Back

Fecal pH as a marker of stunting among children hospitalized for diarrhea and other non-diarrheal pathologies

Hossain, M. S.; Hoque, M. M.; Mahfuz, M.; Ahmed, T.

2025-08-29 public and global health
10.1101/2025.08.26.25334519 medRxiv
Show abstract

BackgroundFecal pH is a simple, non-invasive diagnostic tool used for initial screening of certain gastrointestinal (GI) diseases. Increased fecal pH indicates reduction of beneficial microbiota in the gut, which has emerged as a key factor contributing to stunting. The purpose of this study was to investigate the association of fecal pH with stunting in hospitalized children. MethodsThis cross-sectional study was conducted on 200 children aged 06-24 months getting admitted in icddr,b Dhaka Hospital with diarrhea and Dhaka Shishu Hospital for other non-diarrheal pathologies. Length-for-age Z scores (LAZ) was measured and data on factors affecting linear growth was recorded. Fecal pH was measured on freshly collected stool samples following standard procedure. Multivariate linear regression was performed to explore relationship between fecal pH and LAZ scores. ResultsThe mean fecal pH of diarrheal and non-diarrheal children was 5.54{+/-}0.98 and 5.95{+/-}0.76, respectively. After inclusion of factors affecting linear growth into the regression model, a statistically significant inverse association between fecal pH and LAZ scores was observed in non-diarrheal children (p<0.01). However, such association did not apply for diarrheal children. ConclusionIncreased fecal pH in non-diarrheal children was found to have significant association with stunted growth, making fecal pH a possible indirect determinant of childhood stunting. However, no such associations were observed in case of diarrheal children.

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.