Effects of collagen peptide supplementation on cardiovascular markers: a systematic review and meta-analysis of randomised, placebo-controlled trials
Bibliographic record
Abstract
Abstract Previous studies have advocated that collagen peptide supplementation (CPS) can positively affect cardiovascular health. However, the widespread impact of CPS on CVD-related markers is not fully resolved. Consequently, the current systematic review and meta-analysis aimed to assess the efficacy of CPS on CVD-related markers. A systematic search in the Scopus, PubMed and ISI Web of Science databases were completed to identify relevant randomised, placebo-controlled trials (RCT) published up to November 2021. Mean Differences were pooled using a random-effects model, while publication bias, sensitivity analyses and heterogeneity were assessed using previously validated methods. Twelve RCT, comprising of a total of eleven measured markers, were selected for the quantitative analysis. Pooled data revealed that CPS significantly decreased fat mass (–1·21 kg; 95 % CI: −2·13, −0·29; I2 = 0·0 %; P = 0·010) and increased fat-free mass, based on body mass percentage (1·49 %; 95 % CI: 0·57, 2·42; I2 = 0·0 %; P = 0·002). Moreover, collagen peptide supplementation led to a significant decrease in serum LDL (–4·09 mg/dl; 95 % CI: −8·13, −0·04; I2 = 93·4 %; P = 0·048) and systolic blood pressure (SBP) (–5·04 mmHg; 95 % CI: −9·22, −0·85; I2 = 98·9 %; P = 0·018). Our analysis also indicated that CPS did not affect glycemic markers. Our outcomes indicate that CPS reduces fat mass, LDL and SBP while increasing fat-free mass. Future investigations with longer CPS duration are needed to expand on our results.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.035 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".