Oxidative Stress and Salvia miltiorrhiza in Aging-Associated Cardiovascular Diseases.
Cheng-Chieh ChangYu-Chun ChangWen Long HuYu Chiang HungPublished in: Oxidative medicine and cellular longevity (2016)
Aging-associated cardiovascular diseases (CVDs) have some risk factors that are closely related to oxidative stress. Salvia miltiorrhiza (SM) has been used commonly to treat CVDs for hundreds of years in the Chinese community. We aimed to explore the effects of SM on oxidative stress in aging-associated CVDs. Through literature searches using Medicine, PubMed, EMBASE, Cochrane library, CINAHL, and Scopus databases, we found that SM not only possesses antioxidant, antiapoptotic, and anti-inflammatory effects but also exerts angiogenic and cardioprotective activities. SM may reduce the production of reactive oxygen species by inhibiting oxidases, reducing the production of superoxide, inhibiting the oxidative modification of low-density lipoproteins, and ameliorating mitochondrial oxidative stress. SM also increases the activities of catalase, manganese superoxide dismutase, glutathione peroxidase, and coupled endothelial nitric oxide synthase. In addition, SM reduces the impact of ischemia/reperfusion injury, prevents cardiac fibrosis after myocardial infarction, preserves cardiac function in coronary disease, maintains the integrity of the blood-brain barrier, and promotes self-renewal and proliferation of neural stem/progenitor cells in stroke. However, future clinical well-designed and randomized control trials will be necessary to confirm the efficacy of SM in aging-associated CVDs.
Keyphrases
- oxidative stress
- ischemia reperfusion injury
- cardiovascular disease
- diabetic rats
- nitric oxide synthase
- signaling pathway
- induced apoptosis
- dna damage
- reactive oxygen species
- hydrogen peroxide
- nitric oxide
- coronary artery disease
- atrial fibrillation
- randomized controlled trial
- endothelial cells
- mental health
- open label
- clinical trial
- metabolic syndrome
- cardiovascular risk factors
- cardiovascular events
- deep learning
- left ventricular
- study protocol