diet (in the rural population). In both figures, the microbes shown are the fifty most abundant. Notably, taxa such as Treponema were consistently associated with LDL cholesterol in both rural and urban participants. To further illustrate the degree of overlap and uniqueness of these microbial associations across cardiovascular outcomes, we generated a Venn diagram (Fig. 4 C), which highlights shared and distinct taxa linked to multiple CVD risk factors. To investigate the functional relevance of the microbial differences reported here, we conducted a mediation analysis to identify taxa that may mediate the impact of S. mansoni infection on cardiovascular disease risk factors. Several taxa significantly mediated the relationship between S. mansoni infection and reduced cardiovascular risk, with all the microbiota shown in Fig. 4 D showing negative effects (p < 0.05) on CVD risk. Treponema was differentially more present in S. m + and linked to insulin sensitivity and diastolic blood pressure. Family_XIII_AD3011 _group, dgA.11_gut_group , and Christensenellaceae_R.7_group were more abundant in the infected group and linked with glucose, diastolic, and systolic blood pressure respectively. Methanobrevibacter and Phoenicibacter were linked with glucose intolerance. For the taxa that were more abundant in the uninfected group, Roseburia, Lachnospiraceae_UCG.004, Granulicatella were linked with insulin sensitivity, systolic blood pressure and LDL-Cholesterol respectively. S. mansoni infection is associated with metabolome differences To dissect whether the effect of S. mansoni infection on the gut microbiome can translate into differences in microbial-related metabolism, we compared faecal metabolomic profiles between S. m + and S. m - individuals. A volcano plot shows the differentially abundant (p < 0.05) metabolites in both groups, highlighting metabolic alterations associated with S. mansoni infection (Fig. 5 A). The 10 most upregulated metabolites in the infected group include HMDB36635, HMDB39448, HMDB10385, HMDB14867, HMDB08887, HMDB30053, HMDB31040, HMDB60963, HMDB11158 and metabolite with mass to charge ratio 6.26_1326581m/z. The 10 most upregulated metabolites in the uninfected group include HMDB46827, HMDB29485, HMDB14388, HMDB36122, HMDB31828, HMDB14377, HMDB10261, HMDB14585, HMDB11367, HMDB11895. Further, partial least squares discriminant analysis (PLS-DA) suggested some degree of separation between the faecal metabolomic profiles of S. m + and S. m - individuals (Fig. 5 B); however, this clustering did not reach statistical significance based on PERMANOVA ( p = 0.48). To evaluate the discriminative capacity of metabolomic features, we trained a PLS-DA–based classification model (Supplementary Fig. 5). The model exhibited limited predictive performance, with an overall accuracy of 53.8%, specificity of 54.8%, and sensitivity of 53.2%, indicating poor ability to discriminate between S. m + and S. m - individuals. Further, in supplementary Fig. 1B we compared metabolomes of participants in the rural setting that were in the intensive anthelminthic treatment arm to those in the standard anthelminthic treatment and we found no difference. Enrichment of lipid-related pathways among metabolites elevated in S. mansoni- infected individuals Comparative analysis revealed a distinct metabolic signature in infected individuals, with a subset of metabolites significantly more abundant compared to uninfected controls. Pathway enrichment analysis of these elevated metabolites was performed using the Integrated Molecular Pathway Level Analysis (IMPaLA) platform and results shown in Table 2 , and Fig. 5 C. Table 2 Biological pathways enriched by metabolites that were more abundant in S. mansoni infected individuals than uninfected participants. Metabolites were extracted from faecal samples of 209 participants and profiled using liquid chromatography-mass spectrometry. Metabolite identification and quantification was done using Progenesis QI software and pathways were analysed using Integrated Molecular Pathway Level Analysis (IMPaLA). Biological pathway enriched by metabolites Pathway source Number of metabolites P- value NR1H2 & NR1H3 regulate gene expression to limit cholesterol uptake Reactome 3 0.0003 NR1H2 & NR1H3 regulate gene expression to control bile acid homeostasis Reactome 3 0.0003 NR1H2 & NR1H3 regulate gene expression linked to gluconeogenesis Reactome 3 0.0003 NR1H2 & NR1H3 regulate gene expression linked to lipogenesis Reactome 3 0.0003 NR1H2 & NR1H3 regulate gene expression linked to triglyceride lipolysis in adipose Reactome 3 0.0003 NR1H3 & NR1H2 regulate gene expression linked to cholesterol transport and efflux Reactome 3 0.0003 Cholesterol biosynthesis with skeletal dysplasias Wikipathways 4 0.0011 This analysis identified an overrepresentation of pathways regulated by the nuclear receptors NR1H2 (LXRβ) and NR1H3 (LXRα), which are central to lipid homeostasis and metabolic regulation. Specifically, six Reactome pathways driven by NR1H2/NR1H3 activity were significantly enriched (all p = 0.0003), including those regulating cholesterol uptake, bile acid homeostasis, gluconeogenesis, lipogenesis, triglyceride lipolysis in adipose tissue, and cholesterol transport and efflux. These findings suggest coordinated transcriptional regulation of lipid metabolic processes in S. mansoni -infected individuals. In addition, a Wikipathways entry linked to cholesterol biosynthesis in the context of skeletal dysplasias was significantly enriched (p = 0.0011). Collectively, these data indicate that S. mansoni infection is associated with a specific faecal metabolic profile marked by enhanced abundance of metabolites involved in lipid signalling and transport. Integrated microbiome–metabolome interactions link to total and LDL cholesterol levels To examine how microbial and metabolic alterations interact to influence lipid metabolism, we conducted integrative correlation analyses, combining microbes and metabolites that were significantly associated with total and LDL cholesterol. Among those microbes and metabolites significantly associated with total cholesterol, a circos plot highlighted robust correlations (r ≥ 0.7) between specific genera and metabolites (Fig. 6 A), which were visualized in detail in a corresponding heatmap (Fig. 6 B). Metabolite classification using ClassyFire revealed enrichment of glycerolipids, steroids and steroid derivatives, and glycerolphospholipids among total cholesterol-associated compounds that are linked to the microbes associated with total cholesterol (Fig. 6 C). A similar analysis was done for microbes and metabolites significantly associated with LDL cholesterol and uncovered a distinct but overlapping set of microbe–metabolite correlations (r ≥ 0.65; Fig. 6 D–E), again featuring key taxa and metabolite classes previously implicated in lipid homeostasis (Fig. 6 F). Metabolite annotation revealed that several cholesterol-associated compounds belonged to key chemical classes, including Glycerol lipids, fatty acyls carboxylic acids and derivatives, steroids and steroid derivatives, and glycerolphospholipids (Fig. 6 F). There is an overlap in the classes of metabolites linked to total and LDL cholesterol. Microbiome–metabolome interactions also relate to blood pressure regulation We extended our integrative approach to blood pressure phenotypes. Diastolic blood pressure was associated with a network of microbiota–metabolite interactions (r ≥ 0.7; Fig. 7 A), and a heatmap visualization confirming the several strong correlations, both positive and negative as shown in Fig. 7 B. Metabolite classification highlighted compounds linked to classes such as glycerolipids, prenol lipids, organooxygen, fatty acyls and steroid and steroid derivatives (Fig. 7 C). Microbiome-metabolome associations were found for systolic blood pressure (Fig. 7 D-F) and similar classes including prenol lipids, gycerolipids, fatty acyls characterised most of the metabolites involved, emphasizing the role of gut microbial metabolites as potential regulators of blood pressure. Similar analysis was done for insulin associated microbes and metabolites and fewer microbe-metabolite associations were seen, as shown in supplementary Fig. 6. S. mansoni -induced microbial changes alter host CVD risk through metabolites To explore potential mechanistic links between schistosomiasis-associated gut microbiota and risk for CVD, we constructed a directed network integrating differentially abundant microbial taxa, correlated faecal metabolites, and associated CVD risk factors. Among the taxa enriched in schistosomiasis-positive individuals (log₂ fold change > 1, FDR-adjusted p < 0.05), we identified several genera—including Lysobacter , Arthrobacter , and Vicinamibacteraceae —that were strongly inversely correlated with specific metabolites, such as HMDB31050 and HMDB32627 (|ρ| ≥ 0.65, FDR-adjusted p < 0.05), shown in Table 3 . These metabolites, in turn, were significantly associated with CVD risk factors, most notably diastolic blood pressure and LDL cholesterol. Visualization of the network (Fig. 8 ) revealed a coherent directional path from microbial taxa to metabolite changes and CVD risk, suggesting a putative microbiome–metabolite–CVD axis modulated by S. mansoni. These findings support the hypothesis that helminth infection may influence CVD risk through metabolic changes mediated by the gut microbiome. Our findings also showed that S. mansoni infection was associated with an enrichment of specific bacterial taxa, including Domibacillus and Gaiella . Notably, these taxa exhibited correlations with metabolites and cardiometabolic risk, detailed in Table 3 and Fig. 8 . Table 3 Differentially Abundant Gut Microbiota in S. mansoni-infected individuals and their correlation with cardiovascular risk-associated metabolites. This table shows microbiota taxa found to be significantly more abundant in individuals infected with S. mansoni, as determined by differential abundance analysis (Log2FoldChange and adjusted p-value). Each taxon is annotated with its associated metabolite and cardiovascular disease (CVD) risk factor as shown in the network Fig. 8 . Microbiota–metabolite correlations are shown as Pearson correlation coefficients. Negative correlation values suggest a potential inverse relationship between microbial abundance and metabolite levels. CVD risk factors include diastolic blood pressure and LDL cholesterol. Log2FoldChange from Differential Abundance analysis Adjusted p. value for microbiota S. mansoni Group where taxa is more abundant Taxa associated with CVD risk Metabolite associated with CVD Microbiota-metabolome correlation value Associated CVD risk 2.1867 2.5768e-12 Infected Altererythrobacter HMDB31050 -0.830 DIASTOLIC BP 2.1129 7.2807e-11 Infected Arthrobacter HMDB31050 -0.850 DIASTOLIC BP 1.3364 9.5039e-06 Infected Devosia HMDB31050 -0.819 DIASTOLIC BP 1.4811 6.2449e-07 Infected Domibacillus HMDB32627 -0.800 DIASTOLIC BP 2.1777 9.7252e-11 Infected Ellin6055 HMDB31050 -0.821 DIASTOLIC BP 1.0694 0.0006 Infected Geodermatophilus HMDB31050 -0.813 DIASTOLIC BP 1.0117 0.0001 Infected Kapabacteriales HMDB32627 -0.767 DIASTOLIC BP 1.5948 1.8261e-07 Infected Kribbella HMDB31050 -0.797 DIASTOLIC BP 1.0321 0.0006 Infected Longimicrobiaceae HMDB31050 -0.798 DIASTOLIC BP 2.6592 2.4792e-16 Infected Lysobacter HMDB31050 -0.853 DIASTOLIC BP 1.0511 0.0034 Infected Nitrospira HMDB31050 -0.810 DIASTOLIC BP 1.8145 4.4458e-07 Infected Pseudarthrobacter HMDB31050 -0.826 DIASTOLIC BP 3.8699 8.8352e-22 Infected Vicinamibacteraceae HMDB32627 -0.777 DIASTOLIC BP 2.2985 2.79650e-11 Infected Gaiella 0.95_764.7010n -0.671 LDL CHOLESTEROL 2.2985 2.7965e-11 Infected Gaiella HMDB56087 -0.655 LDL CHOLESTEROL 1.6089 3.9925e-08 Infected Arenimonas 0.95_764.7010n -0.653 LDL CHOLESTEROL Discussion Our study shows that S. mansoni infection is associated with distinct changes in gut microbial diversity, metabolomic profiles, and microbe–metabolic interactions. We can show that these alterations appear to influence cardiovascular risk through multiple, interlinked pathways, implicating the gut ecosystem as a mediator of S. mansoni -driven cardiometabolic risk modulation in humans. The observed differences in alpha diversity between S. m + and S. m - individuals suggest that parasitic infection significantly alters one’s gut microbial profile. Several studies have reported reduced alpha diversity, typically associated with a less resilient and less functionally diverse microbiome, to be linked to CVD risk. For example, Kelly and colleagues showed an association between increased observed richness and reduced lifetime CVD risk [ 44 ]. Similarly, Fu et al , reported a positive association between bacterial richness and HDL cholesterol [ 45 ] in individuals living in the Netherlands. With such evidence showing that more bacterial diversity and richness is associated reduced CVD risk and improved lipid profiles, therefore we postulated that one way through which S. mansoni infection may improve lipid profiles in the host is by increasing bacterial richness and diversity. In addition to alpha diversity differences, we also observed beta diversity differences in the microbiome profiles between S. mansoni -infected and uninfected individuals, further indicating that infection not only increases microbial richness, but it can also shift the overall composition of the microbial community, leading to distinct clustering of infected and uninfected individuals as seen in participants living in urban settings. We did not observe similar differences in clustering of overall microbial structure (beta diversity) between S. mansoni infected and uninfected living in the rural setting. This could be because, as shown in previous studies, the rural dwellers tend to have higher gut microbiome diversity and stability due to continuous exposure to a wide range of environmental microbes, diverse diets rich in unprocessed fibre-rich foods, and frequent exposure to infections that may buffer the microbiome against significant changes that may be caused by S. mansoni infection [ 46 – 49 ]. Specifically, given the high exposure to S. mansoni in our rural population, a typical island community, it is possible that the individuals that were uninfected at the time of sample collection, might have had longstanding effects of S. mansoni infection from previous exposure that may modify gut microbiome differences observed in our beta diversity analysis. Next, we applied linear discriminant analysis (LDA) to identify microbial taxa that best discriminate between individuals with and without S. mansoni infection, across both rural and urban settings. Unlike statistical tests of differential abundance, which identify taxa that vary significantly in abundance between groups, LDA ranks features based on their ability to separate predefined classes. Notably, microbes such as Prevotella and Streptococcus were found to be consistently more abundant in S. mansoni infected individuals and are known to play pivotal roles in modulating immune responses and inflammation thereby bringing about protection against CVD risk. These findings align with the hypothesis that parasitic infections such as S. mansoni may exert long-term effects on host health by reshaping the microbiome. Importantly, the altered microbial profiles we observed may not only reflect the host’s immune response to infection but could also be directly involved in mediating disease risk through metabolic and inflammatory pathways. We therefore needed to investigate the mediatory role that helminth-induced gut microbiota changes could play in altering one’s CVD risk. As such, we performed mediation analysis to show that indeed S. mansoni infection may influence cardiovascular risk indirectly through its effects on the gut microbiome. Specifically, we show here that changes in microbial composition appear to positively and negatively mediate key cardiovascular risk factors such as LDL cholesterol and blood pressure. Notably, negative mediation effects reported here suggest that the increased abundance of these taxa in infected individuals may partially explain the protective cardiovascular phenotype observed. Certain taxa enriched in infected individuals such as Treponema, Family_XIII_AD3011_group , dgA.11_gut_group , and Christensenellaceae_R.7_group were associated with improvements in insulin sensitivity and reductions in blood pressure and glucose levels, suggesting that helminth-associated microbial shifts may contribute to a more metabolically favorable profile. However, not all taxa enriched in the infected group aligned with this protective pattern. For instance, Methanobrevibacter and Phoenicibacter were associated with glucose intolerance despite being more abundant in infected individuals, highlighting the diverse and sometimes opposing metabolic effects of different microbial members within the same ecological context. Conversely, several taxa more abundant in uninfected individuals, including Roseburia , Lachnospiraceae_UCG.004 , and Granulicatella were linked to both beneficial and adverse cardiovascular traits, such as enhanced insulin sensitivity, elevated systolic blood pressure, and higher LDL cholesterol levels, respectively. These findings underscore the complexity of microbiota–host interactions, indicating that the health impact of a given microbe is not solely determined by its presence or absence, but by its context within the broader microbial community and host environment. This functional diversity reinforces the idea that S. mansoni -associated shifts in microbiome composition may tip the balance of microbial activity toward either protective or deleterious effects, depending on the taxa involved and the pathways engaged. Additionally, positive mediation of microbiota on CVD risk factors such as LDL cholesterol in uninfected individuals shown in supplementary figure could imply that S. mansoni infected individuals have less LDL cholesterol because they lack microbial populations that have been shown to lead to increases in these CVD risk factors. For example, Enterobacter and Lachnospiraceae_UCG.010 mediate increased total cholesterol in individuals without S. mansoni infection, with Enterobacter also elevating LDL cholesterol levels. Enterobacter is linked with systemic inflammation through lipopolysaccharide (LPS)-mediated activation of host immune pathways [ 50 – 52 ], hence altering lipid metabolism. Similarly, Lachnospiraceae , given their capacity to produce short-chain fatty acids such as propionate from microbial fermentation, may promote lipid and cholesterol synthesis in the absence of helminth-induced immunoregulation[ 53 , 54 ]. These findings point to divergent microbial contributions to metabolic and cardiovascular phenotypes depending on infection status. The dual nature of microbial associations emphasizes the need to consider ecological context and host-microbe interactions in interpreting microbiome-mediated health outcomes. Overall, these results suggest that S. mansoni -associated shifts in the gut microbiome may actively contribute to the modulation of cardiovascular risk factors and offer candidate