The gut microbiome and metabolome associate with Schistosoma mansoni infection and cardiovascular disease risk in Uganda

Domain: neuroscience
Published: 2026-02-04
Editorial outcome: major_revision

Abstract
Helminth infections are consistently associated with reduced cardiovascular disease (CVD) risk, yet the biological mechanisms underlying this relationship remain unclear. The gut microbiome and metabolome are key regulators of cardiometabolic health and may mediate infection-associated effects on host physiology. Here we show that Schistosoma mansoni infection associates with distinct gut microbial and metabolic profiles linked to CVD risk in people living in Uganda. In a cross-sectional study of 209 individuals living in communities with contrasting S. mansoni endemicity, we profile the gut microbiome using 16S rRNA gene sequencing and the faecal metabolome using liquid chromatography–mass spectrometry. S. mansoni infection associates with increased gut microbial diversity and distinct taxonomic signatures, including enrichment of taxa such as Treponema and depletion of Prevotella and Streptococcus. Several infection-associated microbial taxa statistically mediate the relationships between S. mansoni infection and cardiovascular disease risk. Faecal metabolomic profiling identifies infection-associated metabolites, and integrative analyses showed linked microbe–metabolite networks associated with cardiovascular risk. These findings identify gut microbiome and metabolome signatures associated with S. mansoni infection and cardiovascular disease risk in Uganda. Although causality cannot be inferred, this work provides insight into host–parasite–microbiome interactions and highlights microbial and metabolic pathways relevant to cardiometabolic health.

Introduction
Globally, cardiovascular diseases (CVDs) pose a significant threat to public health, consistently ranking as the primary cause of mortality over the last thirty years1. For example, CVDs accounted for approximately 20.5 million deaths (over 31% of global deaths) in 2021 alone2. 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 disease3. These risk factors include dietary risks, high systolic blood pressure, dyslipidaemia (particularly high low-density lipoprotein (LDL) cholesterol) and high fasting plasma glucose1.

Several studies have linked cardiovascular risk factors with the immune system response, both in humans and animal models4. Particularly, chronic inflammation has been highlighted as the main immunological feature characterising cardiovascular or metabolic risk5,6. For example, increased circulating tumour necrosis factor (TNF)-α is associated with glucose intolerance, and inhibiting its expression in adipose tissues affects sensitivity to insulin, and tolerance for glucose, in obese individuals6.

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 cholesterol7. Dietary lipids such as omega-3 fatty acids have been linked to production of inflammatory cytokines such as TNF-α and interleukin (IL)−28. 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-α are associated with increased production of triglycerides and LDL cholesterol levels in serum6,9–11. 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 prognosis12. Given the centrality of inflammation in cardiovascular risk, it has been hypothesised that infections that induce immunomodulatory responses protect hosts against metabolic disorders.

One such class of infections with immunomodulatory effects are chronic helminth infections13. These are characterised by a polarised T-helper 2 cell response, important in resisting or eliminating helminth infections in the host14. However, helminths can modulate the host’s immune response to prolong their own survival14–17. For example, helminths induce production of IL-10, a cytokine known to be pivotal in suppressing inflammation, to enhance their survival16,18–20. 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 region21. In another study by Magen et al, individuals with chronic Opisthorchis felineus infection had significantly reduced total cholesterol compared to those without the infection22. Furthermore, Shen et al found an inverse association between previous schistosomiasis infection and triglyceride levels, waist-to-hip ratio and BMI23. In the same study, diastolic blood pressure was significantly lower in subjects with previous schistosomiasis than those without infection23. 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 without24.

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 infection25. This was reaffirmed by Rennie et al who found reduced fasting glucose, glycated haemoglobin (HbA1c) levels, prevalence of T2D and metabolic syndrome in people with helminth infections compared to those without26.

Given that much of the existing evidence linking helminth 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 host27. 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 suggested28.

Schistosomiasis, caused by parasitic trematodes of the genus Schistosoma, remains one of the most prevalent neglected tropical diseases worldwide29, 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 risk30–32. 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 health33,34. Dysbiosis, characterised by abnormal changes in the composition and function of gut microbiota, has been implicated in various pathological conditions, including CVDs34–36. 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 responses37. 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 cholesterol33, 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 cholesterol27. Further, heavy and moderate S. mansoni infection intensities were associated with lower diastolic blood pressure, triglycerides and LDL cholesterol27.

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 and Urban survey study, aiming at deciphering S. mansoni-associated alterations in gut microbial composition and diversity, the potential contribution of gut microbiome to the observed associations with CVD risk factors, 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.

Results
As summarised in Table 1, this study included 209 participants selected based on the availability of lipid profiles (LDL, HDL, total cholesterol, triglycerides) and blood pressure data. Of these, 128 participants (61.2%) were classified as Schistosoma mansoni positive, defined by positive results on both Kato-Katz microscopy and PCR, while 81 participants (38.8%) were negative on both tests.

The cohort comprised 108 males and 101 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), 84 S. mansoni infected participants were included, with 43 of these from the intensive treatment arm. Among the negative individuals, 43 were from the rural setting and 38 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. 1.

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. 2A). On the other hand, Bray-Curtis-based beta diversity analysis did not show significant separation (PERMANOVA p = 0.175; Fig. 2B) 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. 2C). 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. 2), suggesting that anthelminthic treatment was unlikely to meaningfully affect the microbiome–metabolome analyses or introduce bias when comparing rural and urban participants.

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. 2D–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, Faecalibacterium, 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. 3). 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. 4A) and urban (Fig. 4B) 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. 4C) using a previously published tool38, 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. 4D showing negative effects (p < 0.05) on CVD risk. Among these, Treponema mediated reductions in both insulin and glucose levels; Tabrizicola contributed to lower systolic blood pressure, LDL cholesterol, total cholesterol, and insulin; Promicromonospora mediated reductions in LDL cholesterol; Papillibacter mediated lower insulin; Pedomicrobium mediated reduced LDL cholesterol; Catenisphaera mediated lower total cholesterol; and CCD24 mediated reductions in LDL cholesterol. Additional infected-enriched taxa, including Methanobrevibacter, Phoenicibacter, UTCFX1, and Roseomonas mediated decreases in glucose, glucose, systolic blood pressure, and systolic blood pressure respectively. Two taxa were more abundant in uninfected individuals: Lachnospiraceae_UCG.001 and Lachnospiraceae_UCG.004, both of which mediated reductions in systolic blood pressure. Together, these patterns indicate that although most mediating taxa were enriched in infected participants, both infected- and uninfected-abundant microbes exhibited negative mediation effects. Additionally, Enterobacter and Klebsiella had positive mediation effects on total cholesterol and insulin sensitivity respectively, among infected individuals (Supplementary Fig. 4).

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. 5A). The 10 most enriched metabolites in the infected group include HMDB36635, HMDB39448, HMDB10385, HMDB14867, HMDB08887, HMDB30053, HMDB31040, HMDB60963, HMDB11158 and metabolite with mass to charge ratio 6.26_1326581 m/z. The 10 most enriched 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. 5B); however, this clustering did not reach statistical significance based on PERMANOVA (p = 0.48). While these observations provide important insights into potential microbiome-mediated pathways, the metabolomic differences should be interpreted as hypothesis-generating rather than constituting a definitive infection-related metabolic signature, particularly given the modest global separation in untargeted metabolomics. 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. 2 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. 5C.

This analysis identified an overrepresentation of pathways regulated by the nuclear receptors NR1H2 (LXRβ) and NR1H3 (LXRα), which are central to lipid homoeostasis 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 homoeostasis, 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. 6A), which were visualised in detail in a corresponding heatmap (Fig. 6B). 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. 6C).

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).

