A Preclinical Model of Obesity-Independent Metabolic Syndrome for Studying the Effects of Novel Antidiabetic Therapy Beyond Glycemic Control
Bibliographic record
Abstract
Accumulating data from several large, placebo-controlled studies suggests that sodium-glucose transporter 2 (SGLT-2) inhibitors and glucagon-like peptide 1 receptor (GLP-1) receptor agonists offer therapeutic benefits in the management of cardiovascular diseases, regardless of the patient’s diabetic status. In addition to their effects on glucose excretion, SGLT2 inhibitors have a positive impact on systemic metabolism. The aim of this study was to establish a non-invasive preclinical model of metabolic syndrome (MetS) to investigate the effects of novel antidiabetic therapies beyond glucose reduction, independent of obesity. Eighteen healthy adult Beagle dogs were fed an isocaloric Western diet (WD) for ten weeks. Biospecimens were collected at baseline (BAS1) and after ten weeks of WD feeding (BAS2) for measurement of blood pressure (BP), serum chemistry, lipoprotein profiling, fasting blood glucose, glucagon, insulin, NT-proBNP, BUN, creatinine, angiotensins, oxidative stress biomarkers, serum, urine and fecal metabolomics. Differences between BAS1 and BAS2 were analyzed using non-parametric Wilcoxon signed-rank testing with continuity correction. The isocaloric WD model induced significant variations in several markers of MetS, including elevated BP, increased glucose levels, and reduced HDL-cholesterol. It also caused an increase in circulating NT-proBNP levels, a decrease in serum bicarbonate levels, and significant changes in general metabolism, lipids, and biogenic amines. Short-term, isocaloric feeding with a WD in dogs replicates key biological features of MetS while also causing low-grade metabolic acidosis and elevating natriuretic peptides. These findings support the use of the WD canine model for studying the metabolic effects of new antidiabetic therapies independent of obesity.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".