Effects of different protein and glycemic index diets on metabolic profiles and substrate partitioning in lean healthy males
Bibliographic record
Abstract
Dietary glycemic index (GI) and protein affects postprandial insulin responses and consequently 24 h glucose metabolism and therefore substrate partitioning. This study investigated the mechanistic effects of different protein and GI diets on 24 h profiles of metabolic markers and substrate partitioning. After 3 days of diet and physical activity standardization, 10 healthy male subjects (BMI: 22.5 ± 0.6 kg/m(2)) stayed in a respiration chamber 4 times for 36 h each time to measure substrate partitioning. All subjects randomly received four isoenergetic diets: a normal (15En%) dairy protein and low GI (<40 units) (NDP-LGI) diet; a high (25En%) dairy protein and low GI (HDP-LGI) diet; a normal vegetable protein and low GI (NVP-LGI) diet; or a normal dairy protein and high GI (>60 units) (NDP-HGI) diet. During the day, blood was sampled at fixed time points for the measurement of metabolic markers and satiety hormones. The HDP-LGI diet increased 24 h protein oxidation and sleeping metabolic rate (SMR) compared with the NDP-LGI diet (p < 0.002). No significant differences in 24 h carbohydrate and fat oxidation (day and night) were found between all intervention diets. Net incremental area under the curve (net iAUC) of 24 h plasma glucose decreased in the HDP-LGI diet compared with the NDP-LGI diet (p < 0.01), but no effect was observed on insulin levels. No difference in appetite profiles were observed between all intervention diets. The lower 24 h glycemic profile as a result of a high dairy protein diet did not lead to changes in 24 h substrate partitioning in lean healthy subjects with a normal insulin sensitivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".