Impact of adopting a vegan diet or an olestra supplementation on plasma organochlorine concentrations: results from two pilot studies
Bibliographic record
Abstract
The aim of these studies was to evaluate the potential of some nutritional approaches to prevent or reduce the body load of organochlorines (OC) in humans. Study 1 compared plasma OC concentrations between vegans and omnivores while study 2 verified if the dietary fat substitute olestra could prevent the increase in OC concentrations that is generally observed in response to a weight-reducing programme. In study 1, nine vegans and fifteen omnivores were recruited and the concentrations of twenty-six OC (beta-hexachlorocyclohexane (beta-HCH), p, p'-dichlorodiphenyldichloroethane (p, p'-DDE), p, p'-dichlorodiphenyltrichloroethane (p, p'-DDT), hexachlorobenzene, mirex, aldrin, alpha-chlordane, gamma-chlordane, oxychlordane, cis-nonachlor, trans-nonachlor, polychlorinated biphenyl (PCB) nos. 28, 52, 99, 101, 105, 118, 128, 138, 153, 156, 170, 180, 183 and 187, and aroclor 1260) were determined. In study 2, the concentrations of these twenty-six OC were measured before and after weight loss over 3 months in thirty-seven obese men assigned to one of the following treatments: standard group (33 % fat diet; n 13), fat-reduced group (25 % fat diet; n 14) or fat-substituted group (1/3 of dietary lipids substituted by olestra; n 10). In study 1, plasma concentrations of five OC compounds (aroclor 1260 and PCB 99, PCB 138, PCB 153 and PCB 180) were significantly lower in vegans compared with omnivores. In study 2, beta-HCH was the only OC which decreased in the fat-substituted group while increasing in the other two groups (P = 0.045). In conclusion, there was a trend toward lesser contamination in vegans than in omnivores, and olestra had a favourable influence on beta-HCH but did not prevent plasma hyperconcentration of the other OC during ongoing weight loss.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".