Differential Impact of Lactose/Lactase Phenotype on Colonic Microflora
Bibliographic record
Abstract
BACKGROUND: The ability to digest lactose divides the world's population into two phenotypes that may be risk variability markers for several diseases. Prebiotic effects likely favour lactose maldigesters who experience lactose spilling into their colon. OBJECTIVE: To evaluate the effects of fixed-dose lactose solutions on fecal bifidobacteria and lactobacilli in digesters and maldigesters, and to determine whether the concept of a difference in ability to digest lactose is supported. METHODS: A four-week study was performed in 23 lactose maldigesters and 18 digesters. Following two weeks of dairy food withdrawal, subjects ingested 25 g of lactose twice a day for two weeks. Stool bifidobacteria and lactobacilli counts pre- and postintervention were measured as the primary outcome. For secondary outcomes, total anaerobes, Enterobacteriaceae, beta-galactosidase and N-acetyl-beta-D-glucosaminidase activity in stool, as well as breath hydrogen and symptoms following lactose challenge tests, were measured. RESULTS: Lactose maldigesters had a mean change difference (0.72 log10 colony forming unitsg stool; P=0.04) in bifidobacteria counts compared with lactose digesters. Lactobacilli counts were increased, but not significantly. Nevertheless, reduced breath hydrogen after lactose ingestion correlated with lactobacilli (r=-0.5; P<0.001). Reduced total breath hydrogen and symptom scorestogether, with a rise in fecal enzymes after intervention, were appropriate, but not significant. CONCLUSIONS: Despite failure to achieve full colonic adaptation, the present study provided evidence for a differential impact of lactose on microflora depending on genetic lactase status. A prebiotic effect was evident in lactose maldigesters but not in lactose digesters. This may play a role in modifying the mechanisms of certain disease risks related to dairy food consumption between the two phenotypes.
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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 | 0.000 |
| 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".