Gates Open Res Gates Open Research 2572-4754 F1000 Research Limited London, UK 10.12688/gatesopenres.14061.1 Research Article Articles Respiratory and diarrhoeal pathogens in Malawian children hospitalised with diarrhoea and association with short-term growth: A prospective cohort study

[version 1; peer review: 2 approved with reservations]

 Chisala Mphatso Conceptualization Data Curation Formal Analysis Investigation Methodology Project Administration Supervision Validation Visualization Writing – Original Draft Preparation Writing – Review & Editing a 1 Nyangulu Wongani Methodology Project Administration Writing – Original Draft Preparation Writing – Review & Editing 2 Nyirenda James Formal Analysis Investigation Validation Writing – Review & Editing 2 Iroh Tam Pui-Ying Conceptualization Funding Acquisition Investigation Methodology Project Administration Resources Supervision Validation Visualization Writing – Review & Editing https://orcid.org/0000-0002-3682-8892 2 3 4 Institute of Child Health; Department of Population, Policy and Practice, University College London Medical School, London, UK Paediatrics and Child Health Research Group, Malawi-Liverpool Wellcome Research Programme, Blantyre, Malawi Department of Paediatrics, Kamuzu University of Health Sciences, Blantyre, Malawi Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK mphatsochisala@gmail.com

No competing interests were disclosed.

 22 11 2022 2022 6 145 16 11 2022 Copyright: © 2022 Chisala M et al. 2022 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: Pneumonia and diarrhoea are the leading causes of childhood mortality and morbidity worldwide. The gut-lung axis is associated with disease, and these common infections, especially the parasite Cryptosporidium, are associated with malnutrition. We sought to evaluate the association of respiratory and gastrointestinal (GI) pathogens with short-term growth among children hospitalised with diarrhoeal disease.

Methods: In this sub-study, we followed 27 children (two-24 months) who tested positive for Cryptosporidium spp. for eight weeks with two weekly sampling of the respiratory and GI tract. Respiratory and stool pathogens were detected using quantitative molecular methods. Nutritional outcomes were assessed as length-for-age (LAZ), weight-for-length (WLZ) and weight-for-age (WAZ) z-scores. Changes over the study period were compared using repeated analysis of variance and mixed effects model analysis.

Results: In this period,104 sputum and stool samples were collected. All stool samples had at least one pathogen detected, with an average of 5.1 (SD 2.1) stool pathogens, compared to 84% of the sputum samples with an average 3.5 (SD 1.8). Diarrhoeagenic E. coli were the most common stool pathogens (89%), followed by Cryptosporidium (57.6%) and Adenovirus pan (41%). In sputum, Streptococcus pneumoniae was the most prevalent pathogen (84%), followed by hinovirus (56%) and Moraxella catarrhalis (50%). There was a significant change in WAZ over the follow-up period. Children who had ≥3 GI pathogens had significantly a lower LAZ mean score at enrolment (-1.8 [SD 1.4]) and across the follow-up period. No relationship between respiratory pathogens and short-term growth was observed. Out of 49 sputum samples that had ≥3 pathogens, 42 (85%) concurrent stool samples had ≥3 GI pathogens.

Conclusions:Among young children hospitalised with diarrhoea, multiple GI and respiratory pathogens were prevalent over an eight-week follow-up period. The presence of more GI, but not respiratory, pathogens was significantly associated with reduced short-term growth.

 GI pathogens respiratory pathogens nutritional status gut-lung axis Gates Foundation OPP1191165 This work was supported, in whole or in part, by the Gates Foundation [OPP1191165]. Under the grant conditions of the Foundation, a Creative Commons Attribution 4.0 Generic License has already been assigned to the Author Accepted Manuscript version that might arise from this submission. The funding body had no role in the design of the study; collection, analysis, and interpretation of data; and in writing the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Introduction

Lower respiratory infections (LRI) and diarrhoeal diseases are the top leading preventable causes of mortality and morbidity globally in children under five years of age, and are the annual cause of 12% (700,000) and 9% (446,000) of deaths, respectively 1– 3 . Children with frequent and recurrent infections are at risk of malnutrition which also predisposes them to further infection 4 . Malnutrition is one of the most important risk factors for both diarrhoeal disease and LRI 3, 5 , and is associated with about half of all under-five deaths 6 . Reducing the burden of malnutrition could, therefore, concomitantly decrease respiratory and diarrhoeal disease amongst high-risk children. This was estimated in the “Global Burden of Disease” study to be a reduction of 9% of LRI and 12% of diarrhoeal disease over the past three decades 1, 3 .

