Lipid-modifying effects of lean fish and fish-derived protein consumption in humans: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
CONTEXT: Consumption of lean fish and fish-derived proteins were effective for improving lipid profiles in published studies; however, evidence remains inconclusive. OBJECTIVE: To evaluate the effectiveness of lean fish or fish-derived protein on serum/plasma lipid and lipoprotein levels by conducting a systematic review of the literature and meta-analysis of available randomized controlled trials (RCTs). DATA SOURCES: Medline (Ovid), Scopus, CINAHL, and Food and Nutritional Sciences databases were searched from the start date of each database to September 2019 to identify RCTs determining the effect of lean fish on lipid profile. STUDY SELECTION INCLUDED: RCTs investigated lean fish and fish-derived proteins intake and determined at least 1 major lipid or lipoprotein measurement. DATA EXTRACTION: Two reviewers independently evaluated 1217 studies against the inclusion and exclusion criteria. Relevant studies were assessed for risks of bias, and random-effects meta-analysis was conducted to generate average estimates of effect. RESULTS: A total of 24 studies met the inclusion criteria. Meta-analysis of data from 18 to 21 eligible crossover and parallel-design RCTs with a total of 1392 to 1456 participants found triacylglycerol-lowering effects for lean fish compared with no fish consumption. Lean fish intake showed no significant differences related to total cholesterol or lipoprotein levels. Subanalysis showed that parallel-group RCTs tended to find greater reduction effects on circulating triacylglycerol than did crossover RCTs. CONCLUSION: Additional better-designed, longer, and larger RCTs, particularly crossover RCTs, are needed to clarify the impact of lean fish and fish proteins on the serum/plasma lipid profile. Findings from such studies would enable practitioners to provide their patients evidence-based recommendations to meet the American Heart Association guidelines for fish consumption to reduce cardiovascular disease risk.
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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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.034 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".