Inflammation impairs post-hospital discharge growth among children hospitalised with acute illness in sub-Saharan Africa and south Asia

Inflammation impairs post-hospital discharge growth among children hospitalised with acute illness in sub-Saharan Africa and south Asia | 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 Inflammation impairs post-hospital discharge growth among children hospitalised with acute illness in sub-Saharan Africa and south Asia James Njunge, Evans Mudibo, Jasper Bogaert, Benedict Orindi, Charles Sande, and 26 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6127712/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Nov, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Childhood growth can be affected by acute illness, chronic conditions, diet and their home environment. In resource-poor settings, children often experience poor growth following illness, but the mechanisms are poorly understood. This cohort study nested within the CHAIN cohort in six countries in sub-Saharan Africa and south Asia investigated pathways linking inflammation and post-discharge weight gain among children hospitalised with acute illness. We analysed biomarkers of inflammation, enteropathy, growth mediators and other exposures at hospital discharge and examined how they impact post-discharge weight gain during 90 days. Linear mixed models determined associations between exposures and weight gain while structural equation models explained how these exposures influence growth. We show that systemic inflammation impacts mediators of linear growth including the GH/IGF1 axis and bone metabolism to a larger extent and weight gain via enteroendocrine peptide YY and glucagon pathways to a lesser extent. Systemic inflammation negatively affects weight gain directly. Intestinal dysfunction impacts growth through systemic inflammation. Adverse household and chronic medical conditions predominantly influenced weight gain through inflammation. Persistent systemic inflammation at hospital discharge strongly impairs post-discharge linear growth and limits weight gain. It is critical to address inflammation, the intestinal mucosal barrier and other exposures driving inflammation to optimise recovery. One Sentence Summary: Inflammation driven by illness, enteropathy and adverse social factors redirects post-hospital recovery away from linear growth and limits weight gain. Biological sciences/Molecular biology/Proteomics/Protein–protein interaction networks Biological sciences/Physiology/Bone Biological sciences/Immunology/Inflammation/Chronic inflammation Health sciences/Endocrinology/Endocrine system and metabolic diseases/Growth disorders Inflammation weight-gain enteric dysfunction growth mediators SomaScan Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Medical and nutritional management of acutely ill children with or at risk of malnutrition in low- and middle-income countries (LMICs) aim to support convalescence and rapid weight gain. At hospital discharge, vulnerable children are commonly perceived to have ‘recovered’ by clinicians and parents. However, such children commonly have unstable health trajectories post-discharge, remaining at risk of death and poor catch-up growth( 1–4 ). Catch-up growth following illness and/or malnutrition, by definition, requires faster growth than the usual velocity for age and sex. Factors such as prior nutritional status, the type of initial illness and severity, recurrent infections, diet, household exposures, and physical activity may all impact catch-up growth( 5, 6 ). Recent findings from the Childhood Acute Illness and Nutrition (CHAIN) Network cohort study in sub-Saharan Africa and South Asia showed that two-thirds of hospitalised acutely ill children aged 2–23 months were underweight at 6 months post-discharge and stunting increased during this period( 3 ). While poor catch-up is associated with socio-economic disadvantage including age-inappropriate nutrition, adverse caregiver characteristics, household-level exposures, small size at birth, the biological mechanisms linking these exposures and acute illness to growth faltering in these settings are not well understood. Acute illness is associated with altered metabolism and hormonal perturbations driven by complex interactions between prior diet, persistent infections, inflammation, and immunopathology which may persist after apparent clinical resolution. Biomarkers of inflammation and immunosuppression persist in two-thirds of sepsis survivors and are associated with worse long-term outcomes( 7 ). Over two thirds of adult survivors of community-acquired pneumonia continue having increased inflammatory activity in their lung parenchyma for several weeks after clinical resolution( 8 ). Additionally, among Zambian and Zimbabwean children treated for complicated severe malnutrition (CSM), systemic, vascular, and intestinal inflammation did not resolve almost one year following hospitalization( 9 ). The role of systemic inflammation in growth failure is clearly observed in chronic systemic inflammatory diseases where systemic inflammation suppresses linear growth via the growth hormone/insulin-like growth factor 1 (GH/IGF1) axis( 10 ) and has direct effects on long bone growth plate chondrocytes( 11, 12 ). Additionally, systemic inflammation affects adipose and muscle through persistent catabolism and dysregulation of hormonal and metabolic mediators( 13–15 ). A pilot study among Kenyan children treated for CSM suggested that inflammation at hospital discharge negatively impacts recovery from wasting( 16 ). Mudibo and colleagues showed that HIV infection affects post-discharge growth by modulating complement and humoral responses as well as IGF signalling, and bone mineralization among children hospitalised with acute illness in sub-Saharan Africa( 17 ). Persistent subclinical inflammation among children recovering from an acute illness may limit catch-up growth in weight and height. Understanding the interrelationships between infection, inflammation, metabolic reprogramming, background exposures and catch-up growth in LMICs may help improve management. Using data and samples collected from the CHAIN multi-country cohort of children discharged from hospital following acute illness across diverse geographic and epidemiologic settings, we investigated pathways linking inflammation to early post-discharge growth (Fig. 1 A). We analysed a panel of inflammation biomarkers and growth mediators, enteric markers of inflammation and gut permeability, and lipopolysaccharide (LPS); a marker of microbial translocation, and adverse household and chronic medical conditions in relation to post-discharge weight-gain during 90 days. In this cohort study, we provide a mechanistic understanding of why underweight children gain weight but not height after an acute illness and how socio-demographic and environmental exposures, enteric dysfunction and systemic inflammation operate to influence post-discharge weight-gain. MATERIALS AND METHODS Study design, setting and population. This is a secondary analysis of the CHAIN cohort that aimed to characterise the biomedical and social risk factors for mortality in acutely ill young children, described in detail elsewhere( 18 ). Briefly, the CHAIN cohort was conducted between November 2016 and January 2019 at nine hospitals in Africa and South Asia: Dhaka and Matlab Hospitals (Bangladesh), Banfora Referral Hospital (Burkina Faso), Kilifi County, Mbagathi County and Migori County Hospitals (Kenya), Queen Elizabeth Hospital (Malawi), Civil Hospital (Pakistan), and Mulago National Referral Hospital (Uganda) (Fig. 1 B). The hospitals serve vulnerable populations and represent a range of urban and rural environments with varying access to health care and underlying comorbidities such as HIV and malaria. CHAIN enrolled 3,101 acutely ill children aged 2–23 months stratified by anthropometry using mid-upper-arm circumference (MUAC) into: no wasting (MUAC ≥ 12·5 cm [age ≥ 6 months] or MUAC ≥ 12·0 cm [age < 6 months]), moderate wasting (MUAC 11·5–12·5 cm [age ≥ 6 months] or MUAC 11·0–12·0 cm [age < 6 months]), and severe wasting (MUAC < 11·5 cm [age ≥ 6 months] or MUAC < 11·0 cm [age < 6 months], or bilateral pedal oedema [kwashiorkor] unexplained by other medical causes) at hospital admission. Children were then followed for six months after discharge with scheduled visits at days 45 (1.5 months), 90 (3 months) and 180 (6 months) when anthropometry was conducted. For treatment purposes, acutely ill children were classified at admission to hospital as severely wasted or not based on WHO criteria( 19 ). Children with severe wasting were treated in hospital and after discharge at local nutrition clinics with milk-based feeds or ready to use therapeutic feeds (RUTF) according to WHO and national guidelines( 19 ). We collected data on nutritional clinic attendance and therapeutic and supplementary feed receipt, but reliable data on RUTF use, its sharing and other diet at home was not feasible. Definitions, procedures, data, and sample collection and processing were harmonised across sites through staff training and the use of standard operation procedures and case report forms (available online, https://chainnetwork.org/resources/ ) and provide detailed demographic, clinical and social phenotyping, and determination of outcomes including growth (Fig. 1 B). Biological samples were systematically collected at admission, discharge, and scheduled follow-up timepoints and archived at the Kilifi biobank − 80°C freezers in Kenya. This analysis is nested within the CHAIN case cohort (CHAIN NCC) that aims to investigate biological mechanisms leading to mortality through multi-omic approaches among children who died, randomly selected survivors and community children( 20 ). The CHAIN NCC collected data on blood proteome, metabolome, lipidome, lipopolysaccharides (LPS), faecal microbiome, targeted pathogens and faecal biomarkers of enteropathy( 20 ) at admission and discharge from hospital. Because this analysis addressed weight-gain, we excluded children who died, were lost to follow-up or withdrew, had nutritional oedema or lacked plasma proteomics measurements at discharge. This analysis utilised data collected at hospital discharge, including blood proteome, plasma LPS and faecal biomarkers of enteropathy among 550 survivors among the randomly selected participants (Fig. 1 C–D). Ethics Ethical approvals were obtained from each site-affiliated or collaborating institution and from the University of Oxford. All caregivers provided written informed consent for their child to participate in the study. The study protocol was reviewed and approved by the Oxford Tropical Research Ethics Committee, UK; the Kenya Medical Research Institute, Kenya; the University of Washington and Oregon Health and Science University, USA; Makerere University School of Biomedical Sciences Research Ethics Committee and The Uganda National Council for Science and Technology, Uganda; Aga Khan University, Pakistan; International Centre for Diarrhoeal Disease Research, (icddr,b), Bangladesh; The University of Malawi; The University of Ouagadougou and Centre Muraz, Burkina Faso; the Hospital for Sick Children, Canada; and University of Amsterdam, The Netherlands. Anthropometry Measurements included weight, MUAC and length and calculations of respective Z scores according to WHO growth standards are detailed elsewhere( 3 ). Laboratory analysis and data preprocessing The analysis of samples including SomaScan® plasma proteomics, faecal biomarkers of enteric dysfunction; Myeloperoxidase (MPO), Calprotectin (CAL), and Alpha-1-Antitrypsin (AAT) and plasma LPS has been detailed in the CHAIN NCC study protocol( 20 ) (Fig. 1 E). Briefly, the aptamer based 7k SomaScan® assay v4.1 (SomaLogic, USA) was used to quantify the abundances of 7335 proteins in plasma samples according to manufacturer’s instructions( 21 ) and presented in a proprietary text-based format called ADAT. The readat R package was used for importing, transforming and annotating SomaScan® data from the ADAT files( 22 ). The data were log-transformed and standardised. Outliers were replaced with the 5th and 95th percentile values. Several independent aptamers (short oligonucleotides which have binding affinity to a single protein) appeared to detect the same protein and this were excluded if they were highly correlated (r > 0.5). Stool MPO, CAL, and AAT were quantified using an ELISA assay (Immundiagnostik AG, Germany) and absolute concentrations calculated for 15 mg of stool using dose response curves. The plasma LPS levels were measured via a limulus amoebocyte lysate-based, quantitative chromogenic endpoint assay (ThermoFisher, UK) according to manufacturer’s instructions. The faecal biomarker and LPS data were log transformed and scaled since they were skewed. Selection of systemic inflammation proteins and growth mediators from SomaScan assay We selected proteins classified by the UniProt Knowledgebase (UniProtKB), as inflammatory response and innate immunity from the SomaScan® assay and binned them into one group we termed systemic inflammation which comprised 338 proteins (Fig. 1 F). We also selected proteins classified by UniProtKB as Growth arrest, Growth factor, Growth factor binding, Growth factor receptor, Hormones, Obesity, Osteogenesis and Chondrogenesis which were binned into a second group termed growth mediators that consisted of 297 proteins. UniProtKB is a central hub containing functional information on proteins and consists of manually-annotated records with information extracted from literature and curator-evaluated computational analysis, which we used for this analysis, as well as computationally analysed records that await full manual annotation( 23 ). Statistical analysis Baseline analysis Characteristics of study children at hospital discharge including demographic, anthropometry and clinical features were summarised using median with interquartile ranges if continuous