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Cigarette Smoking, Nicotine, and Body Weight
Abstract
Smokers generally gain weight when they quit smoking; this weight gain can lessen some of the health benefits of quitting smoking. We review the effectiveness of behavioral and pharmacological approaches to mitigating weight gain in the context of quitting smoking and consider mechanisms that could potentially account for the effects of smoking and nicotine on body weight. Understanding how nicotine affects body weight may lead to novel pharmacological and behavioral interventions for obesity as well as concurrent obesity and nicotine dependence.Clinical Pharmacology & Therapeutics (2011) 90 1, 164-168. doi:10.1038/clpt.2011.105
... There is a considerable amount of scientific literature on the mechanisms underlying weight gain upon smoking cessation, but these mechanisms are still not fully understood and there is evidence for numerous contributing factors (Chao et al., 2019). Evidence points out that the suppression of nicotine's metabolic effects (Audrain-Mcgovern & Benowitz, 2011) and behavioral changes (e.g., eating behaviors, exercise, or sleep) could account for this weight gain. Regarding eating behaviors, literature notes that caloric intake, snacking and appetite increase after smoking cessation (Bacha et al., 2016;Kadota et al., 2010;Kos et al., 1997;Yannakoulia et al., 2018), and that the increase in the caloric intake is greater among women with higher body mass index (BMI ≥ 27) (Saules et al., 2004). ...
... Notably, individuals with higher nicotine dependence have greater levels of cotinine (Jung et al., 2012;Van Overmeire et al., 2016), and greater postcessation weight gain (Killi et al., 2020;Kmetova et al., 2014;Komiyama et al., 2013). Considering that nicotine intake raises energy expenditure (Audrain-Mcgovern & Benowitz, 2011;Stojakovic et al., 2017), individuals with higher cotinine levels may be more affected by the removal of nicotine's metabolic effects and, therefore, may demonstrate greater weight gain (Audrain-Mcgovern & Benowitz, 2011;Stamford et al., 1986). ...
... Notably, individuals with higher nicotine dependence have greater levels of cotinine (Jung et al., 2012;Van Overmeire et al., 2016), and greater postcessation weight gain (Killi et al., 2020;Kmetova et al., 2014;Komiyama et al., 2013). Considering that nicotine intake raises energy expenditure (Audrain-Mcgovern & Benowitz, 2011;Stojakovic et al., 2017), individuals with higher cotinine levels may be more affected by the removal of nicotine's metabolic effects and, therefore, may demonstrate greater weight gain (Audrain-Mcgovern & Benowitz, 2011;Stamford et al., 1986). ...
Introduction: A more comprehensive understanding of the factors regarding weight control in individuals with overweight or obesity after quitting smoking is needed. The study aimed to analyze the changes of in-treatment variables during a smoking cessation intervention and examine their impact on weight.
Methods: A total of 120 individuals who smoke with overweight or obesity (MBMI = 31.75 ± 4.31; 54.16 % female) participated in a cognitive-behavioral therapy for smoking cessation and weight control or the same treatment plus contingency management. Weight, smoking variables (cotinine and continuous abstinence), eating behaviors (appetite, grazing), exercise, and sleep were assessed weekly throughout the treatment.
Results: More participants gained weight over time with reduced nicotine use or abstinence. There was a tendency during treatment to increase appetite and exercise time, while grazing episodes and sleeping hours remained stable. Higher baseline weight (p < .001), greater cotinine reduction (p = .021) and time (p = .009) were associated with greater weight gain, while more hours of exercise (p = .003), no appetite changes (p = .003) and diminished appetite (p < .001) were associated with less gain over the treatment. Both treatment conditions showed similar results in all in-treatment variables.
Discussion: Individuals with overweight and obesity with higher baseline weight and higher baseline cotinine levels during smoking cessation interventions may require special attention to improve weight outcomes. Exercise and appetite regulation may be useful for mitigating weight gain in smoking cessation interventions for individuals with overweight or obesity.
... Many people seem to believe that smoking can aid in weight loss. Consequently, numerous studies in the past have investigated the impact of smoking on body weight, and there have been reports suggesting such a possibility [2][3][4] . However, the association between smoking and weight loss remains debatable 5,6 . ...
... Many studies have investigated the effect of smoking on body weight. Smoking is generally associated with weight loss [2][3][4] . For example, Wang 3 , using a national cohort, reported that cigarette smoking increased the likelihood of being underweight by 2.7% and having a healthy weight by 12.7%. ...
INTRODUCTION
This descriptive study examined the relationship between body image perception and smoking status among women aged 19–64 years in Korea, using data from the Korea National Health and Nutrition Examination Survey (KNHANES).
METHODS
This study is a secondary analysis of data gathered from the KNHANES between 2014 and 2020, encompassing 12515 women aged 19–64 years. The final study group consisted of 742 current smokers (CS), 132 hidden smokers (HS), and 11641 non-smokers (NS). Hidden smokers were defined as participants who reported being non-smokers but had urine cotinine levels >50 ng/mL. The participants were divided into three age categories: 19–29, 30–49, and 50–64 years. A multiple logistic regression analysis was used to assess the relationship between body image perception and smoking status, by age group.
RESULTS
Baseline statistics indicated that smokers (both CS and HS) generally had a lower socioeconomic status across all age groups. The highest rate of obesity perception was observed in the CS group, followed by the HS group, in both groups of women aged 19–29 and 30–49 years. However, only women aged 19–29 years in the CS group were more likely to perceive themselves as obese than those in the NS group (AOR=2.60; 95% CI: 1.49–4.52; p=0.001). Furthermore, factors such as current smoking status (AOR=2.32; 95% CI: 1.28–4.23; p=0.006), higher body mass index (AOR=2.95; 95% CI: 2.59–3.37; p<0.001), and perceived health status as poor (AOR=3.82; 95% CI: 2.11–6.92; p<0.001), significantly influenced the perception of obesity in this age group.
CONCLUSIONS
This study identified a notable relationship between obesity perception and smoking among women aged 19–29 years only. These findings suggest that interventions aimed at weight reduction or modifying the perception of obesity, could potentially aid smoking cessation efforts in young women.
... Smokers often weigh less than non-smokers [69]. Studies conducted both in vivo and in vitro have also confirmed that cigarette smoke exposure leads to significant weight loss in mice, particularly affecting fat mass more than lean mass. ...
... However, it is worth noting that cigarettes have a complex composition. Besides Cd, nicotine can increase energy expenditure, promote lipolysis in AT, and suppress appetite [69]. The present study found that, compared to non-smokers, smokers exhibited more significant negative correlations between UCd and both FFMI and FMI. ...
The effects of cadmium (Cd) on metabolic physiology remain controversial. Given the varying metabolic impacts associated with different body compositions, investigating the relationship between Cd exposure and body composition may facilitate further research. Here, the associations of body composition and fat distribution with urine Cd (UCd) were evaluated. This analysis included 2979 adult participants from the 2011–2018 National Health and Demographic Survey (NHANES). UCd was measured using inductively coupled plasma mass spectrometry and adjusted for urinary creatinine. Body composition and fat distribution were estimated using dual-energy x-ray absorptiometry (DXA). The study results show that UCd was negatively associated with fat mass index (FMI) and percent fat mass (p for trend < 0.001), and the negative correlation between UCd and FMI was stronger in males and smokers (all p for interaction < 0.05). In terms of abdominal fat distribution, UCd was negatively associated with abdominal subcutaneous adipose tissue (SAT) mass (p for trend < 0.001), but with abdominal visceral adipose tissue (VAT) mass only in those with low percent fat mass (< 32.3%) (p for trend = 0.026 and p for interaction < 0.05). UCd was positively related to percent VAT (p for trend < 0.001) and visceral-to-subcutaneous (VAT/SAT) ratio (p for trend = 0.003). And there was a significant negative association between UCd and android-to-gynoid (A/G) ratio (p for trend = 0.001). Meanwhile, UCd was negatively correlated with fat-free mass index (FFMI) (p for trend < 0.001). And the negative correlation between UCd and FFMI was stronger in males, smokers, and individuals with < 32.3 percent fat mass (all p for interaction < 0.05). We found the association of UCd with body composition and fat distribution, with distinct patterns observed in different demographic groups. These findings underscore the importance of considering UCd exposure in the context of body composition and fat distribution.
... Although longitudinal studies about the effect of smoking status on WC are rare, there is evidence that on average, WC increases 3.9 cm during the first post-cessation year 11 . As a mechanism for this it has been suggested that nicotine increases energy expenditure about 10%, especially during exercise and after eating 29 , and thus individuals who quit smoking tend to increase their body weight 1-4 6 . ...
... Whether longer behavioral and weight management interventions benefit successful cessation and reduce post-cessation abdominal obesity remains to be investigated 29 . ...
BACKGROUND: This follow-up study investigated the associations of smoking status and leisure-time physical activity (LTPA) with weight circumference (WC) change.
METHODS: In the FinnTwin16 cohort, 3,431 twins (47% men) reported smoking status, LTPA, and WC in early adulthood and 10 years later. Regression models were conducted to investigate associations of smoking status and of LTPA change (metabolic equivalent tasks [MET]-h/week) with WC change (cm) during the follow-up. Within-pair associations were analyzed using linear mixed fixed-effect regression among 800 same-sexed (409 identical) pairs.
RESULTS: During the 10-year follow-up, 40% (n=454) quit smoking. Among those who quit smoking, the mean WC increase was 7.4 cm (SD 8.2) and the mean LTPA decrease was -0.02 MET-h/week (SD 35.8). Compared to individuals continuing daily smoking, only quitters who smoked daily at baseline (β 1.87; 95% CI 0.68, 3.06) increased their WC. This association was not robust after shared familial influences were controlled for. Each additional MET-h/week lowered the risk for WC increase among individuals who smoked occasionally (β -0.054; -0,08, -0.003), quitters who smoked daily (β -0.05; -0.06, -0.02) and those who had never smoked (β -0.04; -0.05, -0.03). In the analyses among identical twin pairs, LTPA was associated with less WC increase among those who quit occasional smoking or had never smoked. For quitters from daily smoking, this association approached significance, but no association remained for those continuing smoking.
CONCLUSIONS: Smoking cessation seems to be associated with WC increase, but familial confounding is involved in this process. LTPA may inhibit post-cessation WC increase.
... Nicotine in NNTPs, is known to suppress appetite and increase resting metabolic rate. 6) Furthermore, tobacco companies have been innovating NNTPs to support weight loss. 7) Mantey et al. 8) found that EC users were more likely to have weight loss intentions (odds ratio [OR], 1.38; 95% confidence intervals [CI], 1.07 to 1.78) among high school students. ...
... 15) Nicotine from cigarettes acts as a sympathomimetic, increasing energy expenditure, inducing anorexic effects, and reducing energy intake. 6,16) Recently, it has been reported that the use of EC, a type of NNTPs, is also associated with UWCBs among adolescents. 10,17) Some adolescents believe that NNTPs are safe and attractive alternatives to CCs. ...
Background: Noncombustible nicotine or tobacco product (NNTP) use, and cigarette smoking are associated with a high likelihood of unhealthy weight control behaviors (UWCBs) among adolescents. However, no study has addressed the differences in UWCBs among non-users, single users of combustible cigarettes (CCs) or NNTPs and dual users. This study compared the frequencies of weight control behaviors according to the status of CC and NNTP use among Korean adolescents.Methods: This was a cross-sectional study of 25,094 adolescents who had attempted to reduce or maintain their body weight during the past 30 days, using data from the 15th Korea Youth Risk Behavior Survey, 2019. Data on the status of CC and NNTP use, weight status, and weight control behaviors were obtained using self-report questionnaires. Subjects were categorized into four groups: non-users, cigarette-only users, NNTP-only users, and dual users.Results: Among boys and girls, current smokers and NNTP users were 8.9%±0.3% and 5.5%±0.3%, and 4.2%±0.2% and 1.7%±0.1%, respectively. Among boys, NNTP-only users were more likely to engage in extreme weight control behaviors than non-users. Among girls, users of either CCs or NNTPs were more likely to engage in extreme weight control behaviors and less extreme weight control behaviors than non-users.Conclusion: This study shows that users of either CCs or NNTPs are more likely to engage in UWCBs, and NNTP-only users are the most likely to do so.
... Consequently, their diet is inclined towards refined carbohydrates and fats, meals high in sugar as well as alcohol thus consuming more calories that are low in essential nutrients [12,49,52,53]. Alternatively, a larger waist circumference can be attributed to metabolic effects of nicotine on body weight or composition like low adiponectin levels which modulates insulin sensitivity [48]. Reduced adiponectin levels are one of the known causes of insulin resistance which alters body composition including increased visceral fat [54]. ...
... This could be due to active tobacco smoking as reported by some of the participants and second hand smoking by virtue of association. In general, smokers tend to be leaner than non-smokers [48] possibly due to the effects of nicotine which is believed to increase metabolic rate and therefore increase in energy expenditure [55]. Elsewhere tobacco smoking has been linked to central adiposity and not overall obesity as smokers tend to have a low BMI [56,57]. ...
Introduction
Despite documentation on injection drug use (IDU) in Kenya, the nutritional status of people who inject drugs (PWIDs) is under-explored. Elsewhere studies report under-nutrition among PWIDs which is attributed to food insecurity; competing priorities between drugs and food supply; chaotic lifestyle; reduced food intake; substance use induced malnutrition due to inflammation and comorbidities.