microbial targets for further mechanistic and translational investigation. To complement this analysis, we further employed linear regression to investigate the relationship between microbes and CVD risk factors. By adjusting for key confounders such as age, sex, BMI, and diet, we aimed to isolate the unique contribution of the microbes to CVD risk. The findings revealed significant associations between specific microbial taxa and distinct CVD risk factors, suggesting that these microbes may play a mechanistic role in CVD risk. These results agree with the mediation analysis findings, where the role of S. mansoni infection in modulating CVD risk factors was partially explained by its influence on the microbiome. This suggests a pathway wherein microbes, through their microbial composition, may mediate the relationship between S. mansoni and CVD outcomes. The robustness of these associations, even after adjusting for confounders, underscores the potential of these microbial markers as indicators or contributors to CVD disease pathways. These findings support the notion that S. mansoni infection and the gut microbiota can influence cardiovascular disease (CVD) risk both independently and concertedly. The identification of taxa that significantly mediate the relationship between S. mansoni infection and reduced CVD risk suggests that part of the protective effect of infection may be exerted through infection-induced remodelling of the microbiota–metabolome axis. However, not all associations between S. mansoni and cardiovascular risk were microbiota-mediated, indicating the presence of parallel, microbiota-independent pathways such as immune modulation through which helminth infection may confer CVD risk protection. We observed more significant associations of microbes with CVD risk among S. mansoni infected individuals in the urban compared to those in rural populations. The observed stronger associations in the urban population can be attributed to S. mansoni infection having a greater influence on microbial diversity in this population compared to the rural. Having shown that even from the top 50 abundant microbes, we have evidence of some microbes being associated with CVD risk, we then extracted all the significantly associated microbes from the entire dataset (beyond 50 most abundant microbes) and investigated if there was any relationship (overlap) between microbes that are significantly associated with the different CVD risk factors. Indeed, we can show from that there are several microbes that associated with more than one cardiovascular risk. Triangulating evidence from association and mediation analysis supports the hypothesis that the gut microbiome could play a central role in the regulation of metabolic pathways that are critical to cardiovascular health. For instance, certain microbial taxa produce metabolites such as SCFAs and secondary bile acids that can influence lipid metabolism [ 33 ]. Dysregulation of these pathways may therefore contribute to an increased risk of cardiovascular events. These insights highlight the importance of considering infectious diseases like S. mansoni not only in terms of acute morbidity but also in their potential to influence long-term health outcomes through microbiome-mediated mechanisms. We therefore set out to investigate the metabolome profiles of our participants as a way of assessing if, similar to the microbiome changes observed here, there could be differences in the faecal metabolites between individuals that were infected with S. mansoni infection and those that are not infected. This would enable us to infer functionality of the gut microbiota and how they act to alter one’s cardiovascular risk. Despite no statistically significant global separation of metabolomic profiles between S. mansoni –infected and uninfected individuals as assessed by PLS-DA, univariate linear regression analysis identified several metabolites that were differentially abundant between the two groups, with multiple features reaching nominal significance thresholds. This suggests that S. mansoni infection is associated with specific metabolic alterations rather than broad-scale shifts in the overall metabolome. The lack of clear clustering in multivariate space may reflect substantial inter-individual heterogeneity, or localized metabolic effects of infection, or the multifactorial nature of host metabolic responses. Although we identified differentially abundant metabolites between S. mansoni –infected and uninfected individuals, our PLS-DA classification model demonstrated limited predictive performance, likely reflecting the biology of S. mansoni transmission. Unlike enteric pathogens whose acquisition may be modulated by gut microbial or metabolic environments, S. mansoni is acquired via percutaneous exposure to cercariae-contaminated freshwater. As such, microbiome and metabolome features are unlikely to serve as determinants of infection status. Instead, the observed metabolic shifts are more plausibly consequences of infection, supporting our interpretation that S. mansoni may exert causal effects on host physiology. Our findings from the pathway enrichment analysis revealed that S. mansoni infection is associated with distinct alterations in host lipid metabolism, as evidenced by significant enrichment of NR1H2/NR1H3-regulated pathways among the differentially abundant metabolites. These nuclear receptors (LXRα and LXRβ) are key transcriptional regulators of lipid homeostasis, and their coordinated activation suggests a host response aimed at modulating cholesterol uptake, bile acid turnover, and lipid mobilization during infection. The parallel enrichment of pathways linked to gluconeogenesis and lipogenesis further supports a broader metabolic reprogramming, potentially reflecting shifts in host energy utilization and storage under chronic parasitic infection. These findings align with emerging evidence that helminth infections exert broad systemic effects on host physiology, including lipid and glucose metabolism, and may influence susceptibility to non-communicable diseases such as diabetes and cardiovascular disease [ 27 ]. Future work should investigate whether modulation of LXR signalling contributes to immune tolerance, pathogen persistence, or protection from metabolic disease in endemic populations. Beyond pathways, we performed linear regression analysis to identify the metabolites that were significantly associated with the different CVD risk and whether were there was overlap between these metabolites. We indeed show here that there are metabolites associated with CVD risk and that there is overlap between metabolites that are associated with various CVD risk factors. With a possibility that the microbes found to be associated with the particular risk factors could correlate with metabolites that are similarly associated with the same CVD risk, we integrated these two data modalities- microbiome and metabolome. We identified specific microbiome-metabolome signatures that are associated with cardiovascular risk factors including total- and LDL-cholesterol, DBP and SBP. This integrative approach allows us to capture the complex interactions between the gut microbiome and metabolome, revealing, for each CVD risk factor, how shifts in microbial composition may drive changes in metabolite levels. After identifying the significantly CVD associated microbes and the metabolites that interact together, we further complemented this data with the differential abundance analysis to highlight whether some these CVD-associated microbes interacting with CVD-associated metabolites were enriched in S. mansoni infected individuals. By doing so, this allowed us to generate a Schistosomiasis-microbe-metabolite-CVD risk mechanistic network. Our integrative network analysis reveals that several bacterial taxa enriched in S. mansoni individuals were strongly correlated with faecal metabolites. These metabolites were, in turn, associated with key CVD risk factors, including diastolic blood pressure and LDL cholesterol. This directional pattern supports the hypothesis that helminth-induced alterations in the gut microbiota may influence host cardiovascular risk through downstream effects on the metabolome. The inverse associations between these microbes and adverse CVD phenotypes align with emerging evidence suggesting that certain helminth-driven microbial shifts may exert systemic immunometabolic benefits. Notably, taxa such as Lysobacter and Devosia have been implicated in anti-inflammatory metabolic pathways [ 55 , 56 ], providing a plausible biological basis for these associations. While causality cannot be inferred from this cross-sectional design, these findings raise the possibility that S. mansoni , or its microbiome-mediated effects, may modulate cardiometabolic outcomes in endemic settings. Further longitudinal and interventional studies are warranted to validate these interactions and assess their relevance for biomarker development or CVD therapeutic modulation. In as much as this study provides important insights into the gut microbiome-metabolome-CVD risk interaction in a typical setting with high S. mansoni infestation, there are potential limitations. Firstly, we did not comprehensively profile participants’ dietary habits, which are known to have profound effects on both the microbiome and metabolome and are likely to represent an unmeasured confounder. Given the variability in diet across different regions and individuals, future studies should incorporate detailed dietary assessments such as next generation sequencing methodologies to disentangle the effects of infection from those of diet. Furthermore, given the cross-sectional design of our study, we cannot establish causality in the observed associations between S. mansoni infection, microbiome composition, metabolite profiles, and cardiovascular risk. Temporal dynamics of microbial and metabolic alterations following infection remain unexplored, limiting our ability to infer directionality. As such, longitudinal studies, ideally spanning pre-infection, active infection, and post-treatment phases, would be essential to disentangle cause-effect relationships and capture the evolving host–microbiome–metabolome interactions over time. Incorporating repeated sampling, coupled with temporal metadata such as infection history and treatment timing, would provide a more robust framework for understanding how S. mansoni shapes cardiometabolic risk through microbial and metabolic pathways. Conclusion Our findings provide evidence that S. mansoni infection is associated with significant alterations in the gut microbiome and metabolome profiles, with important implications for cardiovascular disease risk. These microbiome-mediated effects may represent a novel pathway through which parasitic infections influence CVD outcomes. Future studies should focus on refining our understanding of these interactions, with an emphasis on diet, antibiotic use, and circadian regulation of microbial activity. Our study paves the way for targeted therapeutic interventions aimed at modifying the gut microbiome in a way that mimics helminth infections to reduce cardiovascular risk in humans. Declarations Ethical consideration For this current project nested in the two studies, ethical approval was obtained from UVRI Research Ethics Committee (UVRI-REC), London School of Hygiene and Tropical Medicine (LSHTM) and Uganda National Council for Science and Technology (UNCST) (required for amendments to the parent studies) and the Higher Degrees Research and Ethics Committee of the School of Medicine, College of Health Sciences, Makerere University. Both studies LaVIISWA and the Urban Survey based at UVRI, Entebbe were approved by UVRI-REC, the (LSHTM) and the UNCST. Author contributions B.W. Conceived the study, designed the experiments, conducted the parasitological, microbiome and metabolomics conducted the primary statistics and bioinformatics analyses, interpreted the results, prepared the figures, and wrote the first draft of the manuscript. M.A.E.L. Contributed to the microbiome and metabolomics experiments and analyses and contributed to critical manuscript revision. A.J.B. supported the microbiome and metabolomics experiments, and manuscript drafting and revision. J.N. Contributed to the parasitological, microbiome and metabolomics experiments, and contributed to manuscript revision. D.K.T. Contributed to metabolomics data annotation, and offered guidance on how metabolomics analysis was done. G.T. Provided specialist support in metabolomics experiments and pathway analysis contributed to critical manuscript review. R.E.S. Oversaw clinical coordination of parent projects, supported data acquisition, and provided critical input on the interpretation of the cardiovascular risk outcomes. E.L.W. Supervised statistical analysis for the study, advised on analytical strategy, and contributed to manuscript review and editing. D.P.K. Provided supervision of project, contributed to and reviewed the manuscript. R.K.G. provided senior supervision of the project, funding acquisition for this study, contributed to study conceptualization and provided critical manuscript revisions. A.M.E. Provided senior supervision throughout the study, supported study design of the parent projects, funding acquisition of both the parent and current studies, advised on epidemiological methods, and contributed to critical revision of the manuscript. All authors reviewed and approved the final manuscript. Acknowledgments We are grateful to Dr Gyaviira Nkurunungi, Ms Joy Kabagenyi and Mr Alfred Ssekagiri for their expert comments on the manuscript. Funding BW is partially supported by GCRF collaborative Grant (R120442) from the Royal Society awarded to Professors Richard Grencis and Alison Elliott is also partially funded by the National Institute for Health Research (NIHR) under its Global Health Research Group on Vaccines for Vulnerable People in Africa (VAnguard) (Grant Reference Number: NIHR134531), using UK aid from the UK Government to support global health research. This project has also been supported by Wellcome Trust Investigator Award Z10661/Z/18/Z and the Wellcome Centre for Cell Matrix Research Grant 088785/Z/09/Z awarded to Professor Richard Grencis, and Wellcome Trust (grant number 095778) awarded to Professor Alison Elliott. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Government. 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Diabetes Res Clin Pract 120:209–220 Rennie C, Fernandez R, Donnelly S, McGrath KC (2021) The impact of helminth infection on the incidence of metabolic syndrome: A systematic review and meta-analysis. Front Endocrinol 12:728396 Sanya RE, Webb EL, Zziwa C, Kizindo R, Sewankambo M, Tumusiime J et al (2020) The effect of helminth infections and their treatment on metabolic outcomes: results of a cluster-randomized trial. Clin Infect Dis 71(3):601–613 Rajamanickam A, Babu S (2024) Helminth Infections and Diabetes: Mechanisms Accounting for Risk Amelioration. Annu Rev Nutr. ;44 Colley DG, Bustinduy AL, Secor WE, King CH (2014) Human schistosomiasis. Lancet 383(9936):2253–2264 Hussaarts L, García-Tardón N, van Beek L, Heemskerk MM, Haeberlein S, van der Zon GC et al (2015) Chronic helminth infection and helminth‐derived egg antigens promote adipose tissue M2 macrophages and improve insulin sensitivity in obese mice. FASEB J. ;(7):3027–3039 Luo X, Zhu Y, Liu R, Song J, Zhang F, Zhang W et al (2017) Praziquantel treatment after Schistosoma japonicum infection maintains hepatic insulin sensitivity and improves glucose metabolism in mice. Parasites vectors 10:1–12 Thabet HS, Saleh NK, Thabet SS, Abdel-Aziz M, Kalleny NK (2008) Decreased basal non-insulin-stimulated glucose uptake by diaphragm in streptozotocin-induced diabetic mice infected with Schistosoma mansoni. Parasitol Res 103:595–601 Jonsson AL, Bäckhed F (2017) Role of gut microbiota in atherosclerosis. Nat Reviews Cardiol 14(2):79–87 Li W-Z, Stirling K, Yang J-J, Zhang L (2020) Gut microbiota and diabetes: From correlation to causality and mechanism. World J diabetes 11(7):293 Yuan X, Wang R, Han B, Sun C, Chen R, Wei H et al (2022) Functional and metabolic alterations of gut microbiota in children with new-onset type 1 diabetes. Nat Commun 13(1):6356 Levy M, Kolodziejczyk AA, Thaiss CA, Elinav E (2017) Dysbiosis and the immune system. Nat Rev Immunol 17(4):219–232 Floudas A, Aviello G, Schwartz C, Jeffery IB, O'Toole PW, Fallon PG (2019) Schistosoma mansoni worm infection regulates the intestinal microbiota and susceptibility to colitis. Infect Immun 87(8). 10.1128/iai 00275 – 19 Sanya RE, Nkurunungi G, Hoek Spaans R, Nampijja M, O’Hara G, Kizindo R et al (2019) The impact of intensive versus standard anthelminthic treatment on allergy-related outcomes, helminth infection intensity, and Helminth-related morbidity in lake Victoria fishing communities, Uganda: results from the LaVIISWA cluster-randomized trial. Clin Infect Dis 68(10):1665–1674 Sanya RE, Biraro IA, Nampijja M, Zziwa C, Nanyunja C, Nsubuga D et al (2020) Contrasting impact of rural, versus urban, living on glucose metabolism and blood pressure in Uganda. 