MedicinemedRxiv
Heuristic editor, no API keyVerdict: NotableDoes gut microbial richness lost with industrialisation matter for infection? A two-direction test in Gambian toddlers
Background and objectives: Industrialisation is associated with lower gut microbial richness.
Key numbers
- 95% CI 0.51-0.93
- 113 days
Caveats
- Preprint; not yet peer reviewed.
VerdictWorth a reader's time today.
Abstract
Background and objectives: Industrialisation is associated with lower gut microbial richness. The ecological insurance hypothesis proposes that richer communities buffer disturbance via overlapping functions, possibly affecting infection susceptibility. We state predictions, audit the evidence, and test the richness-infection relationship in Gambian children. Methodology: We analysed 452 children aged 6-35 months with Day 85 stool 16S profiles and dated infections over 113 days. Diversity used Hill numbers (q = 0, 1, 2); redundancy was inferred from reference-genome gene content. We examined associations with illness during follow-up and infections after Day 85, adjusting for earlier illness. Results: Richness was lower in children with any infection (93.9 vs 103.1; d = 0.36; adjusted odds ratio 0.989 per species, p = 0.019); abundance-weighted diversity was not. Most illness preceded sampling (295 of 315); the difference lay in common and intermediate-prevalence species. Richness was lowest around recent illness. Higher richness was associated with fewer later infections (rate ratio 0.69 per SD, 95% CI 0.51-0.93; q = 0.11). Redundancy correlated with richness at q = 0 (rho = 0.93); at q = 1-2 estimates indicated lower infection rates but none survived correction (smallest FDR q = 0.14). Conclusions and implications: Richness and infection were associated in both directions: children who had been ill had lower richness, and greater Day 85 diversity preceded fewer infections. Redundancy was directionally aligned with reduced infection at higher diversity orders, but FDR control and collinearity limit inference. Cohorts sampled before illness, with shotgun data, are needed.
The editor's rubric
| Dimension | Level | Weight | What that level means |
|---|---|---|---|
| Leverage | ███░░ 3 | 10% | A method or resource many groups across the field will adopt within a year. |
| Magnitude | ███░░ 3 | 20% | Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem. |
| Evidence | ████░ 4 | 32% | Strong: large scale, preregistered, independently replicated, or a well-powered randomized trial. |
| Novelty | ██░░░ 2 | 8% | A new combination of known ideas. |
| Trajectory | ██░░░ 2 | 5% | Some room to improve with obvious engineering. |
| Stakes | ██░░░ 2 | 25% | Benefits a professional community (practitioners, clinicians, engineers). |
Editor’s rationale
Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: breadth (many tasks); gains (versus baseline, efficacy); verification (confidence interval, p-value, multiple benchmarks); stakes (major disease).
How the score was computed
- Merit
- 5.9 / 10
- Adjusted merit
- 4.8 / 10
- Attention
- 0%
- Freshness
- 92%
- Citations0 (reference 20, via semantic-scholar, Oct 8, 2026, 06:17 UTC)