and proportions if categorical. We also summarised the clinical diagnosis and nutritional status at admission. Growth analysis The primary outcome of the analysis was growth as assessed by weight-gain. We defined weight-gain by the change in absolute deficits in weight (WAD) from discharge to 3 month post discharge follow-up (Delta WAD). Absolute deficit was calculated as the difference between the measured weight and the median age- and sex-specific value obtained from the WHO 2006 growth standards( 24–26 ). Absolute deficit was used rather than Z scores because changes in standard deviation widths across age or length makes Z scores less appropriate for measuring changes over time among children of different ages( 25 ). Linear mixed models were used to test the association between exposures including systemic inflammation and growth mediator panels, inflammatory cells from haematology, individual measures of enteric dysfunction, and adverse household and chronic medical conditions with growth (Fig. 1 G). The adverse household and chronic medical conditions are detailed in Fig. S1 and have also been described in a previous CHAIN cohort growth analysis( 3 ). Models were adjusted for sex, age, site, baseline WAD, and receipt of therapeutic feeds and corrected for false discovery rate using the Benjamini–Hochberg method and statistical significance set at p < 0.05( 27, 28 ). SEM path models were used to examine how adverse household and chronic medical conditions, enteric dysfunction, systemic inflammation, and growth mediators influence weight gain. We used principal component analysis (PCA) to reduce the dimensions of the individual biomarkers selected for systemic inflammation and growth mediators. Components explaining at least 65% of the variation were included in the analysis. Enteric dysfunction( 29 ) which is a subclinical condition characterised by small intestinal inflammation, abnormal villous architecture, malabsorption and altered gut permeability, was a latent variable measured by CAL, MPO, and AAT in stool. Enteric dysfunction, plasma LPS, systemic inflammation, and growth mediators were considered as biological factors related to growth. The final SEM models included the biological factors, demographic factors comprising age, site and sex, receipt of therapeutic feeds as well as latent variables depicting socioeconomic and medical factors. SEM models were fitted using the full information maximum likelihood estimator (FIML)( 30 ) using the lavaan( 31 ) package version 0.6.17 in R version 4.2.2 using the sem function. We report standardised estimates. Model fit for the SEMs were evaluated using the comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error for approximation (RMSEA), and standardised root mean squared residual (SRMR). A reasonably good model fit is obtained when Chi-square p-value is > 0.05, CFI and TLI are ≥ 0.90, RMSEA is ≤ 0.06 and SRMR is ≤ 0.08( 32 ). Associations with p < 0.05 were considered statistically significant. No imputation of missing data was performed; the analyses are valid under the missing at random (MAR) assumption given the likelihood approach. RESULTS Population characteristics A total of 550 children being discharged from hospital randomly selected from the CHAIN study (excluding deaths, children with oedema and those missing samples) were included for analysis (Fig. 1 B–D). Characteristics of the study children are presented in Table 1 . The Banfora, Dhaka and Kampala sites had larger proportions of study children compared to the other study sites. Selected children were mainly diagnosed with pneumonia and diarrhoea at admission and the proportion of non-wasted to severely wasted was similar. Most haematological parameters were comparable by sex except eosinophils which were increased among males at discharge (p = 0.01; Table S1 ). Several parameters varied by nutritional status (Table S2); albumin and erythrocytes were lower while white blood cells, platelets, neutrophils, and monocytes counts were higher among severely wasted children. Males were more underweight (p = 0.03) and stunted (p < 0.01) and had larger weight deficits at discharge and at 3 months post discharge (p < 0.01; Table S1 ). However, the proportion of the not wasted, moderately wasted and the severely wasted did not vary by sex (Table S1 ). Weight gain The median weight gain was 0.17 kg within three months and the median absolute weight deficit reduced from 2.14 kg at discharge to 1.88 kg during 90 days post-discharge from hospital (Table 1 ). Severely wasted children had larger weight deficits at discharge and 3 months but also larger weight gains during this period compared to the moderate and the not wasted children (p < 0.001; Table S2). While older children had larger weight deficits than younger children, the median weight gained did not vary by age (Table S3). Table 1 Baseline demographic, anthropometric, and clinical characteristics Variable Cohort N = 550 Demographic Age (months) Med. (IQR) 11.3 (7.1 to 16.1) Sex: Female, (%) 223 (41%) Site n (%) Banfora 81 (15%) Blantyre 50 (9.1%) Dhaka 88 (16%) Kampala 83 (15%) Karachi 48 (8.7%) Kilifi 46 (8.4%) Matlab 61 (11%) Migori 45 (8.2%) Nairobi 48 (8.7%) Anthropometric indices WAZ Med. (IQR) -2.40 (-3.52 to -1.27) MUAC (cm) Med. (IQR) 12.2 (11.4 to 13.3) WHZ Med. (IQR) -1.76 (-2.75 to -0.79) HAZ Med. (IQR) -1.96 (-3.09 to -1.10) WAD at Discharge Med. (IQR) 2.14 (1.16 to 3.11) WAD at 3m post-discharge Med. (IQR) 1.88 (1.07 to 2.85) Delta-WAD at 3m post-discharge Med. (IQR) 0.17 (-0.18 to 0.61) Length of hospitalization Days in hospital Med. (IQR) 4.0 (3.0 to 7.0) Clinical and Haematology Albumin, g/ L; Med. (IQR) 39.0 (35.6 to 42.0) Haemoglobin, g/dL; Med. (IQR) 9.60 (8.50 to 10.50) RBC, x10 6 /µL; Med. (IQR) 4.40 (3.79 to 4.86) WBC, x10 3 /µL; Med. (IQR) 12.3 (9.5 to 15.8) Platelets, x10 3 /µL; Med. (IQR) 444 (284 to 590) Neutrophils, x10 3 /µL; Med. (IQR) 2.95 (1.98 to 4.43) Lymphocytes, x10 3 /µL; Med. (IQR) 7.6 (5.5 to 9.9) Eosinophils, x10 3 /µL; Med. (IQR) 0.21 (0.09 to 0.50) Monocytes, x10 3 /µL; Med. (IQR) 0.90 (0.56 to 1.22) Basophils, x10 3 /µL; Med. (IQR) 0.05 (0.02 to 0.14) Biochemistry Alanine transaminase, IU/L; Med. (IQR) 25 (16 to 37) Alkaline Phosphatase, IU/L; Med. (IQR) 189 (146 to 250) Blood urea nitrogen, Mmol/L; Med. (IQR) 1.79 (1.18 to 2.50) Creatinine, µmol/L; Med. (IQR) 18.9 (16.3 to 23.8) Bilirubin, µmol/µL; Med. (IQR) 3.7 (3.0 to 5.2) Phosphate, IU/L; Med. (IQR) 1.68 (1.45 to 1.87) Magnesium, Mmol/L; Med. (IQR) 0.90 (0.83 to 0.99) Calcium, Mmol/L; Med. (IQR) 2.48 (2.37 to 2.60) Clinical illness at admission – N (%) Pneumonia 225 (41%) Diarrhoea 306 (56%) Sepsis 63 (11%) Malaria 92 (17%) Anaemia 108 (20%) Pulmonary TB 8 (1.5%) Nutritional status at admission – N (%) Not wasted 208 (38%) Moderately Wasted 145 (26%) Severely wasted 197 (36%) WAZ = Weight-For-Age Z Score, MUAC = Mid-upper arm circumference, WHZ = weight-for-length/height z-score, HAZ = Height-For-Age Z-Score, WAD = Weight Absolute Deficit, Delta-WAD = Change in WAD Systemic inflammation is negatively associated with post-discharge weight gain. We first examined whether systemic inflammation consisting of preselected proteins from the SomaScan® assay at discharge was associated with weight gain to 90 days post-discharge. The expression of these biomarkers by sex, nutritional status, and age category is presented on Tables S1–3. Our analysis indicated that CC Motif Chemokine Ligand 21 (CCL21), Sodium/potassium-transporting ATPase subunit beta-1 (ATP1B1), Complement C8 Gamma Chain (C8G), complement factor H-related 5 (CFHR5), and Interleukin-1 receptor accessory protein (IL1RAP) inflammatory proteins were associated with weight gain (Fig. 2 A). All these proteins were negatively associated with weight gain suggesting that increased levels of these systemic inflammatory mediators may negatively impact weight gain post-discharge. CCL21 recruits and organizes T cells and dendritic cells in lymphoid tissues and has been shown to be negatively associated with body weight during catch-up growth in juvenile rats( 33 ), while IL1RAP, required for IL-1, -33, and − 36 signalling, is a major upstream inflammatory cytokine whose levels are reduced in obesity( 34 ). We then tested whether inflammatory cells from clinical haematological measurements including platelets, neutrophils, lymphocytes, eosinophils, among others were associated with post discharge weight gain. We observed that increased eosinophil counts were negatively associated with weight gain (Fig. 2 B). We noted that eosinophil counts were higher among males (p = 0.01), but their levels did not differ by nutritional status or age (Table S1 –3). Eosinophils have roles in allergic inflammation, host defence against parasitic infections and in adipose tissue and metabolism where they have been suggested to prevent weight gain and protect against obesity( 35 ). These results suggested that systemic inflammation negatively impacts weight gain directly. Post discharge weight gain is linked to suppression of linear growth mediators . After establishing the association between systemic inflammation and weight-gain, we proceeded to examine whether growth mediators were associated with weight gain. The expression of these mediators is presented on Tables S1–3 stratified by sex, nutritional status, and age. We observed that Insulin-like growth factor binding protein 2 (IGFBP2), Growth/differentiation factor 15 (GDF15), Glucagon (GCG), Peptide YY (PYY) and Cellular repressor of E1A-stimulated genes 1 (CREG1) were positively associated with weight gain. However, thrombospondin-4 (THBS4), aggrecan (ACAN), IGF1, IGFBP-3, and IGFBP-6, among others were negatively associated with weight gain (Fig. 2 C). Further correlation analysis within these biomarkers showed that IGFBP2, GDF15, PYY and GCG were highly correlated (p < 0.001) and both IGFBP2, GDF15 had a strongly negative correlation with IGF1 and most other linear growth promoting mediators including IGFBP3, ACAN, THBS4 and Growth hormone receptor (GHR) (Fig. 2 D). These linear growth promoting mediators were also highly correlated (p < 0.001). IGFBP-3 prolongs the half-life of the IGF1 while IGFBP2 inhibits IGF-mediated growth rate among other roles. GDF15 is a divergent transforming growth factor b (TGFB) family member associated with metabolic adaptation to inflammatory linked aetiologies. While IGFBP-6 was negatively associated with weight gain, it was positively associated with mediators linked to both weight gain and linear growth. IGFBP-6 is proposed to play a role in tissue remodelling, fibrosis, and immunity. Overall, ponderal growth mediators were positively while linear growth mediators were negatively associated with post-discharge weight gain. Since the GH/IGF1 axis is the major regulator of longitudinal bone growth, and consequently height, these results suggest suppression of linear growth within this cohort. Enteric dysfunction and socio-demographic exposures are not associated with weight gain . We were interested in determining whether enteric dysfunction and socio-demographic exposures were directly associated with post-discharge weight gain. Enteric dysfunction was assessed through faecal biomarkers of enteric inflammation (Myeloperoxidase (MPO), calprotectin (CAL)), and permeability (Alpha-1-Antitrypsin (AAT))( 29 ). We also tested whether gut-systemic microbial product translocation (lipopolysaccharides (LPS)) was associated with weight-gain. Distributions of these biomarkers (Fig. 2 E-I) were at increased levels compared to Western standards, but comparable to populations from similar LMIC settings( 36–38 ). These biomarkers did not vary by sex and nutritional status except for LPS whose levels were higher among severely wasted compared to the non-wasted children (p < 0.01; Table S2). MPO and LPS appeared to have a non-linear relationship with age; children < 6 month and those ≥ 12 months had higher levels compared to those between 6–12 months of age (p = 0.02;Table S3). Socioeconomic and medical risk factors were assessed through clinical presentation at admission, underlying chronic conditions, age-inappropriate nutrition, caregiver characteristics, and household-level exposures, as described previously( 3 ). Our adjusted analysis showed that none of the enteric dysfunction biomarkers nor the socioeconomic or medical risk factors were directly associated with post discharge weight gain (Fig I-J). We therefore postulated that socioeconomic factors and enteric dysfunction may operate through systemic mechanisms to impair weight gain. Systemic inflammation impacts growth indirectly through growth mediators. Our previous work on early post discharge growth following acute illness among severely malnourished children suggested that inflammation negatively impacts recovery from wasting( 16 ). We hypothesized that systemic inflammation influences weight-gain directly and indirectly through effects on growth mediators (Fig. 1 A). We postulated that besides intestinal inflammation, systemic inflammation is microbially driven including responses to viral and bacterial targets including lipopolysaccharides (LPS) from translocation or systemic gram-negative infection. Informed by our previous work and hypothesis, we selected TNF, IFNG, IL1B, IL10, CRP, PLA2G2A, LBP and sCD14 from the SomaScan panel as biomarkers for systemic inflammation since they are well characterised. We also selected mediators and regulators THBS4, ACAN, IGFBP6, IGFBP3, IGF1, PYY and GCG that are strongly linked to linear and ponderal growth (Fig. 2 B). The expression of these biomarkers is presented on Tables S1–3 stratified by sex, nutritional status, and age. Principal component analysis of systemic inflammation biomarkers indicated that the first three components explained 66% of variance (Fig. 3 A–C) and were included in the analysis. The first component of systemic inflammation comprised CRP, PLA2G2A, LBP and sCD14 (Fig. 3 D) while the second and third components included TNF, IFNG, IL1B