Uncovering the mechanisms for statin‐mediated dysglycaemia: role of Rac1?
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».