Inflammatory Biomarkers and Physical Function in Older, Obese Adults with Knee Pain and Self‐Reported Osteoarthritis After Intensive Weight‐Loss Therapy
Bibliographic record
Abstract
OBJECTIVES: To describe the relationships between proinflammatory biomarkers and self-reported and performance-based physical function and to examine the effect of weight loss on these markers of inflammation. DESIGN: Randomized, longitudinal, clinical study comparing subjects eating an energy-restricted diet and participating in exercise training with a control group. SETTING: Community-base participants for the Physical Activity, Inflammation and Body Composition Trial. PARTICIPANTS: Eighty-seven obese (body mass index (BMI) >30.0 kg/m(2)) adults aged 60 and older with knee pain and self-report of osteoarthritis. MEASUREMENTS: Inflammatory biomarkers (interleukin 6 (IL-6), tumor necrosis factor alpha (TNFalpha), C-reactive protein, and soluble receptors for TNFalpha (sTNFR1 and sTNFR2)) and self-reported (Western Ontario and McMaster University Osteoarthritis Index questionnaire) and performance-based (6-minute walk distance and stair climb time) measures of physical function at baseline and 6 months. RESULTS: Mean (standard error of the mean) weight loss was 8.7% (0.8%) in the intervention group, compared with 0.0% (0.7%) in the control group. sTNFR1 was significantly less in the intervention group than in the control group at 6 months. sTNFR1 and sTNFR2 predicted stair climb time at baseline. Change across the 6-month intervention for sTNFR2 was an independent predictor for change in 6-minute walk distance. CONCLUSION: These results indicate that an intensive weight-loss intervention in older obese adults with knee pain can help improve inflammatory biomarkers and that changes in these concentrations showed associations with physical function.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".