Beneficial Effects of Incretin-Based Therapy on Glycemic Control, Adipokines, Insulin Sensitivity Parameters and Weight Loss in Overweight or Obese Patients With Newly Diagnosed Type 2 Diabetes: A Prospective Study
Bibliographic record
Abstract
Background: The aim of the study was to examine the incretin-based therapy on glycemic control, insulin sensitivity parameters, weight loss and adipokines in overweight or obese patients with newly diagnosed type 2 diabetes mellitus (T2DM). Methods: This was a 24-week prospective study of 75 enrolled overweight or obese subjects (body mass index (BMI) is greater than or equal to 25 kg/m 2 ) with newly diagnosed T2DM randomly assigned to the liraglutide, metformin, and sitagliptin groups. Outcome measures were fasting blood glucose (FBG), HbA1c, weight, BMI, waist circumstance, plasma lipids, fast insulin, fast C-peptide, HOMA-IR, HOMA-B, leptin, adiponectin, and high-sensitivity C-reactive protein (hsCRP). Results: At the endpoint of 24 weeks, administration of liraglutide, metformin or sitagliptin all resulted in significant improvements of glycemic control, weight loss and insulin sensitivity parameters. It was also demonstrated that liraglutide was superior to metformin and sitagliptin in terms of achieving target HbA1c values and sustaining weight loss after 24-week treatment, but not to the incidence of adverse events (AEs). Moreover, liraglutide administration showed beneficial effects on reducing leptin levels and L/A ratio as well as elevating adiponectin levels compared to the metformin and sitagliptin administration. Conclusion: Liraglutide treatment during 24 weeks in newly diagnosed T2DM patients led to reduction of BMI and improvement of glucose control, insulin sensitivity and resistance parameters. Additionally, circulating levels of adipokines could play an important role in GLP-1 treatment. J Endocrinol Metab. 2015;5(5):284-290 doi: http://dx.doi.org/10.14740/jem304w
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".