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Smoking cessation and weight gain
Abstract
Cigarette smoking is the single most important preventable cause of death and illness. Smoking cessation is associated with substantial health benefits. Weight gain is cited as a primary reason for not trying to quit smoking. There is a great variability in the amount of weight gain but younger ages, lower socio-economic status and heavier smoking are predictors of higher weight gain. Weight change after smoking cessation appears to be influenced by underlying genetic factors. Besides, weight gain after smoking cessation is largely because of increased body fat and some studies suggest that it mostly occurs in the subcutaneous region of the body. The mechanism of weight gain includes increased energy intake, decreased resting metabolic rate, decreased physical activity and increased lipoprotein lipase activity. Although there is convincing evidence for the association between smoking cessation and weight gain, the molecular mechanisms underlying this relationship are not well understood. This review summarizes current information of the effects of nicotine on peptides involved in feeding behaviour. Smoking was shown to impair glucose tolerance and insulin sensitivity and cross-sectional studies have demonstrated that smokers are insulin-resistant and hyperinsulinaemic, as compared with non-smokers. Smoking cessation seems to improve insulin sensitivity in spite of the weight gain. Nicotine replacement - in particular nicotine gum - appears to be effective in delaying post-cessation weight gain. In a group of women who failed to quit smoking because of weight gain, a dietary intervention (intermittent very-low-calorie diet) plus nicotine gum showed to both increase success rate in terms of smoking cessation and prevent weight gain. On the other hand, body weight gain at the end of treatment was significantly lower in the patients receiving bupropion or bupropion plus nicotine patch, compared with placebo. Studies with new drugs available for the treatment of obesity - sibutramine and orlistat - are warranted.
... While approximately 68.0% of individuals who smoke report they want to quit, only 7.4% actually achieve cessation with each quit attempt [2]. Smoking cessation is associated with weight gain due to decreased metabolic rate and increased energy intake [3], with individuals gaining approximately 1.1 kg, 2.3 kg, 2.9 kg, 4.2 kg and 4.7 kg at 1, 2, 3, 6, and 12 months after quitting smoking, respectively [4], with greater weight gains seen among those with a body mass index (BMI) in healthy and overweight ranges [5]. Yet, smoking cessation reduces a person's risk for cardiovascular diseases [6,7], even if they gain weight. ...
... Yet, smoking cessation reduces a person's risk for cardiovascular diseases [6,7], even if they gain weight. However, weight gain is also a commonly cited reason for either not trying to quit smoking or smoking relapse [8][9][10], particularly among women [3,11]. Given the importance of weight gain in relation to smoking cessation, it may be prudent to tackle both issues to effectively promote sustained smoking abstinence. ...
... Lastly, there were some differences in the amount of weight gained or lost based on smoking status at 12-month follow-up, with those who did not quit smoking having higher weight loss compared to those who did quit. Although these values were not statistically significant, they are an important consideration for future research given that those who successfully quit smoking did see less weight loss or more weight gained, and this has been a deterrent to maintaining a quit in previous studies [3,[8][9][10][11]. It will be important for future studies implementing these interventions to continue evaluating differences in weight change by smoking status, as individuals may be at risk for relapse should their weight significantly increase compared to their smoking counterparts. ...
Background/Objectives
Weight gain is a barrier to smoking cessation. Previous interventions targeting weight gain while quitting smoking have largely been unsuccessful. The current study aimed to assess the efficacy of weight stability and weight loss interventions compared to a low-intensity, self-guided bibliotherapy weight management group.
Subjects/Methods
A randomized controlled trial with 12-month follow-up from 2018 to 2022 was conducted with participants (N = 305) who reported smoking at least five cigarettes per day for the last year and interest in quitting initially recruited from the Memphis, TN, USA area. Recruitment was expanded nationally with the onset of the COVID-19 pandemic. Subsequently, 276 completed 12-month follow-up.
Interventions/Methods
The Bibliotherapy group was provided a weight management book. Both the Stability and Loss groups met via telephone for eight weeks to learn strategies for maintaining/losing weight, respectively. All three groups then received the same six-week smoking cessation intervention, with six months of varenicline provided.
Results
Individuals in the Loss group lost more weight (−2.01 kg, SE = 1.58) than individuals in the Bibliotherapy group (+1.08 kg, SE = 1.49, p = 0.0004), while the Stability group (−0.30 kg, SE = 1.56) was not significantly different from the Bibliotherapy group (p = 0.17). Those in the Stability group did not gain a significant amount of weight. Participants in the Loss group did not gain back all weight lost after smoking cessation and ended the study approximately 2.01 kg lower than baseline. The Bibliotherapy group did not gain the amount of weight expected after cessation. There were no significant differences between groups related to self-reported smoking cessation at each time point except at eight-month follow-up (p = 0.005).
Conclusions and relevance
Results indicated the Stability and the Loss interventions were effective for preventing post-smoking cessation weight gain, with the Loss group having the benefit of sustained weight loss. These interventions may be helpful to implement to combat weight gain and potentially facilitate smoking cessation.
Trial Registration
The trial is registered on clinicaltrials.gov (NCT03156660).
... Nicotine reduces body weight by suppressing appetite and food intake, increasing energy expenditure, raising the resting metabolic rate and increasing lipolysis and fat oxidation [9]. Weight gain associated with smoking cessation is largely due to the removal of the effects of nicotine on the central nervous system [9,12,31,32]. Some of these effects are presented in Figure 1. ...
... Nicotine reduces body weight by suppressing appetite and food int increasing energy expenditure, raising the resting metabolic rate and increasing lipo and fat oxidation [9]. Weight gain associated with smoking cessation is largely due to removal of the effects of nicotine on the central nervous system [9,12,31,32]. Some of t effects are presented in Figure 1. ...
... A 10% increase in 24 h energy expenditure corresponds to approximately 200 kcal per 24 h, and under the condition of a stable caloric intake, this change may result in the loss of 10 kg of body weight over 1 year [9]. The variability in the mean resting metabolic rate reduction after smoking cessation ranges from 4% to 16% and is responsible for less than 40% of the PSCWG [31]. ...
Smokers with diabetes mellitus substantially lower their risks of microvascular and macrovascular diabetic complications, in particular cardiovascular disease, by quitting smoking. However, subsequent post-smoking-cessation weight gain may attenuate some of the beneficial effects of smoking cessation and discourage attempts to quit. Weight gain can temporarily exacerbate diabetes and deteriorate glycemic control and metabolic profile. The molecular mechanisms by which quitting smoking leads to weight gain are largely associated with the removal of nicotine’s effects on the central nervous system. This review addresses mechanisms of post-smoking-cessation weight gain, by reviewing the effects of nicotine on appetite, food intake, eating behaviour, energy expenditure, fat oxidation and appetite-regulating peptides. We also highlight correlations between post-cessation weight gain and risk of type 2 diabetes, consequences of weight gain in people with type 2 diabetes and the role of pharmacotherapies, which combine treatment of nicotine addiction and promotion of weight control.
... Smoking is related to obesity, and various association analyses between smoking and obesity-related traits have been conducted. Typically, smoking is associated with weight loss [13,14,[20][21][22]. Previous studies have reported that nicotine causes weight loss by increasing energy expenditure and insulin resistance, as well as reducing appetite and insulin sensitivity [13,20]. ...
... Typically, smoking is associated with weight loss [13,14,[20][21][22]. Previous studies have reported that nicotine causes weight loss by increasing energy expenditure and insulin resistance, as well as reducing appetite and insulin sensitivity [13,20]. However, smoking increases the waist circumference, WHR, and abdominal and visceral fat [15,23,24]. ...
... However, smoking increases the waist circumference, WHR, and abdominal and visceral fat [15,23,24]. Moreover, former smokers tend to increase in body weight and subcutaneous fat for 1-8 years after smoking cessation; however, most of them do not gain excessive weight, and their average BMIs tend to be similar to those of nonsmokers [14,20,21,25,26]. Thus, smoking is a complex environmental factor affecting obesity and obesity-related traits, and more accurate information can be obtained about its effects by identifying genes and pathways that interact with smoking. ...
Introduction:
Although many studies have investigated the association between smoking and obesity, very few have analyzed how obesity traits are affected by interactions between genetic factors and smoking. Here, we aimed to identify the loci that affect obesity traits via smoking status-related interactions in European samples.
Methods:
We performed stratified analysis based on the smoking status using both the UK Biobank (UKB) data (N = 334,808) and the Genetic Investigation of ANthropometric Traits (GIANT) data (N = 210,323) to identify gene-smoking interaction for obesity traits. We divided the UKB subjects into two groups, current smokers and nonsmokers, based on the smoking status, and performed genome-wide association study (GWAS) for body mass index (BMI), waist circumference adjusted for BMI (WCadjBMI), and waist-hip ratio adjusted for BMI (WHRadjBMI) in each group. And then we carried out the meta-analysis using both GWAS summary statistics of UKB and GIANT for BMI, WCadjBMI, and WHRadjBMI, and computed the stratified P-values (Pstratified) based on the differences between meta-analyzed estimated beta coefficients with standard errors in each group.
Results:
We identified four genome-wide significant loci in interactions with the smoking status (Pstratified < 5×10-8); rs336396 (INPP4B) and rs12899135 (near CHRNB4) for BMI, and rs998584 (near VEGFA) and rs6916318 (near RSPO3) for WHRadjBMI. Moreover, we annotated the biological functions of the SNPs using expression quantitative trait loci (eQTL) and GWAS databases, along with publications, which revealed possible mechanisms underlying the association between the smoking status-related genetic variants and obesity.
Conclusions:
Our findings suggest that obesity traits can be modified by the smoking status via interactions with genetic variants through various biological pathways.
... Quitting smoking is undoubtedly a positive move for overall health, but it can introduce challenges related to diet [21,22]. Nicotine suppresses appetite and increases metabolism, which can help smokers with weight control [23]. Yet, cessation often results in increased appetite and cravings, leading to potential weight gain [23]. ...
... Nicotine suppresses appetite and increases metabolism, which can help smokers with weight control [23]. Yet, cessation often results in increased appetite and cravings, leading to potential weight gain [23]. Approximately half of women and one-third of men report hesitancy to quit or relapse due to weight concerns [24,25]. ...
Background
Combining smoking with poor eating habits significantly elevates the risk of chronic illnesses and early death. Understanding of how dietary quality shifts post-smoking cessation remains limited. The objective of this study is to examine dietary quality – using Healthy Eating Index (HEI – 2020) and its 13 components, among current, former, and never smokers, and particularly the impact of quitting and the duration of cessation on dietary habits.
Methods
A cross-sectional analysis of 31,569 adults from the National Health and Nutrition Examination Survey (NHANES) 2005–2018 was conducted. Dietary quality was assessed using HEI-2020 scores, which were determined by NIH developed - simple HEI scoring algorithm per person. Smoking status was categorized into current, former, and never smokers, with further subdivisions for current (heavy/light smokers) and former smokers (duration post-cessation). Descriptive analysis and multiple regression models weighted to represent the US population were performed.
Results
The current smoking rate was 19.4%, with a higher prevalence in males (22.5%) than females (17.5%). Current smokers reported statistically significantly lower HEI total score than both former and never smokers. Former smokers exhibited HEI scores similar to those of never smokers. The adjusted HEI total scores for current, former, and never smokers were 49.2, 54.0, and 53.3, respectively, with a statistically significant difference (p < 0.001). Moreover, light smokers had better total HEI score than heavy smokers (46.8 vs. 50.8, p < 0.001, respectively), but former and never smokers scored even higher. Quitting smoking immediately improved dietary quality, with former smokers reaching the dietary levels of never smokers within 5–10 years (53.8 vs. 53.3, p > 0.05, respectively). Compared to current smokers, former smokers tended to consume more beneficial foods (e.g., fruits, vegetables, greens and beans, whole grains, proteins, and fatty acids), while also consuming more sodium and less added sugar.
Conclusions
Current smokers, particularly heavy smokers, exhibit poorer dietary habits than former and never smokers. The dietary quality of former smokers aligns with never smokers over time, highlighting the positive impact of smoking cessation on diet. This has implications for reducing chronic disease risks associated with poor diet and smoking.
... those with the greatest health impact or highest reach), and c) detecting potential spillover effects (i.e. where targeting one health behaviour leads to compensatory changes in other behaviours) [13]. For instance, when some individuals reduce cigarette smoking, the reward value and consumption of 'treat foods' increases, resulting in weight gain [13]. ...
... where targeting one health behaviour leads to compensatory changes in other behaviours) [13]. For instance, when some individuals reduce cigarette smoking, the reward value and consumption of 'treat foods' increases, resulting in weight gain [13]. ...
Background
Health-risk behaviours such as smoking, unhealthy nutrition, alcohol consumption, and physical inactivity (termed SNAP behaviours) are leading risk factors for multimorbidity and tend to cluster (i.e. occur in specific combinations within distinct subpopulations). However, little is known about how these clusters change with age in older adults, and whether and how cluster membership is associated with multimorbidity.
Methods
Repeated measures latent class analysis using data from Waves 4–8 of the English Longitudinal Study of Ageing (ELSA; n = 4759) identified clusters of respondents with common patterns of SNAP behaviours over time. Disease status (from Wave 9) was used to assess disorders of eight body systems, multimorbidity, and complex multimorbidity. Multinomial and binomial logistic regressions were used to examine how clusters were associated with socio-demographic characteristics and disease status.
Findings
Seven clusters were identified: Low-risk (13.4%), Low-risk yet inactive (16.8%), Low-risk yet heavy drinkers (11.4%), Abstainer yet inactive (20.0%), Poor diet and inactive (12.9%), Inactive, heavy drinkers (14.5%), and High-risk smokers (10.9%). There was little evidence that these clusters changed with age. People in the clusters characterised by physical inactivity (in combination with other risky behaviours) had lower levels of education and wealth. People in the heavy drinking clusters were predominantly male. Compared to other clusters, people in the Low-risk and Low-risk yet heavy drinkers had a lower prevalence of all health conditions studied. In contrast, the Abstainer but inactive cluster comprised mostly women and had the highest prevalence of multimorbidity, complex multimorbidity, and endocrine disorders. High-risk smokers were most likely to have respiratory disorders.
Conclusions
Health-risk behaviours tend to be stable as people age and so ought to be addressed early. We identified seven clusters of older adults with distinct patterns of behaviour, socio-demographic characteristics and multimorbidity prevalence. Intervention developers could use this information to identify high-risk subpopulations and tailor interventions to their behaviour patterns and socio-demographic profiles.