Methods
This was a cross-sectional study that sought to assess the nutritional status of PWIDs in Coastal Kenya. We recruited 752 participants of whom 371(49%) were on IDUs and 75 non-IDUs and 306 non-drug users using respondent driven sampling, traditional snowball, makeshift outreach and purposive sampling methods.
Results
More than one half of the participants (56%) had BMI classified as normal while 35% had BMI < 18.5. The proportion with BMI < 18.5 was higher among IDUs (46%) compared to the non-IDUs (33%) and non-drug users (23%) at P < 0.001. Using the mid upper arm circumference (MUAC), 17% were classified as underweight and the proportion was lowest (11%) among non- drugs users compared to 22% among IDUs (P < 0.001). However, the IDUs had lower proportion of overweight (8.1%) compared to 55% among the non- drug users. The proportion with low waist-for-hip ratio was highest among the IDUs (74%) while high waist-for-hip ratio was lowest in the same group of IDUs (11%) at P < 0.001. One half (50%), of the participants had no signs of anaemia, (47%) had mild/moderate anaemia while 21 (2.8%) had severe anaemia. However, IDUs were more likely to be overweight based on waist circumference as a parameter. The IDUs had the highest proportion (54%) of mild to moderate anaemia compared to non-IDUs (37%) and 40% non- drug users (P < 0.001). In the multivariable models, IDUs (aRRR 2.83 (95%CI 1.84‒4.35)) and non-IDUs (aRRR 1.42 (95%CI 1.07‒1.88)) compared to non- drug users were positively associated with BMI < 18.5. Being an IDU was positively associated with mild or moderate anaemia (aRRR 1.65 (95%CI 1.13‒2.41)) while non-IDUs were positively associated with severe anaemia (aRRR 1.69 (95%CI 1.16‒2.48)).
Conclusion
A significant proportion of the participants were under-nourished with those injecting drugs bearing the heaviest brunt. Being an IDU was positively associated with the low BMI, MUAC, waist for hip ratio and mild or moderate anaemia but high waist circumference. People who inject drugs have high risk for under-nutrition and should be targeted with appropriate interventions.
... Weight control is a common motive for initiation and continued smoking, and fear of postcessation weight gain is cited as an important barrier to smoking cessation and/or relapse (Beebe & Bush, 2015;Pinto et al., 1999;White, 2012). The relations among smoking, appetite, and body weight are complex and incompletely understood, but evidence supports the hypothesis that nicotine delivered alone or via cigarettes reduces appetite, and ultimately body weight, by acting upon brain and hormonal mechanisms (Audrain-McGovern & Benowitz, 2011). Whether these effects extend to other tobacco products such as electronic nicotine delivery systems (ENDS or e-cigarettes) is unknown. ...
... Nicotine use for appetite suppression is complex, and there are multiple mechansims by which nicotine likely exerts this effect (Audrain-McGovern & Benowitz, 2011). For example, nicotine delivered via cigarettes or other methods is thought to act on the hormone leptin found in adipose tissue by increasing its quantity and regulating feelings of hunger and inducing satiety (Jo et al., 2002). ...
Previous work has aimed to disentangle the acute effects of nicotine and smoking on appetite with mixed findings. Electronic nicotine delivery systems (ENDS) have yet to be examined in this regard despite evidence of use for weight control. The present study tested the influence of an ENDS on acute energy intake and associated subjective effects. Participants (n = 34; 18–65 years) with current ENDS use completed two randomly ordered clinical lab sessions after overnight abstinence from tobacco/nicotine/food/drinks (other than water). Sessions differed by the product administered over 20 min: active (20 puffs of a JUUL ENDS device; 5% nicotine tobacco-flavored pod) or control (access to an uncharged JUUL with an empty pod). About 40 min after product administration, participants were provided an ad lib buffet-style meal with 21 food/drink items. Subjective ratings were assessed at baseline, after product use, and before/after the meal. Energy intake (kcal) was calculated using pre–post buffet item weights. Repeated measures analyses of variance and pairwise comparisons were used to detect differences by condition and time (α < .05). Mean ± standard error of the mean energy intake did not differ significantly between active (1011.9 ± 98.8 kcal) and control (939.8 ± 88.4 kcal; p = .108) conditions. Nicotine abstinence symptoms significantly decreased after the active condition, while satiety significantly increased. Following the control condition, satiety remained constant while hunger significantly increased relative to baseline. Findings indicate that acute ENDS use did not significantly impact energy intake, but there was an ENDS-associated subjective increase in satiety and relative decrease in hunger. Results support further investigation of ENDS on appetite.
... [112][113][114] Similarly, 1 study aligns with this, as nonsmokers were less likely to be obese compared to with smokers. 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. ...
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.
... Akan tetapi kondisi sekarang secara global menunjukkan bahwa prevalensi obesitas juga semakin meningkat secara drastis di negaranegara berpenghasilan menengah dan rendah (middle income dan lower income). Pada tahun 2016, diperkirakan 44 persen orang dewasa (lebih dari 2 miliar) di seluruh dunia mengalami kelebihan berat badan dan obesitas, dengan lebih dari 70 persen dari mereka tinggal di negara berpenghasilan rendah atau menengah [3]. Peningkatan obesitas ini juga terjadi seiring dengan meningkatnya pertumbuhan ekonomi [4]. ...
Obesity begins to be experienced by households with high, middle and low incomes. The prevalence of obesity is increasing along with globalization which makes access to food easier and the price of processed food is cheap but has high calories. Most households use their income for food consumption. Therefore, consumption of processed and processed foods is closely related to the risk of obesity. In this study, we investigated the extent to which processed and processed consumption affects obesity in Indonesia using the Instrumental Variable Probit method. We found that processed and processed consumption had a positive and significant effect on the obesity probability of 0.756. The random effect test also shows that fast and processed consumption has a positive and significant effect on the probability of obesity, such as those who live in urban areas increase the probability of experiencing obesity. Another findings show that life style, medical history and insurance status also have positive effect on obesity.
... 9 Smoking and nicotine have been associated with an increase in energy expenditure thus it further tends to lower the BMI. 10 This study will be helpful for further understanding of socio-economic risk factors associated with the onset and progression of tuberculosis, and assessment of best intervention and effectiveness. ...
... Many prior studies indicated effects of residual confounding by smoking and/or an interaction between BMI and smoking in mortality analyses [3,16,[27][28][29]. This is not much surprising, as smoking suppresses appetite, which can lead to a substantial weight loss [30]. At the same time, smoking causes premature mortality since cigarette smoke contains a variety of carcinogenic compounds [31] and is involved in the pathophysiology of arterosclerosis [32,33]. ...
Aims
To investigate the association between body mass index (BMI) at acute myocardial infarction (AMI) and all-cause as well as cause-specific long-term mortality.
Methods
The analysis was based on 10,651 hospitalized AMI patients (age 25–84 years) recorded by the population-based Myocardial Infarction Registry Augsburg between 2000 and 2017. The median follow-up time was 6.7 years [IQR: 3.5–10.0)]. Cause-specific mortality was obtained by evaluating the death certificates. In multivariable-adjusted COX regression models using cubic splines for the variable BMI, the association between BMI and cause-specific mortality (all-cause, cardiovascular, ischemic heart diseases, cancer) was investigated. Additionally, a subgroup analysis in three age groups was performed for all-cause mortality.
Results
Overall, there was a statistically significant U-shaped association between BMI at AMI and long-term mortality with the lowest hazard ratios (HR) found for BMI values between 25 and 30 kg/m². For cancer mortality, higher BMI values > 30 kg/m² were not associated with higher mortality. In patients aged <60 years, there was a significant association between BMI values >35 kg/m² and increased all-cause mortality; this association was missing in 60 to 84 years old patients. For all groups and for each specific cause of mortality, lower BMI (<25kg/m²) values were significantly associated with higher mortality.
Conclusions
Overall, a lower BMI – and also a high BMI in patients younger than 60 years - seem to be a risk factors for increased all-cause mortality after AMI. A BMI in a mid-range between 25 and 30 kg/m² is favorable in terms of long-term survival after AMI.
... However, their long-term efficacy is modest with success rates of <40% at one year (12). Furthermore, these treatments delay, but do not prevent, body weight gain during smoking abstinence (13,14). ...
The glucagon‐like peptide 1 (GLP-1) agonists such as semaglutide (Ozempic®, Wegovy®) and tirzepatide (Mounjaro®) have shown efficacy inducing weight loss in both diabetics and non-diabetics. According to the incentive sensitization theory of addiction, these drugs may prove useful in addictive disorders such as nicotine addiction. Animal data has been suggestive of a potential positive effect but early human studies have been mixed. This manuscript reviews the theory of addiction as well as the few animal and human studies available. Further human studies are needed to show GLP-1 agonist efficacy in smoking cessation.
... In contrast to alcohol consumption, our study found that tobacco consumption is negatively associated with overweight and obesity. Evidence suggests that smoking may reduce body weight by elevating the resting metabolic rate while diminishing the expected increase in food intake associated with this metabolic increase [71]. However, since smoking is a signi cant risk factor for adverse health outcomes [72], using smoking as a weight management strategy is not recommended for adults. ...
Background
Overweight and obesity pose a huge burden on individuals and society. While the relationship between lifestyle factors and overweight and obesity is well-established, the relative contribution of specific lifestyle factors remains unclear. To address this gap in the literature, this study utilizes interpretable machine learning methods to identify the relative importance of specific lifestyle factors as predictors of overweight and obesity in adults.
Methods
Data were obtained from 46,057 adults in the China Health and Nutrition Survey (2004–2011) and the National Health and Nutrition Examination Survey (2007–2014). Basic demographic information, self-reported lifestyle factors, including physical activity, macronutrient intake, tobacco and alcohol consumption, and body weight status were collected. Three machine learning models, namely decision tree, random forest, and gradient-boosting decision tree, were employed to predict body weight status from lifestyle factors. The SHapley Additive exPlanation (SHAP) method was used to interpret the prediction results of the best-performing model by determining the contributions of specific lifestyle factors to the development of overweight and obesity in adults.
Results
The performance of the gradient-boosting decision tree model outperformed the decision tree and random forest models. Analysis based on the SHAP method indicates that sedentary behavior, alcohol consumption, and protein intake were important lifestyle factors predicting the development of overweight and obesity in adults. The amount of alcohol consumption and time spent sedentary were the strongest predictors of overweight and obesity, respectively. Specifically, sedentary behavior exceeding 28–35 h/week, alcohol consumption of more than 7 cups/week, and protein intake exceeding 80 g/day increased the risk of being predicted as overweight and obese.
Conclusion
Pooled evidence from two nationally representative studies suggests that recognizing demographic differences and emphasizing the relative importance of sedentary behavior, alcohol consumption, and protein intake are beneficial for managing body weight status in adults. The specific risk thresholds for lifestyle factors observed in this study can help inform and guide future research and public health actions.
... Furthermore, smoking causes impaired skeletal muscle oxygen delivery to the mitochondria, leading to reduced ability of the muscle to maintain a giver force or power output (muscle fatigue resistance) [43]. This further leads to smoking-related sarcopenia [44], decreased grip strength [45], weight loss [46,47], and lowered exercise capacity and physical activity in older adults [48]. In addition to age-related decline in muscle mass and physical activity, smoking can further accelerate this process [49]. ...
Introduction
Smoking has been recognized as a contributing factor to frailty in older adults. Nevertheless, it remains uncertain whether the degree of smoking has a discernible impact on frailty among older smokers. This cross-sectional study was conducted to investigate the correlation between serum cotinine levels, a biomarker reflecting tobacco exposure, and the presence of frailty within a nationally representative cohort of older adults.
Method
A total of 1626 individuals aged ≥ 60 who identified as smokers were included in the analysis. Participants were selected based on self-reported current smoking status. According to the Fried Phenotype, frailty is assessed through five dimensions: unintentional weight loss, slow walking speed, weakness, self-reported exhaustion, and low physical activity. Participants with three or more of these conditions were categorized as frailty, those with at least one but less than three as pre-frailty, and those with none as robust. Multinomial logistic regression models were employed to explore the relationship between serum cotinine level quartiles, with the lowest quartile as the reference group, and the various frailty statuses, with robustness as the reference category. These models were adjusted for covariates, including age, sex, race/ethnicity, alcohol drinking, daily protein intake, systolic blood pressure, serum albumin level, depressive symptoms, and cognitive function. The data used for this analysis were sourced from the National Health and Nutrition Examination Survey for the years 2011 to 2014.
Results
The median age of the participants was 69.0 years. The majority were male (62.2%) and non-Hispanic White (49.0%). The distribution of frailty statuses among the participants revealed that the highest proportion had pre-frailty (50.7%), followed by robustness (41.1%), and frailty (8.2%). Multinomial logistic regression showed that participants in the 4th quartile of serum cotinine level exhibited a higher probability of pre-frailty versus robustness (Odds ratio [OR] 1.599, 95% confidence interval [CI] 1.017, 2.513, P = 0.042). Participants in the 3rd quartile of serum cotinine level had higher odds of frailty versus robustness (OR 2.403, 95% CI 1.125, 5.134, P = 0.024). Moreover, participants whose serum cotinine levels were higher than the literature cutoffs (≥ 15 ng/ml) were more likely to be pre-frail (Odds ratio [OR] 1.478, 95% confidence interval [CI] 1.017, 2.150, P = 0.035) or frail (Odds ratio [OR] 2.141, 95% confidence interval [CI] 1.054, 4.351, P = 0.041).