Methods

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Journal name: Nature Communications\nManuscript number: NCOMMS-25-57900\nManuscript title: Uncovering the role of the gut microbiome and metabolome in Schistosoma mansoni-induced modulation of cardiovascular disease risk in humans.\nSummary: This study investigated whether Schistosoma mansoni infection influences the gut microbiome and metabolome in relation to cardiovascular disease risk. Among 209 participants from Uganda, S. mansoni infection was linked with greater microbial diversity, shifts in specific taxa, and microbiome-metabolome networks connected to cholesterol metabolism. The study shows that S. mansoni may reduce cardiovascular risk through microbiome-metabolic pathways.\nComments to Authors\nAbstract\nPlease indicate the study design used in the methodology even though it is mentioned under the discussion section in line 521.\nCould you use a plainer or simplified phrase for “coherent microbe–metabolite–CVD networks” for clarity?\nThe interpretation of the result of mediation analysis (lines 45-48) suggests a causal mechanism, but the study is cross-sectional.\nIntroduction\nRegarding the aim, which aspect of dysbiosis were you referring to in line 161?\nThe aim is relevant but more driven towards causality, which a cross-sectional study cannot test. Using milder language focused on associations would make that aim more clearer and more precise.\nMethods\nWhile the treatment status of the rural population was explicitly stated, that of the urban population was not.\nWere there any differences between the S. m+ and S. m.- in terms of treatment?\nWhat were the inclusion and exclusion criteria for the study?\nHow were samples stored after collection and during transport?\nThough stated as a limitation, antibiotic history represents a major confounding factor in this study and could be an exclusion criterion.\nWas any criterion employed in the selection of samples for microbiome profiling?\nWhile microbiome profiling pipelines and processing were rigorous, there was no mention of the sequence depth (number of reads) per sample.\nIncluding effect size calculations like odds ratio could improve rigor more than the use of p-values alone.\nDiscussion\nAccording to lines 182, 223, and 768, dietary data was collected from and adjusted for only rural populations? Are the beta diversity differences observed in the urban group due to S. mansoni infection or due to unmeasured dietary variation in the urban group?\nCheck for potential omissions in lines 447 and 497.\nConclusion\nSince no CVD outcomes were measured but only risk factors, could you rephrase the conclusion to suit what was actually measured, that is, the CVD risk factors?", "reviewer_2": "Walusimbi and colleagues explore the relationship between helminth infection, the gut microbiome, the circulating metabolome, and cardiovascular disease (CVD) risk in rural and urban Ugandans. Using sequencing and metabolomic profiling from ~ 200 individuals with differing endemicity, the authors report that S. mansoni infection is associated with increased diversity, enrichment and depletion of certain taxa, and changes in lipid-related metabolic pathways. Mediation and integrative analyses suggests that certain taxa and their derived metabolites may influence host lipid levels and blood pressure, providing a potential mechanistic basis for epidemiological observations linking helminth infections to reduced cardiometabolic risk.\nThe study advances an important area of interest at the intersection of infectious disease, microbiome science, and noncommunicable disease, offering new human evidence for a helminth-microbiome-CVD connection; however, limitations inherent to the cross-sectional design, limited metabolomic signal, and potential dietary and environmental confounders markedly reduces the impact of mechanistic claims. With revisions to temper conclusions, clarify presentation, and acknowledge these caveats, this work would represent an important contribution to the field.\nMajor Comments\n1. A major concern of this manuscript is the cross-sectional design. This limits the ability to determine causality or temporal dynamics. It remains unclear whether microbiome–metabolome changes precede reductions in CVD risk or are downstream consequences of infection. Longitudinal or interventional approaches would be necessary to strengthen causal inference. Explicitly acknowledging, in the discussion, that the cross-sectional design precludes causal inference with the addition of a short paragraph outlining how future longitudinal and interventional studies could address causality would strengthen the overall manuscript.\n2. While several metabolites were differentially abundant, global metabolomics between infected and uninfected individuals was not statistically significant. This raises concerns about the strength of metabolomic findings and the strength of evidence supporting a distinct infection-related metabolic signature. Clarification or reframing of conclusions to emphasize hypothesis generating observations rather than a conclusive metabolic signature would provide a more reasonable appraisal of the metabolic data. Furthermore, additional supplementary analysis which stratifies the data based on infection status and intensity of the infection (if known) could provide further evidence of robustness of the findings.\n3. This lack a difference in metabolic profiling may in part be due to the limit incorporation of the dietary confounder. Diet is a major determinant of both microbiome and metabolome composition. Although some dietary data are provided, comprehensive dietary profiling appears limited. Without stronger control of dietary variables or analysis which attempts to normalize to diet makes it difficult to disentangle the effects of infection from those of diet. Statements which explicitly discuss the dietary confounder and suggestion of future directions to integrate dietary assessment is encouraged to strengthen the overall impact of the manuscript.\n4. Differences in microbiome-metabolome associations between rural and urban participants suggest that environmental and lifestyle factors may strongly influence findings. The authors should discuss more explicitly how these factors might confound or modify the observed associations.\n5. Mediation analyses identify both protective and potentially deleterious taxa enriched in infected individuals. The discussion could improve through acknowledging the complexity of these microbial associations and avoid framing the effect of infection as uniformly protective. Again, emphasizing the cross-sectional design of the study and avoidance of overgeneralization of the infection effect.\n6. While the study provides novel mechanistic hypotheses, the direct clinical applicability remains limited, and mechanistic hypotheses were not tested (the authors may feel this is outside the scope of the manuscript). The authors should temper statements regarding therapeutic potential and discuss future longitudinal and interventional studies to replicate these findings.\n7. The discussion is good but would benefit from inclusion of studies that have not demonstrated protective associations between helminth infections and metabolic outcomes; this would provide more balance. In addition, certain statements are presented with a degree of certainty that exceeds the evidence provided. The authors claim that S. mansoni infection may improve lipid profiles through increased microbial richness and diversity. However, this is speculative, the authors show an association not causality. Rephrasing such as: “We observed that S. mansoni infection is associated with greater microbial richness and given that higher richness has been linked to improved lipid profiles in prior studies, it is plausible, but not yet demonstrated, that richness could contribute to the protective association between infection and lipid metabolism.” This acknowledges the value of the findings while situating them more accurately within the context of existing evidence.\nMinor Comments\n1. Some figures are dense and could benefit from simplified labeling and clearer figure legends to increase accessibility to the reader. I would suggest the addition of simplified summary diagrams to complement heatmap findings and circus plots.\n2. Report effect sizes alongside p-values throughout, particularly in regression and mediation analyses, to help readers gauge the biological significance of findings.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "REVIEWERS’ COMMENTS\nREVIEWER 1\nJournal name: Nature Communications\nManuscript number: NCOMMS-25-57900\nManuscript title: Uncovering the role of the gut microbiome and metabolome in Schistosoma\nmansoni-induced modulation of cardiovascular disease risk in humans.\nSummary: This study investigated whether Schistosoma mansoni infection influences the gut\nmicrobiome and metabolome in relation to cardiovascular disease risk. Among 209\nparticipants from Uganda, S. mansoni infection was linked with greater microbial diversity,\nshifts in specific taxa, and microbiome-metabolome networks connected to cholesterol\nmetabolism. The study shows that S. mansoni may reduce cardiovascular risk through\nmicrobiome-metabolic pathways.\nComments to Authors\nComment 1\nAbstract\nPlease indicate the study design used in the methodology even though it is\nmentioned under the discussion section in line 521.\nThank you very much. We have now included this in line 35 of the abstract.\nComment 2\nCould you use a plainer or simplified phrase for “coherent microbe–metabolite–CVD\nnetworks” for clarity?\nThank you. We have made changes in line 55 in the abstract, it now reads:\n“linked microbe–metabolite networks associated with CVD risk factors” which we believe is a\nclearer phrase\nComment 3\nThe interpretation of the result of mediation analysis (lines 45-48) suggests a causal\nmechanism, but the study is cross-sectional.\nThank you. We have tempered the tone of the interpretation and this now reads as\n“Mediation analysis identified several taxa, such as Treponema, Lachnospiraceae_UCG.00\n4 and Methanobrevibacter, that significantly contributed to explaining the associations\nobserved between S. mansoni infection and reduced cardiovascular disease (CVD) risk\nfactors” in lines 47-51.\nComment 4\nIntroduction\nRegarding the aim, which aspect of dysbiosis were you referring to in line 161?\nThe aim is relevant but more driven towards causality, which a cross-sectional study\ncannot test. Using milder language focused on associations would make that aim\nmore clearer and more precise.