High fructose rewires gut glucose sensing via glucagon-like peptide 2 to impair metabolic regulation in mice
Bibliographic record
Abstract
OBJECTIVE: Increased fructose consumption contributes to type 2 diabetes (T2D) and metabolic dysfunction-associated steatotic liver disease (MASLD), but the mechanisms are ill-defined. Gut nutrient sensing involves enterohormones like Glucagon-like peptide (Glp)2, which regulates the absorptive capacity of luminal nutrients. While glucose is the primary dietary energy source absorbed in the gut, it is unknown whether excess fructose alters gut glucose sensing to impair blood glucose regulation and liver homeostasis. METHODS: Mice were fed diets where carbohydrates were either entirely glucose (70 %Kcal) or glucose partially replaced with fructose (8.5 %Kcal). Glp2 receptor (Glp2r) was inhibited with Glp2 (3-33) injections. Glucose tolerance, insulin sensitivity, and gut glucose absorption were concomitantly assessed, and enteric sugar transporters and absorptive surface were quantified by RT-qPCR and histological analysis, respectively. RESULTS: High fructose feeding led to impairment of blood glucose disposal, ectopic fat accumulation in the liver, and hepatic (but not muscle or adipose tissue) insulin resistance independent of changes in fat mass. This was accompanied by increased gut glucose absorption, which preceded glucose intolerance and liver steatosis. Fructose upregulated glucose transporters and enlarged the gut surface, but these effects were prevented by Glp2r inhibition. Blocking Glp2r prevented fructose-induced impairments in glucose disposal and hepatic lipid handling. CONCLUSION: Excess fructose impairs blood glucose and liver homeostasis by rewiring gut glucose sensing and exacerbating gut glucose absorption. Our findings are positioned to inform novel early diagnostic tools and treatments tailored to counter high fructose-induced metabolic derangements predisposing to T2D and MASLD.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".