The effect of algae supplementation on lipid profile and blood pressure in adults: A systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
• This systematic review and meta-analysis included 77 RCTs with 3686 participants. • Algae supplementation significantly reduced TG, TC, and LDL levels in adults. • Diastolic blood pressure significantly decreased with algae supplementation. • These findings suggest that algae supplementation can improve cardiovascular health markers. To assess the effects of algae supplementation on lipid profiles and blood pressure in adults, we conducted a systematic search in PubMed, Scopus, Web of Science, and Cochrane Library for relevant randomized controlled trials (RCTs). A total of 77 RCTs with 3686 participants were included. Algae supplementation significantly reduced triglycerides (TG) (WMD: −7.99 mg/dL, 95 % CI: −12.71, −3.26), total cholesterol (TC) (WMD: −11.01 mg/dL, 95 % CI: −14.26, −7.76), and low-density lipoprotein (LDL) (WMD: −10.17 mg/dL, 95 % CI: −13.12, −7.22) levels, while increasing high-density lipoprotein (HDL) (WMD: 1.66 mg/dL, 95 % CI: 0.73, 2.59). No significant changes were observed in the LDL/HDL ratio and systolic blood pressure (SBP), but diastolic blood pressure (DBP) significantly decreased (WMD: −1.71 mmHg, 95 % CI: −2.72, −0.71). These findings suggest that algae supplementation can improve cardiovascular health markers, although further research is needed to address the observed variability.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".