\nThank you. We have now used milder language and also explained the aspect of dysbiosis\nwe are referring to, to make the aim clearer and more precise. For lines 164-170, it now\nreads; “In pursuit of a deeper understanding of how S. mansoni infection could lead to these\nchanges in CVD risk, the current work therefore used samples from our LaVIISWA trial and\nUrban survey study, aiming at deciphering S. mansoni-associated alterations in gut microbial\ncomposition and diversity, the potential contribution of gut microbiome to the observed\nassociations with CVD risk factors, and the gut-microbiome and metabolome interaction in\nthe context of chronic schistosomiasis infection and its impact on cardiovascular risk.”\nComment 5\nMethods\nWhile the treatment status of the rural population was explicitly stated, that of the urban\npopulation was not.\nResponse\nWe thank the reviewer for pointing this out. We have now clarified in the Methods section\nthat, unlike the rural LaVIISWA cohort, which consisted of clusters randomised to either\nintensive or standard anthelminthic treatment, the urban comparison cohort did not undergo\nany community-wide deworming intervention prior to this study, and participants therefore\nhad no prior treatment assignment. This can also be seen in table 1 showing the\ncharacteristics of the study participants from rural and urban communities.\nImportantly, we assessed whether helminth treatment status in the rural population\ninfluenced gut microbiome composition. In analyses comparing microbiome profiles between\nintensively treated and standard-treatment rural participants, we observed no differences in\nalpha diversity, beta diversity, or overall microbial community structure as seen in\nsupplementary figure 1b.\nGiven this absence of treatment-related microbiome differences the lack of a treatment arm\nin the urban cohort is unlikely to introduce bias into our rural–urban comparisons or our\nanalyses associating S. mansoni infection with microbiome features.\nWe have updated the Methods section to explicitly state the treatment context for the urban\ncohort\nWe have made the following changes in the manuscript: lines 654-656\nWe have added to the sentence “In the urban survey, no prior community-wide anthelminthic\ntreatment programme had been implemented, and participants therefore had no treatment\nassignment comparable to the LaVIISWA trial.”\nComment 6\nWere there any differences between the S. m+ and S. m.- in terms of treatment?\nResponse\nThank you for this important point. We examined whether anthelminthic treatment status\ndiffered between S. mansoni–infected (S .m⁺) and uninfected (S.m⁻) participants and\nwhether treatment could therefore confound microbiome differences. As shown in\nSupplementary Fig. 1B, gut microbiome profiles did not differ between the intensive and\nstandard treatment arms within the rural cohort. Specifically, beta-diversity analyses (Bray–\nCurtis) of microbiome composition showed no meaningful separation between treatment\ngroups.\nBecause treatment assignment was uncorrelated with microbiome structure, anthelminthic\ntreatment is unlikely to explain the observed microbiome differences associated with S.\nmansoni infection.\nChanges in the manuscript: lines 206-208\nIn the original manuscript lines 196-199, we had highlighted treatment effect on\nmicrobiome:“In addition, we compared the beta diversity of participants that were under\nintensive anthelminthic treatment to those under standard treatment and we observed no\ndifference in clustering (see supplementary Fig. 1B).” we have now supplemented this with\n…” suggesting that anthelminthic treatment was unlikely to meaningfully affect the\nmicrobiome analyses or introduce bias when comparing rural and urban participants” lines\n206-208.\nComment 7\nWhat were the inclusion and exclusion criteria for the study?\nResponse\nThank you for the thoughtful request. As noted in our methods section, participants were\ndrawn from two parent studies: the LaVIISWA rural trial and the Entebbe Urban Survey.\nDrawing reference from previously published work detailing both studies1, in both settings,\nhousehold surveys were conducted, where a household was defined as a habitable roofed\nresidential structure with at least one active resident. Permission was obtained from a\nhousehold head or an adult household member. Households were excluded only if all\nmembers were absent or declined participation.\n Rural sampling (LaVIISWA): An updated register of households across 26 study\nvillages served as the sampling frame. Using simple random sampling, 70\nhouseholds per village were sampled.\n Urban sampling (Entebbe survey): Sub-wards were mapped onto satellite imagery,\nexcluding uninhabited areas. Each sub-ward was divided into equal geographical\nsegments and segments were randomly selected with probability proportional to sub-\nward population. The midpoint of each selected segment, identified by GIS\ncoordinates, served as the starting point. The nearest house was enrolled and\nsubsequent houses were selected sequentially by proximity. In total, 120 segments\nacross 24 sub-wards were targeted, with four households per segment; empty or\nrefusing households were replaced to achieve sample size targets.\nFor this cross-sectional analysis, we applied additional analytic inclusion criteria at the\nindividual level to ensure complete and comparable infection, microbiome, and\ncardiovascular datasets across cohorts.\n Inclusion criteria:\no Cardiovascular risk factors: Availability of at least six risk factor\nmeasurements (e.g., blood pressure, fasting glucose, insulin, lipid profile\ncomponents included in the study).\no Schistosoma mansoni status: Determined by Kato–Katz\no Stool sample: Availability of a stored stool sample suitable for microbiome\nsequencing.\no Core covariates: Complete data for age, sex, setting/residence and BMI.\no Consent: Informed consent provided within the parent study, including\npermission for secondary analyses.\n Exclusion criteria:\no Data completeness: Individuals missing any required data (cardiovascular\nrisk factors, infection status, stool sample, or core covariates) were excluded.\no Laboratory quality: Individuals with missing or insufficient-quality stool DNA\nfor sequencing were excluded.\no Additional biological exclusions: No further biological exclusions (e.g.,\npregnancy, chronic disease, medication use) were applied beyond those\ndefined or encountered in the parent studies.\nBecause eligibility for the present analysis was determined by the parent cohorts’ sampling\nand consent procedures, our additional inclusion criteria focused on data completeness and\nassay concordance to enable harmonised multi-omics comparisons between rural and urban\nsettings.\nWe have now added the full exclusion and inclusion criteria in the methods section lines 705\nto 723.\nWe now added the following section:\n“Eligibility Criteria\nParticipants included in this study were drawn from two established cohort studies in\nUganda: the LaVIISWA trial (rural) and the Entebbe Urban Survey (urban). Full recruitment\nprocedures for these parent cohorts have been described previously1,2. Briefly, both studies\nconducted household-based sampling, with households eligible if at least one adult resident\nprovided consent. Households were excluded only if all members were absent or declined\nparticipation.\nFor the present study, we applied additional individual-level inclusion and exclusion criteria\nto ensure harmonised infection, microbiome, and cardiometabolic datasets across the rural\nand urban sites. Individuals were included if Schistosoma mansoni infection status could be\ndetermined using our harmonised diagnostic panel comprising Kato–Katz microscopy and\nPCR. Eligible participants were also required to have complete cardiovascular risk factor\ndata, including systolic and diastolic blood pressure, fasting glucose and insulin, and both\ntotal and LDL cholesterol. A further requirement was the availability of a stored stool sample\nof adequate quality for 16S rRNA gene sequencing, along with complete information for core\ncovariates—age, sex, body mass index, and residential setting. Only individuals who had\nprovided informed consent within the parent studies that included permission for secondary\nanalyses of biological samples were eligible.\nComment 8\nHow were samples stored after collection and during transport?\nResponse\nWe thank the reviewer for highlighting this important point. We have now included a detailed\ndescription of stool collection and handling procedures in the Methods section (lines 724-\n740) to ensure full transparency.\nWe have made the following changes in the manuscript: lines 724-740\n“Stool sample collection and processing\nStool samples were collected under standardized conditions to ensure preservation of\nmicrobial community composition and DNA integrity. Each participant was provided with a\nsterile, screw-capped stool collection container and instructed on hygienic self-collection on\nthe morning of the scheduled visit. Upon arrival at the field site, a portion of each sample\nwas immediately processed for Schistosoma mansoni detection using the Kato–Katz\nmethod. The remaining stool was transferred using a sterile wooden spatula into pre-labelled\ncryovials containing 95% molecular-grade ethanol to stabilize microbial DNA and minimize\ncompositional changes during handling.\nAll ethanol-fixed samples were promptly placed in liquid-nitrogen charged dry shippers in the\nfield (typically within one hour of collection) and maintained at cryogenic temperatures\nthroughout transport to the central laboratory. On arrival, samples were transferred to –80 °C\nfreezers for long-term storage until DNA extraction. All field and laboratory personnel were\ntrained in biospecimen handling, and procedures were performed under aseptic conditions\naccording to harmonized quality control protocols. This workflow ensured uniformity of pre-\nanalytical processing and high confidence in the validity of downstream microbiome\nmeasurements.”\nComment 9\nThough stated as a limitation, antibiotic history represents a major confounding\nfactor in this study and could be an exclusion criterion.\nResponse\nWe thank the reviewer for this important observation. We fully agree that antibiotic exposure\nis a relevant consideration in microbiome research. Unfortunately, detailed antibiotic-use\nhistories were not available in either parent study (LaVIISWA or the Urban Survey), and\nretrospective reconstruction of antibiotic use is not feasible in this setting. In Uganda,\nantibiotics are generally accessible without prescription, can be obtained through informal\noutlets, and are rarely documented systematically—particularly in rural fishing communities.