and IFNG, IL1B, IL10 respectively (Fig. 3 E and 3 F). Similar analysis of growth mediators showed that the first two components explained 70% of variance (Fig. 3 G–I). The first growth mediator component explained 42% was predominantly IGF1 and IGFBP3 as well as aggrecan and thrombospondin-4 (Fig. 3 J). The second component of growth mediators explained 28%, driven mostly by PYY and GCG with minor contributions from IGFBP6 and others (Fig. 3 K). Our structural equation modelling analyses are presented in Fig. 3 L showing that systemic inflammation was negatively associated with growth mediators (Figs. 3 L and M). At discharge, systemic inflammation components 1 and 3 were negatively associated with component 1 and 2 of growth mediators respectively. There was no direct relationship between WAD and the 3 systemic inflammation components. Growth mediators, on the other hand, were negatively associated with WAD (underweight children had lower levels of these mediators) implying that inflammation may act indirectly through growth mediators to adversely impact the WAD. Systemic inflammation component 1 had a weak negative direct association with subsequent weight gain (Fig. 3 L). However, other systemic inflammation components were not associated with weight gain. Growth mediators components 1 and 2 were negatively associated with weight gain (Figs. 3 L and M). Component 1 was largely comprised of mediators known to promote linear growth while component 2 comprised mediators linked to ponderal growth. Both growth mediators components were negatively associated with inflammation implying that inflammation impacts mediators of both linear and ponderal growth. Taken together, these results indicate that inflammation impacts mediators of linear growth to a larger extent and those of ponderal growth to a smaller extent thereby favouring weight at the expense of height gain. Enteric dysfunction was positively associated with systemic inflammation component 1 indicating that it is a driver of systemic inflammation (Figs. 3 L and M), however, plasma LPS was not however associated with any of the systemic inflammation components. Severity of illness at admission and adverse nutritional risks were positively associated with enteric disfunction. Larger WAD, therapeutic feeding, adverse nutritional underlying risks, chronic medical conditions, severity of illness at admission and adverse household exposures were associated with components of systemic inflammation and growth mediators (Figs. 3 L and M). Since these exposures were not directly associated with weight gain, this implies that they operate predominantly through inflammatory and other pathways. DISCUSSION This study investigated the effect of inflammation at hospital discharge on post-discharge weight gain, and examined how adverse household and chronic medical conditions, and enteric dysfunction relate to systemic inflammation and weight gain in young vulnerable children hospitalised with acute illness in sub-Saharan Africa and South Asia. As expected, we found that systemic inflammation negatively impacts weight gain. We also found that systemic inflammation impacts mediators of linear growth to a larger extent than those of ponderal growth, thereby favouring weight gain at the expense of linear growth in the early post-discharge period (Fig. 4 ). We also showed that household and nutritional exposures operate both directly and through other pathways to drive systemic inflammation, which in turn negatively impacts weight gain directly, and indirectly through growth mediators. Lastly, we found that intestinal dysfunction mainly impacts growth through systemic inflammation. Despite apparent clinical recovery, many patients treated for common illness such as pneumonia and sepsis may be discharged from hospital with ongoing subclinical inflammation, which has been associated with an increased risk of death, readmission and long-term sequelae( 7, 9, 39, 40 ). As clinical signs resolve after an acute illness, children generally regain appetite and improve feeding, enhancing catch-up growth. Our previous analysis showed that an inflammatory profile (IL17A, IL2, MIP1B, sCD14, LBP, SAP, and β2M) was negatively associated with weight and mid-upper arm circumference gain in the early post-discharge period among Kenyan children treated for CSM ( 16 ). However, in southern Africa, enteric and systemic inflammation, endothelial activation, and gut epithelial repair at hospital admission were not associated with change in weight-for-length/height z-score over 48 weeks among children treated for CSM( 9 ). The present study revealed that systemic inflammation negatively impacts weight gain directly and indirectly through growth mediators. In the direct pathway, we observed that inflammatory proteins and eosinophils were negatively associated with weight gain. CCL21 is produced by lymphatic endothelial cells and lymph node stromal cells and is involved in organizing the thymic architecture and homing of T-cells and antigen-presenting dendritic cells to lymph nodes( 41–43 ). IL1RAP is a component of the receptors for interleukins 1, 33, and 36 that result in the activation of interleukin 1-responsive genes( 44 ). IL1B is known to act directly on the growth plate cartilage and suppress longitudinal bone growth through processes such as reducing proteoglycan synthesis, aggrecan, type II and X collagens( 45, 46 ). C8G belongs to the lipocalin family and is one of the three subunits that constitutes complement component 8 which participates in the formation of the membrane attack complex on bacterial cell membranes. Our analysis also showed that systemic eosinophils were negatively associated with weight gain. Eosinophils are constitutively released from the bone marrow into the circulation at a low rate which increases during parasitic helminth infections or in allergic conditions( 47 ). Recent studies in mice suggest that adipose tissue eosinophils may protect against obesity through increasing metabolism and thermogenesis( 35 ). However, while such observations have not been supported by human studies, parasitic infections are common in LMIC settings( 48, 49 ) likely with consequences of tissue eosinophilia. Taken together, these results implicate systemic inflammation in impeding weight recovery. Studies in LMICs have shown that there is early rapid weight gain while linear growth does not improve or decreases especially among undernourished children discharged from hospital following an acute illness despite therapeutic or supplementary feeding( 3, 50–52 ). Inflammation is clearly implicated in suppressing linear growth mainly through GH/IGF1 axis and long bone growth plate chondrocytes( 10–12 ). Our results confirm suppression of the IGF1 axis likely linked to GH resistance and increased levels of IGFBP2 at discharge among hospitalised children. GH resistance is thought to be linked to decreased hepatic GH receptors, low leptin levels or a post-receptor defect resulting in an inability of GH to stimulate IGF1 production( 53 ). IGFBP2 on the other hand, is known to affect growth by reducing local IGF1 bioavailability, metabolism, and bone among others ( 54 ). Malnutrition in neonatal rats causes reductions in systemic IGF1 and 2 and elevation of IGFBP2( 55 ). In transgenic mice, overexpression of IGFBP2 reduces postnatal weight gain linked to reductions in skeletal muscle and gain in body fat ( 56 ). The relationship between IGFBP2 and body weight has been reported in patients with anorexia nervosa or cancer linked malnutrition who have elevated circulating levels while low levels are demonstrated in obesity, metabolic syndrome, type 2 diabetes, and that administration of IGFBP2 can prevent adipogenesis( 57–60 ). Malnutrition within the CHAIN cohort children likely underlies increased levels of IGFBP2 and its consequences could be perturbed metabolism and growth impairments. Our results further show that there was downregulation of proteins involved in cartilage and bone formation and homeostasis. ACAN, THBS4, IGFs and their binding proteins are associated with height in a recent genome-wide association study of 5.4 million individuals of diverse ancestries( 61 ). More than 12k independent SNPs were associated with height accounting for 40% and 10–20% of phenotypic variance in populations of European and other ancestry respectively( 61 ). Further, IGF1 and 2, GHR, and ACAN have been curated from the Online Mendelian Inheritance in Man database as containing pathogenic mutations that cause syndromes of abnormal skeletal growth( 62 ). The downregulation of these proteins appears to be part of the wider systemic mechanism linking inflammation to poor linear growth post-discharge. Our results indicate that study children promoted enteroendocrine ponderal growth mediators that modulate appetite, nutrient intake and colonic motility. PYY is a hormone secreted by enteroendocrine L-cells of the ileum and colon in response to nutrients, mainly fat, but also bile acids, gastric acid and cholecystokinin and slows gastric emptying and induction of satiety( 63 ). Further, CREG1 which was associated with weight gain is essential for early development and is known to play roles in cell growth and proliferation( 64 ). CREG1 heterozygous mice models on a high fat diet gained 30% more body weight compared with wild-type controls and displayed a prominent obese phenotype, developed insulin resistance and adipose tissue inflammation suggesting a role in energy regulation and metabolism( 65 ). We also observed increased GDF15 was associated with weight gain among the study children. GDF15 has been linked to appetite suppression and anorexic metabolic programming, with impacts on metabolic health and body weight regulation. In this context, GDF15 is hypothetically a tolerogenic strategy linking metabolic adaptation to systemic inflammation driven by infectious and toxin-induced stress in contrast to driving appetite suppression and anorexia( 66 ). In our analysis, the increased expression of mediators promoting nutrient intake and weight gain was coupled with extensive downregulation of mediators linked to height gain. Taken together, these results indicate that among these children, weight gain is prioritised at the expense of height gain in the early post-discharge period. These results agree with previous observations indicating weight gain precedes linear growth spurts especially in undernourished children( 67–69 ). We were interested in generating mechanistic insights into pathways leading to poor weight recovery by examining how enteric dysfunction, systemic inflammation, growth mediators, and growth relate while also accounting for the role of nutritional and social risk factors. Overall, we demonstrated that systemic inflammation negatively impacts growth indirectly through growth mediators which were in turn negatively associated with weight deficits at discharge and post-discharge weight gain. Systemic inflammation has been suggested as one of the mechanisms that explains associations between environmental enteropathy and poor growth in LMIC settings( 70 ). Our results demonstrate that enteric dysfunction is a driver of systemic inflammation and indirectly associated with linear but not ponderal growth mediators. This is consistent with previous studies linking enteric dysfunction with impaired linear growth( 71, 72 ). In the Malnutrition and Enteric Disease (MAL-ED) birth cohort study in community settings of southern Asia, Latin America and sub-Saharan Africa, children had frequent enteric infections among which enteroinvasive, and mucosal disrupting pathogens were indirectly associated with reduced linear and ponderal growth via gut and systemic inflammation. They showed that systemic inflammation had a stronger impact on linear growth while gut inflammation was linked to reduced ponderal growth( 70 ). Surprisingly, in our study, circulating lipopolysaccharides at discharge, likely arising from the gut-systemic translocation axis, was not associated with systemic inflammation nor growth. Hypothetically, at discharge, effects of lipopolysaccharides are moderated by inpatient treatment including antibiotics. However, in a related analysis focusing on mortality, plasma LPS at admission to hospital was indirectly associated with mortality through systemic inflammation (unpublished observations). The lack of direct association between enteric dysfunction and growth is consistent with our previous demonstration that enteric permeability may not be an important direct determinant of post-discharge growth( 73 ). Previous studies have demonstrated that variability in child growth globally is more due to socio-economic and demographic factors than to genetics( 74, 75 ). Adverse clinical factors such as HIV infection, small birth size, chronic conditions, illness severity and social determinants including age-inappropriate nutrition, household level exposures, and more adverse caregiver characteristics have both been associated with mortality and poor growth post-discharge( 2, 3, 17 ). While complex relationships likely operate between these clinical, nutritional and socio-economic factors to influence catch-up growth, the ultimate biological mechanisms are likely to include enteric dysfunction and inflammation. Our analysis showed that adverse household exposure, nutritional risk factors and severity of illness appeared to drive systemic inflammation both directly and through promoting enteric dysfunction providing a biological pathway linking poor socio-economic conditions to poor growth. This therefore implies that interventions to improve ponderal and linear growth need to be multifaceted targeting both biological and socio-environmental determinants. Strengths includes nesting this study within the CHAIN cohort that captured children from diverse geographical and epidemiological settings thereby enhancing generalisability of findings. The study also analysed extensive panels of inflammatory and growth mediators and employed approaches such as structural equation modelling to interrogate relationships between biological and socio-economic factors. Weaknesses include not examining the trajectory of biomarkers over time post-discharge, since this analysis focussed on the hospital discharge timepoint and early weight-gain. It was not possible to assess the role of nutritional intake and therapeutic or supplementary feeding post-discharge on weight gain. However, the analyses were adjusted for receipt of therapeutic feeds which started in hospital and continued in the community for severely wasted children. In conclusion, systemic inflammation among children in LMICs at hospital discharge, following resolution of clinical signs of acute illness, redirects anthropometric recovery away from linear growth and limits post-discharge ponderal growth. This occurs through a set of clear biological pathways resulting from a combination of nutritional, infective, mucosal barrier and background exposures. Interventions targeting these pathways will likely need to be multifaceted. Declarations Acknowledgments: We thank the CHAIN study including the participants and their families, the study hospitals, and communities within participating sites. Funding: Bill and Melinda Gates Foundation grant OPP1131320/INV-003225 (The CHAIN Network). Wellcome Trust Intermediate Fellowship grant 222967/B/21/Z (JMN) Author contributions: Conceptualization: JMN, HHU, KT, RHJB, JLW, JAB Materials and Methodology: JMN, EOM, CT, MMN, NN, EO, WB, RM, MT, SM, AG, JT, EM, CLL, BOS, AHD, RMB, MJC, ASMSBS, TA, AFS, SAA, HHU, KT. Data management: CT, MMN, NN Analysis and Visualization: JMN, EOM, JP, BO, CJS, CB. Funding acquisition: JMN, KT, RHJB, JLW, JAB. Writing – original draft: JMN. Writing – review & editing: JMN, EOM, JB, BO, CJS, CB, CLL, ASMSBS, TA, HHU, KT, JLW, JAB. Competing interests: The authors declare no competing interest. 