Longitudinal changes in weight in relation to smoking cessation in participants of the EPIC-PANACEA study

The effects of cigarette smoking and alcohol drinking on salty taste preferences based on Korean Community Health Survey data

BACKGROUND/OBJECTIVES

Excessive sodium intake, cigarette smoking, and alcohol consumption are risk factors for a wide range of diseases. This study aimed to determine whether smokers and drinkers are more likely to enjoy their food with more salt, and whether the combination of smoking and drinking is associated with salty taste preferences.

SUBJECTS/METHODS

This study analyzed the data of over 16 million Koreans from two four-year Korean Community Health Survey cycles (i.e., 2010 to 2013 and 2014 to 2017). The respondents' preferences for salty foods (i.e., their salt intake levels, whether they added salt or soy sauce to foods served on the table, and whether they dipped fried foods in salt or soy sauce), and the odds ratio (OR) of their preference were examined among smokers and drinkers when adjusted for sex, age, body mass index, educational level, household income, marital status, and cigarette smoking or alcohol consumption status.

RESULTS

Cigarette smoking and alcohol consumption were correlated with the consumption of salty food. Based on the adjusted model, cigarette smokers and alcohol drinkers preferred adding salt or soy sauce or dipping fried foods in soybean more than non-smokers and non-drinkers. In addition, people who smoked and consumed alcohol reported a more significant stacking effect regarding the salty taste preference.

CONCLUSION

This large population-based study found that both cigarette smoking and alcohol consumption were correlated with salty taste preferences, which may cause excessive sodium intake.

Is the ongoing obesity epidemic partly explained by concurrent decline in cigarette smoking? Insights from a longitudinal population study. The Tromsø Study 1994-2016

The increase of obesity coincides with a substantial decrease in cigarette smoking. We assessed post-cessation weight change and its contribution to the obesity epidemic in a general population in Norway. A total of 14,453 participants (52.6% women), aged 25-54 years in 1994, who attended at least two of four surveys in the Tromsø Study between 1994 and 2016, were included in the analysis. Hereof 77% participated in both the first and the last survey. Temporal trends in mean body mass index (BMI), prevalence of obesity (BMI ≥ 30 kg/m²) and daily smoking were estimated with generalized estimation equations. We assessed BMI change by smoking status (ex-smoker, quitter, never smoker, daily smoker), and also under a scenario where none quit smoking. In total, the prevalence of daily smoking was reduced over the 21 years between Tromsø 4 (1994-1995) and Tromsø 7 (2015-2016) by 22 percentage points. Prevalence of obesity increased from 5 - 12% in 1994-1995 to 21-26% in 2015-2016, where obesity in the youngest (age 25-44 in 1994) increased more than in the oldest (p < 0.0001). Those who quit smoking had a larger BMI gain compared to the other three smoking subgroups over the 21 years (p < 0.0001). The scenario where none quit smoking would imply a 13% reduction in BMI gain in the population, though substantial age-related differences were noted. We conclude that smoking cessation contributed to the increase in obesity in the population, but was probably not the most important factor. Public health interventions should continue to target smoking cessation, and also target obesity prevention.

Association of smoking with abdominal adipose deposition and muscle composition in Coronary Artery Risk Development in Young Adults (CARDIA) participants at mid-life: A population-based cohort study

BACKGROUND

Smokers have lower risk of obesity, which some consider a "beneficial" side effect of smoking. However, some studies suggest that smoking is simultaneously associated with higher central adiposity and, more specifically, ectopic adipose deposition. Little is known about the association of smoking with intermuscular adipose tissue (IMAT), an ectopic adipose depot associated with cardiovascular disease (CVD) risk and a key determinant of muscle quality and function. We tested the hypothesis that smokers have higher abdominal IMAT and lower lean muscle quality than never smokers.