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Circul Res 119(8):956–964 Fu J, Bonder MJ, Cenit MC, Tigchelaar EF, Maatman A, Dekens JA et al (2015) The gut microbiome contributes to a substantial proportion of the variation in blood lipids. Circul Res 117(9):817–824 Das B, Ghosh TS, Kedia S, Rampal R, Saxena S, Bag S et al (2018) Analysis of the gut microbiome of rural and urban healthy Indians living in sea level and high altitude areas. Sci Rep 8(1):10104 Tamburini FB, Maghini D, Oduaran OH, Brewster R, Hulley MR, Sahibdeen V et al (2022) Short-and long-read metagenomics of urban and rural South African gut microbiomes reveal a transitional composition and undescribed taxa. Nat Commun 13(1):926 Hansen ME, Rubel MA, Bailey AG, Ranciaro A, Thompson SR, Campbell MC et al (2019) Population structure of human gut bacteria in a diverse cohort from rural Tanzania and Botswana. Genome Biol 20:1–21 Ayeni FA, Biagi E, Rampelli S, Fiori J, Soverini M, Audu HJ et al (2018) Infant and adult gut microbiome and metabolome in rural Bassa and urban settlers from Nigeria. Cell Rep 23(10):3056–3067 Baldelli V, Scaldaferri F, Putignani L, Del Chierico F (2021) The role of Enterobacteriaceae in gut microbiota dysbiosis in inflammatory bowel diseases. Microorganisms 9(4):697 Mass M, Kubera M, Leunis J-C (2008) The gut-brain barrier in major depression: intestinal mucosal dysfunction with an increased translocation of LPS from gram negative enterobacteria (leaky gut) plays a role in the inflammatory pathophysiology of depression. Neuroendocrinol Lett 29(1):117–124 Dinh DM, Volpe GE, Duffalo C, Bhalchandra S, Tai AK, Kane AV et al (2015) Intestinal microbiota, microbial translocation, and systemic inflammation in chronic HIV infection. J Infect Dis 211(1):19–27 Vacca M, Celano G, Calabrese FM, Portincasa P, Gobbetti M, De Angelis M (2020) The controversial role of human gut lachnospiraceae. Microorganisms 8(4):573 Gao X, Lin S-H, Ren F, Li J-T, Chen J-J, Yao C-B et al (2016) Acetate functions as an epigenetic metabolite to promote lipid synthesis under hypoxia. Nat Commun 7(1):1–14 Zhao L, Li X, Ji C, Rong X, Liu S, Zhang J et al (2016) Protective effect of Devosia sp. ANSB714 on growth performance, serum chemistry, immunity function and residues in kidneys of mice exposed to deoxynivalenol. Food Chem Toxicol 92:143–149 de Bruijn I, Cheng X, de Jager V, Expósito RG, Watrous J, Patel N et al (2015) Comparative genomics and metabolic profiling of the genus Lysobacter. BMC Genomics 16:1–16 Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7053382","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481072396,"identity":"a8d3fc39-fa66-4f93-88f0-9ba69cae7511","order_by":0,"name":"Bridgious Walusimbi","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-2878-4623","institution":"MRC/UVRI and LSHTM Uganda Research Unit","correspondingAuthor":true,"prefix":"","firstName":"Bridgious","middleName":"","lastName":"Walusimbi","suffix":""},{"id":481072478,"identity":"214bbae3-9de9-48bb-a8f3-601b375d477a","order_by":1,"name":"Melissa AE Lawson","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Melissa","middleName":"AE","lastName":"Lawson","suffix":""},{"id":481072517,"identity":"e2b579bb-c37c-408b-b046-5bed532210d5","order_by":2,"name":"Allison J. Bancroft","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Allison","middleName":"J.","lastName":"Bancroft","suffix":""},{"id":481075104,"identity":"30126ff1-bdb5-4d99-8740-86878f879b76","order_by":3,"name":"Jacent Nassuuna","email":"","orcid":"","institution":"MRC/UVRI and LSHTM Uganda Research Unit","correspondingAuthor":false,"prefix":"","firstName":"Jacent","middleName":"","lastName":"Nassuuna","suffix":""},{"id":481075105,"identity":"070b52e5-2a27-44d3-a769-9a01f3e36502","order_by":4,"name":"Drupard K Trivedi","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Drupard","middleName":"K","lastName":"Trivedi","suffix":""},{"id":481075106,"identity":"4bc7f189-2a54-4e42-9f58-9a3b0b8d19cb","order_by":5,"name":"George Taylor","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"George","middleName":"","lastName":"Taylor","suffix":""},{"id":481075107,"identity":"535a9925-4667-4b8e-a493-8c83e8369b6f","order_by":6,"name":"Richard E. 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Elliott","email":"","orcid":"","institution":"MRC/UVRI and LSHTM Uganda Research Unit","correspondingAuthor":false,"prefix":"","firstName":"Alison","middleName":"M.","lastName":"Elliott","suffix":""}],"badges":[],"createdAt":"2025-07-05 13:35:03","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7053382/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7053382/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86213831,"identity":"2164c704-8b72-4425-a906-eda431fbef92","added_by":"auto","created_at":"2025-07-08 05:31:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":238549,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental and analytical flowchart. Created from BioRender\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7053382/v1/904495503e68e435fefb8d7a.png"},{"id":86213801,"identity":"51456967-cc13-4503-a4b1-465c2d832ea4","added_by":"auto","created_at":"2025-07-08 05:31:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":681849,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobial diversity comparison of \u003cem\u003eS. mansoni\u003c/em\u003e infected (\u003cem\u003eS. m\u003c/em\u003e+) and uninfected individuals (\u003cem\u003eS. m-\u003c/em\u003e). \u003cstrong\u003eA\u003c/strong\u003e Box plots showing that alpha diversity measurements (Shannon index and observed richness) were both significantly higher in \u003cem\u003eS. m\u003c/em\u003e+ than \u003cem\u003eS. m- \u003c/em\u003e(p= 0.048) and (p=0.008) respectively. Kruskal-Wallis test was the statistical test of comparison. \u003cstrong\u003eB \u003c/strong\u003ecomparison of Beta diversity (using Bray-Curtis distance) in \u003cem\u003eS. m\u003c/em\u003e+ and \u003cem\u003eS. m-\u003c/em\u003eindividuals living in rural Uganda. These were similar \u003cstrong\u003e(\u003c/strong\u003ePERMANOVA p=0.175\u003cstrong\u003e)\u003c/strong\u003e. \u003cstrong\u003eC\u003c/strong\u003e \u003cem\u003eS. m\u003c/em\u003e+ showing an overall microbiome structure that is different from the \u003cem\u003eS. m-\u003c/em\u003eindividuals (PERMANOVA, p=0.011). \u003cstrong\u003eD\u003c/strong\u003e and \u003cstrong\u003eE\u003c/strong\u003e Linear Discriminant Analysis (LDA) scores of bacteria that are differentially abundant between (\u003cem\u003eS. m\u003c/em\u003e+) and (\u003cem\u003eS. m-\u003c/em\u003e)\u003cstrong\u003e.\u003c/strong\u003e LDA scores show the measure of effect of each genus. Bacteria with LDA score \u0026gt; 2 were differentially enriched in a particular group. \u003cstrong\u003eD \u003c/strong\u003eshows LDA results for \u003cem\u003eS. m\u003c/em\u003e+ and \u003cem\u003eS. m- \u003c/em\u003eliving in the rural setting while \u003cstrong\u003eE\u003c/strong\u003e shows LDA results for \u003cem\u003eS. m\u003c/em\u003e+ and \u003cem\u003eS. m- \u003c/em\u003eliving in the urban setting. The red bars represent microbes that are more abundant in the \u003cem\u003eS. m- \u003c/em\u003eindividuals while the green bars show those that are more abundant in the \u003cem\u003eS. m\u003c/em\u003e+ individuals.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7053382/v1/a25aa7cc1fd0c20471516150.png"},{"id":86214201,"identity":"d824f840-f8d6-465b-8012-a36f86d4303d","added_by":"auto","created_at":"2025-07-08 05:39:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":95721,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential abundance of gut microbial taxa by \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eS. mansoni\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e-infection status.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVolcano plot showing the log₂ fold change in abundance (x-axis) versus the –log₁₀ adjusted \u003cem\u003ep\u003c/em\u003e-value (y-axis) for microbial taxa differentially abundant between \u003cem\u003eS. mansoni\u003c/em\u003e-infected and -uninfected individuals. Each point represents a microbial taxon. Taxa to the right of the vertical dashed line are enriched in \u003cem\u003eS. mansoni\u003c/em\u003e-infected individuals, while those to the left are depleted. Points are coloured by their log₂ fold change, with red indicating higher abundance and blue indicating lower abundance in \u003cem\u003eS. mansoni\u003c/em\u003e-infected. Labels highlight taxa with statistically significant differences (FDR-adjusted \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Also, coloured taxa denoted by triangular dots are both significantly enriched in \u003cem\u003eS. mansoni\u003c/em\u003eparticipants and associated with CVD risk. Those taxa shown by circular dots are significantly impacted by \u003cem\u003eS. mansoni\u003c/em\u003e infection but are not associated with CVD risk\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7053382/v1/7d69f61c76e75529b1de0635.png"},{"id":86213796,"identity":"43ba8f80-23a9-40bf-9dcd-0e08c6e17166","added_by":"auto","created_at":"2025-07-08 05:31:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":441280,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe link between S. mansoni infection, microbiota and CVD. A \u003c/strong\u003eAssociation of microbes and CVD risk in \u003cem\u003eS. m\u003c/em\u003e+ and \u003cem\u003eS. m- \u003c/em\u003eindividuals living in rural setting: The asterisks (*) show the significant association between microbes and the different CVD risk factors in the rural population, following linear regression analysis where potential confounders including age, sex, body mass index (BMI) and diet were adjusted for. These are results from the top 50 abundant microbes. ** indicative of p \u0026lt;0.01, while * indicative of a p-value \u0026lt;0.05. BP is blood pressure, Total Chol is total cholesterol while LDL Chol is low density lipoprotein cholesterol. Taxa such as \u003cem\u003eTreponema\u003c/em\u003e were consistently associated with LDL cholesterol in both rural and urban populations in \u003cstrong\u003eB\u003c/strong\u003e. \u003cstrong\u003eB\u003c/strong\u003e Linear regression analysis similar to what is shown in B but for S. m+ and S. m-\u003cem\u003e \u003c/em\u003eindividuals living in\u003cem\u003e \u003c/em\u003eurban setting. Potential confounders including age, sex and body mass index (BMI) were adjusted for. *** show p. value \u0026lt;0.001, ** indicative of p \u0026lt;0.01, while * shows p-value \u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.\u003c/strong\u003e Venn diagram showing overlap amongst microbes that are significantly associated with different cardiovascular risk factors. After performing linear regression (adjusting for age, sex, BMI, diet), we extracted all (not only the top 50 shown in A and B) microbes that were found to be significantly associated with the various CVD risk factors (p\u0026lt;0.05). D\u003cstrong\u003e.\u003c/strong\u003e Mediation analysis illustrating the mediatory role of microbes in \u003cem\u003eS. mansoni\u003c/em\u003e-driven CVD risk resolution\u003cstrong\u003e. \u003c/strong\u003eAn alluvial plot showing microbes through which helminths may alter one’s CVD risk. Results from mediation analysis using bias-correct non-parametric bootstrap method in the pingouin.mediation analysis package in R. Microbial taxa shown were differentially abundant (p=0.05) in either the \u003cem\u003eS. m\u003c/em\u003e+ and \u003cem\u003eS. m- \u003c/em\u003egroups (shown on the Y-axis). All the microbes above had a significant negative mediation effect (p\u0026lt;0.05, 97.5% CI) on CVD risk factors shown.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7053382/v1/bd1dbb47c72c43969a210c2e.png"},{"id":86213818,"identity":"43beca10-fa5c-4398-aa1a-72c75c7e302a","added_by":"auto","created_at":"2025-07-08 05:31:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":233160,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential abundance of metabolites by \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eS. mansoni\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e-infection status\u003c/strong\u003e. \u003cstrong\u003e(A) \u003c/strong\u003eVolcano plot shows metabolites that are differentially abundant between the S. m-infected and uninfected groups. Red dots at the upper left area are significantly upregulated in the uninfected group\u003cem\u003e, \u003c/em\u003eblue dots located at the upper right area are significantly upregulated in \u003cem\u003eS. m\u003c/em\u003e -infected individuals. p ≤ 0.05 and fold change ≥1.0 was used to identify the metabolites that were significantly more abundant in infected while p ≤ 0.05 and fold change \u0026lt; -1.0 was used to identify metabolites that were significantly more abundant in \u003cem\u003eS. m\u003c/em\u003e- uninfected group\u003cem\u003e. \u003c/em\u003eThe top 10 most differentially abundant metabolites from either group were labelled.\u003cem\u003e \u003c/em\u003e(\u003cstrong\u003eB)\u003c/strong\u003ePartial least squares-discriminant analysis (PLS-DA) score plot showing differences in clustering of metabolites in \u003cem\u003eS. m\u003c/em\u003e+ and \u003cem\u003eS. m\u003c/em\u003e- individuals. The blue represents \u003cem\u003eS. m\u003c/em\u003e- while yellow represents the \u003cem\u003eS. m\u003c/em\u003e+ individuals. We assessed group-level differences in metabolomic profiles using PERMANOVA on scaled Euclidean distances. \u003cem\u003eS. m+\u003c/em\u003e vs \u003cem\u003eS. m-\u003c/em\u003e profiles were not significantly different (\u003cem\u003ep\u003c/em\u003e = 0.48) (\u003cstrong\u003eC)\u003c/strong\u003e Biological pathways enriched by metabolites that were more abundant in \u003cem\u003eS. m\u003c/em\u003e+ \u0026nbsp;compared to \u003cem\u003eS. m\u003c/em\u003e- participants. Metabolites were profiled from faecal samples (n = 212) using liquid chromatography–mass spectrometry and identified using Progenesis QI software. Pathway enrichment analysis was performed using Integrated Molecular Pathway Level Analysis (IMPaLA). The bar plot shows the −log₁₀-transformed \u003cem\u003eP\u003c/em\u003e-values for the top enriched biological pathways, with bar colour intensity reflecting the strength of statistical significance. Higher −log₁₀(\u003cem\u003eP\u003c/em\u003e-value) indicates stronger enrichment.\u003cbr\u003e\nNotably, multiple pathways linked to lipid metabolism and cholesterol regulation were significantly enriched.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7053382/v1/03dce9190824df63ed38d2e7.png"},{"id":86213809,"identity":"4f2ddcfe-9278-4f6f-802d-451a8dbee7a6","added_by":"auto","created_at":"2025-07-08 05:31:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":568078,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between cholesterol-associated microbiota and metabolites. \u0026nbsp;A \u003c/strong\u003eCircos plot showing the topmost correlations between variables of microbiome (blue blocks) and metabolome (green blocks) data that were significantly associated with total cholesterol following association analysis described in Fig. 3 where age, sex, BMI were adjusted for, and setting included as interaction term in our model.\u003cstrong\u003e \u003c/strong\u003eThe correlation cut-off for this circos plot is r ≥ 0.7. \u003cstrong\u003eB\u003c/strong\u003e After extracting only those microbes and metabolites shown to interact in A, a heatmap was used to clearly show the most significant correlations between the microbiota genera and metabolites that were significantly associated with total cholesterol levels. Correlation strength (Spearman’s correlation coefficient, r, value) is shown by the depth of the red colour (the deepest red indicates the strongest correlation positive correlation, and blue shows negative correlation). \u003cstrong\u003eC \u003c/strong\u003eAnnotation of metabolites into classes. Going beyong individual metabolites, this annotation was done using the CLASSYFIRE to show the classes they fall under. The blue horizontal bars represent the number of metabolites matching with x-axes. The vertical axis represents the annotated classes of metabolites, and the graph shows the number of metabolites annotated to a class. \u003cstrong\u003eD \u003c/strong\u003eCircos plot showing the topmost correlations between variables of microbiome (blue blocks) and metabolome (green blocks) data that were significantly associated with LDL-Cholesterol following association analysis described in Fig. 3 where factors such as age, sex, BMI were adjusted for, and setting included as interaction term in our model.\u003cstrong\u003e \u003c/strong\u003eThe correlation cut-off for this circos plot is r ≥ 0.65. \u003cstrong\u003eE\u003c/strong\u003e Similar approach to B. Correlation strength (Spearman’s correlation coefficient, r, value) is described as per the key in B. \u003cstrong\u003eF \u003c/strong\u003eAnnotation of LDL-Cholesterol associated metabolites into classes. Going beyong individual metabolites, this annotation was done using the CLASSYFIRE to show the classes they fall under.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7053382/v1/4d7939ab94d434605bcf79d0.png"},{"id":86213798,"identity":"ac9dfa26-5d21-47ab-ace3-ea065e6629e9","added_by":"auto","created_at":"2025-07-08 05:31:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":695842,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between blood pressure-associated microbiota and metabolites. A \u003c/strong\u003eCircos plot showing the topmost correlations between variables of microbiome (blue blocks) and metabolome (green blocks) data that were significantly associated with diastolic bloop pressure following association analysis described in Fig. 3 where factors such as age, sex, BMI were adjusted for, and setting included as interaction term in our model.\u003cstrong\u003e \u003c/strong\u003eThe correlation cut-off for this circos plot is r ≥ 0.7. \u003cstrong\u003eB\u003c/strong\u003eAfter extracting only those microbes and metabolites shown to interact in A, a heatmap was used to clearly show the most significant correlations between the microbiota and metabolites that were significantly associated with diastolic Blood pressure. Correlation strength (Spearman’s correlation coefficient, r, value) is shown by the depth of the red colour (the deepest red indicates the strongest correlation positive correlation, and blue shows negative correlation). \u003cstrong\u003eC \u003c/strong\u003eAnnotation of metabolites into classes. Going beyond individual metabolites, this annotation was done using the CLASSYFIRE to show the classes they fall under. The blue horizontal bars represent the number of metabolites matching with x-axes. The vertical axis represents the annotated classes of metabolites, and the graph shows the number of metabolites annotated to a class. \u003cstrong\u003eD \u003c/strong\u003eCircos plot showing the topmost correlations between variables of microbiome (blue blocks) and metabolome (green blocks) data that were significantly associated with systolic blood pressure following association analysis described in Fig. 3 where factors such as age, sex, BMI were adjusted for, and setting included as interaction term in our model.\u003cstrong\u003e \u003c/strong\u003eThe correlation cut-off for this circos plot is r ≥ 0.70. \u003cstrong\u003eE\u003c/strong\u003e Similar approach to B, correlation strength (Spearman’s correlation coefficient, r, value) is described as per the key in B. \u003cstrong\u003eF \u003c/strong\u003eAnnotation of metabolites associated systolic blood pressure, into classes. Going beyond individual metabolites, this annotation was done using the CLASSYFIRE to show the classes they fall under.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7053382/v1/fe8c610328a0a590052c5816.png"},{"id":86213811,"identity":"13c778ce-7911-4e6a-9eb4-6693e5300c12","added_by":"auto","created_at":"2025-07-08 05:31:50","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":42923,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNetwork representation of microbiome–metabolome–cardiovascular risk in individuals with \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eS. mansoni\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e This network shows directional associations between gut microbial taxa (dark blue nodes), faecal metabolites (orange nodes), and cardiovascular disease (CVD) risk factors LDL cholesterol (green) and diastolic blood pressure (red). Arrows indicate significant correlations between nodes, derived from Spearman’s correlation analysis. Microbial taxa nodes represent bacteria found to be significantly enriched (log₂ fold change \u0026gt; 1, FDR-adjusted p \u0026lt; 0.05) in individuals with schistosomiasis. Metabolites (orange) are detected in serum and significantly correlated with these taxa. Cardiovascular risk factors are represented by green (LDL cholesterol) and red (diastolic blood pressure) nodes, denoting the specific clinical trait associated with the metabolite and microbe. Edge directionality flows from microbe → metabolite → CVD risk factor, showing a hypothesized mediation path through which \u003cem\u003eS. mansoni\u003c/em\u003e-associated microbial changes may influence metabolic and cardiovascular profiles\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7053382/v1/4291eab3c4e18f5a8fe96771.png"},{"id":86215589,"identity":"e8bd39be-3d93-4ce6-a4fc-e8ecffd9b30c","added_by":"auto","created_at":"2025-07-08 05:55:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4464568,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7053382/v1/15527bed-55f3-47f8-bdb3-2f58315837c5.pdf"},{"id":86213800,"identity":"0f3a5111-f618-40ef-88ea-16a181cd3db1","added_by":"auto","created_at":"2025-07-08 05:31:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1941245,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7053382/v1/e633bcf97b8e30bc4b9bdb10.