The association between types of seafood intake and the risk of type 2 diabetes: a systematic review and meta-analysis of prospective cohort studies
Bibliographic record
Abstract
Background: Seafood is the main source of long-chain n-3 polyunsaturated fatty acids (n-3 PUFAs) with beneficial health effects; however, findings on the association between the consumption of different types of seafood and type 2 diabetes mellitus (T2DM) are conflicting. Our objective was to perform a systematic review and meta-analysis examining the relationship between different types of fish/seafood and the risk of T2DM in adult populations. Methods: A systematic search of PubMed/Medline, Scopus, and Web of Science (ISI) databases was performed for cohort studies, published in English, before 1 September 2017. Multivariate adjusted relative risk (RR) estimates with 95% confidence intervals (CIs) for each category of seafood were pooled to examine the association. Results: Comparing the highest vs. lowest fatty fish intake categories indicated that there was a significant inverse association between the consumption of fatty fish and onset of T2DM (RR: 0.89; 95 % CI: 0.82, 0.98; I2: 0%, P = 0.54). However, after performing sensitivity analysis, we found that eliminating one study resulted in a non-significant association (RR: 0.93; 95 % CI: 0.80, 1.09). There were no significant associations between lean fish (RR: 1.03; 95% CI: 0.87, 1.22, I2: 51.0%, P = 0.08), seafood other than fish (RR: 0.95; 95% CI: 0.83, 1.10, I2: 71.2%, P = 0.002), fish products (RR: 0.96; 95% CI: 0.82, 1.13, I2:0%, P = 0.62), and fried fish (RR: 1.02; 95% CI: 0.83, 1.26, I2:71.2%, P = 0.06) and T2DM risk. Conclusion: The risk of T2DM was not associated with the intake of lean fish, seafood other than fish, and fish products. However, due to the low robustness of findings regarding protective roles of oily fish, more longitudinal studies are needed to clarify this association.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".