Genetic Variation Predicting Lactose Intolerance (LCT -13910C>T), Dairy Intake, 25-Hydroxyvitamin D and Risk of Cardiometabolic Disease
Bibliographic record
Abstract
Background: The LCT-13910C>T variant is associated with lactose intolerance (LI) in >70 ethnic populations. In Canada, the prevalence of the LCT -13910C>T variant is not known. Individuals with LI might avoid dairy, which is a rich source of calcium and vitamin D. Dairy has been associated with increased risk of cardiometabolic diseases, but findings have been equivocal. Objectives: To determine the prevalence of LI risk genotypes in major ethnic groups living in Canada and their association with 25(OH)D levels and biomarkers of cardiometabolic disease, and to determine food predictors of calcium and vitamin D in different LCT genotypes. Methods: A total of 1,495 participants from the Toronto Nutrigenomics and Health (TNH) study were used for the present study. Fasting blood samples were obtained for genotyping, 25(OH)D, biomarkers of cardiometabolic disease, and plasma proteomics. Dairy intake was assessed using a 196-item semi-quantitative food frequency questionnaire. Results: Approximately 32% of Caucasians, 99% of East Asians, 74% of South Asians, and 59% of those with other ethnicities had the CC genotype associated with LI. In Caucasians, compared to the TT genotype, those with the CC genotype had lower dairy intake, and plasma 25(OH)D levels. The CT and CC genotypes were associated with lower calcium intake and increased risk of suboptimal (
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".