Does supplementation with pine bark extract improve cardiometabolic risk factors? A systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it