Anagliptin Monotherapy for Six Months in Patients With Type 2 Diabetes Mellitus and Hyper-Low-Density Lipoprotein Cholesterolemia Reduces Plasma Levels of Fasting Low-Density Lipoprotein Cholesterol and Lathosterol: A Single-Arm Intervention Trial
Bibliographic record
Abstract
BACKGROUND: Anagliptin, a dipeptidyl peptidase-4 (DPP-4) inhibitor, has been shown to decrease plasma low-density lipoprotein cholesterol (LDL-C) levels. The objective of our study was to elucidate the mechanisms responsible for the anagliptin-mediated improvements in high LDL-C levels (hyper-LDL cholesterolemia). METHODS: We prospectively examined the effects of anagliptin monotherapy on fasting plasma lathosterol, sitosterol, and campesterol levels in patients with type 2 diabetes mellitus and hyper-LDL cholesterolemia for 6 months. We examined 14 patients who did not use hypoglycemic or lipid-lowering drugs for 4 months before initiating the study. Plasma variables related to glucose and lipid metabolism were measured before and after 6 months of treatment and pre- and postprandially using the cookie-loading test. RESULTS: After treatment, anagliptin monotherapy (n = 14) significantly decreased fasting LDL-C (175.6 to 148.5 mg/dL, mean values before and after the treatment, respectively) and plasma lathosterol levels (3.56 to 2.49 mg/dL), whereas it did not lower fasting sitosterol or campesterol levels. Furthermore, fasting plasma lathosterol levels were negatively correlated with preprandial glucagon-like peptide-1 (GLP-1) levels after anagliptin treatment. CONCLUSIONS: Anagliptin monotherapy may have a beneficial effect on lipid metabolism, which could be mediated by the inhibition of hepatic cholesterol synthesis rather than the inhibition of intestinal lipid transport.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".