... Additionally, the role of smoking in cancer development is worsened by Quitting smoking is undoubtedly a positive move for overall health, but it can introduce challenges related to diet [21,22]. Nicotine suppresses appetite and increases metabolism, which can help smokers with weight control [23]. Yet, cessation often results in increased appetite and cravings, leading to potential weight gain [23]. ...
... Nicotine suppresses appetite and increases metabolism, which can help smokers with weight control [23]. Yet, cessation often results in increased appetite and cravings, leading to potential weight gain [23]. Approximately half of women and one-third of men report hesitancy to quit or relapse due to weight concerns [24,25]. ...
Background
Combining smoking with poor eating habits significantly elevates the risk of chronic illnesses and early death. Understanding of how dietary quality shifts post-smoking cessation remains limited. The objective of this study is to examine dietary quality – using Healthy Eating Index (HEI – 2020) and its 13 components, among current, former, and never smokers, and particularly the impact of quitting and the duration of cessation on dietary habits.
Methods
A cross-sectional analysis of 31,569 adults from the National Health and Nutrition Examination Survey (NHANES) 2005-2018 was conducted. Dietary quality was assessed using HEI-2020 scores, which were determined by NIH developed - simple HEI scoring algorithm per person. Smoking status was categorized into current, former, and never smokers, with further subdivisions for current (heavy/light smokers) and former smokers (duration post-cessation). Descriptive analysis and multiple regression models weighted to represent the US population were performed.
Results
The current smoking rate was 19.4%, with a higher prevalence in males (22.5%) than females (17.5%). Current smokers reported statistically significantly lower HEI total score than both former and never smokers. Former smoker exhibited HEI scores similar to those of never smokers. The adjusted HEI total scores for current, former, and never smokers were 49.2, 54.0, and 53.3, respectively, with a statistically significant difference (p < 0.001). Moreover, light smokers had better total HEI score than heavy smokers (46.8 vs. 50.8, p < 0.001, respectively), but former and never smokers scored even higher. Quitting smoking immediately improved dietary quality, with former smokers reaching the dietary levels of never smokers within 5-10 years (53.8 vs. 53.3, p > 0.05, respectively). Compared to current smokers, former smokers tend to consume more beneficial foods (e.g., fruits, vegetables, greens and beans, whole grains, proteins, and fatty acids), while also consuming more sodium and less added sugar.
Conclusions
Current smokers, particularly heavy smokers, exhibit poorer dietary habits than former and never smokers. The dietary quality of former smokers aligns with never smokers over time, highlighting the positive impact of smoking cessation on diet. This has implications for reducing chronic disease risks associated with poor diet and smoking.
... Previous studies showed that smokers, experienced weight gain after quitting smoking. 25,28,29 This present study also showed the same result of feeling weight gain after smoking cessation. Gaining weight is most likely related to a higher energy intake with physical inactivity, low resting metabolic rate and high lipoprotein lipase activity. ...
... However, the molecular mechanisms causing weight gain after smoking cessation are poorly understood. 15,29 In this study, alcohol consumption was found to have a significant association with BMI. Alcohol consumption has been shown to be associated with metabolic syndrome consisting of increased abdominal fat, diabetes mellitus, high blood pressure, and dyslipidemia. ...
Introduction: Overweight and obesity caused by excess body fat accumulation is a leading public health problem globally. The objective of this study was to investigate the overweight and obesity prevalence and its accompanying factors among educational office staff in Kuantan, Malaysia. Materials and Methods: A cross-sectional study was conducted among the staff of Widad University College (WUC) and Widad College (WC) using a self-administered validated questionnaire. Socio-demographic characteristics of the participants and obesity-related health information such as body mass index (BMI), smoking, alcoholics, physical activities and blood pressure status were obtained. The BMI was determined considering the participants weight in kilograms (kg) divided by the square of height in meters (kg/m2). Correlating the BMI with sociodemographic and health-related factors, the participants were characterized as underweight, normal, overweight and obese and presented as mean, frequency and percentage distribution. A p-value of <0.05 means a statistically significant result. Results: Overweight prevalence was 28.3% while obesity was 16.2 %. There were 13% of respondents underweight. A higher rate was found in males and Malay participants. Age, education, occupation, income, smoking, alcoholics, physical inactivity and hypertension were significantly linked to BMI. Conclusions: Overweight and obese constitute around 44% of the staff at WUC and WC, while 13 % were underweight. It is necessary to motivate employees through an effective training program organized by the employer towards maintaining a healthy lifestyle and thereby maintaining good health and improving work performance. Bangladesh Journal of Medical Science Vol. 22 No. 04 October’23 Page : 833-841
... 23 Prospective population surveys and trials show that weight gain and fear of weight gain can create a reluctance to quit smoking and remain abstinent; this may be especially true among women and initially heavier smokers. [24][25][26] A meta-analysis study reported an average gain of 4.67 kg [95% confidence interval (CI) 3.96 to 5.38 kg] after 12 months of abstinence. 27 Among a range of options for preventing long-term weight gain after smoking cessation, PA appears to be one of the most promising, 28 by increasing energy expenditure and metabolic rate, and also by self-regulation of energy intake, particularly emotional snacking. ...
... As it was only possible from the data to identify individuals who were non-smokers 1 year after previously describing themselves as current smokers, it was necessary to account for those smokers who attempted to quit but relapsed before being surveyed the following year. We estimate relatively high rates of quit attempts (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25) ...
Background
Physical activity can support smoking cessation for smokers wanting to quit, but there have been no studies on supporting smokers wanting only to reduce. More broadly, the effect of motivational support for such smokers is unclear.
Objectives
The objectives were to determine if motivational support to increase physical activity and reduce smoking for smokers not wanting to immediately quit helps reduce smoking and increase abstinence and physical activity, and to determine if this intervention is cost-effective.
Design
This was a multicentred, two-arm, parallel-group, randomised (1 : 1) controlled superiority trial with accompanying trial-based and model-based economic evaluations, and a process evaluation.
Setting and participants
Participants from health and other community settings in four English cities received either the intervention ( n = 457) or usual support ( n = 458).
Intervention
The intervention consisted of up to eight face-to-face or telephone behavioural support sessions to reduce smoking and increase physical activity.
Main outcome measures
The main outcome measures were carbon monoxide-verified 6- and 12-month floating prolonged abstinence (primary outcome), self-reported number of cigarettes smoked per day, number of quit attempts and carbon monoxide-verified abstinence at 3 and 9 months. Furthermore, self-reported (3 and 9 months) and accelerometer-recorded (3 months) physical activity data were gathered. Process items, intervention costs and cost-effectiveness were also assessed.
Results
The average age of the sample was 49.8 years, and participants were predominantly from areas with socioeconomic deprivation and were moderately heavy smokers. The intervention was delivered with good fidelity. Few participants achieved carbon monoxide-verified 6-month prolonged abstinence [nine (2.0%) in the intervention group and four (0.9%) in the control group; adjusted odds ratio 2.30 (95% confidence interval 0.70 to 7.56)] or 12-month prolonged abstinence [six (1.3%) in the intervention group and one (0.2%) in the control group; adjusted odds ratio 6.33 (95% confidence interval 0.76 to 53.10)]. At 3 months, the intervention participants smoked fewer cigarettes than the control participants (21.1 vs. 26.8 per day). Intervention participants were more likely to reduce cigarettes by ≥ 50% by 3 months [18.9% vs. 10.5%; adjusted odds ratio 1.98 (95% confidence interval 1.35 to 2.90] and 9 months [14.4% vs. 10.0%; adjusted odds ratio 1.52 (95% confidence interval 1.01 to 2.29)], and reported more moderate-to-vigorous physical activity at 3 months [adjusted weekly mean difference of 81.61 minutes (95% confidence interval 28.75 to 134.47 minutes)], but not at 9 months. Increased physical activity did not mediate intervention effects on smoking. The intervention positively influenced most smoking and physical activity beliefs, with some intervention effects mediating changes in smoking and physical activity outcomes. The average intervention cost was estimated to be £239.18 per person, with an overall additional cost of £173.50 (95% confidence interval −£353.82 to £513.77) when considering intervention and health-care costs. The 1.1% absolute between-group difference in carbon monoxide-verified 6-month prolonged abstinence provided a small gain in lifetime quality-adjusted life-years (0.006), and a minimal saving in lifetime health-care costs (net saving £236).
Conclusions
There was no evidence that behavioural support for smoking reduction and increased physical activity led to meaningful increases in prolonged abstinence among smokers with no immediate plans to quit smoking. The intervention is not cost-effective.
Limitations
Prolonged abstinence rates were much lower than expected, meaning that the trial was underpowered to provide confidence that the intervention doubled prolonged abstinence.
Future work
Further research should explore the effects of the present intervention to support smokers who want to reduce prior to quitting, and/or extend the support available for prolonged reduction and abstinence.
Trial registration
This trial is registered as ISRCTN47776579.
Funding
This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment ; Vol. 27, No. 4. See the NIHR Journals Library website for further project information.
... those with the greatest health impact or highest reach), and c) detecting potential spillover effects (i.e. where targeting one health behaviour leads to compensatory changes in other behaviours) [13]. For instance, when some individuals reduce cigarette smoking, the reward value and consumption of 'treat foods' increases, resulting in weight gain [13]. ...
... where targeting one health behaviour leads to compensatory changes in other behaviours) [13]. For instance, when some individuals reduce cigarette smoking, the reward value and consumption of 'treat foods' increases, resulting in weight gain [13]. ...
... People who quit smoking gain more weight than those who continue smoking and more than those who have never smoked [5,6]. The mechanism behind the weight gain is related to decreased metabolic rate, decreased physical activity and eating habits [7]. Mortality and risk of chronic diseases related to smoking seem to decrease over the long term after smoking cessation even though there is substantial weight gain [6,[8][9][10]. ...
... Previous studies, in accordance with ours, have shown that smoking cessation is related to weight gain [5][6][7]11]. In a previous long-term follow-up study, more weight gain occurred in those who quit smoking compared to those who continued smoking or had never smoked [5]. ...
Background
Smoking is the biggest preventable factor causing mortality and morbidity and the health benefits of smoking cessation are commonly known. Smoking cessation-related weight gain is well documented. We evaluated the association between smoking cessation and the incidence of obesity-related morbidities such as hypertension, diabetes and metabolic syndrome as well as mortality. We also evaluated telomere length related to smoking cessation.
Material and methods
This study was part of the OPERA (Oulu Project Elucidating Risk of Atherosclerosis) study. The mean follow up time among the 600 study subjects was 20 years. We divided the study subjects into four groups by smoking status (“never”, “current”, “ex-smokers” and “quit”) and analyzed their health status. “Ex-smokers” had quit smoking before baseline and “quit” quit during the follow-up time. Information about total mortality between the years 2013–2020 was also utilized.
Results
During the follow-up time systolic blood pressure decreased the most in the “current” and in the “ex-smoker” groups. Office SBP decreased the least in the “quit” group (p = 0.001). BMI increased the most in the “quit” and the least in the “ex-smokers” group (p = 0.001). No significant increases were seen in the incidence of obesity-related-diseases, such as metabolic syndrome, hypertension and diabetes was seen. There was no significant difference in the shortening of telomeres. Odds of short-term mortality was increased in the “current” group (2.43 (CI 95% 1.10; 5.39)), but not in the “quit” (1.43 (CI 95% 0.73–2.80)) or “ex-smoker” (1.02 (CI 95% 0.56–1.86)) groups when compared to “never” group.
Conclusions
Even though, the blood pressure levels were unfavorable in the “quit” group, there was no significant increase in the incidence of obesity-related-diseases, and a noticeable benefit in short-term mortality was seen during the 6-year follow-up. The benefits of smoking cessation outweigh the disadvantages in the long-term.
... Several studies have shown that individuals who quit smoking tend to increase their body weight. [110] The possibility of gaining weight can stop smokers from quitting and increases the risk of relapse, particularly in women. [111] However, Sherrill-Mittleman et al. [112] in the study involving 35,986 young US Air Force recruits (mean age 20 years) reported that although there was a statistically significant relationship between smoking and body weight in White males, the effect range was about 1 kg. ...
... This can be explained by several mechanisms such as increased energy intake, decreased resting metabolic rate, decreased physical activity and increased lipoprotein lipase activity. [100,110] Furthermore, smoking cessation is associated with increased plasma adiponectin levels. [117] Early abstinence from cigarette smoking seems to be associated with increased plasma concentration of ghrelin. ...
The metabolic syndrome (MetS) is a common cluster of pre-morbid, modified metabolic–vascular risk factors/diseases (visceral obesity, hyperglycaemia, dyslipidaemia and hypertension) associated with increased cardiovascular (CV) morbidity, fatty liver and risk of cancer. Several studies reported a higher incidence of MetS in smokers. Cigarette smoking plays a substantial role in the pathogenesis of numerous chronic diseases such as CV disease (CVD), cancer, lung disease and others. However, due to the existence of the so-called “smoking paradox”, the impact of this risk factor on CVD mortality is still not clear. Smoking cigarettes may increase risk of MetS or worsen it by numerous mechanisms. Furthermore, individuals who quit smoking tend to increase their body weight. The possibility of gaining weight can stop smokers from quitting and increases the risk of relapse, particularly in women. Herein, we review the cigarette smoking status (active/cessation) in relation to the MetS.
... It has been suggested that nicotine has a catabolic effect on fat mass but, at the same time, promotes the redistribution of fat toward the abdominal region [13]. Furthermore, smoking cessation is often accompanied by weight gain, which could lead to greater visceral fat accumulation if not accompanied by increased physical activity or dietary changes [14]. ...
Citation: Gordito Soler, M.; López-González, Á.A.; Tárraga López, P.J.; Martínez-Almoyna Rifá, E.; Martorell Sánchez, C.; Vicente-Herrero, M.T.; Paublini, H.; Ramírez-Manent, J.I. Association of Sociodemographic Variables and Healthy Habits with Body and Visceral Fat Values in Spanish Workers. Medicina 2025, 61, 150. Abstract: Background and Objectives: The accumulation of fat in the body, especially visceral fat, is associated with various cardiometabolic conditions such as diabetes mellitus and fatty liver. The reasons for the accumulation of this fat are diverse. Some studies, also in the working population, have shown a clear association between sociodemographic variables and health habits with scales that assess overweight and obesity. This study aims to determine how certain sociodemographic variables, such as age, gender, and socioeconomic level, as well as certain healthy habits like physical activity and tobacco consumption, affect the levels of body and visceral fat. Materials and Methods: We conducted a descriptive and cross-sectional study involving 8590 Spanish workers. The percentage of body and visceral fat was measured using a bioimpedance analysis with a Tanita DC 430MA device. Results: Both the average values and the prevalence of elevated body and visceral fat increase with age and decrease with social class and lower levels of physical activity. These values are higher in smokers. A multivariate analysis shows that the variables most influential in increasing the risk of high levels of both body and visceral fat are age and low levels of physical activity. Conclusions: The profile of a person at high risk of having elevated body and visceral fat levels is an older male with a low socioeconomic status who smokes and leads a sedentary lifestyle.
