Exploring interindividual differences in fasting and postprandial insulin sensitivity adaptations in response to sprint interval exercise training
Bibliographic record
Abstract
ABSTRACT Previous studies have concluded that wide variance in changes in insulin sensitivity markers following exercise training demonstrates heterogeneity in individual trainability. However, these studies frequently don't account for technical, biological, and random within‐subject measurement error. We used the standard deviation of individual responses (SDIR) to determine whether interindividual variability in trainability exists for fasting and postprandial insulin sensitivity outcomes following low‐volume sprint interval training (SIT). We pooled data from 63 untrained participants who completed 6 weeks of SIT (n = 49; VO2max: 35 (7) mL⋅kg−1⋅min−1) or acted as no‐intervention controls (n = 14; VO2max: 34 (6) mL⋅kg−1⋅min−1). Fasting and oral glucose tolerance test (OGTT)‐derived measures of insulin sensitivity were measured pre‐ and post‐intervention. SDIR values were positive and exceeded a small effect size threshold for changes in fasting glucose (SDIR = 0.27 [95%CI 0.07,0.38] mmol⋅L−1), 2‐h OGTT glucose (SDIR = 0.89 [0.22,1.23] mmol⋅L−1), glucose area‐under‐the‐curve (SDIR = 66.4 [−81.5,124.3] mmol⋅L−1⋅120min−1) and The Cederholm Index (SDIR = 7.2 [−16.0,19.0] mg⋅l2⋅mmol−1⋅mU−1⋅min−1), suggesting meaningful individual responses to SIT, whilst SDIR values were negative for fasting insulin, fasting insulin resistance and insulin AUC. For all variables, the 95% CIs were wide and/or crossed zero, highlighting uncertainty about the existence of true interindividual differences in exercise trainability. Only 2–22% of participants could be classified as responders or non‐responders with more than 95% certainty. Our findings demonstrate it cannot be assumed that variation in changes in insulin sensitivity following SIT is attributable to inherent differences in trainability, and reiterate the importance of accounting for technical, biological, and random error when examining heterogeneity in health‐related training adaptations.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".