Genomic adaptations to cereal‐based diets contribute to mitigate metabolic risk in some human populations of East Asian ancestry
Bibliographic record
Abstract
Adoption of diets based on some cereals, especially on rice, signified an iconic change in nutritional habits for many Asian populations and a relevant challenge for their capability to maintain glucose homeostasis. Indeed, rice shows the highest carbohydrates content and glycemic index among the domesticated cereals and its usual ingestion represents a potential risk factor for developing insulin resistance and related metabolic diseases. Nevertheless, type 2 diabetes and obesity epidemiological patterns differ among Asian populations that rely on rice as a staple food, with higher diabetes prevalence and increased levels of central adiposity observed in people of South Asian ancestry rather than in East Asians. This may be at least partly due to the fact that populations from East Asian regions where wild rice or other cereals such as millet have been already consumed before their cultivation and/or were early domesticated have relied on these nutritional resources for a period long enough to have possibly evolved biological adaptations that counteract their detrimental side effects. To test such a hypothesis, we compared adaptive evolution of these populations with that of control groups from regions where the adoption of cereal-based diets occurred many thousand years later and which were identified from a genome-wide dataset including 2,379 individuals from 124 East Asian and South Asian populations. This revealed selective sweeps and polygenic adaptive mechanisms affecting functional pathways involved in fatty acids metabolism, cholesterol/triglycerides biosynthesis from carbohydrates, regulation of glucose homeostasis, and production of retinoic acid in Chinese Han and Tujia ethnic groups, as well as in people of Korean and Japanese ancestry. Accordingly, long-standing rice- and/or millet-based diets have possibly contributed to trigger the evolution of such biological adaptations, which might represent one of the factors that play a role in mitigating the metabolic risk of these East Asian populations.
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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".