Enteropathogen detection and early mortality among people with advanced HIV disease in sub-Saharan Africa

Enteropathogen detection and early mortality among people with advanced HIV disease in sub-Saharan Africa | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Enteropathogen detection and early mortality among people with advanced HIV disease in sub-Saharan Africa Helen Jones, Victor Riitho, Roisin Connon, Buxton Ndemera, Agnes Gwela, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6718521/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract One-third of people with HIV in sub-Saharan Africa initiate treatment with advanced HIV disease, with high associated mortality. Whether enteropathogens and intestinal inflammation contribute to mortality remains unclear. We leveraged participant samples from the Reduction of Early Mortality in HIV-infected Adults and Children Starting Antiretroviral Therapy (REALITY) trial (ISRCTN43622374) to investigate associations between enteropathogens (assayed by multiplex nucleic acid detection), immune biomarkers (assayed by ELISA and Luminex) and mortality, and to explore the mechanism by which an enhanced prophylaxis bundle (containing albendazole, azithromycin, fluconazole, and isoniazid/pyridoxine) significantly reduced mortality. Cox models were adjusted for age, sex, CD4, WHO stage, viral load, BMI, site, and biomarkers. In 265 participants initiating treatment with median CD4 36 cells/ml, we found extensive sub-clinical enteropathogen carriage and frequent co-infection. Enteropathogens had differential associations with gut biomarkers and mortality. Clostridium difficile was associated with elevated faecal neopterin and higher mortality (adjusted hazard ratio (aHR)=3.78, 95%CI 1.31-10.93; P=0.014) while Giardia was associated with lower faecal myeloperoxidase and reduced mortality (aHR=0.20, 95%CI 0.07-0.60; P=0.004). Enhanced antimicrobial prophylaxis reduced Shigella . Our findings highlight the interactions between sub-clinical pathogens, enteropathy and immunosuppression. The effects of azithromycin on the intestinal milieu may confer mortality benefits in people with advanced HIV disease. Health sciences/Pathogenesis/Infection Health sciences/Biomarkers/Predictive markers Figures Figure 1 Figure 2 INTRODUCTION Advanced HIV disease, defined by a CD4 count below 200 cells/µl or World Health Organization (WHO) stage 3 or 4 clinical disease, is associated with a high risk of hospitalization 1 or death 2 , 3 , even in the first months after initiation of antiretroviral therapy (ART). In sub-Saharan Africa, the mean CD4 count at presentation in 2002 was 251 cells/µl, and at ART initiation was 152 cells/µl 4 ; by 2013, mean CD4 counts had not significantly increased. There are an estimated 20–25 million people with HIV in sub-Saharan Africa, resulting in 260,000 deaths in 2022 5 . In people with advanced HIV disease, most HIV-related deaths with known aetiology are due to co-infections including tuberculosis, severe bacterial infections, and cryptococcal disease 1 , 2 , 6 – 10 . However, many deaths arise from unknown or multiple causes, due to the overlapping and interacting factors that drive mortality in the setting of advanced immunosuppression, malnutrition and clinical disease 10 . The pathogenesis of advanced HIV disease is complex and incompletely understood 11 . CD4 decline arises due to chronic immune activation, pyroptosis, and failed immune reconstitution, leading to opportunistic infections and a reinforcing cycle of chronic inflammation and immunosuppression. The impact of CD4 depletion is particularly profound in the gut, and even when peripheral blood shows CD4 cell recovery following ART initiation, the gut CD4 population does not reconstitute as effectively. CCR9 + β7 + (including Th17) CD4 + T-cells exhibit a defective gut-homing phenotype with CCR9 + and α4β7 + cells remaining in the circulation and positively correlating with plasma markers of mucosal damage and microbial translocation 12 . Other markers of enteropathy such as I-FABP, lipopolysaccharide and sCD14 are also elevated in people with HIV and are strongly associated with mortality 13 . Taken together, HIV enteropathy is characterised by reduced mucosal barrier function, and increased microbial translocation, particularly in advanced HIV disease, and likely drives both local and systemic inflammation 14 . Enteropathy and an altered local inflammatory milieu may predispose to pathobionts and co-infections 11 , 15 . A model of simian immunodeficiency virus (SIV) infection reported a reduction of Th17 cells in the gut and an associated expansion of Salmonella typhimurium 16 . Early HIV infection has been associated with shedding of adenovirus in faecal samples among rhesus macaques 15 . It is increasingly apparent that asymptomatic carriage of enteropathogens is common 17 – 20 . Cotrimoxazole prophylaxis reduces infection from a range of opportunistic pathogens 21 including gut pathogens such as Cystoisospora belli 7 , and directly and indirectly reduces intestinal and systemic inflammation 22 . It is unclear whether implementing a broader bundle of antimicrobials at the time of ART initiation, to target a greater range of enteropathogens, would have further benefits for gut inflammation. Here, we explore the hypothesis that enteropathogens in people with advanced HIV disease drive enteropathy, intestinal inflammation, and mortality in sub-Saharan Africa, where there is high pathogen pressure. We leverage data and specimens from the Reduction of Early Mortality in HIV Infected Adults and Children Starting Antiretroviral Therapy (REALITY) trial, which enrolled adults and older children with advanced HIV disease in four African countries. The trial tested an enhanced prophylaxis bundle, which reduced overall mortality by 27% 9,10,23 , and demonstrated significant reductions in reports of tuberculosis, cryptococcal disease, candidiasis, and deaths from unknown causes. We hypothesised that the antimicrobial bundle reduced enteropathogen burden, and ameliorated enteropathy and systemic inflammation, thereby providing an additional mechanistic explanation for its mortality reduction benefits. RESULTS Enteropathogen prevalence was measured using a multiplexed nucleic acid panel on the Luminex platform in 265 participants with advanced HIV disease enrolled in the REALITY trial in Zimbabwe and Kenya. Trial eligibility required CD4 counts below 100 cells/ml, and this analysis included participants with available baseline stools samples (Supplementary Figure 1). The median age at ART initiation was 38 years (3% below 18 years old), median CD4 count was 36 cells/ml, and 72% had WHO stage 3 or 4 disease (Table 1). Multiple enteropathogen detection is common and associated with baseline CD4 count Overall, there was a high prevalence of enteropathogens at ART initiation (Table 1), with 38% of participants having carriage of one pathogen and 38% with carriage of two or more pathogens. The commonest baseline enteropathogens were Shigella (31% of participants), Norovirus (21%), Giardia (19%) and Campylobacter (18%). There was a high degree of co-carriage; in particular, E. histolytica , E. coli O157, Salmonella , STEC, Shigella, Campylobacter and C. difficile were commonly identified with an additional enteropathogen (Supplementary Figure 2). Enteropathogen burden was associated with baseline CD4 count. Participants with a CD4 count <50 cells/ml had a higher prevalence of Cryptosporidium (19% versus 9% in those with CD4 count ≥50 cells/ml, respectively; P=0.04),norovirus(27% vs. 11%; P=0.004), and Campylobacter (22% vs. 13%; P=0.06) , but a lower prevalence of E. coli 0157 (2% vs. 10%; P=0.005) (Table 1). Despite the high prevalence of enteropathogens, diarrhoea was only reported by 30/265 (11%) participants at baseline. Shigella was the pathogen most commonly identified in participants with diarrhoea (57% of those with diarrhoea) followed by Campylobacter, Cryptosporidium, and norovirus(20%) (Supplementary Table 1). Specific enteropathogens are independently associated with mortality risk in advanced HIV Overall, 56 participants died within the first 24 weeks (cases) and 209 survived (non-cases, see Methods). We assessed whether carriage of specific pathogens at baseline was associated with higher or lower risk of mortality after ART initiation, using multivariable Cox regression models, adjusted for CD4 count, age, stage at enrolment, body mass index, viral load, sex, site and enhanced prophylaxis randomisation. E. histolytica was independently associated with a 5-fold higher risk of mortality (adjusted Hazard Ratio (aHR)=5.33, 95%CI 1.10-25.91; P=0.038). There was weak evidence that C. difficile was independently associated with higher mortality (aHR=2.59, 95%CI 0.87-7.71; P=0.088) and E. coli O157 with lower mortality (aHR=0.07, 95%CI 0.00-1.37; P=0.079) (Figure 1A). To assess whether the relationship between enteropathogens and mortality was mediated by their effects on systemic or enteric inflammation, we next included a panel of baseline plasma biomarkers (CRP, IFN-g, IL-23, IL-2, IL-6, IP10, RANTES) and gut biomarkers (plasma I-FABP, and faecal alpha-1 antitrypsin, neopterin, and myeloperoxidase) in the model. When adjusted for these biomarkers, there was evidence that C. difficile was independently associated with higher mortality (aHR=3.78, 95%CI 1.31-10.93; P=0.014) while Giardia (aHR=0.20, 95%CI 0.07-0.60; P=0.004) was associated with lower mortality. However, after adjusting for biomarkers, there was no evidence that E. histolytica was associated with higher mortality (aHR=3.47, 95%CI 0.39-30.99; P=0.266), and weak evidence that E. coli O157 was associated with lower mortality (aHR=0.01, 95%CI 0.00-1.01; P=0.051) (Figure 1B). We next extended the regression models to explore the effects of pathogen load on mortality. There was no evidence of an association between the total number of baseline enteropathogens and subsequent mortality (aHR=1.05 per pathogen higher, 95%CI 0.88-1.24; P=0.61). However, given their differential effects on mortality, we next categorised enteropathogens as either “beneficial” ( Giardia and E coli 0157), or “harmful” ( C. difficile and E. histolytica ). Identification of any harmful pathogen at baseline was associated with a 3-fold higher subsequent risk of death (aHR=3.17, 95%CI 1.32-7.62; P=0.01), whereas the isolation of any beneficial pathogen was associated with 2-fold lower mortality (aHR=0.43, 95%CI 0.20-0.95; P=0.04). Identification of any other pathogen, not categorised as beneficial or harmful, was not associated with mortality (aHR=1.01, 95%CI 0.51- 2.01; P=0.97). After adjusting for systemic inflammation, the relationship between harmful pathogens and elevated mortality (aHR=4.45, 95%CI 1.73- 11.44; P=0.002), and the relationship between beneficial pathogens and reduced mortality (aHR=0.13, 95%CI 0.04-0.43; P=0.001) both remained significant. However, after adjusting for gut biomarkers, effects were attenuated (Supplementary Table 2). There was an interaction between carriage of beneficial pathogens and baseline CD4 count (P=0.02), whereby the mortality benefits appeared stronger in those with CD4 > 50 cells/ml (aHR=0.02, 95%CI 0.002-0.24; P=0.002) vs. those with CD4<50 cells/ml (aHR=0.38, 95%CI 0.12=1.13; P=0.08) (heterogeneity p=0.02). Enteropathogens are differentially associated with markers of enteropathy Given the findings from our mortality models, which highlighted the contribution of gut biomarkers, we next hypothesised that specific enteropathogens may differentially modulate enteropathy. We reasoned that Giardia may modify the gut environment in beneficial ways, given previous data suggesting that this protozoon is immunoregulatory 24,25 , while C. difficile may exacerbate enteropathy, given existing data that it drives gut inflammation 26 . Giardia carriage was associated with lower faecal myeloperoxidase (805 ng/mL (IQR 770-3,144) vs. 1,987 ng/mL (IQR 770-4,833) among those without Giardia (P=0.03) (Table 2). By contrast, participants with C. difficile had higher concentrations of faecal neopterin than those without C. difficile (1,183 nmol/L (IQR 345-1,742) versus 236 nmol/L (80-1,065); P=0.005). Taken together, these findings suggest that enteropathogens have differential associations with mortality among people with advanced HIV starting ART, and that harmful and beneficial pathogens may mediate their effects by exacerbating or ameliorating the inflammatory milieu of the gut, respectively. Enhanced antimicrobial prophylaxis reduced prevalence of Shigella among people with advanced HIV disease Given our finding that specific pathogens are associated with mortality in advanced HIV disease, we finally hypothesised that the enhanced antimicrobial prophylaxis bundle may partly reduce mortality by modifying the presence of enteropathogens over time. We therefore leveraged the randomised trial design using logistic mixed models to explore changes in enteropathogens by weeks 4 and 12 post-intervention among 282 participants (17 with longitudinal samples but no baseline sample), comparing those randomised to standard prophylaxis (who received