A185 A GUT MICROBIOME TRANSITION: THE JOURNEY FROM INDIA TO CANADA
Bibliographic record
Abstract
Abstract Background Young Indian immigrants and Indo-Canadians face a significantly higher risk of inflammatory bowel disease (IBD) in westernized countries. While the etiology of IBD remains unclear, a gut microbiome that is no longer symbiotic with its host is a key player. However, Indians are underrepresented in microbiome research, therefore we cannot accurately assess the role of their gut microbiome in IBD. To understand why Indians are at a greater risk for IBD in Canada, we must first characterize their gut microbiome. Aims This study explores how immigration to Canada impacts the gut microbiome of Indian populations, potentially elevating their risk to IBD. Methods Stool samples from healthy volunteers (ages 17-53) were collected from Indians in India, Indo-Immigrants, Indo-Canadians, Euro-Canadians and Euro-Immigrants. DNA was extracted for 16S sequencing on the Illumina MiSeq platform and shotgun sequencing on the Illumina NovaSeq platform. Dietary data was processed in ESHA. Results Weighted Unifrac revealed distinct clusters of Indian and Indo-Immigrant samples from the westernized cohorts. The most pronounced difference was between Indian and Euro-Canadians with a pseudo-F value of 49.18 (q = 0.00125). Indian versus Indo-Immigrants resulted in a pseudo-F value of 7.707 (q = 0.00125), and Indian versus Indo-Canadian had a pseudo-F value of 15.95 (q = 0.00125). BugBase estimated a significantly higher stress tolerance in the Indian gut, with Proteobacteria driving this phenotypic prediction. Compared to Euro-Canadians, a significantly higher pathogenic potential was predicted in Indians (pFDR= 1.04e-11) and Indo-Immigrants (pFDR= 1.41e-04), driven mainly from Proteobacteria and Bacteroidetes. Multiple comparisons with LDA revealed Prevotella was over 5 times more abundant in Indians residing in India. The gut microbiome in Indians were also enriched with taxa associated with a plant-based diet. Dietary data showed that 59% of Indians do not consume meat, and they also had the lowest consumption (12% caloric intake) of group 4 ultra-processed food (UPF), whereas the highest consumption of UPF was in Indo-Canadians (61% caloric intake) (pbonf ampersand:003C 0.0001). A frequent trend was observed in Indians living in Canada, which was a reduction in key nutrients including soluble fiber, zinc, iron, and folate, some of which are commonly found in plant-based foods. Conclusions Overall findings reveal that a loss of Prevotella abundance was observed in Indo-Immigrants and Indo-Canadians, which was associated with a reduction in key nutrients that are common to a plant-based diet, indicating a transition away from their traditional diet. Their dietary changes may be a driver to the displacement of Prevotella and other taxa commonly found in the Indian gut microbiome. Funding Agencies NSERC, Killam
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".