Adaptation to Lactose in Lactase Non Persistent People: Effects on Intolerance and the Relationship between Dairy Food Consumption and Evalution of Diseases
Bibliographic record
Abstract
Dairy foods contain complex nutrients which interact with the host. Yet, evolution of lactase persistence has divided the human species into those that can or cannot digest lactose in adulthood. Such a ubiquitous trait has differential effects on humanity. The literature is reviewed to explore how the divide affects lactose handling by lactase non persistent persons. There are two basic differences in digesters. Firstly, maldigesters consume less dairy foods, and secondly, excess lactose is digested by colonic microflora. Lactose intolerance in maldigesters may occur with random lactose ingestion. However, lactose intolerance without maldigestion tends to detract from gaining a clear understanding of the mechanisms of symptoms formation and leads to confusion with regards to dairy food consumption. The main consequence of intolerance is withholding dairy foods. However, regular dairy food consumption by lactase non persistent people could lead to colonic adaptation by the microbiome. This process may mimic a prebiotic effect and allows lactase non persistent people to consume more dairy foods enhancing a favorable microbiome. This process then could lead to alterations in outcome of diseases in response to dairy foods in lactose maldigesters. The evidence that lactose is a selective human prebiotic is reviewed and current links between dairy foods and some diseases are discussed within this context. Colonic adaptation has not been adequately studied, especially with modern microbiological techniques.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".