Cheese can reduce indexes that estimate fatty acid desaturation. Results from the Oslo Health Study and from experiments with human hepatoma cells
Bibliographic record
Abstract
Previously, cheese intake was shown to be inversely related to serum triglycerides, raising the possibility that cheese might inhibit triglyceride synthesis, which is governed by fatty acid desaturases. Therefore, analyses were done to study whether cheese intake was associated with indexes that reflect fatty acid desaturation in 121 healthy ethnic Norwegians aged 40-45 years, a subsample from the Oslo Health Study (N = 18 777). Experiments with human hepatoma cells (HepG2) were done to clarify whether cheese might have a causal effect on desaturases. Fatty acid distribution in lipids of human sera and HepG2 cells was determined by gas chromatography. Δ9-Desaturase was estimated by the (16:1,n-7)/(16:0) and (18:1,n-9)/(18:0) ratios, abbreviated ds9_1 and ds9_2, and Δ5-desaturase (ds5) by the (20:4,n-6)/(18:2,n-6) ratio. Correlation, ANOVA, and multiple linear regression models were used to study associations. Oslo Health Study: Subjects with cheese intake >4-6 times per week had 33% lower ds9_1 and 16% lower ds5 than subjects with intake ≤ 4-6 times per week. The cheese intake vs. ds5 association prevailed when adjusting for sex, time since last meal, fatty fish, vegetables, fruit-berries, fruit juice, cod liver oil, coffee, alcohol, body mass index, physical activity, length of education, and smoking. HepG2 cells: An ethanol extract of Jarlsberg cheese lowered the desaturase indexes. Inhibition of ds9_1 increased with increasing amount cheese extract added. Thus, cheese may contain inhibitors of desaturases, thereby providing an explanation for the previously reported negative association between cheese intake and triglycerides.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".