The effect of a short-term low-carbohydrate, high-fat diet with or without postmeal walks on glycemic control and inflammation in type 2 diabetes: a randomized trial
Bibliographic record
Abstract
Lowering carbohydrate consumption effectively lowers glucose, but impacts on inflammation are unclear. The objectives of this study were to: 1) determine whether reducing hyperglycemia by following a low-carbohydrate, high-fat (LC) diet could lower markers of innate immune cell activation in type 2 diabetes (T2D) and 2) examine if the combination of an LC diet with strategically timed postmeal walking was superior to an LC diet alone. Participants with T2D ( n = 11) completed a randomized crossover study involving three 4-day diet interventions: 1) low-fat low-glycemic index (GL), 2) and 3) LC with 15-min postmeal walks (LC+Ex). Four-day mean glucose was significantly lower in the LC+Ex group as compared with LC (-5%, P < 0.05), whereas both LC+Ex (-16%, P < 0.001) and LC (-12%, P < 0.001) conditions were lower than GL. A significant main effect of time was observed for peripheral blood mononuclear cells phosphorylated c-Jun N-terminal kinase ( P < 0.001), with decreases in all three conditions (GL: -32%, LC: -45%, and LC+Ex: -44%). A significant condition by time interaction was observed for monocyte microparticles ( P = 0.040) with a significant decrease in GL (-76%, P = 0.035) and a tendency for a reduction in LC (-70%, P = 0.064), whereas there was no significant change in LC+Ex (0.5%, P = 0.990). Both LC (-27%, P = 0.001) and LC+Ex (-35%, P = 0.005) also led to significant reductions in circulating proinsulin. An LC diet improved 4-day glycemic control and fasting proinsulin levels when compared with GL, with added glucose-lowering benefits when LC was combined with postmeal walking.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".