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Hatløy, Determinants of childhood stunting in the Democratic Republic of Congo: further analysis of Demographic and Health Survey 2013–14. BMC Public Health 18 , 74 (2017). E. A. Frongillo, M. de Onis, K. M. P. Hanson, Socioeconomic and Demographic Factors Are Associated with Worldwide Patterns of Stunting and Wasting of Children12. J Nutr 127 , 2302-2309 (1997). Additional Declarations There is NO Competing Interest. Supplementary Files NjungeSupplementaryInfo27022025.docx Cite Share Download PDF Status: Published Journal Publication published 28 Nov, 2025 Read the published version in Nature Communications → 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. 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10:35:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6127712/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6127712/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-025-66245-2","type":"published","date":"2025-11-28T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78945665,"identity":"e3319065-7234-42dd-8579-95970ce39b7d","added_by":"auto","created_at":"2025-03-21 07:52:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1084972,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual framework, study design and approach. A\u003c/strong\u003e.\u003cstrong\u003e \u003c/strong\u003eResearch questions are presented on the left panel while the conceptual framework is presented on the right panel.\u003cstrong\u003e \u003c/strong\u003eThe framework was developed to cross-examine how adverse household and chronic medical conditions, enteric dysfunction, systemic inflammation, and growth mediators influence weight gain. The socio-environmental and medical exposures are related to malnutrition and infection and impact growth through enteric dysfunction and systemic inflammation. The framework would generate mechanistic insights into pathways leading to poor growth, including those driven by nutritional and social risk factors. \u003cstrong\u003eB\u003c/strong\u003e.\u003cstrong\u003e \u003c/strong\u003eMap showing the CHAIN study participant countries. \u003cstrong\u003eC.\u003c/strong\u003e CHAIN enrolled acutely ill children aged 2-23 months at hospital admission and followed them for six months after discharge with scheduled visits at days 45 (D45), 90 (D90) and 180 (D180). CHAIN case cohort (CHAIN NCC) analysed samples collected at admission and discharge for a subset of participants within CHAIN cohort. This study focussed on data collected at discharge including clinical, anthropometry and biomarkers and 3 months follow-up anthropometry but also included socio-demographic and medical factors collected at admission. \u003cstrong\u003eD\u003c/strong\u003e. Consort showing the selection of study participants for the inflammation and growth analysis. CHAIN NCC had selected 1008 children within CHAIN that included a randomly 24% sample of the enrolled cohort and deaths outside the 24%. This study included surviving children selected within the CHAIN NCC substudy and excluded children that died, had oedema or lacked proteomics measurements. \u003cstrong\u003eE\u003c/strong\u003eThis study used data from the SomaScan plasma proteomics, plasma LPS and biomarkers of ED quantified from stool quantified at discharge for cohort children and from CP. \u003cstrong\u003eF\u003c/strong\u003e. Selection of systemic inflammation and growth mediator proteins from the SomaScan assay. Proteins from the SomaScan assay classified by the UniProt Knowledgebase (UniProtKB), as inflammatory response and/or innate immunity from the SomaScan assay were binned into one group we termed systemic inflammation and comprised of 338 proteins. Additionally, proteins classified by UniProtKB as Growth arrest, Growth factor, Growth factor binding, Growth factor receptor, Hormones, Obesity, Osteogenesis and Chondrogenesis were binned into a second group termed growth mediators (GM) and comprised of 297 proteins. \u003cstrong\u003eG\u003c/strong\u003e. Approaches used in data analysis to decipher molecular mechanisms including differential expression analysis and structural equation modelling analysis.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6127712/v1/c60aa37cd51b4aeb0de42eef.png"},{"id":78945652,"identity":"83f80446-f045-4f2e-abdd-7c243dffec98","added_by":"auto","created_at":"2025-03-21 07:52:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":532645,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential expression analysis to identify proteins associated with weight-gain.\u003c/strong\u003e Forest plots showing inflammatory cells (\u003cstrong\u003eA\u003c/strong\u003e) and differentially expressed inflammation proteins (\u003cstrong\u003eB\u003c/strong\u003e) and growth mediators (\u003cstrong\u003eC\u003c/strong\u003e) associated with growth from generalised linear models adjusted for WAD at discharge, sex, age, receipt of therapeutic feeds and site and controlled for FDR (p\u0026lt;0.05). \u003cstrong\u003eD\u003c/strong\u003e. Correlation plot among the inflammatory proteins and growth mediators significantly associated with growth (Delta WAD from discharge to 90 days; “DWAD Day90”). The significance level for correlations are coded as \"***\" for p\u0026lt;0.0005, \"**\" for p\u0026lt;0.005, \"*\" for p\u0026lt;0.05 and \"-\" for p≥0.05. The variables in \u003cstrong\u003eD\u003c/strong\u003e are ordered according to the PCA-based re-ordering in the corrgram package in R. Boxplots (mean interquartile range) depicting the distribution of enteric biomarkers (\u003cstrong\u003eE\u003c/strong\u003e) myeloperoxidase (MPO), (\u003cstrong\u003eF\u003c/strong\u003e) Calprotectin (CAL), (\u003cstrong\u003eG\u003c/strong\u003e) Alpha-1-antitrypsin (AAT) and (\u003cstrong\u003eH\u003c/strong\u003e) Lipopolysaccharides (LPS) at discharge. Cutoffs (dashed lines on the boxplots) based on Western standards (CAL\u0026gt;200 μg/ml, MPO\u0026gt;2000 ng/ml, AAT\u0026gt;270 μg/ml) show that 43%, 38% and 26% of children had elevated levels of the biomarkers respectively. Forest plots showing results from generalised linear models testing the association between enteric dysfunction biomarkers (\u003cstrong\u003eI\u003c/strong\u003e) and socio-economic and chronic medical conditions (\u003cstrong\u003eI\u003c/strong\u003e) and weight gain after adjusting for WAD at discharge, sex, age, receipt of therapeutic feeds and site.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6127712/v1/81c0dcd7a8ecabf8afe661cb.png"},{"id":78946654,"identity":"2892d396-c6c9-437f-9143-d2a685ecfeb3","added_by":"auto","created_at":"2025-03-21 08:00:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":747373,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBiomarkers, principal component analysis and relationships with growth using structural equation models.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Principal Component Analysis (PCA) biplot for components 1 and 2 for common biomarkers for systemic inflammation; TNF, IFNG, IL1B, IL10, CRP, PLA2G2A, LBP and sCD14. (\u003cstrong\u003eB\u003c/strong\u003e), Corrgram plot showing individual contribution of the biomarkers for systemic inflammation across all the dimensions. (\u003cstrong\u003eC\u003c/strong\u003e) Scree plot showing the percentage variance explained by the individual dimensions from the PCA. Individual biomarker contribution towards the first (\u003cstrong\u003eD\u003c/strong\u003e), second (\u003cstrong\u003eE\u003c/strong\u003e) and third (\u003cstrong\u003eF\u003c/strong\u003e) dimension of the PCA for systemic inflammation. (\u003cstrong\u003eG\u003c/strong\u003e). PCA biplots for components 1 and 2 for common biomarkers for growth mediators; THBS4, ACAN, IGFBP6, IGFBP3, IGF1, PYY and GCG. (\u003cstrong\u003eH\u003c/strong\u003e), Corrgram plot showing individual contribution of the biomarkers for growth mediators across all the dimensions. (\u003cstrong\u003eI\u003c/strong\u003e) Scree plot showing the percentage variance explained by the individual dimensions from the PCA. Individual biomarker contribution towards the first (\u003cstrong\u003eJ\u003c/strong\u003e), second (\u003cstrong\u003eK\u003c/strong\u003e) and third (\u003cstrong\u003eL\u003c/strong\u003e) dimension of the PCA for growth mediators. (\u003cstrong\u003eM\u003c/strong\u003e). A forest plot showing results from regression analysis from a structural equation model (SEM) examining the relationships between the first three components of both systemic inflammation and growth mediators and growth and how they relate to basal WAD at discharge, receipt of therapeutic and socio-economic, demographic and medical factors. Significant associations (p\u0026lt;0.05) in the forest plot are shown in red error bars. (\u003cstrong\u003eN\u003c/strong\u003e). A cartoon display of the significant associations displayed in M. The latent variables are described in detail in Supplementary Table S2. SI = systemic inflammation, GM = growth mediators, Dim = dimension.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6127712/v1/ddcbec11fb0d0826e12f6f01.png"},{"id":78945654,"identity":"4c9b12dd-2d8c-48b4-a740-b0a6545b587e","added_by":"auto","created_at":"2025-03-21 07:52:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":297177,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMechanisms underlying impaired post-discharge growth among after an acute illness episode in children. \u003c/strong\u003eSystemic inflammation negatively impacts on the mediators for linear growth to a larger extent and those promoting weight gain to a smaller extent thereby tilting the balance in favour of weight gain at the expense of linear growth. Intestinal dysfunction does impact linear growth mediators through systemic inflammation. Acute illness and underlying conditions and household/carer exposures appear to act through systemic inflammation and other pathways to influence weight gain and linear growth post-discharge.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6127712/v1/4b974a53b70af90c4772d360.png"},{"id":97040337,"identity":"0c0f0609-ced2-48d9-96fa-9794e9f93f1d","added_by":"auto","created_at":"2025-11-29 08:15:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3999397,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6127712/v1/b3731229-4389-43bb-ac16-be5712758a8f.pdf"},{"id":78945656,"identity":"8d407556-e15a-49c0-a39d-4d415c6d1920","added_by":"auto","created_at":"2025-03-21 07:52:38","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":191480,"visible":true,"origin":"","legend":"","description":"","filename":"NjungeSupplementaryInfo27022025.docx","url":"https://assets-eu.researchsquare.com/files/rs-6127712/v1/b2283b8310a216e6d676809b.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Inflammation impairs post-hospital discharge growth among children hospitalised with acute illness in sub-Saharan Africa and south Asia","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMedical and nutritional management of acutely ill children with or at risk of malnutrition in low- and middle-income countries (LMICs) aim to support convalescence and rapid weight gain. At hospital discharge, vulnerable children are commonly perceived to have \u0026lsquo;recovered\u0026rsquo; by clinicians and parents. However, such children commonly have unstable health trajectories post-discharge, remaining at risk of death and poor catch-up growth(\u003cem\u003e1\u0026ndash;4\u003c/em\u003e). Catch-up growth following illness and/or malnutrition, by definition, requires faster growth than the usual velocity for age and sex. Factors such as prior nutritional status, the type of initial illness and severity, recurrent infections, diet, household exposures, and physical activity may all impact catch-up growth(\u003cem\u003e5, 6\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eRecent findings from the Childhood Acute Illness and Nutrition (CHAIN) Network cohort study in sub-Saharan Africa and South Asia showed that two-thirds of hospitalised acutely ill children aged 2\u0026ndash;23 months were underweight at 6 months post-discharge and stunting increased during this period(\u003cem\u003e3\u003c/em\u003e). While poor catch-up is associated with socio-economic disadvantage including age-inappropriate nutrition, adverse caregiver characteristics, household-level exposures, small size at birth, the biological mechanisms linking these exposures and acute illness to growth faltering in these settings are not well understood.