Recently, large-scale studies including the “Etiology, Risk Factors and Interactions of Enteric Infections and Malnutrition and Consequences for Child Health and Development” (MAL-ED) study and the “Global Enteric Multi-site” (GEMS) study explored the association between gastrointestinal (GI) pathogens, malnutrition and gut function with other long-term effects to understand the pathophysiology of malnutrition 7– 9 . These studies noted that subclinical infections and quantity of pathogens are negatively associated with linear growth, and that this persists until two years of age 8– 10 . Cumulative insults of infections like diarrhoeal on children likely cause failure of catch-up growth, resulting in growth faltering and decreased cognitive development 11– 14 . However, the association between these pathogens and respiratory infections and short-term growth has not been explored.

The gut and lungs both have the same embryonic origin. Thus, while the mechanisms are not understood, studies have shown that the two sites interact in health and disease. Specifically, animal studies have demonstrated how the ‘gut-lung axis’ of host-associated gut and respiratory microbiota appear to influence local and systemic immunity 15– 18 . However, while these studies have focused on how gut microbiota is broadly protective against respiratory infection 19 , there has been less attention paid to the reverse relationship, i.e. respiratory microbiota and pathogens influencing the gut, and subsequent association with growth. Studies have indicated a link between respiratory infections and growth 20, 21 although generally the pathways between growth, nutrition and infection are likely bidirectional.

Cryptosporidium infection is a common cause of diarrhoeal infection, malnutrition and excess mortality amongst children in developing countries 9, 10 . The objective of this study was to describe detection of diarrhoeal and respiratory pathogens in children who were hospitalised with diarrhoeal disease (with detection of Cryptosporidium) at a tertiary hospital in Malawi, and to examine the association with short-term growth over an eight-week follow-up period.

 Methods Study design, setting, and participants

This is a secondary data analysis from a prospective longitudinal observational study evaluating respiratory cryptosporidiosis in paediatric diarrhoeal disease 22, 23 . The main study recruited children presenting with primary gastrointestinal (GI) symptoms to Queen Elizabeth Central Hospital in Blantyre, Malawi, from March 2019 to April 2020. Children were eligible to participate if they were two-24 months of age and had at least three or more loose stools within the past 48 hours and lived 15km outside of the study 22, 23 . Children were excluded if they had visible blood in loose stools, or dysentery 24 . Parents provided written informed consent. The study was approved by the University of Malawi College of Medicine Research Ethics Committee (P.07/18/2438) and the Liverpool School of Tropical Medicine Research Ethics Committee (18-066).

Clinical procedures

Participants positive for Cryptosporidium spp. in either respiratory or GI tract specimens at enrolment were followed up every two weeks until eight weeks post-enrolment. At each visit, history and physical exam were conducted, and induced sputum and stool were collected. The induced sputum procedure has been previously described in detail elsewhere 22, 23 . In brief, sputum samples were obtained via oropharyngeal suctioning after nebulized 3% sodium chloride treatment and processed for microscopy and multiplex PCR testing.

For the main study, children with a positive PCR for Cryptosporidium in any of the collected samples were followed up, and they constitute the sample included in this sub-analysis 23 . At enrolment, Cryptosporidium spp. in stool/sputum/ NP were detected using PCR analysis specifically for Cryptosporidium spp. Diarrhoea was defined as ≥3 loose stools within the past 48 hours. A diarrhoeal episode was termed symptomatic if the participant reported any GI symptoms (to include abdominal pain/tenderness, dehydration, vomiting, and/or poor feeding) and the stool sample collected was PCR-positive for any pathogen, and asymptomatic if the participant did not report any GI symptoms but the stool was PCR-positive for any pathogen. Respiratory symptoms included cough, runny nose, difficulty in breathing, wheezing, chest indrawing/retractions, and/or crackles.

Nutrition indices were defined according to WHO growth standards 25 . We defined wasted, underweight or stunted, as weight-for-length z score (WLZ), weight-for-age z scores (WAZ) and length-for-age z score (LAZ) <-2, respectively.

 Laboratory procedures

We extracted DNA from stool samples using a QIAamp Fast DNA Mini Kit (Qiagen, Hilden, Germany) with a procedure modified from that of the manufacturer as previously described 26 . Briefly, 200mg solid stool or 200µL liquid fecal samples were first mixed with InhibitEX buffer and glass beads before bead beating (Tissue Lyser II, Qiagen). Resulting lysates were heated at 95°C for 5 minutes prior to proceeding according to the manufacturer’s protocol. Sputum samples for Cryptosporidium detection were extracted using QIAamp DNA mini kit. Briefly the 180µl ATL buffer and 20ul proteinase K was added to 300ul sample and incubated at 56°C for 1–3 hours with occasional vortexing during the incubation. This was followed by addition of 200µl Buffer AL and the sample was incubated at 70°C for 10 minutes. 200ul of absolute ethanol was added and this was followed by washing using 500µl buffer AW1 and 500µl buffer AW2. DNA was eluted using 200µl of elution buffer.