continuous cotrimoxazole) with those randomised to enhanced prophylaxis (who received continuous cotrimoxazole plus single-dose albendazole, 5 days’ azithromycin, and 12 weeks’ fluconazole and isoniazid/pyridoxine). Detection of Shigella was decreased by the enhanced prophylaxis bundle (Figure 2, Supplementary Figure 3), while the overall burden of beneficial or harmful enteropathogens (Supplementary Figure 4) or other specific species, did not differ between groups (Supplementary Table 3). Previously, we showed that enhanced antimicrobial prophylaxis reduced biomarkers of enteropathy (specifically, I-FABP, faecal alpha-1 antitrypsin and faecal myeloperoxidase) 27 . We therefore went on to explore whether reductions in Shigella might explain these changes in biomarkers. Detection of Shigella was associated with higher baseline faecal myeloperoxidase compared to participants without Shigella (3,594 nmol/L (IQR 770-12,274) versus 1,093 nmol/L (770-3,294); P=<0.001; Table 2). Taken together, these findings strongly suggest that an antimicrobial bundle containing azithromycin decreases Shigella carriage, which in turn is associated with a reduction in faecal myeloperoxidase. This highlights the interplay between pathogens and enteropathy, and how an antimicrobial intervention may modulate the intestinal milieu and thereby confer benefits to people with advanced HIV disease. DISCUSSION Advanced HIV remains a major challenge in high-burden countries. Between 2015–2017, 25–32% of people presenting with HIV to primary care settings in sub-Saharan Africa had advanced HIV disease 28 , 29 , whichis associated with high mortality despite ART. It has long been apparent that the gastrointestinal tract is central to the pathogenesis of HIV, with growing evidence of the interplay between enteropathy, immunosuppression, and the gut microbiome 11 – 14 , 30 . Here, we assessed enteropathogen dynamics in advanced HIV disease, their contribution to mortality, and associations with the intestinal inflammatory milieu. We have four major findings: First, we document extensive subclinical gastrointestinal pathogen carriage in advanced HIV disease, even in people without diarrhoea. Second, we show the differential mortality effects of baseline enteropathogens, with some harmful and some beneficial in the first 6 months after ART initiation. Third, we find that the relationship between enteropathogens and mortality is partly dependent on an effect on the gut inflammatory milieu. Finally, we show that the benefits of an enhanced antimicrobial bundle, previously shown to reduce mortality by 27% 9 , may in part be mediated by reduced Shigella prevalence, due to the azithromycin component of the bundle. Collectively, these findings highlight the central role of the gut in people with advanced HIV disease, and the therapeutic potential of modifying the infectious and inflammatory intestinal milieu. Our results highlight the prevalence of subclinical carriage of gastrointestinal pathogens in ART-naive people with low CD4 counts during HIV infection. Most participants had one or more enteropathogens detected at baseline, most commonly Shigella , norovirus, Giardia or Campylobacter , particularly in those with CD4 counts < 50 cells/µl. Our results contribute to mounting evidence from recent studies highlighting extensive sub-clinical enteropathogen carriage through the application of molecular methods across multiple ages, demographics and geographies 18 – 20 , 31 , 32 . Furthermore, global studies focusing on populations from low/middle-income countries report frequent co-carriage of multiple microorganisms 18–20,32−34 . We identified distinct associations between specific pathogens and mortality; specifically, E. histolytica and C. difficile were associated with higher mortality, whereas E. coli O157 and Giardia were associated with lower mortality after ART initiation. Given the well-characterised relationship between systemic inflammation and mortality, our multivariable analyses adjusted for a panel of inflammatory biomarkers, which modified these relationships. Following adjustments for inflammation, C. difficile was independently associated with 4-fold higher mortality, while Giardia was independently associated with 80% lower mortality. Previous research from high-income settings has identified advanced HIV disease as a risk factor for C. difficile carriage or infection 35 , 36 . While C. difficile is a known prevalent pathogen in sub-Saharan Africa, associations to date with advanced HIV disease have been heterogeneous 37 , 38 . Relationships between C. difficile and mortality in the current study remained after adjusting for systemic inflammation, suggesting other pathways, such as toxin production or disruption of the gut mucosal barrier, may drive mortality. We found a strong positive association between C. difficile infection and faecal neopterin, suggesting that enteropathy may be exacerbated by carriage of C. difficile , which is consistent with its known role in enterocolitis 26 . Conversely, our results suggest that Giardia carriage was ‘protective’, with an 80% reduction in mortality during the first 6 months of ART. Asymptomatic carriage of Giardia has previously been reported 39 , 40 , with mixed findings regarding reduction of co-carriage 39 . Giardia has been hypothesised to reduce the risk of diarrhoea, modulate the host immune response 24 , 41 , and reduce morbidity 40 by attenuating intestinal neutrophil infiltration and decreasing expression of associated cytokines 25 . We found that Giardia carriage was negatively correlated with faecal myeloperoxidase, a marker of neutrophil activity, suggesting reduced neutrophilic inflammation in the gut. This is consistent with previous evidence from animal models that Giardia attenuates neutrophil infiltration into the colon by modulating gut expression of neutrophil chemoattractants including interleukin-8 25 . We hypothesise that the protective effects of Giardia may be principally driven by immunomodulation in the gastrointestinal compartment, similar to previous reports of proinflammatory response attenuation 24 , 25 . Overall, E. histolytica , C. difficile and Giardia appear to modulate the gut inflammatory milieu in different ways, and these effects on the gastrointestinal environment may contribute to their differing effects on the risk of mortality in the context of advanced HIV disease. Other mechanisms could include local or distant effects of toxins and other virulence factors, their effects on microbial communities, alteration of metabolic pathways, and co-infection dynamics. The REALITY trial showed that a bundle of enhanced prophylaxis, including two antimicrobials with activity against gut pathogens (single-dose albendazole and 5 days’ azithromycin), reduced mortality by 27% over 24 weeks in this population of adults and older children initiating ART with advanced HIV disease. We previously showed that the enhanced prophylaxis bundle modified enteropathy by reducing I-FABP, faecal A1AT and faecal myeloperoxidase 27 . Here, we extend these findings by showing a direct effect of the enhanced prophylaxis bundle in reducing Shigella prevalence over time, most likely due to azithromycin. Shigella is a known cause of diarrhoea in people living with HIV 42 , 43 , but here we show that Shigella may also shape the gastrointestinal inflammatory milieu, since we found positive associations between Shigella and faecal myeloperoxidase. Azithromycin has been previously reported to reduce faecal myeloperoxidase, A1AT and calprotectin, as well as the prevalence of enteropathogens in a randomised trial among healthy infants in India 44 , and cotrimoxazole has similarly been shown to reduce faecal myeloperoxidase in people living with HIV 22 , highlighting the immunomodulatory properties of antibiotics. Taken together, our findings suggest that azithromycin may confer survival benefits by modulating the inflammatory environment of the gut, both through its direct effects on Shigella , and its broad immunomodulatory effects 44 . This provides a mechanistic insight into this component of the enhanced prophylaxis bundle, which may therefore have contributed to mortality reductions, despite no direct evidence of fewer deaths from serious bacterial infections in the original trial; however, the absence of serious bacterial infections may be linked to challenges in detection and diagnosis given the significant reductions identified in deaths from unknown causes, which may have been sepsis-related. The strengths of this study lie in the large cohort of participants with advanced HIV disease, with longitudinal sample collection, and the ability to assess the causal effect of the randomised multi-component enhanced prophylaxis intervention. We measured a wide range of enteropathogens using molecular methods, with paired enteric and systemic inflammatory data; however, the pathogen identification technique is limited by its semi-quantitative detection. The current substudy was restricted to two countries with available stool samples, meaning the generalizability to other settings is unclear. The study is limited by an absence of pre-enrolment data therefore our models do not adjust for previous hospitalisation and infection events. In summary, we provide evidence for the important role that enteropathogens play in advanced HIV disease, including their independent associations with mortality. Our findings support a contribution of azithromycin to the mortality benefits of the enhanced prophylaxis bundle used in the REALITY trial, in reducing Shigella prevalence and modulating the enteropathy that characterises advanced HIV disease 22 , 27 , 44 . Targeting inflammation in the gastrointestinal compartment is a plausible intervention strategy, similar to recent findings in children with severe acute malnutrition 45 . Further investigation is required to explore the role of ‘protective’ pathogens such as Giardia , and to dissect the interplay between enteropathogens, the commensal microbiota, and inflammation, to inform the optimal use of antimicrobials in people with advanced HIV disease. METHODS REALITY trial The REALITY trial (ISRCTN43622374) was conducted in Kenya, Malawi, Uganda and Zimbabwe between 2013 and 2016. Participants were ART-naïve adults and children (over 5 years) with HIV and CD4 counts < 100 cells/µl. Participants were randomised to three interventions at ART initiation in a 2x2x2 factorial design: enhanced antimicrobial prophylaxis, adjunctive raltegravir therapy, and ready-to-use supplementary food, as previously reported 3 , 9 , 46 , 47 . Participants in the standard-of-care arm received cotrimoxazole alone. The bundle of enhanced infection prophylaxis comprised continuous cotrimoxazole plus 5 days’ azithromycin, single-dose albendazole, 12 weeks’ fluconazole and at least 12 weeks’ isoniazid/pyridoxine as a single fixed-dose combination tablet 12 . All deaths were reviewed by a blinded endpoint review committee with independent chair, to adjudicate cause of death. REALITY sample collection and storage Blood was collected into EDTA tubes at the screening and baseline visits, then at weeks 4, 12, 24, 36 and 48 post-randomisation. Blood was processed within 2 hours, and plasma and buffy coat cells were collected, as previously described 47 . Stool was collected at baseline, 4, 12 and 48 weeks into a plain container by participants prior to scheduled clinic visits in Harare, Zimbabwe, and Kilifi, Kenya, only; samples were transferred using a spatula into a plain storage vial. All samples were stored at -80°C. Immunology substudy We used a case-cohort design, which randomly sampled 45% of participants from the Kenya and Zimbabwe sites, where plasma, buffy coat cells, baseline cell pellet, and stool were collected from participants (sample size determined by available funding). Sampling was stratified by CD4 count (0–24, 25–49, 50–99 cells/µl, in approximate terciles). We selected all 65 participants who died by 24 weeks in Kenya and Zimbabwe as cases, and randomly sampled non-cases still alive and in follow-up at 48 weeks if they had complete biological specimens to week 24 plus baseline CD8 + T-cell count data using a case-cohort design (Supplementary Fig. 2). We added any deaths by 24 weeks which had not been selected by the random sampling, creating a final population of 290 participants from Kenya and Zimbabwe. The study focused on early changes in enteropathogens (from baseline to 12 weeks) since the enhanced prophylaxis bundle was only given for the first 12 weeks and most deaths occurred before 24 weeks 10 , 27 . Enteropathogen detection The Luminex xTAG Gastrointestinal Pathogen Panel (GPP) assay (Luminex Corp, Austin, Texas) was conducted on thawed stool samples according to the manufacturer’s instructions. The assay is a multiplexed nucleic acid test for the qualitative detection of 15 bacterial, viral and parasitic gastrointestinal pathogens: Campylobacter , Clostridium difficile toxin A/B, Escherichia coli O157, Enterotoxigenic E.coli (ETEC) LT/ST, Shiga-like toxin-producing E.coli (STEC) stx1/stx2, Salmonella, Shigella , Vibrio cholerae , Yersinia enterocolitica , Adenovirus 40/41, Norovirus GI/GII, Rotavirus A, Cryptosporidium , Entamoeba histolytica and Giardia . The data were acquired using a Luminex MagPix instrument, with analysis and interpretation carried out using TDAS data analysis software (TDAS GPP version 1.11). The positivity of each pathogen probe (or set of probes) was determined by a Mean Florescent Intensity (MFI) cut-off provided by the manufacturer, except for Cryptosporidium which was adjusted from 250 to 500 due to hypersensitivity of the probe and reactivity in negative controls, following discussions with the manufacturer. Enteropathy and inflammatory biomarkers Plasma biomarkers were measured by ELISA (Human FABP2/I-FABP Quantikine ELISA; R&D Systems Inc, Minneapolis, MN, USA) and by multiplex analysis using a preconfigured ProcartaPlex 34-plex human cytokine and chemokine panel (ThermoFisher Scientific/Life Technologies Ltd) and two customised 3-plex Luminex assays (R&D Systems Inc, Minneapolis, MN, USA), as previously described 27 . Stool samples were tested by ELISA for neopterin (GenWay Biotech Inc, San Diego, CA, USA), myeloperoxidase (Immundianostik, Bensheim, Germany), and alpha-1 antitrypsin (BioVendor, Brno, Czech Republic). All multiplex assays were run in singlicate on a Luminex MagPix machine with xPonent 4.2 software. Biomarker concentrations were determined against the respective standard curves and samples above the upper limit of detection were re-run at lower dilutions. Statistical Analysis All statistical analyses were performed in Stata version 18.5. Baseline prevalence of each pathogen was summarised by frequency. Chi-squared tests were used to test for difference between those with baseline CD4 < 50 and ≥ 50 cells/µl. Associations between enteropathogens and mortality were estimated with hazard ratios from Cox models weighted according to probability of inclusion in the substudy. Deaths were weighted as 1, and non-deaths were weighted according to the inverse probability of selection into the substudy. All models adjusted for clinical factors: age, sex, baseline CD4, WHO stage at enrolment, viral load, BMI, and site. Selected models additionally included either number of pathogens or plasma biomarkers (CRP, IFN-γ, IL-23, IL-2, IL-6, IP10, RANTES) and gut biomarkers (A1AT, myeloperoxidase, neopterin, IFABP). Biomarkers were truncated at the 1st and 95th percentile and effects estimated on a log scale (i.e. per fold-change). All pathogens were included together in the models, with the exception of adenovirus, rotavirus A, V. cholerae , and Y. enterocolitica , which were excluded from models due to low number of positive samples. Associations were explored in models adjusting for i) clinical factors only, ii) clinical factors and plasma biomarkers, and iii) clinical factors, plasma and gut biomarkers. Following this, backwards elimination with a threshold p = 0.2 was used to identify pathogens with any evidence of association with mortality (exploratory models, not directly adjusted for multiple testing but with results interpreted in the context of the number of factors considered). Selected pathogens were classified as ‘beneficial’, ‘harmful’ or ‘no effect’ depending on the direction of their association with mortality in the final model adjusted for clinical factors alone. The number of pathogens identified in each of these categories were then included as factors in Cox models, adjusting for clinical factors and biomarkers as before. Interactions were used to test whether the effect of beneficial or harmful pathogens differed depending on baseline CD4 (categorised as < 50 or ≥ 50 cells/µl); or randomisation (enhanced vs. standard prophylaxis). The change in pathogen prevalence from week 0 to week 12 was analysed using mixed effects logistic regression. Site, sex, timepoint, and baseline CD4 were included as fixed effects, and participant as a random effect with unstructured covariance. Models were weighted according to inverse probability of selection into the substudy. A sensitivity analysis restricted the model to include only participants who survived to week 12. The effect of enhanced prophylaxis on detection of each pathogen was explored by including interactions between randomised group and timepoint. We used Wilcoxon rank-sum test used to test for differences in gut biomarker concentrations between participants who were positive vs. negative for each pathogen at baseline, calculating medians and interquartile ranges for each group. Declarations Ethics and inclusion statement Researchers from Zimbabwe, Kenya, Malawi and Uganda were integral to the design, implementation, analysis and interpretation of the REALITY trial. The current manuscript includes clinical and laboratory data generated in Kenya and Zimbabwe, where stool samples were stored. Clinicians and laboratory scientists conducting this work were involved in the generation and interpretation of the study findings, and are included as authors. The trial steering committee comprised researchers and independent members from each country. Adult participants provided written informed consent, and parents/guardians provided written informed consent for children below the age of 18 years to enrol in the trial. Informed consent included storage of biological specimens, including faecal and blood samples, for subsequent analysis. Older children additionally provided assent, according to national guidelines. The trial and the laboratory work in this study were approved by ethics committees in Kenya (Kenya Medical Research Institute Ethics Review Committee), Zimbabwe (Joint Parirenyatwa Hospital and College of Health Sciences Research Ethics Committee and the Medical Research Council of Zimbabwe), and the UK (University College London Ethics Committee). Data availability Source data will be provided with this paper. The full dataset used in the analyses presented in this manuscript will be available on Figshare as part of the editorial process. Author contributions AJP, ASW, DMG, NK, JAB, MBD, AS conceptualised the project. AJP, ASW, DMG, NK, and JAB acquired funding. AJP, ASW, VR, AS designed the methodology. VR, HJ, RC, BN, AG, GM-w, AT, GM-u, SM-w, SM-u, TE, AS conducted investigations, laboratory, and statistical analysis. HJ, RC, AJP, VR wrote the original draft. All authors critically reviewed and edited the paper. AJP supervised the project. Competing interests The authors declare no competing or conflicting interests. Acknowledgements This study was funded by MRC (grant MR/P022251/1). We thank the participants, collaborating sites, and the REALITY trial team. References Anglaret X et al (2012) AIDS and non-AIDS morbidity and mortality across the spectrum of CD4 cell counts in HIV-infected adults before starting antiretroviral therapy in Cote d’Ivoire. Clin Infect Dis 54:714–723 Lucas SB et al (1993) The mortality and pathology of HIV infection in a west African city. Aids 7:1569–1579 Kityo C et al (2018) Raltegravir-intensified initial antiretroviral therapy in advanced HIV disease in Africa: A randomised controlled trial. PLoS Med 15:e1002706 Siedner MJ et al (2015) Trends in CD4 count at presentation to care and treatment initiation in sub-Saharan Africa, 2002–2013: a meta-analysis. Clin Infect Dis 60:1120–1127 HIV/AIDS GJ (2023) U. N. P. o. UNAIDS epidemiological estimates. 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Microbiome 7:1–17 Raffatellu M et al (2008) Simian immunodeficiency virus–induced mucosal interleukin-17 deficiency promotes Salmonella dissemination from the gut. Nat Med 14:421–428 Levine MM, Robins-Browne RM (2012) Factors that explain excretion of enteric pathogens by persons without diarrhea. Clin Infect Dis 55:S303–S311 Lindsay B et al (2013) Quantitative PCR for detection of Shigella improves ascertainment of Shigella burden in children with moderate-to-severe diarrhea in low-income countries. J Clin Microbiol 51:1740–1746 Liu J et al (2016) Optimization of quantitative PCR methods for enteropathogen detection. PLoS ONE 11:e0158199 Tickell KD et al (2020) The effect of acute malnutrition on enteric pathogens, moderate-to-severe diarrhoea, and associated mortality in the Global Enteric Multicenter Study cohort: a post-hoc analysis. 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PLoS ONE 9:e109087. https://doi.org/10.1371/journal.pone.0109087 Kelly CP, Pothoulakis C, LaMont JT (1994) Clostridium difficile colitis. N Engl J Med 330:257–262 Riitho V et al (2024) Biomarkers of mortality in adults and adolescents with advanced HIV in sub-Saharan Africa. Nat Commun 15:5492 Leeme TB et al (2021) Utility of CD4 count measurement in the era of universal antiretroviral therapy: an analysis of routine laboratory data in Botswana. HIV Med 22:1–10 Carmona S et al (2018) Persistent high burden of advanced HIV disease among patients seeking care in South Africa’s national HIV program: data from a nationwide laboratory cohort. Clin Infect Dis 66:S111–S117 Vujkovic-Cvijin I et al (2013) Dysbiosis of the gut microbiota is associated with HIV disease progression and tryptophan catabolism. Sci Transl Med 5:193ra191–193ra191 Kotloff KL et al (2013) Burden and aetiology of diarrhoeal disease in infants and young children in developing countries (the Global Enteric Multicenter Study, GEMS): a prospective, case-control study. lancet 382:209–222 Platts-Mills JA et al (2015) Pathogen-specific burdens of community diarrhoea in developing countries: a multisite birth cohort study (MAL-ED). Lancet Global Health 3:e564–e575 Barletta F et al (2011) Quantitative real-time polymerase chain reaction for enteropathogenic Escherichia coli: a tool for investigation of asymptomatic versus symptomatic infections. Clin Infect Dis 53:1223–1229 Quiroga M et al (2000) Asymptomatic infections by diarrheagenic Escherichia coli in children from Misiones, Argentina, during the first twenty months of their lives. Revista do Instituto de Medicina Tropical de São Paulo 42:9–15 Haines CF et al (2013) Clostridium difficile in a HIV-infected cohort: incidence, risk factors, and clinical outcomes. Aids 27:2799–2807 Pulvirenti JJ et al (2002) Epidemiology and outcome of Clostridium difficile infection and diarrhea in HIV infected inpatients11Currently at Metro Infectious Disease Consultants, L.I.C., Hinsdale, Illinois. 22Currently at Illinois Department of Public Health, Chicago, Illinois. Diagnostic Microbiology and Infectious Disease 44, 325–330 https://doi.org/https://doi.org/10.1016/S0732-8893(02)00462-5 Keeley AJ et al (2016) Clostridium difficile: A healthcare-associated infection of unknown significance in adults in sub-Saharan Africa. Malawi Med J 28:66–69 Seugendo M et al (2015) Clostridium difficile infections among adults and children in Mwanza/Tanzania: is it an underappreciated pathogen among immunocompromised patients in sub-Saharan Africa? New microbes new infections 8:99–102 Bilenko N et al (2004) Does co-infection with Giardia lamblia modulate the clinical characteristics of enteric infections in young children? 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BMC Infect Dis 23:255 Grassly NC et al (2016) The effect of azithromycin on the immunogenicity of oral poliovirus vaccine: a double-blind randomised placebo-controlled trial in seronegative Indian infants. Lancet Infect Dis 16:905–914 Chandwe K et al (2024) Malnutrition enteropathy in Zambian and Zimbabwean children with severe acute malnutrition: A multi-arm randomized phase II trial. Nat Commun 15:2910 Mallewa J et al (2018) Effect of ready-to-use supplementary food on mortality in severely immunocompromised HIV-infected individuals in Africa initiating antiretroviral therapy (REALITY): an open-label, parallel-group, randomised controlled trial. Lancet HIV 5:e231–e240. https://doi.org/https://doi.org/10.1016/S2352-3018(18)30038-9 Hanekom WA et al (2004) Novel application of a whole blood intracellular cytokine detection assay to quantitate specific T-cell frequency in field studies. J Immunol Methods 291:185–195. https://doi.org/10.1016/j.jim.2004.06.010 Tables Table 1. Demographics and prevalence of gastrointestinal pathogens by CD4 at ART initiation Participants with baseline sample (N=265) CD4 <50 / m l (N=169) CD4 ≥50 / m l (N=96) p-value Demographic and clinical factors Age, years 38 (31, 45) 38 (30, 45) 38 (32, 44) 0.52 Sex, female 128 (48%) 78 (46%) 50 (52%) 0.35 BMI (kg/m 2 ) 19 (17, 21) 19 (17, 21) 19 (18, 21) 0.030 WHO Stage at enrolment: 3-4 190 (72%) 133 (79%) 57 (59%) <0.001 Enhanced prophylaxis 128 (48%) 81 (48%) 47 (49%) 0.87 Plasma HIV RNA, log 10 copies/mL 5.4 (5.0, 5.8) 5.4 (5.1, 5.8) 5.4 (5.0, 5.8) 0.83 Centre: Harare 202 (76%) 127 (75%) 75 (78%) 0.58 Kilifi 63 (24%) 42 (25%) 21 (22%) Pathogen prevalence Adenovirus 40/41 1 (0%) 1 (1%) 0 (0%) 0.45 Campylobacter 49 (18%) 37 (22%) 12 (12%) 0.06 C. difficile 25 (9%) 13 (8%) 12 (12%) 0.20 Cryptosporidium 41 (15%) 32 (19%) 9 (9%) 0.04 E. coli O157 14 (5%) 4 (2%) 10 (10%) 0.005 E. histolytica 6 (2%) 3 (2%) 3 (3%) 0.48 ETEC LT/ST 19 (7%) 11 (7%) 8 (8%) 0.58 Giardia 50 (19%) 35 (21%) 15 (16%) 0.31 Norovirus 56 (21%) 45 (27%) 11 (11%) 0.004 Rotavirus A 2 (1%) 2 (1%) 0 (0%) 0.28 Salmonella 39 (15%) 27 (16%) 12 (12%) 0.44 Shigella 82 (31%) 54 (32%) 