Conclusions
A higher serum cotinine level is linked to an elevated probability of pre-frailty and frailty among older smokers. Initiatives geared towards assisting older smokers in reducing or quitting their smoking habits might possibly play a crucial role in preventing pre-frailty and frailty.
... Expert have not reached a consensus about the biological mechanism on how smokers have lower BMI than non-smokers. However, it is widely held view that nicotine in cigarettes can increase the basal metabolism rate and reduce smokers' appetite 24 . In addition, the determinants of someone having a BMI ≥25 kg/m 2 in Indonesia are physical inactivity, living in an urban area, lower education level, higher socio-economic status, and consumption of instant and junk foods 25 . ...
Latar Belakang: Berdasarkan data Riskesdas tahun 2018, prevalensi penyakit tidak menular (PTM) seperti penyakit jantung, diabetes melitus dan hipertensi di wilayah Sulawesi Tengah masih lebih tinggi dibandingkan dengan prevalensi Nasional. Selain itu Sulawesi Tengah juga menjadi rumah bagi 31,3% perokok aktif. Penyebab utama PTM adalah gaya hidup tidak sehat seperti rendahnya aktivitas fisik, merokok, minum alkohol, serta memiliki pola makan tidak sehat (seperti sering mengonsumsi makanan tinggi lemak, makanan manis, rendah serat, tinggi sodium dan kalori berlebihan). Faktor-faktor risiko ini tidak hanya berdiri sendiri, namun juga saling menguatkan dalam meningkatkan morbiditas dan mortalitas. Kombinasi dari faktor-faktor risiko ini menyulitkan Pemerintah dalam upaya menurunkan prevalensi PTM di Sulawesi Tengah. Tujuan: Penelitian ini bertujuan untuk mengetahui pola makan dan status gizi penduduk dewasa di Sulawesi Tengah berdasarkan status merokok mereka. Metode: Penelitian ini merupakan penelitian cross-sectional dengan menggunakan data sekunder yaitu data dari Riset Kesehatan Dasar tahun 2018 (Riskesdas 2018) yang dilaksanakan oleh Badan Penelitian dan Pengembangan Kesehatan Kementerian Kesehatan RI. Dari total 21.904 individu yang diwawancarai, sebanyak 12.211 responden yang dimasukkan ke dalam analisis karena memenuhi kriteria umur (≥ 18 tahun) dan memiliki data yang lengkap untuk semua variabel. Variabel yang diuji adalah status merokok, pola makan, dan status gizi. Variabel pola makan terdiri dari konsumsi makanan manis, konsumsi minuman manis, konsumsi makanan berlemak, konsumsi makanan asin, konsumsi sayur dan konsumsi buah. Sedangkan variabel status gizi diukur berdasarkan indeks massa tubuh (IMT) dan lingkar pinggang. Uji statistik digunakan adalah chi-square untuk melihat ada tidaknya perbedaan sebaran frekuensi setiap variabel dan generalized linear model (GLM) untuk melihat pola makan yang spesifik pada kelompok perokok. Hasil: Sebanyak 36,2% dari seluruh responden mengaku menghisap rokok dan atau mengunyah tembakau baik setiap hari maupun kadang-kadang dalam 1 bulan terakhir. Dimana mayoritas kelompok perokok adalah laki-laki (94,54%), memiliki IMT normal (68,07%), tidak mengalami obesitas sentral (86,7%), berusia 30-39 tahun (26,38%), lulusan SD (32,48%) dan bekerja sebagai petani/buruh tani (51,46%). Sedangkan kelompok non-perokok didominasi oleh perempuan (77,2%), memiliki IMT normal (50,3%), tidak mengalami obesitas sentral (50,9%), berusia 30-39 tahun (24,4%), tamat SD (30,3%) dan mayoritas tidak memiliki pekerjaan (39,42%). Setelah dilakukan penyesuaian terhadap variabel lainnya, prevalensi kelebihan berat badan (IMT ≥25kg/m2) dan obesitas sentral pada kelompok perokok lebih rendah (p<0,05) dibandingkan dengan non-perokok. Dalam hal pola makan, perokok mengonsumsi minuman manis minimal 1 kali/minggu dengan rasio prevalens yang semakin tinggi seiring dengan peningkatan frekuensi konsumsi per minggu, dan mengonsumsi sayur maksimal 3-4 porsi/hari dengan rasio prevalens yang lebih rendah, serta mengonsumsi makanan asin sebanyak 1-2 kali/minggu. Kesimpulan: Meskipun memiliki risiko kelebihan berat badan dan obesitas sentral yang lebih rendah, perokok dewasa di Sulawesi Tengah memiliki pola makan yang tidak sehat yaitu konsumsi minuman manis dan makanan asin yang tinggi dan konsumsi sayur yang rendah. Penelitian lebih lanjut dengan metode longitudinal dan jumlah sampel yang representatif diperlukan untuk dapat menentukan hubungan sebab akibat antar variabel. Kata Kunci: Perokok, status gizi, pola makan.
... Similarly, smoking is the leading cause of preventable death worldwide, linked to a myriad of adverse health outcomes such as lung cancer, respiratory diseases, and cardiovascular disorders [4]. Despite their distinct etiologies, recent research has begun to elucidate a complex relationship between smoking and obesity, highlighting the need for integrated public health strategies to address these dual epidemics [5,6]. ...
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.
... increases energy expenditure and reduces appetite 8) . METs was more prevalent in ex-smokers than in smokers in a three-year follow-up study 9) . ...
Aim: The Fukushima Daiichi Nuclear Power Plant accident caused lifestyle changes and psychological distress in residents living near the plant. This study clarified the associations between changes in residents’ lifestyles and psychological factors with the onset of metabolic syndrome (METs) after the accident.
Methods: This longitudinal study included 10,373 residents who underwent the Comprehensive Health Check and Mental Health and Lifestyle Survey in Fiscal Year (FY) 2013. Follow-up surveys were conducted between FY 2014 and FY 2017. Lifestyle changes and the METs incidence were evaluated using a logistic regression model.
Results: METs developed in 14.0% of subjects. In addition to metabolic factors, such as the body mass index, hypertension, dyslipidemia, and diabetes mellitus, there were differences in physical activity, fast walking, eating fast, eating habits before bedtime, skipping breakfast, current smoking, and alcohol intake between subjects with and without new-onset METs. Eating fast, current smoking, and drinking alcohol were positively associated with new-onset METs, whereas starting physical activity and fast walking were inversely associated with new-onset METs.
Conclusions: Disaster-related lifestyle changes, such as eating fast, starting to smoke, and continued alcohol intake, were risk factors for new-onset METs after the Fukushima Daiichi Nuclear Power Plant accident.
... Smokers weigh 4-5 kg less than non-smokers. 22 High BMI has been found to be a risk factor for OSA. 10,[14][15][16] Mass load effect by obesity on the upper airway increases its collapsibility, leading to OSA. 16 Higher rates of smoking and more severe OSA were seen in obese patients. ...
Obstructive Sleep Apnea (OSA) and smoking are important global health issues that are widely prevalent. Both are independently associated with cardiovascular, respiratory, metabolic, neurological, psychiatric, and endocrinological abnormalities and cancer, leading to premature death. Whether coexistent OSA and smoking worsen multisystem abnormalities is inconclusive. Therefore, this study was done to find the association between OSA and other morbidities among smokers and non-smokers. The objectives of this study were i) to evaluate the association of OSA with co-morbidities in smokers and non-smokers and ii) to compare the severity of OSA in smokers and non-smokers.
... This aligns with general findings that higher BMI is a risk factor for hypertension. Nicotine increases energy expenditure, suppresses appetite, and can mimic satisfaction derived from food [42,43]. Smoking one cigarette has been shown to increase energy expenditure by 3% within 30 min [44], while smoking four cigarettes can increase it by 3.3% within 3 h [45]. ...
Smoking is a major global health issue that contributes to various chronic diseases, while hypertension and obesity are considered significant health concerns due to their associated complications, such as cardiovascular diseases and metabolic disorders. In this study, we investigated the associations between current smoking status, hypertension, and obesity among the Korean population, excluding individuals with high blood pressure (systolic blood pressure ≥ 160 mmHg or diastolic blood pressure ≥ 100 mmHg) and those taking antihypertensive medications. Data from the 2015 Korea National Fitness Assessment, encompassing 3457 individuals, were analyzed. Logistic regression analysis was used to examine the effects of current smoking and other variables on hypertension and obesity. The results showed that, among the population that excludes specific hypertension criteria, current smoking status was not significantly associated with hypertension or obesity. However, sex and body mass index were significantly associated with hypertension, and age, sex, and blood pressure were significantly associated with obesity. Future research should utilize larger sample sizes and longitudinal designs to confirm these findings and include a broader range of hypertensive participants to better control for potential confounding variables.
... Withdrawal of nicotine as in quitting of cigarette smoking can result in intense cravings for nicotine, irritability, depression, insomnia, hunger, and difficulty concentrating [18]. The increased appetite associated with nicotine withdrawal could be due to reactivation of pathways that were previously blocked by nicotine [19]. Studies suggest that the nicotine withdrawal symptoms could result from the brain having no opposing force (dopamine) to dampen its effects. ...
Background to the study: Nicotine, which is the most abundant constituent of tobacco cigarette is one of the most commonly abused drugs. The duo of obesity and tobacco smoking increases the risk of cardiometabolic disorders. Methodology: The experiment involved 48 male wistar rats separated into 4 groups of 8 rats each. All the animals were placed on high-fat diet (Margarine; blue band) in addition to the different doses of nicotine in the experimental groups. Group 1 served as control and received distilled water, Groups 2, 3 and 4 received 200µg/kg, 400µg/kg and 800µg/kg of nicotine oral solution respectively. The experimental groups were placed on their respective doses of nicotine solution for an initial four weeks. Thereafter, four animals in each group were sacrificed and blood samples collected to determine their nicotine exposure plasma levels of glucose, insulin and glycated hemoglobin. The remaining four animals in each group continued without nicotine for another four weeks after which they were sacrificed and blood samples collected to determine their nicotine withdrawal plasma levels of glucose, insulin and glycated hemoglobin. Results: The results showed that exposure to the three concentrations of nicotine (200, 400 and 800µg/kg respectively) significantly caused a rise in plasma insulin levels but dose-dependent reduction in both glucose and glycated haemoglobin compared to their control groups. Four weeks after withdrawal of initial 200µg/kg nicotine there were no significant changes in the levels of plasma glucose, insulin and glycated haemoglobin compared to the nicotine exposed groups. Withdrawal, after initial exposure to 400µg/kg of nicotine was associated with significant rise in both the plasma glucose and glycated haemoglobin but no significant change in insulin compared to their respective nicotine exposed groups. Cessation, after initial exposure 800µg/kg of nicotine resulted in significant rise in plasma levels of glucose, glycated haemoglobin and insulin compared to their nicotine exposed groups. Conclusion: The possible reduction in food consumption and increased physical activity together with the increased levels of insulin in the plasma could contribute to the lowered plasma glucose shown in this study. The reduction in the levels of glycated haemoglobin following exposure to nicotine may be potentially beneficial in diabetic management. Therefore, reversal of plasma glucose and glycated haemoglobin levels could be achieved four weeks after cessation of 800µg/kg of nicotine. This result could suggest a possible association of nicotine cessation with decreased insulin sensitivity.
... Factors that encourage e-cigarettes usage for weight management include the perception of them being healthier alternatives to smoking traditional cigarettes and availability of flavour-infused e-liquids [3,38]. Individuals trying to lose weight may find nicotine appealing, as it suppresses hunger, reduces cravings for sweet foods, and increases both the resting metabolic rate and daily calorie burn [60,61]. ...
Background: Electronic cigarettes or vapes are battery-operated devices that heat a liquid, often containing nicotine and flavouring substances, to produce an inhalable aerosol. Despite being used as an alternative to traditional smoking, many studies have reported their health risks and ineffectiveness in smoking cessation. The impact of e-cigarettes on weight control behaviours, a known effect of traditional cigarette smoking, is unclear. Herein, a systematic review was conducted to explore the relationship between e-cigarette use and body weight changes in adolescents and young adults. Methods: The existing literature from databases such as PubMed, Cochrane Library, Embase, Science Direct, Web of Science, Scopus, and Google Scholar until October 2023 was searched and included in the review. The methodological quality of all selected studies was assessed using the Joanna Briggs Institute’s (JBI) Critical Appraisal Checklists for Studies. Results: Out of 5117 citations, 20 publications featuring cross-sectional studies with adolescent participants were qualitatively analysed. The high rates of e-cigarette usage seemed to correlate with increased weight concerns, particularly among females. Regular e-cigarette users who reported being overweight and used calorie restriction for weight reduction were more likely to view vaping as a weight loss or control strategy. Young adults (<24 years) may consume more flavoured e-cigarettes than older users (>25 years). Conclusions: This study revealed a significant use of e-cigarettes among high school students, driven by taste preferences, weight management, and perceived harm reduction. Particularly among girls facing body image pressures, vaping serves as a weight control method. This highlights the need to assess cardiovascular risks and advocate for further research, including longitudinal studies, to inform public health strategies effectively.
