The Effects of Teneligliptin on Lipid Profile: A Prospective Study for Comparison of Biomarkers Before and After a Meal
Bibliographic record
Abstract
Background: In the treatment of diabetes mellitus, it is important to prevent dyslipidemia, which is one of the complications. However, no study has examined the long-term effects of teneligliptin on the blood parameters of apolipoprotein B (apoB) after a meal in patients with type 2 diabetes. Methods: The effects of teneligliptin on blood glucose and lipids were examined by measurement of biomarkers before and after a meal. We gathered data before and after 6-month treatment in diabetic patients. Results: After treating 31 patients with teneligliptin for 6 months, the blood level of apoB-48, expressed as total area under the curve (tAUC), was significantly decreased. A multiple regression analysis of factors affecting the decreases in apoB-48 tAUC indicated that apoB-48 is more likely to decrease if it is higher at the start of testing, and that the apoB-48 tAUC value is more likely to fall in women than in men. Conclusions: Teneligliptin may be beneficial for the treatment of postprandial hyperlipidemia in diabetic patients. J Endocrinol Metab. 2020;10(3-4):79-88 doi: https://doi.org/10.14740/jem679
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".