\u003c/p\u003e \u003cp\u003eAcute illness is associated with altered metabolism and hormonal perturbations driven by complex interactions between prior diet, persistent infections, inflammation, and immunopathology which may persist after apparent clinical resolution. Biomarkers of inflammation and immunosuppression persist in two-thirds of sepsis survivors and are associated with worse long-term outcomes(\u003cem\u003e7\u003c/em\u003e). Over two thirds of adult survivors of community-acquired pneumonia continue having increased inflammatory activity in their lung parenchyma for several weeks after clinical resolution(\u003cem\u003e8\u003c/em\u003e). Additionally, among Zambian and Zimbabwean children treated for complicated severe malnutrition (CSM), systemic, vascular, and intestinal inflammation did not resolve almost one year following hospitalization(\u003cem\u003e9\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eThe role of systemic inflammation in growth failure is clearly observed in chronic systemic inflammatory diseases where systemic inflammation suppresses linear growth via the growth hormone/insulin-like growth factor 1 (GH/IGF1) axis(\u003cem\u003e10\u003c/em\u003e) and has direct effects on long bone growth plate chondrocytes(\u003cem\u003e11, 12\u003c/em\u003e). Additionally, systemic inflammation affects adipose and muscle through persistent catabolism and dysregulation of hormonal and metabolic mediators(\u003cem\u003e13\u0026ndash;15\u003c/em\u003e). A pilot study among Kenyan children treated for CSM suggested that inflammation at hospital discharge negatively impacts recovery from wasting(\u003cem\u003e16\u003c/em\u003e). Mudibo and colleagues showed that HIV infection affects post-discharge growth by modulating complement and humoral responses as well as IGF signalling, and bone mineralization among children hospitalised with acute illness in sub-Saharan Africa(\u003cem\u003e17\u003c/em\u003e). Persistent subclinical inflammation among children recovering from an acute illness may limit catch-up growth in weight and height. Understanding the interrelationships between infection, inflammation, metabolic reprogramming, background exposures and catch-up growth in LMICs may help improve management.\u003c/p\u003e \u003cp\u003eUsing data and samples collected from the CHAIN multi-country cohort of children discharged from hospital following acute illness across diverse geographic and epidemiologic settings, we investigated pathways linking inflammation to early post-discharge growth (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). We analysed a panel of inflammation biomarkers and growth mediators, enteric markers of inflammation and gut permeability, and lipopolysaccharide (LPS); a marker of microbial translocation, and adverse household and chronic medical conditions in relation to post-discharge weight-gain during 90 days. In this cohort study, we provide a mechanistic understanding of why underweight children gain weight but not height after an acute illness and how socio-demographic and environmental exposures, enteric dysfunction and systemic inflammation operate to influence post-discharge weight-gain.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003eStudy design, setting and population.\u003c/strong\u003e This is a secondary analysis of the CHAIN cohort that aimed to characterise the biomedical and social risk factors for mortality in acutely ill young children, described in detail elsewhere(\u003cem\u003e18\u003c/em\u003e). Briefly, the CHAIN cohort was conducted between November 2016 and January 2019 at nine hospitals in Africa and South Asia: Dhaka and Matlab Hospitals (Bangladesh), Banfora Referral Hospital (Burkina Faso), Kilifi County, Mbagathi County and Migori County Hospitals (Kenya), Queen Elizabeth Hospital (Malawi), Civil Hospital (Pakistan), and Mulago National Referral Hospital (Uganda) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). The hospitals serve vulnerable populations and represent a range of urban and rural environments with varying access to health care and underlying comorbidities such as HIV and malaria.\u003c/p\u003e\n\u003cp\u003eCHAIN enrolled 3,101 acutely ill children aged 2\u0026ndash;23 months stratified by anthropometry using mid-upper-arm circumference (MUAC) into: no wasting (MUAC\u0026thinsp;\u0026ge;\u0026thinsp;12\u0026middot;5 cm [age\u0026thinsp;\u0026ge;\u0026thinsp;6 months] or MUAC\u0026thinsp;\u0026ge;\u0026thinsp;12\u0026middot;0 cm [age\u0026thinsp;\u0026lt;\u0026thinsp;6 months]), moderate wasting (MUAC 11\u0026middot;5\u0026ndash;12\u0026middot;5 cm [age\u0026thinsp;\u0026ge;\u0026thinsp;6 months] or MUAC 11\u0026middot;0\u0026ndash;12\u0026middot;0 cm [age\u0026thinsp;\u0026lt;\u0026thinsp;6 months]), and severe wasting (MUAC\u0026thinsp;\u0026lt;\u0026thinsp;11\u0026middot;5 cm [age\u0026thinsp;\u0026ge;\u0026thinsp;6 months] or MUAC\u0026thinsp;\u0026lt;\u0026thinsp;11\u0026middot;0 cm [age\u0026thinsp;\u0026lt;\u0026thinsp;6 months], or bilateral pedal oedema [kwashiorkor] unexplained by other medical causes) at hospital admission. Children were then followed for six months after discharge with scheduled visits at days 45 (1.5 months), 90 (3 months) and 180 (6 months) when anthropometry was conducted.\u003c/p\u003e\n\u003cp\u003eFor treatment purposes, acutely ill children were classified at admission to hospital as severely wasted or not based on WHO criteria(\u003cem\u003e19\u003c/em\u003e). Children with severe wasting were treated in hospital and after discharge at local nutrition clinics with milk-based feeds or ready to use therapeutic feeds (RUTF) according to WHO and national guidelines(\u003cem\u003e19\u003c/em\u003e). We collected data on nutritional clinic attendance and therapeutic and supplementary feed receipt, but reliable data on RUTF use, its sharing and other diet at home was not feasible.\u003c/p\u003e\n\u003cp\u003eDefinitions, procedures, data, and sample collection and processing were harmonised across sites through staff training and the use of standard operation procedures and case report forms (available online, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://chainnetwork.org/resources/\u003c/span\u003e\u003c/span\u003e) and provide detailed demographic, clinical and social phenotyping, and determination of outcomes including growth (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). Biological samples were systematically collected at admission, discharge, and scheduled follow-up timepoints and archived at the Kilifi biobank \u0026minus;\u0026thinsp;80\u0026deg;C freezers in Kenya.\u003c/p\u003e\n\u003cp\u003eThis analysis is nested within the CHAIN case cohort (CHAIN NCC) that aims to investigate biological mechanisms leading to mortality through multi-omic approaches among children who died, randomly selected survivors and community children(\u003cem\u003e20\u003c/em\u003e). The CHAIN NCC collected data on blood proteome, metabolome, lipidome, lipopolysaccharides (LPS), faecal microbiome, targeted pathogens and faecal biomarkers of enteropathy(\u003cem\u003e20\u003c/em\u003e) at admission and discharge from hospital. Because this analysis addressed weight-gain, we excluded children who died, were lost to follow-up or withdrew, had nutritional oedema or lacked plasma proteomics measurements at discharge. This analysis utilised data collected at hospital discharge, including blood proteome, plasma LPS and faecal biomarkers of enteropathy among 550 survivors among the randomly selected participants (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC\u0026ndash;D).\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eEthics\u003c/h2\u003e\n \u003cp\u003eEthical approvals were obtained from each site-affiliated or collaborating institution and from the University of Oxford. All caregivers provided written informed consent for their child to participate in the study. The study protocol was reviewed and approved by the Oxford Tropical Research Ethics Committee, UK; the Kenya Medical Research Institute, Kenya; the University of Washington and Oregon Health and Science University, USA; Makerere University School of Biomedical Sciences Research Ethics Committee and The Uganda National Council for Science and Technology, Uganda; Aga Khan University, Pakistan; International Centre for Diarrhoeal Disease Research, (icddr,b), Bangladesh; The University of Malawi; The University of Ouagadougou and Centre Muraz, Burkina Faso; the Hospital for Sick Children, Canada; and University of Amsterdam, The Netherlands.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eAnthropometry\u003c/h3\u003e\n\u003cp\u003eMeasurements included weight, MUAC and length and calculations of respective Z scores according to WHO growth standards are detailed elsewhere(\u003cem\u003e3\u003c/em\u003e).\u003c/p\u003e\n\u003ch3\u003eLaboratory analysis and data preprocessing\u003c/h3\u003e\n\u003cp\u003eThe analysis of samples including SomaScan\u0026reg; plasma proteomics, faecal biomarkers of enteric dysfunction; Myeloperoxidase (MPO), Calprotectin (CAL), and Alpha-1-Antitrypsin (AAT) and plasma LPS has been detailed in the CHAIN NCC study protocol(\u003cem\u003e20\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE). Briefly, the aptamer based 7k SomaScan\u0026reg; assay v4.1 (SomaLogic, USA) was used to quantify the abundances of 7335 proteins in plasma samples according to manufacturer\u0026rsquo;s instructions(\u003cem\u003e21\u003c/em\u003e) and presented in a proprietary text-based format called ADAT. The \u003cem\u003ereadat\u003c/em\u003e R package was used for importing, transforming and annotating SomaScan\u0026reg; data from the ADAT files(\u003cem\u003e22\u003c/em\u003e). The data were log-transformed and standardised. Outliers were replaced with the 5th and 95th percentile values. Several independent aptamers (short oligonucleotides which have binding affinity to a single protein) appeared to detect the same protein and this were excluded if they were highly correlated (r\u0026thinsp;\u0026gt;\u0026thinsp;0.5). Stool MPO, CAL, and AAT were quantified using an ELISA assay (Immundiagnostik AG, Germany) and absolute concentrations calculated for 15 mg of stool using dose response curves. The plasma LPS levels were measured via a limulus amoebocyte lysate-based, quantitative chromogenic endpoint assay (ThermoFisher, UK) according to manufacturer\u0026rsquo;s instructions. The faecal biomarker and LPS data were log transformed and scaled since they were skewed.\u003c/p\u003e\n\u003ch3\u003eSelection of systemic inflammation proteins and growth mediators from SomaScan assay\u003c/h3\u003e\n\u003cp\u003eWe selected proteins classified by the UniProt Knowledgebase (UniProtKB), as inflammatory response and innate immunity from the SomaScan\u0026reg; assay and binned them into one group we termed \u003cem\u003esystemic inflammation\u003c/em\u003e which comprised 338 proteins (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eF). We also selected proteins classified by UniProtKB as Growth arrest, Growth factor, Growth factor binding, Growth factor receptor, Hormones, Obesity, Osteogenesis and Chondrogenesis which were binned into a second group termed \u003cem\u003egrowth mediators\u003c/em\u003e that consisted of 297 proteins. UniProtKB is a central hub containing functional information on proteins and consists of manually-annotated records with information extracted from literature and curator-evaluated computational analysis, which we used for this analysis, as well as computationally analysed records that await full manual annotation(\u003cem\u003e23\u003c/em\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003eBaseline analysis\u003c/h2\u003e\n \u003cp\u003eCharacteristics of study children at hospital discharge including demographic, anthropometry and clinical features were summarised using median with interquartile ranges if continuous and proportions if categorical. We also summarised the clinical diagnosis and nutritional status at admission.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eGrowth analysis\u003c/h3\u003e\n\u003cp\u003eThe primary outcome of the analysis was growth as assessed by weight-gain. We defined weight-gain by the change in absolute deficits in weight (WAD) from discharge to 3 month post discharge follow-up (Delta WAD). Absolute deficit was calculated as the difference between the measured weight and the median age- and sex-specific value obtained from the WHO 2006 growth standards(\u003cem\u003e24\u0026ndash;26\u003c/em\u003e). Absolute deficit was used rather than Z scores because changes in standard deviation widths across age or length makes Z scores less appropriate for measuring changes over time among children of different ages(\u003cem\u003e25\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eLinear mixed models were used to test the association between exposures including systemic inflammation and growth mediator panels, inflammatory cells from haematology, individual measures of enteric dysfunction, and adverse household and chronic medical conditions with growth (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eG). The adverse household and chronic medical conditions are detailed in Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e and have also been described in a previous CHAIN cohort growth analysis(\u003cem\u003e3\u003c/em\u003e). Models were adjusted for sex, age, site, baseline WAD, and receipt of therapeutic feeds and corrected for false discovery rate using the Benjamini\u0026ndash;Hochberg method and statistical significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05(\u003cem\u003e27, 28\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eSEM path models were used to examine how adverse household and chronic medical conditions, enteric dysfunction, systemic inflammation, and growth mediators influence weight gain. We used principal component analysis (PCA) to reduce the dimensions of the individual biomarkers selected for systemic inflammation and growth mediators. Components explaining at least 65% of the variation were included in the analysis. Enteric dysfunction(\u003cem\u003e29\u003c/em\u003e) which is a subclinical condition characterised by small intestinal inflammation, abnormal villous architecture, malabsorption and altered gut permeability, was a latent variable measured by CAL, MPO, and AAT in stool. Enteric dysfunction, plasma LPS, systemic inflammation, and growth mediators were considered as biological factors related to growth. The final SEM models included the biological factors, demographic factors comprising age, site and sex, receipt of therapeutic feeds as well as latent variables depicting socioeconomic and medical factors.