28 (29%) 0.64 STEC 19 (7%) 10 (6%) 9 (9%) 0.29 V. cholerae 3 (1%) 1 (1%) 2 (2%) 0.27 Y. enterocolitica 0 (0%) 0 (0%) 0 (0%) 1.00 Note: bold shows differences with univariable p<0.05 between pre-ART CD4 subgroups. Table 2. Concentrations of gut biomarkers in participants with or without baseline enteropathogens Pathogen name I-FABP Alpha-1 antitrypsin Myeloperoxidase Neopterin Median (IQR) p Median (IQR) p Median (IQR) p Median (IQR) p Positive Negative Positive Negative Positive Negative Positive Negative Adenovirus 40/41 2,237 (2,237, 2,237) 2,231 (1,403, 3,751) 0.99 81,465 (81,465, 81,465) 300,907 (144,179, 564,226) 0.19 4,815 (4,815, 4,815) 1,693 (770, 4,738) 0.37 33 (33, 33) 291 (83, 1,192) 0.12 Campylobacter 3,148 (1,697, 4,916) 2,192 (1,331, 3,609) 0.06 350,129 (127,138, 515,226) 287,626 (148,952, 573,500) 0.98 1,663 (770, 6,584) 1,727 (770, 4,131) 0.11 410 (98, 1,379) 254 (81, 1,184) 0.33 C. difficile 1,967 (1,380, 3,109) 2,271 (1,403, 3,925) 0.30 352,380 (186,198, 496,905) 270,288 (136,497, 573,500) 0.51 2,633 (770, 3,967) 1,663 (770, 4,771) 0.31 1,183 (345, 1,742) 236 (80, 1,065) 0.005 Cryptosporidium 2,884 (1,539, 4,845) 2,217 (1,370, 3,611) 0.19 334,479 (127,822, 555,813) 293,539 (144,369, 556,920) 0.96 1,774 (770, 6,935) 1,710 (770, 4,269) 0.79 304 (110, 1,188) 286 (80, 1,236) 0.39 E. coli O157 3,659 (1,757, 6,045) 2,219 (1,381, 3,610) 0.04 424,817 (73,659, 496,905) 293,539 (144,179, 573,500) 0.82 5,003 (770, 9,443) 1,691 (770, 4,285) 0.34 234 (90, 594) 296 (83, 1,258) 0.39 E. histolytica 3,902 (3,189, 6,045) 2,225 (1,397, 3,696) 0.11 391,405 (318,829, 467,051) 279,874 (142,799, 564,226) 0.68 770 (770, 770) 1,814 (770, 4,771) 0.07 280 (168, 594) 286 (83, 1,192) 0.74 ETEC LT/ST 1,969 (1,634, 2,340) 2,299 (1,397, 3,892) 0.47 191,538 (79,406, 401,926) 318,829 (144,558, 575,699) 0.09 770 (770, 3,400) 1,811 (770, 4,833) 0.15 302 (83, 911) 285 (82, 1,273) 0.61 Giardia 2,619 (1,746, 3,978) 2,191 (1,322, 3,610) 0.14 366,426 (182,277, 573,500) 265,977 (132,525, 549,613) 0.18 805 (770, 3,144) 1,987 (770, 4,833) 0.03 321 (99, 831) 258 (79, 1,236) 0.35 Norovirus 2,534 (1,517, 3,979) 2,219 (1,381, 3,610) 0.23 359,517 (177,459, 602,465) 269,044 (135,460, 547,932) 0.43 1,801 (770, 3,786) 1,691 (770, 5,142) 0.83 393 (133, 1,243) 242 (72, 1,192) 0.08 Rotavirus A 6,542 (3,289, 9,795) 2,230 (1,397, 3,748) 0.11 555,949 (106,089, 1,005,808) 300,180 (144,179, 549,613) 0.73 1,685 (866, 2,505) 1,710 (770, 4,771) 0.98 593 (54, 1,131) 286 (83, 1,192) 0.82 Salmonella 2,023 (1,265, 3,574) 2,243 (1,427, 3,806) 0.61 340,071 (161,071, 437,235) 299,452 (136,497, 578,677) 0.69 2,287 (770, 5,892) 1,663 (770, 4,481) 0.54 493 (112, 1,492) 244 (81, 1,170) 0.15 Shigella 1,999 (1,326, 3,281) 2,458 (1,485, 3,978) 0.09 255,859 (149,717, 564,226) 310,725 (136,497, 549,613) 0.52 3,594 (770, 12,274) 1,093 (770, 3,294) <0.001 175 (56, 686) 331 (90, 1,411) 0.02 STEC 2,473 (1,259, 3,334) 2,231 (1,424, 3,859) 0.64 361,413 (206,032, 564,226) 272,121 (138,245, 549,613) 0.39 2,397 (770, 7,577) 1,663 (770, 4,285) 0.23 160 (54, 2,354) 291 (87, 1,184) 0.72 V. cholerae 2,220 (1,856, 2,735) 2,237 (1,392, 3,806) 0.94 176,262 (82,475, 333,977) 300,907 (144,179, 564,226) 0.28 770 (770, 815) 1,811 (770, 4,771) 0.10 286 (51, 3,189) 290 (83, 1,188) 0.80 Note: showing medians and IQR, with ranksum p-values. Bold indicates comparisons with p<0.05. Additional Declarations There is NO Competing Interest. Supplementary Files 250310AppendixREALITY.docx Supplementary results Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6718521","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":466401690,"identity":"172db3a5-e87f-4e77-ac28-f2a41a7a346e","order_by":0,"name":"Helen 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17:00:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6718521/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6718521/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84190488,"identity":"9a988d7f-b9b2-4b48-9de1-2dd607a5cf0f","added_by":"auto","created_at":"2025-06-09 06:44:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":120430,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between mortality and baseline enteropathogen detection. \u003c/strong\u003ePanel A. Cox model adjusted for centre, CD4, age, stage at enrolment, BMI, viral load, sex and enhanced prophylaxis randomisation. Panel B. Adjusted for centre, CD4, age, stage at enrolment, BMI, viral load, sex and enhanced prophylaxis randomisation and additionally: CRP, IFNg, IL-23, IL-2, IL-6, IP10, RANTES.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6718521/v1/125c53de36e0ab6989f5a13d.png"},{"id":84190935,"identity":"f70f26df-eac1-4156-bce9-0c53d17a2a86","added_by":"auto","created_at":"2025-06-09 06:52:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":154482,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredicted prevalence of pathogens over time by enhanced prophylaxis randomisation. \u003c/strong\u003eNote\u003cstrong\u003e \u003c/strong\u003efrom logistic mixed model in all participants, see Supplementary Figure 4 restricted to survivors (\u0026gt;12 weeks). Adjusted for centre, sex, and baseline CD4. P-values from test of interaction between randomised group and time.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6718521/v1/ec3967f838511d3baa470b4d.png"},{"id":84193363,"identity":"ea2ebd92-686a-4e35-88a3-700460990aab","added_by":"auto","created_at":"2025-06-09 07:09:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1530179,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6718521/v1/221a3b26-a5a7-4a28-b9ee-8785f998c0f1.pdf"},{"id":84190495,"identity":"3258f851-b606-448f-bf69-51a216aefe21","added_by":"auto","created_at":"2025-06-09 06:44:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1010087,"visible":true,"origin":"","legend":"Supplementary results","description":"","filename":"250310AppendixREALITY.docx","url":"https://assets-eu.researchsquare.com/files/rs-6718521/v1/b881b41833756fbf62655bc6.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Enteropathogen detection and early mortality among people with advanced HIV disease in sub-Saharan Africa","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAdvanced HIV disease, defined by a CD4 count below 200 cells/\u0026micro;l or World Health Organization (WHO) stage 3 or 4 clinical disease, is associated with a high risk of hospitalization\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e or death\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, even in the first months after initiation of antiretroviral therapy (ART). In sub-Saharan Africa, the mean CD4 count at presentation in 2002 was 251 cells/\u0026micro;l, and at ART initiation was 152 cells/\u0026micro;l\u003csup\u003e4\u003c/sup\u003e; by 2013, mean CD4 counts had not significantly increased. There are an estimated 20\u0026ndash;25\u0026nbsp;million people with HIV in sub-Saharan Africa, resulting in 260,000 deaths in 2022\u003csup\u003e5\u003c/sup\u003e. In people with advanced HIV disease, most HIV-related deaths with known aetiology are due to co-infections including tuberculosis, severe bacterial infections, and cryptococcal disease\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. However, many deaths arise from unknown or multiple causes, due to the overlapping and interacting factors that drive mortality in the setting of advanced immunosuppression, malnutrition and clinical disease\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe pathogenesis of advanced HIV disease is complex and incompletely understood\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. CD4 decline arises due to chronic immune activation, pyroptosis, and failed immune reconstitution, leading to opportunistic infections and a reinforcing cycle of chronic inflammation and immunosuppression. The impact of CD4 depletion is particularly profound in the gut, and even when peripheral blood shows CD4 cell recovery following ART initiation, the gut CD4 population does not reconstitute as effectively. CCR9\u003csup\u003e+\u003c/sup\u003eβ7\u003csup\u003e+\u003c/sup\u003e (including Th17) CD4\u003csup\u003e+\u003c/sup\u003e T-cells exhibit a defective gut-homing phenotype with CCR9\u003csup\u003e+\u003c/sup\u003e and α4β7\u003csup\u003e+\u003c/sup\u003e cells remaining in the circulation and positively correlating with plasma markers of mucosal damage and microbial translocation\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Other markers of enteropathy such as I-FABP, lipopolysaccharide and sCD14 are also elevated in people with HIV and are strongly associated with mortality\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Taken together, HIV enteropathy is characterised by reduced mucosal barrier function, and increased microbial translocation, particularly in advanced HIV disease, and likely drives both local and systemic inflammation\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEnteropathy and an altered local inflammatory milieu may predispose to pathobionts and co-infections\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. A model of simian immunodeficiency virus (SIV) infection reported a reduction of Th17 cells in the gut and an associated expansion of \u003cem\u003eSalmonella typhimurium\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e. Early HIV infection has been associated with shedding of adenovirus in faecal samples among rhesus macaques\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. It is increasingly apparent that asymptomatic carriage of enteropathogens is common\u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Cotrimoxazole prophylaxis reduces infection from a range of opportunistic pathogens\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e including gut pathogens such as \u003cem\u003eCystoisospora belli\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e, and directly and indirectly reduces intestinal and systemic inflammation\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. It is unclear whether implementing a broader bundle of antimicrobials at the time of ART initiation, to target a greater range of enteropathogens, would have further benefits for gut inflammation.\u003c/p\u003e \u003cp\u003eHere, we explore the hypothesis that enteropathogens in people with advanced HIV disease drive enteropathy, intestinal inflammation, and mortality in sub-Saharan Africa, where there is high pathogen pressure. We leverage data and specimens from the Reduction of Early Mortality in HIV Infected Adults and Children Starting Antiretroviral Therapy (REALITY) trial, which enrolled adults and older children with advanced HIV disease in four African countries. The trial tested an enhanced prophylaxis bundle, which reduced overall mortality by 27%\u003csup\u003e9,10,23\u003c/sup\u003e, and demonstrated significant reductions in reports of tuberculosis, cryptococcal disease, candidiasis, and deaths from unknown causes. We hypothesised that the antimicrobial bundle reduced enteropathogen burden, and ameliorated enteropathy and systemic inflammation, thereby providing an additional mechanistic explanation for its mortality reduction benefits.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eEnteropathogen prevalence was measured using a multiplexed nucleic acid panel on the Luminex platform in 265 participants with advanced HIV disease enrolled in the REALITY trial in Zimbabwe and Kenya. Trial eligibility required CD4 counts below 100 cells/ml, and this analysis included participants with available baseline stools samples (Supplementary Figure 1). The median age at ART initiation was 38 years (3% below 18 years old), median CD4 count was 36\u0026nbsp;cells/ml, and 72% had WHO stage 3 or 4 disease (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultiple enteropathogen detection is common and associated with baseline CD4 count\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, there was a high prevalence of enteropathogens at ART initiation (Table 1), with 38% of participants having carriage of one pathogen and 38% with carriage of two or more pathogens. The commonest baseline enteropathogens were \u003cem\u003eShigella\u003c/em\u003e (31% of participants), Norovirus (21%), \u003cem\u003eGiardia\u003c/em\u003e (19%) and \u003cem\u003eCampylobacter\u003c/em\u003e (18%). There was a high degree of co-carriage; in particular, \u003cem\u003eE. histolytica\u003c/em\u003e, \u003cem\u003eE. coli\u0026nbsp;\u003c/em\u003eO157, \u003cem\u003eSalmonella\u003c/em\u003e, STEC, \u003cem\u003eShigella,\u003c/em\u003e \u003cem\u003eCampylobacter\u003c/em\u003e and \u003cem\u003eC. difficile\u003c/em\u003e were commonly identified with an additional enteropathogen (Supplementary Figure 2). Enteropathogen burden was associated with baseline CD4 count. Participants with a CD4 count \u0026lt;50 cells/ml\u0026nbsp;had a higher prevalence of \u003cem\u003eCryptosporidium\u0026nbsp;\u003c/em\u003e(19% versus 9% in those with CD4 count\u0026nbsp;≥50 cells/ml, respectively; P=0.04),norovirus(27% vs. 11%; P=0.004), and \u003cem\u003eCampylobacter\u003c/em\u003e (22% vs. 13%; P=0.06)\u003cem\u003e,\u0026nbsp;\u003c/em\u003ebut a lower prevalence of \u003cem\u003eE. coli\u003c/em\u003e 0157 (2% vs. 10%; P=0.005) (Table 1). Despite the high prevalence of enteropathogens, diarrhoea was only reported by 30/265 (11%) participants at baseline. \u003cem\u003eShigella\u003c/em\u003e was the pathogen most commonly identified in participants with diarrhoea (57% of those with diarrhoea) followed by \u003cem\u003eCampylobacter, Cryptosporidium,\u0026nbsp;\u003c/em\u003eand norovirus(20%) (Supplementary Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpecific enteropathogens are independently associated with mortality risk in advanced HIV\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, 56 participants died within the first 24 weeks (cases) and 209 survived (non-cases, see Methods). We assessed whether carriage of specific pathogens at baseline was associated with higher or lower risk of mortality after ART initiation, using multivariable Cox regression models, adjusted for CD4 count, age, stage at enrolment, body mass index, viral load, sex, site and enhanced prophylaxis randomisation. \u003cem\u003eE. histolytica\u003c/em\u003e was independently associated with a 5-fold higher risk of mortality (adjusted Hazard Ratio (aHR)=5.33, 95%CI 1.10-25.91; P=0.038). There was weak evidence that \u003cem\u003eC. difficile\u003c/em\u003e was independently associated with higher mortality (aHR=2.59, 95%CI 0.87-7.71; P=0.088) and \u003cem\u003eE. coli\u0026nbsp;\u003c/em\u003eO157 with lower mortality (aHR=0.07, 95%CI 0.00-1.37; P=0.079) (Figure 1A).