Effect of glycemic index and fructose content in lunch on substrate utilization during subsequent brisk walking
Bibliographic record
Abstract
The purpose of the present study was to investigate the effect of glycemic index (GI) and fructose content in lunch on substrate utilization during subsequent brisk walking. Ten healthy young males completed 3 main trials in a counterbalanced crossover design. They completed 60 min of brisk walking at approximately 50% maximal oxygen consumption after consuming a standard breakfast and 1 of 3 lunch meals, i.e., a low GI meal without fructose (LGI), a low GI meal that included fructose beverage (LGIF), or a high GI meal (HGI). The 3 lunch meals were isocaloric and provided 1.0 g·kg⁻¹ carbohydrate. Substrate utilization was measured using indirect respiratory calorimetry method. Blood samples were collected at certain time points. During the 2-h postprandial period after lunch, the incremental area under the blood response curve values of glucose and insulin were higher (p < 0.05) in the HGI trial than those in the LGI and LGIF trials (HGI vs. LGI and LGIF: glucose, 223.5 ± 24.4 vs. 92.5 ± 10.4 and 128.0 ± 17.7 mmol·min·L⁻¹; insulin, 3603 ± 593 vs. 1425 ± 289 and 1888 ± 114 mU·min·L⁻¹). During brisk walking, decreased carbohydrate oxidation was observed (p < 0.05) in the LGI trial than in the LGIF and HGI trials (LGI vs. LGIF and HGI: 60.8 ± 4.0 vs. 68.1 ± 6.0 and 74.4 ± 4.7 g). No difference was found in fat oxidation among the 3 trials (LGI vs. LGIF vs. HGI: 21.6 ± 2.3 vs. 19.2 ± 2.3 vs. 16.4 ± 2.2 g). It appeared that fructose content was an important influencing factor when considering the effect of different GI lunch meals on substrate utilization during subsequent moderate intensity exercise.
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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) | 0.001 | 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".