A Significant Effect of Oral Semaglutide on Cardiovascular Risk Factors in Patients With Type 2 Diabetes
Why this work is in the frame
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Bibliographic record
Abstract
Background: The once-daily glucagon-like peptide 1 (GLP-1) analogue, liraglutide has been shown to reduce major adverse cardiovascular events (MACE) and progression of chronic kidney disease (CKD). The once-weekly GLP-1 analogue, semaglutide also reduced MACE and renal events. Based on the evidence for GLP-1 analogues on MACE and renal events, the guideline recommended to treat high-risk diabetic individuals with GLP-1 analogues to reduce MACE and CKD progression. Recently, a once-daily oral semaglutide was developed and shown to reduce MACE. However, its effects on renal outcome and cardiovascular metabolic risk factors remain unknown. Methods: We retrospectively picked up patients who had taken oral semaglutide from March 2021 to June 2022 and compared metabolic parameters at baseline with the data at 3, 6 months after the start of oral semaglutide. Results: We found 47 patients who had taken oral semaglutide. Body weight significantly decreased at 3 and 6 months after the start of oral semaglutide, and systolic blood pressure significantly decreased after 6 months. Hemoglobin A1c (HbA1c) tended to decrease after 3 months and significantly deceased after 6 months. Serum low-density lipoprotein cholesterol (LDL-C) levels significantly decreased after 6 months and non-high-density lipoprotein-cholesterol (non-HDL-C) levels tended to decrease after 6 months. Urinary albumin to creatinine ratio (UACR) tended to decrease after 3 and 6 months. Such favorable metabolic changes by oral semaglutide were observed more prominently in patients who had not ever used GLP-1 analogues than in patients who switched from subcutaneous GLP-1 analogues. Conclusions: Our study showed that oral semaglutide improved body weight, blood pressure, HbA1c, LDL-C, non-HDL-C and UACR, in type 2 diabetic obese patients, especially, in patients who had not ever used GLP-1 analogues.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it