Acute high-intensity interval exercise reduces human monocyte Toll-like receptor 2 expression in type 2 diabetes
Bibliographic record
Abstract
Type 2 diabetes (T2D) is characterized by chronic low-grade inflammation that contributes to disease pathophysiology. Exercise has anti-inflammatory effects, but the impact of high-intensity interval training (HIIT) is not known. The purpose of this study was to determine the impact of a single session of HIIT on cellular, molecular, and circulating markers of inflammation in individuals with T2D. Participants with T2D ( n = 10) and healthy age-matched controls (HC; n = 9) completed an acute bout of HIIT (7 × 1 min at ~85% maximal aerobic power output, separated by 1 min of recovery) on a cycle ergometer with blood samples obtained before (Pre), immediately after (Post), and at 1 h of recovery (1-h Post). Inflammatory markers on leukocytes were measured by flow cytometry, and TNF-α was assessed in both LPS-stimulated whole blood cultures and plasma. A single session of HIIT had an overall anti-inflammatory effect, as evidenced by 1) significantly lower levels of Toll-like receptor (TLR) 2 surface protein expression on both classical and CD16+ monocytes assessed at Post and 1-h Post compared with Pre ( P < 0.05 for all); 2) significantly lower LPS-stimulated TNF-α release in whole blood cultures at 1-h Post ( P < 0.05 vs. Pre); and 3) significantly lower levels of plasma TNF-α at 1-h Post ( P < 0.05 vs. Pre). There were no differences between T2D and HC, except for a larger decrease in plasma TNF-α in HC vs. T2D (group × time interaction, P < 0.05). One session of low-volume HIIT has immunomodulatory effects and provides potential anti-inflammatory benefits to people with, and without, T2D.
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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) | 0.001 | 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 | 0.001 |
| 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".