... This is in line with our findings, where participants shared that smoking adversely affected their ability to taste the food [8]. This effect may be attributed to nicotine's widely recognized capacity to suppress appetite, potentially prompting individuals to turn to smoking as a substitute behavior for eating [31]. ...
People with substance use disorders often have unhealthy diets, high in sweets and processed foods but low in nutritious items like fruits and vegetables, increasing noncommunicable disease risks. This study investigates healthy eating perceptions and barriers among individuals with opioid use disorder undergoing opioid agonist therapy. Interviews with 14 participants at opioid agonist therapy clinics in Western Norway, using a semi-structured guide and systematic text condensation for analysis, reveal that most participants view their diet as inadequate and express a desire to improve for better health. Barriers to healthy eating included oral health problems, smoking habits, and limited social relations, while economic factors were less of a concern for the participants. Participants did find healthy eating easier when they were in social settings. This study underscores the importance of understanding and addressing these barriers and facilitators to foster healthier eating patterns in this population, potentially enhancing overall health and well-being.
... Smoking has been reported to have an array of physiological and psychological effects that some people who smoke identify as reasons for continuing to smoke, including effects on emotion and cognitive function [8,9]. Nicotinic acetylcholine receptor activation via agonists, such as nicotine, can facilitate the release of an array of neurotransmitters that have been shown to be involved in cognitive functioning [10]. ...
Background
As well as being associated with serious negative health outcomes, smoking has been reported to have an array of physiological and psychological effects, including effects on mood and cognitive function. Post-cessation, loss of such effects (including temporary deficits in cognitive function) have been cited as reasons for resumption of smoking. The effects of e-cigarettes and nicotine delivered by e-cigarettes on these functions have not been widely researched but may play a role in the effectiveness of e-cigarettes as a satisfactory alternative to combustible cigarettes for people who smoke, and in encouraging individuals who would otherwise continue to smoke, to transition to e-cigarettes.
Methods
The study was an exploratory, randomised, partially-blinded, single-centre, five-arm crossover trial that recruited 40 healthy male and female people who smoke. At 5 study sessions, following a 12-h period of nicotine abstinence, participants were randomly assigned to use either a combustible cigarette, an e-cigarette of three varying nicotine strengths (18 mg/mL, 12 mg/mL or 0 mg/mL respectively) or observe a no product usage session. Participants completed pre- and post-product usage assessments to examine the product usage effect on cognitive performance (using the Cambridge Neuropsychological Test Automated Battery (CANTAB)), subjective mood and smoking urges.
Results
A significant improvement in sustained attention task performance was observed following use of both the nicotine containing e-cigarettes and combustible cigarette compared to no product use. Additionally, there were no significant differences between the nicotine containing products, indicating that nicotine use enhanced sustained attention regardless of delivery format. Nicotine containing e-cigarette and combustible cigarette use also significantly improved overall mood of participants compared to no product use, with no significant differences observed between the nicotine containing products. Nicotine containing e-cigarette and combustible cigarette use significantly reduced smoking urges compared to no product use, though combustible cigarette use elicited the greatest reduction in smoking urges.
Conclusions
Overall, the nicotine containing products improved sustained attention and mood while reducing smoking urges, with the studied e-cigarettes having comparable effects to combustible cigarettes across the assessed cognitive parameters and mood measures. These results demonstrate the potential role of e-cigarettes to provide an acceptable alternative for combustible cigarettes among people who would otherwise continue to smoke.
Trial registration ISRCTN (identifier: ISRCTN35376793).
... Considering our results, we hypothesize that this association concerns postmenopausal rather than late pre-and perimenopausal women. According to the review by Audrain-McGovern et al. (2011), most of the effects of smoking on body weight and fat deposits are likely to be mediated through nicotine. An old animal study on rodents demonstrated that nicotine increases sympathetic nervous system activity and thermogenesis in adipose tissues, thus increasing whole-body metabolism, which might subsequently affect the decrease in adiposity (Yuki et al. 2015). ...
Menopause and its related hormonal changes are associated with the variation of body composition, especially impacting adipose tissue metabolism and the reduction of lean mass. The purpose of the present study was to investigate the impact of smoking during menopause on the subsequent effects on body composition. The sample comprised of 572 Slovak women aged between 39 and 65 years (49.67±6.2). Standard anthropometric techniques were used to collect anthropometric measurements, whereas bioelectrical parameters were measured utilizing a mono-frequency bioimpedance analyzer (BIA 101). Data on menopausal status, physical activity, and smoking habits were obtained via a specific questionnaire. In postmenopausal women, our results showed a statistically significant difference between smokers and non-smokers in BMI, TBW%, ECW%, ICW%, MM%, FFM%, FM% (p < 0.05). No significant differences were observed in premenopausal women, although two-way analysis of covariance revealed a significant interaction between smoking and menopausal status on the FM% (p < 0.001), FFM% (p < 0.001), and MM% (p = 0.002), whilst controlling for age and physical activity. In our sample group of middle-aged women, the combined impact of menopause and smoking appeared to influence anthropometric parameters and body composition.
... Specifically, the rates of cigarette smoking among people with EDs are three times higher than the national average (Solmi et al., 2016). Tobacco use has long been associated with eating/weight control due to the appetite suppressant effects of nicotine (Audrain-McGovern & Benowitz, 2011;Glover et al., 2016). Post-tobacco cessation weight gain is also common and concern related to weight gain may be even more pronounced among individuals with disordered eating behaviors (Germeroth & Levine, 2018). ...
Objective: Tobacco use is elevated among individuals with eating disorders (EDs). Yet, further research is needed to understand associations between cigarette and e-cigarette use patterns and ED symptomatology. To gain a more comprehensive understanding of tobacco use and EDs, this study characterized ED symptomatology and tobacco use patterns, including exclusive cigarette use, e-cigarette use, dual use, and nonuse. Method: Young adults aged 18-24 years who self-reported exclusive cigarette, e-cigarette, dual, or nonuse (N = 2500) were recruited via Lucid, an online survey management company. Participants completed questionnaires assessing body dissatisfaction, global ED psychopathology, binge eating and self-induced vomiting frequency, and demographics. ED diagnostic groups included: anorexia nervosa (AN), bulimia spectrum eating disorders (BSED), atypical AN, and night eating syndrome (NES). Results: Multinomial logistic models revealed those with AN were more likely to be dual users, those with atypical AN were more likely to be exclusive e-cigarette users, and participants with a BSED or NES were more likely to be exclusive e-cigarette or dual users, compared to those without an ED. General linear models suggested body dissatisfaction and global ED psychopathology were higher among exclusive e-cigarette and dual users, while binge eating and self-induced vomiting frequencies were greater among all tobacco use groups compared to nonusers. Discussion: Our findings suggest young adults with ED symptomatology were more likely to be users of e-cigarettes exclusively or dual users. It will be necessary to examine how these associations manifest using longitudinal and clinical populations in future research.
... These findings were expected since no significant differences were observed in the BMI of participants (Table 1). However, it is expected that smoking habits can increase the individuals' metabolic rate and contribute to the loss of appetite, thus lowering the BMI of some individuals (Audrain-McGovern et al., 2011). Moreover, cigarette smoking can increase PAH exposure while simultaneously decreasing BMI, explaining the negative correlations. ...
... The duration -dependent increase in the body weight of the experimental animals as recorded in this study following administration of varying doses of nicotine especially on the 2 nd and 3 rd weeks may be due to the inability of nicotine to impact negatively on appetite in view of acute exposure. Despite the weightsuppressive effects of nicotine being studied extensively, the mechanism by which nicotine acts to suppress body weight remains poorly understood [12,13]. However, the reduction in the body weight of the animal models over a prolonged period of time observed in the study is consistent with the findings of Audi et al., [14] which showed that administration of nicotine to rats caused a significant decrease in the body weight and food intake. ...
Considering the popular usage of nicotine, the impact of prolonged exposure of nicotine on haematological parameters in male Wistar rats was evaluated by the current study. A total of thirty (30) Wistar rats were grouped into 5 groups of 6 rats each. Group 1: served as (control) and received distilled water and rat chow ad libitum. Groups 2 to 4 served as test groups; and received 250, 500, 750 and 1000 mg/kg/bw of Nicotine respectively. All treatments were administered orally using oral gavage and it lasted for 28 consecutive days. Thereafter samples were harvested and laboratory analyses done. Upon statistical analysis of data using the SPSS, there was a dependent increase in the body weight of experimental animals administered with 200 mg/kg and 500 mg/kg of the nicotine extract during week 2 and week 3. Aside from that of the 500 mg/kg treated group with marked (P < 0.05) decreased, all other nicotine treated groups compared to control group, indicated significantly raised levels of RBC. Similarly, there were non–significant (p>0.05) increases in the packed cell volume and haemoglobin concentration for the groups administered with 200 mg/kg, 750 mg/kg and 1000 mg/kg of Nicotine compared to the control. There was a significant (P < 0.05) increase in the level of neutrophils and reduction in eosinophil compared to control. In conclusion, chronic exposure to increasing doses of nicotine may have the potential to raise the levels of RBC, PCV, haemoglobin and platelet count; thus leading to a possible raised viscosity/hypercoagulable state of blood that could result in haemodynamic and other related dysfunction.
... 43 Adult smokers are less likely to gain weight and it is reported that their body weight is usually about 5 kg less than nonsmokers. 43,44 This issue could be explained by the cigarette smoker's less desire for food which in turn results from nicotine introduction and its associated early satiety and fullness. 45 Besides, it has been reported that nicotine increases the body's basal metabolic rate and decreases the metabolic efficiency. ...
Background and Aims
Obesity is considered a major growing threat to public health which could negatively affect the quality of life. The current cross‐sectional study was conducted to investigate the population‐based prevalence of metabolically healthy obesity (MHO) and healthy overweight (MHOW) and associated factors in southern Iran.
Methods
Baseline data from the Pars Cohort Study was analyzed. Metabolically healthy participants were identified based on the definition of the American Heart Association for the metabolic syndrome. The prevalence of MHOW and MHO and their 95% confidence intervals were estimated. Poisson regression was applied for the calculation of prevalence ratios (PRs).
Results
Gender‐ and age‐standardized prevalences of MHOW and MHO were 6.3% (6.0%–6.6%) and 2.3% (2.1%–2.5%), respectively. The following factors were associated with being MHOW compared with those with normal weight: Being younger, female gender (1.31, 1.20–1.43), higher socioeconomic status, being noncurrent cigarette smoker (1.27, 1.11–1.45), low level of physical activity (1.14, 1.03–1.25), having normal overweight during adolescence, and overweight (1.35, 1.24–1.48) or obesity (1.68, 1.53–1.86) during young adulthood. We also found strong associations between MHO and younger age groups, female gender (2.87, 2.40–3.42), being married (1.57, 1.08–2.27), Fars ethnicity (1.25, 1.10–1.43), higher socioeconomic status, ever use of tobacco (1.14, 1.00–1.30), never use of opium (1.85, 1.19–2.86), lower physical activity (1.45, 1.20–1.72), being normal weight in 15‐year body pictogram and being overweight (1.87, 1.59–2.20) or obese (3.20, 2.74–3.72) in 30‐year body pictogram when considering those with normal weight or MHO.
Conclusion
Potentially modifiable factors including physical activity should be more emphasized. Furthermore, our study issued that it would be more reasonable that the prevention of unhealthy obesity be initiated before the development of MHO, where there are more protective factors and they could be more effective.
Introduction
Snus is suggested as a risk factor for cardiometabolic disease, but little is known about health effects in young populations, particularly in women. We aimed to investigate associations between snus and cardiometabolic health markers among young men and women.
Method
This study was conducted within the BAMSE birth cohort and included participants followed up around 24 years (n=2256) and 26 years (n=1011). Snus use was assessed at 24 years by questionnaires. Cardiometabolic health markers were recorded at clinical examinations at 24 and 26 years. Associations between snus use and cardiometabolic markers were assessed by multivariable linear regression.
Results
Snus was used by 6.4% (n=81) among women and 21.9% (n=219) among men. Compared to no tobacco use, daily exclusive snus use among women at 24 years was associated with higher body mass index (BMI) (adjusted β: 1.93 kg/m2, 95% CI: 0.54, 3.33) and waist circumference (WC) (aβ: 3.80 cm, 95% CI: 0.41, 7.18) at 24 years, and with higher BMI (aβ: 2.73 kg/m2, 95% CI: 0.53-4.93) at 26 years. Among men, using ≥4 cans/week was associated with increased BMI (aβ: 2.48 kg/m2, 95% CI: 0.73, 4.24) and a tendency toward increased body fat (aβ: 2.31%, 95% CI: -0.53, 5.14) at 26 years. Snus use was not associated with glycemic status or blood pressure.
Conclusion
Our results suggest that snus is associated with increased BMI, and possibly other measures of adiposity, among young women and heavy using young men. Given the cross-sectional study design, the results should be interpreted with caution.
Implications
We found cross-sectional associations between snus use and measures of increased adiposity in a cohort of Swedish young adults, including BMI and waist circumference among women and BMI among heavy snus using men. We did not find associations between snus use and body fat %, glycemic status or blood pressure. This is one of few studies to investigate health effect of snus among both women and men as well as cardiometabolic health markers in young adults. Given the recent trends of increased snus use among young adults, our findings highlight the need for further research on snus on cardiometabolic health.