... According to previously published data from our unit, the prevalence of smoking during pregnancy was higher among obese women (BMI ≥ 30 kg/m 2 ), which was in accordance with the findings of the current study [9]. Several studies have suggested that smoking behavior is significantly related with body weight and obesity [24][25][26][27]. Regarding parity, the literature suggests that multiparous women are less likely to smoke before pregnancy (OR: 0.77; 95% CI: 0.67-0.89), ...
Background and Objectives: Smoking has adverse effects on both maternal and fetal health and its incidence varies among different countries. The aim of this study was to identify the prevalence of smoking during pregnancy and to identify factors associated with smoking. Materials and Methods: This was a retrospective study conducted at the Third Department of Obstetrics and Gynecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece, during an 11-year period (2013–2023). All women receiving antenatal care in our unit were eligible to participate when they attended the prenatal unit for the first trimester nuchal translucency scan (11+0–13+6 weeks). Results: Of the 12,074 pregnant women included in the study, 5005 (41.5%) reported themselves as smokers before pregnancy; the smoking cessation rate due to pregnancy was 70.2% (3516/5005) and the prevalence of smoking in pregnancy was 12.3% (1489/12,074). Multiparity was associated with less odds of smoking before pregnancy (OR: 0.79; 95% CI: 0.73–0.85), whereas advanced maternal age (OR: 1.17; 95% CI: 1.07–1.27) and obesity (OR: 1.44; 95% CI: 1.29–1.6) were associated with higher odds of smoking before pregnancy. Smoking prevalence in pregnancy was lower in women that conceived via assisted reproductive techniques (ARTs) (OR: 0.52; 95% CI: 0.38–0.70) and higher in cases of multiparity (OR: 1.12; 95% CI: 1.008–1.26) and maternal obesity (OR: 1.55; 95% CI: 1.20–2.00). Conception via ARTs was associated with higher odds of smoking cessation (OR: 1.9; 95% CI: 1.38–2.69), whereas multiparous (OR: 0.7; 95% CI: 0.62–0.8) and obese women (OR: 0.72; 95% CI: 0.61–0.85) were less likely to quit smoking. Conclusions: Pregnancy is a strong motivator for women to quit smoking, especially in primiparous women and those undergoing ARTs. Our findings highlight the need for more consistent smoking prevention and health promotion strategies in Greece as a very high proportion of women smoke before pregnancy and a substantial proportion continue in pregnancy.
... 115 The possible reason for increasing weight after quitting smoking post-disaster include nicotine withdrawal, which contributes to increased food consumption and decreased energy expenditure, potentially increasing the risk of obesity. 116 Other reasons for quitting smoking include concerns about family members' health, lack of designated smoking areas, and overall health concerns. Overall, smoking poses significant risks to respiratory health, decreases lung function, intensifies asthma, causes chronic obstructive pulmonary disease, and contributes to cardiovascular problems. ...
Background
Natural disasters occur unexpectedly, leading to long-term consequences like obesity. That contributes to various noncommunicable diseases such as cardiovascular disease, diabetes, and cancer. This review aimed to examine the link between natural disasters and obesity, along with related risk factors.
Objective
This systematic review aimed to examine the relationship between natural disasters and obesity, as well as the associated risk factors.
MethodsA thorough search was conducted using electronic databases such as PubMed, Scopus, Web of Science, HINARI, and Google Scholar. Additional articles were manually searched. Studies that reported weight gain and risk factors were included. The quality of the studies was assessed using the Joanna Briggs Institute (JBI) tools. Data were collected from eligible articles and synthesized.
ResultsThe participants in this research ranged from 3 months to 67 years old. Of the 17 articles, 11 focused on children, while the 5 focused on adults and 1 on adolescents. All studies followed a cohort design, with follow-up periods varying from 6 months to 15.5 years. Results indicated weight gain post-disaster, with risk factors including sedentary behavior, unhealthy eating habits, maternal high Body Mass Index (BMI), mixed feeding, stress, alcohol consumption, coastal residence, temporary housing, and timing from disaster onset.
Conclusions
This research emphasizes the significance of addressing post-disaster obesity as a pivotal aspect of public health, suggesting its integration with immediate priorities such as trauma management. Emphasizing its long-lasting effects across generations, the study offers policymakers valuable insights to develop effective approaches in tackling post-disaster obesity.
... The propensity for ailments such as lung cancer, heart disease, and chronic obstructive pulmonary disease is equivalent for men and women with smoking habits (Centers for Disease Control and Prevention 2014). The correlation between tobacco consumption and diminished insulin sensitivity has been extensively documented in the literature (Filozof, Fernández Pinilla, and Fernández-Cruz 2004), implicating it in the pathogenesis of metabolic syndrome (Roberts, Hevener, and Barnard 2013). Moreover, a meta-analysis of 13 prospective studies reported a significant positive association between active smoking and risk of metabolic syndrome (pooled relative risk = 1.26, 95% confidence interval = 1.10-1.44) ...
Objective
Smoking, a risk factor for metabolic syndrome, is associated with stress relief and pleasure among women, which can hinder efforts to quit smoking, particularly in workplaces. We investigated the metabolic syndrome indicators among working Korean women based on smoking status and workplace size to devise tailored smoking cessation policies.
Design
Retrospective longitudinal study.
Sample
Data from 53,126 working Korean women aged 15–64 years were collected between 2009 and 2015.
Measurements
Data were collected from the Female Employees Database derived from the National Health Insurance Service. To assess the trend of metabolic syndrome indicators among working Korean women according to smoking status and workplace size, repeated‐measures analysis of variance was used.
Results
Significant interactions were found between time and group for waist circumference (WC), diastolic blood pressure (DBP), and fasting glucose (FG) levels. Trends of metabolic syndrome were more prevalent in small‐ and medium‐sized enterprises (SMSEs) than in large‐sized enterprises (LSEs). Current smokers in the LSE group had the highest WC, triglyceride, systolic blood pressure, and FG values.
Conclusions
These insights may be valuable for devising policies and interventions to improve metabolic health among women working in SMSEs and current smokers in LSEs.
... Nicotine, the primary addictive substance in cigarettes, increases metabolic rate. When smoking is discontinued, the metabolic rate decreases, leading to fewer calories being burned at rest [161]. Research indicates that quitting smoking results in an average weight gain of 4-5 kg within the first year, though this can vary widely among individuals [162]. ...
Background: Obesity remains a widely debated issue, often criticized for the limitations in its identification and classification. This study aims to compare two distinct systems for classifying obesity: body mass index (BMI) and body fat percentage (BFP) as assessed by bioelectrical impedance analysis (BIA). By examining these measures, the study seeks to clarify how different metrics of body composition influence the identification of obesity-related risk factors. Methods: The study enrolled 1255 adults, comprising 471 males and 784 females, with a mean age of 36 ± 12 years. Participants exhibited varying degrees of weight status, including optimal weight, overweight, and obesity. Body composition analysis was conducted using the TANITA Body Composition Analyzer BC-418 MA III device (T5896, Tokyo, Japan), evaluating the following parameters: current weight, basal metabolic rate (BMR), adipose tissue (%), muscle mass (%), and hydration status (%). Results: Age and psychological factors like cravings, fatigue, stress, and compulsive eating were significant predictors of obesity in the BMI model but not in the BFP model. Additionally, having a family history of diabetes was protective in the BMI model (OR: 0.33, 0.11–0.87) but increased risk in the BFP model (OR: 1.66, 1.01–2.76). The BMI model demonstrates exceptional predictive ability (AUC = 0.998). In contrast, the BFP model, while still performing well, exhibits a lower AUC (0.975), indicating slightly reduced discriminative power compared to the BMI model. Conclusions: BMI classification demonstrates superior predictive accuracy, specificity, and sensitivity. This suggests that BMI remains a more reliable measure for identifying obesity-related risk factors compared to the BFP model.
... The relationship between smoking and body weight is paradoxical. While smoking has long been perceived as a weight control tool due to its appetite-suppressing effects [7], evidence suggests that smokers may have higher levels of abdominal fat compared to non-smokers [8]. Additionally, smoking cessation is often associated with weight gain, further complicating the relationship between these two factors [9]. ...
Obesity and smoking are two major public health challenges, both contributing significantly to morbidity and mortality worldwide. This study investigates the association between smoking behaviors and obesity among men in Taiwan, focusing on body mass index (BMI) and waist circumference (WC) as indicators of general and abdominal obesity. The sample consisted of 27,908 men categorized into five groups based on their smoking status: never smoking (NS), former smoking (FS), light-intensity smoking (LIS), moderate-intensity smoking (MIS), and heavy-intensity smoking (HIS). Our findings reveal a significant association between smoking and increased obesity risk, particularly among light- and moderate-intensity smokers. Socioeconomic factors such as education and income levels were also found to influence these behaviors. These results underscore the importance of integrated public health strategies that address both smoking cessation and obesity prevention.
... The point estimates of the potential-outcome means corresponding to different treatment levels both fell within the interval [0, 1]. The point estimates of the risk difference and the risk ratio indicated that smoking reduces obesity rates, which is consistent with some previous studies [43,47]. This is mainly because nicotine is thought to suppress appetite and increase metabolism, potentially leading to lower body weight among smokers. ...
Potential outcomes play a fundamental and important role in many causal inference problems. If the potential-outcome means are identifiable, a series of causal effect measures, including the risk difference, the risk ratio, and the treatment benefit rate, among others, can also be identified. However, current identification and estimation methods for these means often implicitly assume that the collected data for analysis are measured precisely. In many fields such as medicine and economics, the collected variables may be subject to measurement errors, such as medical diagnostic results and individual wage data. Misclassification, as a non-classic measurement error, can lead to severely biased estimates in causal inference. In this paper, we leverage a combined sample to study the identifiability of potential-outcome means corresponding to different treatment levers under a plausible misclassification assumption for the outcome, allowing the misclassification probability to depend on not only the true outcome but also the covariates. Furthermore, we propose the multiply-robust and semiparametric efficient estimators for the means, consistent even under partial misspecification of the observed data law, based on the semiparametric theory framework. The simulation studies and real data analysis demonstrate the satisfactory performance of the proposed method.
... Nicotine, the primary addictive substance in cigarettes, increases metabolic rate. When smoking is discontinued, the metabolic rate decreases, leading to fewer calories being burned at rest [132]. Research indicates that quitting smoking results in an average weight gain of 4-5 kilograms within the first year, though this can vary widely among individuals [133]. ...
Background: Obesity remains a widely debated issue, often criticized for the limitations in its identification and classification. This study aims to compare two distinct systems for classifying obesity: Body Mass Index (BMI) and body fat percentage (BFP) as assessed by bioelectrical impedance analysis (BIA). By examining these measures, the study seeks to clarify how different metrics of body composition influence the identification of obesity-related risk factors. Methods: The study enrolled 1255 adults, comprising 471 males and 784 females, with a mean age of 36 ± 11.90 years. Participants exhibited varying degrees of weight status, including optimal weight, overweight, and obesity. Body composition analysis was conducted using the TANITA Body Composition Analyzer BC-418 MA III device (T5896, Tokyo, Japan), evaluating the following parameters: current weight, basal metabolic rate (BMR), adipose tissue (%), muscle mass (%), and hydration status (%). Results: Age and psychological factors like cravings, fatigue, stress, and compulsive eating were significant predictors of obesity in the BMI model but not in the BFP model. Additionally, having a family history of diabetes was protective in the BMI model (OR: 0.33) but increased risk in the BFP model (OR: 1.66). The BMI model demonstrates exceptional predictive ability (AUC=0.998). In contrast, the BFP model, while still performing well, exhibits a lower AUC (0.975), indicating slightly reduced discriminative power compared to the BMI model. Conclusions:BMI classification demonstrates superior predictive accuracy, specificity, and sensitivity. This suggests that BMI remains a more reliable measure for identifying obesity-related risk factors compared to the BFP model.
... There are a few possible explanations for why former smoking status may have a higher association with arthritis. Smoking cessation can lead to weight gain, and this extra weight can put additional stress on the joints, which may increase the association between arthritis and former smoking status [89]. On the other hand, in current smokers, the anti-inflammatory effect of nicotine may mask the symptoms of arthritis [90]. ...
Arthritis is associated with health challenges. Lifestyle traits are believed to influence arthritis development and progression; however, data to support personalized treatment regimens based on holistic lifestyle factors are missing. This study aims to provide a comprehensive list of associations between lifestyle traits and the health status of individuals with arthritis in the Canadian population, using binary logistic regression analysis on data from the Canadian Community Health Survey, which includes 104,359 respondents. Firstly, we explored the association between arthritis and various aspects of health status including self-reported lifestyle factors. Secondly, we examined the associations between self-reported dietary intake and smoking status with general, mental, and oral health, and sleep disturbance among individuals both with and without arthritis. Our analysis revealed that individuals with arthritis reported considerably poorer general, mental, and oral health, and poorer sleep quality compared to those without arthritis. Associations were also found between self-reported dietary intake and various measures of health status in individuals with arthritis. Smoking and exposure to passive smoking were associated not only with arthritis but also with compromised sleep quality and poorer general, mental, and oral health in people with and without arthritis. This study highlights the need for personalized and holistic approaches that may include a combination of dietary interventions, oral health improvements, sleep therapies, and smoking cessation for improved arthritis prevention and care.
... Quitting smoking in early pregnancy/still smoking had significantly higher RRs for all four not having a normal BMI. People believe smoking is an effective way to reduce body weight [45], and girls who are overweight or obese are more likely to initiate smoking [46]. As our analysis was cross-sectional, the higher RRs for overweight and obesity were reflected in overweight or obese women who wanted to lose weight by smoking. ...