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo assess whether the relationship between enteropathogens and mortality was mediated by their effects on systemic or enteric inflammation, we next included a panel of baseline plasma biomarkers (CRP, IFN-g, IL-23, IL-2, IL-6, IP10, RANTES) and gut biomarkers (plasma I-FABP, and faecal alpha-1 antitrypsin, neopterin, and myeloperoxidase) in the model. When adjusted for these biomarkers, there was evidence that \u003cem\u003eC. difficile\u003c/em\u003e was independently associated with higher mortality (aHR=3.78, 95%CI 1.31-10.93; P=0.014) while \u003cem\u003eGiardia\u003c/em\u003e (aHR=0.20, 95%CI 0.07-0.60; P=0.004) was associated with lower mortality. However, after adjusting for biomarkers, there was no evidence that \u003cem\u003eE. histolytica\u003c/em\u003e was associated with higher mortality (aHR=3.47, 95%CI 0.39-30.99; P=0.266), and weak evidence that \u003cem\u003eE. coli\u0026nbsp;\u003c/em\u003eO157 was associated with lower mortality (aHR=0.01, 95%CI 0.00-1.01; P=0.051) (Figure 1B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe next extended the regression models to explore the effects of pathogen load on mortality. There was no evidence of an association between the total number of baseline enteropathogens and subsequent mortality (aHR=1.05 per pathogen higher, 95%CI 0.88-1.24; P=0.61). However, given their differential effects on mortality, we next categorised enteropathogens as either “beneficial” (\u003cem\u003eGiardia\u0026nbsp;\u003c/em\u003eand \u003cem\u003eE coli\u0026nbsp;\u003c/em\u003e0157), or “harmful” (\u003cem\u003eC. difficile\u003c/em\u003e and \u003cem\u003eE. histolytica\u003c/em\u003e). Identification of any harmful pathogen at baseline was associated with a 3-fold higher subsequent risk of death (aHR=3.17, 95%CI 1.32-7.62; P=0.01), whereas the isolation of any beneficial pathogen was associated with 2-fold lower mortality (aHR=0.43, 95%CI 0.20-0.95; P=0.04). Identification of any other pathogen, not categorised as beneficial or harmful, was not associated with mortality (aHR=1.01, 95%CI 0.51- 2.01; P=0.97). After adjusting for systemic inflammation, the relationship between harmful pathogens and elevated mortality (aHR=4.45, 95%CI 1.73- 11.44; P=0.002), and the relationship between beneficial pathogens and reduced mortality (aHR=0.13, 95%CI 0.04-0.43; P=0.001) both remained significant. However, after adjusting for gut biomarkers, effects were attenuated (Supplementary Table 2). There was an interaction between carriage of beneficial pathogens and baseline CD4 count (P=0.02), whereby the mortality benefits appeared stronger in those with CD4 \u003cu\u003e\u0026gt;\u003c/u\u003e50 cells/ml\u0026nbsp;(aHR=0.02, 95%CI 0.002-0.24; P=0.002) vs. those with CD4\u0026lt;50 cells/ml\u0026nbsp;(aHR=0.38, 95%CI 0.12=1.13; P=0.08) (heterogeneity p=0.02).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnteropathogens are differentially associated with markers of enteropathy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the findings from our mortality models, which highlighted the contribution of gut biomarkers, we next hypothesised that specific enteropathogens may differentially modulate enteropathy. We reasoned that \u003cem\u003eGiardia\u003c/em\u003e may modify the gut environment in beneficial ways, given previous data suggesting that this protozoon is immunoregulatory\u003csup\u003e24,25\u003c/sup\u003e, while \u003cem\u003eC. difficile\u003c/em\u003e may exacerbate enteropathy, given existing data that it drives gut inflammation\u003csup\u003e26\u003c/sup\u003e. \u003cem\u003eGiardia\u003c/em\u003e carriage was associated with lower faecal myeloperoxidase (805 ng/mL (IQR 770-3,144) vs. 1,987 ng/mL (IQR 770-4,833) among those without \u003cem\u003eGiardia\u003c/em\u003e (P=0.03) (Table 2). By contrast, participants with \u003cem\u003eC. difficile\u003c/em\u003e had higher concentrations of faecal neopterin than those without \u003cem\u003eC. difficile\u003c/em\u003e (1,183 nmol/L (IQR 345-1,742) versus 236 nmol/L (80-1,065); P=0.005).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTaken together, these findings suggest that enteropathogens have differential associations with mortality among people with advanced HIV starting ART, and that harmful and beneficial pathogens may mediate their effects by exacerbating or ameliorating the inflammatory milieu of the gut, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnhanced antimicrobial prophylaxis reduced prevalence of \u003cem\u003eShigella\u003c/em\u003e among people with advanced HIV disease\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven our finding that specific pathogens are associated with mortality in advanced HIV disease, we finally hypothesised that the enhanced antimicrobial prophylaxis bundle may partly reduce mortality by modifying the presence of enteropathogens over time. We therefore leveraged the randomised trial design using logistic mixed models to explore changes in enteropathogens by weeks 4 and 12 post-intervention among 282 participants (17 with longitudinal samples but no baseline sample), comparing those randomised to standard prophylaxis (who received continuous cotrimoxazole) with those randomised to enhanced prophylaxis (who received continuous cotrimoxazole plus single-dose albendazole, 5 days’ azithromycin, and 12 weeks’ fluconazole and isoniazid/pyridoxine). Detection of \u003cem\u003eShigella\u0026nbsp;\u003c/em\u003ewas decreased by the enhanced prophylaxis bundle (Figure 2, Supplementary Figure 3), while the overall burden of beneficial or harmful enteropathogens (Supplementary Figure 4) or other specific species, did not differ between groups (Supplementary Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePreviously, we showed that enhanced antimicrobial prophylaxis reduced biomarkers of enteropathy (specifically, I-FABP, faecal alpha-1 antitrypsin and faecal myeloperoxidase)\u003csup\u003e27\u003c/sup\u003e. We therefore went on to explore whether reductions in \u003cem\u003eShigella\u003c/em\u003e might explain these changes in biomarkers.\u0026nbsp;Detection of \u003cem\u003eShigella\u003c/em\u003e was associated with higher baseline faecal myeloperoxidase compared to participants without\u0026nbsp;\u003cem\u003eShigella\u003c/em\u003e (3,594\u0026nbsp;nmol/L (IQR 770-12,274) versus 1,093 nmol/L (770-3,294); P=\u0026lt;0.001; Table 2). Taken together, these findings strongly suggest that an antimicrobial bundle containing azithromycin decreases \u003cem\u003eShigella\u003c/em\u003e carriage, which in turn is associated with a reduction in faecal myeloperoxidase. This highlights the interplay between pathogens and enteropathy, and how an antimicrobial intervention may modulate the intestinal milieu and thereby confer benefits to people with advanced HIV disease.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eAdvanced HIV remains a major challenge in high-burden countries. Between 2015\u0026ndash;2017, 25\u0026ndash;32% of people presenting with HIV to primary care settings in sub-Saharan Africa had advanced HIV disease\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, whichis associated with high mortality despite ART. It has long been apparent that the gastrointestinal tract is central to the pathogenesis of HIV, with growing evidence of the interplay between enteropathy, immunosuppression, and the gut microbiome\u003csup\u003e\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Here, we assessed enteropathogen dynamics in advanced HIV disease, their contribution to mortality, and associations with the intestinal inflammatory milieu. We have four major findings: First, we document extensive subclinical gastrointestinal pathogen carriage in advanced HIV disease, even in people without diarrhoea. Second, we show the differential mortality effects of baseline enteropathogens, with some harmful and some beneficial in the first 6 months after ART initiation. Third, we find that the relationship between enteropathogens and mortality is partly dependent on an effect on the gut inflammatory milieu. Finally, we show that the benefits of an enhanced antimicrobial bundle, previously shown to reduce mortality by 27%\u003csup\u003e9\u003c/sup\u003e, may in part be mediated by reduced \u003cem\u003eShigella\u003c/em\u003e prevalence, due to the azithromycin component of the bundle. Collectively, these findings highlight the central role of the gut in people with advanced HIV disease, and the therapeutic potential of modifying the infectious and inflammatory intestinal milieu.\u003c/p\u003e \u003cp\u003eOur results highlight the prevalence of subclinical carriage of gastrointestinal pathogens in ART-naive people with low CD4 counts during HIV infection. Most participants had one or more enteropathogens detected at baseline, most commonly \u003cem\u003eShigella\u003c/em\u003e, norovirus, \u003cem\u003eGiardia\u003c/em\u003e or \u003cem\u003eCampylobacter\u003c/em\u003e, particularly in those with CD4 counts\u0026thinsp;\u0026lt;\u0026thinsp;50 cells/\u0026micro;l. Our results contribute to mounting evidence from recent studies highlighting extensive sub-clinical enteropathogen carriage through the application of molecular methods across multiple ages, demographics and geographies\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Furthermore, global studies focusing on populations from low/middle-income countries report frequent co-carriage of multiple microorganisms\u003csup\u003e18\u0026ndash;20,32\u0026minus;34\u003c/sup\u003e. We identified distinct associations between specific pathogens and mortality; specifically, \u003cem\u003eE. histolytica\u003c/em\u003e and \u003cem\u003eC. difficile\u003c/em\u003e were associated with higher mortality, whereas \u003cem\u003eE. coli\u003c/em\u003e O157 and \u003cem\u003eGiardia\u003c/em\u003e were associated with lower mortality after ART initiation. Given the well-characterised relationship between systemic inflammation and mortality, our multivariable analyses adjusted for a panel of inflammatory biomarkers, which modified these relationships. Following adjustments for inflammation, \u003cem\u003eC. difficile\u003c/em\u003e was independently associated with 4-fold higher mortality, while \u003cem\u003eGiardia\u003c/em\u003e was independently associated with 80% lower mortality. Previous research from high-income settings has identified advanced HIV disease as a risk factor for \u003cem\u003eC. difficile\u003c/em\u003e carriage or infection\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. While \u003cem\u003eC. difficile\u003c/em\u003e is a known prevalent pathogen in sub-Saharan Africa, associations to date with advanced HIV disease have been heterogeneous\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Relationships between \u003cem\u003eC. difficile\u003c/em\u003e and mortality in the current study remained after adjusting for systemic inflammation, suggesting other pathways, such as toxin production or disruption of the gut mucosal barrier, may drive mortality. We found a strong positive association between \u003cem\u003eC. difficile\u003c/em\u003e infection and faecal neopterin, suggesting that enteropathy may be exacerbated by carriage of \u003cem\u003eC. difficile\u003c/em\u003e, which is consistent with its known role in enterocolitis\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConversely, our results suggest that \u003cem\u003eGiardia\u003c/em\u003e carriage was \u0026lsquo;protective\u0026rsquo;, with an 80% reduction in mortality during the first 6 months of ART. Asymptomatic carriage of \u003cem\u003eGiardia\u003c/em\u003e has previously been reported\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, with mixed findings regarding reduction of co-carriage\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eGiardia\u003c/em\u003e has been hypothesised to reduce the risk of diarrhoea, modulate the host immune response\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, and reduce morbidity\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e by attenuating intestinal neutrophil infiltration and decreasing expression of associated cytokines\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. We found that \u003cem\u003eGiardia\u003c/em\u003e carriage was negatively correlated with faecal myeloperoxidase, a marker of neutrophil activity, suggesting reduced neutrophilic inflammation in the gut. This is consistent with previous evidence from animal models that \u003cem\u003eGiardia\u003c/em\u003e attenuates neutrophil infiltration into the colon by modulating gut expression of neutrophil chemoattractants including interleukin-8\u003csup\u003e25\u003c/sup\u003e. We hypothesise that the protective effects of \u003cem\u003eGiardia\u003c/em\u003e may be principally driven by immunomodulation in the gastrointestinal compartment, similar to previous reports of proinflammatory response attenuation\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Overall, \u003cem\u003eE. histolytica\u003c/em\u003e, \u003cem\u003eC. difficile\u003c/em\u003e and \u003cem\u003eGiardia\u003c/em\u003e appear to modulate the gut inflammatory milieu in different ways, and these effects on the gastrointestinal environment may contribute to their differing effects on the risk of mortality in the context of advanced HIV disease. Other mechanisms could include local or distant effects of toxins and other virulence factors, their effects on microbial communities, alteration of metabolic pathways, and co-infection dynamics.