Strawberries decrease circulating levels of tumor necrosis factor and lipid peroxides in obese adults with knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Knee osteoarthritis (OA) is increasingly prevalent in obese people, who often have high cardio-metabolic risk factors. Among the few available non-surgical approaches, nutraceuticals have gained popularity, and dietary berries have mitigated arthritis symptoms in observational and animal studies. Clinical studies in OA are sparse, but recently we reported that strawberry supplementation can mitigate pain and reduce inflammatory markers in adults with knee OA. This study extends those observations. METHODS: We conducted a randomized cross-over double-blind placebo-controlled trial on the effects of dietary freeze-dried strawberries on obesity-related hormones, biomarkers of inflammation and lipid peroxidation. Seventeen subjects (4 men, 13 women; age 57 ± 3 year) were randomized to strawberry supplements (50 g day-1 for 12 weeks) vs. placebo (50 g day-1, matched for calories and fiber), for two 12-week intervention periods, separated by 2-week washout phase. RESULTS: Among 24 biomarkers of inflammation examined (Bioplex-Pro human inflammation panel), 12 were detectable in all samples. Among these, high-sensitivity TNF-α (hs-TNF-α) and the soluble tumor necrosis factor receptor (sTNF-R2) were significantly decreased after strawberry consumption (p < 0.05). There were no changes in other biomarkers of the TNF super family, such as APRIL and BAFF. Among serum biomarkers of oxidative stress, 4-hydroxy-2-nonenal (4-HNE) and conjugated dienes were also reduced (p < 0.05). No changes were observed in body weight, serum obesity-related hormones, or osteocalcin. CONCLUSION: Strawberries lowered TNF-α, and lipid peroxidation products in obese adults with knee OA. Since, they also mitigate pain, these findings merit further investigation in larger trials.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".