Assessment of Stress Biomarkers in the Saliva of Smokers and Nonsmokers via UV Photospectrometry and POMS

Abstract

Smoking is one of the major health catastrophes. Smoking is believed to be the major cause of chronic diseases like Cardiovascular complications, stroke, pregnancy issues, respiratory failure, etc. There are three important transdiagnostic emotional factors that make the population vulnerable to initiation of smoking i.e. anhedonia anxiety sensitivity, distress tolerance. Research studies for the past five decades have proven the adverse effect of stress on brain physiology and functioning. The human body responds to trauma (physical or non-physical stress) in a definite manner. This response of the body can be qualitatively and quantitatively monitored through several chemicals in the bloodstream, saliva, or urine; responding to the stress, called stress biomarkers i.e. Brain-derived neurotrophic factor, cortisol, cytokines etc. Saliva delivers an efficient specimen for various diagnostic procedures due to the presence of different biological products and secondary metabolites of xenobiotics and helps in determining the disease progressions as well as therapy outcomes depending on the variations in the markers/triggers. The nature of mindset and mood states are evaluated by a scales designed to rate the behavior of an individual towards the environmental stimuli that may be physical or psychological in nature. This psychological rating scale is known as the profile of mood state (POMS). This scale was initially originated by McNair, Droppleman, and Lorr in 1971. This scale is presented in the form a questionnaire including different questions regarding the mode and feelings of a subject.This research work aims to further elucidate the utilization of UV Photospectrometry for quantitatively assessing POMS and its relation to the stress biomarker. The samples were collected form the vicinity of the university campus H-12 Islamabad. The samples were processed and stored at the biomedical laboratory of School of Mechanical and Manufacturing Engineering (SMME), NUST. A total of twenty-four (24) male subjects were analyzed. A total of two groups were considered. Group 1 included the non-smoking participants, while group 2 included smoking participants. Simple spitting technique was used for the collection of unstimulated saliva. About 4 ml unstimulated saliva was collected in the sterile falcon tube. Saliva was temporarily stored in cool boxes at 4°C and immediatelyII shifted to the facility. Centrifugation of the salivary sample was done at 4°C for 5 minutes and 10,000 rpm. Saliva sample was frozen at -80°C until sample collection span was completed. The mood state of the participants was also evaluated using the profile of mood state technique used initially by McNair, Lorr, and Droppleman in 1971. The total mood disturbance (TMD) score was calculated that ranges from -32 to 200. The questionnaire was accessed from ―Mackenzie, B. (2001) Profile of Mood States (POMS) [WWW] Available from: https://www.brianmac.co.uk/poms.htm [Accessed 26/6/2022]‖. Simulated neural networking (SNN) was applied to the collected data from smokers and non-smokers for accuracy scoring. The required statistical analysis was performed and the data was statistically analyzed through a software ―GraphPad Prism 8.0‖ and the respective graphs were plotted. UV spectrophotometry studies provided peak plasma concentration peaks at the lower UV range of 190 to 210 nm, but with no significant difference, representing the presence of biological stress markers. The profile of mood state evaluation studies concluded that the smoking participants were presented with a significantly higher level of individual mood profile scores i.e. anger (****, P<0.0001), confusion (**, P<0.0014), fatigue (*, P<0.0354), tension (*, P<0.0422) and stress as compared to nonsmoking participants. The vigorous score was significantly high in the nonsmoking individuals (****, P<0.0001). Similarly, total mood disturbance score was also significantly high in the smoking participants. The application of artificial neural networking through artificial machine learning scored the accuracy of the results 84% which is a reliable outcome. The current research work concludes that different stress stimuli including physiological stress and psychological stress tends to initiate/increase the smoking behavior among the community. Likewise, it is also concluded that smoking initiation may not be always triggered in response to stress. Numerous factors i.e. lack of education, negative inspiration, or behavior to impress are also involved. Furthermore, the adaptation of smoking behavior as a result of stressful stimuli is not a valid approach to reduce the noxious/stressful stimuli. The stress may further be exaggerated by smoking.