All samples were spiked with Phocine herpes virus (PhHV) and MS2 phage to be used as extraction controls. One extraction blank (200µL nuclease-free water as the sample) was included in each batch of extractions to monitor for contamination.

We performed qPCR as previously described 27 . These qPCRs were carried out using the ViiA7 or QuantStudio 7 Flex Real-Time PCR instruments (Thermo Fisher, Waltham, MA, USA). Primers (Crypto F primer: GGGTTGTATTTATTAGATAAAGAACCA, Crypto R primer: AGGCCAATACCCTACCGTCT) and probe (<FAM>GTGACATATCATTCAAGTTTCTGAC<BHQ1>). These were sourced from Integrated DNA Technologies (IDT, Coralville, Iowa, USA) and Sigma (Sigma-Aldrich, Haverhill, UK). All resulting qPCR data were analyzed using QuantStudio 6 and 7 Flex Real-Time PCR System Software, version 1.3 (Thermo Fisher). For initial denaturation and Taq activation we used one cycle at 95°C for 3 minutes, for amplification and subsequent target detection we used a total of 40 cycles (95°C for 10 seconds and 60°C for 1 minute). An analytical cutoff of 35 cycles was applied to the data ( i.e. C t values ≥35.0 were considered negative).

In sputum, multiplex testing detected bacteria ( S. pneumoniae, Staphylococcus aureus, M. catarrhalis, Bordetella pertussis, Haemophilus influenzae and H. influenzae type b, Chlamydia pneumoniae, Mycoplasma pneumoniae, Klebsiella pneumoniae, Legionella pneumophila, and Salmonella species); viruses (influenza A/B/C, RSV A/B, parainfluenza virus types 1-4, coronaviruses NL63, 229E, OC43, and HKU1, human metapneumovirus A/B, rhinovirus, adenovirus, enterovirus, parechovirus, bocavirus, cytomegalovirus); and parasites ( Pneumocystis jirovecii). A cycle threshold (Ct) of <38 was considered as a positive for any of these pathogens. Cryptosporidium spp. detection of respiratory specimens were measured using quantitative polymerase chain reaction (qPCR), with a positive result corresponding to a Ct <35.

In stool, GI pathogens were detected using qPCR in a TaqMan Array Card (Thermo Fisher, Waltham, MA) using a custom design developed at the Houpt Laboratory (Charlottesville, VA [25]). These were done at week 2 to week 8. Multiplex testing detected rotavirus, norovirus GII, adenovirus, astrovirus, sapovirus, enterotoxigenic Escherichia coli (ETEC), enteropathogenic E. coli (EPEC), enteroaggregative E. coli (EAEC), Shiga-toxigenic E. coli (STEC), Shigella/enteroinvasive E. coli (EIEC), Salmonella, Campylobacter jejuni/coli, Vibrio cholerae, Clostridium difficile, Cryptosporidium spp , C. parvum, C. hominis, Giardia lamblia, Entamoeba histolytica, Ascaris lumbricoides, and Trichuris trichiura). We considered a pathogen as present if Ct was <35 in all pathogens. For pathogens that were positive and are associated with diarrhoea in children under five years old 8, 13 , we calculated the prevalence of diarrhoeagenic Ct cut-offs (diarrhoea-associated Ct quantity) based on the GEMS study to estimate the prevalence of diarrhoeal samples in this population 27 . An increased pathogen load was defined as at least three pathogens present in each sample per participant per study visit to compare how these relate with other demographics 28 .

 Statistical analysis

At enrolment, categorical variables were compared using Pearson’s X2 test or Fisher’s exact test. Continuous variables were compared using Student’s t-tests or nonparametric Mann-Whitney U tests where data were nonnormally distributed. We presented diarrhoeagenic quantitative cut-off based on the GEMS study to estimate the burden of diarrhoea attributed to the common causes of diarrhoeal pathogens in this population. These cut-off values are useful in studies that have no controls or that do not have diarrhoea data like our study. Comparison for different characteristics across the eight-week study period was done using one-way repeated measures analysis of variance and mixed-effect model analysis. Statistical significance was set at 0.05, characteristics that showed a significant change over the follow-up period were included in a mixed-effect model analysis as confounders. Statistical analysis was performed using Stata software, version 16 (StataCorp. 2019. College Station, TX, USA).

 Results Participants

From March 2019 to April 2020, 755 children were screened and 162 were recruited into the study. Of the 162 enrolled, 37 (23%) were positive for Cryptosporidium spp., 36 were entered into follow-up, and 27 children (75%) completed the 8-week follow-up, which was discontinued early due to COVID-19. Of these, the median age was 5.5 (IQR 2,14) months and 18 (64%) were male. Only 1 (3%) was HIV-infected, but HIV status was unknown in over half of the children (20/27). The enrolled study population is described elsewhere 29 .

