Gut Microbiota Differences with Age and among North American, European, and Australian Regions
Bibliographic record
Abstract
The study analyzed the gut microbiota composition from over 3600 ambulatory donor samples undergoing routine medical evaluation from the USA, Canada, UK, Denmark, Australia, New Zealand, and other regions, spanning ages from 2 to over 70 years using the GA-map® multiplex polymerase chain reaction assay. The research aimed to identify differences in dysbiosis index (DI) score, diversity, and gastrointestinal (GI) microbiota across age categories and regions. Bacterial DNA was extracted, followed by amplification, hybridization, and detection of target organisms. The DI score showed an inverse but nonsignificant change with age. Significantly reduced DI scores were observed in adult cohorts from the UK and Denmark compared to North America and Australia, likely due to reduced bacterial diversity. Nonsignificant reduction in bacterial diversity was observed in ages over 70 years. Bacterial species such as Actinobacteria, Bifidobacterium spp., Dialister invisus and Megasphaera micronuciformis, and Streptococcus salivarius spp. were more abundant in younger cohorts. Older donors showed increased abundance of Proteobacteria. In European and Australian cohorts, lower levels of Bacteroides spp. and Prevotella spp. were detected relative to the US among other differences. This study demonstrated significant age- and region-specific differences in gut microbiota composition in an ambulatory population free from GI pathogens, inflammation typically associated with irritable bowel syndrome or inflammatory bowel disease, or active diarrhea.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".