The Effect of Alogliptin and Metformin Combination Therapy in Type 2 Diabetes: A Pilot Study
Bibliographic record
Abstract
Background: As type 2 diabetes is characterized by both insulin resistance and impaired insulin secretion, oral anti-diabetic treatments including dipeptidyl peptidase-4 inhibitors, which stimulate insulin secretion, and biguanides, which enhance insulin sensitivity should be considered. Therefore, we performed a pilot study using a combination therapy with agents from both of these classes of drugs. We selected alogliptin and metformin as representative drugs and evaluated their combinational efficacy in patients with type 2 diabetes. Methods: Continuous glucose monitoring (CGM) was performed throughout the study for two patients with type 2 diabetes and one control subject. First, the subjects received no medication (the 5-day washout period), and later, all subjects received three patterns of medication: alogliptin alone, alogliptin and metformin co-administration, and metformin alone. Blood was sampled before and 1 h after breakfast and lunch on representative days during each treatment condition. Results: The CGM results indicated that combination of alogliptin and metformin attenuated the escalation and fluctuation of glucose levels. The patterns of insulin and glucagon secretion with alogliptin alone, alogliptin and metformin co-administration, and metformin alone varied among subjects. When alogliptin and metformin were co-administered, glucagon-like peptide-1 (GLP-1) levels 1 h after lunch were higher in all subjects compared to those at any other time point. Postprandial glucose-dependent insulinotropic peptide (GIP) levels varied according to medication and the subject. Conclusions: Thus, CGM results revealed that a combination of alogliptin and metformin effectively reduced postprandial glucose fluctuation and stabilized blood glucose levels. The study subjects exhibited completely different response patterns of insulin, glucagon, GLP-1, and GIP with medications alone or in combination, suggesting that individual hormone-dependent glycemic responses to these drugs are complicated and multifactorial. J Endocrinol Metab. 2013;3(4-5):111-118 doi: https://doi.org/10.4021/jem196e
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".