METHODS AND FINDINGS

We measured abdominal muscle total, lean, and adipose volumes (in cubic centimeters) and attenuation (in Hounsfield units [HU]) along with subcutaneous (SAT) and visceral adipose tissue (VAT) volumes using computed tomography (CT) in 3,020 middle-aged Coronary Artery Risk Development in Young Adults (CARDIA) participants (age 42-58, 56.3% women, 52.6% white race) at the year 25 (Y25) visit. The longitudinal CARDIA study was initiated in 1985 with the recruitment of young adult participants (aged 18-30 years) equally balanced by female and male sex and black and white race at 4 field centers located in Birmingham, AL, Chicago, IL, Minneapolis, MN, and Oakland, CA. Multivariable linear models included potential confounders such as physical activity and dietary habits along with traditional CVD risk factors. Current smokers had lower BMI than never smokers. Nevertheless, in the fully adjusted multivariable model with potential confounders, including BMI and CVD risk factors, adjusted mean (95% CI) IMAT volume was 2.66 (2.55-2.76) cm3 in current smokers (n = 524), 2.36 (2.29-2.43) cm3 in former smokers (n = 944), and 2.23 (2.18-2.29) cm3 in never smokers (n = 1,552) (p = 0.007 for comparison of former versus never smoker, and p < 0.001 for comparison of current smoker versus never and former smoker). Moreover, compared to participants who never smoked throughout life (41.6 [41.3-41.9] HU), current smokers (40.4 [39.9-40.9] HU) and former smokers (40.8 [40.5-41.2] HU) had lower lean muscle attenuation suggesting lower muscle quality in the fully adjusted model (p < 0.001 for comparison of never smokers with either of the other two strata). Among participants who had ever smoked, pack-years of smoking exposure were directly associated with IMAT volume (β [95% CI]: 0.017 [0.010-0.025]) (p < 0.001). Despite having less SAT, current smokers also had higher VAT/SAT ratio than never smokers. These findings must be viewed with caution as residual confounding and/or reverse causation may contribute to these associations.

CONCLUSIONS

We found that, compared to those who never smoked, current and former smokers had abdominal muscle composition that was higher in adipose tissue volume, a finding consistent with higher CVD risk and age-related physical deconditioning. These findings challenge the belief that smoking-associated weight loss or maintenance confers a health benefit.

BMI modified the association of current smoking with the incidence of hypertension in Chinese population: a 22-year cohort study

Background

There was little known on how the interaction effect between obesity and current smoking affected the incidence of hypertension. The aim of this study was to investigate how body mass index (BMI) modified the effect of current smoking on the incidence of hypertension.

Methods

Data were obtained from the China Health and Nutrition Survey (CHNS). According to the WHO recommendations for Chinese people, the normal weight, overweight, and obesity were defined using the BMI cutoff values 18.5 kg/m², 23.0 kg/m², and 27.5 kg/m, respectively. Current smokers were defined as having smoked at least 100 cigarettes or electronic cigarettes, 20 cigars, or 20 tobacco pipes and other type of tobacco in the last 30 days preceding the survey. Hypertension was defined as systolic blood pressure (SBP)/ diastolic blood pressure (DBP) ≥ 140/90 mmHg, use of anti-hypertensive medications, or a self-reported diagnosis.

Results

This study included 12,900 subjects. There were interaction effects between obesity and current smoking in females (P = 0.030) and the 50–59 years group (P = 0.049). Current smoking was a significant predictor of incident hypertension only in the total and female populations with normal weight (HR: 1.119 and 1.274; HR 95% CI: 1.013–1.236 and 1.143–1.415; and P = 0.027 and 0.040, respectively). Stratified by age, current smoking affected the development of hypertension only in the 50–59 years subjects with the normal weight (HR: 1.356; HR 95% CI: 1.084–1.697; and P = 0.008).

Conclusions

Current smoking was a significant predictor of incident hypertension only in the female and middle-age populations with normal weight but not in the overweight and obesity as well as the younger and elder populations.

A Prospective Study of Weight Gain in Women Diagnosed with Early-Stage Invasive Breast Cancer, Ductal Carcinoma In Situ, and Women Without Breast Cancer

Background: Weight gain after breast cancer has been associated with recurrence and mortality. We therefore examined factors associated with ≥5% weight gain over 2-year follow-up of a cohort of newly diagnosed early-stage invasive breast cancer (EIBC) and ductal carcinoma in situ (DCIS) patients and age-matched controls without a breast cancer history. Materials and Methods: We interviewed participants 4-6 weeks after definitive surgical treatment (patients) or a negative/benign screening mammogram (controls). Multivariable logistic regression models were used to identify socioeconomic, psychosocial, and treatment factors associated with ≥5% weight gain over 2-year follow-up. Results: Overall, 88 (24%) of 362 EIBC patients, 31 (17%) of 178 DCIS patients, and 82 (15%) of 541 controls had ≥5% weight gain during follow-up. EIBC patients were more likely to experience ≥5% weight gain than DCIS patients (Odds ratio [OR] = 2.16; 95% confidence interval [95% CI] = 1.19-3.95) and controls (OR = 1.76; 95% CI = 1.23-2.51). Among EIBC patients, older patients (OR = 0.96; 95% CI = 0.93-0.99), patients who underwent endocrine therapy (OR = 0.43; 95% CI = 0.19-0.95), smokers (OR = 0.35; 95% CI = 0.14-0.86), and African Americans (OR = 0.23; 95% CI = 0.09-0.58) were less likely to have ≥5% weight gain than their respective counterparts. Among DCIS patients, older patients (OR = 0.94; 95% CI = 0.89-0.99) were less likely to have ≥5% weight gain. Among controls, smokers were more likely to have ≥5% weight gain (OR = 3.03; 95% CI = 1.49-6.17). Conclusions: EIBC patients were more likely than DCIS patients and controls to experience ≥5% weight gain over follow-up. Studies are necessary to elucidate mechanisms of weight gain in early-stage breast cancer survivors.

