The impact of dairy consumption on circulating cholesterol levels is modulated by common single nucleotide polymorphisms in cholesterol synthesis‐ and transport‐related genes (1038.4)
Bibliographic record
Abstract
Dairy consumption impacts circulating cholesterol levels, a traditional risk marker for cardiovascular health, however, whether genetics play a role in response to dairy has not been established. Our objective was to evaluate 24 candidate SNPs within 13 cholesterol synthesis‐ and transport‐associated genes in relation to the cholesterol metabolism response to an intake of conventional and commonly‐consumed dairy products in Canada. Normolipidemic adults (n=101) consumed 3 servings/d of dairy (1% fat milk, 1.5% fat yogurt, and 34% fat cheese) or energy‐matched control products for 28 d using a multicentre, randomized, free‐living, crossover design. Relative to the control diet, dairy intake was associated with an increase in plasma levels of total cholesterol (TC) and LDL‐C only in carriers of the cholesterol transport gene ABCG5 SNP rs6720173‐G/G (4.86±0.20 vs. 5.05±0.20 mmol/L, n=72, p=0.008) and (2.86±0.14 vs. 3.03±0.14 mmol/L, p=0.002), cholesterol synthesis gene SREBF2 SNP rs2228314‐G/G (4.98±0.16 vs. 5.21±0.16 mmol/L, n=50, p=0.02) and (3.06±0.15 vs. 3.24±0.15 mmol/L, p=0.03), and bile acid synthesis gene CYP7A1 SNP rs3808607‐G/T (5.00±0.16 vs. 5.21±0.16 mmol/L, n=53, p=0.04) and (2.93±0.14 vs. 3.11±0.14 mmol/L, p=0.01) genotypes, respectively. These findings accordingly suggest the existence of a gene‐diet interaction modulating the impact of dairy intake on circulating cholesterol levels. Grant Funding Source : Supported by Dairy Farmers of Canada (DFC) and Agriculture and Agri‐Food Canada (AAFC)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".