Ancient Grains as Functional Foods: Integrating Traditional Knowledge with Contemporary Nutritional Science
Bibliographic record
Abstract
Ancient grains, including wild rice, millet, fonio, teff, quinoa, amaranth, and sorghum, are re-emerging as vital components of modern diets due to their dense nutritional profiles and diverse health-promoting bioactive compounds. Rich in high-quality proteins, dietary fiber, essential micronutrients, and a broad spectrum of bioactive compounds such as phenolic acids, flavonoids, carotenoids, phytosterols, and betalains, these grains exhibit antioxidant, anti-inflammatory, antidiabetic, cardioprotective, and immunomodulatory properties. Their health-promoting effects are underpinned by multiple interconnected mechanisms, including the reduction in oxidative stress, modulation of inflammatory pathways, regulation of glucose and lipid metabolism, support for mitochondrial function, and enhancement of gut microbiota composition. This review provides a comprehensive synthesis of the essential nutrients, phytochemicals, and functional properties of ancient grains, with particular emphasis on the nutritional and molecular mechanisms through which they contribute to the prevention and management of chronic diseases such as cardiovascular disease, type 2 diabetes, obesity, and metabolic syndrome. Additionally, it highlights the growing application of ancient grains in functional foods and nutrition-sensitive dietary strategies, alongside the technological, agronomic, and consumer-related challenges limiting their broader adoption. Future research priorities include well-designed human clinical trials, standardization of compositional data, innovations in processing for nutrient retention, and sustainable cultivation to fully harness the health, environmental, and cultural benefits of ancient grains within global food systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".