Nutritional benefits of sourdoughs: A systematic review
Bibliographic record
Abstract
Food fermentation using sourdough-i.e., consortia of lactic bacteria and yeasts-is increasingly considered among the public as a natural transformation yielding nutritional benefits; however, it is unclear whether its alleged properties are validated by science. The aim of this study was to systematically review the clinical evidence related to the effect of sourdough bread on health. Bibliographic searches were performed in 2 different databases (The Lens and PubMed) up to February 2022. Eligible studies were randomized controlled trials involving adults, healthy or not, given any type of sourdough bread compared with those given any type of yeast bread. A total of 573 articles were retrieved and investigated, of which 25 clinical trials met the inclusion criteria. The 25 clinical trials included a total of 542 individuals. The main outcomes investigated in the retrieved studies were glucose response (N = 15), appetite (N = 3), gastrointestinal markers (N = 5), and cardiovascular markers (N = 2). Overall, it is currently difficult to establish a clear consensus with regards to the beneficial effects of sourdough per se on health when compared with other types of bread because a variety of factors, such as the microbial composition of sourdough, fermentation parameters, cereals, and flour types potentially influence the nutritional properties of bread. Nonetheless, in studies using specific strains and fermentation conditions, significant improvements were observed in parameters related to glycemic response, satiety, or gastrointestinal comfort after bread ingestion. The reviewed data suggest that sourdough has great potential to produce a variety of functional foods; however, its complex and dynamic ecosystem requires further standardization to conclude its clinical health benefits.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".