Dairy product consumption is associated with a lowering of linoleic acid within serum TAG in adolescent females with overweight or obesity: a secondary analysis
Bibliographic record
Abstract
Dairy fat is rich in SFA such as palmitic acid (16:0) but low in linoleic acid (18:2n-6). The natural carbon 13 enrichment (δ13C) of 16:0 is higher in dairy fat than in most of the food supply. In adults, serum levels of pentadecanoic acid (15:0) and heptadecanoic acid (17:0) are recognised as biomarkers of dairy intake. In adolescents, no study has evaluated serum fatty acid levels or δ13C in response to chronic dairy consumption. The objectives of this study were to evaluate whether increased dairy product consumption can modulate (1) serum fatty acid levels and (2) 16:0 δ13C in adolescents with overweight/obesity who followed a 12-week weight management programme. This secondary analysis of a randomised control trial included two groups of adolescent females: recommended dairy (RDa; n 23) and low dairy (LDa; n 23). The RDa group was given 4 servings/d of dairy products while the LDa group maintained dairy intakes at ≤ 2 servings/d. Blood was sampled before and after the intervention. Lipids were extracted and separated, and fatty acids were quantified by GC. Isotope ratio MS was used to assess 16:0 δ13C. There were no group differences on serum changes of 15:0 or 17:0. Within TAG, 18:2n-6 was lowered by 7·4 % only in the RDa group (P = 0·040). The difference in delta 16:0 δ13C between the LDa and RDa groups did not reach statistical significance (P = 0·070). Reductions in serum 18:2n-6 by dairy consumption could have positive health implications, but more studies are needed to confirm this assertion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".