BACKGROUNDExposure to nicotine via tobacco smoking may influence leptin release and decrease food intake among smokers. However, the effect of nicotine exposure on leptin and food intake among different nicotine dependent groups is unclear.OBJECTIVEWe aimed to measure leptin and calorie intake among different nicotine dependent groups.DESIGNCross-sectional study.SETTINGResearch department in school of medical sciences.PATIENTS AND METHODSSubjects were selected by purposive (non-probability) sampling and categorized as having low, moderate and high nicotine dependency based on the Fagerstrom Test for Nicotine Dependence (FTND) score. Diet was recorded by interview. Anthropometry, blood pressure, body composition, lipid profile, and physical activity level were measured accordingly. Fasting serum leptin was measured using a commercial ELISA kit.MAIN OUTCOME MEASURE(S)Nicotine dependency, 24-hour diet, clinical anthropometric and clinical measurements.RESULTSIn 107 Malay male smokers leptin concentration was inversely correlated with nicotine dependence. However, body weight, smoking period, blood pressure, body composition, lipid profile and physical activity level were not significantly different among low, moderately and highly dependent smoking groups. Leptin concentration and total calorie intake were also not significantly different among these groups.CONCLUSIONLeptin concentration was inversely correlated with nicotine dependence, but leptin concentration and total calorie intake status were not significantly different among our different nicotine dependency subjects.LIMITATIONSPurposive sampling for subject recruitment and inaccurate information in the self-administered questionnaire.

No abstract

Ingestion of tobacco is detrimental to overall health that could affect the normal function of several health parameters in the human body. While it has been generally documented that smoking is injurious to health, the influence of tobacco on certain health parameters with regards to the duration of smoking in individuals without any apparent health issues is yet to be fully investigated. The current study investigated the effect of smoking on health parameters of healthy male smokers. A total of 107 (37 ± 9.42) years were enlisted randomly from different locations in Malaysia and several health-related parameters were measured. A kmeans cluster analysis was employed to classify the smokers into groups based on their smoking period while analysis of variance (ANOVA) was used to examine the differences in the health parameters status of the smokers. The k-means clustering analysis identified two distinct groups namely; chronic and acute smokers whilst the ANOVA indicated that the chronic smokers are older and characterised with a considerably higher diastolic blood pressure levels, total cholesterol, visceral fat, leptin as well as high and low-density lipoprotein. Moreover, chronic smokers are found to be highly dependent on nicotine p < 0.05. Nonetheless, no significant difference was found in basal metabolic rate systolic blood pressure, heart rate, nicotine level as well as calories intake amongst the smoking groups p > 0.05. The long-term smokers are predisposed to higher risks of cardiovascular-related problems, high fat accumulation as well as dependence on nicotine, among others.

Anthropometric variables (AV) are shown to be essential in assessing health status and to serve as markers for evaluating health-related risks in different populations. Studying the impact of physical activity (PA) on AV and its relationship with smoking is a non-trivial task from a public health perspective. In this study, a total of 107 healthy male smokers (37 ± 9.42 years) were recruited from different states in Malaysia. Standard procedures of measurement of several anthropometric indexes were carried out, and the International Physical Activity Questionnaire (IPPQ) was used to ascertain the PA levels of the participants. A principal component analysis was employed to examine the AV associated with physical activity, k-means clustering was used to group the participants with respect to the PA levels, and discriminant analysis models were utilized to determine the differential variables between the groups. A logistic regression (LR) model was further employed to ascertain the efficacy of the discriminant models in classifying the two smoking groups. Six AV out of twelve were associated with smoking behaviour. Two groups were obtained from the k-means analysis, based on the IPPQ and termed partially physically active smokers (PPAS) or physically nonactive smokers (PNAS). The PNAS were found to be at high risk of contracting cardiovascular problems, as compared with the PPAS. The PPAS cluster was characterized by a desirable AV, as well as a lower level of nicotine compared with the PNAS cluster. The LR model revealed that certain AV are vital for maintaining good health, and a partially active lifestyle could be effective in mitigating the effect of tobacco on health in healthy male smokers.

Body mass index (BMI) is a significant marker in assessing the health risk factors of an individual. Although, the discovery of BMI is over 200 years, however, its application as a measure of health is fairly new. Hitherto, the prevalence of higher BMI amongst university students is on the rise. Consequently, the present study endeavor to investigate the association of BMI and other health-related parameters namely; per cent body fat, visceral fat, basal metabolic rate (BMR), systolic and diastolic blood pressure, resting heart rate, core and upper muscle endurance, maximum oxygen consumption (V02max) and metabolic equivalent (MET). A total number of 232 university students were enrolled and completed the physical fitness assessments and health indicator measurement of the variables. A multiple Linear Regression (MLR) was used to observe the association of the BMI as the dependent variable with the physical fitness as well as health parameters as independent variables. A significant regression model was obtained F (3.225, 5) = 301.104, P <0.0001, R2 = 0.869 demonstrating that the model has accounted for about 87% variability of the whole dataset. Sensitivity analysis demonstrated that per cent body fat, visceral fat, BMR, as well as VO2max, are the major contributors towards the model prediction P <0.001. Moreover, positive significant relationships were detected between the BMI, per cent body fat, visceral fat, BMR, systolic and diastolic blood pressure whilst negative association between the BMI and performance in upper muscle endurance and VO2max were noted. BMI index could be a potential marker of assessing university students’ health-related risks that would consequently reveal vital information about their overall health status.