Exercise training reduces circulating cytokines in male patients with coronary artery disease and type 2 diabetes: A pilot study
Bibliographic record
Abstract
Low-grade inflammation is central to coronary artery disease (CAD) and type 2 diabetes (T2D) and is reduced by exercise training. The objective of this study was to compare the anti-inflammatory potential of moderate-to-vigorous intensity continuous training (MICT) and high-intensity interval training (HIIT) in patients with CAD with or without T2D. The design and setting of this study is based on a secondary analysis of registered randomized clinical trial NCT02765568. Male patients with CAD were randomly assigned to either MICT or HIIT, with subgroups divided according to T2D status (non-T2D-HIIT n = 14 and non-T2D-MICT n = 13; T2D-HIIT n = 6 and T2D-MICT n = 5). The intervention was a 12-week cardiovascular rehabilitation program consisting of either MICT or HIIT (twice weekly sessions) and circulating cytokines measured pre- and post-training as inflammatory markers. The co-occurrence of CAD and T2D was associated with increased plasma IL-8 (p = 0.0331). There was an interaction between T2D and the effect of the training interventions on plasma FGF21 (p = 0.0368) and IL-6 (p = 0.0385), which were further reduced in the T2D groups. An interaction between T2D, training modalities, and the effect of time (p = 0.0415) was detected for SPARC, with HIIT increasing circulating concentrations in the control group, while lowering them in the T2D group, and the inverse occurring with MICT. The interventions also reduced plasma FGF21 (p = 0.0030), IL-6 (p = 0.0101), IL-8 (p = 0.0087), IL-10 (p < 0.0001), and IL-18 (p = 0.0009) irrespective of training modality or T2D status. HIIT and MICT resulted in similar reductions in circulating cytokines known to be increased in the context of low-grade inflammation in CAD patients, an effect more pronounced in patients with T2D for FGF21 and IL-6.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".