Objective
Night eating syndrome (NES) has been associated with psychological issues and academic performance among university students in several countries, yet research on NES in Bangladesh remains limited. Therefore, the purpose of the study was to examine the prevalence and factors associated with NES among university students in the country.
Methods
This cross‐sectional study included 500 students from five public universities in Bangladesh. A validated 14‐item night eating questionnaire was used to assess NES as the outcome variable. Demographic factors, depressive symptoms measured via the Patient Health Questionnaire (PHQ‐9), and internet addiction levels measured via Orman's Internet Addiction Survey (OIAS) were explored as predictor variables. A multiple binary logistic regression model was fitted to identify the correlation of NES and its associated factors, with results presented as adjusted odds ratio (AOR) and level of significance set at p values < 0.05.
Results
The prevalence of NES among participants was 16.6% (mean age = 21.6 years, 53.6% male). Adjusted binary logistic regression revealed that male participants (AOR = 2.03, 95% CI = 1.09–3.74, p = 0.024), smoking (AOR = 1.92, 95% CI = 1.02–4.44, p = 0.044), depressive symptoms (AOR = 2.17, 95% CI = 1.26–3.72, p = 0.005), and severe internet addiction (AOR = 2.69, 95% CI = 1.28–5.62, p = 0.009) were significantly associated with increased odds of experiencing NES.
Conclusions
These findings underscore the need for heightened healthy eating awareness programs along with targeted mental health interventions with students attending Bangladeshi universities. Further research that explores longitudinal patterns of NES and the risk factors addressed in this study is warranted to better understand and inform the development of future interventions to benefit the Bangladeshi university student population.
The prevalence of central obesity in Indonesia, especially in women aged 45-54 years, has increased according to the results of the Basic Health Research (Riskesdas, Riset Kesehatan Dasar). Central obesity is associated with a higher risk of diseases such as diabetes, cardiovascular disease, dyslipidemia, and hypertension. This study aimed to identify factors associated with the incidence of central obesity in women aged 45-54 years in Indonesia using the 2018 Riskesdas data with a cross-sectional study design. The study sample consisted of 60,557 individuals, with 64.54% having central obesity. Marital status (PR=1.12; 95%CI 1.09-1.15), low physical activity (PR=1.18; 95%CI 1.14-1.21), and consumption of high-risk foods (PR=1.03; 95%CI 1.01-1.05) were found to be associated with an increased risk of central obesity. In contrast, primary education (PR=0.86; 95%CI 0.83-0.89), employment status (PR=0.91; 95%CI 0.90-0.93), and smoking status (PR=0.89; 95%CI 0.84-0.95) acted as protective factors against central obesity. The results of this study suggest the need for increased nutrition education and physical activity in women aged 45-54 years as well as health support programs for married women.
Study Design
Retrospective study.
Objective
The objective of this study is to investigate the association of waterpipe smoking with lumbar intervertebral disc degeneration (IVDD).
Methods
This is a retrospective chart review study. A total of 286 adults who underwent a lumbar magnetic resonance imaging (MRI) at a tertiary medical center were included and divided into three groups. Group 1 (n = 125) included non-smokers, group 2 (n = 80) smoked cigarettes only, and group 3 (n = 81) smoked waterpipe only. The intervertebral discs were graded using the Pfirmann disc degeneration grading system.
Results
The study showed higher lumbar disc degeneration scores for waterpipe and cigarette smokers compared to non-smokers at all spinal levels. Specifically, post hoc analysis showed that there was a significant difference at L1-L2 between cigarette smokers and non-smokers ( P = 0.007) and between waterpipe smokers and non-smokers ( P = 0.013), and a significant difference at L3-L4 and L4-L5 between non-smokers and cigarettes smokers ( P < .001 and P = .029 respectively).
Conclusion
Waterpipe smoking is associated with lumbar intervertebral disc degeneration.
Systolic blood pressure (SBP) time in target (TTR) over months were associated with lower risk of adverse clinical outcomes in hypertensive patients, whether short-term of 24-h SBP TTR was effective in predicting heart failure (HF) risk in the general population remained unclear. This prospective study aimed to investigate the association of 24-h SBP TTR with HF in the real-world settings. Based on Kailuan study, 24-h SBP target range defined as 110-140 mmHg was calculated with linear interpolation. Among 5152 participants included in the analysis, 186 (3.61%) cases of incident HF occurred during a median follow-up of 6.96 years. Compared with participants with SBP TTR of 0 to <25%, those with TTR of 75% to 100% had 47% lower risk of HF (hazard ratio [HR], 0.53; 95% confidence interval [CI], 0.32-0.89). The restricted spline curve depicted an inverse relationship between SBP TTR and incident HF. Additionally, the addition of SBP TTR, rather than mean SBP and SBP variation, to a conventional risk model had an incremental effect on the predictive value for HF, with integrated discrimination improvement value of 0.31% (P = 0.0003) and category-free net reclassification improvement value of 19.79% (P = 0.0081). Higher SBP TTR was associated with a lower risk of incident HF. Efforts to attain SBP within 110 to 140 mmHg may be an effective strategy to prevent HF.
Aim: The harmful effects of smoking and its impact on respiratory functions and exercise capacity are well-recognized health concerns in the present day, demanding further investigation. This study aims to assess the association between smoking and respiratory functions and exercise capacity while exploring factors contributing to smoking addiction. Methods: A total of 893 volunteers (mean age: 27.24 years, range: 17-67) participated in this study, with 77% having a university education and 58% being female. Participants were categorized into active smokers, passive smokers, and non-smokers. Maximum Voluntary Breath-holding Time , 6-minute walk test distance, and 30-second chair stand test were used to assess respiratory functions and exercise capacity. Participants provided information about their smoking habits, which was analyzed and reported. Results: There were no statistically significant differences in Maximum Voluntary Breath-holding Time, 6-minute walk test distance, and 30-second chair stand test scores between the groups (p>0.05). However, all smokers exhibited psychological dependence on cigarettes, with 88% showing signs of physical dependence. Smoking initiation was significantly influenced by friends (64%). While 61% of active smokers acknowledged potential health issues associated with smoking, only 92% had not sought medical advice to quit smoking. Conclusion: This study did not demonstrate a conclusive effect of cigarette consumption on respiratory functions and exercise capacity. However, it revealed a strong association between smoking habits and social factors such as friendships. Additionally, most smokers exhibited psychological and physical dependence on cigarettes, emphasizing the need for further research and awareness campaigns to highlight the detrimental consequences of smoking.
Obesity and its associated health problems are an issue, especially when weight gain is not outwardly visible. Individuals with the same Body Mass Index (BMI) may differ in body fat percentage and may unknowingly suffer from normal weight obesity (NWO). Middle-aged women are at high risk if their body composition changes due to factors such as age, health status or reproductive history. This study investigated the relationship between NWO and lipid profile in middle-aged women. A sample of 122 women aged 38 to 59 years (47.17 ± 5.24) from Slovakia participated in this study. Using a questionnaire, participants answered questions about lifestyle, health background, sociodemographic classification, reproductive and menstrual history. The anthropometric parameters were determined using standard methods. Body composition was measured using a bioelectrical impedance analyzer. Biochemical parameters were determined from morning blood samples. Blood pressure was assessed in a sitting position using a digital sphygmomanometer. The primary aim of this study was to assess the differences in lipid profiles between NOW women and normal weight-lean (NWL) women. Our results showed statistically significantly higher values of uric acid in the women with NWO compared to NWL women (237.39 ± 54.11 μmol/l vs. 213.02 ± 52.64 μmol/l; p = 0.009). Moreover, significant differences were noted between NWO women and NWL women in body height, body weight, waist and hip circumference, WHR, BMI and fat mass (%, p < 0.05). Other biochemical variables showed no statistically significant differences between the study groups of women. Elevated uric acid levels in women diagnosed with NWO may serve as an indication of metabolic imbalance associated with undetected obesity. These results underscore the importance of implementing early detection and intervention methods for NWO to prevent related health issues. Further research is necessary to investigate the underlying factors contributing to these connections and evaluate the efficacy of customized interventions.
Background: To investigate the association between body mass index (BMI) at acute myocardial infarction (AMI) and all-cause as well as cause-specific long-term mortality.
Methods: The analysis was based on 10,651 hospitalized AMI patients (age 25-84 years) recorded by the population-based Myocardial Infarction Registry Augsburg between 2000 and 2017. The median follow-up time was 6.7 years [IQR: 3.5-10.0)]. Cause-specific mortality was obtained by evaluating the death certificates. In multivariable-adjusted COX regression models using cubic splines for the variable BMI, the association between BMI and cause-specific mortality (all-cause, cardiovascular, ischemic heart diseases, cancer) was investigated. Additionally, a subgroup analysis in three age groups was performed for all-cause mortality.
Results: Overall, there was a statistically significant U-shaped association between BMI at AMI and long-term mortality with the lowest hazard ratios (HR) found for BMI values between 25 and 30 kg/m2. For cancer mortality, higher BMI values > 30 kg/m2 were not associated with higher mortality. In patients aged <60 years, there was a significant association between BMI values > 35 kg/m2 and increased all-cause mortality; this association was missing in 60 to 84 years old patients. For all groups and for each specific cause of mortality, lower BMI (< 25kg/m2) values were significantly associated with higher mortality.
Conclusions: Overall, a lower BMI and also a high BMI in patients younger than 60 years - seem to be a risk factors for increased all-cause mortality after AMI. A BMI in a mid-range between 25 and 30 kg/m2 is favorable in terms of long-term survival after AMI.
Objective: The purpose of this study was to elucidate comorbidity between body dissatisfaction and nicotine vaping. Participants: Participants were 121 college students (M age = 20.51 years; 75.0% female; 75.2% White) who participated in a 14-day daily diary study. Methods: Logistic regression was used to test links between baseline trait body dissatisfaction and vaping frequency across 14 days. Multilevel logistic regression was used to test within-person, daily links between body dissatisfaction and nicotine vaping. Results: Each additional unit of trait body dissatisfaction increased the odds of frequent vaping by 33% (95% CI [1.00, 1.77]) and daily vaping by 54% (95% CI [1.10, 2.15]). Within-person, daily associations between body dissatisfaction and vaping were nonsignificant. Conclusions: Body dissatisfaction may increase college students' risk for engaging in frequent nicotine vaping. However, daily changes in body dissatisfaction may not predict same- or next-day vaping. College students with body dissatisfaction may benefit from nicotine interventions.
The Nicotiana tabacum L. plant, a medicinal resource, holds significant potential for benefiting human health, as evidenced by its use in Native American and ancient Chinese cultures. Modern medical and pharmaceutical studies have investigated that the abundant and distinctive function metabolites in tobacco including nicotine, solanesol, cembranoid diterpenes, essential oil, seed oil and other tobacco extracts, avoiding the toxic components of smoke, mainly have the anti-oxidation, anti-lipid production, pro-lipid oxidation, pro-insulin sensitivity, anti-inflammation, anti-apoptosis and antimicrobial activities. They showed potential pharmaceutical value mainly as supplements or substitutes for treating neurodegenerative diseases including Alzheimer’s and Parkinson’s disease, inflammatory diseases including colitis, arthritis, sepsis, multiple sclerosis, and myocarditis, and metabolic syndrome including Obesity and fatty liver. This review comprehensively presents the research status and the molecular mechanisms of tobacco and its metabolites basing on almost all the English and Chinese literature in recent 20 years in the field of medicine and pharmacology. This review serves as a foundation for future research on the medicinal potential of tobacco plants.
INTRODUCTION
Knowledge of the impact of smoking on healthcare costs is important for establishing the external effects of smoking and for evaluating policies intended to modify this behavior. Conventional analysis of this association is difficult because of omitted variable bias, reverse causality, and measurement error.
METHODS
We approached these challenges using a Mendelian Randomization study design; genetic variants associated with smoking behaviors were used in instrumental variables models with inpatient hospital costs (calculated from electronic health records) as the outcome. We undertook genome wide association studies to identify genetic variants associated with smoking initiation and a composite smoking index (reflecting cumulative health impacts of smoking) on up to 300,045 individuals (mean age: 57 years at baseline, range 39 to 72 years) in the UK Biobank. We followed individuals up for a mean of six years.
RESULTS
Genetic liability to initiate smoking (ever versus never smoking) was estimated to increase mean per-patient annual inpatient hospital costs by £477 (95% confidence interval (CI): £187 to £766). A one-unit change in genetic liability to the composite smoking index (range: 0-4.0) increased inpatient hospital costs by £204 (95% CI: £105 to £303) per unit increase in this index. There was some evidence that the composite smoking index causal models violated the instrumental variable assumptions, and all Mendelian Randomization models were estimated with considerable uncertainty. Models conditioning on risk tolerance were not robust to weak instrument bias.
CONCLUSIONS
Our findings have implications for the potential cost-effectiveness of smoking interventions.
IMPLICATIONS
We report the first Mendelian Randomization analysis of the causal effect of smoking on healthcare costs. Using two distinct smoking phenotypes, we identified substantial impacts of smoking on inpatient hospital costs, although the causal models were associated with considerable uncertainty. These results could be used alongside other evidence on the impact of smoking to evaluate the cost-effectiveness of anti-smoking interventions and to understand the scale of externalities associated with this behaviour.