Socioeconomic status and smoking are reportedly associated with underweight and obesity; however, their associations among pregnant women are unknown. This study aimed to investigate whether socioeconomic factors, namely educational attainment, household income, marital status, and employment status, were associated with pre-pregnancy body mass index (BMI) categories, including severe-moderate underweight (BMI ≤ 16.9 kg/m²), mild underweight (BMI, 17.0–18.4 kg/m²), overweight (BMI, 25.0–29.9 kg/m²), and obese (BMI ≥ 30.0 kg/m²) among Japanese pregnant women using data from the Japan Environment and Children’s Study (JECS). In total, pregnant women were included 96,751. Age- and parity-adjusted multivariable multinomial logistic regression analyses assessed socioeconomic factors and smoking associations with falling within abnormal BMI categories (normal BMI as the reference group). Lower education and lower household were associated with overweight and obesity, and, especially, lowest education and household income had relatively higher point estimate relative ratios (RRs) of 3.97 and 2.84, respectively. Regarding the risks for underweight, however, only junior high school education had a significantly higher RR for severely to moderately underweight. Regarding occupational status, homemakers or the unemployed had a higher RR for severe-moderate underweight, overweight, and obesity. Unmarried, divorced, or bereaved women had significantly higher RRs for mildly underweight status. Quitting smoking early in pregnancy/still smoking had higher RRs for all four not having normal BMI outcomes; however, quitting smoking before pregnancy had a higher RR only for obese individuals. Lower educational attainment and smoking are essential intervention targets for obesity and severe-moderate underweight prevention in younger women. Lower household income is also a necessary target for obesity.
... However, a different study concluded that heavy smokers significantly gain weight due to the higher consumption of alcohol in comparison to light smokers, although there is no considerable difference in BMI between smokers and nonsmokers, heavy smokers exhibit an unfavourable distribution of metabolic fat [36]. Besides a study showed that heavy smokers are heavier in comparison to light smokers which suggests that their weight loss may be a fall in lean body mass rather than a decrease in fat mass [37]. ...
... Analyzing the association between the amount (area) of adipose tissue and cardiovascular risk factors, we found that smokers/ex-smokers had a lower quantity of subcutaneous adipose tissue and patients with hypertension had a larger visceral adipose tissue area. These data are in accordance with the literature [41][42][43]. We reported a negative and moderate correlation between smoking load and subcutaneous adipose tissue area, which can be explained by lipogenesis inhibition in the subcutaneous region of the body caused by nicotine inhalation [44]. ...
The prevalence of obesity has doubled, with a concomitant increase in cardiovascular disease. This study aimed to compare the characteristics of visceral, subcutaneous and peri-aortic adipose tissue determined with computed tomography (CT) scans and to correlate them with cardiovascular risk factors, anthropometric measures and medication. An observational and prospective study was conducted, and 177 subjects were included. Peri-aortic adipose tissue had the highest density, while the subcutaneous adipose tissue had the lowest. The density of subcutaneous adipose tissue differs from the density of visceral (p = 0.00) and peri-aortic adipose tissue (p = 0.00). Smokers/ex-smokers had a lower area (p = 0.00) and density (p = 0.02) of subcutaneous adipose tissue. Multiple linear regression analysis showed that sex was a predictor of subcutaneous adipose tissue area (β = −0.27, t = −3.12, p = 0.00) but smoking habits were not. After controlling for sex, we found that the association between smokers/ex-smokers and area of subcutaneous adipose tissue was lost, but the association with density persisted. Patients with hypertension had a higher visceral adipose tissue area, and this relationship was maintained even after adjusting for gender. Peri-aortic adipose tissue is similar to visceral and distinct from subcutaneous adipose tissue. Cardiovascular risk factors have different influences in distinct adipose compartments.
... It is important to note that smoking has both short-and long-term effects on body weight [186]. In the short term, smoking can suppress appetite and increase metabolism, thereby resulting in weight loss [187,188]. It is believed that this effect is due to stimulation of the nervous system by nicotine, which leads to increased energy expenditure and decreased appetite. ...
Tobacco smoking is a major cause of chronic obstructive pulmonary disease (COPD) and atherosclerotic cardiovascular disease (ASCVD). These diseases share common pathogenesis and significantly influence each other’s clinical presentation and prognosis. There is increasing evidence that the mechanisms underlying the comorbidity of COPD and ASCVD are complex and multifactorial. Smoking-induced systemic inflammation, impaired endothelial function and oxidative stress may contribute to the development and progression of both diseases. The components present in tobacco smoke can have adverse effects on various cellular functions, including macrophages and endothelial cells. Smoking may also affect the innate immune system, impair apoptosis, and promote oxidative stress in the respiratory and vascular systems. The purpose of this review is to discuss the importance of smoking in the mechanisms underlying the comorbid course of COPD and ASCVD.
... As data was not normally distributed, Shapiro-Wilk test was applied for normality and then compared by independent t-test and value of ≤ 0.05 was taken significant. 46 were nonsmokers. This study result showed that 20 (10%) of individuals were underweight; 90 (45%) were normal, 64 (32%) were pre obese, 20 (10%) belonged to obese class 1 and 6 (3%) were of obese class 2. Independent t test was applied for comparison of BMI with smokers and non-smokers and showed that BMI is associated with smoking as value is of .017 ...
Objective: To compare Body Mass Index among smokers and non-smokers. Study Design: Cross-sectional study. Setting: Aziz Fatima Medical and Dental College. Period: January to September 2018. Material & Methods: Study was conducted and 200 male students of age 18 to 25 years of college were included in this study. Height and weight were measured and data was collected and analyzed by SPSS version 22. As data was not normally distributed, Shapiro-Wilk test was applied for normality and then compared by independent t-test and value of ≤ 0.05 was taken significant. Results: Out of 200 study participants, 116 (58%) with mean and St. Deviation of 25.2886 ± 4.417 were smokers and 96 (42%) with mean and St. Deviation of 23.1026 ±4.46 were nonsmokers. This study result showed that 10% of individuals were underweight; 45% were normal, 32% were pre obese, 10% belonged to obese class 1 and 3% were of obese class 2. Results showed that BMI is associated with smoking as value is of .017 which is highly significant. Conclusion: Our study underlined the comparison of BMI among smokers and nonsmokers among students. The study results showed that association between smokers and obesity according to their BMI was statistically significant.
... In favor of our study numerous studies have shown that persons who quit smoking are likely to gain weight,the prospect of gaining weight can discourage smokers from quitting.as reviewed by Ward et al. (2001) 21 and Filozof et al. (2004) 22. The largest and most significant smoking group, with 59.3% of smokers, was between the ages of 21 and 30. ...
Awareness among smokers related to adverse effects of tobacco smoking on periodontal health and the challenges faced to quit the habit: a cross sectional survey Abstract Background: Periodontitis is an inflammatory disease of tooth-supporting structures caused mainly by specific microorganisms or groups of specific microorganisms present in dental plaque. Smoking is considered as a global epidemic. Its adverse effects are well known which ranges from staining of teeth to life threatening diseases such as cancer. Aims & objective: The aim of the study is to assess awareness of smokers about the adverse effects of tobacco smoking on periodontal health and the challenges faced to quit the habit-a cross sectional survey. Material& Methods: A total number of 993 male smoker subjects were included, divided into three study groups. Group I-young age adults 18-35 yrs. (n=333), GroupII-middle age adults 36-55 yrs. (n=331) and Group III-old age adults ≥ 55 yrs. (n=329). Participants were subcategorized by education into primary (I to VIII), secondary (IX to XII) and tertiary education (graduate, postgraduate and PhD.) in individual each age group. Case history and questionnaire was recorded.
... The underlying mechanism of post-cessation WG is unclear (Chao et al., 2019). Studies prompt that a decrease in the basal metabolic rates, an imbalance in caloric intake due to the nicotine withdrawal, an increase in appetite, or eating behavior changes, stand out as feasible explanations (Filozof et al., 2004;Gottfredson & Sokol, 2019;Jo et al., 2002;Mineur, 2011;Stojakovic et al., 2017). ...
Objective: Weight gain (WG) is one of the most widespread consequences of smoking cessation, although there is a great variability of post-cessation weight changes among smokers. Its approach is critical because it depicts an important barrier to trying to quit smoking and because it has been considered as a long-term predictor of relapse. Notwithstanding, little is known about post-cessation WG specifically among depressed smokers. The current study sought to: (1) describe the WG at posttreatment and follow-ups in smokers with depression, (2) examine the predictors of posttreatment WG, and (3) analyze whether post-cessation WG predicts smoking relapse at 6-month follow-up. Methods: The sample was comprised of 125 smokers with depression who achieved tobacco abstinence at posttreatment following a psychological smoking cessation intervention. Smoking abstinence was biochemically verified through carbon monoxide and urine cotinine. Multiple linear and hierarchical logistic regressions were conducted to examine predictors of WG at posttreatment and tobacco relapse at 6-month follow-up, respectively. Results: Abstinent participants gained an average of 3.55 kg at 6-month follow-up compared to 1.49 kg among participants who relapsed. Greater nicotine dependence (β = .372, p = .001) and diastolic pressure at baseline (β = .252, p = .021) predicted higher WG at end of treatment. WG at posttreatment increased the likelihood of relapse 6 months later (B = .303, OR = 1.354; 95% CI [1.006, 1.822]). Limitations: Weight concerns, disordered eating, and BMI were not recorded, and they could be related to the present findings. Conclusions: These results suggest that individuals with depression during treatment for smoking cessation should be regularly screened and offered treatment to prevent WG.
... While some studies have shown no signi cant association between smoking status (number of smokers) and body mass index (BMI) (Zbikowski, et al. 2011), others have suggested that smoking may be associated with lower BMI (Klesges, et al. 1989) and smoking cessation is associated with increased BMI (Munafò, et al. 2009). Also, several studies have shown that smoking behavior is closely related to body weight and the incidence of obesity (Albanes, et al. 1987;Filozof, et al. 2004) and showed that people who smoked were heavier than people who did not have ever been smokers (Flegal, et al. 1995;Mackay, et al. 2013). It is possible that this relationship re ects an inverse factor since overweight individuals who are trying to lose weight are likely to be more motivated to start smoking (Chiolero, et al. 2008). ...
Today, the inequality of distribution of built environments leads to the formation of advantaged and disadvantaged neighborhoods. Advantage neighborhoods have good accessibility (Distance to) and availability (Number of) service centers. If the neighborhoods have some service centers that don’t provide healthy lifestyles, especially in increasing obesity, they can decline community health in these areas. So, this research tries to have a spatial view of obesity as a dependent variable. Independent variables are the number of and distance to food, smoking, and physical activity centers that are based on theoretical concepts. We analyzed them with dependent variables on SEIFA Clusters at SA2 level. This research has used spatial analysis methods such as BILISA Cluster on Local Moran I for clustering, GWR for spatial correlation which is a base of the analysis method. The results in the Great Melbourne Area (GMA) show that the level of accessibility is more important than availability and some SA2s in the low levels of SEIFA haven’t good access to a healthy built environment and which makes them more obese.
... As a result, thorough monitoring of body weight and promotion of lifestyle modifications should be part of smoking cessation programs. In this sensitive demographic, the combination of bupropion and nicotine replacement, particularly nicotine gum, may assist to avoid weight gain [83]. ...
Obesity is a chronic, multifactorial condition described as an abnormal or excessive buildup of body fat that endangers an individual's health. It is a huge global public health problem. Prednisone and anti-psychotic medicines are known to cause weight gain and obesity. In this era of precision medicine, it is critical to identify individuals who are most likely to gain weight as a result of pharmaceutical use. Hypoglycemic anti-diabetes medicines, psychotropic agents (atypical antipsychotics, antidepressants), anticonvulsant and mood stabilizer agents, and different hormones are the most regularly reported prescription groups that may cause weight gain. The obesity pandemic is multifaceted, but medication-induced weight gain may play a role. While doctors may aim to pick pharmacotherapies with the least detrimental influence on weight, the literature on the weight effects of medicine is frequently inadequate and empty of alternative treatments. Because of the devastating consequences of adolescents obesity, new therapies are desperately needed. Real-world data reveal that the majority of teenagers do not lose weight over time, and pharmacological therapy should be recommended.
... Studies have found that leptin, neuropeptide Y, and orexins may be involved in nicotine-induced changes in food intake and energy expenditure. 37,38 The most unique contribution of our research to the literature is our finding of a lack of association between e-cigarette use during pregnancy and the risk of low GWG. Although the true reasons for this lack of association are not fully understood, there are several possible explanations. ...
Objective
To evaluate the risk of low gestational weight gain (GWG) in women who use electronic cigarettes (e‐cigarettes), combustible cigarettes, or both e‐cigarettes and combustible cigarettes (dual use) during pregnancy.
Methods
We conducted a secondary analysis of the data from 176 882 singleton pregnancies in the 2016–2020 US Pregnancy Risk Assessment Monitoring System (PRAMS). Postpartum women self‐reported their use of e‐cigarettes and/or cigarettes during the last 3 months of pregnancy. Low GWG was defined as the total GWG less than 12.7 kg, less than 11.3 kg, less than 6.8 kg, and less than 5.0 kg (<28, <25, <15, and < 11 lb) for women with underweight, normal weight, overweight, and obesity, respectively. We used multivariable logistic regression to estimate the odds ratios (ORs) of low GWG, adjusting for confounders.
Results
In this national sample, 921 (weighted percentage, 0.5%) of women were e‐cigarette users and 1308 (0.7%) were dual users during late pregnancy. Compared with non‐users during late pregnancy (40 090, 22.1%), cigarette users (4499, 28.0%) and dual users (427, 26.0%) had a higher risk of low GWG, but e‐cigarette users had a similar risk (237, 22.1%). Adjustment for sociodemographic and pregnancy confounders moderately attenuated these associations: confounder‐adjusted ORs 1.26 (95% confidence interval [CI] 1.18–1.35) for cigarette users, 1.18 (95% CI 0.96–1.44) for dual users, and 0.99 (95% CI 0.78–1.27) for e‐cigarette users.
Conclusions
Unlike combustible cigarette use, e‐cigarette use during late pregnancy does not appear to be a risk factor for low GWG.
... The combination of inexpensive, high caloric, fat-laden foods and decreased physical activity over the last few decades are often listed as significant contributors to the prevalence of obesity (20). In addition, the cessation of smoking may be a contributor to the obesity pandemic, as weight gain is a common consequence of smoking cessation (20,24). As the etiological factors that lead to obesity are multifactorial and often difficult to counteract, efforts on improving treatments and vaccines for individuals with obesity are essential. ...
To combat the immense toll on global public health induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), new vaccines were developed. While these vaccines have protected the populations who received them from severe SARS-CoV-2 infection, the effectiveness and durability of these vaccines in individuals with obesity are not fully understood. Our uncertainty of the ability of these novel vaccines to induce protective immunity in humans with obesity stems from historical data that revealed obesity-associated immune defects to influenza vaccines. This review analyzes the efficacy of SARS-CoV-2 vaccines in humans with obesity. According to the vaccine safety and efficacy information for the Pfizer, Moderna, and Johnson & Johnson formulations, these vaccines showed a similar efficacy in both individuals with and without obesity. However, clinical trials that assess BMI and central obesity showed that induced antibody titers are lower in individuals with obesity when compared to healthy weight subjects, highlighting a potential early waning of vaccine-induced antibodies linked to obesity rates. Thus, the desired protective effects of SARS-CoV-2 vaccination were potentially diminished in humans with obesity when compared to the healthy weight population, but further studies outlining functional implications of the link between obesity and lower antibody titers need to be conducted to understand the full impact of this immune phenomenon. Further, additional research must be completed to truly understand the immune responses mounted against SARS-CoV-2 in patients with obesity, and whether these responses differ from those elicited by previously studied influenza viruses.