We tested 104 stool and sputum samples from the 27 participants that had completed follow-up from week 2 to week 8 post-enrolment. At least one pathogen was detected in all the 104 stool samples, while 87/104 (84%) of the sputum samples had at least one pathogen detected. Amongst the 104 stool samples collected, diarrhoeagenic E. coli was the most abundant pathogen (92/104 [89%]), of which EAEC was the most common subtype (88/92 [95%]) followed by typical EPEC (42/92 [45%]). Cryptosporidium spp. (60/104 [(57.6%]) were the second most common stool pathogen, of which a third were C. hominis (20/55), one sample was C. parvum and the rest were not identified. Adenovirus pan (44/104 [42%]),

Campylobacter pan (43/104 [41%]), Campylobacter jejuni/coli (34/104 [33%]), rotavirus (36/104 [34%] and norovirus (34/104 [32%]) were the other common pathogens identified. Over half (58/104 [56%]) of the follow-up stool samples with a pathogen associated with diarrhoea were below the diarrhoeagenic cut-offs as presented in the GEMS study ( Figure 1). Out of the 104 induced sputum samples, S. pneumoniae was the most abundant pathogen 87/104 (84%) followed by Human Rhinovirus (58/104 (56%)), M. catarrhalis (52/104 [50%]), Adenovirus (29/104 [28%]) and H. influenzae (21/104 [20%]). The majority of stool samples (80/104 [77%]) and almost half of sputum samples (49/104 [47%]) had at least three pathogens detected (data not shown). Of the 49 sputum samples that had a high pathogen load, 42/49 (85%) simultaneously had ≥3 stools pathogens, although this was not statistically significant.

Positive stool samples with diarrhoeagenic cut-offs as described in the GEMS study.

stETEC - heat-stable enterotoxin-producing E. coli; tEPEC – typical enteropathogenic E. coli; EIEC – enteroinvasive E. coli; H. pylori –Helicobacter pylori; V. cholera- Vibrio cholera.

GI pathogens were detected in all stool samples from two weeks to eight weeks post-enrolment. In contrast, respiratory pathogens were detected in all sputum samples at 2 weeks and in 20/25 (80%) samples at the end of the eight weeks. On average, there were 5.1 (SD 2.1) stool pathogens detected per participant per study visit over the follow-up period, while an average of 3.5 (SD 1.8) sputum pathogens were detected per participant per study visit over the follow-up period ( Table 1). There was an average of 3.1 (SD 1.6) bacteria compared to 1.0 (SD 0.8) parasites and 1.0 (SD 0.9) viruses in stool samples collected, and an average of 2.0 (SD 1.1) bacteria, 1.4 (SD 1.1) viruses and 0.3 (SD 0.5) parasites in sputum samples ( Figure 2).

Characteristics of and change in study population over 8 weeks.
Characteristics Study period p-value
Enrolment * 2w 4w 6w 8w Total
Weight-for-age z score, mean (SD) -1.2 (0.9) -0.9 (1.0) -0.8 (1.0) -0.8 (0.9) -0.8 (0.9) - 0.002
Length-for-age z score, mean (SD) -1.8 (1.4) -1.7 (1.3) -1.4 (1.4) -1.5 (1.3) -1.4 (1.1) - 0.204
Weight-for-length z score, mean (SD) -1.2 (0.9) -1.2 (1.4) -0.2 (1.2) -0.0 (1.3) -0.1 (1.2) - 0.673
Total number of respiratory pathogens detected/participant/visit (mean, SD)) - 3.7 (1.6) 3.6 (1.8) 3.6 (1.8) 2.8 (1.8) 3.5(1.8) 0.115
    Mean number of bacterial respiratory pathogens/visit (N=202) - 2.2 (1.0) 1.8 (1.1) 1.7 (1.0) 2.1 (1.2) 2.0(1.1) 0.347
    Mean number of viral pathogens per visit (N=141) - 1.4 (1.1) 1.6 (1.0) 1 (1.0) 1.5 (1.1) 1.4(1.1) 0.148
    Mean number of parasitic pathogens per participant/visit (N= 48) - 0.5 (0.6) 0.5 (0.7) 0.3 (0.5) 0.3 (0.4) 0.3(0.5) 0.343
Visit to health centre with respiratory symptoms in the past 7 days (%) - 10/27 (37%) 6/24 (25%) 5/27 (18%) 7/24 (29%) - 0.107
    Cough - 5/10 (50%) 2/6 (33%) 2/5 (40%) 5/7 (71%) - 0.147
    Rhinorrhoea - 7/10 (70%) 4/6 (66%) 1/4 (20%) 3/7 (43%) - 0.347
Visit to a health facility for a diarrhoea episode in past 7 days 0/24 (0%) 2/ 24 (8%) 4/27 (15%) 3/24 (13%) 0.141
Total number of diarrhoea pathogens detected/participant/visit (mean, SD) - 5.3 (2.0) 5.1 (2.1) 4.7 (2.1) 5.2 (2.5) 5.1 (2.1) 0.817
    Number of bacterial GI pathogens detected/ participant/visit (mean, SD) d (N=320) - 2.8 (1.6) 3.1 (1.8) 3.1 (1.4) 3.4 (1.8) 3.1 (1.6) 0.375
    Number of viral GI pathogens detected/ participant/visit (mean, SD) e (N=10) - 1.3 (1.0) 1.0 (1.0) 0.8 (1.0) 0.8 (1.0) 1.0 (0.8) 0.087
    Number of parasitic GI pathogens detected/ participant/visit (mean, SD) f (N=98) - 1.2 (0.7) 1.0 (0.9) 0.7 (0.8) 1.0 (0.9) 1.0 (0.8) 0.099