\u003c/p\u003e\n\u003cp\u003eSEM models were fitted using the full information maximum likelihood estimator (FIML)(\u003cem\u003e30\u003c/em\u003e) using the lavaan(\u003cem\u003e31\u003c/em\u003e) package version 0.6.17 in R version 4.2.2 using the sem function. We report standardised estimates. Model fit for the SEMs were evaluated using the comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error for approximation (RMSEA), and standardised root mean squared residual (SRMR). A reasonably good model fit is obtained when Chi-square p-value is \u0026gt;\u0026thinsp;0.05, CFI and TLI are \u0026ge;\u0026thinsp;0.90, RMSEA is \u0026le;\u0026thinsp;0.06 and SRMR is \u0026le;\u0026thinsp;0.08(\u003cem\u003e32\u003c/em\u003e). Associations with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. No imputation of missing data was performed; the analyses are valid under the missing at random (MAR) assumption given the likelihood approach.\u003c/p\u003e\n"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePopulation characteristics\u003c/h2\u003e \u003cp\u003eA total of 550 children being discharged from hospital randomly selected from the CHAIN study (excluding deaths, children with oedema and those missing samples) were included for analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB\u0026ndash;D). Characteristics of the study children are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The Banfora, Dhaka and Kampala sites had larger proportions of study children compared to the other study sites. Selected children were mainly diagnosed with pneumonia and diarrhoea at admission and the proportion of non-wasted to severely wasted was similar. Most haematological parameters were comparable by sex except eosinophils which were increased among males at discharge (p\u0026thinsp;=\u0026thinsp;0.01; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Several parameters varied by nutritional status (Table S2); albumin and erythrocytes were lower while white blood cells, platelets, neutrophils, and monocytes counts were higher among severely wasted children. Males were more underweight (p\u0026thinsp;=\u0026thinsp;0.03) and stunted (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and had larger weight deficits at discharge and at 3 months post discharge (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). However, the proportion of the not wasted, moderately wasted and the severely wasted did not vary by sex (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWeight gain\u003c/h2\u003e \u003cp\u003eThe median weight gain was 0.17 kg within three months and the median absolute weight deficit reduced from 2.14 kg at discharge to 1.88 kg during 90 days post-discharge from hospital (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Severely wasted children had larger weight deficits at discharge and 3 months but also larger weight gains during this period compared to the moderate and the not wasted children (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table S2). While older children had larger weight deficits than younger children, the median weight gained did not vary by age (Table S3).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline demographic, anthropometric, and clinical characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;550\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e Demographic\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (months) Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.3 (7.1 to 16.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSex: Female, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223 (41%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eSite n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBanfora\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (15%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlantyre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDhaka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKampala\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83 (15%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKarachi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKilifi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (8.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatlab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (11%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMigori\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNairobi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnthropometric indices\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWAZ Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.40 (-3.52 to -1.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMUAC (cm) Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.2 (11.4 to 13.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWHZ Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.76 (-2.75 to -0.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHAZ Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.96 (-3.09 to -1.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWAD at Discharge Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.14 (1.16 to 3.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWAD at 3m post-discharge Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.88 (1.07 to 2.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDelta-WAD at 3m post-discharge Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17 (-0.18 to 0.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLength of hospitalization\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDays in hospital Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0 (3.0 to 7.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical and Haematology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAlbumin, g/ L; Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.0 (35.6 to 42.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHaemoglobin, g/dL; Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.60 (8.50 to 10.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRBC, x10\u003csup\u003e6\u003c/sup\u003e/\u0026micro;L; Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.40 (3.79 to 4.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWBC, x10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L; Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.3 (9.5 to 15.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePlatelets, x10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L; Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e444 (284 to 590)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNeutrophils, x10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L; Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.95 (1.98 to 4.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLymphocytes, x10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L; Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.6 (5.5 to 9.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEosinophils, x10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L; Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21 (0.09 to 0.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMonocytes, x10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L; Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90 (0.56 to 1.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBasophils, x10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L; Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05 (0.02 to 0.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBiochemistry\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAlanine transaminase, IU/L; Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (16 to 37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAlkaline Phosphatase, IU/L; Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189 (146 to 250)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBlood urea nitrogen, Mmol/L; Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.79 (1.18 to 2.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCreatinine, \u0026micro;mol/L; Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.9 (16.3 to 23.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBilirubin, \u0026micro;mol/\u0026micro;L; Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7 (3.0 to 5.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePhosphate, IU/L; Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.68 (1.45 to 1.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMagnesium, Mmol/L; Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90 (0.83 to 0.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCalcium, Mmol/L; Med. (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.48 (2.37 to 2.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical illness at admission \u0026ndash;\u003c/b\u003e \u003cb\u003eN\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225 (41%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDiarrhoea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e306 (56%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSepsis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (11%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMalaria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (17%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAnaemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108 (20%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePulmonary TB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNutritional status at admission \u0026ndash;\u003c/b\u003e \u003cb\u003eN\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNot wasted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e208 (38%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModerately Wasted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145 (26%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSeverely wasted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197 (36%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eWAZ\u0026thinsp;=\u0026thinsp;Weight-For-Age Z Score, MUAC\u0026thinsp;=\u0026thinsp;Mid-upper arm circumference, WHZ\u0026thinsp;=\u0026thinsp;weight-for-length/height z-score, HAZ\u0026thinsp;=\u0026thinsp;Height-For-Age Z-Score, WAD\u0026thinsp;=\u0026thinsp;Weight Absolute Deficit, Delta-WAD\u0026thinsp;=\u0026thinsp;Change in WAD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSystemic inflammation is negatively associated with post-discharge weight gain.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe first examined whether systemic inflammation consisting of preselected proteins from the SomaScan\u0026reg; assay at discharge was associated with weight gain to 90 days post-discharge. The expression of these biomarkers by sex, nutritional status, and age category is presented on Tables S1\u0026ndash;3. Our analysis indicated that CC Motif Chemokine Ligand 21 (CCL21), Sodium/potassium-transporting ATPase subunit beta-1 (ATP1B1), Complement C8 Gamma Chain (C8G), complement factor H-related 5 (CFHR5), and Interleukin-1 receptor accessory protein (IL1RAP) inflammatory proteins were associated with weight gain (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). All these proteins were negatively associated with weight gain suggesting that increased levels of these systemic inflammatory mediators may negatively impact weight gain post-discharge. CCL21 recruits and organizes T cells and dendritic cells in lymphoid tissues and has been shown to be negatively associated with body weight during catch-up growth in juvenile rats(\u003cem\u003e33\u003c/em\u003e), while IL1RAP, required for IL-1, -33, and \u0026minus;\u0026thinsp;36 signalling, is a major upstream inflammatory cytokine whose levels are reduced in obesity(\u003cem\u003e34\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eWe then tested whether inflammatory cells from clinical haematological measurements including platelets, neutrophils, lymphocytes, eosinophils, among others were associated with post discharge weight gain. We observed that increased eosinophil counts were negatively associated with weight gain (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). We noted that eosinophil counts were higher among males (p\u0026thinsp;=\u0026thinsp;0.01), but their levels did not differ by nutritional status or age (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u0026ndash;3). Eosinophils have roles in allergic inflammation, host defence against parasitic infections and in adipose tissue and metabolism where they have been suggested to prevent weight gain and protect against obesity(\u003cem\u003e35\u003c/em\u003e). These results suggested that systemic inflammation negatively impacts weight gain directly.