In Germany, at the time of the last microcensus survey (2017), 26.4% of men and 18.6% of women older than 15 years smoked. Today, smoking is considered the most significant single risk factor for a variety of serious illnesses and premature death in industrialized countries. Very few clinical studies have so far investigated smoking behavior in patients with bulimia nervosa (BN) or anorexia nervosa (AN). An examination of smoking motives showed that women with an eating disorder had a significantly higher motivation to smoke compared to a control group with depression. Smoking is used as a means of weight control and serves to cope with anxiety and stress. Patients with BN have an increased risk of smoking and often develop a strong tobacco addiction. In particular, in the case of obesity, smoking cessation is an essential factor in reducing morbidity and mortality.
Background
Addiction to tobacco and nicotine products has adverse health effects and afflicts more than a billion people worldwide. Therefore, there is an urgent need for new treatments to reduce tobacco and nicotine use. Glucocorticoid receptor blockade shows promise as a novel treatment for drug abuse and stress-related disorders.
Aim
These studies aim to investigate whether glucocorticoid receptor blockade with mifepristone diminishes the reinforcing properties of nicotine in rats with intermittent or daily long access to nicotine.
Methods
The rats self-administered 0.06 mg/kg/inf of nicotine for 6 h per day, with either intermittent or daily access for 4 weeks before treatment with mifepristone. Daily nicotine self-administration models regular smoking, while intermittent nicotine self-administration models occasional smoking. To determine whether the rats were dependent, they were treated with the nicotinic acetylcholine receptor antagonist mecamylamine, and somatic signs were recorded.
Results
The rats with intermittent access to nicotine had a higher level of nicotine intake per session than those with daily access but only the rats with daily access to nicotine showed signs of physical dependence. Furthermore, mecamylamine increased nicotine intake during the first hour of access in rats with daily access but not in those with intermittent access. Mifepristone decreased total nicotine intake in rats with intermittent and daily access to nicotine. Moreover, mifepristone decreased the distance traveled and rearing in the open field test and operant responding for food pellets.
Conclusion
These findings indicate that mifepristone decreases nicotine intake but this effect may be partially attributed to the sedative effects of mifepristone.
Although quitting smoking lowers the risk of developing chronic conditions, it usually leads to weight gain. Literature on the association between weight gain after quitting smoking and the future development of hypertension is scarce. Among 234 596 individuals who visited our health center, 856 who had quit smoking for whom data were available at least 6 years after smoking cessation were included. We evaluated changes in blood pressure and antihypertensive drug prescription rate at 1 and 6 years after smoking cessation. We also compared weight and blood pressure between the smoking cessation and continued smoking groups after 6 years. Multiple regression analyses were performed to identify predictors of changes in systolic and diastolic blood pressures using covariates affecting blood pressure. Since a median weight gain of 1.8 kg was observed at 1 year after smoking cessation, we divided the participants into high and low-weight gain groups. No significant intergroup difference in the antihypertensive drug prescription rate was observed after 6 years. The high weight gain group showed significant increases in systolic and diastolic blood pressures after 6 years. Multiple regression analyses revealed that systolic blood pressure was affected by age and high weight gain, while diastolic blood pressure was affected by high weight gain. Our findings suggest that weight gain following smoking cessation leads to blood pressure elevation: the smoking cessation group gained more weight and had higher blood pressure than the continued smoking group. Therefore, weight loss guidance may be useful for individuals who want to quit smoking. Participants in the high weight gain group showed significant increases in systolic and diastolic blood pressures at 6 years after smoking cessation that were significantly different from those observed in participants in the low weight gain group and the continued smoking group.
In past decades the prevalence of overweight and obesity had grown rapidly. There are numerous factors contributing to this unfavorable change in people’s health. This review article investigates the environmental factors which may play a role in the prevalence of overweight and obesity and additionally the novel factors which appeared after the beginning of the COVID-19 pandemic, which caused the increase in BMI during the lockdown period. Most of the studies reveal that the COVID-19 pandemic and lockdown contributed to the growth of BMI in numerous countries and, eventually the prevalence of overweight and obesity increased. Studies suggest that the physical activity was decreased while sleep time and screen time were increased and the amount of food consumed increased, additionally more processed food with long shelf life was consumed. The diverse environmental factors may have an impact on obesity and overweight development taking into account policy and local school policy issues, socioeconomic status, lifestyle including physical activity, diet habits, and amongst others, more trivial causes such as uninteresting neighborhoods, lack of sense of security outside the place of residence or a long distance from shops. Still, this is the object of debate if air pollution is an environmental risk factor influencing the unfavorable trends towards increasing body weight.
The link between smoking and obesity is complex and not fully understood, and published studies have produced conflicting results. The increase in MT associated with smoking cessation is largely due to an increase in energy intake and a decrease in energy expenditure. By design, 68 patients who planned to quit smoking participated in the study. The study was conducted within the framework of the social project "Diabetes and overweight school" of the city health center in the city of Odesa. According to the study design, all patients were ready to quit smoking. when dividing patients into groups, we had an equal distribution of anthropometric indicators and indicators of body composition. The average value among the examined patients corresponds to the diagnosis of "excess body weight" (Overweight). The patients were divided into groups according to their willingness to implement certain changes in their daily life. After making changes during the observation period, the patients of the main group acquired changes, namely BMI changed from 27.80±1.72 kg/m2 to 26.90±0.59 kg/m2, the percentage of body fat decreased from 32.55±1 .05% by 30.01±0.51% (р<0.05) and the level of visceral fat from 11.66±0.98 to 10.11±0.76 UA. In addition, the patients of the main group increased the percentage of muscle mass from 30.11±0.69% to 31.34±0.59% during the 3 months of observation. Patients of the main group had positive dynamics according to the SF-36 questionnaire during observation, the most positive changes were noted in the parameters of: physical functioning (PF) from 59.12±1.31 points to 64.11±1.46 (р<0.05 ); general state of health (GH) from 62.19±2.83 to 68.15±2.46; vital activity (VT) from 58.92±1.72 to 65.56±1.29 points (р<0.05); role functioning due to emotional state (RE) 62.91±2.81 to 67.49±2.15 points. Other indicators also had positive dynamics, but not so significant.
Abstract
Background: Smoking is associated with an increase in macrovascular and microvascular complications in people with diabetes. In addition to other concomitant vascular disturbances, it also appears to affect cardio-metric parameters. This may partly explain the acceleration of vascular complications in people with diabetes. This study aimed to investigate the relationship between smoking and its cessation in cardio-metric parameters in diabetes.
Materials and Methods: Searches were conducted in Medline, Embase, and CINAHL. After screening 6866 studies, 14 observational studies with a total of 98 978 participants with type 1 or type 2 diabetes were selected for the investigation.
Results: Meta-analysis demonstrated that the mean difference of hemoglobin A1c (HBA1C) between non-smokers and smokers was 0.61% (95% confidence interval [CI] -0.88 to -0.33, P < 0.001). The difference in low-density lipoprotein cholesterol between non-smokers and smokers was 0.11 mmol/L (95% CI -20.21 to 0.01, P = 0.04). The difference in high-density lipoprotein cholesterol between non-smokers and smokers was 0.12 mmol/L (95% CI 0.08-0.15, P < 0.001). However, there was no statistically significant difference in blood pressure (BP) between the two groups. The difference in HBA1C between quitters and continuing smokers was not statistically significant at 10.0.10% (95 CI -0.42 to 0.21, P = 0.53). Conversely, the results revealed that over 10 years, HBA1C was comparable between non-smokers and quitters.
Conclusion: Smoking in patients with diabetes was widespread and should be the target of smoking cessation campaigns. Smoking cessation does not lead to an increase in HBA1C in the long term and may reduce vascular complications in diabetes by its desirable effect on the lipid profile.
To examine the relationships between different shift patterns and Type 2 diabetes mellitus (T2DM) risk, and determine whether physical exercise reduced the incidence of T2DM in shift workers in the oil industry.
Baseline data were collected from participants in May 2013 who were then followed for 4 years in a prospective cohort study. The cohort initially consisted of 3,002 workers and ultimately included 2,827 people. Baseline and follow-up questionnaires were sent to participants every 2 years (in May 2015 and May 2017) to update medical and lifestyle information during the follow-up period. The risk of T2DM among two shift workers [relative risk (RR) = 3.442, 95% CI: 1.904–6.799)], three shift workers (RR = 2.534, 95% CI: 1.484–4.571), and four shift workers (RR = 4.230, 95% CI: 2.680–7.518) was higher than that among day workers. An increasing trend was observed with respect to T2DM risk, with the lowest risk in three shift workers, moderate risk in two shift workers, and highest risk in four shift workers. In the interactive analysis between shift work and physical exercise, taking part in mild physical exercise increased the risk of T2DM for workers. Four shift workers who took part in mild physical exercise had an increased risk of T2DM. The relative excess risk due to interaction (RERI) was 33.769 (0.398–67.140). The attributable proportion due to interaction [API (%)] was 0.704 (0.529–0.880). The synergy index (SI) was 3.563 (1.900–6.683). Shift work is significantly correlated with increased incidence of T2DM. Risk of T2DM is lowest risk in three shift workers, moderate in two shift workers, and highest in four shift workers. Shift workers who participated in moderate and severe physical exercise had reduced risk of developing T2DM.
Introduction
DNA methylation in the CpG sites of intron 1 of HIF3A is associated with body mass index (BMI). This cross-sectional study investigated correlations between DNA methylation of HIF3A and BMI or adiposity parameters in the Japanese population.
Method
DNA methylation of HIF3A was quantified via pyrosequencing.
Result
DNA methylation of HIF3A differed only in women; DNA methylation level at cg27146050 was associated with visceral adipose tissue thickness and correlated with BMI and percent (%) body fat after excluding smokers.
Conclusion
Peripheral blood DNA methylation at the CpG site (cg27146050) of HIF3A correlated with VAT thickness in Japanese women.
Background
Obesity among the elderly imposes a significant health and economic burden. The purpose of this study was to measure the obesity prevalence and income-related inequality among older adults in China and to explore the determinants of the inequity.
Methods
Data were obtained from 4,541 older adults (60 years and older) participating in the China Family Panel Study, 2018. Obesity was defined as body mass index (BMI) ≥28 kg/m². Normalized concentration index and concentration curve were calculated to measure the income-related inequality. Decomposition analysis was used to measure the contribution of each factor to the overall unfairness.
Results
The prevalence of obesity among the respondents was 7.99%. The 95% confidence interval for the overall prevalence was 7.20–8.78%. The normalized concentration index of obesity in the elderly was 0.075 (95% confidence interval: 0.047–0.103), indicating that obesity was more concentrated among the rich (p < 0.05). Socioeconomic factors contributed the most to the overall inequality (68.73%). Health behavior factors explained 16.38% of the observed income-related inequality in obesity among the elderly in China.
Conclusions
In 2018, obesity was more concentrated among the elderly with higher incomes in China. The pro-poor income-related inequality was mainly due to the higher socioeconomic status of higher-income older adults. Health behaviors and psychosocial factors could also exacerbate the inequality. To prevent the heavy burden of obesity on the health and finances of older adults, more attention should be paid to those who are financially better off, especially those who smoke and are physically inactive, while extroverted older adults also need to be focused on. For developing countries, concern needs to be given to the obesity of the wealthy elderly as a result of economic development.
In Deutschland rauchten bei der letzten Mikrozensuserhebung (2017) 26,4 % der Männer und 18,6 % der Frauen, die älter als 15 Jahre sind. Rauchen gilt heute in den Industriestaaten als der bedeutsamste einzelne Risikofaktor für eine Vielzahl von schwerwiegenden Erkrankungen und einen vorzeitigen Tod. Sehr wenige klinische Studien untersuchten bislang das Rauchverhalten bei Patientinnen mit einer Bulimie oder Anorexie. Wie die Untersuchung von Rauchmotiven zeigte, wiesen Frauen mit einer Essstörung im Vergleich zu einer depressiven Kontrollgruppe eine signifikant höhere Motivation auf, zu rauchen. Rauchen wird als Mittel zur Gewichtskontrolle eingesetzt und dient zur Bewältigung von Angst und Stress. Bulimie-Patientinnen haben ein erhöhtes Risiko zu rauchen und entwickeln häufig eine starke Tabakabhängigkeit. Insbesondere bei Adipositas stellt die Tabakentwöhnung einen wesentlichen Faktor zur Reduzierung von Morbidität und Mortalität dar.
Background
Schizophrenia and antipsychotic use are associated with clinically significant weight gain and subsequent increased mortality. Despite weight loss medications (WLMs) licensed by regulatory bodies (FDA, EMA, and MHRA) being available, current psychiatric guidelines recommend off-label alternatives, which differ from non-psychiatric guidelines for obesity.
Objective
Evaluate the efficacy of licensed WLMs on treating antipsychotic-induced weight gain (AIWG) and obesity in schizophrenia and psychosis (OSP).
Method
A literature search was conducted using Medline, EMBASE, PsycINFO and Cochrane Library online databases for human studies using licensed WLMs to treat AIWG and OSP.
Results
Three RCTs (two liraglutide, one naltrexone-bupropion), one unpublished open-label trial (naltrexone-bupropion), and seven observational studies (five liraglutide, one semaglutide, one multiple WLMs) were identified. Results for liraglutide showed statistically significant improvement in weight, BMI, waist circumference, HbA1c, cholesterol, and LDL readings on meta-analysis. Evidence was mixed for naltrexone-bupropion with no detailed studies conducted for setmelanotide, or stimulants.
Conclusion
Evidence is strongest for liraglutide compared to other licensed WLMs. The findings, particularly the inclusion of human trial data, provide evidence for liraglutide use in treating AIWG and OSP, which would better align psychiatric practice with non-psychiatric practices around obesity. The findings also identify continued literature gaps regarding other licensed WLMs.
