Comparison of Lipid-Lowering Effects of Anagliptin and Miglitol in Patients With Type 2 Diabetes: A Randomized Trial
Bibliographic record
Abstract
BACKGROUND: Recently, we reported that the level of lathosterol, a cholesterol synthesis marker, was suppressed after 1 month of treatment with anagliptin, a dipeptidyl peptidase-4 inhibitor. In this study, we administered either anagliptin or miglitol, an alpha-glucosidase inhibitor, for 3 months in patients with type 2 diabetes and compared the lipid-lowering effects of anagliptin with those of miglitol. METHODS: This study was a 12-week, open-label, prospective, randomized, parallel-group comparison trial. Fifty-two patients with type 2 diabetes who aged 20 - 70 years with a low-density lipoprotein cholesterol (LDL-C) level of over 120 mg/dL, and with no history of treatment with antihyperlipidemic drugs were enrolled. Patients were randomly assigned to either the anagliptin group or miglitol group. The 100 mg of anagliptin was administered twice a day for the anagliptin group and 50 mg of miglitol was administered thrice a day for miglitol group. The changes in lipids, cholesterol synthesis, and absorption markers were evaluated after 12 weeks. RESULTS: Fifty-two participants were initially enrolled in the trial, and 47 of them completed the protocol. There was no significant difference in LDL-C, cholesterol synthesis, and the absorption markers between anagliptin and miglitol groups. CONCLUSIONS: Anagliptin and miglitol are similarly effective on lipid and glycemic control.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".