\nExcluding participants on the basis of unverifiable or incomplete antibiotic histories would\ntherefore introduce selection bias and reduce generalisability without guaranteeing accurate\nexposure classification.\nNonetheless, several factors substantially reduce the likelihood that unmeasured antibiotic\nexposure introduced systematic bias into our findings:\n1. We examined all available medication data, including antibiotics, in the rural cohort,\nwhere medication information had been collected as part of our rural survey. In this\npopulation, antibiotic use was reported by only six individuals, and other medication\ncategories were similarly rare (antiretrovirals n=11; antihypertensives n=1; allergy\nmedications n=2; other medications n=2). No participants reported antidiabetic\ntherapy or herbal medicine use. These small numbers indicate that medication\nexposure, including antibiotics, was infrequent and unlikely to meaningfully confound\nmicrobiome–phenotype associations. We have now included these descriptive\nresults as Supplementary table 1 for transparency.\n2. Absence of parallel medication data in the urban site precludes meaningful statistical\nadjustment, as asymmetrical and sparse medication variables would produce\nunstable estimates and do not constitute a valid adjustment set.\n3. Our analytical strategy was guided by a directed acyclic graph (DAG). The updated\nDAG identifies age, sex, and setting as the minimal sufficient adjustment set required\nto block backdoor paths between S. mansoni, the microbiome, and CVD risk factors.\nAntibiotic use is conceptually important but does not function as a confounder of\nthe S. mansoni - microbiome - CVD pathway under this structure. We now clarify this\nrationale explicitly in the revised Methods and Discussion.\nWe have strengthened the limitations section to acknowledge the absence of detailed\nantibiotic histories, clarified its implications in the causal framework, and added the available\nmedication data in the supplementary materials. We believe these additions address the\nreviewer’s concern while preserving the validity and transparency of our analytical approach.\nWe have made the following changes in the manuscript: line 580 - 596\n“Secondly, antibiotic exposure represents an important but incompletely measured source of\npotential variability in microbiome studies. In these Ugandan settings, antibiotics are\ngenerally accessible without prescription and can be obtained through informal outlets,\nmaking accurate self-report challenging and frequently unreliable. In the rural cohort, limited\nself-reported medication data were collected as part of the survey. Within these constraints,\nreported antibiotic use was uncommon, with only six participants reporting use, and other\nmedication categories similarly rare (Supplementary Table 1). Comparable medication data\nwere not available for the urban cohort, precluding meaningful statistical adjustment.\nOur analytical strategy was guided by a directed acyclic graph, which identified age, sex,\nand setting as the minimal sufficient adjustment set required to block backdoor paths\nbetween S. mansoni infection, the gut microbiome, and cardiometabolic risk factors. While\nantibiotic exposure is conceptually important, it could not be empirically incorporated into the\nadjustment set given the absence of systematic and harmonised data, and exclusion or\nadjustment based on incomplete or asymmetrically measured information would introduce\nbias without improving causal interpretability. We therefore acknowledge the absence of\ndetailed antibiotic-use data as a limitation and highlight prospective, systematic assessment\nof antibiotic exposure as a priority for future studies.. .”\nComment 10\nWas any criterion employed in the selection of samples for microbiome profiling?\nResponse\nYes. Sample selection for microbiome profiling followed a predefined, non-selective, data-\ndriven set of criteria to ensure completeness and comparability across participants from the\ntwo parent cohorts (LaVIISWA and the Urban Survey). No biological or outcome-based\ncriteria were used.\nFrom among participants who met these eligibility criteria, samples were randomly\nselected for sequencing. This approach ensured that inclusion in microbiome profiling was\nnot influenced by infection status, cardiometabolic outcomes, or other biological\ncharacteristics.\n1. Availability of a stored stool sample of sufficient quantity and quality for DNA\nextraction and 16S rRNA sequencing.\n2. Confirmed Schistosoma mansoni infection status, as assessed by Kato–Katz\nand PCR.\n3. Complete cardiometabolic data, including at least six cardiovascular risk factor\nmeasurements.\n4. Availability of core covariates required for adjusted analyses (age, sex, BMI, and\nresidence).\n5. Consent within the parent study that permitted secondary laboratory analyses.\nExclusion criteria were limited to:\n Missing stool samples or insufficient DNA quality for sequencing.\n Missing CVD risk factor data or incomplete infection diagnostics.\nImportantly, selection criteria were applied independently of clinical phenotype, infection\nintensity, microbiome characteristics, treatment status, or other biological features, thereby\navoiding sampling bias. The same criteria were applied uniformly in both rural and urban\nsettings to preserve comparability.\nWe added the exclusion and inclusion criteria in the methods section lines 705 to 723 as\npreviously shared in response to comment #7\nComment 11\nWhile microbiome profiling pipelines and processing were rigorous, there was no\nmention of the sequence depth (number of reads) per sample.\nResponse\nWe thank the reviewer for highlighting the need to report sequencing depth. The biodiversity\ncurves demonstrated that sequencing depth was sufficient for reliable microbiome profiling.\n“The rarefaction curves (cutoff = 82,695 reads) for all samples rapidly approached a plateau,\nindicating that the captured sequencing depth was adequate and that additional reads would\nnot meaningfully increase observed alpha diversity. Likewise, the species accumulation\nanalysis, based on >10 samples as recommended, showed stable asymptotes in species\nrichness, confirming that both sequencing depth and sample size were sufficient to\ncharacterise microbial community structure.” These details have now been added to the\nMethods lines 783-788 and the new Supplementary Figure 12.\nComment 12\nIncluding effect size calculations like odds ratio could improve rigor more than the\nuse of p-values alone.\nResponse:\nWe thank the reviewer for highlighting the importance of reporting effect sizes to support\ninterpretation of biological relevance. In response, we have now added complete effect-size\noutputs for both the regression and mediation analyses, and these are provided in the\nSupplementary Materials.\n1. Regression analyses\nFor every microbe–CVD risk association, stratified by S. mansoni infection status, we now\nreport:\n unstandardised regression coefficients (β) and their 95% confidence intervals\n standardised coefficients (β_std) and their 95% confidence intervals\n p-values\n infection group (Infected vs Uninfected)\nThese are included in:\n Supplementary Table S1 - Rural cohort regression effect sizes\n Supplementary Table S2 - Urban cohort regression effect sizes\nThese tables allow readers to compare not only statistical significance but also the direction\nand magnitude of effects across settings and infection strata.\n2. Mediation analyses\nFor each microbe-metabolite-CVD risk pathway tested in our causal mediation framework,\nwe now report:\n Average Causal Mediation Effect (ACME; indirect effect)\n Standard error\n p-values\n 95% confidence intervals\n significance calls\n corresponding microbe and CVD outcome\nThese results are compiled in:\n Supplementary Table S3 — Mediation effect-size summary\nThis enables clearer assessment of the strength and biological relevance of the indirect\npathways through metabolite mediators.\nComment 13\nAccording to lines 182, 223, and 768, dietary data was collected from and adjusted for\nonly rural populations? Are the beta diversity differences observed in the urban group\ndue to S. mansoni infection or due to unmeasured dietary variation in the urban\ngroup?\nResponse\nWe thank the reviewer for this thoughtful and important comment. As noted, detailed dietary\ndata were available only for the rural LaVIISWA cohort. To evaluate whether unmeasured\ndietary variation in the urban population could plausibly confound the observed beta-diversity\ndifferences, we used a Directed Acyclic Graph (DAG) to formally encode known and\nhypothesised relationships between S. mansoni infection, demographic determinants,\nlifestyle factors, microbiome composition, and cardiovascular risk.\nIn this DAG (implemented using the dagitty package), S. mansoni infection is specified as a\nfunction of age, sex, and residential setting—reflecting established epidemiology that\ninfection risk is driven primarily by water exposure, occupation, and demographic factors.\nMicrobiome structure is influenced by infection status, age, sex, BMI, diet, antibiotics, and\nsetting. Cardiovascular risk markers are modelled as influenced by infection, microbiome\nfeatures, and demographic factors. Lifestyle variables (BMI, smoking, alcohol use,\noccupation) are placed downstream of age, sex, and setting to avoid implausible or reversed\ncausal pathways.\nRunning adjustmentSets() on this DAG identified age, sex, and setting as the minimal\nsufficient adjustment set for estimating the total and microbiome-mediated effects of S.\nmansoni infection on CVD risk. Crucially, diet does not appear in this minimal set, not\nbecause dietary variation is unimportant biologically, but because in the causal structure\nsupported by Ugandan epidemiology, diet is conceptualised as downstream of residential\nsetting. Under this structure, adjusting for diet would represent overadjustment and could\nopen collider paths, biasing estimates rather than improving confounding control.\nTo further contextualise this, evidence from national and regional dietary surveys including\nthe Uganda Food Consumption Survey (2008) and subsequent analyses3—indicates that\nboth rural and urban populations share a broadly similar dietary foundation dominated by\nstarchy staples (cereals, plantain, roots, tubers) and legumes, with consistently low intake of\nfruit, vegetables, and animal-source foods across settings. While some urban populations\nconsume relatively more processed and animal-source foods, these differences occur within\na highly conserved national dietary pattern and are unlikely to fully explain the distinct\nmicrobiome signatures associated with S. mansoni infection in our dataset.