Estimates of postcessation weight gain vary widely. This study determined the magnitude of weight gain in a cohort using both point prevalence and continuous abstinence criteria for cessation. Participants were 196 volunteers who participated in a smoking cessation program and who either continuously smoked (n = 118), were continuously abstinent (n = 51), or who were point prevalent abstinent (n = 27) (i.e., quit at the 1-year follow-up visit but not at others). Continuously abstinent participants gained over 13 lbs. (5.90 kg) at 1 year, significantly more than continuously smoking (M = 2.4 lb.) and point prevalent abstinent participants (M = 6.7 lbs., or 3.04 kg). Individual growth curve analysis confirmed that weight gain and the rate of weight gain (pounds per month) were greater among continuously smoking participants and that these effects were independent of gender, baseline weight, smoking and dieting history, age, and education. Results suggest that studies using point prevalence abstinence to estimate postcessation weight gain may be underestimating postcessation weight gain.
Despite their growing popularity, worksite health-promotion programs have generally been characterized as having low participation rates, high attrition rates, and modest outcomes. This investigation identified the predictors of participation, attrition, and outcome of worksite smoking-cessation program. Subjects were regular cigarette smokers recruited from two worksites. Of 66 eligible smokers in the two worksites, 44 (67%) agreed to participate in the program. Fifty-five percent (24 of 44) of these completed the program. Of those completing the program, 29% had quit smoking by posttest and 17% were abstinent at the 6-month follow-up. Results indicated that a different set of variables predicted participation, attrition, and outcome. The significant predictors of smokers who participated were the length of cessation in previous abstinence attempts, the number of years they smoked, and the belief regarding personal vulnerability in contracting a smoking-related disease. Levels of pretest carbon monoxide along with attitudes regarding the adoption of smoking restrictions in the worksite predicted attrition. Posttest cessation was related to nicotine levels of cigarette brand smoked at pretest and pretest beliefs regarding postcessation weight gain. Abstinence at the 6-month follow-up was predicted by the number of co-workers who smoked and pretest concerns related to postcessation weight gain. The results are discussed in terms of future evaluation and intervention efforts.
Key words: smoking, health, stop-smoking programs
Fluoxetine's effect (30 mg, 60 mg, and placebo) on postcessation weight gain was studied among participants from a randomized, double-blind 10-week smoking cessation trial who met strict criteria for abstinence and drug levels. It was hypothesized that (a) fluoxetine would dose-dependently suppress postcessation weight gain and (b) drug discontinuation would produce dose-dependent weight rebound. During the on-drug phase, placebo participants gained weight linearly (
M = 2.61 kg), exceeding both fluoxetine groups (30-mg
M = 1.33 kg, 60-mg
M = 1.25 kg). Weight suppression was initially greater for 60 mg than 30 mg, but both were followed by weight gain. Six months off-drug produced greater dose-dependent weight rebound for 60 mg than 30 mg or placebo. Considering both on- and off-drug phases, weight gain for 60 mg of fluoxetine (
M = 6.5 kg) was comparable with that for placebo (
M = 4.7 kg) but greater than that for 30 mg (
M = 3.6 kg). Fluoxetine appears to forestall postcessation weight gain, allowing time for the weight-conscious smoker to focus on quitting smoking rather than on preventing weight gain. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Two studies were performed to investigate the association between body fat mass and fat oxidation. The first, a cross-sectional study of 106 obese women maintaining stable body weight, showed that these two variables were significantly correlated (r = 0.56, P less than 0.001) and the regression coefficient indicated that a 10-kg change in fat mass corresponded to a change in fat oxidation of approximately 20 g/d. The second, a prospective study, validated this estimate and quantifies the long-term adaptations in fat oxidation resulting from body fat loss. Twenty-four moderately obese women were studied under controlled dietary conditions at stable weight before and after mean weight and fat losses of 12.7 and 9.8 kg, respectively. The reduction in fat oxidation was identical to that predicted by the above regression. We conclude that changes in fat mass significantly affect fat oxidation and that this process may contribute to the long-term regulation of fat and energy balance in obese individuals.
This article provides a comprehensive review of the research on smoking and body weight. The relationships between smoking and body weight are evaluated in 70 cross-sectional and longitudinal investigations. The mechanisms responsible for differences in body weight are discussed, the weight-related issues that promote smoking behavior are reviewed, methods for reducing postcessation weight gain are summarized, and future research directions are proposed. A proposed working research model for studying the relationship between smoking, energy balance, and weight gain is offered. It is concluded that smoking and body weight relationships are closely related and pose significant challenges for smoking researchers.
The present study examined nicotine replacement effects on postcessation weight gain in smoking cessation clinic volunteers using objective indices of cigarette smoking, gum use, and body weight. After they achieved abstinence, subjects were randomly assigned to either active nicotine or placebo gum conditions for 10 weeks, during which smoking status was carefully monitored. Analyses revealed strong evidence for a gum effect on weight gain, with active gum users gaining a mean total of 3.8 lb compared with 7.8 lb for placebo gum users at the end of the 10-week trial. Evidence for a dose-response relation was found, suggesting that more gum use (greater than or equal to 6.5 pieces/day) resulted in greater weight suppression. Placebo gum subjects reported greater postcessation increases in eating and hunger compared with active gum subjects. The implications of the weight suppression effect of nicotine gum for smoking cessation treatments are discussed.
Thirteen sedentary adult females successfully quit smoking cigarettes for 48 days. Mean daily caloric consumption increased 227 kcal and mean weight gain was 2.2 kg. There were no measurable acute effects of smoke inhalation and no chronic net effects of smoking cessation on resting metabolic rate, as determined by oxygen consumption and respiratory exchange ratio. After 1 yr, subjects who continued to abstain gained an average of 8.2 kg. HDL-cholesterol increased 7 mg/dl in 48 days; however, this effect was lost in those who returned to smoking. Increased caloric consumption accounted for 69% of weight gained immediately following smoking cessation. Factors other than changes in caloric consumption and metabolic rate may be responsible for a significant proportion (31%) of the weight gained in individuals who quit smoking.
The proportion of U.S. adults 35 to 74 years of age who were overweight increased by 9.6 percent for men and 8.0 percent for women between 1978 and 1990. Since the prevalence of smoking declined over the same period, smoking cessation has been suggested as a factor contributing to the increasing prevalence of overweight.
To estimate the influence of smoking cessation on the increase in the prevalence of overweight, we analyzed data on current and past weight and smoking status for a national sample of 5247 adults 35 years of age or older who participated in the third National Health and Nutrition Examination Survey, conducted from 1988 through 1991. The results were adjusted for age, sociodemographic characteristics, level of physical activity, alcohol consumption, and (for women) parity.
The weight gain over a 10-year period that was associated with the cessation of smoking (i.e., the gain among smokers who quit that was in excess of the gain among continuing smokers) was 4.4 kg for men and 5.0 kg for women. Smokers who had quit within the past 10 years were significantly more likely than respondents who had never smoked to become overweight (odds ratios, 2.4 for men and 2.0 for women). For men, about a quarter (2.3 of 9.6 percentage points) and for women, about a sixth (1.3 of 8.0 percentage points) of the increase in the prevalence of overweight could be attributed to smoking cessation within the past 10 years.
Although its health benefits are undeniable, smoking cessation may nevertheless be associated with a small increase in the prevalence of overweight.
Data from two surveys of the National Academy of Sciences-National Research Council Twin Registry, conducted 16 years apart, were used to determine characteristics of individuals that were predictive of excessive weight gain after smoking cessation.
Over the follow-up, 2179 men quit smoking and averaged a weight gain of 3.5 kg. Quitters were grouped into four categories of weight change: lost weight, no change, gained weight, and excessive weight gain ("super-gainers").
In comparison with quitters reporting no change in weight, super-gainers were younger, were of lower socioeconomic status, and differed on a number of health habits before quitting (all Ps < .05). At follow-up, super-gainers reported changes in health habits that were significantly different from those seen in quitters reporting stable weight (all Ps < .05). Pairwise concordance for weight change in 146 monozygotic and 111 dizygotic twin pairs in which both twins quit smoking was significantly greater in monozygotic than dizygotic pairs (P < .01).
These results indicate that super-gainers differ in important ways from those who do not gain weight after smoking cessation and that these weight changes may be influenced by underlying genetic factors.
To determine the risk factors for noninsulin dependent diabetes in a cohort representative of middle aged British men.
Prospective study.
7735 men aged 40-59, drawn from one group practice in each of 24 towns in Britain. Known and probable cases of diabetes at screening (n = 158) were excluded.
Non-insulin dependent diabetes (doctor diagnosed) over a mean follow up period of 12.8 years.
There were 194 new cases of non-insulin dependent diabetes. Body mass index was the dominant risk factor for diabetes, with an age adjusted relative risk (upper fifth to lower fifth) of 11.6; 95% confidence interval 5.4 to 16.8. Men engaged in moderate levels of physical activity had a substantially reduced risk of diabetes, relative to the physically inactive men, after adjustment for age and body mass index (0.4; 0.2 to 0.7), an association which persisted in full multivariate analysis. A nonlinear relation between alcohol intake and diabetes was observed, with the lowest risk among moderate drinkers (16-42 units/week) relative to the baseline group of occasional drinkers (0.6; 0.4 to 1.0). Additional significant predictors of diabetes in multivariate analysis included serum triglyceride concentration, high density lipoprotein cholesterol concentration (inverse association), heart rate, uric acid concentration, and prevalent coronary heart disease.
These findings emphasise the interrelations between risk factors for non-insulin dependent diabetes and coronary heart disease and the potential value of an integrated approach to the prevention of these conditions based on the prevention of obesity and the promotion of physical activity.
To examine the temporal relationship between stopping smoking and total mortality rates among middle-aged women.
Prospective cohort study with 12 years of follow-up.
Registered nurses residing in the United States.
117,001 female registered nurses, ages 30 to 55 years, who were free of manifest coronary heart disease, stroke, and cancer (except nonmelanoma skin cancer) in 1976.
Total mortality, further categorized into deaths from cardiovascular diseases, cancers, and violent deaths.
A total of 2847 deaths (933 among "never smokers," 799 among former smokers, and 1115 among current smokers) occurred during 1.37 million person-years of follow-up. The multivariate relative risks for total mortality compared with never smokers were 1.87 (95% CI, 1.65 to 2.13) for current smokers and 1.29 (CI, 1.14 to 1.46) for former smokers. Participants who started smoking before the age of 15 years had the highest risks for total mortality (multivariate relative risk, 3.15; CI, 2.16 to 4.59), cardiovascular disease mortality (relative risk, 9.94; CI, 5.15 to 19.19), and deaths from external causes of injury (relative risk, 5.39; CI, 1.84 to 15.78). Compared with continuing smokers, former smokers had a 24% reduction in risk for cardiovascular disease mortality within 2 years of quitting. The excess risks for total mortality and both cardiovascular disease and total cancer mortality among former smokers approached the level of that for never smokers after 10 to 14 years of abstinence. The health benefits of cessation were clearly present regardless of the age at starting and daily number of cigarettes smoked.
The risk of cigarette smoking on total mortality among former smokers decreases nearly to that of never smokers 10 to 14 years after cessation.
The purpose of this study was to examine prospectively whether exercise can modify weight gain after smoking cessation in women.
Data were analyzed from a 2-year follow-up period (1986-1988) in the Nurses' Health Study, an ongoing cohort of 121,700 US women aged 40 to 75 in 1986.
The average weight gain over 2 years was 3.0 kg in the 1474 women who stopped smoking, and 0.6 kg among the 7832 women who continued smoking. Among women smoking 1 to 24 cigarettes per day, those who quit without changing their levels of exercise gained an average of 2.3 kg more (95% confidence interval [CI] = 1.9, 2.6) than women who continued smoking. Women who quit and increased exercise by between 8 to 16 MET-hours (the work metabolic rate divided by the resting metabolic rate) per week gained 1.8 kg (95% CI = 1.0, 2.5), and the excess weight gain was only 1.3 kg (95% CI = 0.7, 1.9) in women who increased exercise by more than 16 MET-hours per week.
Smoking cessation is associated with a net excess weight gain of about 2.4 kg in middle-aged women. However, this weight gain is minimized if smoking cessation is accompanied by a moderate increase in the level of physical activity.
To investigate the acute effect of cigarette smoking on glucose tolerance, insulin sensitivity, serum lipids, blood pressure, and heart rate.
This nonrandomized experimental control trial in a tertiary care center included 20 healthy chronic smokers and 20 age-, sex-, and BMI-matched healthy volunteers. Two oral glucose tolerance tests (OGTTs) were performed on each subject. Three cigarettes were smoked during the first 30 min in one of the tests. Serum glucose, insulin, and C-peptide levels were measured every 30 min; the area under the curve (AUC) and the insulin sensitivity index (ISI) were calculated; serum total cholesterol, LDL cholesterol, HDL cholesterol, and triglyceride levels were measured at 0 and 180 min; and blood pressure and heart rate were recorded every 5 min throughout 180 min.
Smoking acutely impaired glucose tolerance: the AUC for glucose in smokers was 25.5 +/- 1.03 mmol/l (mean +/- SE) (95% CI 22.9-28) during the smoking OGTT and 21.8 +/- 0.85 mmol/l (CI 19.2-24.3) in the control OGTT (P < 0.01); in nonsmokers, it was 19.7 +/- 0.3 mmol/l (CI 18.8-20.5) in the smoking OGTT and 18.7 +/- 0.35 mmol/l (CI 17.8-19.5) in the control OGTT (P < 0.05). Smoking acutely increased serum insulin and C-peptide levels and decreased ISI only in smokers: ISI in smokers was 55 +/- 2.8 (CI 47.4-62.6) in the control OGTT and 43 +/- 2.7 (CI 35.4-50.6) in the smoking OGTT (P < 0.05). Smoking acutely caused a rise of serum total cholesterol levels in both groups and increased LDL cholesterol and triglyceride serum levels significantly only in smokers (P < 0.05). A significant rise of blood pressure and heart rate while smoking was present in all the subjects.
Smoking acutely impaired glucose tolerance and insulin sensitivity, enhanced serum cholesterol and triglyceride levels, and raised blood pressure and heart rate. These findings support the pathogenetic role of cigarette smoking on cardiovascular risk factors.
Although commonly accepted, there is essentially no direct support for the assumption that preventing weight gain after smoking cessation will improve the likelihood of remaining abstinent from smoking. First, developing interventions to prevent this weight gain has proven to be extremely difficult. To date, no adjunct behavioral treatment has been shown to be successful in attenuating cessation-induced weight gain in healthy ex-smokers. Reasons for this failure may stem from inadequate intensity of interventions, poor compliance due to the complexity of interventions aimed at both eliminating smoking and controlling weight, or to biological pressures for weight to increase after removal of the weight suppressing effects of smoking (i.e. altered energy regulation). Pharmacological interventions, despite being successful during brief periods of active use, have not been studied beyond several months' duration of treatment. Second and more importantly, prospective studies have not reported that weight gain after cessation directly predicts relapse, and results of a recent intervention indicate that adjunct weight control treatment may be associated with greater smoking relapse. Although contrary to common belief these findings are very consistent with a large body of basic animal research showing that food or weight restriction increases drug intake. In light of these observations, a more prudent and fruitful approach for basic and clinical research may be to re-examine the fundamental relationships among smoking, eating, body weight, and perhaps weight-related attitudes, rather than developing intensive strategies for combating weight gain after smoking cessation. If further study continues to find no positive relationship between this weight gain and risk of subsequent smoking relapse, it may be necessary to consider developing treatments designed to help ex-smokers accept, rather than fight, weight gain after cessation.