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePost discharge weight gain is linked to suppression of linear growth mediators\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eAfter establishing the association between systemic inflammation and weight-gain, we proceeded to examine whether growth mediators were associated with weight gain. The expression of these mediators is presented on Tables S1\u0026ndash;3 stratified by sex, nutritional status, and age. We observed that Insulin-like growth factor binding protein 2 (IGFBP2), Growth/differentiation factor 15 (GDF15), Glucagon (GCG), Peptide YY (PYY) and Cellular repressor of E1A-stimulated genes 1 (CREG1) were positively associated with weight gain. However, thrombospondin-4 (THBS4), aggrecan (ACAN), IGF1, IGFBP-3, and IGFBP-6, among others were negatively associated with weight gain (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Further correlation analysis within these biomarkers showed that IGFBP2, GDF15, PYY and GCG were highly correlated (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and both IGFBP2, GDF15 had a strongly negative correlation with IGF1 and most other linear growth promoting mediators including IGFBP3, ACAN, THBS4 and Growth hormone receptor (GHR) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). These linear growth promoting mediators were also highly correlated (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). IGFBP-3 prolongs the half-life of the IGF1 while IGFBP2 inhibits IGF-mediated growth rate among other roles. GDF15 is a divergent transforming growth factor b (TGFB) family member associated with metabolic adaptation to inflammatory linked aetiologies. While IGFBP-6 was negatively associated with weight gain, it was positively associated with mediators linked to both weight gain and linear growth. IGFBP-6 is proposed to play a role in tissue remodelling, fibrosis, and immunity. Overall, ponderal growth mediators were positively while linear growth mediators were negatively associated with post-discharge weight gain. Since the GH/IGF1 axis is the major regulator of longitudinal bone growth, and consequently height, these results suggest suppression of linear growth within this cohort.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEnteric dysfunction and socio-demographic exposures are not associated with weight gain\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eWe were interested in determining whether enteric dysfunction and socio-demographic exposures were directly associated with post-discharge weight gain. Enteric dysfunction was assessed through faecal biomarkers of enteric inflammation (Myeloperoxidase (MPO), calprotectin (CAL)), and permeability (Alpha-1-Antitrypsin (AAT))(\u003cem\u003e29\u003c/em\u003e). We also tested whether gut-systemic microbial product translocation (lipopolysaccharides (LPS)) was associated with weight-gain. Distributions of these biomarkers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-I) were at increased levels compared to Western standards, but comparable to populations from similar LMIC settings(\u003cem\u003e36\u0026ndash;38\u003c/em\u003e). These biomarkers did not vary by sex and nutritional status except for LPS whose levels were higher among severely wasted compared to the non-wasted children (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Table S2). MPO and LPS appeared to have a non-linear relationship with age; children\u0026thinsp;\u0026lt;\u0026thinsp;6 month and those \u003cb\u003e\u0026ge;\u003c/b\u003e12 months had higher levels compared to those between 6\u0026ndash;12 months of age (p\u0026thinsp;=\u0026thinsp;0.02;Table S3). Socioeconomic and medical risk factors were assessed through clinical presentation at admission, underlying chronic conditions, age-inappropriate nutrition, caregiver characteristics, and household-level exposures, as described previously(\u003cem\u003e3\u003c/em\u003e). Our adjusted analysis showed that none of the enteric dysfunction biomarkers nor the socioeconomic or medical risk factors were directly associated with post discharge weight gain (Fig I-J). We therefore postulated that socioeconomic factors and enteric dysfunction may operate through systemic mechanisms to impair weight gain.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSystemic inflammation impacts growth indirectly through growth mediators.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOur previous work on early post discharge growth following acute illness among severely malnourished children suggested that inflammation negatively impacts recovery from wasting(\u003cem\u003e16\u003c/em\u003e). We hypothesized that systemic inflammation influences weight-gain directly and indirectly through effects on growth mediators (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). We postulated that besides intestinal inflammation, systemic inflammation is microbially driven including responses to viral and bacterial targets including lipopolysaccharides (LPS) from translocation or systemic gram-negative infection. Informed by our previous work and hypothesis, we selected TNF, IFNG, IL1B, IL10, CRP, PLA2G2A, LBP and sCD14 from the SomaScan panel as biomarkers for systemic inflammation since they are well characterised. We also selected mediators and regulators THBS4, ACAN, IGFBP6, IGFBP3, IGF1, PYY and GCG that are strongly linked to linear and ponderal growth (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The expression of these biomarkers is presented on Tables S1\u0026ndash;3 stratified by sex, nutritional status, and age.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrincipal component analysis of systemic inflammation biomarkers indicated that the first three components explained 66% of variance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u0026ndash;C) and were included in the analysis. The first component of systemic inflammation comprised CRP, PLA2G2A, LBP and sCD14 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) while the second and third components included TNF, IFNG, IL1B and IFNG, IL1B, IL10 respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Similar analysis of growth mediators showed that the first two components explained 70% of variance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG\u0026ndash;I). The first growth mediator component explained 42% was predominantly IGF1 and IGFBP3 as well as aggrecan and thrombospondin-4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ). The second component of growth mediators explained 28%, driven mostly by PYY and GCG with minor contributions from IGFBP6 and others (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eK).\u003c/p\u003e \u003cp\u003eOur structural equation modelling analyses are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eL showing that systemic inflammation was negatively associated with growth mediators (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eL and M). At discharge, systemic inflammation components 1 and 3 were negatively associated with component 1 and 2 of growth mediators respectively. There was no direct relationship between WAD and the 3 systemic inflammation components. Growth mediators, on the other hand, were negatively associated with WAD (underweight children had lower levels of these mediators) implying that inflammation may act indirectly through growth mediators to adversely impact the WAD.\u003c/p\u003e \u003cp\u003eSystemic inflammation component 1 had a weak negative direct association with subsequent weight gain (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eL). However, other systemic inflammation components were not associated with weight gain. Growth mediators components 1 and 2 were negatively associated with weight gain (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eL and M). Component 1 was largely comprised of mediators known to promote linear growth while component 2 comprised mediators linked to ponderal growth. Both growth mediators components were negatively associated with inflammation implying that inflammation impacts mediators of both linear and ponderal growth. Taken together, these results indicate that inflammation impacts mediators of linear growth to a larger extent and those of ponderal growth to a smaller extent thereby favouring weight at the expense of height gain.\u003c/p\u003e \u003cp\u003eEnteric dysfunction was positively associated with systemic inflammation component 1 indicating that it is a driver of systemic inflammation (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eL and M), however, plasma LPS was not however associated with any of the systemic inflammation components. Severity of illness at admission and adverse nutritional risks were positively associated with enteric disfunction.\u003c/p\u003e \u003cp\u003eLarger WAD, therapeutic feeding, adverse nutritional underlying risks, chronic medical conditions, severity of illness at admission and adverse household exposures were associated with components of systemic inflammation and growth mediators (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eL and M). Since these exposures were not directly associated with weight gain, this implies that they operate predominantly through inflammatory and other pathways.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study investigated the effect of inflammation at hospital discharge on post-discharge weight gain, and examined how adverse household and chronic medical conditions, and enteric dysfunction relate to systemic inflammation and weight gain in young vulnerable children hospitalised with acute illness in sub-Saharan Africa and South Asia. As expected, we found that systemic inflammation negatively impacts weight gain. We also found that systemic inflammation impacts mediators of linear growth to a larger extent than those of ponderal growth, thereby favouring weight gain at the expense of linear growth in the early post-discharge period (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). We also showed that household and nutritional exposures operate both directly and through other pathways to drive systemic inflammation, which in turn negatively impacts weight gain directly, and indirectly through growth mediators. Lastly, we found that intestinal dysfunction mainly impacts growth through systemic inflammation.\u003c/p\u003e \u003cp\u003eDespite apparent clinical recovery, many patients treated for common illness such as pneumonia and sepsis may be discharged from hospital with ongoing subclinical inflammation, which has been associated with an increased risk of death, readmission and long-term sequelae(\u003cem\u003e7, 9, 39, 40\u003c/em\u003e). As clinical signs resolve after an acute illness, children generally regain appetite and improve feeding, enhancing catch-up growth. Our previous analysis showed that an inflammatory profile (IL17A, IL2, MIP1B, sCD14, LBP, SAP, and β2M) was negatively associated with weight and mid-upper arm circumference gain in the early post-discharge period among Kenyan children treated for CSM (\u003cem\u003e16\u003c/em\u003e). However, in southern Africa, enteric and systemic inflammation, endothelial activation, and gut epithelial repair at hospital admission were not associated with change in weight-for-length/height z-score over 48 weeks among children treated for CSM(\u003cem\u003e9\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eThe present study revealed that systemic inflammation negatively impacts weight gain directly and indirectly through growth mediators. In the direct pathway, we observed that inflammatory proteins and eosinophils were negatively associated with weight gain. CCL21 is produced by lymphatic endothelial cells and lymph node stromal cells and is involved in organizing the thymic architecture and homing of T-cells and antigen-presenting dendritic cells to lymph nodes(\u003cem\u003e41\u0026ndash;43\u003c/em\u003e). IL1RAP is a component of the receptors for interleukins 1, 33, and 36 that result in the activation of interleukin 1-responsive genes(\u003cem\u003e44\u003c/em\u003e). IL1B is known to act directly on the growth plate cartilage and suppress longitudinal bone growth through processes such as reducing proteoglycan synthesis, aggrecan, type II and X collagens(\u003cem\u003e45, 46\u003c/em\u003e). C8G belongs to the lipocalin family and is one of the three subunits that constitutes complement component 8 which participates in the formation of the membrane attack complex on bacterial cell membranes. Our analysis also showed that systemic eosinophils were negatively associated with weight gain. Eosinophils are constitutively released from the bone marrow into the circulation at a low rate which increases during parasitic helminth infections or in allergic conditions(\u003cem\u003e47\u003c/em\u003e). Recent studies in mice suggest that adipose tissue eosinophils may protect against obesity through increasing metabolism and thermogenesis(\u003cem\u003e35\u003c/em\u003e). However, while such observations have not been supported by human studies, parasitic infections are common in LMIC settings(\u003cem\u003e48, 49\u003c/em\u003e) likely with consequences of tissue eosinophilia. Taken together, these results implicate systemic inflammation in impeding weight recovery.