\u003c/p\u003e \u003cp\u003eThe REALITY trial showed that a bundle of enhanced prophylaxis, including two antimicrobials with activity against gut pathogens (single-dose albendazole and 5 days\u0026rsquo; azithromycin), reduced mortality by 27% over 24 weeks in this population of adults and older children initiating ART with advanced HIV disease. We previously showed that the enhanced prophylaxis bundle modified enteropathy by reducing I-FABP, faecal A1AT and faecal myeloperoxidase\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Here, we extend these findings by showing a direct effect of the enhanced prophylaxis bundle in reducing \u003cem\u003eShigella\u003c/em\u003e prevalence over time, most likely due to azithromycin. \u003cem\u003eShigella\u003c/em\u003e is a known cause of diarrhoea in people living with HIV\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, but here we show that \u003cem\u003eShigella\u003c/em\u003e may also shape the gastrointestinal inflammatory milieu, since we found positive associations between \u003cem\u003eShigella\u003c/em\u003e and faecal myeloperoxidase. Azithromycin has been previously reported to reduce faecal myeloperoxidase, A1AT and calprotectin, as well as the prevalence of enteropathogens in a randomised trial among healthy infants in India\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, and cotrimoxazole has similarly been shown to reduce faecal myeloperoxidase in people living with HIV\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, highlighting the immunomodulatory properties of antibiotics. Taken together, our findings suggest that azithromycin may confer survival benefits by modulating the inflammatory environment of the gut, both through its direct effects on \u003cem\u003eShigella\u003c/em\u003e, and its broad immunomodulatory effects\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. This provides a mechanistic insight into this component of the enhanced prophylaxis bundle, which may therefore have contributed to mortality reductions, despite no direct evidence of fewer deaths from serious bacterial infections in the original trial; however, the absence of serious bacterial infections may be linked to challenges in detection and diagnosis given the significant reductions identified in deaths from unknown causes, which may have been sepsis-related.\u003c/p\u003e \u003cp\u003eThe strengths of this study lie in the large cohort of participants with advanced HIV disease, with longitudinal sample collection, and the ability to assess the causal effect of the randomised multi-component enhanced prophylaxis intervention. We measured a wide range of enteropathogens using molecular methods, with paired enteric and systemic inflammatory data; however, the pathogen identification technique is limited by its semi-quantitative detection. The current substudy was restricted to two countries with available stool samples, meaning the generalizability to other settings is unclear. The study is limited by an absence of pre-enrolment data therefore our models do not adjust for previous hospitalisation and infection events.\u003c/p\u003e \u003cp\u003eIn summary, we provide evidence for the important role that enteropathogens play in advanced HIV disease, including their independent associations with mortality. Our findings support a contribution of azithromycin to the mortality benefits of the enhanced prophylaxis bundle used in the REALITY trial, in reducing \u003cem\u003eShigella\u003c/em\u003e prevalence and modulating the enteropathy that characterises advanced HIV disease\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Targeting inflammation in the gastrointestinal compartment is a plausible intervention strategy, similar to recent findings in children with severe acute malnutrition\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Further investigation is required to explore the role of \u0026lsquo;protective\u0026rsquo; pathogens such as \u003cem\u003eGiardia\u003c/em\u003e, and to dissect the interplay between enteropathogens, the commensal microbiota, and inflammation, to inform the optimal use of antimicrobials in people with advanced HIV disease.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cstrong\u003eREALITY trial\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe REALITY trial (ISRCTN43622374) was conducted in Kenya, Malawi, Uganda and Zimbabwe between 2013 and 2016. Participants were ART-na\u0026iuml;ve adults and children (over 5 years) with HIV and CD4 counts\u0026thinsp;\u0026lt;\u0026thinsp;100 cells/\u0026micro;l. Participants were randomised to three interventions at ART initiation in a 2x2x2 factorial design: enhanced antimicrobial prophylaxis, adjunctive raltegravir therapy, and ready-to-use supplementary food, as previously reported\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Participants in the standard-of-care arm received cotrimoxazole alone. The bundle of enhanced infection prophylaxis comprised continuous cotrimoxazole plus 5 days\u0026rsquo; azithromycin, single-dose albendazole, 12 weeks\u0026rsquo; fluconazole and at least 12 weeks\u0026rsquo; isoniazid/pyridoxine as a single fixed-dose combination tablet\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. All deaths were reviewed by a blinded endpoint review committee with independent chair, to adjudicate cause of death.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eREALITY sample collection and storage\u003c/h3\u003e\n\u003cp\u003eBlood was collected into EDTA tubes at the screening and baseline visits, then at weeks 4, 12, 24, 36 and 48 post-randomisation. Blood was processed within 2 hours, and plasma and buffy coat cells were collected, as previously described\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Stool was collected at baseline, 4, 12 and 48 weeks into a plain container by participants prior to scheduled clinic visits in Harare, Zimbabwe, and Kilifi, Kenya, only; samples were transferred using a spatula into a plain storage vial. All samples were stored at -80\u0026deg;C.\u003c/p\u003e\n\u003ch3\u003eImmunology substudy\u003c/h3\u003e\n\u003cp\u003eWe used a case-cohort design, which randomly sampled 45% of participants from the Kenya and Zimbabwe sites, where plasma, buffy coat cells, baseline cell pellet, and stool were collected from participants (sample size determined by available funding). Sampling was stratified by CD4 count (0\u0026ndash;24, 25\u0026ndash;49, 50\u0026ndash;99 cells/\u0026micro;l, in approximate terciles). We selected all 65 participants who died by 24 weeks in Kenya and Zimbabwe as cases, and randomly sampled non-cases still alive and in follow-up at 48 weeks if they had complete biological specimens to week 24 plus baseline CD8\u003csup\u003e+\u003c/sup\u003e T-cell count data using a case-cohort design (Supplementary Fig. 2). We added any deaths by 24 weeks which had not been selected by the random sampling, creating a final population of 290 participants from Kenya and Zimbabwe. The study focused on early changes in enteropathogens (from baseline to 12 weeks) since the enhanced prophylaxis bundle was only given for the first 12 weeks and most deaths occurred before 24 weeks\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eEnteropathogen detection\u003c/h2\u003e\n \u003cp\u003eThe Luminex xTAG Gastrointestinal Pathogen Panel (GPP) assay (Luminex Corp, Austin, Texas) was conducted on thawed stool samples according to the manufacturer\u0026rsquo;s instructions. The assay is a multiplexed nucleic acid test for the qualitative detection of 15 bacterial, viral and parasitic gastrointestinal pathogens: \u003cem\u003eCampylobacter\u003c/em\u003e, \u003cem\u003eClostridium difficile\u003c/em\u003e toxin A/B, \u003cem\u003eEscherichia coli\u003c/em\u003e O157, Enterotoxigenic \u003cem\u003eE.coli\u003c/em\u003e (ETEC) LT/ST, Shiga-like toxin-producing \u003cem\u003eE.coli\u003c/em\u003e (STEC) stx1/stx2, \u003cem\u003eSalmonella, Shigella\u003c/em\u003e, \u003cem\u003eVibrio cholerae\u003c/em\u003e, \u003cem\u003eYersinia enterocolitica\u003c/em\u003e, Adenovirus 40/41, Norovirus GI/GII, Rotavirus A, \u003cem\u003eCryptosporidium\u003c/em\u003e, \u003cem\u003eEntamoeba histolytica\u003c/em\u003e and \u003cem\u003eGiardia\u003c/em\u003e. The data were acquired using a Luminex MagPix instrument, with analysis and interpretation carried out using TDAS data analysis software (TDAS GPP version 1.11). The positivity of each pathogen probe (or set of probes) was determined by a Mean Florescent Intensity (MFI) cut-off provided by the manufacturer, except for \u003cem\u003eCryptosporidium\u003c/em\u003e which was adjusted from 250 to 500 due to hypersensitivity of the probe and reactivity in negative controls, following discussions with the manufacturer.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eEnteropathy and inflammatory biomarkers\u003c/h2\u003e\n \u003cp\u003ePlasma biomarkers were measured by ELISA (Human FABP2/I-FABP Quantikine ELISA; R\u0026amp;D Systems Inc, Minneapolis, MN, USA) and by multiplex analysis using a preconfigured ProcartaPlex 34-plex human cytokine and chemokine panel (ThermoFisher Scientific/Life Technologies Ltd) and two customised 3-plex Luminex assays (R\u0026amp;D Systems Inc, Minneapolis, MN, USA), as previously described\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Stool samples were tested by ELISA for neopterin (GenWay Biotech Inc, San Diego, CA, USA), myeloperoxidase (Immundianostik, Bensheim, Germany), and alpha-1 antitrypsin (BioVendor, Brno, Czech Republic). All multiplex assays were run in singlicate on a Luminex MagPix machine with xPonent 4.2 software. Biomarker concentrations were determined against the respective standard curves and samples above the upper limit of detection were re-run at lower dilutions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eAll statistical analyses were performed in Stata version 18.5. Baseline prevalence of each pathogen was summarised by frequency. Chi-squared tests were used to test for difference between those with baseline CD4\u0026thinsp;\u0026lt;\u0026thinsp;50 and \u0026ge;\u0026thinsp;50 cells/\u0026micro;l. Associations between enteropathogens and mortality were estimated with hazard ratios from Cox models weighted according to probability of inclusion in the substudy. Deaths were weighted as 1, and non-deaths were weighted according to the inverse probability of selection into the substudy. All models adjusted for clinical factors: age, sex, baseline CD4, WHO stage at enrolment, viral load, BMI, and site. Selected models additionally included either number of pathogens or plasma biomarkers (CRP, IFN-\u0026gamma;, IL-23, IL-2, IL-6, IP10, RANTES) and gut biomarkers (A1AT, myeloperoxidase, neopterin, IFABP). Biomarkers were truncated at the 1st and 95th percentile and effects estimated on a log scale (i.e. per fold-change).