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eUncovering the role of the gut microbiome and metabolome in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eSchistosoma mansoni\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e-induced modulation of cardiovascular disease risk in humans\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, cardiovascular diseases (CVDs) pose a significant threat to public health, consistently ranking as the primary cause of mortality over the last thirty years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. For example, CVDs accounted for approximately 20.5\u0026nbsp;million deaths (over 31% of global deaths) in 2021 alone [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. More than 75% of CVD related deaths have been reported to occur in low and middle income countries, demonstrating a critical need for intensified research to address the risk factors for CVD that can be modulated to reduce the incidence of disease [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These risk factors include dietary risks, high systolic blood pressure, dyslipidaemia (particularly high low-density lipoprotein (LDL) cholesterol) and high fasting plasma glucose [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral studies have linked cardiovascular risk factors with the immune system response, both in humans and animal models [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Particularly, chronic inflammation has been highlighted as the main immunological feature characterizing cardiovascular or metabolic risk [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For example, increased circulating tumour necrosis factor (TNF)-⍺ is associated with glucose intolerance, and inhibiting its expression in adipose tissues affects sensitivity to insulin, and tolerance for glucose, in obese individuals [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdditionally, inflammatory pathways involving the activation of macrophages, dendritic cells, and mast cells have been shown to rely on the availability of dietary lipids such as saturated fats and cholesterol [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Dietary lipids such as omega-3 fatty acids have been linked to production of inflammatory cytokines such as TNF-⍺ and interleukin (IL)-2 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. As such, this lipid-inflammation interplay suggests that inflammation alters lipid profiles and metabolism in hosts. Multiple lines of evidence show that cytokines such as IL-6, IL-1 and TNF-⍺ are associated with increased production of triglycerides and LDL cholesterol levels in serum [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Atherosclerosis, a chronic inflammatory condition typified by thickening of arterial walls, mediated by accumulation of lipids and cells such as macrophages in the vascular intima, is central in cardiovascular disease prognosis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Given the centrality of inflammation in cardiovascular risk, it has been hypothesized that infections that induce immunomodulatory responses protect hosts against metabolic disorders.\u003c/p\u003e\u003cp\u003eOne such class of infections with immunomodulatory effects are chronic helminth infections [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These are characterized by a polarized T-helper 2 cell response, important in resisting or eliminating helminth infections in the host [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, helminths can modulate the host\u0026rsquo;s immune response to prolong their own survival [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. For example, helminths induce production of IL-10, a cytokine known to be pivotal in suppressing inflammation, to enhance their survival [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, owing to their ability to downregulate inflammation in the host, the hypothesis that helminths may be protective against CVD risk is plausible.\u003c/p\u003e\u003cp\u003eSeveral epidemiological studies have shown an inverse association between chronic helminth infection and metabolic risk factors such as LDL cholesterol and high blood pressure. For example, Wiria \u003cem\u003eet al.\u003c/em\u003e, reported reduced total cholesterol, LDL cholesterol, body mass index (BMI) and waist-to-hip ratio in people infected with soil transmitted helminth (STH) compared to those without STH infection among Indonesian subjects living in helminth endemic region [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In another study by Magen \u003cem\u003eet al\u003c/em\u003e, individuals with chronic \u003cem\u003eOpisthorchis felineus\u003c/em\u003e infection had significantly reduced total cholesterol compared to those without the infection [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Furthermore, Shen \u003cem\u003eet al\u003c/em\u003e found an inverse association between previous schistosomiasis infection and triglyceride levels, waist-to-hip ratio and BMI [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In the same study, diastolic blood pressure was significantly lower in subjects with previous schistosomiasis than those without infection [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Moreover, in separate cross-sectional studies investigating whether previous schistosome infection protects against development of diabetes and metabolic syndrome, Chen \u003cem\u003eet al\u003c/em\u003e found lowered systolic blood pressure (SBP) and diastolic blood pressure (DBP) in people with schistosomiasis infection compared to those without [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe aforementioned evidence is further supported by meta-analyses that have begun to show the importance of the inverse association of helminths with CVD risk. Tracey \u003cem\u003eet al\u003c/em\u003e reported an association of lower glucose levels, insulin resistance, metabolic syndrome, and a 50% reduced likelihood of susceptibility to CVD risk factors such as type 2 diabetes (T2D), with helminth infection [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This was reaffirmed by Rennie \u003cem\u003eet al\u003c/em\u003e who found reduced fasting glucose, glycated hemoglobin (HbA1c) levels, prevalence of T2D and metabolic syndrome in people with helminth infections compared to those without [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven that much of the existing evidence linking helmith infections to cardiometabolic protection is informed by studies focusing on STH, it would be informative to investigate how helminths acquired through alternative routes, such as \u003cem\u003eS. mansoni\u003c/em\u003e, might affect one\u0026rsquo;s cardiovascular risk, in a population with a high prevalence of \u003cem\u003eS. mansoni\u003c/em\u003e infection, and in case of a protective effect, study the mechanisms by which these parasites bring about this benefit to the host [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Despite the well-reviewed importance of the anti-inflammatory effect of helminths such as \u003cem\u003eS. mansoni\u003c/em\u003e in protecting the host against CVD, other possible pathways have been suggested [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSchistosomiasis, caused by parasitic trematodes of the genus \u003cem\u003eSchistosoma\u003c/em\u003e, remains one of the most prevalent neglected tropical diseases worldwide [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], affecting millions of individuals, primarily in low-resource regions. Beyond its direct pathological effects, emerging evidence suggests a complex interplay between schistosomiasis infection and the modulation of host immune responses, metabolic pathways, and disease susceptibility, and more recently the potential of infection to have immune-mediated protective effect against cardiovascular disease risk [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, the mechanisms underlying these potential benefits remain poorly understood.\u003c/p\u003e\u003cp\u003eOne promising area of investigation is the gut microbiota, a complex ecosystem of trillions of microorganisms that profoundly shape host immunity, metabolism, and overall health [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Dysbiosis, characterized by abnormal changes in the composition and function of gut microbiota, has been implicated in various pathological conditions, including CVDs [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Helminth infections are known to modulate the gut microbiome, but it remains unclear whether such changes play a mediating role in the relationship between \u003cem\u003eS. mansoni\u003c/em\u003e infection and cardiovascular risk.\u003c/p\u003e\u003cp\u003eThere is growing evidence suggesting that the gut microbiota and the metabolites they produce serve as critical mediators of host-parasite interactions. In the context of schistosomiasis, the parasite-host interaction within the gut environment can influence microbial composition and metabolic activity, leading to systemic effects on host physiology and immune responses [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Moreover, specific microbial metabolites, such as short-chain fatty acids (SCFAs), and trimethylamine N-oxide (TMAO), have been implicated in modulating CVD risk factors such as blood pressure and cholesterol [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and may contribute to the observed protective effect of schistosomiasis against CVD risk.\u003c/p\u003e\u003cp\u003eDespite the emerging evidence separately implicating the gut microbiome and its metabolites, and \u003cem\u003eS. mansoni\u003c/em\u003e, the precise mechanisms underlying this complex helminth-microbiome interplay in driving cardiovascular risk modulation remain poorly understood. Our previous findings from a cluster-randomised trial involving 1,898 participants (the Lake Victoria Island Intervention Trial on Worms and Allergy-related Diseases [LaVIISWA], which was extended to investigate metabolic outcomes) showed that \u003cem\u003eSchistosoma mansoni\u003c/em\u003e infection was associated with lower levels of total and LDL cholesterol, while intensive anthelmintic treatment led to an increase in LDL cholesterol [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Further, heavy and moderate \u003cem\u003eS. mansoni\u003c/em\u003e infection intensities were associated with lower diastolic blood pressure, triglycerides and LDL cholesterol [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn pursuit of a deeper understanding of how \u003cem\u003eS. mansoni\u003c/em\u003e infection could lead to these changes in CVD risk, the current work therefore used samples from our LaVIISWA trial, aiming at deciphering \u003cem\u003eS. mansoni\u003c/em\u003e-associated dysbiosis, the mediatory role of gut microbiome in altering CVD risk, and the gut-microbiome and metabolome interaction in the context of chronic schistosomiasis infection and its impact on cardiovascular risk.\u003c/p\u003e\u003cp\u003eBy using an integrative, multi-omics approach including microbiome and metabolomics, we unravel the molecular pathways and microbial signatures associated with the protective effect of schistosomiasis on CVD risk. Ultimately, this research provides novel insights into host-parasite interactions, microbial dysbiosis, and metabolic influence, with implications for the development of targeted interventions to mitigate cardiovascular disease risk in humans.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eFirstly, we established a comprehensive framework to investigate how \u003cem\u003eSchistosoma mansoni\u003c/em\u003e infection influences cardiovascular disease risk through alterations in the gut microbiome and metabolome (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe used samples from rural participants in the LaVIISWA trial [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and a second, well-characterized survey in a nearby urban setting in Uganda [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLaVIISWA was a cluster-randomised trial conducted among Lake Victoria Island fishing communities in Mukono district, Uganda. The study was conducted in 27 fishing villages: one was selected for piloting the study and the remaining 26 were randomised in a 1:1 ratio to standard deworming (single dose praziquantel given once a year and single dose albendazole twice a year) or intensive deworming (single dose praziquantel and triple dose albendazole four times a year). The samples we used were collected during the metabolic survey undertaken after 4 years of intervention. Contemporaneously, the rest of the samples for our study were collected from participants of an Urban Survey that was conducted in Entebbe municipality (an urban setting) found on shores north of Lake Victoria. Entebbe is in Wakiso district approximately 40km southwest of Kampala, the Ugandan capital city. We have previously reported that the rural population showed a markedly higher burden of helminth infections, with \u003cem\u003eSchistosoma mansoni\u003c/em\u003e detected significantly more frequently than in the urban group, as illustrated by both stool Kato-Katz microscopy (31.7% vs 9.9%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and stool PCR analysis (47.6% vs 22.2%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn both studies, following overnight fasting, stool and blood samples were collected from the participants. Metabolic outcomes measured are: fasting blood sugar, insulin levels, serum lipid levels, body mass index (BMI), waist and hip circumference and blood pressure (systolic and diastolic).\u003c/p\u003e\u003cp\u003e\u003cb\u003eExposure and Outcome Assessment\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSociodemographic and Anthropometric Data\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAge and sex were documented using a structured survey tool. Body weight was recorded to the nearest 0.1 kg using a digital scale (SECA model 875), with participants lightly clothed and barefoot. Standing height was measured to the nearest 0.1 cm using a portable stadiometer (SECA model 213). Waist circumference was measured midway between the lower rib and iliac crest, while hip circumference was taken at the level of the greater trochanters, both using a non-elastic measuring tape. Body mass index (BMI) was calculated as weight (kg) divided by height (m\u0026sup2;), and waist-to-hip ratio was derived accordingly.\u003c/p\u003e\u003cp\u003e\u003cem\u003eParasitological Assessment\u003c/em\u003e\u003c/p\u003e\u003cp\u003eStool samples were analysed for helminth infections using both microscopy and molecular techniques. The Kato-Katz method was used to quantify \u003cem\u003eS. mansoni\u003c/em\u003e and \u003cem\u003eT. trichiura\u003c/em\u003e eggs as desribed by Sanya \u003cem\u003eet al\u003c/em\u003e [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Real-time PCR assays were employed to detect DNA of \u003cem\u003eS. mansoni\u003c/em\u003e, hookworm (\u003cem\u003eN. americanus\u003c/em\u003e), and \u003cem\u003eS. stercoralis\u003c/em\u003e, the latter being exclusively detected by PCR. PCR data were prioritized for diagnostic confirmation of hookworm due to slide timing variability.\u003c/p\u003e\u003cp\u003e\u003cem\u003eDietary Intake\u003c/em\u003e\u003c/p\u003e\u003cp\u003eDietary patterns were evaluated using a semi-quantitative food frequency questionnaire (FFQ) tailored to reflect local Ugandan food consumption habits. The FFQ assessed usual intake over the past month, covering major food categories such as cereals, legumes, animal proteins, dairy, fruits, vegetables, oils, and sugary drinks. Responses were used to compute dietary diversity scores to adjust for potential confounding in downstream microbiome and metabolome analyses.\u003c/p\u003e\u003cp\u003e\u003cem\u003eBlood Pressure\u003c/em\u003e\u003c/p\u003e\u003cp\u003eBlood pressure was measured three times at five-minute intervals in a seated, rested position using a validated automatic sphygmomanometer (OMRON M2, HEM-7121-E), with the average of the last two readings used for analysis. Blood pressure monitors were routinely calibrated through the Uganda National Bureau of Standards to ensure accuracy.\u003c/p\u003e\u003cp\u003e\u003cem\u003eCardiometabolic Biomarkers\u003c/em\u003e\u003c/p\u003e\u003cp\u003eFasting venous blood samples were collected into EDTA, fluoride oxalate, and serum separator tubes following an overnight fast (\u0026ge;\u0026thinsp;8 hours). Participants were advised to avoid physical exertion and tobacco use prior to sampling. Plasma and serum were separated within one hour of collection and cryopreserved in liquid nitrogen.\u003c/p\u003e\u003cp\u003eAll biochemical analyses were performed at the MRC/UVRI \u0026amp; LSHTM Uganda Research Unit laboratory (Entebbe) using a Roche Cobas 6000 platform (c 501 module). Fasting plasma glucose and serum lipids\u0026mdash;total cholesterol, triglycerides, HDL-c, and LDL-c were quantified using enzymatic colorimetric methods. HbA1c was assessed in whole blood using a turbidimetric inhibition immunoassay, and fasting insulin via electrochemiluminescence immunoassay (ECLIA). Insulin resistance was calculated using the Homeostasis Model Assessment (HOMA-IR) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eMicrobiome profiling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSelected samples were prepared from MRC/UVRI and LSHTM-Uganda Research Unit and shipped to Novogene for 16S \u003cem\u003erRNA\u003c/em\u003e sequencing. From stool samples collected from these participants at the time the CVD measurements were done, we profiled gut microbial diversity and performed untargeted metabolomics. Genomic microbial DNA was extracted from 150 mg of faecal sample of every selected individual, using the QIAamp DNA Stool kits (Qiagen, Hilden, Germany) according to the manufacturer's instructions.