\nWe also acknowledge that our rural participants reside in fishing communities with higher\nfish consumption. However, because setting is included in the final adjustment set, this\nvariable indirectly captures broad environmental and lifestyle contrasts—including diet—\nthereby substantially mitigating bias from unmeasured dietary differences.\nAs an additional check, we compared mediation results obtained without formal adjustment\nto those obtained after adjusting for the DAG-derived set (age, sex, setting). A substantial\nnumber of taxa continued to exhibit significant indirect effects after adjustment, indicating\nthat the core microbiome-mediated associations are robust to potential dietary confounding.\nWe now make these causal assumptions, supporting evidence, and robustness checks\nexplicit in the revised Discussion. We also highlight, as part of our Recommendations, the\nneed for future work employing high-resolution dietary profiling such as detailed dietary\nsurveys, diet-associated metabolomic signatures, or next-generation sequencing\napproaches to more precisely disentangle diet–infection–microbiome pathways.\nTogether, these considerations—causal-structure analysis, national dietary evidence, and\nstability of results after applying the correct adjustment set, support the inference that the\nobserved microbiome variation associated with S. mansoni infection rather than unmeasured\ndietary differences.\nWe have made the following changes in the methods section of manuscript: lines 891-901\n“We applied a prespecified inference framework to estimate the extent to which gut\nmicrobiome features mediated the association between Schistosoma mansoni infection and\ncardiometabolic risk traits. We constructed a directed acyclic graph (DAG; Supplementary\nFig. 13) to formalise prior biological and epidemiological knowledge and to determine the\nminimally sufficient adjustment set required to block back-door confounding paths. Running\nthe adjustmentSets() function on this DAG identified age, sex, and setting as the minimal\nconfounder set for estimating both total effects and microbiome-mediated effects.\nAccordingly, all mediation analyses adjusted exclusively for these variables (supplementary\ntable 4).\nFor each cardiometabolic outcome, we fitted counterfactual-based causal mediation models\nthat partitioned the total effect of S. mansoni infection into natural direct effects and natural\nindirect effects operating through individual microbial taxa, analysed one mediator at a time.“\nand discussed these results in lines 401-464.\nComment 14\nCheck for potential omissions in lines 447 and 497.\nThese have been addressed in line 493 and 539 of the updated document.\nComment 15\nConclusion\nSince no CVD outcomes were measured but only risk factors, could you rephrase the\nconclusion to suit what was actually measured, that is, the CVD risk factors?\nWe thank the reviewer for this important clarification. We agree that our study\nassessed cardiovascular risk factors, not clinical cardiovascular disease (CVD) events or\noutcomes. We have revised the Conclusion accordingly to ensure that the interpretation\nremains aligned with the measured data. The updated text now emphasises that S.\nmansoni infection was associated with microbiome and metabolomic alterations that relate\nto CVD risk factors, rather than CVD outcomes. We have removed any language suggesting\ndirect effects on clinical CVD and reframed the implications to reflect risk modification rather\nthan disease endpoints.\nWe have made the following changes in the conclusion section of the manuscript in\nlines 607-617\n“Our findings provide evidence that S. mansoni infection is associated with significant\nalterations in the gut microbiome and metabolome profiles, with important implications for\ncardiovascular risk. These microbiome-mediated effects may represent a novel pathway\nthrough which parasitic infections influence CVD risk. Future studies should focus on refining\nour understanding of these interactions, with an emphasis on diet, antibiotic use, and\ncircadian regulation of microbial activity. Our study paves the way for developing\nmicrobiome-targeted interventions that that may mimic helminth infections to reduce\ncardiovascular risk in humans, but such approaches will require rigorous mechanistic and\ninterventional validation.”\nReviewer 2\nGeneral Comment\nWalusimbi and colleagues explore the relationship between helminth infection, the\ngut microbiome, the circulating metabolome, and cardiovascular disease (CVD) risk in\nrural and urban Ugandans. Using sequencing and metabolomic profiling from ~ 200\nindividuals with differing endemicity, the authors report that S. mansoni infection is\nassociated with increased diversity, enrichment and depletion of certain taxa, and\nchanges in lipid-related metabolic pathways. Mediation and integrative analyses\nsuggests that certain taxa and their derived metabolites may influence host lipid\nlevels and blood pressure, providing a potential mechanistic basis for\nepidemiological observations linking helminth infections to reduced cardiometabolic\nrisk.\nThe study advances an important area of interest at the intersection of infectious\ndisease, microbiome science, and noncommunicable disease, offering new human\nevidence for a helminth-microbiome-CVD connection; however, limitations inherent to\nthe cross-sectional design, limited metabolomic signal, and potential dietary and\nenvironmental confounders markedly reduces the impact of mechanistic claims. With\nrevisions to temper conclusions, clarify presentation, and acknowledge these\ncaveats, this work would represent an important contribution to the field.\nResponse:\nWe thank the reviewer for their thoughtful and encouraging assessment of our work, and we\nare grateful for the recognition of the study’s contribution to understanding helminth–\nmicrobiome–cardiometabolic interactions in human populations. We fully agree that the\ncross-sectional design and inherent constraints of field-based metabolomics warrant caution\nin interpreting mechanistic pathways. In response, we have tempered causal language\nthroughout the manuscript and more explicitly acknowledged these limitations in the\nDiscussion. We applied a prespecified causal-inference framework, constructed a directed\nacyclic graph to identify the minimally sufficient adjustment set, and conducted mediation\nanalyses that were robust to sensitivity testing. Importantly, key microbially mediated\nassociations persisted under the DAG-derived confounder set, supporting the internal\ncoherence of our findings. Together, these revisions provide a clearer and more balanced\ninterpretation while preserving the strength of the empirical evidence linking S.\nmansoni infection, microbial alterations, and cardiometabolic risk traits. We appreciate the\nreviewer’s positive evaluation and believe that the clarified and strengthened manuscript\nnow more accurately reflects both the novelty and the appropriate scope of our conclusions\nMajor Comments\nComment 1.\n1. A major concern of this manuscript is the cross-sectional design. This limits the\nability to determine causality or temporal dynamics. It remains unclear whether\nmicrobiome–metabolome changes precede reductions in CVD risk or are downstream\nconsequences of infection. Longitudinal or interventional approaches would be\nnecessary to strengthen causal inference. Explicitly acknowledging, in the\ndiscussion, that the cross-sectional design precludes causal inference with the\naddition of a short paragraph outlining how future longitudinal and interventional\nstudies could address causality would strengthen the overall manuscript.\nResponse:\nWe thank the reviewer for highlighting this critical issue. We fully agree that the cross-\nsectional design limits our ability to determine temporal ordering and therefore precludes\ndefinitive causal inference. In response, we have revised the Discussion to clearly and\nexplicitly acknowledge this limitation and to temper any mechanistic wording.\nIn lines 566-571, we have this phrase “. While causality cannot be inferred from this cross-\nsectional design, these findings raise the possibility that S. mansoni, or its microbiome-\nmediated effects, may modulate cardiometabolic outcomes in endemic settings. Further\nlongitudinal and interventional studies are warranted to validate these interactions and\nassess their relevance for biomarker development or CVD therapeutic modulation.” As\nrecommended by reviewer\nAt the same time, we emphasise that our analytic strategy was designed to maximise\nepidemiologial transparency within the constraints of cross-sectional data. As outlined in our\nresponse to Reviewer 1, we employed a prespecified causal-inference framework based on\na directed acyclic graph (DAG), which identified age, sex, and residential setting as the\nminimally sufficient confounder set for estimating the total and mediated effects of S.\nmansoni infection on cardiometabolic risk. All mediation analyses were re-examined using\nthis DAG-derived confounder set, and several microbially mediated associations persisted.\nThis strengthens confidence that the observed links between infection, microbial features,\nand cardiometabolic traits are not simply artefacts of demographic or environmental\nconfounding, although we acknowledge that it does not demonstrate causality.\nIn line with the reviewer’s suggestion, we have now incorporated a dedicated section (lines\n569- 606) in the Discussion outlining limitations and how future studies can establish\ntemporality and strengthen causal inference. This includes:\n Longitudinal cohort designs to track microbial and metabolic changes before and\nafter alterations in helminth infection status or environmental exposures.\n Interventional studies, such as controlled deworming or targeted microbiome-\nmodifying strategies, to establish whether microbial shifts mediate downstream\ncardiometabolic effects.\n Enhanced exposure characterisation, including systematic capture of dietary intake,\nmedication history (particularly antibiotic use), and comorbidity-factors that would\nfurther strengthen confounder control.\n Targeted metabolite profiling, particularly of short-chain fatty acids (SCFAs), which\nwere not captured by our LC–MS platform but represent critical microbial mediators\nof blood pressure and metabolic regulation.