The Inuit gut microbiome is dynamic over time and shaped by traditional foods
Bibliographic record
Abstract
BACKGROUND: The human gut microbiome represents a diverse microbial community that varies across individuals and populations, and is influenced by factors such as host genetics and lifestyle. Diet is a major force shaping the gut microbiome, and the effects of dietary choices on microbiome composition are well documented. However, it remains poorly known how natural temporal variation in diet can affect the microbiome. The traditional Inuit diet is primarily based on animal products, which are thought to vary seasonally according to prey availability. We previously investigated the Inuit gut microbiome sampled at a single time point, and found no detectable differences in overall microbiome community composition attributable to the traditional Inuit diet. RESULTS: To determine whether seasonal changes in the Inuit diet might induce more pronounced changes in the microbiome, we collected stool and toilet paper samples, and dietary information from Inuit volunteers living in Resolute Bay (Nunavut, Canada), and compared them to individuals of European descent living in Montréal (Québec, Canada) consuming a typical Western diet. We sequenced the V4 region of the 16S rRNA gene to characterize microbiome diversity and composition, and compared samples collected with toilet paper or from stool. Our results show that these sampling methods provide similar, but non-identical portraits of the microbiome. Based on toilet paper samples, we found that much of the variation in microbiome community composition could be explained by individual identity (45-61% of variation explained, depending on the beta diversity metric used), with small but significant variation (3-5%) explained by sex or geography (Nunavut or Montréal). In contrast with our previous study at one time point, sampling over the course of a year revealed that diet explains 11% of variation in community composition across all participants, and 17% of variation specifically among Nunavut participants. However, we observed no clear seasonal shifts in the microbiomes of participants from either Nunavut or Montréal. Within-individual microbial diversity fluctuated more over time in Nunavut than in Montréal, consistent with a more variable and highly individualized diet in Nunavut. CONCLUSIONS: Together, these results shows that the traditional Inuit diet and lifestyle has an impact on the composition, diversity and stability of the Inuit gut microbiome, even if the seasonality of the diet is less pronounced than expected, perhaps due to an increasingly westernized diet.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".