Changes In Fitness Levels Prevent Against The Adverse Responder Phenotype In Individuals Living With Type 2 Diabetes
Bibliographic record
Abstract
Exercise training is a cornerstone for type 2 diabetes management. Although the terms exercise non-responder and adverse responder have been coined to describe the absence of benefits or deterioration with exercise training, little is known about the potential predictors associated with several responder phenotypes. PURPOSE: To investigate the associations between Type 2 diabetes responder phenotype and changes in body composition and fitness levels following 9 months of exercise training. METHODS: Participants (n = 188) with Type 2 diabetes mellitus [glycated hemoglobin (HbA1c) ≥6.5%) were randomized to a control group or 9 months of exercise training. Changes in body composition were evaluated using body weight, body mass index, waist circumference, percent body fat, and total fat mass. Changes in aerobic fitness were evaluated using treadmill time to exhaustion (TTE) and the highest estimated metabolic equivalent tasks (METs) during a treadmill-graded exercise test. Participants randomized to exercise training were categorized to responder phenotype based on the change HbA1c: 1) adverse responder (HbA1c > 1% CV), 2) non-responder (HbA1c ± 1% CV, and 3) responder (HbA1c < 1% CV). RESULTS: The proportion of adverse responders, non-responders, and responders was 38.8%, 16.5% and 44.5%. Participants were more likely to be a responder in comparison to the control group if they improved their body composition (weight, body mass index, waist circumference, percent body fat, and total fat mass (all p ≤ 0.05)). Participants who increased their fitness level (TTE or estimated METs) were 1.93 (95%CI: 1.43-2.58) and 4.50 (95%CI:1.22-9.25) times more likely to be exercise responders than the control group. Finally, only participants who changed TTE [1.35 (95%CI:1.12-1.60)] or estimated METs [1.99 (95%CI:1.25-3.16)] were more likely to be a responder compared to an adverse responder phenotype after adjusting for confounders. CONCLUSIONS: Even if exercise normally leads to an improvement in HbA1c, some individuals do not improve beyond the coefficient of variation in the measure. More studies are needed to enhance responder phenotype in individuals with type 2 diabetes. Our secondary analysis suggests that improvement in fitness is associated with a better responder phenotype in type 2 diabetes individuals.
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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) | 0.000 | 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".