Diet changes due to urbanization in South Africa are linked to microbiome and metabolome signatures of Westernization and colorectal cancer
Bibliographic record
Abstract
Transition from traditional high-fiber to Western diets in urbanizing communities of Sub-Saharan Africa is associated with increased risk of non-communicable diseases (NCD), exemplified by colorectal cancer (CRC) risk. To investigate how urbanization gives rise to microbial patterns that may be amenable by dietary intervention, we analyzed diet intake, fecal 16 S bacteriome, virome, and metabolome in a cross-sectional study in healthy rural and urban Xhosa people (South Africa). Urban Xhosa individuals had higher intakes of energy (urban: 3,578 ± 455; rural: 2,185 ± 179 kcal/d), fat and animal protein. This was associated with lower fecal bacteriome diversity and a shift from genera favoring degradation of complex carbohydrates (e.g., Prevotella) to taxa previously shown to be associated with bile acid metabolism and CRC. Urban Xhosa individuals had higher fecal levels of deoxycholic acid, shown to be associated with higher CRC risk, but similar short-chain fatty acid concentrations compared with rural individuals. Fecal virome composition was associated with distinct gut bacterial communities across urbanization, characterized by different dominant host bacteria (urban: Bacteriodota; rural: unassigned taxa) and variable correlation with fecal metabolites and dietary nutrients. Food and skin microbiota samples showed compositional differences along the urbanization gradient. Rural-urban dietary transition in South Africa is linked to major changes in the gut microbiome and metabolome. Further studies are needed to prove cause and identify whether restoration of specific components of the traditional diet will arrest the accelerating rise in NCDs in Sub-Saharan Africa.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".