The effect of smoking on obesity: Evidence from a randomized trial

This paper aims to identify the causal effect of smoking on body mass index (BMI) using data from the Lung Health Study, a randomized trial of smoking cessation treatments. Since nicotine is a metabolic stimulant and appetite suppressant, quitting or reducing smoking could lead to weight gain. Using randomized treatment assignment to instrument for smoking, we estimate that quitting smoking leads to an average long-run weight gain of 1.8-1.9 BMI units, or 11-12 pounds at the average height. Semi-parametric models provide evidence of a diminishing marginal effect of smoking on BMI, while subsample regressions show that the impact is largest for younger individuals, those with no college degree, and those in the lowest quartile of baseline BMI.

Plasma MicroRNA signature predicting weight gain among Mexican-American women

OBJECTIVE

There is growing evidence that circulating microRNAs (miRNAs) play an important role in obesity. However, whether they can contribute to adult weight gain is still unclear.

METHODS

In the training set with 40 nonsmoking, healthy women identified from the Mano-A-Mano Mexican American Cohort study, global circulating miRNA profiles in plasma samples were assessed. Cox proportional hazard regression was used to assess the effects of plasma miRNAs on significant weight gain during a 5-year follow-up. Plasma miRNAs associated with significant weight gain were further validated in two testing sets (N = 160 and 100, respectively).

RESULTS

A total of 23 significant plasma miRNAs were identified in the training set. Among them, eight were validated in two testing sets. They were miR-142, miR-122, miR-125b, miR-15b, miR-130b, miR222, miR-519d, and miR-31. Using those eight miRNAs, a risk score for significant weight gain was created. Study participants with a high risk score had 3.01-fold increased risk of having significant weight gain in the whole study population (hazard ratio: 3.01, 95% confidence interval: 1.70-5.47).

CONCLUSIONS

The findings provide evidence that circulating miRNAs play important roles in obesity and weight gain and suggest new targets for understanding the mechanisms of weight gain and developing weight loss intervention strategies.

Tobacco cessation and household spending on non-tobacco goods: results from the US Consumer Expenditure Surveys

Objectives

To estimate the impact of tobacco cessation on household spending on non-tobacco goods in the USA.

Methods

Using 2006–2015 Consumer Expenditure Survey data, 9130 tobacco-consuming households were followed for four quarters. Households were categorised during the fourth quarter as having: (1) recent tobacco cessation, (2) long-term cessation, (3) relapsed cessation or (4) no cessation. Generalised linear models were used to compare fourth quarter expenditures on alcohol, food at home, food away from home, housing, healthcare, transportation, entertainment and other goods between the no-cessation households and those with recent, long-term or relapsed cessation. The full sample was analysed, and then analysed by income quartile.

Results

In the full sample, households with long-term and recent cessation had lower spending on alcohol, food, entertainment and transportation (p<0.001). Recent cessation was further associated with reduced spending on food at home (p<0.001), whereas relapsed cessation was associated with higher spending on healthcare and food away from home (p<0.001). In the highest income quartile, long-term and recent cessations were associated with reduced alcohol spending only (p<0.001), whereas in the lowest income quartile, long-term and recent cessations were associated with lower spending on alcohol, food at home, transportation and entertainment (p<0.001).

Conclusions

Households that quit tobacco spend less in areas that enable or complement their tobacco cessation, most of which may be motivated by financial strain. The most robust association between tobacco cessation and spending was the significantly lower spending on alcohol.

Smoking Cessation and Changes in Body Mass Index Among Middle Aged and Older Adults

Objective: This study contributes to the body of literature examining smoking cessation and body mass index (BMI) for adults aged 50 and older. Method: Utilizing the 2004 and 2010 waves of the RAND Health and Retirement Study, this analysis utilized Fixed Effects (FE) regression on a sample of 1,316 adults aged 50 and older. Results: Older adults undergo a small change in BMI after a transition from smoking to nonsmoking during a 6-year period, and this occurs after accounting for individual-level unobserved heterogeneity. More specifically, men experience a BMI gain of 1.24 ( p< .01) and women experience a BMI gain of 1.58 ( p< .01). Discussion: Gerontologists/health professionals can use these results to inform older adults about the potential for a small increase in BMI and, in the process, assuage any apprehensions about excessive weight gain. This insight may encourage a greater number of older adults to cease smoking.