\u003c/p\u003e\n \u003cp\u003eAll pathogens were included together in the models, with the exception of adenovirus, rotavirus A, \u003cem\u003eV. cholerae\u003c/em\u003e, and \u003cem\u003eY. enterocolitica\u003c/em\u003e, which were excluded from models due to low number of positive samples. Associations were explored in models adjusting for i) clinical factors only, ii) clinical factors and plasma biomarkers, and iii) clinical factors, plasma and gut biomarkers. Following this, backwards elimination with a threshold p\u0026thinsp;=\u0026thinsp;0.2 was used to identify pathogens with any evidence of association with mortality (exploratory models, not directly adjusted for multiple testing but with results interpreted in the context of the number of factors considered). Selected pathogens were classified as \u0026lsquo;beneficial\u0026rsquo;, \u0026lsquo;harmful\u0026rsquo; or \u0026lsquo;no effect\u0026rsquo; depending on the direction of their association with mortality in the final model adjusted for clinical factors alone. The number of pathogens identified in each of these categories were then included as factors in Cox models, adjusting for clinical factors and biomarkers as before. Interactions were used to test whether the effect of beneficial or harmful pathogens differed depending on baseline CD4 (categorised as \u0026lt;\u0026thinsp;50 or \u0026ge;\u0026thinsp;50 cells/\u0026micro;l); or randomisation (enhanced vs. standard prophylaxis).\u003c/p\u003e\n \u003cp\u003eThe change in pathogen prevalence from week 0 to week 12 was analysed using mixed effects logistic regression. Site, sex, timepoint, and baseline CD4 were included as fixed effects, and participant as a random effect with unstructured covariance. Models were weighted according to inverse probability of selection into the substudy. A sensitivity analysis restricted the model to include only participants who survived to week 12. The effect of enhanced prophylaxis on detection of each pathogen was explored by including interactions between randomised group and timepoint.\u003c/p\u003e\n \u003cp\u003eWe used Wilcoxon rank-sum test used to test for differences in gut biomarker concentrations between participants who were positive vs. negative for each pathogen at baseline, calculating medians and interquartile ranges for each group.\u003c/p\u003e\n\u003c/div\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics and inclusion statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearchers from Zimbabwe, Kenya, Malawi and Uganda were integral to the design, implementation, analysis and interpretation of the REALITY trial. The current manuscript includes clinical and laboratory data generated in Kenya and Zimbabwe, where stool samples were stored. Clinicians and laboratory scientists conducting this work were involved in the generation and interpretation of the study findings, and are included as authors. The trial steering committee comprised researchers and independent members from each country. Adult participants provided written informed consent, and parents/guardians provided written informed consent for children below the age of 18 years to enrol in the trial. Informed consent included storage of biological specimens, including faecal and blood samples, for subsequent analysis. Older children additionally provided assent, according to national guidelines. The trial and the laboratory work in this study were approved by ethics committees in Kenya (Kenya Medical Research Institute Ethics Review Committee), Zimbabwe (Joint Parirenyatwa Hospital and College of Health Sciences Research Ethics Committee and the Medical Research Council of Zimbabwe), and the UK (University College London Ethics Committee).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource data will be provided with this paper. The full dataset used in the analyses presented in this manuscript will be available on Figshare as part of the editorial process.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAJP, ASW, DMG, NK, JAB, MBD, AS conceptualised the project. AJP, ASW, DMG, NK, and JAB acquired funding. AJP, ASW, VR, AS designed the methodology. VR, HJ, RC, BN, AG, GM-w, AT, GM-u, SM-w, SM-u, TE, AS conducted investigations, laboratory, and statistical analysis. HJ, RC, AJP, VR wrote the original draft. All authors critically reviewed and edited the paper. AJP supervised the project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing or conflicting interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by MRC (grant MR/P022251/1). We thank the participants, collaborating sites, and the REALITY trial team.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnglaret X et al (2012) AIDS and non-AIDS morbidity and mortality across the spectrum of CD4 cell counts in HIV-infected adults before starting antiretroviral therapy in Cote d\u0026rsquo;Ivoire. 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Clin Infect Dis 66:S132\u0026ndash;S139. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/cid/cix1141\u003c/span\u003e\u003cspan address=\"10.1093/cid/cix1141\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastillo-Rozas G, Lopez MN, Soto-Rifo R, Vidal R, Cortes CP (2023) Enteropathy and gut dysbiosis as obstacles to achieve immune recovery in undetectable people with HIV: a clinical view of evidence, successes, and projections. AIDS 37:367\u0026ndash;378\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMavigner M et al (2012) Altered CD4\u0026thinsp;+\u0026thinsp;T cell homing to the gut impairs mucosal immune reconstitution in treated HIV-infected individuals. 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Sci Transl Med 5:193ra191\u0026ndash;193ra191\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKotloff KL et al (2013) Burden and aetiology of diarrhoeal disease in infants and young children in developing countries (the Global Enteric Multicenter Study, GEMS): a prospective, case-control study. lancet 382:209\u0026ndash;222\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlatts-Mills JA et al (2015) Pathogen-specific burdens of community diarrhoea in developing countries: a multisite birth cohort study (MAL-ED). Lancet Global Health 3:e564\u0026ndash;e575\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarletta F et al (2011) Quantitative real-time polymerase chain reaction for enteropathogenic Escherichia coli: a tool for investigation of asymptomatic versus symptomatic infections. Clin Infect Dis 53:1223\u0026ndash;1229\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuiroga M et al (2000) Asymptomatic infections by diarrheagenic Escherichia coli in children from Misiones, Argentina, during the first twenty months of their lives. Revista do Instituto de Medicina Tropical de S\u0026atilde;o Paulo 42:9\u0026ndash;15\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaines CF et al (2013) Clostridium difficile in a HIV-infected cohort: incidence, risk factors, and clinical outcomes. 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BMC Infect Dis 23:255\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrassly NC et al (2016) The effect of azithromycin on the immunogenicity of oral poliovirus vaccine: a double-blind randomised placebo-controlled trial in seronegative Indian infants. Lancet Infect Dis 16:905\u0026ndash;914\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChandwe K et al (2024) Malnutrition enteropathy in Zambian and Zimbabwean children with severe acute malnutrition: A multi-arm randomized phase II trial. Nat Commun 15:2910\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMallewa J et al (2018) Effect of ready-to-use supplementary food on mortality in severely immunocompromised HIV-infected individuals in Africa initiating antiretroviral therapy (REALITY): an open-label, parallel-group, randomised controlled trial. Lancet HIV 5:e231\u0026ndash;e240. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/S2352-3018(18)30038-9\u003c/span\u003e\u003cspan address=\"10.1016/S2352-3018(18)30038-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanekom WA et al (2004) Novel application of a whole blood intracellular cytokine detection assay to quantitate specific T-cell frequency in field studies. J Immunol Methods 291:185\u0026ndash;195. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jim.2004.06.010\u003c/span\u003e\u003cspan address=\"10.1016/j.jim.2004.06.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Demographics and prevalence of gastrointestinal pathogens by CD4 at ART initiation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipants with baseline sample (N=265)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD4 \u0026lt;50\u003c/strong\u003e\u003cstrong\u003e/\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003el\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=169)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD4\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026ge;50\u003c/strong\u003e\u003cstrong\u003e/\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003el\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=96)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 601px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic and clinical factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e38 (31, 45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e38 (30, 45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e38 (32, 44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eSex, female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e128 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e78 (46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e50 (52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e19 (17, 21)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e19 (17, 21)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e19 (18, 21)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eWHO Stage at enrolment: 3-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e190 (72%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e133 (79%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e57 (59%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eEnhanced prophylaxis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e128 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e81 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e47 (49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003ePlasma HIV RNA, log\u003csub\u003e10\u003c/sub\u003e copies/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e5.4 (5.0, 5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e5.4 (5.1, 5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e5.4 (5.0, 5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eCentre: Harare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e202 (76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e127 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e75 (78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eKilifi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e63 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e42 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e21 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 601px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathogen prevalence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eAdenovirus 40/41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cem\u003eCampylobacter\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e49 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e37 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e12 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cem\u003eC. difficile\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e25 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e13 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e12 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cem\u003eCryptosporidium\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e41 (15%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e32 (19%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9 (9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e O157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14 (5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4 (2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10 (10%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cem\u003eE. histolytica\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e6 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e3 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e3 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eETEC LT/ST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e19 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e11 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e8 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cem\u003eGiardia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e50 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e35 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e15 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eNorovirus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e56 (21%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e45 (27%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e11 (11%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eRotavirus A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e2 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e2 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e39 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e27 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e12 