Sustained release bupropion (amfebutamone) is a non-nicotine agent that is indicated as an aid to smoking cessation.
In 2 large well designed clinical trials, sustained release bupropion 300 mg/day (the recommended dose) for 7 or 9 weeks was associated with considerably and significantly higher smoking abstinence rates (continuous abstinence and 7-day point prevalence rates) than placebo during treatment and at follow-up at 6 and 12 months. Point prevalence rates at 12 months in 2 studies were 23.1 and 30.3% with bupropion, whereas values for placebo were 12.4 and 15.6%. Continuous abstinence rates at 12 months, available from 1 trial, were 18.4% with bupropion and 5.6% with placebo. Furthermore, bupropion was associated with significantly higher quitting rates than nicotine patch in a comparative study. Combination therapy with bupropion and nicotine patch provided slightly higher abstinence rates than bupropion alone, although differences were not statistically significant. The combination was superior to nicotine patch alone.
Data from a preliminary report of long term bupropion treatment (52 weeks) showed that the drug was associated with significantly higher continuous abstinence rates than placebo only to 6 months. However, point prevalence abstinence rates were significantly higher with bupropion than placebo to 18 months.
Bupropion 300 mg/day recipients reported nicotine withdrawal symptoms during treatment; however, the symptoms were significantly less severe with bupropion than placebo. Patients receiving bupropion 300 mg/day or bupropion in combination with nicotine patch for smoking cessation generally gained less body weight than placebo recipients. The benefits of bupropion for preventing weight gain persisted after the completion of long term, but not short term therapy.
Bupropion was well tolerated in clinical trials, and the only adverse events that were significantly more common with bupropion than placebo were insomnia and dry mouth. Data published so far suggest that sustained release bupropion has a low potential for inducing seizures (seizure rate ≈0.1% in patients with depression).
Conclusions: Bupropion is an effective and well tolerated smoking cessation intervention. Further studies with long term follow-up will be useful in determining whether abstinence rates are maintained with bupropion. In addition, clarification of its efficacy in comparison with other therapies used for smoking cessation would help to establish its clinical value. The reduced potential for weight gain with bupropion and the ability to use bupropion in combination with nicotine replacement therapy make the drug a useful treatment option for smoking cessation.
Pharmacology
The mechanism by which bupropion (amfebutamone) acts as an aid in smoking cessation is unknown. However, bupropion is thought to produce its therapeutic antidepressant effects via the inhibition of noradrenaline and/or dopamine reuptake. Bupropion does not affect serotonin reuptake.
Bupropion showed dependence potential in animal models, but not at therapeutic dosages in individuals who abuse drugs or in healthy volunteers. Post-marketing surveillance data have shown that bupropion has a very low abuse potential.
Maximum plasma concentrations of sustained release bupropion are reached approximately 3 hours after an oral 150mg dose. Bupropion is highly plasma protein bound, and is extensively metabolised to 3 active metabolites. A single 150mg dose of sustained release bupropion has a mean elimination half-life of 18 to 19 hours. Around 84% and 9% of an oral dose of bupropion was recovered in the urine and faeces, respectively, within 72 hours after administration.
There is little available data on the effects of the concomitant administration of bupropion and other drugs on the metabolism of each drug. However, there is potential for interactions between bupropion and drugs that affect the cytochrome P450 (CYP) 2B6 isoenzyme. In addition, bupropion inhibits the activity of the CYP2D6 isoenzyme, which metabolises certain antidepressants (including tricyclic antidepressants and selective serotonin reuptake inhibitors), β-blockers, antiarrhythmics and antipsychotics. It is recommended that coadministration of bupropion and such drugs is approached with caution.
There are no significant differences in the pharmacokinetics of sustained release bupropion between smokers and nonsmokers. Sustained release bupropion is bioequivalent to the immediate release formulation in humans.
Therapeutic Efficacy
Sustained release bupropion 300 mg/day for 7 or 9 weeks significantly increased smoking cessation rates (continuous abstinence and 7-day point prevalence rates) during treatment and at follow-up at 6 and 12 months versus placebo in 2 large well designed studies. Point prevalence rates at 12 months were ≤30.3% with bupropion, whereas values for placebo were ≤15.6%. In 1 trial, continuous abstinence rates at 12 months were 18.4% with bupropion and 5.6% with placebo. Furthermore, bupropion was associated with significantly higher quitting rates than nicotine patch in the only comparison. Combination therapy with bupropion and nicotine patch provided slightly higher abstinence rates than bupropion alone, although differences were not statistically significant. The combination was superior to nicotine patch alone.
Data from a preliminary report of long term bupropion treatment (52 weeks) showed that the drug was associated with significantly higher continuous abstinence rates than placebo only to 6 months. However, point prevalence abstinence rates were significantly higher with bupropion than placebo to 18 months.
Bupropion 300 mg/day recipients reported significant withdrawal symptoms during treatment; however, the symptoms were significantly less with bupropion than placebo. In the preliminary report of a long term (52 weeks’ treatment) study, bupropion recipients had significantly less craving for cigarettes than placebo recipients and craving was less likely to be the reason for relapse with bupropion than placebo.
Bodyweight gain was generally less in patients receiving bupropion 300 mg/day or bupropion in addition to nicotine patch for smoking cessation than in placebo recipients. The benefits of bupropion for preventing weight gain persisted after the completion of long term, but not short term therapy.
Tolerability
Short term treatment with sustained release bupropion 300 mg/day was well tolerated in clinical trials of the drug for smoking cessation. The only adverse events that were significantly more common with bupropion than placebo were insomnia and dry mouth. Sustained release bupropion appears to have a lower propensity to cause seizures than the immediate release formulation (≈0.1 vs 0.4% for therapeutic dosages); however, no direct comparison of seizure rates between the formulations has been made.
Immediate release bupropion was generally well tolerated in patients with pre-existing heart disease. The cardiovascular effects of bupropion have not been assessed in patients with unstable heart disease or recent myocardial infarction, although studies are ongoing.
Dosage and Administration
It is recommended that sustained release bupropion 300 mg/day (twice daily) is given for 7 to 12 weeks for smoking cessation in adults. A target quitting date should generally be set for within the first 2 weeks of treatment. Patients are able to continue smoking while they take bupropion. In patients requiring continuous treatment, bupropion can be continued for up to 6 months (US) or a year (Canada). Bupropion can be given with transdermal nicotine. Patients with hepatic or renal disease should be treated with reduced dosages of bupropion.
Bupropion is contraindicated in patients with bulimia or anorexia nervosa and in patients with seizure disorders. In addition, bupropion should be given with caution to patients with risk factors for seizures. Mothers should not continue breastfeeding infants while taking bupropion.
Orexins are two recently discovered neuropeptides that can stimulate food intake. As the chronic use of tobacco typically leads to a reduction in body weight, it is of interest to determine whether nicotine, the major biologically active tobacco ingredient, has an effect on orexin metabolism in the brain. Using a semiquantitative RT-PCR technique, the levels of messenger RNA (mRNA) for prepro-orexin, orexin A (OX1-R) and orexin B (OX2-R) receptors were 20–50% higher in rats receiving nicotine for 14 days at the level of 2–4 mg/kg·day compared with rats receiving saline solvent alone. In animals treated with nicotine at 4 mg/kg·day, the expression levels of mRNA for prepro-orexin, OX1-R, and OX2-R were significantly higher compared with those in either the free-feeding control or pair-fed saline control rats. RIA data indicated that both orexin A and orexin B peptide levels were significantly elevated (45–54%; P < 0.01) in the dorsomedial nucleus (DMH) of the nicotine-treated rats compared with either solvent-only or pair-fed controls. Additionally, orexin B was significantly elevated (83%; P < 0.01), over levels in both types of the control animals, in the paraventricular nucleus (PVN) region. In summary, we demonstrated that an inverse association between nicotine and food intake as well as body weight held with doses comparable to those consumed by average human smokers. Moreover, our data indicated that chronic exposure to nicotine can induce a long-term increase in the expression levels of prepro-orexin and their receptor mRNA in the rat hypothalamus and in the levels of orexin A in the DMH and orexin B in the DMH and PVN among the six hypothalamic regions that we examined.
Background: Smoking relapse is common after successful pharmacologic treatment for smoking cessation. No previous studies have examined long-term drug therapy used expressly for prevention of smoking relapse. Objective: To evaluate the efficacy of bupropion to prevent smoking relapse. Design: Randomized, placebo-controlled trial. Participants: 784 healthy community volunteers who were motivated to quit smoking and who smoked at least 15 cigarettes per day. Intervention: The participants received open-label, sustained-release bupropion, 300 mg/d, for 7 weeks. Participants who were abstinent throughout week 7 of open-label treatment were randomly assigned to receive bupropion, 300 mg/d, or placebo for 45 weeks and were subsequently followed for an additional year after the conclusion of the medication phase. Participants were briefly counseled at all follow-up visits. At the end of open-label bupropion treatment, 461 of 784 participants (58.8%) were abstinent from smoking. Measurement: Self-reported abstinence was confirmed by an expired air carbon monoxide concentration of 10 parts per million or less. Results: The point prevalence of smoking abstinence was significantly higher in the bupropion group than in the placebo group at the end (week 52) of drug therapy (55.1% vs. 42.3%, respectively; P = 0.008) and at week 78 (47.7% vs. 37.7%; P = 0.034) but did not differ at the final (week 104) follow-up visit (41.6% vs. 40.0%). The median time to relapse was significantly greater for bupropion recipients than for placebo recipients (156 days vs. 65 days; P = 0.021). The continuous abstinence rate was higher in the bupropion group than in the placebo group at study week 24 (17 weeks after randomization) (52.3% vs. 42.3%; P = 0.037) but did not differ between groups after week 24. Weight gain was significantly less in the bupropion group than in the placebo group at study weeks 52 (3.8 kg vs. 5.6 kg; P = 0.002) and 104 (4.1 kg vs. 5.4 kg; P = 0.016). Conclusions: In persons who stopped smoking with 7 weeks of bupropion treatment, sustained-release bupropion for 12 months delayed smoking relapse and resulted In less weight gain.
Hyperinsulinemia, dyslipidemia, and endothelial dysfunction are characteristic findings in insulin-resistant individuals, and all of these abnormalities have been identified as increasing cardiovascular disease (CVD) risk. Smokers tend to be relatively insulin resistant, hyperinsulinemic, and dyslipidemic, with evidence of endothelial dysfunction, as compared with nonsmokers, and recent epidemiologic data have suggested that CVD in smokers is primarily seen in those individuals who also have the characteristic findings of insulin resistance. Based on these observations, it is argued that insulin resistance and its consequences represent a major mechanistic link between cigarette smoking and CVD. It is also postulated that the enhanced CVD risk in smokers, resulting from hyperinsulinemia, abnormalities of lipoprotein metabolism, and endothelial dysfunction, will primarily be present in those smokers who are insulin resistant. As a corollary, it is suggested that CVD risk in individuals who cannot, or will not, stop smoking can be reduced by therapeutic efforts aimed at attenuating the adverse effects of insulin resistance and its consequences.
The effect of cigarette smoking on other cardiovascular risk factors, serum lipids, body weight, blood pressure and blood sugar was assessed in a randomized control trial of reduction or cessation of cigarette smoking. In the intervention group (n = 107), reported cigarette use fell from 28.5 ± 1.2 ( ± SEM) to 10.6 ± 1.2 cigarettes/day and serum thiocyanate, a biochemical indicator of the extent of tobacco exposure, decreased −42.8 ± 5.5 μmol/l (P < 0.001). Compared to the control group, the intervention group showed significant (P < 0.05) decreases in reported cigarette consumption and serum thiocyanate and significant (P < 0.05) increases in body weight and skinfold thickness. Change in thiocyanate correlated significantly (P < 0.05) and inversely with change in HDL-C, body weight and skinfold thickness, but not with change in LDL-C, triglycerides or blood pressure. These relationships remained significant even after adjusting in multivariate analysis, for initial measurements of these variables or regression to the mean. For those who quit smoking (n = 35) HDL-C increased 5.9 ± 1.7 mg/dl (P < 0.01). The usual inverse relationship between body weight and HDL-C does not exist with cessation of cigarette smoking. Thus, benefits of stopping cigarette smoking extend to favourable alterations in HDL-C and there are no adverse effects on blood pressure, fasting blood sugar, triglycerides or LDL-C.
Objective:
To summarize the Smoking Cessation Clinical Practice Guideline that provides recommendations for 3 groups of professionals: primary care clinicians, smoking cessation specialists, and health care administrators, insurers, and purchasers.
Participants:
An independent panel of scientists, clinicians, consumers, and methodologists selected by the US Agency for Health Care Policy and Research.
Evidence:
English-language, peer-reviewed literature published between 1975 and 1994 that addresses the assessment and treatment of tobacco dependence, nicotine addiction, and clinical practice.
Consensus process:
Four panel meetings were held over 2 years to evaluate meta-analytic and other results, to synthesize the results, and to develop recommendations. The Guideline was repeatedly reviewed and revised.
Conclusions:
The panel recommendations address 3 audiences. Major recommendations for primary care clinicians are to use officewide systems to identify smokers, treat every smoker with a cessation or motivational intervention, offer nicotine replacement except in special circumstances, and schedule follow-up contact to occur after cessation. Major recommendations to smoking cessation specialists are to use multiple individual or group counseling sessions lasting at least 20 minutes each with sessions spanning multiple weeks, offer nicotine replacement, and provide problem-solving and social support counseling. Major recommendations for health care administrators, insurers, and purchasers are that tobacco-user identification systems be used in all clinics and that smoking cessation treatment be supported through staff education and training, dedicated staff, changes in hospital policies, and the provision of reimbursement for tobacco-dependence treatment.
Challenges the commonly-held assumption that prevention of weight gain after smoking cessation will prevent smoking relapse. The successes or failures of recent pharmacological and nonpharmacological treatments aimed at preventing postcessation weight gain are evaluated. No behavioral treatment has been shown to be successful in attenuating cessation-induced weight gain in healthy ex-smokers, but prospective studies have not reported that weight gain after cessation directly predicts relapse and that adjunct weight-control treatment may be associated with greater smoking relapse. Rather than developing intensive strategies for combating weight gain after smoking cessation, a more prudent approach for researchers may be to re-examine the fundamental relationship among smoking, eating, body weight, and weight-related attitudes. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Objective: To determine whether attempts to prevent weight gain will increase success rates for stopping smoking.