Introduction
The subjective experience of positive and negative effects of e-cigarette use has been shown to relate to e-cigarette use outcomes in adults, but no validated measure of e-cigarette subjective response exists for adolescents and young adults (AYAs). In the current study, the psychometric properties of the Modified E-cigarette Evaluation Questionnaire (MECEQ) were evaluated for use with AYAs.
Methods
997 AYAs who endorsed using nicotine e-cigarettes at least 4 days per week completed an anonymous, online survey in 2022 (51.1% male; 17.39 [1.88] years old; 33.9% Hispanic; 68% White). Analyses included factor analysis to determine the latent structure of the MECEQ, internal consistency, measurement invariance, between-group differences, and test-criterion relationships with vaping frequency and dependence.
Results
The originally-proposed, five-factor structure and a novel four-factor structure were supported, and each subscale was internally consistent. Both models reached scalar invariance for all participant subgroups tested (e.g., sex, daily vaping status), and several between-groups differences were observed. For instance, compared to less frequent vaping, daily vaping was associated with increased Craving Reduction (5-factor), reduced Stimulant Effects (4-factor), and reduced Aversion (5-factor; 4-factor). Adjusted relationships between both MECEQ versions and vaping frequency and dependence provided evidence of concurrent validity.
Conclusions
Similar to adults, a five-factor and four-factor version of the MECEQ were supported and evidenced internal consistency, scalar measurement invariance, and concurrent relationships with vaping frequency and dependence. Moving forward, researchers are encouraged to include the MECEQ in future studies to better understand the importance of subjective response in AYA vaping behavior.
Implications
The Modified E-cigarette Evaluation Questionnaire (MCEQ), an e-cigarette-adapted version of the Modified Cigarette Evaluation Questionnaire (MCEQ), recently was validated to assess subjective e-cigarette effects among adults. Here, we demonstrate that the MECEQ can be used among adolescents and young adults (AYAs). The MECEQ can be scored using the original, five-factor MCEQ structure or using a novel, four-factor structure. Both versions were internally consistent, reached scalar measurement invariance, and evidenced concurrent relationships with vaping frequency and dependence. In sum, this study provides the field with the first psychometrically-sound measure of subjective effects of e-cigarette use for use with AYAs.
Heart rate variability (HRV) is a simple way to explore autonomic nervous activity. Current studies emphasize the need to identify a dependent relationship between smoking habit and HRV. The findings of these studies could demonstrate that smoking can have both an acute and a chronic effect on HRV. The aim of this study is to investigate the relationship between smoking and anthropometric data such as body weight, body fat distribution and heart rate var-iability. Our study involves measuring HRV parameters at rest and during two tests that are part of the Ewing test battery. Through the obtained data we can demonstrate that active smoking is associated with a reduction in HRV. At the same time, we can state that HRV is affected by dif-ferent behavior patterns of the smoker.complex individualized rehabilitation treatment.
Introduction and objectives:
Smoking is associated with various health risks, including cancer, cardiovascular disease, and chronic obstructive pulmonary disease. In this retrospective cohort study, we aimed to determine whether smoking is harmful to the whole metabolic system.
Methods:
We collected data from 340 randomly selected participants who were divided into three groups: smokers (n=137), non-smokers (n=134), and ex-smokers (n=69). We obtained information on participants' body mass index, waist circumference, indicators of glucose metabolism, lipid metabolism, bone metabolism, and uric acid from health screen data during the past three years. A cluster analysis was used to synthesize each participant's overall metabolic characteristics.
Results:
According to the cluster analysis, the 340 participants were divided into three groups: excellent metabolizers (137, 40.3%), adverse metabolizers (32, 9.4%), and intermediate metabolizers (171, 50.3%). The Chi-squared test analysis shows that people with different smoking statuses have different metabolic patterns. Non-smokers had the highest proportion of excellent metabolizers (56%), and current smokers had the highest proportion of adverse metabolizers (15.3%). The proportion of adverse metabolizers (5.8%) in the ex-smoker group was clinically relevantly lower than that of current smokers.
Conclusion:
The statistically significant differences in the distribution of smokers into different metabolic clusters indicate that smoking has adverse effects on the whole metabolic system of the human body, which further increases the existing global burden of metabolic disorders.
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.
To examine the effect of body mass index (BMI) on weight change over 8 years in a cohort of continuing smokers and a cohort that quit and remained abstinent.
8 year prospective cohort study.
Participants smoking >15 cigarettes daily enrolled in a clinical trial of nicotine patch or placebo in Oxfordshire general practices and were reviewed 8 years later.
832 male and female participants. Abstainers were 85 participants who …
Drugs and food exert their reinforcing effects in part by increasing dopamine (DA) in limbic regions, which has generated interest in understanding how drug abuse/addiction relates to obesity. Here, we integrate findings from positron emission tomography imaging studies on DA's role in drug abuse/addiction and in obesity and propose a common model for these two conditions. Both in abuse/addiction and in obesity, there is an enhanced value of one type of reinforcer (drugs and food, respectively) at the expense of other reinforcers, which is a consequence of conditioned learning and resetting of reward thresholds secondary to repeated stimulation by drugs (abuse/addiction) and by large quantities of palatable food (obesity) in vulnerable individuals (i.e. genetic factors). In this model, during exposure to the reinforcer or to conditioned cues, the expected reward (processed by memory circuits) overactivates the reward and motivation circuits while inhibiting the cognitive control circuit, resulting in an inability to inhibit the drive to consume the drug or food despite attempts to do so. These neuronal circuits, which are modulated by DA, interact with one another so that disruption in one circuit can be buffered by another, which highlights the need of multiprong approaches in the treatment of addiction and obesity.
Past therapies for the treatment of obesity have typically involved pharmacological agents usually in combination with a calorie-controlled diet. This paper reviews the efficacy and safety of pharmacotherapies for obesity focusing on drugs approved for long-term therapy (orlistat), drugs approved for short-term use (amfepramone [diethylpropion], phentermine), recently withdrawn therapies (rimonabant, sibutamine) and drugs evaluated in Phase III studies (taranabant, pramlintide, lorcaserin and tesofensine and combination therapies of topiramate plus phentermine, bupropion plus naltrexone, and bupropion plus zonisamide). No current pharmacotherapy possesses the efficacy needed to produce substantial weight loss in morbidly obese patients. Meta-analyses support a significant though modest loss in bodyweight with a mean weight difference of 4.7 kg (95% CI 4.1 to 5.3 kg) for rimonabant, 4.2 kg (95% CI 3.6 to 4.8 kg) for sibutramine and 2.9 kg (95% CI 2.5 to 3.2 kg) for orlistat compared to placebo at ≥12 months. Of the Phase III pharmacotherapies, lorcaserin, taranabant, topiramate and bupropion with naltrexone have demonstrated significant weight loss compared to placebo at ≥12 months. Some pharmacotherapies have also demonstrated clinical benefits. Further studies are required in some populations such as younger and older people whilst the long term safety continues to be a major consideration and has led to the withdrawal of several drugs.
Background
We previously documented that cognitive behavioral therapy for smoking-related weight concerns (CONCERNS) improves cessation rates. However, the efficacy of combining CONCERNS with cessation medication is unknown. We sought to determine if the combination of CONCERNS and bupropion therapy would enhance abstinence for weight-concerned women smokers.
Methods
In a randomized, double-blind, placebo-controlled trial, weight-concerned women (n = 349; 86% white) received smoking cessation counseling and were randomized to 1 of 2 adjunctive counseling components: CONCERNS or STANDARD (standard cessation treatment with added discussion of smoking topics but no specific weight focus), and 1 of 2 medication conditions: bupropion hydrochloride sustained release (B) or placebo (P) for 6 months. Rates and duration of biochemically verified prolonged abstinence were the primary outcomes. Point-prevalent abstinence, postcessation weight gain, and changes in nicotine withdrawal, depressive symptoms, and weight concerns were evaluated.
Results
Women in the CONCERNS + B group had higher rates of abstinence (34.0%) and longer time to relapse than did those in the STANDARD + B (21%; P = .05) or CONCERNS + P (11.5%; P = .005) groups at 6 months, although rates of prolonged abstinence in the CONCERNS + B and STANDARD + B groups did not differ significantly at 12 months. Abstinence rates and duration did not differ in the STANDARD + B group (21% and 19%) compared with the STANDARD + P group (10% and 7%) at 6 and 12 months, respectively. There were no differences among abstinent women in postcessation weight gain or weight concerns, although STANDARD + B produced greater decreases in nicotine withdrawal and depressive symptoms than did STANDARD + P.
Conclusions
Weight-concerned women smokers receiving the combination of CONCERNS + B were most likely to sustain abstinence. This effect was not related to differences in postcessation weight gain or changes in weight concerns.
Trial Registration
clinicaltrials.gov Identifier: NCT00006170
Obesity is associated with coronary heart disease, stroke, certain cancers, hypertension, and type 2 diabetes. Concern about obesity among older adults is growing, and research to examine behaviors associated with risk for increased weight in this population is needed. We examined differences by sex in behaviors associated with overweight and obesity among older adults (aged > or =50 years).
We analyzed data from the 2005 National Health Interview Survey using logistic regression to predict the likelihood of overweight (body mass index [BMI], 25.0-29.9 kg/m2) and obesity (BMI > or =30.0 kg/m2) relative to healthy weight (BMI, 18.5-24.9 kg/m2) among older adults. We used self-reported weights and heights. Correlates were risk behaviors for chronic disease (smoking status, alcohol intake, consumption of fruits and vegetables, leisure-time physical activity, walking for leisure, walking for transportation, and strength training).
Among older men, the prevalence of overweight was 46.3%, and the prevalence of obesity was 25.1%. Among older women, the prevalence of overweight was 33.4%, and the prevalence of obesity was 28.8%. In adjusted logistic regression models, sex differences were observed in the significance of most risk factors for overweight and obesity. Men who were occasional, light, or moderate drinkers were 28% more likely to be obese than men who were nondrinkers; women who were heavy drinkers were 55% less likely to be obese than women who were nondrinkers. Compared with men and women who were regularly active during leisure time, inactive men were 39% more likely to be obese, and inactive women were 28% more likely to be obese.
Several risk behaviors for chronic disease appear to be associated with overweight and obesity among older adults. Modification of these behaviors has the potential to reduce weight.
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.
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.
The relationship between thermogenic and potentially atherogenic effects of cigarette smoking (CS) and its cessation was investigated. Heavy smokers (n = 7, serum cotinine > 200 ng/ml, > 20 cigarettes/d) were maintained on isoenergetic, constant diets for 2 wk, 1 wk with and 1 wk without CS. Stable isotope infusions with indirect calorimetry were performed on day 7 of each phase, after an overnight fast. CS after overnight abstention increased resting energy expenditure by 5% (not significant vs. non-CS phase; P = 0.18). CS increased the flux of FFA by 77%, flux of glycerol by 82%, and serum FFA concentrations by 73% (P < 0.02 for each), but did not significantly affect fat oxidation. Hepatic reesterification of FFA increased more than threefold (P < 0.03) and adipocyte recycling increased nonsignificantly (P = 0.10). CS-induced lipid substrate cycles represented only 15% (estimated 11 kcal/d) of observed changes in energy expenditure. De novo hepatic lipogenesis was low (< 1-2 g/d) and unaffected by either acute CS or its chronic cessation. Hepatic glucose production was not affected by CS, despite increased serum glycerol and FFA fluxes. Cessation of CS caused no rebound effects on basal metabolic fluxes. In conclusion, a metabolic mechanism for the atherogenic effects of CS on serum lipids (increased hepatic reesterification of FFA) has been documented. Increased entry of FFA accounts for CS-induced increases in serum FFA concentrations. The thermogenic effect of CS is small or absent in heavy smokers while the potentially atherogenic effect is maintained, and cessation of CS does not induce a rebound lipogenic milieu that specifically favors accrual of body fat in the absence of increased food intake.
The authors examine weight gains associated with smoking cessation in the Lung Health Study (1986–1994) over a 5-year follow-up
period. A cohort of 5,887 male and female smokers in the United States and Canada, aged 35–60 years, were randomized to either
smoking intervention or usual care. Among participants who achieved sustained quitting for 5 years, women gained a mean of
5.2 (standard error, 5.0) kg in year 1 and a mean of 3.4 (standard error, 5.5) kg in years 1–5. Men gained a mean of 4.9 (standard
error, 4.9) kg in year 1 and a mean of 2.6 (standard error, 5.8) kg in years 1–5. In regression analyses, smoking-change variables
were the most potent predictors of weight change. Participants going from smoking to quit-smoking in a given year had mean
weight gains of 2.95 kg/year (3.61%) in men and 3.09 kg/year (4.69%) in women. Over 5 years, 33% of sustained quitters gained
≥10 kg compared with 6% of continuing smokers. Also among sustained quitters, 7.6% of men and 19.1% of women gained ≥20% of
baseline weight; 60% of the gain occurred in year 1, although significant weight gains continued through year 5. The average
gains and the high proportions of sustained and intermittent quitters who gained excessive weight suggest the need for more
effective early interventions that address both smoking cessation and weight control. Am J Epidemiol 1998; 148:821–30.
The effect of weight control concerns on smoking among adults is unclear. We examined the association between smoking behavior and weight control efforts among US adults.
