Carbohydrate- and lipid-enriched meals acutely disrupt glycemic homeostasis by inducing transient insulin resistance in rats
Bibliographic record
Abstract
Chronic intake of high-carbohydrate or high-lipid diets is a well-known insulin resistance inducer. This study investigates the immediate effect (1-6 h) of a carbohydrate- or lipid-enriched meal on insulin sensitivity. Fasted rats were refed with standard, carbohydrate-enriched (C), or lipid-enriched (L) meal. Plasma insulin, glucose, and non-esterified fatty acids (NEFA) were measured at 1, 2, 4, and 6 h of refeeding. The glucose-insulin index showed that either carbohydrates or lipids decreased insulin sensitivity at 2 h of refeeding. At this time point, insulin tolerance tests (ITTs) and glucose tolerance tests (GTTs) detected insulin resistance in C rats, while GTT confirmed it in L rats. Reduced glycogen and phosphorylated AKT and GSK3 content revealed hepatic insulin resistance in C rats. Reduced glucose uptake in skeletal muscle subjected to the fatty acid concentration that mimics the high NEFA level of L rats suggests insulin resistance in these animals is mainly in muscle. In conclusion, carbohydrate- or lipid-enriched meals acutely disrupt glycemic homeostasis, inducing a transient insulin resistance, which seems to involve liver and skeletal muscle, respectively. Thus, the insulin resistance observed when those types of diets are chronically consumed may be an evolution of repeated episodes of this transient insulin resistance.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".