Impact of Foods and Dietary Supplements Containing Hydroxycinnamic Acids on Cardiometabolic Biomarkers: A Systematic Review to Explore Inter-Individual Variability
Bibliographic record
Abstract
Plant-based diets rich in bioactive compounds such as polyphenols have been shown to positively modulate the risk of cardiometabolic (CM) diseases. The inter-individual variability in the response to these bioactives may affect the findings. This systematic review aimed to summarize findings from existing randomized clinical trials (RCTs) evaluating the effect of hydroxycinnamic acids (HCAs) on markers of CM health in humans. Literature searches were performed in PubMed and the Web of Science. RCTs on acute and chronic supplementation of HCA-rich foods/extracts on CM biomarkers were included. Forty-four RCTs (21 acute and 23 chronic) met inclusion criteria. Comparisons were made between RCTs, including assessments based on population health status. Of the 44 RCTs, only seven performed analyses on a factor exploring inter-individual response to HCA consumption. Results demonstrated that health status is a potentially important effect modifier as RCTs with higher baseline cholesterol, blood pressure and glycaemia demonstrated greater overall effectiveness, which was also found in studies where specific subgroup analyses were performed. Thus, the effect of HCAs on CM risk factors may be greater in individuals at higher CM risk, although future studies in these populations are needed, including those on other potential determinants of inter-individual variability. PROSPERO, registration number CRD42016050790.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".