Uncovering the mechanisms for statin‐mediated dysglycaemia: role of Rac1?
Bibliographic record
Abstract
In a recent issue of The Journal of Physiology, Sylow and colleagues (2015) provided novel data on the role of the small GTPase Rac1 in regulating stretch-stimulated glucose transport in skeletal muscle. Convincingly, they demonstrated that both pharmacological inhibition of Rac1 and muscle-specific Rac1 knockout led to reduced stretch-stimulated glucose uptake in the isolated soleus and extensor digitorum longus muscles. These findings build upon their previous work (Sylow et al. 2013, 2014) and highlight the critical importance of Rac1 for glucose uptake by the skeletal muscle. Undiscussed are the translational insights these findings offer into the mechanisms contributing to, or responsible for, statin-mediated hyperglycaemia. Statins (HMG-CoA reductase inhibitors) are one of the most prescribed medications worldwide, owing to their ability to inhibit the mevalonate pathway required for endogenous production of cholesterol and improve blood lipid profiles (Liao & Laufs, 2005). At the same time, chronic statin therapy can increase fasting blood glucose and glycosylated haemoglobin levels, leading to a greater risk of developing type II diabetes (Sattar et al. 2010). The mechanisms responsible for these metabolic side-effects remain unknown. Based on the mounting evidence demons-trating a role of Rac1 in stretch- and contraction-mediated (Sylow et al. 2014, 2015) and insulin-stimulated glucose transport (Sylow et al. 2013), it is important to remember that in addition to their actions on cholesterol production, statins also inhibit the synthesis of isoprenoid intermediates (farnesyl pyrophosphate and geranylgeranyl pyrophosphate) necessary for small G-protein function. As a consequence, statins inhibit Rac1 (Rashid et al. 2009; Antoniades et al. 2010). Whether this pathway contributes to the risk of dysglycaemia with chronic statin therapy is unknown but warrants future investigation. Independent of Rac1, statins are also associated with myalgia and can reduce physical activity in those over 55 years of age (Parker et al. 2013), ensuring that not only is the machinery necessary for contraction-mediated glucose uptake impaired but the stimulus is attenuated. If Rac1 is involved in statin-mediated dysglycaemia, it begs the question why the overall risk of developing new-incidence type II diabetes is so low? This may be explained by parallel pleiotropic or cholesterol-independent benefits of statin therapy, induced by inhibiting Rac1, RhoA and Ras GTPases (Liao & Laufs, 2005). For example, through its association with NADPH oxidase, inhibition of Rac1 leads to a reduction in reactive oxygen species, while inhibition of Ras results in increased bioavailability of nitric oxide (Liao & Laufs, 2005). It is through these pathways that statins are thought to mediate improvements in vascular function, inflammation, oxidative stress and autonomic balance (Liao & Laufs, 2005; Millar & Floras, 2014). One adaptation that could balance the dysglycaemic effects of inhibiting Rac1 is a reduction in central sympathetic outflow (McGowan et al. 2014; Millar & Floras, 2014), a treatment strategy shown to improve insulin sensitivity in diabetic hypertensives (DeRosa et al. 2007). The net effect of statins on blood glucose levels may therefore be the balance of pleiotropic actions with metabolic consequences. In conclusion, the findings by Sylow et al. (2015) present new data that clarify the role of Rac1 in glucose uptake by the skeletal muscle and may offer an unrecognized mechanism explaining why statin therapy is associated with an increased risk of developing type II diabetes. None declared.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".