Elevated Muscle Inflammatory Response After Protein‐Dense Food Ingestion in Obese Adults
Bibliographic record
Abstract
Skeletal muscle inflammation related to alterations in the toll‐like receptor 4 (TLR4) signaling may lead to insulin resistance and poor muscle remodeling. However, the effect of food ingestion on modulating TLR4/ myeloid differentiation factor 88 (MyD88) signaling with or without excess fat mass has not been examined. Therefore, we examined skeletal muscle TLR4 and MyD88 protein content before and after protein‐dense food ingestion across a range of body mass indexes (BMIs) in healthy, sedentary adults. Eight healthy‐weight (HW: age; 25±1 y, BMI: 23.0±0.4 kg/m 2 , HOMA‐IR: 1.3±0.2), 8 overweight (OW: age; 25±2 y, BMI: 27.3±0.4 kg/m 2 , HOMA‐IR: 1.3±0.1), and 8 obese (OB: age; 29±3 y, BMI; 36.0±1.4 kg/ m 2 , HOMA‐IR: 6.1±0.8) men and women ingested 170 g of lean pork (36 g protein, 3 g fat). Repeated blood samples and muscle biopsies were collected from the vastus lateralis in the basal‐state and over a 5 h postprandial period. Plasma non‐esterified fatty acid (NEFA) concentrations declined during the postprandial period and these values did not differ between groups (P=0.61). Skeletal muscle TLR4 and MyD88 protein content were not different between groups at baseline ( P> 0.05). However, pork ingestion increased muscle TLR4 (1.9‐fold from baseline) and MyD88 protein content (1.6‐fold from baseline) (both P<0.05) in the OB group at 5 h of the postprandial period when compared to the OW and HW groups. These data show that food ingestion modulates the muscle inflammatory effect related to TLR signaling in obese adults, but not overweight and healthy weight adults, and appears to be independent of circulating plasma NEFA concentrations. The elevated muscle inflammatory response to food ingestion may, at least partly, contribute to the impaired postprandial glucose and protein handling commonly observed in obese adults. Support or Funding Information The National Pork Board
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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 | 0.000 |
| 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) | 0.001 | 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".