\u003c/p\u003e\u003cp\u003eFollowing extraction, DNA concentration and purity were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific), and integrity was evaluated via 2% agarose gel electrophoresis. DNA samples (5 \u0026micro;L) were mixed with 1 \u0026micro;L of 6\u0026times; loading dye and loaded alongside a 1 kb DNA ladder (Thermo Scientific) into a 2% agarose gel prepared with Tris-Acetate-EDTA (1 xTAE) buffer and stained with ethidium bromide (0.5 \u0026micro;g/mL). Electrophoresis was performed at 100 volts for approximately 45 minutes. Gels were visualized using a UV transilluminator, and high molecular weight DNA was confirmed by the presence of a distinct, unsmeared band above 10 kb. Only samples with high-quality, intact DNA were retained for downstream amplification and sequencing.\u003c/p\u003e\u003cp\u003eThe 16S library preparation protocol (Reference No: GHFS-LH-039) from Institute of Food Research was used to amplify the V3-V4 hypervariable regions of the bacterial 16S rRNA genes to profile the gut microbiota. The same amount of PCR products from each sample was pooled, end-repaired, A-tailed and further ligated with Illumina adapters. Libraries were sequenced on a paired-end Illumina platform to generate 250bp paired-end raw reads.\u003c/p\u003e\u003cp\u003eThe library was checked with Qubit and real-time PCR for quantification and bioanalyzer for size distribution detection. Quantified libraries were pooled and sequenced on Illumina platforms, according to effective library concentration and data amount required. Paired-end reads were assigned to samples based on their unique barcodes and were truncated by cutting off the barcodes and primer sequences. Paired-end reads were merged using FLASH (Version 1.2.11) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], a fast and accurate analysis tool designed to merge paired-end reads when at least some of the reads overlap with the reads generated from the opposite end of the same DNA fragment, and the splicing sequences were called Raw Tags. Quality filtering on the raw tags was performed using the fastp (Version 0.20.0) software to obtain high-quality Clean Tags. The Clean Tags were compared with the reference database (Silva database \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.arb-silva.de\u003c/span\u003e\u003cspan address=\"https://www.arb-silva.de\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using Vsearch (Version 2.15.0) to detect the chimera sequences, and then the chimera sequences were removed to obtain the EffectiveTags [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFor the Effective Tags obtained previously, denoise was performed with DADA2 to obtain initial Amplicon Sequence Variants (ASVs) and then ASVs with abundance less than 5 were filtered out[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Species annotation was performed using QIIME2 software. The annotation database used was Silva Database. To study phylogenetic relationship of each ASV and the differences of the dominant species among different samples(groups), multiple sequence alignment was performed using QIIME2 software. The absolute abundance of ASVs was normalized using a standard of sequence number corresponding to the sample with the least sequences. Subsequent analyses of alpha diversity and beta diversity were performed based on the output normalized data.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMetabolite extraction and profiling from stool samples\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMetabolites were extracted from stool samples using a solid-phase extraction (SPE) approach. Briefly, 1.25 mL of 80:20 methanol:water solution was added to each fecal sample, followed by vortexing and addition of 1 mL of the resulting mixture to 9 mL of molecular-grade water in a 15 mL conical tube. The mixture was centrifuged at 4,000 rpm for 1 minute, and 5 mL of the resulting supernatant was loaded onto SPE cartridges \u003cem\u003evia\u003c/em\u003e a syringe. The cartridges were dried by pushing air through them twice using the same syringe and then sealed for shipment to the analytical laboratory in Manchester. A blank control sample without faecal material was prepared alongside the experimental samples.\u003c/p\u003e\u003cp\u003eUpon receipt, metabolite elution was performed using 1.5 mL of 85:15 acetonitrile:methanol, drawn into a 2 mL luer-lock syringe and passed through the cartridge into a sterile 1.5 mL microcentrifuge tube. After allowing 1 minute of solvent equilibration to re-solvate the stationary phase, the eluate was collected at a rate of approximately one drop per second. Samples were then subjected to nitrogen blowdown drying in batches of up to 50, using a 60-position dryer platform.\u003c/p\u003e\u003cp\u003eDried samples as provided were resuspended in 100 \u0026micro;l 5:95 acetonitrile/water and centrifuged at 20,000 x \u003cem\u003eg\u003c/em\u003e for 3 min. The top 80 \u0026micro;l supernatant was transferred to a glass autosampler vial with 300 \u0026micro;l insert and capped. Quality control samples were made by pooling 5 \u0026micro;l from each sample.\u003c/p\u003e\u003cp\u003eLiquid chromatography-mass spectrometry analysis was performed using a Thermo-Fisher Ultimate 3000 HPLC system consisting of an HPG-3400RS high pressure gradient pump, TCC 3000SD column compartment and WPS 3000 Autosampler, coupled to a SCIEX 6600 TripleTOF Q-TOF mass spectrometer with TurboV ion source. The system was controlled by SCIEX Analyst 1.7.1, DCMS Link and Chromeleon Xpress software.\u003c/p\u003e\u003cp\u003eA sample volume of 5 \u0026micro;L was injected by pulled loop onto a 5 \u0026micro;L sample loop with 150 \u0026micro;l post-injection needle wash with 5:95 acetonitrile and water. Injection cycle time was 1 minute per sample. Separations were performed using a Thermo Accucore C18 column with dimensions of 150 mm length, 2.1 mm diameter and 2.6 \u0026micro;m particle size equipped with a guard column of the same phase. Mobile phase A was water with 0.1% formic acid; mobile phase B was acetonitrile with 0.1% formic acid. Separation was performed by gradient chromatography at a flow rate of 0.3 ml/min, starting at 5% B for 1 minute, ramping to 100% B over 7 minutes, hold at 100% B for 2 minutes, then back to 5% B. Re-equilibration time was 4 min. Total run time including 1 minute injection cycle was 15 minutes.\u003c/p\u003e\u003cp\u003eThe mass spectrometer was run in positive mode under the following source conditions: curtain gas pressure, 50 psi; ionspray voltage, 5500 V; temperature, 400\u0026deg;C; ESI nebulizer gas pressure, 50 psi; heater gas pressure, -70 psi; declustering potential, -80 V.\u003c/p\u003e\u003cp\u003eData were acquired in an information dependent manner across 10 high sensitivity product ion scans, each with an accumulation time of 100 ms and a TOF survey scan with accumulation time of 250 ms. Total cycle time was 1.3 s. Collision energy was determined using the formula CE (V)\u0026thinsp;=\u0026thinsp;0.084 x \u003cem\u003em/z\u003c/em\u003e\u0026thinsp;+\u0026thinsp;12 up to a maximum of 55 V. Isotopes within 4 Da were excluded from the scan.\u003c/p\u003e\u003cp\u003eAcquired data were checked in PeakView 2.2 and imported into Progenesis Qi 2.4 for metabolomics, where they were aligned, peaks were picked, normalised to all compounds and deconvoluted according to standard Progenesis workflows. Annotations were made by searching the accurate mass, MS/MS spectrum and isotope distribution ratios of acquired data against the NIST MS/MS metabolite library. Metabolites were identified by searching retention times and accurate masses against an in-house chemical standard library. A validated identification is only given if identical hits are made against both the NIST MS/MS and in-house chemical standard libraries.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical and Computational Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eMicrobiome data analysis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo analyze the diversity, richness and uniformity of the communities in the sample, alpha diversity was calculated from indices, including Shannon, observed richness and Pielou_e. Statistical comparisons between infected and uninfected groups was done using the Krusal-Wallis test.\u003c/p\u003e\u003cp\u003eBeta diversity was evaluated using Bray\u0026ndash;Curtis dissimilarity to compare community structure between samples. Principal coordinates analysis (PCoA) was used to visualize ordination, and PERMANOVA (Adonis) implemented in the `vegan` R package, was used to assess statistical differences in beta diversity across infection groups.\u003c/p\u003e\u003cp\u003e\u003cem\u003eDifferential abundance analysis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo identify microbial taxa differentially abundant between \u003cem\u003eSchistosoma mansoni\u003c/em\u003e\u0026ndash;infected and uninfected individuals, we performed differential abundance (DA) testing using the DESeq2 method implemented within the phyloseq R package. A Wald test was applied to estimate log₂ fold changes in microbial abundance between the two groups. Resulting \u003cem\u003ep\u003c/em\u003e-values were adjusted for multiple testing using the Benjamini\u0026ndash;Hochberg false discovery rate (FDR) procedure. Taxa with an FDR-adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. A volcano plot was generated to visualize the results, displaying the log₂ fold change on the x-axis and the \u0026ndash;log₁₀ FDR-adjusted \u003cem\u003ep\u003c/em\u003e-value on the y-axis.\u003c/p\u003e\u003cp\u003e\u003cem\u003eLinear discriminant analysis (LDA) effect size (LEfSe)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo complement DESeq2-based DA testing, we additionally applied linear discriminant analysis (LDA) using the LEfSe algorithm to identify microbial features that consistently discriminate between \u003cem\u003eS. mansoni\u003c/em\u003e\u0026ndash;infected and uninfected individuals. While DESeq2 provides robust statistical inference and effect size estimates for individual taxa across groups, LDA ranks taxa based on their ability to explain group differences by combining statistical significance with biological consistency and effect relevance. This approach helps prioritize taxa most likely to contribute to distinguishing phenotypic states.\u003c/p\u003e\u003cp\u003eTaxa with a logarithmic LDA score\u0026thinsp;\u0026gt;\u0026thinsp;2.0 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significantly enriched. Analyses were stratified by community type (rural vs. urban) to account for environmental and lifestyle heterogeneity. Visualization of LEfSe results was done via bar plots showing LDA scores.\u003c/p\u003e\u003cp\u003eBy integrating both DA and LDA approaches, we capture a broader perspective on microbiome differences\u0026mdash;identifying statistically robust changes (via DESeq2) while also highlighting microbial signatures with high discriminatory power (via LEfSe) that may serve as candidate biomarkers.\u003c/p\u003e\u003cp\u003e\u003cem\u003eMicrobiome\u0026ndash;CVD risk associations\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo evaluate the direct associations between microbial taxa and specific CVD risk factors (e.g., blood pressure, total cholesterol, LDL cholesterol), multivariate linear regression models were fitted separately for infected and uninfected groups within rural and urban settings. Models were adjusted for age, sex, BMI, and diet (in the rural population). Significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and false discovery rate (FDR) correction was applied. The top 50 most abundant taxa were prioritized for analysis. Results were visualized with heatmaps and annotated by significance level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003e\u003cem\u003eMediation analysis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo investigate potential microbial mediation of the relationship between \u003cem\u003eS. mansoni\u003c/em\u003e infection and CVD risk, non-parametric bootstrap-based mediation analysis was conducted using the `pingouin.mediation_analysis` package in R. This approach estimated the indirect effect of differentially abundant taxa (from differential abundance analysis (FDR-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)) on CVD risk scores, with 5,000 bootstrap iterations and 97.5% confidence intervals. Microbes demonstrating significant negative or positive mediation effects (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were visualized using alluvial plots to capture the pathway from infection status to cardiovascular outcome through the microbiome.\u003c/p\u003e\u003cp\u003e\u003cem\u003eMetabolite\u0026ndash;CVD risk associations\u003c/em\u003e\u003c/p\u003e\u003cp\u003eSimilar linear regression models as described above were used to assess the associations between individual metabolites and specific CVD risk factors (blood pressure, total cholesterol, LDL cholesterol). A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 was used to identify significantly associated metabolites that were taken forward for integrative omics analysis with CVD-associated microbes.\u003c/p\u003e\u003cp\u003e\u003cem\u003eMetabolomic Profiling and Pathway Enrichment Analysis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eUntargeted metabolomics was conducted using liquid chromatography\u0026ndash;mass spectrometry (LC-MS) on faecal samples. Differential metabolite abundance between \u003cem\u003eS. m\u0026thinsp;+\u003c/em\u003e\u0026thinsp;and \u003cem\u003eS. m\u003c/em\u003e- individuals was assessed using volcano plots with thresholds set at p\u0026thinsp;\u0026le;\u0026thinsp;0.05 and log 2-fold change (FC)\u0026thinsp;\u0026ge;\u0026thinsp;1.0 (for upregulation in infected) or FC\u0026thinsp;\u0026lt;\u0026thinsp;1.0 (for upregulation in uninfected). To evaluate whether the metabolomic profiles could discriminate between groups, we employed Partial Least Squares Discriminant Analysis (PLS-DA) using the caret package in R. The dataset was split into training and test sets using stratified sampling to maintain class balance. Model training was performed using 5-fold cross-validation, repeated three times, to optimize model parameters and assess classification performance. Model accuracy, sensitivity, and specificity were calculated on the test set, and the discriminative capacity was further evaluated by constructing Receiver Operating Characteristic (ROC) curve\u003cb\u003es\u003c/b\u003e and calculating the area under the curve (AUC) with 95% confidence intervals using the pROC package. We assessed group-level differences in metabolomic profiles using PERMANOVA on scaled Euclidean distances. Feature importance was derived to identify the most discriminative metabolites.\u003c/p\u003e\u003cp\u003ePathway enrichment analysis was conducted using Integrated Molecular Pathway Level Analysis (IMPaLA), incorporating Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and other curated databases. Samples were grouped according to specified criteria provided. For statistical analysis, a minimum fold change between sample groups of at least 1.5-fold, ANOVA p values of \u0026lt;\u0026thinsp;0.05 were used in IMPaLA.\u003c/p\u003e\u003cp\u003e\u003cem\u003eMetabolite annotation and functional classification\u003c/em\u003e\u003c/p\u003e\u003cp\u003eDetected features were matched against reference libraries using mass-to-charge ratio (m/z) and retention time (RT) as primary identifiers. To improve matching precision, m/z values were rounded to five decimal places and RTs to one decimal place, generating a unique combined feature ID for each metabolite. These IDs were used to merge the detected features with annotation outputs from the xMSannotator platform, which provides multi-parameter chemical identification including adduct patterns, isotope distributions, and pathway associations. Annotation confidence was further refined by cross-referencing putative matches with the Human Metabolome Database. When available, we prioritized annotations with the highest confidence scores as assigned by the xMSannotator workflow. Significantly altered metabolites associated with insulin resistance and other cardiometabolic risk factors were annotated into functional classes using LIPID MAPS and CLASSYFIRE The annotated metabolites were grouped by chemical class and displayed via bar plots (for CLASSYFIRE categories) and pathway clusters (via RaMP-DB). The significance of pathway enrichment was determined using Fisher\u0026rsquo;s exact test with multiple testing correction. Results were visualized via bar plots and redundancy-aware \u0026ldquo;lollipop\u0026rdquo; plots generated using RaMP-DB, clustering functionally overlapping pathways shown in supplementary Figs.\u0026nbsp;7\u0026ndash;11.\u003c/p\u003e\u003cp\u003e\u003cem\u003eMultimodal integration\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWhere relevant, co-association networks and integrative heatmaps were generated to explore relationships between \u003cem\u003eS. mansoni\u003c/em\u003e infection, microbial taxa, metabolites, and CVD phenotypes. Correlation networks were built using the Spearman\u0026rsquo;s rank correlation in the mixomics package.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAs summarised in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, this study included 216 participants selected based on the availability of lipid profiles (LDL, HDL, total cholesterol, triglycerides) and blood pressure data. Of these, 135 participants (62.5%) were classified as \u003cem\u003eSchistosoma mansoni\u003c/em\u003e positive, defined by positive results on both Kato-Katz microscopy and PCR, while 81 participants (37.5%) were negative on both tests.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of the study participants from rural and urban communities.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInfected\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;135\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUninfected\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;81\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;216\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemales/ Males\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43/92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60/21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103/113\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge group (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; 10\u0026ndash;19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; 20\u0026ndash;29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; 30\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; 40\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSetting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; Rural\u003c/p\u003e\u003cp\u003eI. Intensive treatment\u003c/p\u003e\u003cp\u003eII. Standard treatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89\u003c/p\u003e\u003cp\u003e47\u003c/p\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003cp\u003e33\u003c/p\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e133\u003c/p\u003e\u003cp\u003e80\u003c/p\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; Urban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe cohort comprised 113 males and 103 females. Among females, 43 were \u003cem\u003eS. mansoni\u003c/em\u003e positive and 60 were negative. Participants were distributed across age groups: 10\u0026ndash;19 years, 20\u0026ndash;29 years, 30\u0026ndash;39 years, and 40 years and above.