GEMS, Global Enteric Multicenter Study; GI, gastrointestinal; SD, standard deviation; w, week

*TaqMan Array was only run at follow up visits

a S. pneumoniae, S. aureus, M. catarrhalis, B. pertussis, H. influenzae and H. influenzae type b, C. pneumoniae, M. pneumoniae, K. pneumoniae, L. pneumophila, and Salmonella species

bInfluenza A/B/C, RSV A/B, parainfluenza virus types 1–4, coronaviruses NL63, 229E, OC43, and HKU1, human metapneumovirus A/B, rhinovirus, adenovirus, enterovirus, parechovirus, bocavirus, cytomegalovirus

c Cryptosporidium, P. jirovecii

dEnterotoxigenic Escherichia coli (ETEC), enteropathogenic E. coli (EPEC), enteroaggregative E. coli (EAEC), Shiga-toxigenic E. coli (STEC), Shigella/enteroinvasive E. coli (EIEC), Salmonella, Campylobacter jejuni/C. coli, Vibrio cholerae, Clostridium difficile

eRotavirus, norovirus GII, adenovirus, astrovirus, sapovirus

f Cryptosporidium, Giardia lamblia, Entamoeba histolytica, Ascaris lumbricoides, and Trichuris trichiura

Average number of pathogens per study visit (mean, SD) over an eight-week period.

A) Stool B) Sputum.

Figure 3A shows the changes in WAZ, WLZ and LAZ across the study period. Participants had low LAZ and WAZ at enrolment, and the average change in WAZ, LAZ, and WLZ scores over the eight-week period were 0.5 (0.6), 0.4 (1.4) and 0.4 (1.4), respectively. There was a significant change in WAZ across the follow-up period (p=0.002), but no significant changes were seen in LAZ and WLZ. Children with ≥3 GI pathogens in a sample had lower LAZ compared to those with <3 pathogens at two weeks (-2.0±1.1 vs 0.1±0.5), four weeks (-1.6±1.4 vs -0.5±0.8), six weeks (-1.8±1.4 vs -1.0±1.2) and eight weeks (-1.6±1.2 vs -0.6±0.9), and this was statistically significant ( Figure 3B). This was not noted for WLZ and WAZ scores. No obvious changes in WLZ, WAZ and LAZ were noted with respiratory pathogen detection over the study period ( Figure 3C). There was also no difference in any of the nutritional indices amongst children with ≥3 of both respiratory and GI pathogens compared to those with <3 ( Figure 3D).

Change in mean WHZ, HAZ, LAZ scores over the 8-week study period.

A) overall; B) in relation to average GI pathogens of i) <3 and ii) ≥3; C) in relation to average respiratory pathogens of i) <3 and ii) ≥3. D) in relation to respiratory and GI pathogens *p-value 0.002 **p-value 0.001 WLZ score LAZ score WAZ score

 Discussion

This is the first description, to our knowledge, of both respiratory and GI pathogens in young children in a low- and middle-income country and association with short-term growth in the eight weeks after hospitalisation with diarrhoea. We found that our population had low anthropometric indices, and that these indices showed minimal change over the eight weeks after hospitalisation. A high average number of GI pathogens was detected throughout the eight weeks, and this was associated with GI symptoms. A high average of respiratory pathogens was also detected throughout the eight weeks, predominantly without associated respiratory symptoms. Significant changes were only noted in WAZ and not the other anthropometric measures over the eight-week follow-up period. Additionally, participants with ≥3 GI pathogens had a lower mean LAZ score at all follow-up visits. Having a high number of both respiratory and GI pathogens was not associated with changes in nutritional indices over the follow-up period.

