Does supplementation with pine bark extract improve cardiometabolic risk factors? A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Supplementation with pine bark extract (PBE) may improve risk factors associated with cardiometabolic syndrome (CMS). The effects of PBE supplementation on cardiometabolic risk factors were evaluated in this systematic review and meta-analysis of randomized controlled trials (RCTs). METHODS: A comprehensive search of various databases was performed to identify relevant RCTs published up to September 2024. A random-effects model was employed for the meta-analysis, which included 27 RCTs with 1,685 participants. RESULTS: The findings indicated that PBE supplementation significantly reduced systolic blood pressure (SBP) (weighted mean difference (WMD): -2.26 mmHg, 95% confidence interval (CI): -3.73, -0.79; P = 0.003), diastolic blood pressure (DBP) (WMD: -2.62 mmHg, 95% CI: -3.71, -1.53; P < 0.001), fasting blood sugar (FBS) (WMD: -6.25 mg/dL, 95% CI: -9.97, -2.53; P = 0.001), hemoglobin A1c (HbA1c) (WMD: -0.32%, 95% CI: -0.54, -0.11; P = 0.003), body weight (WMD: -1.37 kg, 95% CI: -1.86, -0.88; P < 0.001), and low-density lipoprotein (LDL) cholesterol (WMD: -5.07 mg/dL, 95% CI: -9.21, -0.94; P = 0.016) in the PBE-treated group compared to their untreated counterparts. However, no significant impact of PBE was observed on waist-to-hip ratio (WHR), body mass index (BMI), waist circumference (WC), or serum levels of insulin, high-density lipoprotein (HDL) cholesterol, triglycerides (TG), and total cholesterol (TC). CONCLUSIONS: Supplementation with PBE may ameliorate specific cardiometabolic risk factors, as indicated by reductions in body weight, DBP, SBP, FBS, LDL, and HbA1c levels. This approach can be regarded as an adjunct therapeutic strategy for CMS management. Further high-quality trials with larger sample sizes and longer durations are required to validate these findings.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| 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 | 0.003 | 0.002 |
| 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".