\u003c/p\u003e\u003cp\u003eFrom the rural survey (LaVIISWA trial), 89 \u003cem\u003eS. mansoni\u003c/em\u003e positive participants were included, with 33 of these from the intensive treatment arm. Among the negative individuals, 44 were from the rural setting and 37 from the urban setting. This distribution provides a balanced comparison across infection status, sex, age, and environmental exposure. A diet distribution analysis of the participants in rural communities showed that the majority (63%) were predominantly fish eaters, while 27.7% consumed a mixed diet. The other diet types were vegetarians and meat eaters that accounted for 5.9% and 3.4%, respectively, of the rural participants. The algorithm used for the diet distribution analysis is shown in supplementary Fig.\u0026nbsp;4.\u003c/p\u003e\u003cp\u003e\u003cb\u003eS. mansoni\u003c/b\u003e \u003cb\u003einfection is associated with altered gut microbial diversity and composition\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe first performed \u003cem\u003e16S rRNA\u003c/em\u003e amplicon sequencing on faecal samples from all the individuals used in this study. We found that sample gut microbial diversity (alpha diversity) was significantly higher in \u003cem\u003eS. m\u003c/em\u003e\u0026thinsp;+\u0026thinsp;compared to \u003cem\u003eS. m\u003c/em\u003e- (p\u0026thinsp;=\u0026thinsp;0.048 and p\u0026thinsp;=\u0026thinsp;0.008, for Shannon index and observed richness, respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). On the other hand, Bray-Curtis-based beta diversity analysis did not show significant separation (PERMANOVA p\u0026thinsp;=\u0026thinsp;0.175; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) between \u003cem\u003eS. m\u003c/em\u003e\u0026thinsp;+\u0026thinsp;and \u003cem\u003eS. m\u003c/em\u003e- individuals living in the rural setting, although a difference in the overall microbial community structure was observed between the \u003cem\u003eS. m\u003c/em\u003e\u0026thinsp;+\u0026thinsp;and \u003cem\u003eS. m\u003c/em\u003e- individuals living in the urban setting (PERMANOVA p\u0026thinsp;=\u0026thinsp;0.011; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). In addition, we compared the beta diversity of participants that were under intensive anthelminthic treatment to those under standard treatment and we observed no difference in clustering (see supplementary Fig.\u0026nbsp;1B).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe next asked if there were specific bacteria genera that could be used to discriminate between \u003cem\u003eS. m\u003c/em\u003e\u0026thinsp;+\u0026thinsp;and \u003cem\u003eS. m\u003c/em\u003e\u0026thinsp;\u0026minus;\u0026thinsp;individuals. Linear discriminant analysis (LDA) using LEfSe identified bacterial genera that best discriminated between \u003cem\u003eS. m\u003c/em\u003e\u0026thinsp;+\u0026thinsp;and \u003cem\u003eS. m\u003c/em\u003e\u0026thinsp;\u0026minus;\u0026thinsp;individuals based on relative abundance patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u0026ndash;E). Genera with LDA scores\u0026thinsp;\u0026gt;\u0026thinsp;2 were considered to have a meaningful effect size in separating the groups. Among rural participants, taxa such as \u003cem\u003eAcinetobacter, Methanosphaera, Jeotgalibaca\u003c/em\u003e, and \u003cem\u003eRuminococcus\u003c/em\u003e were enriched in \u003cem\u003eS. m\u003c/em\u003e\u0026thinsp;+\u0026thinsp;individuals, while \u003cem\u003eStreptococcus, Prevotella\u003c/em\u003e, and \u003cem\u003eRoseburia\u003c/em\u003e were enriched in the \u003cem\u003eS. m\u0026thinsp;\u0026minus;\u003c/em\u003e\u0026thinsp;group. In the urban cohort, LDA highlighted \u003cem\u003eRomboutsia, Succinivibrio, Clostridium_sensu_stricto_1, Treponema, Pseudomonas, Butyrivibrio\u003c/em\u003e, and \u003cem\u003eGastranaerophilales as enriched in S. m+\u003c/em\u003e, whereas \u003cem\u003ePrevotella, Streptococcus, Dialister, Facalibacterium\u003c/em\u003e, and \u003cem\u003eAgathobacter\u003c/em\u003e were enriched in \u003cem\u003eS. m\u0026thinsp;\u0026minus;\u0026thinsp;individuals.\u003c/em\u003e Notably, \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e were consistently enriched in the \u003cem\u003eS. m\u003c/em\u003e\u0026thinsp;\u0026minus;\u0026thinsp;group across both settings.\u003c/p\u003e\u003cp\u003eTo further investigate the impact of \u003cem\u003eS. mansoni\u003c/em\u003e infection on the gut microbiome, we compared microbial taxonomic abundance profiles between \u003cem\u003eS. m\u0026thinsp;+\u003c/em\u003e\u0026thinsp;and \u003cem\u003eS. m\u003c/em\u003e-individuals. Differential abundance analysis revealed a set of taxa significantly enriched in \u003cem\u003eS. m\u003c/em\u003e\u0026thinsp;+\u0026thinsp;individuals (FDR adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including \u003cem\u003eAltererythrobacter, Arthrobacter Devosia, Domibacillus\u003c/em\u003e, and \u003cem\u003eLysobacter\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and supplementary Fig.\u0026nbsp;2). \u003cem\u003eListeria\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e and \u003cem\u003eCetobacterium\u003c/em\u003e were significantly depleted in the \u003cem\u003eS. m\u003c/em\u003e\u0026thinsp;+\u0026thinsp;individuals.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpecific microbial taxa mediate the relationship between\u003c/b\u003e \u003cb\u003eS. mansoni\u003c/b\u003e \u003cb\u003einfection and cardiovascular risk\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRegression analyses revealed distinct microbe\u0026ndash;CVD risk factor associations present in rural (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) and urban (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) sample populations after adjusting for confounding factors including age, sex, BMI, and diet (in the rural population).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn both figures, the microbes shown are the fifty most abundant. Notably, taxa such as \u003cem\u003eTreponema\u003c/em\u003e were consistently associated with LDL cholesterol in both rural and urban participants.\u003c/p\u003e\u003cp\u003eTo further illustrate the degree of overlap and uniqueness of these microbial associations across cardiovascular outcomes, we generated a Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), which highlights shared and distinct taxa linked to multiple CVD risk factors.\u003c/p\u003e\u003cp\u003eTo investigate the functional relevance of the microbial differences reported here, we conducted a mediation analysis to identify taxa that may mediate the impact of \u003cem\u003eS. mansoni\u003c/em\u003e infection on cardiovascular disease risk factors. Several taxa significantly mediated the relationship between \u003cem\u003eS. mansoni\u003c/em\u003e infection and reduced cardiovascular risk, with all the microbiota shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD showing negative effects (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) on CVD risk. \u003cem\u003eTreponema\u003c/em\u003e was differentially more present in \u003cem\u003eS. m\u003c/em\u003e\u0026thinsp;+\u0026thinsp;and linked to insulin sensitivity and diastolic blood pressure. \u003cem\u003eFamily_XIII_AD3011\u003c/em\u003e_group, \u003cem\u003edgA.11_gut_group\u003c/em\u003e, and \u003cem\u003eChristensenellaceae_R.7_group\u003c/em\u003e were more abundant in the infected group and linked with glucose, diastolic, and systolic blood pressure respectively. \u003cem\u003eMethanobrevibacter\u003c/em\u003e and \u003cem\u003ePhoenicibacter\u003c/em\u003e were linked with glucose intolerance. For the taxa that were more abundant in the uninfected group, \u003cem\u003eRoseburia, Lachnospiraceae_UCG.004, Granulicatella\u003c/em\u003e were linked with insulin sensitivity, systolic blood pressure and LDL-Cholesterol respectively.\u003c/p\u003e\u003cp\u003e\u003cb\u003eS. mansoni\u003c/b\u003e \u003cb\u003einfection is associated with metabolome differences\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo dissect whether the effect of \u003cem\u003eS. mansoni\u003c/em\u003e infection on the gut microbiome can translate into differences in microbial-related metabolism, we compared faecal metabolomic profiles between \u003cem\u003eS. m\u003c/em\u003e\u0026thinsp;+\u0026thinsp;and \u003cem\u003eS. m\u003c/em\u003e- individuals. A volcano plot shows the differentially abundant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) metabolites in both groups, highlighting metabolic alterations associated with \u003cem\u003eS. mansoni\u003c/em\u003e infection (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The 10 most upregulated metabolites in the infected group include HMDB36635, HMDB39448, HMDB10385, HMDB14867, HMDB08887, HMDB30053, HMDB31040, HMDB60963, HMDB11158 and metabolite with mass to charge ratio 6.26_1326581m/z. The 10 most upregulated metabolites in the uninfected group include HMDB46827, HMDB29485, HMDB14388, HMDB36122, HMDB31828, HMDB14377, HMDB10261, HMDB14585, HMDB11367, HMDB11895. Further, partial least squares discriminant analysis (PLS-DA) suggested some degree of separation between the faecal metabolomic profiles of \u003cem\u003eS. m\u0026thinsp;+\u003c/em\u003e\u0026thinsp;and \u003cem\u003eS. m\u003c/em\u003e- individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB); however, this clustering did not reach statistical significance based on PERMANOVA (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.48). To evaluate the discriminative capacity of metabolomic features, we trained a PLS-DA\u0026ndash;based classification model (Supplementary Fig.\u0026nbsp;5). The model exhibited limited predictive performance, with an overall accuracy of 53.8%, specificity of 54.8%, and sensitivity of 53.2%, indicating poor ability to discriminate between \u003cem\u003eS. m\u0026thinsp;+\u003c/em\u003e\u0026thinsp;and \u003cem\u003eS. m\u003c/em\u003e- individuals. Further, in supplementary Fig.\u0026nbsp;1B we compared metabolomes of participants in the rural setting that were in the intensive anthelminthic treatment arm to those in the standard anthelminthic treatment and we found no difference.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEnrichment of lipid-related pathways among metabolites elevated in\u003c/b\u003e \u003cb\u003eS. mansoni-\u003c/b\u003e\u003cb\u003einfected individuals\u003c/b\u003e\u003c/p\u003e\u003cp\u003eComparative analysis revealed a distinct metabolic signature in infected individuals, with a subset of metabolites significantly more abundant compared to uninfected controls. Pathway enrichment analysis of these elevated metabolites was performed using the Integrated Molecular Pathway Level Analysis (IMPaLA) platform and results shown in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBiological pathways enriched by metabolites that were more abundant in S. mansoni infected individuals than uninfected participants. Metabolites were extracted from faecal samples of 209 participants and profiled using liquid chromatography-mass spectrometry. Metabolite identification and quantification was done using Progenesis QI software and pathways were analysed using Integrated Molecular Pathway Level Analysis (IMPaLA).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBiological pathway enriched by metabolites\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePathway source\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of metabolites\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-\u003c/p\u003e\u003cp\u003evalue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNR1H2 \u0026amp; NR1H3 regulate gene expression to limit cholesterol uptake\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReactome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNR1H2 \u0026amp; NR1H3 regulate gene expression to control bile acid homeostasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReactome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNR1H2 \u0026amp; NR1H3 regulate gene expression linked to gluconeogenesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReactome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNR1H2 \u0026amp; NR1H3 regulate gene expression linked to lipogenesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReactome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNR1H2 \u0026amp; NR1H3 regulate gene expression linked to triglyceride lipolysis in adipose\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReactome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNR1H3 \u0026amp; NR1H2 regulate gene expression linked to cholesterol transport and efflux\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReactome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCholesterol biosynthesis with skeletal dysplasias\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWikipathways\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis analysis identified an overrepresentation of pathways regulated by the nuclear receptors NR1H2 (LXRβ) and NR1H3 (LXRα), which are central to lipid homeostasis and metabolic regulation. Specifically, six Reactome pathways driven by NR1H2/NR1H3 activity were significantly enriched (all p\u0026thinsp;=\u0026thinsp;0.0003), including those regulating cholesterol uptake, bile acid homeostasis, gluconeogenesis, lipogenesis, triglyceride lipolysis in adipose tissue, and cholesterol transport and efflux. These findings suggest coordinated transcriptional regulation of lipid metabolic processes in \u003cem\u003eS. mansoni\u003c/em\u003e-infected individuals. In addition, a Wikipathways entry linked to cholesterol biosynthesis in the context of skeletal dysplasias was significantly enriched (p\u0026thinsp;=\u0026thinsp;0.0011). Collectively, these data indicate that \u003cem\u003eS. mansoni\u003c/em\u003e infection is associated with a specific faecal metabolic profile marked by enhanced abundance of metabolites involved in lipid signalling and transport.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIntegrated microbiome\u0026ndash;metabolome interactions link to total and LDL cholesterol levels\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo examine how microbial and metabolic alterations interact to influence lipid metabolism, we conducted integrative correlation analyses, combining microbes and metabolites that were significantly associated with total and LDL cholesterol. Among those microbes and metabolites significantly associated with total cholesterol, a circos plot highlighted robust correlations (r\u0026thinsp;\u0026ge;\u0026thinsp;0.7) between specific genera and metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), which were visualized in detail in a corresponding heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Metabolite classification using ClassyFire revealed enrichment of glycerolipids, steroids and steroid derivatives, and glycerolphospholipids among total cholesterol-associated compounds that are linked to the microbes associated with total cholesterol (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA similar analysis was done for microbes and metabolites significantly associated with LDL cholesterol and uncovered a distinct but overlapping set of microbe\u0026ndash;metabolite correlations (r\u0026thinsp;\u0026ge;\u0026thinsp;0.65; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD\u0026ndash;E), again featuring key taxa and metabolite classes previously implicated in lipid homeostasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003eMetabolite annotation revealed that several cholesterol-associated compounds belonged to key chemical classes, including Glycerol lipids, fatty acyls carboxylic acids and derivatives, steroids and steroid derivatives, and glycerolphospholipids (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). There is an overlap in the classes of metabolites linked to total and LDL cholesterol.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMicrobiome\u0026ndash;metabolome interactions also relate to blood pressure regulation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe extended our integrative approach to blood pressure phenotypes. Diastolic blood pressure was associated with a network of microbiota\u0026ndash;metabolite interactions (r\u0026thinsp;\u0026ge;\u0026thinsp;0.7; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), and a heatmap visualization confirming the several strong correlations, both positive and negative as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB. Metabolite classification highlighted compounds linked to classes such as glycerolipids, prenol lipids, organooxygen, fatty acyls and steroid and steroid derivatives (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Microbiome-metabolome associations were found for systolic blood pressure (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD-F) and similar classes including prenol lipids, gycerolipids, fatty acyls characterised most of the metabolites involved, emphasizing the role of gut microbial metabolites as potential regulators of blood pressure. Similar analysis was done for insulin associated microbes and metabolites and fewer microbe-metabolite associations were seen, as shown in supplementary Fig.\u0026nbsp;6.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eS. mansoni\u003c/b\u003e\u003cb\u003e-induced microbial changes alter host CVD risk through metabolites\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo explore potential mechanistic links between schistosomiasis-associated gut microbiota and risk for CVD, we constructed a directed network integrating differentially abundant microbial taxa, correlated faecal metabolites, and associated CVD risk factors. Among the taxa enriched in schistosomiasis-positive individuals (log₂ fold change\u0026thinsp;\u0026gt;\u0026thinsp;1, FDR-adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), we identified several genera\u0026mdash;including \u003cem\u003eLysobacter\u003c/em\u003e, \u003cem\u003eArthrobacter\u003c/em\u003e, and \u003cem\u003eVicinamibacteraceae\u003c/em\u003e\u0026mdash;that were strongly inversely correlated with specific metabolites, such as HMDB31050 and HMDB32627 (|ρ| \u0026ge; 0.65, FDR-adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. These metabolites, in turn, were significantly associated with CVD risk factors, most notably diastolic blood pressure and LDL cholesterol. Visualization of the network (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) revealed a coherent directional path from microbial taxa to metabolite changes and CVD risk, suggesting a putative microbiome\u0026ndash;metabolite\u0026ndash;CVD axis modulated by \u003cem\u003eS. mansoni.