Previous studies that have evaluated GI pathogens in young children after hospitalisation and association with short-term growth have typically focused on a single pathogen 12, 30– 32 . However, co-infection with GI pathogens is common amongst children under two years and has significant effects on growth compared to single pathogens 33– 36 . Short term growth after an infection in children under two years old is important because it allows for catch-up growth in this critical period for children to attain their optimal weight and height increment over time 11, 37 . However, catch-up growth typically occurs in the absence of diarrhoea, whether clinical or subclinical 14, 38 . We noted that throughout the follow-up period, children who had a higher number of GI pathogens were more stunted than those with a lower number of GI pathogens. These changes may be due to environmental enteropathy, a poorly understood, chronic condition associated with enteropathogens, gut inflammation and a leaky gut seen in children from developing countries 39 . Diarrhoeal pathogens in the MAL-ED study, specifically EAEC, Campylobacter, and Shigella, were associated with a reduction in linear growth (LAZ) after three months 38 . Additionally, 95% of stool specimens in the MAL-ED study, from predominantly non-diarrhoeal episodes, detected at least one enteropathogen – which, if persistently present, can lead to altered growth 13, 40 .

The linkage between respiratory infection and growth is not well explored. In the Pneumonia Etiology Research for Child Health (PERCH) study, multiple respiratory pathogens were noted among children hospitalised with severe pneumonia, with an average of 3.8 (SD 1.5) pathogens/participant and 3·6 (SD 1·5) pathogens/participant among age and sex-matched controls 41 which is comparable to the average number of pathogens found in our study. Additionally, higher numbers of respiratory pathogens were consistently found in malnourished children, suggesting a possible link between respiratory pathogens and growth 42, 43 . Our participants generally did not have respiratory symptoms, and we did not see an association between prevalence of respiratory pathogens with short-term growth. Colonisation is unlikely to affect short-term growth 41, 44, 45 .

It is however worth noting that the complex interaction between nutrition, infection and immunity puts children at risk of persistent and recurrent colonisation and subsequent infections including those of the respiratory tract 16– 19, 44 . There was a high prevalence of stunting in this study population. At enrolment, over a third of our study population showed stunted growth and the mean LAZ was -1.8 (1.4) 23 , similar to the national stunting prevalence amongst children <24 months 46 . There was a significant positive change in WAZ over the follow-up period, which would be explained by catch-up growth achieved from reduced incidence of diarrhoea/infection over the follow-up period 11, 12, 14 . We did not see any change in WLZ and WAZ. The majority of samples from this population that had a high number of stool pathogens also had a high number of pathogens in sputum. While an unhealthy gut microbiome composition is thought to be a risk factor for respiratory infections 16, 19 , we cannot make any inferences or conclusions from these data.

Our study has limitations. The sample size was small, and not powered to detect changes in short-term growth. This was a secondary analysis, and we did not have a comparison group of children with no diarrhoea/ Cryptosporidium spp. infection at baseline. Stool or respiratory samples collected at the time of recruitment were not evaluated for pathogens beyond Cryptosporidium, and therefore we could not assess change in pathogens from enrolment. Although we collected HIV status, data were not available for 84% of the participants. However, the strengths of our study include the serial sampling, clinical, and anthropometry data collected over an eight-week period, the collection of induced sputum from the respiratory tract, and the high sensitivity and specificity of the molecular methods we used 26, 45 .

In summary, among young children hospitalised with diarrhoea, multiple gut and respiratory pathogens were prevalent in the participants over the following eight weeks, and the presence of more GI pathogens, but not respiratory pathogens, was associated with reduced short-term growth. Further study of larger cohorts is warranted, to delineate how gut and respiratory pathogens interact and contribute to linear deficits, during a period where insults that occur can impact long-term growth, developmental, and cognitive outcomes 26 .

 Consent

Written informed consent for publication of the participants’ details was obtained from the participants.

 Data availability Underlying data

Figshare: Recruitment and follow-up data of participants recruited in the sub-analysis of the CryptoResp study, https://doi.org/10.6084/m9.figshare.21266142 47

This project contains the following underlying data:

Data file 1: A dataset of participants presenting with diarrhoea and followed up over an 8-week period at Queen Elizabeth Central Hospital.

 Reporting guidelines

Figshare: CONSORT checklist and flow chart for ‘Respiratory and diarrhoeal pathogens in Malawian children hospitalised with diarrhoea and association with short-term growth’ https://doi.org/10.6084/m9.figshare.21268572 48

Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

 Acknowledgements

We thank the patients and parents for participating in this study. We thank the research clinical and laboratory staff for conducting the study. We thank David Moore and Tanja Adams with training the CryptoResp clinical and laboratory teams. We thank Neema Toto for interim study support, Thokozani Ganiza for assistance with data management, and Wes Van Voorhis for reviewing a draft of the manuscript and providing critical feedback.