Design: 16 week, open, randomised study with 1 year follow up.
Setting: Obesity unit.
Subjects: 287 female smokers who had quit smoking before but started again because of weight concerns.
Intervention: Combination of a standard smoking cessation programme with nicotine gum and a behavioural weight control programme including a very low energy diet. A control group was treated with the identical programme but without the diet.
Main outcome measure: Sustained cessation of smoking.
Results: After 16 weeks, 68/137 (50%) women had stopped smoking in the diet group versus 53/150 (35%) in the control group (P=0.01). Among these women, weight fell by mean 2.1 (95% confidence interval 2.9 to 1.3) kg in the diet group but increased by 1.6 (0.9 to 2.3) kg in the control group (P<0.001). After 1 year the success rates in the diet and control groups were 38/137 (28%) and 24/150 (16%) respectively (P<0.05), but there was no statistical difference in weight gain.
Conclusions: Combining the smoking cessation programme with an intervention to control weight helped women to stop smoking and control weight.
Background:
Although the cessation of smoking reduces the increased risk for ischemic heart disease, it is associated with marked weight gain and presumably insulin resistance, both of which heighten the risk of coronary heart disease.
Hypothesis:
We investigated the isolated effect of nicotine on body weight and insulin resistance during smoking cessation.
Methods:
Eleven healthy, middle-aged heavy smokers were studied. Insulin sensitivity was assessed by an insulin-enhanced, frequently sampled intravenous glucose tolerance test with minimal model analysis. The subjects were studied at baseline (last day of smoking) (phase 1), at the end of the 6-week nicotine replacement program (phase 2), and after 8 weeks without smoking or nicotine replacement (phase 3).
Results:
The subjects started to gain weight during nicotine replacement (phase 2) (0.3 +/- 0.2 kg/week, mean +/- standard deviation) and continued to do so at a steady rate after nicotine replacement was stopped (0.2 +/- 0.2 kg/week) (p = 0.3). Insulin sensitivity decreased by 14 +/- 2.6% during nicotine replacement but increased by 16 +/- 5.1% (compared with phase 2) during phase 3, even though the weight gain continued (p = 0.047; 95% confidence interval: 0.05-5.73).
Conclusions:
Smoking cessation is associated with weight gain and improvement in insulin resistance. Nicotine is the main ingredient in cigarette smoke causing insulin resistance, but the withdrawal of another, unknown ingredient in cigarette smoke is responsible for the weight gain associated with smoking cessation.
Epidemiological studies have shown an inverse relationship between cigarette smoking and body weight. In rodents, a negative correlation between nicotine and body weight has been reported, but this observation was largely derived from studies where relatively high doses of nicotine (∼12 mg/kg/day) were used. In the current study, we showed that a negative relationship also holds for low doses of nicotine that are comparable to that consumed by average human smokers (<6 mg/kg/day). We also demonstrated that 14 days of nicotine administration (4 mg/kg/day) reduced average daily food intake by 19.5% (P<0.01) in the free-feeding nicotine-treated group compared to saline controls. No significant differences in body weight were detected between the nicotine-treated and pair-fed groups. To determine whether the effects of nicotine on food intake and body weight were related to neuropeptide Y (NPY) expression, semi-quantitative reverse transcription-polymerase chain reaction (RT-PCR) and radioimmunoassay were utilized to measure NPY mRNA and peptide levels in various regions of the hypothalamus. Significantly higher levels of NPY mRNA (ca. 20–50%) and peptide (ca. 24–69%) were only detected in the nicotine-treated groups. In addition, significantly higher NPY contents were also obtained in two hypothalamic areas of pair-fed control animals. In summary, our data suggest that the pharmacological effects of nicotine on food intake and body weight may be mediated by changes in hypothalamic NPY levels, a neuropeptide that is pivotal to the hypothalamic regulation of food intake.
This study examined the effect of cessation from smoking on body weight, body fat, resting metabolic rate (RMR), and caloric consumption. Twenty-six women aged 25 to 45 years (mean, 37.2 +/- 4.7) who smoked 20 or more cigarettes per day for the past 5 years served as volunteers. Twelve subjects abstained from smoking for a period of 60 days (EXSMOKERS). Six stopped smoking for 30 days, then resumed the habit for an additional 30 days (RESMOKERS). Eight subjects continued to smoke for the entire 60 days (SMOKERS). Additionally, 10 women who had never smoked served as nonsmoking controls (NONSMOKERS). Body weight was recorded weekly and body fat was calculated from body density as determined by hydrostatic weighing. RMR was assessed by open-circuit spirometry. Caloric intake was obtained from 3-day food records using a computerized nutrient data base. Group means for body weight, body fat, RMR, and caloric intake were compared using a repeated measures ANOVA with a Scheffe post hoc at day 0 (baseline), day 30, and day 60 of cessation from smoking. NONSMOKERS weighed significantly (P less than .05) more, but were no fatter than all smoker groups at day 0. Body weight significantly increased by 1.8 kg (EXSMOKERS) and 2.1 kg (RESMOKERS) at day 30 of cessation. By day 60 EXSMOKERS' body weight had increased an additional 1.8 kg to 61.6 +/- 6.4 kg, while return to smoking (RESMOKERS) resulted in a 3.1 kg loss of body weight to 57.9 +/- 7.9 kg.(ABSTRACT TRUNCATED AT 250 WORDS)
Tobacco smoking reduces appetite and body weight (BW). Cessation of smoking leads to hyperphagia and weight gain. Daily food intake (FI) is a function of meal number (MN) and meal size (MZ), i.e., FI=MN×MZ. Under normal conditions, the female Fischer rat has a periodic reciprocal fluctuation between MZ and MN corresponding to phase of estrous cycle. Wide fluctuations between MZ and MN compensate each other to keep FI constant. Nicotine (5 mg/kg BW/day) was infused via osmotic minipump for 7 days. Controls received saline. FI, MZ, and MN were measured by an Automated Computerized Rat Eater Meter. Nicotine significantly decreased BW and FI via a decrease in MZ without compensatory increase of MN. Nicotine cessation led to hyperphagia, normalizing BW loss via an increase in MZ, which exceeded a compensatory decrease in MN. Nicotine significantly prolonged the estrous cycle by an extension of proestrous phase. Nicotine significantly lengthened the intermeal interval (IMI), delaying the start of the next meal and simultaneously decreasing subsequent MZ. Stopping nicotine led to normalization of IMI and MZ. Data show that nicotine alters the usual reciprocal regulation between MZ and MN and leads to a prolongation of the estrous cycle.
Cigarette smoking is associated with increases in plasma triglycerides and decreases in plasma high density-lipoprotein-cholesterol concentration. These changes not only increase risk of coronary heart disease but also are secondary to resistance to insulin-stimulated glucose uptake or hyperinsulinaemia. To see whether there is a relation between cigarette smoking and insulin-mediated glucose uptake we measured plasma lipid and lipoprotein concentrations, plasma glucose and insulin response to an oral glucose challenge, and insulin-mediated glucose uptake in 40 matched healthy volunteers (20 non-smokers, 20 smokers). Smokers had significantly higher mean (SEM) very-low-density-lipoprotein triglycerides (0.66 [0.10] vs 0.39 [0.03] mmol/l, p less than 0.02) and cholesterol (0.45 [0.06] vs 0.23 [0.04] mmol/l, p less than 0.005) concentrations and lower high-density-lipoprotein cholesterol concentrations (1.16 [0.05] vs 1.51 [0.08] mmol/l, p less than 0.001). Although plasma glucose concentrations in response to the oral glucose load were similar in the two groups, plasma insulin response of the smokers was significantly higher (p less than 0.001). Finally, smokers had higher steady-state plasma glucose concentrations in response to a continuous infusion of glucose, insulin, and somatostatin (8.4 [0.2] vs 5.0 [0.3] mmol/l, p less than 0.001), despite similar steady-state plasma insulin concentrations. The findings show that chronic cigarette smokers are insulin resistant, hyperinsulinaemic, and dyslipidaemic compared with a matched group of non-smokers, and may help to explain why smoking increases risk of coronary heart disease.
Weight gain is a consistent sequela of smoking cessation. A successful intervention might attract smokers who fear weight gain. If the gain causes smoking relapse, such an intervention might reduce smoking relapse risk.
Using a sample of 158 smokers who completed a 2-week smoking treatment program, we compared an innovative weight gain prevention intervention with both a nonspecific treatment and standard treatment. Subjects were assessed on weight and smoking behavior and followed for 1 year.
A disturbing, unexpected finding was that subjects in both the innovative and nonspecific conditions had a higher risk of smoking relapse than did standard treatment subjects. Some differences were observed between abstinent and smoking subjects in weight gain by treatment condition.
Both active interventions may have been so complicated that they detracted from nonsmoking. Also, caloric restriction may increase the reinforcing value of nicotine, a psychoactive drug, thereby increasing smoking relapse risk. The magnitude of weight gain after smoking cessation may not merit interventions that increase smoking risk. Perhaps attitudinal modifications are the most appropriate.
Many believe that the prospect of weight gain discourages smokers from quitting. Accurate estimates of the weight gain related to the cessation of smoking in the general population are not available, however.
We related changes in body weight to changes in smoking status in adults 25 to 74 years of age who were weighed in the First National Health and Nutrition Examination Survey (NHANES I, 1971 to 1975) and then weighed a second time in the NHANES I Epidemiologic Follow-up Study (1982 to 1984). The cohort included continuing smokers (748 men and 1137 women) and those who had quit smoking for a year or more (409 men and 359 women).
The mean weight gain attributable to the cessation of smoking, as adjusted for age, race, level of education, alcohol use, illnesses related to change in weight, base-line weight, and physical activity, was 2.8 kg in men and 3.8 kg in women. Major weight gain (greater than 13 kg) occurred in 9.8 percent of the men and 13.4 percent of the women who quit smoking. The relative risk of major weight gain in those who quit smoking (as compared with those who continued to smoke) was 8.1 (95 percent confidence interval, 4.4 to 14.9) in men and 5.8 (95 percent confidence interval, 3.7 to 9.1) in women, and it remained high regardless of the duration of cessation. For both sexes, blacks, people under the age of 55, and people who smoked 15 cigarettes or more per day were at higher risk of major weight gain after quitting smoking. Although at base line the smokers weighed less than those who had never smoked, they weighed nearly the same at follow-up.
Major weight gain is strongly related to smoking cessation, but it occurs in only a minority of those who stop smoking. Weight gain is not likely to negate the health benefits of smoking cessation, but its cosmetic effects may interfere with attempts to quit. Effective methods of weight control are therefore needed for smokers trying to quit.
To examine the association between cigarette smoking in adults and serum lipid and lipoprotein concentrations the results of 54 published studies were analysed. Overall, smokers had significantly higher serum concentrations of cholesterol (3.0%), triglycerides (9.1%), very low density lipoprotein cholesterol (10.4%), and low density lipoprotein cholesterol (1.7%) and lower serum concentrations of high density lipoprotein cholesterol (-5.7%) and apolipoprotein AI (-4.2%) compared with nonsmokers. Among non-smokers and light, moderate, and heavy smokers a significant dose response effect was present for cholesterol (0, 1.8, 4.3, and 4.5% respectively), triglycerides (0, 10.7, 11.5, and 18.0%), very low density lipoprotein cholesterol (0, 7.2, 44.4, and 39.0%), low density lipoprotein cholesterol (0, -1.1, 1.4, and 11.0%), high density lipoprotein cholesterol (0, -4.6, -6.3, and -8.9%), and apolipoprotein AI (0, -3.7 and -5.7% in non-smokers and light and heavy smokers). These dose response effects may provide new evidence for a causal relation between exposure to cigarette smoke and changes in serum lipid and lipoprotein concentrations whether as a direct result of physiological changes or of dietary changes induced by smoking. Adequate prospective data to estimate the excess risk of coronary artery disease existed only for cholesterol concentration. When that information was combined with data from the present study, and given that smokers as a group face an average overall excess risk of coronary artery disease of 70%, it was estimated that the observed increased serum cholesterol concentration in smokers may account for at least 9% of that excess risk. Furthermore, the dose response effect of smoking on serum cholesterol concentration suggests a gradient of increased absolute risk of coronary artery disease between light and heavy smokers.
Studies of brain monoamines and neuropeptides have provided extensive evidence in support of their role in the control of normal eating behavior. In this process, the medial and lateral portions of the hypothalamus, working in conjunction with forebrain and hindbrain sites and with peripheral autonomic-endocrine systems, have a critical responsibility in balancing signals for hunger and satiety. Via its rich and biologically active neurotransmitter substances, the hypothalamus monitors and integrates the complex sensory and metabolic input concerning the nutritional status of the organism and transduces this information into appropriate quantitative and qualitative adjustments in food intake. The specific neurotransmitters for which there is the most extensive evidence for a physiological function include the eating-stimulatory substances norepinephrine (alpha 2), opioid peptides, pancreatic polypeptides, growth hormone-releasing factor, and gamma-aminobutyric acid; the eating-inhibitory substances dopamine, epinephrine, serotonin, cholecystokinin, neurotensin, calcitonin, glucagon, and corticotropin-releasing factor; and possibly other gut-brain peptides. From biochemical, pharmacological, and anatomical studies, hypotheses have been generated to explain the role of these various monoamines and neuropeptides in controlling total energy intake, in determining the amount and pattern of macronutrient selection, and in maintaining normal energy and nutrient stores under fluctuating conditions within the external environment.
Cross-sectional associations between smoking habits, body mass index, and waist-hip ratio (WHR) were examined in 1122 men aged 19 to 102 years. Weight and body mass index were significantly lower in cigarette smokers than in nonsmokers when age was taken into account. The WHR in smokers was significantly higher than in nonsmokers. A graded dose-response relationship was found between the number of cigarettes smoked and the WHR. Longitudinal associations between changes in smoking habits and changes in the WHR were examined during follow-up visits. In the period between these pairs of visits, weight increased when subjects quit smoking and decreased when they started smoking, as expected. The increase in WHR among those who quit smoking was, however, significantly less than the expected increase if smoking had continued. The WHR in those who started smoking actually increased despite their loss of weight. These paradoxical changes in WHR indicate that there are harmful effects of cigarette smoking on the pattern of distribution of body fat. These facts introduce still another reason to suggest that the decision to initiate or to continue smoking to control body weight is unwise.
(JAMA 1989;261:1169-1173)