\nWe believe these additions substantially strengthen the manuscript by clarifying the\ninterpretive limits of the current study while providing a clear roadmap for future longitudinal\nand interventional investigations. We thank the reviewer for prompting these important\nimprovements.\nComment 2.\nWhile several metabolites were differentially abundant, global metabolomics between\ninfected and uninfected individuals was not statistically significant. This raises\nconcerns about the strength of metabolomic findings and the strength of evidence\nsupporting a distinct infection-related metabolic signature. Clarification or reframing\nof conclusions to emphasize hypothesis generating observations rather than a\nconclusive metabolic signature would provide a more reasonable appraisal of the\nmetabolic data. Furthermore, additional supplementary analysis which stratifies the\ndata based on infection status and intensity of the infection (if known) could provide\nfurther evidence of robustness of the findings.\nResponse\nWe thank the reviewer for this thoughtful and important comment. We agree that\ninterpretation of metabolomic data requires care, particularly when global multivariate\nseparation is modest. We have therefore revised the text to emphasise that these findings\nare hypothesis-generating and not intended to represent a definitive infection-specific\nmetabolic signature. This can be seen in lines 278-282 where we say, “While these\nobservations provide important insights into potential microbiome-mediated pathways, the\nmetabolomic differences should be interpreted as hypothesis-generating rather than\nconstituting a definitive infection-related metabolic signature, particularly given the modest\nglobal separation in untargeted metabolomics”\nRegarding the reviewer’s concern that global metabolomics did not differ significantly\nbetween infected and uninfected individuals: this pattern is biologically and analytically\nplausible. In heterogeneous, free-living human populations, broad metabolic profiles are\noften dominated by inter-individual lifestyle, dietary, and microbiome variability, which can\ndilute group-level separation in multivariate space. Importantly, lack of global separation\ndoes not preclude biologically meaningful changes in specific metabolites, particularly when\nthese metabolites lie on tightly regulated pathways that respond discretely to perturbations\nsuch as helminth infections. This is well established in environmental and nutritional\nmetabolomics, where local pathway-specific shifts frequently occur in the absence of global\nclustering differences. In our study, this manifests as:\n no significant separation in PLS-DA/PERMANOVA\n reliable, directionally coherent differential abundance of individual metabolites, many\nof which map onto lipid and cholesterol-related pathways that are mechanistically\nrelevant to both S. mansoni infection and cardiovascular physiology or risk.\nWe have clarified this interpretation in the revised manuscript.\nImportantly, the reviewer requested stratified analyses based on infection status. These\nanalyses were performed and are presented in Figure 5, which directly contrasts S.\nmansoni–infected and uninfected individuals. We now draw clearer attention to this in the\nResults and figure legend to ensure that the stratified approach is more visible. As described\nin the legend (Fig. 5), we present:\n volcano plots of differentially abundant metabolites,\n PLS-DA with PERMANOVA statistics, and\n pathway enrichment analyses of metabolites more abundant in infected individuals.\nWith respect to infection intensity, our analytical framework for this study was explicitly\ndesigned around the contrast between S. mansoni–infected and uninfected individuals,\nwhich represents the most statistically reliable and epidemiologically meaningful comparison\ngiven the structure of the dataset. Although infection intensity can be classified using WHO\ncut-offs based on Kato–Katz egg counts, intensity estimates derived from the different\ndiagnostic methods used in this study (KK and PCR) do not always align, introducing\nadditional heterogeneity. For this reason, metabolomic analyses were performed using\ninfection status rather than intensity. We agree that intensity-stratified analyses, particularly\nthose based on standardized WHO Kato–Katz thresholds, would be highly informative in\nlarger cohorts with sufficient power and harmonized intensity measurements.\nComment 3.\nThis lack a difference in metabolic profiling may in part be due to the limit\nincorporation of the dietary confounder. Diet is a major determinant of both\nmicrobiome and metabolome composition. Although some dietary data are provided,\ncomprehensive dietary profiling appears limited. Without stronger control of dietary\nvariables or analysis which attempts to normalize to diet makes it difficult to\ndisentangle the effects of infection from those of diet. Statements which explicitly\ndiscuss the dietary confounder and suggestion of future directions to integrate\ndietary assessment is encouraged to strengthen the overall impact of the manuscript.\nResponse\nWe thank the reviewer for highlighting the important role of diet in shaping both the gut\nmicrobiome and metabolome and agree that dietary intake is a major upstream determinant\nof metabolic variation. We also acknowledge that the dietary variables available in the\npresent study, derived from a structured food-frequency questionnaire, lack the granularity\nrequired for comprehensive metabolome normalization (e.g., quantitative intake, portion\nsizes, micronutrient content, or temporal dietary patterns). We have now clarified this\nlimitation explicitly in the revised manuscript. In lines 572 – 579 we say, ”In as much as this\nstudy provides important insights into the gut microbiome-metabolome-CVD risk interaction\nin a typical setting with high S. mansoni infestation, there are potential limitations. Firstly, we\ndid not comprehensively profile participants’ dietary habits, which are known to have\nprofound effects on both the microbiome and metabolome and are likely to represent an\nunmeasured confounder. Given the variability in diet across different regions and individuals,\nfuture studies should incorporate detailed dietary assessments such as next generation\nsequencing methodologies to disentangle the effects of infection from those of diet.”\nAt the same time, several considerations reduce the likelihood that incomplete dietary\ncapture materially confounded the infection–metabolome associations reported here. First,\nconsistent with Ugandan epidemiology, S. mansoni infection in this community is\nenvironmentally driven, principally determined by water contact, occupation, age, and sex—\nrather than by household dietary disparities. This results in broadly similar underlying dietary\npatterns between infected and uninfected individuals. This interpretation is supported by\nnational and regional dietary surveys, including the Uganda Food Consumption Survey\n(2008) and subsequent analyses3—which show that both rural and urban populations\noverwhelmingly consume starchy staples (cereals, plantain, roots, tubers) and legumes, with\nuniversally low intake of fruits, vegetables, and animal-source foods. Although some\nsubgroups may consume more animal-source or processed foods, these differences occur\nwithin a largely conserved national dietary profile and are unlikely to generate systematic\nmetabolomic patterns that mimic infection status.\nSecond, to formally assess the role of diet as a confounder, we constructed a Directed\nAcyclic Graph (DAG) encoding the causal relationships between S. mansoni infection,\ndemographic variables, lifestyle factors, diet, microbiome composition, and cardiovascular\nrisk. In this DAG, infection is determined by age, sex, and residential setting; microbiome\nstructure is influenced by infection, age, sex, BMI, diet, antibiotics, and setting; and dietary\npatterns are conceptualised as downstream of setting rather than upstream of infection.\nUnder this causal structure, adjusting for diet would represent overadjustment and could\nintroduce collider bias rather than improve confounding control. Running adjustmentSets()\non this DAG identified age, sex, and setting as the minimal sufficient adjustment set for\nestimating the total and microbiome-mediated effects of infection on cardiovascular risk.\nThird, our mediation analyses showed that a substantial number of taxa remained significant\nmediators even after adjusting for the DAG-derived minimal adjustment set (age, sex,\nsetting) as detailed in lines 891-901. This stability suggests that the principal microbiome-\nmediated associations are unlikely to be explained by unmeasured dietary differences.\nWith respect to metabolomics, we agree that the lack of strong global separation between\ninfected and uninfected individuals warrants cautious interpretation. We have reframed our\nconclusions to emphasise that these findings are hypothesis-generating rather than\nindicative of a definitive infection-specific metabolic signature in lines 278-282. Importantly,\nin heterogeneous free-living human populations, global metabolomic profiles often reflect\nbroad lifestyle variation, even when specific metabolites show biologically meaningful shifts.\nIn our dataset, while global clustering was limited, several individual metabolites were\ndifferentially abundant and mapped to lipid- and cholesterol-related pathways relevant to\nboth S. mansoni infection and cardiovascular physiology.\nWe fully agree with the reviewer that future work would benefit from incorporating high-\nresolution dietary profiling—such as repeated 24-hour recalls, quantitative nutrient\nestimation, diet-associated metabolomic signatures, or next-generation sequencing\napproaches—to disentangle diet–infection–microbiome pathways with greater precision. We\nhave strengthened the Recommendations section to explicitly highlight this as a key priority\nfor future research.\nWe hope these clarifications sufficiently address the reviewer’s concern and improve the\ntransparency and interpretability of our metabolomic findings.\nComment 4.\nDifferences in microbiome-metabolome associations between rural and urban\nparticipants suggest that environmental and lifestyle factors may strongly influence\nfindings. The authors should discuss more explicitly how these factors might\nconfound or modify the observed associations.\nWe thank the reviewer for this thoughtful and important comment. We fully agree that\nenvironmental and lifestyle heterogeneity including sanitation, water exposure, subsistence\npatterns, physical activity, dietary structure, and broader ecological exposures, strongly\ninfluence microbiome composition, metabolomic profiles, and cardiometabolic traits. We\nhave now substantially strengthened both the Methods and Discussion to clarify how these\nfactors were addressed analytically, and where key limitations remain.