Seafood intake and the development of obesity, insulin resistance and type 2 diabetes
Bibliographic record
Abstract
We provide an overview of studies on seafood intake in relation to obesity, insulin resistance and type 2 diabetes. Overweight and obesity development is for most individuals the result of years of positive energy balance. Evidence from intervention trials and animal studies suggests that frequent intake of lean seafood, as compared with intake of terrestrial meats, reduces energy intake by 4-9 %, sufficient to prevent a positive energy balance and obesity. At equal energy intake, lean seafood reduces fasting and postprandial risk markers of insulin resistance, and improves insulin sensitivity in insulin-resistant adults. Energy restriction combined with intake of lean and fatty seafood seems to increase weight loss. Marine n-3 PUFA are probably of importance through n-3 PUFA-derived lipid mediators such as endocannabinoids and oxylipins, but other constituents of seafood such as the fish protein per se, trace elements or vitamins also seem to play a largely neglected role. A high intake of fatty seafood increases circulating levels of the insulin-sensitising hormone adiponectin. As compared with a high meat intake, high intake of seafood has been reported to reduce plasma levels of the hepatic acute-phase protein C-reactive protein level in some, but not all studies. More studies are needed to confirm the dietary effects on energy intake, obesity and insulin resistance. Future studies should be designed to elucidate the potential contribution of trace elements, vitamins and undesirables present in seafood, and we argue that stratification into responders and non-responders in randomised controlled trials may improve the understanding of health effects from intake of seafood.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".