Dietary supplementation with fish gelatine modifies nutrient intake and leads to sex-dependent responses in TAG and C-reactive protein levels of insulin-resistant subjects
Bibliographic record
Abstract
Previous studies have shown that fish protein, as well as marine n-3 PUFA, may have beneficial effects on cardiovascular risk profile. The objectives of this study were to investigate the combined effects of fish gelatine (FG) and n-3 PUFA supplementation on (1) energy intake and body weight, (2) lipid profile and (3) inflammatory and CVD markers in free-living insulin-resistant males and females. Subjects were asked to consume, in a crossover study design with two experimental periods of 8 weeks each, an n-3 PUFA supplement and n-3 PUFA supplement plus FG (n-3 PUFA + FG). n-3 PUFA + FG led to an increase in protein intake and a decrease in carbohydrate intake compared with n-3 PUFA (P < 0·02) in males and females. Sex-treatment interactions were observed for TAG (P = 0·03) and highly sensitive C-reactive protein (hsCRP) (P = 0·001) levels. In females, n-3 PUFA reduced plasma TAG by 8 % and n-3 PUFA + FG by 23 %, whereas in males, n-3 PUFA reduced plasma TAG by 25 % and n-3 PUFA + FG by 11 %. n-3 PUFA increased serum hsCRP by 13 % and n-3 PUFA + FG strongly reduced hsCRP by 40 % in males, whereas in females, n-3 PUFA reduced serum hsCRP by 6 % and n-3 PUFA + FG increased hsCRP by 20 %. In conclusion, supplementation with FG may enhance the lipid-lowering effect of marine n-3 PUFA in females and beneficially counteract the effect of n-3 PUFA on serum hsCRP in males. Further studies are needed to identify the sex-dependent mechanisms responsible for the divergent effects of FG on TAG and hsCRP levels in females and males, respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".