Effect Of Exercise And Glycemic Index Of Carbohydrate Feeding On Postprandial Fat Oxidation
Bibliographic record
Abstract
Exercise performed shortly before (i.e. within half of day) of a high fat meal is beneficial for stimulating fat oxidation after the meal. This benefit of exercise is unfortunately negated if the after-exercise food choice to replace the calories expended during exercise is one containing high-glycemic index (HGI) carbohydrates . PURPOSE: We determined the effect of consuming low-glycemic index (LGI) carbohydrates after an exercise session on fat oxidation and free fatty acid (FFA) levels after a subsequent high fat meal. METHODS: Using a randomized, counter-balanced cross-over design, 23 overweight or obese individuals (BMI>25 kg/m2) performed: 1) Walking exercise (90min) at 6pm followed by no meal (EX); 2) Exercise followed by a meal with LGI carbohydrates (i.e. lentils, EX-LGI); 3) Exercise followed by a meal with HGI carbohydrates (i.e. potatoes, EX-HGI); 4) A control condition with no exercise or meal (CNT). In all conditions, after a 10 hour fast, participants were given a standardized high fat meal. Fat oxidation was estimated before, and for 6h after the meal from respiratory gas measures, and FFA was determined from blood samples. RESULTS: There was a condition main effect for fat oxidation (n=23) with EX (0.115 SD 0.033 g/min) higher than EX-HGI (0.105 SD 0.026 g/min) and CNT (0.099 SD 0.029 g/min) (p<0.05), and EX-LGI (0.110 SD 0.032) g/min) higher than CNT (p<0.05). There was a main effect for FFA, with greater FFA for EX-LGI (0.53 SD 0.08 mmol/L) and EX (0.52 SD 0.13 mmo/L) compared to EX-HGI (0.43 SD 0.07 mmol/L) and CNT (0.44 SD 0.06 mmol/L). There was no difference between EX-HGI and CNT for any measure. CONCLUSION: A meal containing HGI carbohydrates consumed after an evening exercise session cancels the beneficial effect of exercise for stimulating fat mobilization and oxidation after a subsequent high-fat meal. Consuming a post-exercise meal with LGI carbohydrates does not cancel this benefit and may therefore be more beneficial for maintaining metabolic health. Supported by the Heart and Stroke Foundation of Canada
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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 | 0.001 |
| 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) | 0.002 | 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".