4,5-dicaffeyolquinic acid improves high-fat diet-induced cognitive dysfunction through the regulation of insulin degrading enzyme.
Jin Yong KangSeon Kyeong ParkJong Min KimSu Bin ParkSeul Ki YooHye Ju HanDae Ok KimHo-Jin HeoPublished in: Journal of food biochemistry (2019)
This study was performed to investigate the effects of Artemisia argyi and 4,5-dicaffeyolquinic acid (4,5-diCQA) as a main compound of ethyl acetate fraction from Artemisia argyi (EFAA) on high-fat diet (HFD)-induced cognitive dysfunction. Both EFAA and 4,5-diCQA were effective in improving cognitive function on HFD-induced cognitive dysfunction. In brain tissue analysis, it was confirmed that EFAA and 4,5-diCQA inhibited the reduction of neurotransmitters as well as oxidative stress and mitochondrial dysfunction. In addition, they inhibited amyloid β (Aβ) accumulation by increasing the expression of insulin-degrading enzyme and consequently prevented apoptosis. In conclusion, it is presumed that Artemisia argyi may help to improve the cognitive impairment due to the HFD, and it is considered that this effect is closely related to the physiological activity of 4,5-diCQA. PRACTICAL APPLICATIONS: Artemisia argyi is used in traditional herbal medicine in Asia. Type 2 diabetes mellitus has been proven by a variety of epidemiological studies to be a risk factor for cognitive impairment, such as Alzheimer's disease. This study confirmed that 4,5-diCQA is a bioactive compound of Artemisia argyi on improving HFD-induced cognitive dysfunction. Therefore, this study can provide useful information to the effect of Artemisia argyi and related substance.
Keyphrases
- high fat diet
- oxidative stress
- cognitive impairment
- insulin resistance
- diabetic rats
- type diabetes
- high fat diet induced
- cell death
- poor prognosis
- glycemic control
- metabolic syndrome
- multiple sclerosis
- cardiovascular disease
- dna damage
- signaling pathway
- ischemia reperfusion injury
- brain injury
- long non coding rna
- social media
- cognitive decline
- binding protein
- data analysis