1590-P: Post-Oral Glucose Load Hyperinsulinemia in Individuals with Metabolic Syndrome and Metabolic-Associated Fatty Liver Disease—First Steps toward the Development of Type 2 Diabetes?
Bibliographic record
Abstract
Introduction: Metabolic associated fatty liver disease (MAFLD) increases the risk of incident type 2 diabetes (T2D), but mechanisms involved have not been clearly elucidated. Objective: To assess post-oral glucose tolerance test (OGTT) glucose metabolism, insulin resistance, secretion and clearance in individuals with metabolic alterations, with or without increased liver fat. Methods: Individuals with a BMI >25 kg/m2 and at least 1 criteria for metabolic syndrome were included in this analysis. Liver fat was quantified using MRI and participants were assigned to 2 groups according to > 5% liver fat (MAFLD group) or ≤ 5%. A 3h frequently-sampled OGTT was performed and oral minimal model was used to measure insulin sensitivity, secretion and disposition index. Results: Fifty participants were included in this analysis and 50% had a liver fat > 5%. Post-OGTT glucose concentrations and iAUC glucose were not different between groups (p -≥ 0.50), whereas post-OGTT insulin concentrations and iAUC were significantly higher in the MAFLD group (p < 0.03) and significantly associated with liver fat. The components of insulin secretion (dynamic, static and total) were not different between groups (p -≥ 0.27). Insulin sensitivity and dynamic disposition indices were lower in the MAFLD group and significantly associated with liver fat. but in a multivariable regression model including sex and BMI, liver fat was not statistically associated with these indices. Insulin clearance indices were significantly lower in the MAFLD group, and liver fat statistically explained most of the variance in the multivariate model (p< 0.02). Conclusion: Among individuals with metabolic alterations, the presence of MAFLD is associated with impaired insulin clearance and post-oral glucose load hyperinsulinemia. Insulin sensitivity and dynamic beta-cell function are also altered, features of the early pathophysiology of T2D. Disclosure G.Trépanier: None. T.Gignac: None. M.Pradeau: None. A.Morissette: None. A.Agrinier: None. C.Gagnon: Advisory Panel; Novo Nordisk, Other Relationship; Ascendis Pharma A/S, Research Support; Novo Nordisk. M.Vohl: None. A.Marette: Advisory Panel; Valbiotis, Amancia, Plexus, Acasti. A.Carreau: Advisory Panel; Novartis, Consultant; Valbiotis, Speaker's Bureau; Novo Nordisk Canada Inc.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".