A total of 17 317 adults responded to the Year 2000 Supplement of the 1995 National Health Interview Survey (83% combined response rate). Respondents provided sociodemographic and health information, including their smoking history and whether they were trying to lose weight, maintain weight, or gain weight.
Rates of smoking were lower among adults who were trying to lose or maintain weight than among those not trying to control weight (25% vs 31%; P<.001). After adjustment for sex, race, education, income, marital status, region of the country, and body mass index, the relationship between trying to lose weight and current smoking varied according to age. Among adults younger than 30 years, those trying to lose weight were more likely to smoke currently (odds ratio, 1.36 [95% confidence interval, 1.09-1.70]), whereas older adults trying to lose weight were as likely or less likely to smoke compared with adults not trying to control weight. After adjustment, smokers of all ages who were trying to lose weight were more likely to express a desire to quit smoking. Results were similar after stratification by sex and body mass index.
Adults younger than 30 years are more likely to smoke if they are trying to lose weight. However, smokers of all ages who are trying to lose weight are more likely to want to stop smoking. Patients' weight control efforts should not discourage clinicians from counseling about smoking cessation. Education about smoking and healthy weight control methods should target young adults.
Women smokers concerned about weight gain (N = 219) were randomly assigned to 1 of 3 adjunct treatments accompanying group smoking cessation counseling: (a) behavioral weight control to prevent weight gain (weight control); (b) cognitive-behavioral therapy (CBT) to directly reduce weight concern, in which dieting was discouraged; and (c) standard counseling alone (standard), in which weight gain was not explicitly addressed. Ten sessions were conducted over 7 weeks, and no medication was provided. Continuous abstinence was significantly higher at posttreatment and at 6 and 12 months of follow-up for CBT (56%, 28%, and 21%, respectively), but not for weight control (44%, 18%, and 13%, respectively), relative to standard (31%, 12%, and 9%, respectively). However, weight control, and to a lesser extent CBT, was associated with attenuation of negative mood after quitting. Prequit body mass index, but not change in weight or in weight concerns postquit, predicted cessation outcome at 1 year. In sum, CBT to reduce weight concerns, but not behavioral weight control counseling to prevent weight gain, improves smoking cessation outcome in weight-concerned women.
Bupropion is an atypical antidepressant and the only non-nicotine-based therapy approved for smoking cessation. Its use has raised much debate as to how a non-nicotine-based agent can aid in smoking cessation.
We assessed the effects of bupropion on brain reward function under baseline conditions and subsequent to withdrawal from chronic nicotine administration in rats.
A discrete-trial intracranial self-stimulation paradigm procedure was used that provides one with current intensity thresholds, a measure of reward in rats under baseline conditions and subsequent to withdrawal from chronic nicotine (3.16 mg/kg per day for 7 days via osmotic minipump). Somatic signs were recorded based on a checklist of nicotine abstinence signs in animals withdrawn from nicotine.
Bupropion (10-60 mg/kg) dose-dependently lowered reward thresholds in non-withdrawing subjects indicating an increase in reward. Interestingly, a sub-effective dose of bupropion (5 mg/kg) blocked completely the threshold lowering effects of acute nicotine (0.25 mg/kg). Animals withdrawn from chronic nicotine exhibited increases in somatic signs of withdrawal and elevated brain reward thresholds, which is indicative of "diminished interest or pleasure" (i.e. anhedonia) in the rewarding stimuli. Bupropion (10-40 mg/kg) reversed both the reward deficit and the somatic signs, with the highest dose (40 mg/kg) inducing a protracted reversal of the threshold elevation.
Bupropion acts on multiple levels to alter brain reward circuits influenced by nicotine, in addition to reducing the expression of somatic signs of withdrawal. First, bupropion, unlike other antidepressants, increases brain reward function under baseline conditions in non-withdrawing subjects. Second, at low doses bupropion blocks the rewarding effects of nicotine. Third, bupropion reverses the negative affective aspects of nicotine withdrawal. Such actions are likely to act in concert to mediate the unique anti-smoking properties of bupropion.
It has recently been recognised that adipose tissue, which accounts for more than 10% of body weight, is not only a reservoir for energy storage, but is also an endocrine tissue.1 Adiponectin, which is structurally related to collagen, is a novel adipose specific gene product that circulates at high concentrations.2 Adiponectin has an anti-inflammatory effect on endothelial cells, inhibits the proliferation of vascular smooth muscle cells, and suppresses the conversion of macrophages to foam cells.1 Plasma adiponectin concentrations are significantly lower in obese subjects than in non-obese subjects, significantly lower in patients with coronary artery disease (CAD) than in control subjects, and associated with insulin resistance in animal models.2–4
Cigarette smoking is one of the major coronary risk factors for CAD. Indeed, major epidemiological prospective studies have unequivocally demonstrated the relation between cigarette smoking and CAD …
Nicotine is a major component of tobacco smoke contributing to the initiation and persistence of the harmful tobacco habit in human smokers. The reinforcing effects of nicotine likely arise through its ability to stimulate brain circuitry mediating the detection and experiencing of natural rewards. Nevertheless, remarkably little is known concerning the acute or long-lasting actions of nicotine on brain reward systems in vivo. Here, we investigated the effects of intravenously self-administered nicotine (0.03 mg/kg/infusion, free base) on the sensitivity of brain reward systems, reflected in alterations of intracranial self-stimulation (ICSS) thresholds in rats. Rats self-administered nicotine during 1 or 12 h daily sessions, with reward thresholds assessed 1 h before and 15 min after each self-administration session. Control rats remained nicotine naïve throughout. Nicotine self-administration increased the sensitivity of brain reward systems, detected by post-nicotine lowering of reward thresholds in 1 and 12 h rats. This nicotine-enhanced sensitivity of reward systems was reversed by the high-affinity nicotinic receptor antagonist dihydro-beta-erythroidine (DHbetaE; 3 mg/kg). Surprisingly, nicotine-induced excitation of reward systems persisted for at least 36 days after nicotine self-administration had ceased. Overall, these data demonstrate that rats can voluntarily consume quantities of nicotine sufficient to increase the sensitivity of brain reward systems, an action likely crucial in establishing and maintaining the nicotine habit. Moreover, self-administered nicotine resets the sensitivity of reward systems to a new increased level, thereby imprinting an indelible 'memory' of its effects in reward systems, an action that so far appears unique to nicotine among drugs of abuse.
Our aim was to critically evaluate the relations among smoking, body weight, body fat distribution, and insulin resistance as reported in the literature. In the short term, nicotine increases energy expenditure and could reduce appetite, which may explain why smokers tend to have lower body weight than do nonsmokers and why smoking cessation is frequently followed by weight gain. In contrast, heavy smokers tend to have greater body weight than do light smokers or nonsmokers, which likely reflects a clustering of risky behaviors (eg, low degree of physical activity, poor diet, and smoking) that is conducive to weight gain. Other factors, such as weight cycling, could also be involved. In addition, smoking increases insulin resistance and is associated with central fat accumulation. As a result, smoking increases the risk of metabolic syndrome and diabetes, and these factors increase risk of cardiovascular disease. In the context of the worldwide obesity epidemic and a high prevalence of smoking, the greater risk of (central) obesity and insulin resistance among smokers is a matter of major concern.
Discontinuing nicotine intake usually results in weight gain partially due to heightened energy intake from between-meal snacks. This experiment tested the hypothesis that the reinforcing value of palatable carbohydrate-rich snacks increases for female smokers during nicotine deprivation. Eighteen smokers and 18 nonsmokers completed a concurrent-schedules operant computer task on two separate days. Smokers were bioverified abstinent at the second testing. The operant task allowed participants to earn points redeemable for either carbohydrate snacks or money on concurrent variable-ratio schedules of reinforcement. There were five different probabilities of earning points redeemable for snacks (8%, 16%, 25%, 50%, 75%), while the probability of earning points redeemable for money remained fixed at 25%. Reward value of snacks was measured by switch point: the reinforcement ratio at which the effort required to earn snacks exceeded their value to the respondent, as signified by a shift to working for money. Results showed that smokers undergoing nicotine deprivation persisted in working for snacks into leaner reinforcement schedules than nonsmokers (P=.026). Furthermore, nicotine deprivation increased smokers' allocation of effort to earn snack foods relative to their own behavior when smoking (P=.006). Variation in palatability or hunger did not explain these differences in snack reward value. Findings indicate that nicotine deprivation is associated with a heightened reward value of appealing snack foods for female smokers.
Background:
Cigarette smoking is an established predictor of incident type 2 diabetes mellitus, but the effects of smoking cessation on diabetes risk are unknown.
Objective:
To test the hypothesis that smoking cessation increases diabetes risk in the short term, possibly owing to cessation-related weight gain.
Design:
Prospective cohort study.
Setting:
The ARIC (Atherosclerosis Risk in Communities) Study.
Patients:
10,892 middle-aged adults who initially did not have diabetes in 1987 to 1989.
Measurements:
Smoking was assessed by interview at baseline and at subsequent follow-up. Incident diabetes was ascertained by fasting glucose assays through 1998 and self-report of physician diagnosis or use of diabetes medications through 2004.
Results:
During 9 years of follow-up, 1254 adults developed type 2 diabetes. Compared with adults who never smoked, the adjusted hazard ratio of incident diabetes in the highest tertile of pack-years was 1.42 (95% CI, 1.20 to 1.67). In the first 3 years of follow-up, 380 adults quit smoking. After adjustment for age, race, sex, education, adiposity, physical activity, lipid levels, blood pressure, and ARIC Study center, compared with adults who never smoked, the hazard ratios of diabetes among former smokers, new quitters, and continuing smokers were 1.22 (CI, 0.99 to 1.50), 1.73 (CI, 1.19 to 2.53), and 1.31 (CI, 1.04 to 1.65), respectively. Further adjustment for weight change and leukocyte count attenuated these risks substantially. In an analysis of long-term risk after quitting, the highest risk occurred in the first 3 years (hazard ratio, 1.91 [CI, 1.19 to 3.05]), then gradually decreased to 0 at 12 years.
Limitation:
Residual confounding is possible even with meticulous adjustment for established diabetes risk factors.
Conclusion:
Cigarette smoking predicts incident type 2 diabetes, but smoking cessation leads to higher short-term risk. For smokers at risk for diabetes, smoking cessation should be coupled with strategies for diabetes prevention and early detection.
To examine the association between weight change and baseline body mass index (BMI) over 8 years in a cohort of continuing and quitting smokers.
Prospective cohort.
Oxfordshire general practices nicotine patch/placebo trial with 8-year follow-up.
Eighty-five participants were biochemically proven abstinent at 3, 6, 12 months and 8 years (abstainers). A total of 613 smoked throughout the 8 years (smokers), 26 quit for a whole year but were smoking again by 8 years (relapsed); 116 smoked for the first year but were abstinent at 8 years (late abstainers).
Weight and BMI was measured at baseline and at 8 years. Regression models were used to examine weight gain by smoking status and the association of BMI at the time of quitting.
Abstainers gained 8.79kg [standard deviation (SD) 6.36; 95% confidence interval (CI) 7.42, 10.17]. Smokers gained 2.24 kg (SD 6.65; 95% CI 1.7, 2.77). Relapsed smokers gained 3.28 kg (SD 7.16; 95% CI 0.328, 6.24). Late abstainers gained 8.33 kg (SD 8.04; 95% CI 6.85, 9.81). The association between baseline BMI and weight change was modified by smoking status. In smokers there was a negative linear association of BMI, while in abstainers a J-shaped curve fitted best. These models estimated weight change over 8 years in abstainers of +9.8 kg, +7.8kg, +10.2kg, +19.4kg and in smokers of +3.9kg, +2.6kg, 1.0kg and -0.8kg, where BMI was 18, 23, 29 and 36, respectively.
Obese smokers gain most weight on quitting smoking, while obese continuing smokers are likely to remain stable or lose weight. Obese quitters have the greatest need for interventions to ameliorate weight gain.
This study investigated the effect of binge eating on smoking cessation outcomes.
Participants (n = 186) reported binge eating status at baseline and at a 6-week postquit evaluation during a larger clinical trial for smoking cessation. Binge eating was defined with a single self-report questionnaire item from the Dieting and Bingeing Severity Scale. Participant groups defined by binge eating status were compared on abstinence rates.
Among participants, 22% reported binge eating at baseline, 17% denied binge eating at baseline but endorsed binge eating by 6 weeks, and 61% denied binge eating at both timepoints. Participants who reported binge eating prior to or during treatment had lower quit rates at 6-week postquit and at the 24-week follow-up point than those without binge eating; the groups did not differ at the 12-week follow-up point. The group that experienced an emergence of binge eating reported significantly more weight gain than the other groups.
These results suggest that treatments addressing problematic eating behaviors during smoking cessation are warranted.
Nicotine is central to maintaining tobacco use. Understanding how nicotine sustains smoking provides a necessary basis for optimal smoking cessation therapy. This article updates several earlier reviews on the pharmacology of nicotine addiction.5, 7 and 8
Cigarette smoking is an established predictor of incident type 2 diabetes mellitus, but the effects of smoking cessation on diabetes risk are unknown.
To test the hypothesis that smoking cessation increases diabetes risk in the short term, possibly owing to cessation-related weight gain.
Prospective cohort study.
The ARIC (Atherosclerosis Risk in Communities) Study.