\u003c/em\u003e These findings support the hypothesis that helminth infection may influence CVD risk through metabolic changes mediated by the gut microbiome. Our findings also showed that \u003cem\u003eS. mansoni\u003c/em\u003e infection was associated with an enrichment of specific bacterial taxa, including \u003cem\u003eDomibacillus\u003c/em\u003e and \u003cem\u003eGaiella\u003c/em\u003e. Notably, these taxa exhibited correlations with metabolites and cardiometabolic risk, detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifferentially Abundant Gut Microbiota in\u0026nbsp;S. mansoni-infected individuals and their correlation with cardiovascular risk-associated metabolites. This table shows microbiota taxa found to be significantly more abundant in individuals infected with\u0026nbsp;S. mansoni, as determined by differential abundance analysis (Log2FoldChange and adjusted p-value). Each taxon is annotated with its associated metabolite and cardiovascular disease (CVD) risk factor as shown in the network Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. Microbiota\u0026ndash;metabolite correlations are shown as Pearson correlation coefficients. Negative correlation values suggest a potential inverse relationship between microbial abundance and metabolite levels. CVD risk factors include diastolic blood pressure and LDL cholesterol.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog2FoldChange from Differential Abundance analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdjusted p. value for microbiota\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eS. mansoni\u003c/em\u003e Group where taxa is more abundant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTaxa associated with CVD risk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMetabolite associated with CVD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMicrobiota-metabolome correlation value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAssociated CVD risk\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.1867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.5768e-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInfected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eAltererythrobacter\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHMDB31050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDIASTOLIC BP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.1129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.2807e-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInfected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eArthrobacter\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHMDB31050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDIASTOLIC BP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.3364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.5039e-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInfected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eDevosia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHMDB31050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDIASTOLIC BP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.4811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.2449e-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInfected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eDomibacillus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHMDB32627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDIASTOLIC BP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.1777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.7252e-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInfected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eEllin6055\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHMDB31050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDIASTOLIC BP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.0694\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInfected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eGeodermatophilus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHMDB31050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDIASTOLIC BP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.0117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInfected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eKapabacteriales\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHMDB32627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDIASTOLIC BP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.5948\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.8261e-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInfected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eKribbella\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHMDB31050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.797\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDIASTOLIC BP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.0321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInfected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eLongimicrobiaceae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHMDB31050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDIASTOLIC BP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.6592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.4792e-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInfected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eLysobacter\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHMDB31050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDIASTOLIC BP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.0511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInfected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eNitrospira\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHMDB31050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDIASTOLIC BP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.8145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.4458e-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInfected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ePseudarthrobacter\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHMDB31050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDIASTOLIC BP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3.8699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.8352e-22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInfected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eVicinamibacteraceae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHMDB32627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDIASTOLIC BP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.2985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.79650e-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInfected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eGaiella\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.95_764.7010n\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLDL CHOLESTEROL\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.2985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.7965e-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInfected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eGaiella\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHMDB56087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLDL CHOLESTEROL\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.6089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.9925e-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInfected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eArenimonas\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.95_764.7010n\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLDL CHOLESTEROL\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study shows that \u003cem\u003eS. mansoni\u003c/em\u003e infection is associated with distinct changes in gut microbial diversity, metabolomic profiles, and microbe\u0026ndash;metabolic interactions. We can show that these alterations appear to influence cardiovascular risk through multiple, interlinked pathways, implicating the gut ecosystem as a mediator of \u003cem\u003eS. mansoni\u003c/em\u003e-driven cardiometabolic risk modulation in humans.\u003c/p\u003e\u003cp\u003eThe observed differences in alpha diversity between \u003cem\u003eS. m\u003c/em\u003e\u0026thinsp;+\u0026thinsp;and \u003cem\u003eS. m\u003c/em\u003e- individuals suggest that parasitic infection significantly alters one\u0026rsquo;s gut microbial profile. Several studies have reported reduced alpha diversity, typically associated with a less resilient and less functionally diverse microbiome, to be linked to CVD risk. For example, Kelly and colleagues showed an association between increased observed richness and reduced lifetime CVD risk [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Similarly, Fu \u003cem\u003eet al\u003c/em\u003e, reported a positive association between bacterial richness and HDL cholesterol [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] in individuals living in the Netherlands. With such evidence showing that more bacterial diversity and richness is associated reduced CVD risk and improved lipid profiles, therefore we postulated that one way through which \u003cem\u003eS. mansoni\u003c/em\u003e infection may improve lipid profiles in the host is by increasing bacterial richness and diversity.\u003c/p\u003e\u003cp\u003eIn addition to alpha diversity differences, we also observed beta diversity differences in the microbiome profiles between \u003cem\u003eS. mansoni\u003c/em\u003e-infected and uninfected individuals, further indicating that infection not only increases microbial richness, but it can also shift the overall composition of the microbial community, leading to distinct clustering of infected and uninfected individuals as seen in participants living in urban settings. We did not observe similar differences in clustering of overall microbial structure (beta diversity) between \u003cem\u003eS. mansoni\u003c/em\u003e infected and uninfected living in the rural setting. This could be because, as shown in previous studies, the rural dwellers tend to have higher gut microbiome diversity and stability due to continuous exposure to a wide range of environmental microbes, diverse diets rich in unprocessed fibre-rich foods, and frequent exposure to infections that may buffer the microbiome against significant changes that may be caused by \u003cem\u003eS. mansoni\u003c/em\u003e infection [\u003cspan additionalcitationids=\"CR47 CR48\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Specifically, given the high exposure to \u003cem\u003eS. mansoni\u003c/em\u003e in our rural population, a typical island community, it is possible that the individuals that were uninfected at the time of sample collection, might have had longstanding effects of \u003cem\u003eS. mansoni\u003c/em\u003e infection from previous exposure that may modify gut microbiome differences observed in our beta diversity analysis.\u003c/p\u003e\u003cp\u003eNext, we applied linear discriminant analysis (LDA) to identify microbial taxa that best discriminate between individuals with and without \u003cem\u003eS. mansoni\u003c/em\u003e infection, across both rural and urban settings. Unlike statistical tests of differential abundance, which identify taxa that vary significantly in abundance between groups, LDA ranks features based on their ability to separate predefined classes. Notably, microbes such as \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e were found to be consistently more abundant in \u003cem\u003eS. mansoni\u003c/em\u003e infected individuals and are known to play pivotal roles in modulating immune responses and inflammation thereby bringing about protection against CVD risk.\u003c/p\u003e\u003cp\u003eThese findings align with the hypothesis that parasitic infections such as \u003cem\u003eS. mansoni\u003c/em\u003e may exert long-term effects on host health by reshaping the microbiome. Importantly, the altered microbial profiles we observed may not only reflect the host\u0026rsquo;s immune response to infection but could also be directly involved in mediating disease risk through metabolic and inflammatory pathways. We therefore needed to investigate the mediatory role that helminth-induced gut microbiota changes could play in altering one\u0026rsquo;s CVD risk.\u003c/p\u003e\u003cp\u003eAs such, we performed mediation analysis to show that indeed \u003cem\u003eS. mansoni\u003c/em\u003e infection may influence cardiovascular risk indirectly through its effects on the gut microbiome. Specifically, we show here that changes in microbial composition appear to positively and negatively mediate key cardiovascular risk factors such as LDL cholesterol and blood pressure. Notably, negative mediation effects reported here suggest that the increased abundance of these taxa in infected individuals may partially explain the protective cardiovascular phenotype observed.\u003c/p\u003e\u003cp\u003eCertain taxa enriched in infected individuals such as \u003cem\u003eTreponema, Family_XIII_AD3011_group\u003c/em\u003e, \u003cem\u003edgA.11_gut_group\u003c/em\u003e, and \u003cem\u003eChristensenellaceae_R.7_group\u003c/em\u003e were associated with improvements in insulin sensitivity and reductions in blood pressure and glucose levels, suggesting that helminth-associated microbial shifts may contribute to a more metabolically favorable profile. However, not all taxa enriched in the infected group aligned with this protective pattern. For instance, \u003cem\u003eMethanobrevibacter\u003c/em\u003e and \u003cem\u003ePhoenicibacter\u003c/em\u003e were associated with glucose intolerance despite being more abundant in infected individuals, highlighting the diverse and sometimes opposing metabolic effects of different microbial members within the same ecological context.\u003c/p\u003e\u003cp\u003eConversely, several taxa more abundant in uninfected individuals, including \u003cem\u003eRoseburia\u003c/em\u003e, \u003cem\u003eLachnospiraceae_UCG.004\u003c/em\u003e, and \u003cem\u003eGranulicatella\u003c/em\u003e were linked to both beneficial and adverse cardiovascular traits, such as enhanced insulin sensitivity, elevated systolic blood pressure, and higher LDL cholesterol levels, respectively. These findings underscore the complexity of microbiota\u0026ndash;host interactions, indicating that the health impact of a given microbe is not solely determined by its presence or absence, but by its context within the broader microbial community and host environment. This functional diversity reinforces the idea that \u003cem\u003eS. mansoni\u003c/em\u003e-associated shifts in microbiome composition may tip the balance of microbial activity toward either protective or deleterious effects, depending on the taxa involved and the pathways engaged.\u003c/p\u003e\u003cp\u003eAdditionally, positive mediation of microbiota on CVD risk factors such as LDL cholesterol in uninfected individuals shown in supplementary figure could imply that \u003cem\u003eS. mansoni\u003c/em\u003e infected individuals have less LDL cholesterol because they lack microbial populations that have been shown to lead to increases in these CVD risk factors.\u003c/p\u003e\u003cp\u003eFor example, \u003cem\u003eEnterobacter\u003c/em\u003e and \u003cem\u003eLachnospiraceae_UCG.010\u003c/em\u003e mediate increased total cholesterol in individuals without \u003cem\u003eS. mansoni\u003c/em\u003e infection, with \u003cem\u003eEnterobacter\u003c/em\u003e also elevating LDL cholesterol levels. \u003cem\u003eEnterobacter\u003c/em\u003e is linked with systemic inflammation through lipopolysaccharide (LPS)-mediated activation of host immune pathways [\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], hence altering lipid metabolism. Similarly, \u003cem\u003eLachnospiraceae\u003c/em\u003e, given their capacity to produce short-chain fatty acids such as propionate from microbial fermentation, may promote lipid and cholesterol synthesis in the absence of helminth-induced immunoregulation[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. These findings point to divergent microbial contributions to metabolic and cardiovascular phenotypes depending on infection status. The dual nature of microbial associations emphasizes the need to consider ecological context and host-microbe interactions in interpreting microbiome-mediated health outcomes. Overall, these results suggest that \u003cem\u003eS. mansoni\u003c/em\u003e-associated shifts in the gut microbiome may actively contribute to the modulation of cardiovascular risk factors and offer candidate microbial targets for further mechanistic and translational investigation.\u003c/p\u003e\u003cp\u003eTo complement this analysis, we further employed linear regression to investigate the relationship between microbes and CVD risk factors. By adjusting for key confounders such as age, sex, BMI, and diet, we aimed to isolate the unique contribution of the microbes to CVD risk. The findings revealed significant associations between specific microbial taxa and distinct CVD risk factors, suggesting that these microbes may play a mechanistic role in CVD risk.\u003c/p\u003e\u003cp\u003eThese results agree with the mediation analysis findings, where the role of \u003cem\u003eS. mansoni\u003c/em\u003e infection in modulating CVD risk factors was partially explained by its influence on the microbiome. This suggests a pathway wherein microbes, through their microbial composition, may mediate the relationship between S. mansoni and CVD outcomes. The robustness of these associations, even after adjusting for confounders, underscores the potential of these microbial markers as indicators or contributors to CVD disease pathways. These findings support the notion that \u003cem\u003eS. mansoni\u003c/em\u003e infection and the gut microbiota can influence cardiovascular disease (CVD) risk both independently and concertedly. The identification of taxa that significantly mediate the relationship between \u003cem\u003eS. mansoni\u003c/em\u003e infection and reduced CVD risk suggests that part of the protective effect of infection may be exerted through infection-induced remodelling of the microbiota\u0026ndash;metabolome axis. However, not all associations between \u003cem\u003eS. mansoni\u003c/em\u003e and cardiovascular risk were microbiota-mediated, indicating the presence of parallel, microbiota-independent pathways such as immune modulation through which helminth infection may confer CVD risk protection.\u003c/p\u003e\u003cp\u003eWe observed more significant associations of microbes with CVD risk among \u003cem\u003eS. mansoni\u003c/em\u003e infected individuals in the urban compared to those in rural populations. The observed stronger associations in the urban population can be attributed to \u003cem\u003eS. mansoni\u003c/em\u003e infection having a greater influence on microbial diversity in this population compared to the rural. Having shown that even from the top 50 abundant microbes, we have evidence of some microbes being associated with CVD risk, we then extracted all the significantly associated microbes from the entire dataset (beyond 50 most abundant microbes) and investigated if there was any relationship (overlap) between microbes that are significantly associated with the different CVD risk factors. Indeed, we can show from that there are several microbes that associated with more than one cardiovascular risk.