Intestinal microbiota of indigenous peoples of the North: a systematic review
Bibliographic record
Abstract
BACKGROUND: The microbiota of the indigenous population of the North may play a pivotal role in the development of the polar (northern) type of metabolism supporting increased energy demands and maintaining body homeostasis in extreme cold climates. However, this area remains relatively understudied. Sequencing of bacterial 16S rRNA allows for establishing the full taxonomic composition of microbial communities, thereby facilitating novel insights into the interplay between microbiota, environmental conditions, and the formation of health in different populations. AIM: The aim of this review is to evaluate the factors and principles of microbiota development in extreme climatic conditions and its potential impact on health in the indigenous peoples of the North. MATERIALS AND METHODS: A systematic review was conducted based on the 2020 PRISMA guidelines. Original studies were searched for across the PubMed, eLibrary, and Google Scholar databases using Russian keywords “микробиота кишечника” (“intestinal microbiota”), “Север” (“North”), and English “gut microbiome,” “16S rRNA,” and “Arctic.” RESULTS: Having filtered the results of the primary selection of articles in accordance with the search criteria, five publications were identified that presented the results of intestinal microbiota 16S rRNA studies in Canadian Inuit, Alaska Natives, and Yakuts of the Sakha Republic (Yakutia). The intestinal microbiota of native residents of the North differs is characterized by inter- and intra-population variability in the diversity and taxonomic composition. Despite similar climatic conditions and dietary patterns, microbiota composition of different Northern populations reflects differences in traditional activities, dietary habits, and surrounding animals. CONCLUSION: Results of available studies are insufficient to form a comprehensive understanding of the northern microbiome and its role in maintaining the health of the indigenous peoples of the North. Nevertheless, the composition of the intestinal microbiota of the northern populations is shown to be diverse and favorable for the metabolic health; further studies are required to identify the mechanisms of the metabolic health formation in cold climate conditions.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".