Infection with gut parasites correlates with gut microbiome diversity across human populations in Africa
Bibliographic record
Abstract
Soil-transmitted helminths (STH) are common in (sub)tropical regions and primarily affect impoverished populations. These parasites reside in the gut, where they interact with both the microbiota and host immunity. Clinical STH detection is laborious and often not performed within the context of gut microbiome studies. Here, we present a proof-of-concept study assessing whether fecal metagenome data could be used to assess STH infection, and to relate STH infection to microbiome features. We leveraged 310 gut metagenomes obtained from mother-child pairs in two different locations in Gabon: one rural and one semi-urban, and assessed the presence of four STH species (Ascaris lumbricoides, Strongyloides stercoralis, Trichuris trichiura, and Necator americanus) using qPCR. Sequence data were used to characterize the microbiomes and to detect these parasites. Metagenomic read mapping and genome coverage metrics closely matched qPCR detection patterns. Within-location analyses revealed that parasite species richness was associated with microbiome diversity and taxonomic composition, with the strongest associations observed in children from the rural site. Applying this approach to published data from five additional African cohorts identified context-specific parasite-microbiome associations, as well as a modest but reproducible association between microbiome alpha diversity and parasite infection. These findings highlight the potential of shotgun metagenomics for concurrent parasite detection and microbiome profiling across diverse geographic and demographic contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".