Exercise timing and blood lactate concentrations in individuals with type 2 diabetes
Bibliographic record
Abstract
The purpose of this study was to characterize how resistance exercise prior to or after a meal alters fasting and postprandial blood lactate concentrations in individuals with type 2 diabetes. Obese individuals with type 2 diabetes (N = 12) completed three 2-day trials, including (i) no exercise (NoEx), (ii) resistance exercise prior to dinner (Ex-M), and (iii) resistance exercise beginning at 45 min postdinner (M-Ex). During day 1 of each trial, fasting and postprandial blood lactate concentrations, perceived exertion, and substrate oxidation were measured, and subsequently on day 2 the following morning fasting blood lactate was measured. The premeal lactate incremental area under the curve (iAUC) during Ex-M (109 ± 66 mmol·L−1·1.6 h−1) was over 100-fold greater (P < 0.01) compared with NoEx (−15 ± 24 mmol·L−1·1.6 h−1) and M-Ex (−2 ± 18 mmol·L−1·1.6 h−1). The postprandial lactate iAUC during M-Ex (304 ± 116 mmol·L−1·4 h−1) was over 2-fold greater (P < 0.01) compared with NoEx (149 ± 74 mmol·L−1·4 h−1) and Ex-M (−140 ± 196 mmol·L−1·4 h−1). Average lactate during exercise was ∼45% greater (P = 0.03) during M-Ex (3.2 ± 0.9 mmol/L) compared with Ex-M (2.2 ± 0.9 mmol/L), but the change in lactate during Ex-M (2.4 ± 1.6 mmol/L) or M-Ex (2.3 ± 1.3 mmol/L) was not different (P > 0.05). Perceived exertion, substrate oxidation, or fasting blood lactate concentrations the day after testing were not different between trials. Blood lactate concentrations during acute resistance exercise are greater when exercise is performed in the postprandial period. Acute resistance exercise performed the night prior does not alter fasting blood lactate concentrations the following morning.
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 | 0.001 |
| 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".