\n1. Confounding by environmental and lifestyle exposures\nRural and urban settings may differ markedly in environmental exposures that could\nindependently influence microbial and metabolic phenotypes. Although our within-setting\nanalyses adjusted for age, sex, BMI, and dietary variables where available, we\nacknowledge, as the reviewer correctly notes, that unmeasured exposures (e.g., sanitation\ninfrastructure, animal contact, water source, environmental microbial load) may contribute to\nsetting-specific differences. These sources of residual confounding and their potential impact\nhave now been explicitly described in the revised Discussion.\n2. How our analysis directly addressed environmental heterogeneity\nTo prevent environmental differences from biasing inference, and recognising that some\nexposures (notably diet) were measured only in the rural cohort—we applied setting-specific\nmodelling strategies across microbiome, metabolome, and mediation analyses. These\nanalytic choices are now fully described in the revised Methods.\n(a) Microbiome-CVD regression models were stratified by setting\nBecause detailed dietary data were only available in the rural cohort, we ran separate\nmodels for rural and urban participants. This prevented asymmetric adjustment and ensured\nthat each model was estimated within a relatively homogeneous environmental context.\n Rural models:\nadjusted for age, sex, BMI, and diet category derived from the food-frequency\nquestionnaire\nlm(CVD ~ microbe + age + sex + bmi + diet_category)\n Urban models:\nadjusted for age, sex, and BMI\nlm(CVD ~ microbe + age + sex + bmi)\nWe have modified this section in our manuscript lines 874-879 which says: “To evaluate the\ndirect associations between microbial taxa and specific CVD risk factors (e.g., blood\npressure, total cholesterol, LDL cholesterol), multivariate linear regression models were fitted\nseparately for infected and uninfected groups within rural and urban settings. Models were\nadjusted for age, sex, BMI, and diet (in the rural population). Full model outputs (effect sizes,\nSEs, 95% CIs, p-values) are provided in supplementary tables 2 and 3. “ This approach\navoids inappropriate pooling of environmentally heterogeneous data.\n(b) Metabolomics models formally incorporated setting as an interaction term\nBecause metabolomic signatures are less sensitive to short-term diet variability,\nmetabolomics analyses were conducted in a combined dataset. The model specification\nincluded:\n age\n sex\n BMI\n S. mansoni infection\n setting × metabolite interaction term\nThis allowed us to formally test whether metabolite–CVD associations differed across\nenvironments.\nWe have further detailed our metabolite-CVD association analysis in the methods section to\ninclude lines 904 - 916 that say “To investigate links between circulating metabolites and\ncardiometabolic traits, we fitted linear regression models for each metabolite using the\ncombined dataset of rural and urban participants. Each model included the metabolite as the\nexposure and the cardiovascular risk factor of interest (systolic blood pressure, diastolic\nblood pressure, total cholesterol, or LDL cholesterol) as the outcome. The models were\nadjusted for age, sex, BMI, and S. mansoni infection status. Because rural and urban\nenvironments differ markedly in lifestyle and ecological exposures, we additionally included a\nsetting-by-metabolite interaction term to assess whether metabolite–CVD associations\ndiffered across environments.\nFor each metabolite, we extracted the effect estimate, standard error, 95% confidence\nintervals, and p-value for the association with the cardiovascular risk factor. Metabolites\ndemonstrating statistical evidence of association at p < 0.01 were taken forward to multi-\nomics integration alongside CVD-associated microbes.”\n(c) Mediation analyses adjusted for the DAG-defined minimal sufficient set\nAs detailed in lines 891-901, using a pre-specified directed acyclic graph, we identified age,\nsex, and setting as the minimal sufficient adjustment set. All mediation models therefore\nincluded these three covariates.\nIndirect effect sizes, confidence intervals, and p-values are provided in Supplementary Table\n4.\n(d) Only adjusted, setting-aware associations were taken forward to multi-omics integration\nTo ensure downstream robustness, only taxa and metabolites that remained significantly\nassociated with CVD risk after the above adjustment strategies were entered into multi-\nomics integration. This prevented environmental artefacts from propagating across modelling\nlayers.\n3. Tempered mechanistic claims and strengthened future directions\nRecognising that environmental heterogeneity cannot be fully eliminated. we now emphasise\nthat our findings are hypothesis-generating rather than conclusive, and that causal pathways\nmust be validated in longitudinal and interventional studies. For example, in lines 278-282,\nwe have added phrase; “While these observations provide important insights into potential\nmicrobiome-mediated pathways, the metabolomic differences should be interpreted\nas hypothesis-generating rather than constituting a definitive infection-related metabolic\nsignature, particularly given the modest global separation in untargeted metabolomics” and\nin our section on on future directions, lines 597-606, we have a section on need for\nlongitudinal studies in the paragrapgh, “Furthermore, given the cross-sectional design of our\nstudy, we cannot establish causality in the observed associations between S.\nmansoni infection, microbiome composition, metabolite profiles, and cardiovascular risk.\nTemporal dynamics of microbial and metabolic alterations following infection remain\nunexplored, limiting our ability to infer directionality. As such, longitudinal studies, ideally\nspanning pre-infection, active infection, and post-treatment phases, would be essential to\ndisentangle cause-effect relationships and capture the evolving host–microbiome–\nmetabolome interactions over time. Incorporating repeated sampling, coupled with temporal\nmetadata such as infection history and treatment timing, would provide a more robust\nframework for understanding how S. mansoni shapes cardiometabolic risk through microbial\nand metabolic pathways”\nComment 5.\nMediation analyses identify both protective and potentially deleterious taxa enriched\nin infected individuals. The discussion could improve through acknowledging the\ncomplexity of these microbial associations and avoid framing the effect of infection\nas uniformly protective. Again, emphasizing the cross-sectional design of the study\nand avoidance of overgeneralization of the infection effect.\nWe thank the reviewer for this insightful comment. We fully agree that mediation effects in\nour study were heterogeneous and that S. mansoni infection should not be framed as\nconferring a uniformly protective metabolic profile. We would like to highlight that our\nDiscussion spanning lines 401-461 explicitly acknowledged this bidirectionality. Specifically,\nwe described both negative and positive mediation effects, noting that taxa enriched in\ninfected individuals were associated with improvements in some cardiometabolic traits (e.g.,\ninsulin sensitivity, lower glucose, reduced blood pressure), whereas others were linked\nto adverse outcomes, such as impaired glucose regulation. Similarly, several taxa enriched\nin uninfected individuals showed both beneficial and deleterious cardiovascular associations.\nThis was deliberate to emphasize the ecological and functional complexity of helminth–\nmicrobiome–host interactions and to avoid overstating a uniformly protective role.\nNonetheless, to further clarify this point in line with the reviewer’s recommendation, we have\nrevised the Discussion to explicitly state that the mediation results reveal divergent and\ncontext-dependent effects, rather than a single directional pattern, and that these findings\nshould be considered hypothesis-generating given the cross-sectional design. We have\nadded a short clarifying statement to ensure readers do not interpret our findings as implying\na consistent protective effect of infection. This statement is found in lines 437-441 and it\nreads ; “Importantly, these mediation patterns do not indicate that S. mansoni infection\nconfers a uniformly protective metabolic profile; rather, they reveal a mixture of positive and\ninverse microbial associationsthat are highly context dependent and should be interpreted\ncautiously given the cross-sectional design.”\nComment 6.\nWhile the study provides novel mechanistic hypotheses, the direct clinical\napplicability remains limited, and mechanistic hypotheses were not tested (the\nauthors may feel this is outside the scope of the manuscript). The authors should\ntemper statements regarding therapeutic potential and discuss future longitudinal\nand interventional studies to replicate these findings.\nResponse\nWe thank the reviewer for this thoughtful comment. We agree that although our findings\nraise novel mechanistic hypotheses linking S. mansoni infection, the gut microbiome, and\ncardiometabolic risk, the direct clinical applicability is constrained by the cross-sectional\nnature of the study and by the fact that mechanistic pathways were not experimentally\nvalidated. We have revised the Discussion to explicitly acknowledge these limitations and to\ntemper statements related to translational or therapeutic potential.\nAs suggested, we now make clear that this study does not evaluate therapeutic\ninterventions, nor does it demonstrate that modifying the microbiome could reproduce\ninfection-associated metabolic effects. Rather, our results should be interpreted as\nhypothesis-generating and as providing a conceptual foundation upon which future\nmechanistic, longitudinal, and interventional studies can build.\nSpecifically, the revised Discussion now emphasises (lines 569–571) that:\n“Further longitudinal and interventional studies are warranted to validate these interactions\nand assess their relevance for biomarker development or CVD therapeutic modulation.”\nIn addition, lines 613–617 have been clarified to ensure appropriately cautious framing:\n“Our study paves the way for developing microbiome-targeted interventions that may mimic\nhelminth infections, but such approaches will require rigorous mechanistic and interventional\nvalidation.”