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Pediatr Infect Dis J. 2021;40(9S):S18–28. 34448741 10.1097/INF.0000000000002767 8448406 Rodrí Guez L Cervantes E Ortiz R : Malnutrition and Gastrointestinal and Respiratory Infections in Children: A Public Health Problem. Int J Environ Res Public Health. 2011;8(4):1174–205. 21695035 10.3390/ijerph8041174 3118884 Park DE Baggett HC Howie SRC : Colonization Density of the Upper Respiratory Tract as a Predictor of Pneumonia— Haemophilus influenzae, Moraxella catarrhalis, Staphylococcus aureus, and Pneumocystis jirovecii. Clin Infect Dis. 2017;64(suppl_3):S328–S336. 28575367 10.1093/cid/cix104 5612712 Global Nutrition Report Country Nutrition Profiles - Global Nutrition Report. [cited 2021 Sep 8]. Reference Source Chisala M : Respiratory and diarrhoeal pathogens in Malawian children hospitalised with diarrhoea and association with short-term growth dataset. figshare. [Dataset].2022. http://www.doi.org/10.6084/m9.figshare.21266142.v2 Chisala M : CONSORT checklist and flow chart for ‘Respiratory and diarrhoeal pathogens in Malawian children hospitalised with diarrhoea and association with short-term growth’. figshare. [Dataset].2022. http://www.doi.org/10.6084/m9.figshare.21268572.v2 10.21956/gatesopenres.15361.r33816 Reviewer response for version 1 Powell Helen 1 Referee https://orcid.org/0000-0002-3127-2012 Department of Epidemiology and Public Health, University of Maryland School of Medicine, Maryland, USA

Competing interests: No competing interests were disclosed.

 24 7 2023 Copyright: © 2023 Powell H 2023 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

General comments:

This is an interesting paper which provides a nice secondary analysis, although on a very small and not particularly generalizable sample.

My main concern is in regard to the analysis being performed. Not nearly enough detail is being provided.

Introduction:

I don’t follow the discussion in the 3 rd paragraph about the reverse relationship. The reverse relationship, as you currently have it written, would be to examine how respiratory microbiota is protective against gut infection. However, you have brought in the discussion of malnutrition. What you are presenting in this paper is interesting, but you have looked largely at gut and respiratory separately and that needs to be clear up front. Your examination of the relationship between malnutrition and pathogens is based on number of pathogens, as opposed to which pathogens were present. Make it clear that this is the focus. You spend considerable time describing the pathogens being tested for, which ones were present, and the diarrhoeagenic cut-offs, but ultimately only look at pathogen load, regardless of pathogen. You also only conduct a single analysis looking at this and it appears to have been a direct comparison of time-point to time-point, as opposed to trends over time.

Clinical procedures:

You define wasted, underweight, and stunted, but used the data as continuous. I think using the continuous data is absolutely fine, but it means that this definition is no longer relevant.

Why have you included the main studies definition of a symptomatic and asymptomatic diarrhoeal episode? I can’t see you making use of this information in this analysis. I assume all children had diarrhoeal disease at enrollment. If this is not the case, how did you adjust for this in your analysis? Previous studies have suggested that it is the diarrhoeal episode which results in malnutrition. If your analysis is suggestive of asymptomatic pathogen carriage resulting in malnutrition then this is quite a statement. I have similar questions/concerns from the respiratory perspective.

Laboratory procedures:

In the 3rd paragraph you state that a Ct >= 35 was considered negative. In the description for the sputum procedures, you used a Ct <38 as positive for all tested pathogens, except crypto. Why the change in threshold and why does it not also apply to crypto?

I have a few questions in regard to the calculation of the diarrhoeagenic Ct cut-offs:

What was the value added of doing this in this paper when it only applies to the stool samples? Do your numbers in table 1 refer to only those samples which had a diarrhoeagenic pathogen? If not, why?

The paper you cite examined crypto in cattle. How do you justify using this method for human stool and all other pathogens? I also believe that others have already worked on this problem with the GEMS data and Ct cut-offs have been previously published, why not use those?

You mention demographics at the end of this section but actually haven’t included any analyses which included demographics.

Statistical analysis:

You mention using a chi-square or Fisher's exact to test between categorical variables but the only place where I believe this was being done was the end of 3rd paragraph in results section. Truthfully, I can't follow what is being tested here and I am not sure what value it has. Either make this clearer or remove it if it is not adding value to the paper.  

How did you use the Ct cut-offs in this paper? You define what is positive or negative for pathogen detection in the laboratory procedures and I am assuming that this cut-off is what is used to describe pathogen load (>= 3 or < 3) and number of samples with/without pathogens in the results piece. 

Where did you use a t-test/Mann-Whitney? I think you did this for Figure 3B results, but it is not clear. If it was used elsewhere, should it have been a paired-t-test?

You mention doing both a repeated measures ANOVA and a mixed effect model. Why both? Also, you need to provide more information about what went into this model. Did you include demographic variables, confounders, interaction terms? If so, which ones? As it stands right now your research isn't reproducible despite the dataset being available. 

In your results section please specify what test was used when a p-value is being reported).