10,892 middle-aged adults who initially did not have diabetes in 1987 to 1989.
Smoking was assessed by interview at baseline and at subsequent follow-up. Incident diabetes was ascertained by fasting glucose assays through 1998 and self-report of physician diagnosis or use of diabetes medications through 2004.
During 9 years of follow-up, 1254 adults developed type 2 diabetes. Compared with adults who never smoked, the adjusted hazard ratio of incident diabetes in the highest tertile of pack-years was 1.42 (95% CI, 1.20 to 1.67). In the first 3 years of follow-up, 380 adults quit smoking. After adjustment for age, race, sex, education, adiposity, physical activity, lipid levels, blood pressure, and ARIC Study center, compared with adults who never smoked, the hazard ratios of diabetes among former smokers, new quitters, and continuing smokers were 1.22 (CI, 0.99 to 1.50), 1.73 (CI, 1.19 to 2.53), and 1.31 (CI, 1.04 to 1.65), respectively. Further adjustment for weight change and leukocyte count attenuated these risks substantially. In an analysis of long-term risk after quitting, the highest risk occurred in the first 3 years (hazard ratio, 1.91 [CI, 1.19 to 3.05]), then gradually decreased to 0 at 12 years.
Residual confounding is possible even with meticulous adjustment for established diabetes risk factors.
Cigarette smoking predicts incident type 2 diabetes, but smoking cessation leads to higher short-term risk. For smokers at risk for diabetes, smoking cessation should be coupled with strategies for diabetes prevention and early detection.
The prospect of weight gain discourages many cigarette smokers from quitting. Practice guidelines offer varied advice about managing weight gain after quitting smoking, but no systematic review and meta-analysis have been available. We reviewed evidence to determine whether behavioral weight control intervention compromises smoking cessation attempts, and if it offers an effective way to reduce post-cessation weight gain.
We identified randomized controlled trials (RCTs) that compared combined smoking treatment and behavioral weight control to smoking treatment alone for adult smokers. English-language studies were identified through searches of PubMed, Ovid MEDLINE, CINAHL, EMBASE, PsycINFO and Cochrane Central Register of Controlled Trials. Of 779 articles identified and 35 potentially relevant RCTs screened, 10 met the criteria and were included in the meta-analysis.
Patients who received both smoking treatment and weight treatment showed increased abstinence [odds ratio (OR) = 1.29, 95% confidence interval (CI) = 1.01, 1.64] and reduced weight gain (g = -0.30, 95% CI = -0.57, -0.02) in the short term (<3 months) compared with patients who received smoking treatment alone. Differences in abstinence (OR = 1.23, 95% CI = 0.85, 1.79) and weight control (g = -0.17, 95% CI = -0.42, 0.07) were no longer significant in the long term (>6 months).
Findings provide no evidence that combining smoking treatment and behavioral weight control produces any harm and significant evidence of short-term benefit for both abstinence and weight control. However, the absence of long-term enhancement of either smoking cessation or weight control by the time-limited interventions studied to date provides insufficient basis to recommend societal expenditures on weight gain prevention treatment for patients who are quitting smoking.
Background:
Most people who stop smoking gain weight, on average about 7 kg in the long term. There are some interventions that have been specifically designed to tackle smoking cessation whilst also limiting weight gain. Many smoking cessation pharmacotherapies and other interventions may also limit weight gain.
Objectives:
This review is divided into two parts. (1) Interventions designed specifically to aid smoking cessation and limit post-cessation weight gain (2) Interventions designed to aid smoking cessation that may also plausibly have an effect on weight
Search strategy:
Part 1: We searched the Cochrane Tobacco Addiction Group's Specialized Register which includes trials indexed in MEDLINE, EMBASE, SciSearch and PsycINFO, and other reviews and conference abstracts. Part 2: We searched the included studies of Cochrane smoking cessation reviews of nicotine replacement therapy, antidepressants, nicotine receptor partial agonists, cannabinoid type 1 receptor antagonists (rimonabant), and exercise interventions, published in Issue 4, 2008 of The Cochrane Library.
Selection criteria:
Part 1: We included trials of interventions designed specifically to address both smoking cessation and post-cessation weight gain that had measured weight at any follow-up point and/or smoking six months or more after quitting.Part 2: We included trials from the selected Cochrane reviews that could plausibly modify post-cessation weight gain if they had reported weight gain by trial arm at end of treatment or later.
Data collection and analysis:
We extracted data in duplicate on smoking and weight for part 1 trials, and on weight only for part 2. Abstinence from smoking is expressed as a risk ratio (RR), using the most rigorous definition of abstinence available in each trial, and biochemically validated rates if available. The outcome is expressed as the difference in weight change between trial arms from baseline. Where appropriate, we performed meta-analysis using the Mantel-Haenszel method for smoking and inverse variance for weight using a fixed-effect model.
Main results:
We found evidence that pharmacological interventions aimed at reducing post-cessation weight gain resulted in a significant reduction in weight gain at the end of treatment (dexfenfluramine (-2.50kg [-2.98kg to -2.02kg], fluoxetine (-0.80kg [-1.27kg to -0.33kg], phenylpropanolamine (PPA) (-0.50kg [-0.80kg to -0.20kg], naltrexone (-0.76kg [-1.51kg to -0.01kg])). No evidence of maintenance of the treatment effect was found at six or 12 months.Among the behavioural interventions, only weight control advice was associated with no reduction in weight gain and with a possible reduction in abstinence. Individualized programmes were associated with reduced weight gain at end of treatment and at 12 months (-2.58kg [-5.11kg to -0.05kg]), and with no effect on abstinence (RR 0.74 [0.39 to 1.43]). Very low calorie diets (-1.30kg (-3.49kg to 0.89kg] at 12 months) and cognitive behavioural therapy (CBT) (-5.20kg (-9.28kg to -1.12kg] at 12 months) were both associated with improved abstinence and reduced weight gain at end of treatment and at long-term follow up.Both bupropion (300mg/day) and fluoxetine (30mg and 60mg/day combined) were found to limit post-cessation weight gain at the end of treatment (-0.76kg [-1.17kg to -0.35kg] I(2)=48%) and -1.30kg [-1.91kg to -0.69kg]) respectively. There was no evidence that the weight reducing effect of bupropion was dose-dependent. The effect of bupropion at one year was smaller and confidence intervals included no effect (-0.38kg [-2.001kg to 1.24kg]).We found no evidence that exercise interventions significantly reduced post-cessation weight gain at end of treatment but evidence for an effect at 12 months (-2.07kg [-3.78kg, -0.36kg]).Treatment with NRT resulted in attenuation of post-cessation weight gain (-0.45kg [-0.70kg, -0.20kg]) at the end of treatment, with no evidence that the effect differed for different forms of NRT. The estimated weight gain reduction was similar at 12 months (-0.42kg [-0.92kg, 0.08kg]) but the confidence intervals included no effect.There were no relevant data on the effect of rimonabant on weight gain.We found no evidence that varenicline significantly reduced post-cessation weight gain at end of treatment and no follow-up data are currently available. One study randomizing successful quitters to 12 more weeks of active treatment showed weight to be reduced by 0.71kg (-1.04kg to -0.38kg). In three studies, participants taking bupropion gained significantly less weight at the end of treatment than those on varenicline (-0.51kg [-0.93kg to -0.09kg]).
Authors' conclusions:
Behavioural interventions of general advice only are not effective and may reduce abstinence. Individualized interventions, very low calorie diets, and CBT may be effective and not reduce abstinence. Exercise interventions are not associated with reduced weight gain at end of treatment, but may be associated with worthwhile reductions in weight gain in the long term, Bupropion, fluoxetine, nicotine replacement therapy, and probably varenicline all reduced weight gain while being used. Although this effect was not maintained one year after quitting for bupropion, fluoxetine, and nicotine replacement, the evidence is insufficient to exclude a modest long-term effect. The data are not sufficient to make strong clinical recommendations for effective programmes.
The inverse relationship between cigarette smoking and body weight, a potent obstacle to stopping smoking, may be due in part to effects of smoking on increasing whole body metabolism. Studies examining chronic and acute metabolic effects of smoking, as well as its constituent nicotine, are reviewed. Evidence suggests the absence of a chronic effect; most studies indicate that smokers and nonsmokers have similar resting metabolic rates (RMR) and that RMR declines very little after smoking cessation. Although an acute effect due to smoking is apparent, its magnitude is inconsistent across studies, possibly because of variability in smoke exposure or nicotine intake. In smokers at rest, the acute effect of smoking (and nicotine intake) appears to be significant but small (less than 10% of RMR) and transient (less than or equal to 30 min). However, the specific situations in which smokers tend to smoke may mediate the magnitude of this effect, inasmuch as smoking during casual physical activity may enhance it while smoking after eating may reduce it. Sympathoadrenal activation by nicotine appears to be primarily responsible for the metabolic effect of smoking, but possible contributions from nonnicotine constituents of tobacco smoke and behavioral effects of inhaling may also be important. Improved understanding of these metabolic effects may lead to better prediction and control of weight gain after smoking cessation, thus increasing the likelihood of maintaining abstinence.
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.
We studied the effect of smoking on energy expenditure in eight healthy cigarette smokers who spent 24 hours in a metabolic chamber on two occasions, once without smoking and once while smoking 24 cigarettes per day. Diet and physical exercise (30 minutes of treadmill walking) were standardized on both occasions. Physical activity in the chamber was measured by use of a radar system. Smoking caused an increase in total 24-hour energy expenditure (from a mean value [+/- SEM] of 2230 +/- 115 to 2445 +/- 120 kcal per 24 hours; P less than 0.001), although no changes were observed in physical activity or mean basal metabolic rate (1545 +/- 80 vs. 1570 +/- 70 kcal per 24 hours). During the smoking period, the mean diurnal urinary excretion of norepinephrine (+/- SEM) increased from 1.25 +/- 0.14 to 1.82 +/- 0.28 micrograms per hour (P less than 0.025), and mean nocturnal excretion increased from 0.73 +/- 0.07 to 0.91 +/- 0.08 micrograms per hour (P less than 0.001). These short-term observations demonstrate that cigarette smoking increases 24-hour energy expenditure by approximately 10 percent, and that this effect may be mediated in part by the sympathetic nervous system. The findings also indicate that energy expenditure can be expected to decrease when people stop smoking, thereby favoring the gain in body weight that often accompanies the cessation of smoking.
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.
Participants in an 8-session, community based smoking cessation intervention rated whether they would stay quit if they experienced weight gain. The majority reported that they would not relapse to smoking, even after a 20-lb, (9.07-kg) weight gain. Those who were weight concerned were more likely to be female, to weight less and be normal or underweight, and to report chronic dieting. This group was also significantly less likely to be abstinent posttreatment, and at the 1-, 6- and 12-month follow-ups. Individuals presenting for formal smoking cessation interventions may be less weight concerned than the general population of smokers. However, weight-concerned smokers who do present for treatment are less likely to quit smoking. Implications for recruitment and intervention are discussed.
This analysis tested the relation between dieting frequency and risk of smoking initiation in a longitudinal sample of adolescents.
From 1995 to 1997, 1295 middle school girls and boys participated in a nutrition and physical activity intervention study. The prospective association between dieting frequency at baseline and smoking initiation 2 years later was tested.
Compared with girls who reported no dieting at baseline, girls who dieted up to once per week had 2 times the adjusted odds of becoming smokers (odds ratio = 2.0; 95% confidence interval = 1.1, 3.5), and girls who dieted more often had 4 times the adjusted odds of becoming smokers (odds ratio = 3.9; 95% confidence interval = 1.5, 10.4).
Dieting among girls may exacerbate risk of initiating smoking, with increasing risk with greater dieting frequency.
To evaluate whether the lipolytic effects of systemic nicotine are not only attributed to indirect adrenergic mechanisms, but also to a direct action of nicotine on fat cells.
The effect of a systemic nicotine infusion (0.5 microg/kg/min for 30 min) on lipolysis in subcutaneous adipose tissue was investigated in situ in 11 non-obese, non-smoking, healthy male subjects under placebo-controlled conditions.
By using microdialysis probes the glycerol levels (lipolysis index) and blood flow were monitored locally in subcutaneous adipose tissue.
Plasma nicotine levels peaked (7.2 ng/ml) at the end of the infusion. Nicotine induced a mean (+/-s.e.) percentage peak increase in adrenaline and noradrenaline plasma levels of 213+/-30% (P<0.01) and 118+/-5% (P<0.05), respectively. Nicotine increased venous plasma glycerol levels by 144+/-9% (P<0.001), arterialized plasma glycerol levels by 148+/-12% (P<0.001) and adipose glycerol levels by 148+/-16% (P<0.001), but did not alter blood flow. By inducing a local cholinoceptor blockade with mecamylamine (10(-5) M) via the microdialysis system, the increase in adipose glycerol levels was inhibited by approximately 45% (P=0.02). A corresponding local beta-adrenoceptor blockade with propranolol (10(-4) M), inhibited the increase in adipose glycerol levels by approximately 60% (P=0.02). Infusion of saline (ie placebo) had no effect on the parameters mentioned above.
Systemically administered nicotine induces lipolysis, in part by activating the classical adrenergic mechanism (mediated by a nicotine-induced release of catecholamines stimulating beta-adrenoceptors), and in part by directly activating a nicotinic cholinergic lipolytic receptor located in adipose tissue.