Correlation between Taste Threshold Sensitivity and MMP-9, Salivary Secretion, Blood Pressure, and Blood Glucose Levels in Smoking and Nonsmoking Women.
Sri TjahajawatiAnggun RafisaNani MurniatiCucu ZubaedahPublished in: International journal of dentistry (2020)
Cigarette smoking can cause taste receptors to increase the taste threshold value. Consequently, the consumption of sugar and salt will not be controlled, therefore causing systemic diseases such as hypertension and diabetes. Nicotine and tobacco in cigarettes can stimulate MMP-9 which plays vital physiological roles in normal tissue growth and repair processes. This study aimed to find the correlation between taste threshold sensitivity and MMP-9, salivary secretion, blood pressure, and blood glucose levels in smoking and nonsmoking women. This was a cross-sectional study consisting of young adult women aged 18-24 years. Subjects were divided into two groups: the nonsmoking and smoking groups. In the combined data of both groups, the sweet taste threshold was correlated with age (r = 0.308, p=0.008), blood glucose levels (r = 0.238, p=0.043), and MMP-9 (r = -0.297, p=0.011). The salt taste threshold was only correlated with systolic blood pressure in the smoking (r = 0.440, p=0.032) and combined data groups (r = 0.260, p=0.026). By using partial correlation, it was shown that the relationship between the salt taste threshold and systolic blood pressure was influenced by smoking habits. The sweet taste threshold in women was found to correlate with age, blood glucose levels, and MMP-9 levels. On the other hand, there was a significant relationship between the salt taste threshold in women with systolic blood pressure, which was the only correlation analyzed in sthis study that was found to be influenced by smoking. However, both sweet and salt taste thresholds were not statistically correlated with salivary secretion.
Keyphrases
- blood glucose
- blood pressure
- smoking cessation
- hypertensive patients
- glycemic control
- heart rate
- polycystic ovary syndrome
- type diabetes
- pregnancy outcomes
- cardiovascular disease
- left ventricular
- young adults
- machine learning
- physical activity
- electronic health record
- insulin resistance
- cervical cancer screening
- deep learning
- pregnant women
- metabolic syndrome
- breast cancer risk