Results:

Why do you only have 104 stool and sputum samples for the 27 participants? I would have expected 108 (27 participants * 4 follow-up points) stool and sputum samples given that each of the 27 participants completed the 8 weeks of follow-up. 

Is it a single participant who is missing all 4 sputum and stool specimens or different participants who are missing different specimens? Based on your numbers I believe there are 2 participants missing this information, but this must only be at particular time points. Please make this clear.

Were the sputum samples which had no pathogens detected excluded from the analysis? Did this result in some participants contributing to some time points and not other time points? How did you deal with this in your analysis?

Table 1:

Footnotes for a, b, and c are provided at the bottom of the table but are not also present in the table.  

What test is being done to produce the p-values reported in this table? What is being tested in this table?

Figure 2:

I would favor including this figure rather than the text in the table. Otherwise it is duplicative. 

Figure 3:

Why is this figure shown as boxplots but figure 2 is shown as averages over time? Is this because of the analysis you did? I see the star above WAZ at enrollment which I would assume is to imply that the mean WAZ at enrollment was significantly less than zero, however your supporting text implies the significance is over time which I would assume means the slope is significantly different from zero. Clarification is needed. 

Discussion:

You note that there was a high average number of GI pathogens detected throughout the 8-weeks and that this was associated with GI symptoms, but you haven’t presented this data. Is that presented in the main study paper? If so make this clear. Also, similarly for the respiratory pathogens.

Learning that only crypto is tested for at enrolment appears here. Include this information higher up as this explains why table 1 is missing a lot of enrolment information.

In the last paragraph add in to the summary that it is young children with diarrhoea and crpto detected (stool or respiratory).

Is the work clearly and accurately presented and does it cite the current literature?

Partly

If applicable, is the statistical analysis and its interpretation appropriate?

Partly

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

No

Reviewer Expertise:

Diarrheal disease, biostatistics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

 10.21956/gatesopenres.15361.r33317 Reviewer response for version 1 Sarmin Monira 1 Referee https://orcid.org/0000-0001-9924-0060 Dhaka Hospital, Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh

Competing interests: No competing interests were disclosed.

 15 6 2023 Copyright: © 2023 Sarmin M 2023 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

Well written and well-organized manuscript where the authors evaluated the association of GI and respiratory pathogen with short term growth. I have the following observations:

If this cohort was only derived from a lager cohort of children having crypto diarrhea at admission, then the title needs the reflection, it should be crypto diarrhea instead of diarrhea only as the primary cohort did not include children having diarrhea by other pathogens.

Did you use GI pathogen and diarrhea pathogen interchangeably? If yes, then please use one, sometime it is confusing. 

How did the study achieve 104 samples; it is not clear from the description, 27 participants, 2 samples (GI and respiratory) from week 2 to week 8.

 Introduction:

4. 1 st para: Last sentence can be rephrased as  “Global Burden of Disease” study reported an estimated reduction of 9% of LRI and 12% of diarrhoeal disease over the past three decades (1,3).

5. Last para: The objective of this study was to describe detection of diarrhoeal and respiratory pathogens in children who were hospitalised with diarrhoeal disease (with detection of Cryptosporidium) at a tertiary hospital in Malawi, and to examine the association with short-term growth over an eight-week follow-up period.

Is it diarrhea with Cryptosporidium only?

Methods:

6. Study design, setting, and participants - This is a secondary data analysis from a prospective longitudinal observational study evaluating respiratory cryptosporidiosis Was the primary study evaluating respiratory cryptosporidiosis?

7. qPCR- for respiratory pathogen Ct value <38 was considered as positive, is there any specific cause? Whereas in the main section it was told if Ct <35, it was positive.

Results:

8. Results: Figure 1: Did you mean symptomatic diarrhea by diarrhoeagenic Ct cut off? Table 1: Mean number of bacterial respiratory pathogens/visit (N=202), you can add respiratory viral pathogen and respiratory parasitic pathogen to remain similar with bacterial/viral/parasitic GI pathogens.

9. Table 1: Weight-for-age z score, mean (SD) at enrolment, -1.2 and at 8 th week, it was -0.8, (difference: 0.4) Weight-for-length z score, mean (SD) at enrolment, -1.2 and at 8 th week, it was -0.1, (difference: 1.1)

Though difference was more in WLZ then WAZ, P value is significant of WAZ. Please confirm the results.

Discussion:

10. Figure 2: Bacterial pathogens are increasing both in stool and sputum, why did it happen? Is there any explanation?

Other comments:

11. Did stool from symptomatic diarrhea have more pathogen then non-symptomatic diarrhea?

12. Did you stratified 3 pathogens or more according to symptomatic vs asymptomatic diarrhea and compare them?

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

I cannot comment. A qualified statistician is required.

Are all the source data underlying the results available to ensure full reproducibility?

Partly

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

I am physician-scientist. My clinical research involves diarrheal diseases, malnutrition, sepsis and critical illnesses.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.