Skeletal muscle TLR4 and TACE are associated with body fat percentage in older adults
Bibliographic record
Abstract
Elevated skeletal muscle expression of toll-like receptor 4 (TLR4) has been linked to increased inflammation in clinical populations. TNFα converting enzyme (TACE), which cleaves membrane-bound TNFα (mTNFα) to its soluble (sTNFα) and more bioactive form, has been linked to chronic disease. In contrast, higher physical activity level is associated with decreased chronic disease risk and inflammation. The purpose of the present study was to examine the relationship between physical activity and skeletal muscle TLR4, TACE, and TNFα in older adults. In 26 older adults (age = 68 ± 4 years, body mass index = 26 ± 3 kg·m(-2)), self-reported physical activity (kcal·week(-1)), estimated maximal oxygen consumption, and body composition (air plethysmography) were measured. TLR4, TACE, mTNFα, and sTNFα were measured in skeletal muscle biopsies (vastus lateralis) using western blot analyses. Pearson product-moment correlations were run between variables. Significance was set at p < 0.05. Skeletal muscle TACE was directly associated with sTNFα (r = 0.53, p < 0.01). Linear regression modeling showed that mTNFα and TACE expression were predictive of sTNFα expression. No correlations were observed between physical activity and TLR4, TACE, or sTNFα. Percent body fat was directly associated with skeletal muscle TLR4 (r = 0.52, p < 0.01) and TACE (r = 0.50, p < 0.01), whereas fasting blood glucose was directly associated with TACE and sTNFα. In conclusion, we found that percent body fat was directly associated with TLR4 and TACE expression in skeletal muscle of older adults. These findings suggest that elevated skeletal muscle expression of TLR4 and TACE may contribute to the augmented inflammation and chronic disease risk observed with increased adiposity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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 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".