Waterpipe tobacco smoke distresses cardiovascular biomarkers in mice: alterations in protein expression of metalloproteinases, endothelin and myeloperoxidase.
Abeer M Rababa'hRaghad W BsoulMohammad J AlkhatatbehKarem H AlzoubiOmar F KhabourPublished in: Inhalation toxicology (2019)
Introduction: Tobacco use is a major risk factor of cardiovascular diseases (CVD) and atherosclerosis in particular. The use of waterpipe smoking (WPS) is increasing due to the misperception that it is less harmful than cigarette smoking due to its flavor and the use of water as a filter. Thus, research that investigates toxic effects of WPS is essential. The aim of this study was to investigate the effect of WPS on major cardiovascular biomarkers that may develop atherosclerosis in mice. Methods: BALB/c mice were exposed to WPS for either two weeks (acute exposure) or eight weeks (chronic exposure). Then, the heart tissue homogenates were analyzed to elucidate the effects of WPS on matrix metalloproteinase (MMPs: isoforms 1, 3, and 9), metallopeptidase inhibitor (TIMP1), endothelin-1 (ET-1) and myeloperoxidase (MPO) using ELISA technique. Results: Current data showed that acute exposure to WPS significantly enhanced the levels of MMP-3, MMP-9, and MPO (p < 0.05) compared to their corresponding control. However, the body was capable to restore the increased levels of these parameters following chronic exposure to WPS for 8 weeks (p > 0.05). Additionally, the levels of ET-1 were significantly higher upon chronic exposure to WPS compared to both control and acute exposure groups (p < 0.05). Conclusions: Waterpipe exposure has a significant negative effect on the cardiovascular system. The enhancement of the atherosclerotic biomarkers (MMP-3, MMP-9, MPO, and ET-1) might represent an early scavenger of compensatory efforts to maintain cardiovascular function after WPS exposure.
Keyphrases
- liver failure
- cardiovascular disease
- drug induced
- respiratory failure
- high fat diet induced
- cell migration
- heart failure
- aortic dissection
- coronary artery disease
- intensive care unit
- gestational age
- insulin resistance
- atrial fibrillation
- big data
- electronic health record
- metabolic syndrome
- machine learning
- adipose tissue
- skeletal muscle
- quality improvement