A Significant Effect of Oral Semaglutide on Cardiovascular Risk Factors in Patients With Type 2 Diabetes
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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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 | 0.001 |
| 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) | 0.001 | 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".