Plasma B-Type Natriuretic Peptide Levels May Increase Because of Fat Mass Loss by Metformin or Sodium-Glucose Transporter 2 Inhibitors Treatment
Bibliographic record
Abstract
Background: Cardiovascular disease (CVD) is the leading cause of death in type 2 diabetes. Metformin reduces cardiovascular events in obese patients with type 2 diabetes, and sodium-glucose transporter 2 (SGLT2) inhibitors decrease cardiovascular events in type 2 diabetic patients with established CVD. However, the underlying mechanisms behind the cardioprotective effects of metformin and SGLT2 inhibitors are unknown. Methods: Fifteen patients with newly diagnosed type 2 diabetes receiving metformin monotherapy, and seven patients with type 2 diabetes receiving SGLT2 inhibitors combined with other hypoglycemic agents were studied. We investigated changes in glycemic control, plasma B-type natriuretic peptide (BNP) levels, and body composition, 3 and 6 months after starting metformin administration, and 3 months after starting SGLT2 inhibitor administration. Results: Plasma BNP levels significantly increased after 3 months in both metformin and SGLT2 inhibitors treatment groups (7.9 ± 7.9 pg/mL to 17 ± 16.9 pg/mL, P = 0.012; 8.8 ± 7.2 pg/mL to 15.5 ± 14.3 pg/mL, P = 0.018, respectively). Fat mass significantly decreased in the first 3 months of metformin administration (25.7 ± 10.3 kg to 23.0 ± 11.4 kg, P = 0.046), while fat mass and visceral fat area decreased in three patients receiving SGLT2 inhibitors. Conclusions: Plasma BNP levels increased because of fat mass loss caused by treatment with metformin and SGLT2 inhibitors. Our results suggest that metformin and SGLT2 inhibitors could reduce the risk of CVD by exerting cardioprotective effects through elevated BNP levels in patients with type 2 diabetes. J Endocrinol Metab. 2016;6(1):12-17 doi: http://dx.doi.org/10.14740/jem333w
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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 | 0.000 |
| 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 | 0.001 |
| 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".