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cem\u003eShigella\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e82 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e54 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e28 (29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003eSTEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e19 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e10 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e9 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cem\u003eV. cholerae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e3 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e2 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cem\u003eY. enterocolitica\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: bold shows differences with univariable p\u0026lt;0.05 between pre-ART CD4 subgroups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Concentrations of gut biomarkers in participants with or without baseline enteropathogens\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"731\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ePathogen name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eI-FABP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eAlpha-1 antitrypsin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMyeloperoxidase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eNeopterin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdenovirus 40/41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,237 (2,237, 2,237)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,231 (1,403, 3,751)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e81,465 (81,465, 81,465)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e300,907 (144,179, 564,226)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,815 (4,815, 4,815)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,693 (770, 4,738)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33 (33, 33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e291 (83, 1,192)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCampylobacter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3,148 (1,697, 4,916)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,192 (1,331, 3,609)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e350,129 (127,138, 515,226)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e287,626 (148,952, 573,500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,663 (770, 6,584)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,727 (770, 4,131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e410 (98, 1,379)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e254 (81, 1,184)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC. difficile\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,967 (1,380, 3,109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,271 (1,403, 3,925)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e352,380 (186,198, 496,905)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e270,288 (136,497, 573,500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,633 (770, 3,967)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,663 (770, 4,771)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1,183 (345, 1,742)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e236 (80, 1,065)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCryptosporidium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,884 (1,539, 4,845)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,217 (1,370, 3,611)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e334,479 (127,822, 555,813)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e293,539 (144,369, 556,920)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,774 (770, 6,935)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,710 (770, 4,269)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e304 (110, 1,188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e286 (80, 1,236)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE. coli O157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3,659 (1,757, 6,045)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2,219 (1,381, 3,610)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e424,817 (73,659, 496,905)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e293,539 (144,179, 573,500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5,003 (770, 9,443)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,691 (770, 4,285)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e234 (90, 594)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e296 (83, 1,258)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE. histolytica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3,902 (3,189, 6,045)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,225 (1,397, 3,696)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e391,405 (318,829, 467,051)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e279,874 (142,799, 564,226)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e770 (770, 770)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,814 (770, 4,771)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e280 (168, 594)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e286 (83, 1,192)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eETEC LT/ST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,969 (1,634, 2,340)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,299 (1,397, 3,892)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e191,538 (79,406, 401,926)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e318,829 (144,558, 575,699)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e770 (770, 3,400)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,811 (770, 4,833)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e302 (83, 911)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e285 (82, 1,273)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGiardia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,619 (1,746, 3,978)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,191 (1,322, 3,610)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e366,426 (182,277, 573,500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e265,977 (132,525, 549,613)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e805 (770, 3,144)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1,987 (770, 4,833)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e321 (99, 831)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e258 (79, 1,236)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNorovirus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,534 (1,517, 3,979)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,219 (1,381, 3,610)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e359,517 (177,459, 602,465)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e269,044 (135,460, 547,932)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,801 (770, 3,786)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,691 (770, 5,142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e393 (133, 1,243)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e242 (72, 1,192)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRotavirus A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6,542 (3,289, 9,795)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,230 (1,397, 3,748)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e555,949 (106,089, 1,005,808)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e300,180 (144,179, 549,613)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,685 (866, 2,505)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,710 (770, 4,771)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e593 (54, 1,131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e286 (83, 1,192)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSalmonella\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,023 (1,265, 3,574)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,243 (1,427, 3,806)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e340,071 (161,071, 437,235)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e299,452 (136,497, 578,677)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,287 (770, 5,892)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,663 (770, 4,481)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e493 (112, 1,492)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e244 (81, 1,170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eShigella\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,999 (1,326, 3,281)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,458 (1,485, 3,978)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e255,859 (149,717, 564,226)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e310,725 (136,497, 549,613)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3,594 (770, 12,274)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1,093 (770, 3,294)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e175 (56, 686)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e331 (90, 1,411)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSTEC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,473 (1,259, 3,334)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,231 (1,424, 3,859)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e361,413 (206,032, 564,226)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e272,121 (138,245, 549,613)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,397 (770, 7,577)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,663 (770, 4,285)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e160 (54, 2,354)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e291 (87, 1,184)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eV. cholerae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,220 (1,856, 2,735)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,237 (1,392, 3,806)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e176,262 (82,475, 333,977)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e300,907 (144,179, 564,226)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e770 (770, 815)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,811 (770, 4,771)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e286 (51, 3,189)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e290 (83, 1,188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: showing medians and IQR, with ranksum p-values. Bold indicates comparisons with p\u0026lt;0.05.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6718521/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6718521/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOne-third of people with HIV in sub-Saharan Africa initiate treatment with advanced HIV disease, with high associated mortality. Whether enteropathogens and intestinal inflammation contribute to mortality remains unclear. We leveraged participant samples from the Reduction of Early Mortality in HIV-infected Adults and Children Starting Antiretroviral Therapy (REALITY) trial (ISRCTN43622374) to investigate associations between enteropathogens (assayed by multiplex nucleic acid detection), immune biomarkers (assayed by ELISA and Luminex) and mortality, and to explore the mechanism by which an enhanced prophylaxis bundle (containing albendazole, azithromycin, fluconazole, and isoniazid/pyridoxine) significantly reduced mortality. Cox models were adjusted for age, sex, CD4, WHO stage, viral load, BMI, site, and biomarkers. In 265 participants initiating treatment with median CD4 36 cells/ml, we found extensive sub-clinical enteropathogen carriage and frequent co-infection. Enteropathogens had differential associations with gut biomarkers and mortality. \u003cem\u003eClostridium difficile\u003c/em\u003e was associated with elevated faecal neopterin and higher mortality (adjusted hazard ratio (aHR)=3.78, 95%CI 1.31-10.93; P=0.014) while \u003cem\u003eGiardia\u003c/em\u003e was associated with lower faecal myeloperoxidase and reduced mortality (aHR=0.20, 95%CI 0.07-0.60; P=0.004). Enhanced antimicrobial prophylaxis reduced \u003cem\u003eShigella\u003c/em\u003e. Our findings highlight the interactions between sub-clinical pathogens, enteropathy and immunosuppression. The effects of azithromycin on the intestinal milieu may confer mortality benefits in people with advanced HIV disease.\u003c/p\u003e","manuscriptTitle":"Enteropathogen detection and early mortality among people with advanced HIV disease in sub-Saharan Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 06:44:54","doi":"10.21203/rs.3.rs-6718521/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"04121305-994c-4cd3-9b22-499f405cae4d","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":49517106,"name":"Health sciences/Pathogenesis/Infection"},{"id":49517107,"name":"Health sciences/Biomarkers/Predictive markers"}],"tags":[],"updatedAt":"2026-05-08T11:05:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-09 06:44:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6718521","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6718521","identity":"rs-6718521","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}