Dairy Consumption, <i>LCT‐13910C>T</i> Genotype and the Plasma Proteome
Bibliographic record
Abstract
Background The LCT ‐13910C>T genotype, associated with lactose intolerance (LI), has been associated with differences in the microbiome, in particular, differences in Bifidobacterium. This may be due to the involvement of the colonic microbiota in the metabolism of lactose in those with LI. Metabolites of the gut microbiota have been linked to the risk of various health conditions. The objective of this study was to examine the association between dairy consumption and a panel of 54 high abundance plasma proteins by LCT‐ 13910C>T genotypes in a population of young adults. Methods Fasting blood samples were drawn from non‐smoking Caucasians aged 20–29 years (n=508) from the Toronto Nutrigenomics and Health Study, for genotyping of the ‐13910C>T variant in the LCT gene and to measure 54‐plasma proteins by mass spectrometry. Dairy intake was assessed using a one‐month 196‐items food frequency questionnaire. Multiple regressions models were used to determine the association between dairy consumption and 54‐plasma proteome in the CC, CT, and TT genotype of the LCT ‐13910 variant. Results In the CC genotype, associated with lactose intolerance, positive associations were observed between total dairy intake and ten positive acute phase proteins, as well as three negative acute phase proteins ( p < 0.05) . In the CT genotype, associated with mild LI, positive associations were observed between total dairy intake and two negative acute phase proteins, and one protein of lipolysis cascade ( p < 0.05) . No association was observed between total dairy intake and plasma proteins in the TT genotype, which does not have LI. Conclusion The association between dairy consumption and disease risk might be influenced by genotypes of LCT gene. This may be due, in part, to the differences in the colonic bacterial response to lactose metabolism between LCT genotypes. Support or Funding Information King Abdulaziz University
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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".