\u003c/p\u003e \u003cp\u003eStudies in LMICs have shown that there is early rapid weight gain while linear growth does not improve or decreases especially among undernourished children discharged from hospital following an acute illness despite therapeutic or supplementary feeding(\u003cem\u003e3, 50\u0026ndash;52\u003c/em\u003e). Inflammation is clearly implicated in suppressing linear growth mainly through GH/IGF1 axis and long bone growth plate chondrocytes(\u003cem\u003e10\u0026ndash;12\u003c/em\u003e). Our results confirm suppression of the IGF1 axis likely linked to GH resistance and increased levels of IGFBP2 at discharge among hospitalised children. GH resistance is thought to be linked to decreased hepatic GH receptors, low leptin levels or a post-receptor defect resulting in an inability of GH to stimulate IGF1 production(\u003cem\u003e53\u003c/em\u003e). IGFBP2 on the other hand, is known to affect growth by reducing local IGF1 bioavailability, metabolism, and bone among others (\u003cem\u003e54\u003c/em\u003e). Malnutrition in neonatal rats causes reductions in systemic IGF1 and 2 and elevation of IGFBP2(\u003cem\u003e55\u003c/em\u003e). In transgenic mice, overexpression of IGFBP2 reduces postnatal weight gain linked to reductions in skeletal muscle and gain in body fat (\u003cem\u003e56\u003c/em\u003e). The relationship between IGFBP2 and body weight has been reported in patients with anorexia nervosa or cancer linked malnutrition who have elevated circulating levels while low levels are demonstrated in obesity, metabolic syndrome, type 2 diabetes, and that administration of IGFBP2 can prevent adipogenesis(\u003cem\u003e57\u0026ndash;60\u003c/em\u003e). Malnutrition within the CHAIN cohort children likely underlies increased levels of IGFBP2 and its consequences could be perturbed metabolism and growth impairments. Our results further show that there was downregulation of proteins involved in cartilage and bone formation and homeostasis. ACAN, THBS4, IGFs and their binding proteins are associated with height in a recent genome-wide association study of 5.4\u0026nbsp;million individuals of diverse ancestries(\u003cem\u003e61\u003c/em\u003e). More than 12k independent SNPs were associated with height accounting for 40% and 10\u0026ndash;20% of phenotypic variance in populations of European and other ancestry respectively(\u003cem\u003e61\u003c/em\u003e). Further, IGF1 and 2, GHR, and ACAN have been curated from the Online Mendelian Inheritance in Man database as containing pathogenic mutations that cause syndromes of abnormal skeletal growth(\u003cem\u003e62\u003c/em\u003e). The downregulation of these proteins appears to be part of the wider systemic mechanism linking inflammation to poor linear growth post-discharge.\u003c/p\u003e \u003cp\u003eOur results indicate that study children promoted enteroendocrine ponderal growth mediators that modulate appetite, nutrient intake and colonic motility. PYY is a hormone secreted by enteroendocrine L-cells of the ileum and colon in response to nutrients, mainly fat, but also bile acids, gastric acid and cholecystokinin and slows gastric emptying and induction of satiety(\u003cem\u003e63\u003c/em\u003e). Further, CREG1 which was associated with weight gain is essential for early development and is known to play roles in cell growth and proliferation(\u003cem\u003e64\u003c/em\u003e). CREG1 heterozygous mice models on a high fat diet gained 30% more body weight compared with wild-type controls and displayed a prominent obese phenotype, developed insulin resistance and adipose tissue inflammation suggesting a role in energy regulation and metabolism(\u003cem\u003e65\u003c/em\u003e). We also observed increased GDF15 was associated with weight gain among the study children. GDF15 has been linked to appetite suppression and anorexic metabolic programming, with impacts on metabolic health and body weight regulation. In this context, GDF15 is hypothetically a tolerogenic strategy linking metabolic adaptation to systemic inflammation driven by infectious and toxin-induced stress in contrast to driving appetite suppression and anorexia(\u003cem\u003e66\u003c/em\u003e). In our analysis, the increased expression of mediators promoting nutrient intake and weight gain was coupled with extensive downregulation of mediators linked to height gain. Taken together, these results indicate that among these children, weight gain is prioritised at the expense of height gain in the early post-discharge period. These results agree with previous observations indicating weight gain precedes linear growth spurts especially in undernourished children(\u003cem\u003e67\u0026ndash;69\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eWe were interested in generating mechanistic insights into pathways leading to poor weight recovery by examining how enteric dysfunction, systemic inflammation, growth mediators, and growth relate while also accounting for the role of nutritional and social risk factors. Overall, we demonstrated that systemic inflammation negatively impacts growth indirectly through growth mediators which were in turn negatively associated with weight deficits at discharge and post-discharge weight gain. Systemic inflammation has been suggested as one of the mechanisms that explains associations between environmental enteropathy and poor growth in LMIC settings(\u003cem\u003e70\u003c/em\u003e). Our results demonstrate that enteric dysfunction is a driver of systemic inflammation and indirectly associated with linear but not ponderal growth mediators. This is consistent with previous studies linking enteric dysfunction with impaired linear growth(\u003cem\u003e71, 72\u003c/em\u003e). In the Malnutrition and Enteric Disease (MAL-ED) birth cohort study in community settings of southern Asia, Latin America and sub-Saharan Africa, children had frequent enteric infections among which enteroinvasive, and mucosal disrupting pathogens were indirectly associated with reduced linear and ponderal growth via gut and systemic inflammation. They showed that systemic inflammation had a stronger impact on linear growth while gut inflammation was linked to reduced ponderal growth(\u003cem\u003e70\u003c/em\u003e). Surprisingly, in our study, circulating lipopolysaccharides at discharge, likely arising from the gut-systemic translocation axis, was not associated with systemic inflammation nor growth. Hypothetically, at discharge, effects of lipopolysaccharides are moderated by inpatient treatment including antibiotics. However, in a related analysis focusing on mortality, plasma LPS at admission to hospital was indirectly associated with mortality through systemic inflammation (unpublished observations). The lack of direct association between enteric dysfunction and growth is consistent with our previous demonstration that enteric permeability may not be an important direct determinant of post-discharge growth(\u003cem\u003e73\u003c/em\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies have demonstrated that variability in child growth globally is more due to socio-economic and demographic factors than to genetics(\u003cem\u003e74, 75\u003c/em\u003e). Adverse clinical factors such as HIV infection, small birth size, chronic conditions, illness severity and social determinants including age-inappropriate nutrition, household level exposures, and more adverse caregiver characteristics have both been associated with mortality and poor growth post-discharge(\u003cem\u003e2, 3, 17\u003c/em\u003e). While complex relationships likely operate between these clinical, nutritional and socio-economic factors to influence catch-up growth, the ultimate biological mechanisms are likely to include enteric dysfunction and inflammation. Our analysis showed that adverse household exposure, nutritional risk factors and severity of illness appeared to drive systemic inflammation both directly and through promoting enteric dysfunction providing a biological pathway linking poor socio-economic conditions to poor growth. This therefore implies that interventions to improve ponderal and linear growth need to be multifaceted targeting both biological and socio-environmental determinants.\u003c/p\u003e \u003cp\u003eStrengths includes nesting this study within the CHAIN cohort that captured children from diverse geographical and epidemiological settings thereby enhancing generalisability of findings. The study also analysed extensive panels of inflammatory and growth mediators and employed approaches such as structural equation modelling to interrogate relationships between biological and socio-economic factors. Weaknesses include not examining the trajectory of biomarkers over time post-discharge, since this analysis focussed on the hospital discharge timepoint and early weight-gain. It was not possible to assess the role of nutritional intake and therapeutic or supplementary feeding post-discharge on weight gain. However, the analyses were adjusted for receipt of therapeutic feeds which started in hospital and continued in the community for severely wasted children.\u003c/p\u003e \u003cp\u003eIn conclusion, systemic inflammation among children in LMICs at hospital discharge, following resolution of clinical signs of acute illness, redirects anthropometric recovery away from linear growth and limits post-discharge ponderal growth. This occurs through a set of clear biological pathways resulting from a combination of nutritional, infective, mucosal barrier and background exposures. Interventions targeting these pathways will likely need to be multifaceted.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eWe thank the CHAIN study including the participants and their families, the study hospitals, and communities within participating sites.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBill and Melinda Gates Foundation grant OPP1131320/INV-003225 (The CHAIN Network).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWellcome Trust Intermediate Fellowship grant 222967/B/21/Z (JMN)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: JMN, HHU, KT, RHJB, JLW, JAB\u003c/p\u003e\n\u003cp\u003eMaterials and Methodology: JMN, EOM, CT, MMN, NN, EO, WB, RM, MT, SM, AG, JT, EM, CLL, BOS, AHD, RMB, MJC, ASMSBS, TA, AFS, SAA, HHU, KT.\u003c/p\u003e\n\u003cp\u003eData management: CT, MMN, NN\u003c/p\u003e\n\u003cp\u003eAnalysis and Visualization: JMN, EOM, JP, BO, CJS, CB.\u003c/p\u003e\n\u003cp\u003eFunding acquisition: JMN, KT, RHJB, JLW, JAB.\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; original draft: JMN.\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; review \u0026amp; editing: JMN, EOM, JB, BO, CJS, CB, CLL, ASMSBS, TA, HHU, KT, JLW, JAB. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare no competing interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and Codes availability:\u0026nbsp;\u003c/strong\u003eThe data and analysis code are archived on the Harvard Dataverse (https://doi.org/10.7910/DVN/TBQYSF) and may be requested via email to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eM. 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Hanson, Socioeconomic and Demographic Factors Are Associated with Worldwide Patterns of Stunting and Wasting of Children12. \u003cem\u003eJ Nutr\u003c/em\u003e \u003cstrong\u003e127\u003c/strong\u003e, 2302-2309 (1997).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Inflammation, weight-gain, enteric dysfunction, growth mediators, SomaScan","lastPublishedDoi":"10.21203/rs.3.rs-6127712/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6127712/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChildhood growth can be affected by acute illness, chronic conditions, diet and their home environment. In resource-poor settings, children often experience poor growth following illness, but the mechanisms are poorly understood. This cohort study nested within the CHAIN cohort in six countries in sub-Saharan Africa and south Asia investigated pathways linking inflammation and post-discharge weight gain among children hospitalised with acute illness. We analysed biomarkers of inflammation, enteropathy, growth mediators and other exposures at hospital discharge and examined how they impact post-discharge weight gain during 90 days. Linear mixed models determined associations between exposures and weight gain while structural equation models explained how these exposures influence growth. We show that systemic inflammation impacts mediators of linear growth including the GH/IGF1 axis and bone metabolism to a larger extent and weight gain via enteroendocrine peptide YY and glucagon pathways to a lesser extent. Systemic inflammation negatively affects weight gain directly. Intestinal dysfunction impacts growth through systemic inflammation. Adverse household and chronic medical conditions predominantly influenced weight gain through inflammation. Persistent systemic inflammation at hospital discharge strongly impairs post-discharge linear growth and limits weight gain. It is critical to address inflammation, the intestinal mucosal barrier and other exposures driving inflammation to optimise recovery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOne Sentence Summary: \u003c/strong\u003eInflammation driven by illness, enteropathy and adverse social factors redirects post-hospital recovery away from linear growth and limits weight gain.\u003c/p\u003e","manuscriptTitle":"Inflammation impairs post-hospital discharge growth among children hospitalised with acute illness in sub-Saharan Africa and south Asia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-21 07:52:34","doi":"10.21203/rs.3.rs-6127712/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":"ddef4b60-21f5-423e-b9cf-0f632d497ff5","owner":[],"postedDate":"March 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":45892674,"name":"Biological sciences/Molecular biology/Proteomics/Protein\u0026#x2013;protein interaction networks"},{"id":45892675,"name":"Biological sciences/Physiology/Bone"},{"id":45892676,"name":"Biological sciences/Immunology/Inflammation/Chronic inflammation"},{"id":45892677,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Growth disorders"}],"tags":[],"updatedAt":"2025-11-29T08:15:16+00:00","versionOfRecord":{"articleIdentity":"rs-6127712","link":"https://doi.org/10.1038/s41467-025-66245-2","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2025-11-28 05:00:00","publishedOnDateReadable":"November 28th, 2025"},"versionCreatedAt":"2025-03-21 07:52:34","video":"","vorDoi":"10.1038/s41467-025-66245-2","vorDoiUrl":"https://doi.org/10.1038/s41467-025-66245-2","workflowStages":[]},"version":"v1","identity":"rs-6127712","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6127712","identity":"rs-6127712","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}