The Effect of Berry Consumption on Oxidative Stress Biomarkers: A Systematic Review of Randomized Controlled Trials in Humans
Bibliographic record
Abstract
Bioactive compounds in berries may scavenge reactive oxygen and nitrogen species by donating electrons to free radicals, thereby protecting DNA, proteins, and lipids from oxidative damage. Evidence shows that berry consumption has beneficial health effects, though it remains unclear whether berries exert a significant impact on oxidative stress in humans. Thus, we performed a systematic review of randomized controlled trials (RCT) to examine the effects of non-acute (more than a single dose and ≥7 days) berry consumption on biomarkers of oxidative stress. Searches were conducted in PubMed, Cochrane Library, and Scopus; results were imported into Covidence for screening and data extraction. The literature search identified 622 studies that were screened, and 131 full-text studies assessed for eligibility. Ultimately, 28 RCTs met the eligibility criteria. Common biomarkers of oxidative stress (antioxidants, DNA damage, isoprostanes, malondialdehyde, and oxidized LDL) were systematically reviewed, and results were reported narratively. Of the approximate 56 oxidative stress biomarkers evaluated in the 28 RCTs, 32% of the biomarkers were reported to have statistically significant beneficial results and 68% of the biomarkers were reported as having no statistically significant differences. More well-designed and longer-term berry RCTs are needed to evaluate biomarkers of oxidative stress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.006 | 0.007 |
| 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) | 0.007 | 0.001 |
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".