Inflammatory profiles in febrile children with moderate and severe malnutrition presenting at-hospital in Uganda are associated with increased mortality
Bibliographic record
Abstract
BACKGROUND: Children in Africa carry a disproportionate burden of malnutrition and infectious disease. Together, malnutrition and infection are major contributors to global child mortality; however, their collective impact on immune activation are not well described. METHODS: This was a secondary analysis of a prospective cohort study of children hospitalized with acute febrile illness at a single centre in Uganda. We investigated the association between malnutrition (determined using the mid-upper arm circumference, MUAC), immune activation (as measured by inflammatory markers IL-6, IL-8, CXCL10, CHI3L1, sTNFR1, Cystatin C, granzyme B, and sTREM-1), and mortality. FINDINGS: Of the 1850 children eligible for this secondary analysis, 71 (3.8%) and 145 (11.7%) presented with severe acute malnutrition (SAM, MUAC <115 mm) and moderate malnutrition (MUAC 115 to < 125 mm), respectively. SAM was associated with increased concentrations of CHI3L1, sTNFR1, Cystatin C, and sTREM-1, and decreased concentrations of CXCL10 and granzyme B, even after controlling for age, sex, and disease severity at presentation. There were 77 deaths (4.2%). SAM was associated with a 9.2-fold (95% CI 4.8-46), 17-fold (95% CI 3.9-74), and 13-fold (95% CI 3.5-52) increased odds of death in children with pneumonia, malaria, and diarrheal illness, respectively. Mediation analysis implicated sTREM-1 and CHI3L1 in the effect of SAM on mortality, suggesting that enhanced activation of these inflammatory pathways is associated with the increased mortality in undernourished children with pneumonia and malaria. INTERPRETATION: Collectively, these data highlight systemic inflammation as a common pathway associated with malnutrition and infection that could be targeted to mitigate the burden of acute febrile illness in LMICs. FUNDING: This work was supported in part by the Canadian Institutes of Health Research, and by kind donations from The Tesari Foundation and Kim Kertland. The funders had no role in design, analysis, or reporting of these studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".