Significant Differences in Effects of Sitagliptin Treatment on Body Weight and Lipid Metabolism Between Obese and Non-Obese Patients With Type 2 Diabetes
Bibliographic record
Abstract
Background: We previously reported that HbA1c levels and body weight significantly decreased by 0.6% and by 0.8 kg, respectively, at 6 months after sitagliptin treatment started. We found a significant and negative correlation between change in body weight and body mass index (BMI) at baseline. Methods: We retrospectively sub-analyzed effects of 6-month treatment with sitagliptin on glucose and lipid metabolism, blood pressure, body weight and renal function in patients with type 2 diabetes, by dividing 173 type 2 diabetic subjects into obese group (BMI is greater than or equal to 25) and non-obese group (BMI < 25). Results: At baseline, obese group was significantly younger than non-obese group. Diastolic blood pressure, low-density lipoprotein-cholesterol (LDL-C), triglyceride (TG), and estimated glomerular filtration rate (eGFR) in obese group were significantly higher than in non-obese group. Serum high-density lipoprotein-cholesterol (HDL-C) in obese group was significantly lower than in non-obese group. At 6 months after the start of sitagliptin use, body weight significantly decreased in obese group, while body weight did not change in non-obese group. HbA1c significantly decreased in both groups. Serum HDL-C significantly decreased in obese group, while serum HDL-C did not change in non-obese group. Serum TG significantly decreased in obese group, while serum TG significantly increased in non-obese group. Change in serum TG was significantly and inversely correlated with BMI at baseline. Conclusions: We found significant differences in effects of sitagliptin treatment on body weight and lipid metabolism between obese and non-obese patients with type 2 diabetes. Sitagliptin improved HbA1c regardless of the existence of obesity. In obese people, sitagliptin significantly reduced body weight and serum TG. Sitagliptin reduced serum TG in a baseline-BMI-dependent manner. J Endocrinol Metab. 2014;4(5-6):136-142 doi: http://dx.doi.org/10.14740/jem243w
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".