\nWe appreciate the reviewer’s guidance, which has strengthened the manuscript by ensuring\nthat the conclusions are presented with appropriate restraint while clearly outlining the\nnecessary next steps for evaluating the mechanistic hypotheses generated by this work.\nComment 7.\nThe discussion is good but would benefit from inclusion of studies that have not\ndemonstrated protective associations between helminth infections and metabolic\noutcomes; this would provide more balance. In addition, certain statements are\npresented with a degree of certainty that exceeds the evidence provided. The authors\nclaim that S. mansoni infection may improve lipid profiles through increased\nmicrobial richness and diversity. However, this is speculative, the authors show an\nassociation not causality. Rephrasing such as: “We observed that S. mansoni\ninfection is associated with greater microbial richness and given that higher richness\nhas been linked to improved lipid profiles in prior studies, it is plausible, but not yet\ndemonstrated, that richness could contribute to the protective association between\ninfection and lipid metabolism.” This acknowledges the value of the findings while\nsituating them more accurately within the context of existing evidence\nResponse.\nWe thank the reviewer for this important comment and agree fully that the Discussion\nbenefits from a more balanced integration of studies reporting no protective or even neutral\nmetabolic effects of helminth infections. We have now expanded the Discussion to reference\nrelevant work f demonstrating that helminth infections do not universally improve metabolic\noutcomes. Incorporating these studies helps situate our findings within the broader variability\nreported in the literature and strengthens the overall interpretive balance of the manuscript.\nIn our revised discussion lines 441-445, we have a paragraph “Several human and animal\nstudies show that chronic infection can contribute to CVD and hypertension, driven by\ninflammation around parasite eggs and longer-term changes in vascular structure4-6. These\nobservations highlight that helminth infections can exert harmful cardiovascular effects, even\nas they may show more favourable metabolic associations in some contexts.”\nWe also agree that some statements in the original Discussion could have implied a level of\ncertainty that exceeds the evidence derived from our cross-sectional dataset. In response,\nwe have carefully revised the relevant passages to avoid causal phrasing and to frame\ninfection-associated shifts in microbial richness and diversity as associations rather than\nmechanistic drivers.\nSpecifically, we have reworded the section discussing microbial richness and lipid\nmetabolism in line with the reviewer’s helpful suggestion. The revised text in lines 402- 413\nnow reads:\n“Specifically, we observed that variation in microbial composition was statistically associated\nwith both higher and lower levels of key cardiovascular risk factors, including LDL cholesterol\nand blood pressure. Several taxa enriched in infected individuals exhibited negative indirect\neffects, consistent with a pattern that could contribute to the more favourable lipid and blood\npressure profiles observed in infected participants. However, these mediation findings reflect\nassociations rather than demonstrated causal pathways. Although higher microbial richness\nand infection-related shifts in community structure have been linked to improved metabolic\noutcomes in prior studies, our data cannot establish that these microbial differences explain\nor drive the cardiometabolic phenotype. Instead, our findings should be interpreted as\nidentifying plausible microbiome-related pathways that warrant further mechanistic and\nlongitudinal investigation”\nMinor Comments\n1. Some figures are dense and could benefit from simplified labeling and clearer\nfigure legends to increase accessibility to the reader. I would suggest the addition of\nsimplified summary diagrams to complement heatmap findings and circus plots.\nWe thank the reviewer for this thoughtful suggestion. We agree that some of the multi-omics\nfigures are inherently dense due to the complexity of the datasets. In response, we have\ncarefully revised several figure legends to enhance clarity, simplified labeling where\npossible, and added more descriptive annotations to guide interpretation. To improve\naccessibility, we have undertaken several targeted revisions across both the cholesterol-\nand blood pressure-associated figures:\n Figure legends have been streamlined by simplifying language, removing\nredundancies, and adopting a consistent panel-by-panel structure.\n Terminology has been standardized (e.g., “microbiota genera” to “microbial genera”)\nto ensure clarity and uniformity.\n Color coding and correlation descriptions have been made explicit in both circos plots\nand heatmaps, clearly indicating positive versus negative correlations.\n Annotations and axis labels have been clarified to better guide interpretation of\nmetabolite classifications and correlation strengths.\n Parallel formatting across panels (A–F) has been introduced to highlight symmetry\nand aid reader navigation.\nWhile we did not introduce new summary diagrams, we believe these revisions substantially\nimprove the readability and interpretability of the existing figures, thereby addressing the\nreviewer’s concern about accessibility in a consistent manner across the manuscript.\nFollowing these changes, our new legends are:\nFig 6: Correlation between cholesterol-associated microbiota and\nmetabolites. (A) Circos plot illustrating the strongest correlations (Spearman’s r ≥ 0.7)\nbetween microbiome (blue blocks) and metabolome (green blocks) features significantly\nassociated with total cholesterol. Associations were identified using models adjusted for age,\nsex, BMI, and setting as an interaction term (see Fig. 3). Purple lines indicate positive\ncorrelations; red lines indicate negative correlations. (B) Heatmap showing the correlation\nstrength between microbial genera and metabolites extracted from panel A. Red reflects\npositive correlation strength; blue indicates negative correlations. (C) Classification of total\ncholesterol-associated metabolites using CLASSYFIRE. Horizontal bars represent the\nnumber of metabolites per chemical class (y-axis). (D) Circos plot showing top correlations\n(r ≥ 0.65) between microbiome and metabolome features significantly associated with LDL-\ncholesterol, using the same model adjustments as in panel A. Green lines indicate positive\ncorrelations; red lines indicate negative correlations. (E) Heatmap of LDL-cholesterol-\nassociated microbial genera and metabolites, following the same format and color scale as\npanel B. (F) Classification of LDL-cholesterol-associated metabolites using CLASSYFIRE,\nas in panel C.\nFig. 7 Correlation between blood pressure-associated microbiota and\nmetabolites. (A) Circos plot illustrating the strongest correlations (Spearman’s r ≥ 0.7)\nbetween microbiome (blue blocks) and metabolome (green blocks) features significantly\nassociated with diastolic blood pressure. Associations were identified using models adjusted\nfor age, sex, BMI, and setting as an interaction term (see Fig. 3). Purple lines indicate positive\ncorrelations; red lines indicate negative correlations.(B) Heatmap showing\ncorrelation strength between microbial genera and metabolites extracted from panel A. Red\nintensity reflects positive correlation strength; blue indicates negative\ncorrelations. (C) Classification of diastolic blood pressure-associated metabolites using\nCLASSYFIRE. Horizontal bars represent the number of metabolites per chemical class (y-\naxis). (D) Circos plot showing the strongest correlations (r ≥ 0.70) between microbiome and\nmetabolome features significantly associated with systolic blood pressure, using the same\nmodel adjustments as in panel A. Green lines indicate positive correlations; red lines indicate\nnegative correlations. (E) Heatmap of systolic blood pressure-associated microbial genera\nand metabolites, following the same format and color scale as panel B. (F) Classification of\nsystolic blood pressure-associated metabolites using CLASSYFIRE, as in panel C.\n2. Report effect sizes alongside p-values throughout, particularly in regression and\nmediation analyses, to help readers gauge the biological significance of findings.\nResponse:\nWe thank the reviewer for highlighting the importance of reporting effect sizes to support\ninterpretation of biological relevance. In response, we have now added complete effect-size\noutputs for both the regression and mediation analyses, and these are provided in the\nSupplementary Materials.\n1. Regression analyses (Rural and Urban cohorts)\nFor every microbe–CVD risk association, stratified by S. mansoni infection status, we now\nreport:\n unstandardised regression coefficients (β) and their 95% confidence intervals.\n standardised coefficients (β_std) and their 95% confidence intervals.\n p-values\n infection group (Infected vs Uninfected)\nThese are included in:\n Supplementary Table S1 -Rural cohort regression effect sizes\n Supplementary Table S2 -Urban cohort regression effect sizes\nThese tables allow readers to compare not only statistical significance but also the direction\nand magnitude of effects across settings and infection strata.\n2. Mediation analyses\nFor each microbe-metabolite-CVD risk pathway tested in our causal mediation framework,\nwe now report:\n Average Causal Mediation Effect (ACME; indirect effect)\n Standard error\n p-values\n 95% confidence intervals\n significance calls\n corresponding microbe and CVD outcome\nThese results are compiled in:\n Supplementary Table 4 — Mediation effect-size summary\nThis enables clearer assessment of the strength and biological relevance of the indirect\npathways through metabolite mediators.\nReferences.\nmetabolism and blood pressure in Uganda. Wellcome open research 5, 39\n(2020).\nmetabolic outcomes: results of a cluster-randomized trial. Clinical infectious\ndiseases 71, 601-613 (2020).\nUganda and their influencing factors: a critical review. African Journal of Food,\nAgriculture, Nutrition and Development 23, 22328-22353 (2023).\nSchistosomiasis-associated pulmonary hypertension. Pulmonary circulation 4,\n596-611 (2014).\nSchistosomiasis-associated pulmonary hypertension: pulmonary vascular\ndisease: the global perspective. Chest 137, 20S-29S (2010).\ndisease associated with parasitic infection—the role of schistosomiasis.\nClinical microbiology and infection 17, 15-24 (2011)."}]

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