\u003c/p\u003e\u003cp\u003eTriangulating evidence from association and mediation analysis supports the hypothesis that the gut microbiome could play a central role in the regulation of metabolic pathways that are critical to cardiovascular health. For instance, certain microbial taxa produce metabolites such as SCFAs and secondary bile acids that can influence lipid metabolism [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Dysregulation of these pathways may therefore contribute to an increased risk of cardiovascular events. These insights highlight the importance of considering infectious diseases like \u003cem\u003eS. mansoni\u003c/em\u003e not only in terms of acute morbidity but also in their potential to influence long-term health outcomes through microbiome-mediated mechanisms.\u003c/p\u003e\u003cp\u003eWe therefore set out to investigate the metabolome profiles of our participants as a way of assessing if, similar to the microbiome changes observed here, there could be differences in the faecal metabolites between individuals that were infected with \u003cem\u003eS. mansoni\u003c/em\u003e infection and those that are not infected. This would enable us to infer functionality of the gut microbiota and how they act to alter one\u0026rsquo;s cardiovascular risk.\u003c/p\u003e\u003cp\u003eDespite no statistically significant global separation of metabolomic profiles between \u003cem\u003eS. mansoni\u003c/em\u003e\u0026ndash;infected and uninfected individuals as assessed by PLS-DA, univariate linear regression analysis identified several metabolites that were differentially abundant between the two groups, with multiple features reaching nominal significance thresholds. This suggests that \u003cem\u003eS. mansoni\u003c/em\u003e infection is associated with specific metabolic alterations rather than broad-scale shifts in the overall metabolome. The lack of clear clustering in multivariate space may reflect substantial inter-individual heterogeneity, or localized metabolic effects of infection, or the multifactorial nature of host metabolic responses. Although we identified differentially abundant metabolites between \u003cem\u003eS. mansoni\u003c/em\u003e\u0026ndash;infected and uninfected individuals, our PLS-DA classification model demonstrated limited predictive performance, likely reflecting the biology of \u003cem\u003eS. mansoni\u003c/em\u003e transmission. Unlike enteric pathogens whose acquisition may be modulated by gut microbial or metabolic environments, \u003cem\u003eS. mansoni\u003c/em\u003e is acquired \u003cem\u003evia\u003c/em\u003e percutaneous exposure to cercariae-contaminated freshwater. As such, microbiome and metabolome features are unlikely to serve as determinants of infection status. Instead, the observed metabolic shifts are more plausibly consequences of infection, supporting our interpretation that \u003cem\u003eS. mansoni\u003c/em\u003e may exert causal effects on host physiology.\u003c/p\u003e\u003cp\u003eOur findings from the pathway enrichment analysis revealed that \u003cem\u003eS. mansoni\u003c/em\u003e infection is associated with distinct alterations in host lipid metabolism, as evidenced by significant enrichment of NR1H2/NR1H3-regulated pathways among the differentially abundant metabolites. These nuclear receptors (LXRα and LXRβ) are key transcriptional regulators of lipid homeostasis, and their coordinated activation suggests a host response aimed at modulating cholesterol uptake, bile acid turnover, and lipid mobilization during infection. The parallel enrichment of pathways linked to gluconeogenesis and lipogenesis further supports a broader metabolic reprogramming, potentially reflecting shifts in host energy utilization and storage under chronic parasitic infection.\u003c/p\u003e\u003cp\u003eThese findings align with emerging evidence that helminth infections exert broad systemic effects on host physiology, including lipid and glucose metabolism, and may influence susceptibility to non-communicable diseases such as diabetes and cardiovascular disease [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Future work should investigate whether modulation of LXR signalling contributes to immune tolerance, pathogen persistence, or protection from metabolic disease in endemic populations.\u003c/p\u003e\u003cp\u003eBeyond pathways, we performed linear regression analysis to identify the metabolites that were significantly associated with the different CVD risk and whether were there was overlap between these metabolites. We indeed show here that there are metabolites associated with CVD risk and that there is overlap between metabolites that are associated with various CVD risk factors.\u003c/p\u003e\u003cp\u003eWith a possibility that the microbes found to be associated with the particular risk factors could correlate with metabolites that are similarly associated with the same CVD risk, we integrated these two data modalities- microbiome and metabolome. We identified specific microbiome-metabolome signatures that are associated with cardiovascular risk factors including total- and LDL-cholesterol, DBP and SBP. This integrative approach allows us to capture the complex interactions between the gut microbiome and metabolome, revealing, for each CVD risk factor, how shifts in microbial composition may drive changes in metabolite levels.\u003c/p\u003e\u003cp\u003eAfter identifying the significantly CVD associated microbes and the metabolites that interact together, we further complemented this data with the differential abundance analysis to highlight whether some these CVD-associated microbes interacting with CVD-associated metabolites were enriched in \u003cem\u003eS. mansoni\u003c/em\u003e infected individuals. By doing so, this allowed us to generate a Schistosomiasis-microbe-metabolite-CVD risk mechanistic network. Our integrative network analysis reveals that several bacterial taxa enriched in \u003cem\u003eS. mansoni\u003c/em\u003e individuals were strongly correlated with faecal metabolites. These metabolites were, in turn, associated with key CVD risk factors, including diastolic blood pressure and LDL cholesterol.\u003c/p\u003e\u003cp\u003eThis directional pattern supports the hypothesis that helminth-induced alterations in the gut microbiota may influence host cardiovascular risk through downstream effects on the metabolome. The inverse associations between these microbes and adverse CVD phenotypes align with emerging evidence suggesting that certain helminth-driven microbial shifts may exert systemic immunometabolic benefits. Notably, taxa such as \u003cem\u003eLysobacter\u003c/em\u003e and \u003cem\u003eDevosia\u003c/em\u003e have been implicated in anti-inflammatory metabolic pathways [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], providing a plausible biological basis for these associations. While causality cannot be inferred from this cross-sectional design, these findings raise the possibility that \u003cem\u003eS. mansoni\u003c/em\u003e, or its microbiome-mediated effects, may modulate cardiometabolic outcomes in endemic settings. Further longitudinal and interventional studies are warranted to validate these interactions and assess their relevance for biomarker development or CVD therapeutic modulation.\u003c/p\u003e\u003cp\u003eIn as much as this study provides important insights into the gut microbiome-metabolome-CVD risk interaction in a typical setting with high \u003cem\u003eS. mansoni\u003c/em\u003e infestation, there are potential limitations. Firstly, we did not comprehensively profile participants\u0026rsquo; dietary habits, which are known to have profound effects on both the microbiome and metabolome and are likely to represent an unmeasured confounder. Given the variability in diet across different regions and individuals, future studies should incorporate detailed dietary assessments such as next generation sequencing methodologies to disentangle the effects of infection from those of diet.\u003c/p\u003e\u003cp\u003eFurthermore, given the cross-sectional design of our study, we cannot establish causality in the observed associations between \u003cem\u003eS. mansoni\u003c/em\u003e infection, microbiome composition, metabolite profiles, and cardiovascular risk. Temporal dynamics of microbial and metabolic alterations following infection remain unexplored, limiting our ability to infer directionality. As such, longitudinal studies, ideally spanning pre-infection, active infection, and post-treatment phases, would be essential to disentangle cause-effect relationships and capture the evolving host\u0026ndash;microbiome\u0026ndash;metabolome interactions over time. Incorporating repeated sampling, coupled with temporal metadata such as infection history and treatment timing, would provide a more robust framework for understanding how \u003cem\u003eS. mansoni\u003c/em\u003e shapes cardiometabolic risk through microbial and metabolic pathways.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings provide evidence that \u003cem\u003eS. mansoni\u003c/em\u003e infection is associated with significant alterations in the gut microbiome and metabolome profiles, with important implications for cardiovascular disease risk. These microbiome-mediated effects may represent a novel pathway through which parasitic infections influence CVD outcomes. Future studies should focus on refining our understanding of these interactions, with an emphasis on diet, antibiotic use, and circadian regulation of microbial activity. Our study paves the way for targeted therapeutic interventions aimed at modifying the gut microbiome in a way that mimics helminth infections to reduce cardiovascular risk in humans.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical consideration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor this current project nested in the two studies, ethical approval was obtained from UVRI Research Ethics Committee (UVRI-REC), London School of Hygiene and Tropical Medicine (LSHTM) and Uganda National Council for Science and Technology (UNCST) (required for amendments to the parent studies) and the Higher Degrees Research and Ethics Committee of the School of Medicine, College of Health Sciences, Makerere University. Both studies LaVIISWA and the Urban Survey based at UVRI, Entebbe were approved by UVRI-REC, the (LSHTM) and the UNCST.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.W.\u003c/strong\u003e Conceived the study, designed the experiments, conducted the parasitological, microbiome and metabolomics conducted the primary statistics and bioinformatics analyses, interpreted the results, prepared the figures, and wrote the first draft of the manuscript. \u003cstrong\u003eM.A.E.L.\u003c/strong\u003e Contributed to the microbiome and metabolomics experiments and analyses and contributed to critical manuscript revision. \u003cstrong\u003eA.J.B.\u003c/strong\u003e supported the microbiome and metabolomics experiments, and manuscript drafting and revision. \u003cstrong\u003eJ.N.\u003c/strong\u003e Contributed to the parasitological, microbiome and metabolomics experiments, and contributed to manuscript revision. \u003cstrong\u003eD.K.T.\u003c/strong\u003e Contributed to metabolomics data annotation, and offered guidance on how metabolomics analysis was done. \u003cstrong\u003eG.T.\u003c/strong\u003e Provided specialist support in metabolomics experiments and pathway analysis contributed to critical manuscript review. \u003cstrong\u003eR.E.S.\u003c/strong\u003e Oversaw clinical coordination of parent projects, supported data acquisition, and provided critical input on the interpretation of the cardiovascular risk outcomes. \u003cstrong\u003eE.L.W.\u003c/strong\u003e Supervised statistical analysis for the study, advised on analytical strategy, and contributed to manuscript review and editing. \u003cstrong\u003eD.P.K.\u003c/strong\u003e Provided supervision of project, contributed to and reviewed the manuscript.\u0026nbsp;\u003cstrong\u003eR.K.G.\u003c/strong\u003e provided senior supervision of the project, funding acquisition for this study, contributed to study conceptualization and provided critical manuscript revisions.\u003cbr\u003e\u003cstrong\u003eA.M.E.\u003c/strong\u003e Provided senior supervision throughout the study, supported study design of the parent projects, funding acquisition of both the parent and current studies, advised on epidemiological methods, and contributed to critical revision of the manuscript. All authors reviewed and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to Dr Gyaviira Nkurunungi, Ms Joy Kabagenyi and Mr Alfred Ssekagiri for their expert comments on the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBW is partially supported by\u0026nbsp;GCRF collaborative Grant (R120442) from the Royal Society awarded to Professors Richard Grencis and Alison Elliott is also partially funded by the National Institute for Health Research (NIHR) under its \u003cem\u003eGlobal Health Research Group on Vaccines for Vulnerable People in Africa (VAnguard)\u003c/em\u003e (Grant Reference Number: NIHR134531), using UK aid from the UK Government to support global health research. This project has also been supported by Wellcome Trust Investigator Award Z10661/Z/18/Z and the Wellcome Centre for Cell Matrix Research Grant 088785/Z/09/Z awarded to Professor Richard Grencis, and Wellcome Trust (grant number 095778) awarded to Professor Alison Elliott. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Government. The MRC/UVRI and LSHTM Uganda Research Unit is jointly funded by the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funders were not involved in the conceptualization of the study, writing of the paper and the decision to submit it for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRoth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM et al (2020) Global Burden of Cardiovascular Diseases and Risk Factors, 1990\u0026ndash;2019: Update From the GBD 2019 Study. 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BMC Genomics 16:1\u0026ndash;16\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"a2710f0b-ce15-4619-bc97-b371590ce083","identifier":"10.13039/501100000288","name":"Royal Society","awardNumber":"R120442","order_by":0},{"identity":"8dd165ed-3da3-407d-9827-a430c70a7935","identifier":"10.13039/100010269","name":"Wellcome Trust","awardNumber":"095778","order_by":1}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"MRC/UVRI and LSHTM Uganda Research Unit","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7053382/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7053382/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHelminth infections have been consistently associated with reduced cardiovascular disease (CVD) risk, yet the underlying mechanisms remain poorly understood. We hypothesized that \u003cem\u003eSchistosoma mansoni\u003c/em\u003e infection alters the gut microbiome and metabolome in ways that modulate CVD risk factors—specifically high total cholesterol, LDL cholesterol, and blood pressure. We further hypothesized that \u003cem\u003eS. mansoni\u003c/em\u003e-associated microbes linked with these risk factors would correlate with similarly associated metabolites.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe profiled the gut microbiome (using 16S\u003cem\u003e rRNA\u003c/em\u003e gene sequencing) and faecal metabolome (using liquid chromatography–mass spectrometry) of 216 individuals from two settings in Uganda with contrasting \u003cem\u003eS. mansoni\u003c/em\u003e endemicity. We conducted differential abundance, linear discriminant, linear regression, mediation, pathway enrichment, and integrative multi-omics analyses to investigate associations between \u003cem\u003eS. mansoni\u003c/em\u003einfection, microbial and metabolite profiles, and CVD risk factors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eS. mansoni\u003c/em\u003e (\u003cem\u003eS. m\u003c/em\u003e) infection was associated with increased microbiome alpha diversity (Shannon index \u003cem\u003ep\u003c/em\u003e = 0.048; observed richness \u003cem\u003ep\u003c/em\u003e = 0.008), though beta diversity separation was observed only in urban communities (PERMANOVA \u003cem\u003ep\u003c/em\u003e = 0.011). Linear discriminant analysis (LDA) and differential abundance testing revealed distinct taxa enriched in \u003cem\u003eS. m\u003c/em\u003e+ individuals, including \u003cem\u003eLysobacter\u003c/em\u003e, \u003cem\u003eDomibacillus\u003c/em\u003e, and \u003cem\u003eTreponema\u003c/em\u003e, while \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e were consistently depleted. Mediation analysis identified several taxa such as \u003cem\u003eTreponema, Roseburia\u003c/em\u003e, \u003cem\u003eLachnospiraceae_UCG.00 4 \u003c/em\u003eand\u003cem\u003eMethanobrevibacter\u003c/em\u003e, that significantly mediated the relationship between \u003cem\u003eS. mansoni \u003c/em\u003einfection and reduced cardiovascular disease (CVD) risk factors.\u003c/p\u003e\n\u003cp\u003eMetabolomic profiling of faecal samples identified differentially abundant metabolites (p\u0026lt; 0.05) present in each group, although the overall global metabolomic separation between \u003cem\u003eS. m\u003c/em\u003e+ and \u003cem\u003eS. m\u003c/em\u003e– groups was not significant. Integrative analyses revealed coherent microbe–metabolite–CVD networks present; one example identified were those bacterial species belonging to \u003cem\u003eLysobacter\u003c/em\u003e and \u003cem\u003eArthrobacter\u003c/em\u003e were inversely correlated with metabolites such as glycerolipids, steroids, and fatty acyls which are associated with both total and LDL cholesterol levels found in blood. These findings support a putative helminth–microbiome–metabolome axis that may modulate host cardiometabolic risk.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOur findings reveal a gut microbiome–metabolome pathway induced from \u003cem\u003eS. m \u003c/em\u003einfection that may reduce CVD risk. Collectively this research provides novel insight into host-parasite interactions and identifying microbial and metabolic signatures that could offer new strategies to mimic the cardioprotective effects of helminth infections.\u003c/p\u003e","manuscriptTitle":"Uncovering the role of the gut microbiome and metabolome in Schistosoma mansoni-induced modulation of cardiovascular disease risk in humans","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-08 05:31:44","doi":"10.21203/rs.3.rs-7053382/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3d2fa643-375c-48bd-9622-22416f4d9cce","owner":[],"postedDate":"July 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51086411,"name":"Bioinformatics"},{"id":51086412,"name":"Parasitology"},{"id":51086413,"name":"General Microbiology"}],"tags":[],"updatedAt":"2025-07-08T05:31:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-08 05:31:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7053382","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7053382","identity":"rs-7053382","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}