P13 Anti-inflammatory effects of metformin – useful in cardiovascular disease?
Bibliographic record
Abstract
Background Metformin is the first line drug treatment for type 2 diabetes (T2D). Along with its anti-hyperglycaemic properties, metformin is also associated with beneficial effects in cardiovascular disease (CVD). In observational studies, metformin has been associated with fewer adverse CV events. 1 How metformin induces these effects is unclear as its mechanism of action is still to be determined. Inflammation, including NF-κB signalling, has been recognised as a contributing factor to both diabetes and CVD with metformin recently reported to have effects upon inflammatory signalling. 2,3 In this study, we have utilised primary cells and human plasma to investigate the anti-inflammatory effects of metformin and how this may be useful in CVD. Results In mouse hepatocytes, TNFα-dependent IκB degradation and expression of pro-inflammatory mediators were inhibited by metformin and the IKKβ inhibitor BI605906. These effects upon NF-κB signalling could be separated from metabolic effects as BI605906 did not replicate metformin’s actions upon lipogenic gene expression, glucose production or AMPK activation. Analysing the plasma from a non-diabetic heart failure cohort, 4 metformin use was associated with suppression of several plasma cytokines. Conclusion This study has demonstrated that the anti-inflammatory effects of metformin are separate from its anti-hyperglycaemic actions. This knowledge indicates that metformin may be harnessed for use in ‘at risk’ CVD groups irrespective of diabetes status. References Evans, et al . Diabetologia 2006; 49 :930–6 Isoda, et al . Arteriosclerosis, Thrombosis, and Vascular Biology 2006; 26 :611–7 Woo, et al . PLoS One 2014; 9 :e91111 Wong, et al . European Journal of Heart Failure 2012; 14 :1303–10
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".