Helicobacter pylori Stool Antigen Positivity and Associated Factors Among Children Attending a Rural School in Pasa, Ecuador: A Cross-Sectional Study with Exploratory Single-Sample Shotgun Metagenomics
Mary Gissela Rosas Morales, Santiago Xavier Mafla Andrade, Lennys Berutti-SuarezHelicobacter pylori infection is commonly acquired in childhood and remains highly prevalent in low- and middle-income settings. Data from rural Ecuadorian schoolchildren are scarce. Methods: We conducted an analytical cross-sectional study among children aged 5–12 years enrolled in a rural elementary school in Pasa (Tungurahua, Ecuador). Stool samples (n = 80; from a planned reference population of 240, voluntary participation) were tested for H. pylori antigen using a rapid immunochromatographic assay. A structured questionnaire captured sociodemographic characteristics, water and sanitation indicators, hygiene practices, and gastrointestinal symptoms. Associations were evaluated using contingency tables and exact tests when appropriate; effect sizes were expressed as odds ratios (ORs) with 95% confidence intervals (CIs). Shotgun metagenomic sequencing was performed on one antigen-positive stool sample (M1805) as an exploratory sub-study. Results: H. pylori stool antigen positivity was 46.3% (37/80; 95% CI 35.7–57.1; Wilson). Several exposures showed complete or near-complete separation (e.g., non-potable water and lack of handwashing before meals), yielding very large unadjusted associations. Exploratory metagenomics generated 31.7 million paired-end reads (9.49 Gb; 93.9% > Q30; GC 44.1%) and reconstructed 45 MAGs dominated by Bacillota (n = 21) and Bacteroidota (n = 13). Conclusions: Water and hygiene exposures showed exploratory unadjusted associations with positivity in this voluntary convenience sample; estimates were unstable for variables with zero cells and should not be interpreted as causal. A low-abundance H. pylori read-level signal (0.005%) was consistent with the positive antigen result. Larger studies with probabilistic sampling and multivariable penalized models are needed. Shotgun metagenomics supports future strain-level investigations.