The Relationship between Body Mass Index and Post-Cessation Weight Gain in the Year after Quitting Smoking: A Cross-Sectional Study

Introduction

There is wide variability in the amount of weight gained when quitting smoking, but little is known about key predictors of weight gain. We examined the impact of body mass index (BMI) category and sociodemographic variables on post-cessation weight gain.

Materials and Methods

We utilized National Health and Nutrition Examination Survey data from five consecutive cycles of data collection from 2003–2004 to 2011–2012 to estimate post-cessation weight gain by BMI category among recent quitters (n = 654). We analyzed data on their “current weight” and their “past year weight”. We also compared the recent quitters with current smokers, in order to estimate the amount of weight that could be attributed to quitting smoking.

Results

Recent quitters gained 1.4 kg (95% CI: 0.8 to 2.0), while current smokers had a non-significant weight change (-0.01 kg (95% CI: -0.3 to 0.2). Weight gain was significant for those in the normal weight (3.1 kg, 95% CI: 2.3 to 3.9) and overweight BMI categories (2.2 kg, 95% CI: 1.1 to 3.2).

Conclusions

BMI category is a key factor in the extent of post-cessation weight gain, with normal and overweight recent quitters gaining significant amounts of weight.

Back on track-Smoking cessation and weight changes over 9 years in a community-based cohort study

OBJECTIVE

To examine the impact of smoking cessation on body weight compared with normal long-term weight development.

METHODS

Of 1970 adults (20-69 years) in a rural town in Denmark invited to take part in the study in 1998-2000, 1374 (70%) participated. After 9 years, 1121 participated in the follow-up study. Weight changes were compared using multivariable regression models.

RESULTS

The mean baseline weight of never-smokers was 76.4 kg (SD 16.0). The adjusted weight of smokers and ex-smokers differed by -4.2 kg (95% CI: -5.9, -2.6), and -0.7 kg (95% CI: -2.5, 1.1), respectively. The adjusted weight gain rate (kg/year) of never-smokers, smokers, and ex-smokers was 0.213, 0.127, and 0.105, respectively. The absolute post cessation weight gain (PCWG) was 5.0 kg (SD 7.0), and the adjusted PCWG was 2.8 kg (95% CI: 1.7, 3.9) compared with never-smokers, and 3.5 kg (95% CI: 2.3, 4.8) compared with smokers. The follow-up weight did not differ between quitters and never-smokers (0.1 kg; 95% CI: -2.4, 2.6).

CONCLUSION

Smokers weigh less than never-smokers. By quitting, they gain weight and end up weighing the same as comparable never-smokers. Weight gain rates differ by smoking status. Consequently, PCWG depends on the length of follow-up. Our graphical model indicates that smoking cessation results in a return to normal weight development.

The association between quitting smoking and weight gain: a systematic review and meta-analysis of prospective cohort studies

This systematic review and meta-analysis aimed to quantify weight gain after smoking cessation and the difference in weight gain between quitters and continuing smokers. Five electronic databases were searched before January 2015. Population-based prospective cohort studies were included if they recorded the weight change of adult smokers from baseline (before smoking cessation) to follow-up (at least 3 months after cessation). Thirty-five cohort studies were identified, including 63,403 quitters and 388,432 continuing smokers. The mean weight gain was 4.10 kg (95% confidence interval [CI]: 2.69, 5.51) and body mass index (BMI) gain was 1.14 kg m(-2) (95% CI: 0.50, 1.79) among quitters. Compared with continuing smoking, quitting smoking was significantly associated with absolute weight (adjusted mean difference [MD]: 2.61 kg; 95% CI: 1.61, 3.60) and BMI gain (adjusted MD: 0.63 kg m(-2) ; 95% CI: 0.46, 0.80). Subgroup analyses using geographic region found that the difference in weight gain was considerably greater in studies from North America than from Asia. Follow-up length was identified as a source of heterogeneity, such that studies with longer follow-up showed greater difference in weight gain. Effective strategies are needed to encourage smokers to quit irrespective of potential weight gain and to help quitters avoid excess weight gain.

Relationship between smoking and obesity: a cross-sectional study of 499,504 middle-aged adults in the UK general population

BACKGROUND

There is a general perception that smoking protects against weight gain and this may influence commencement and continuation of smoking, especially among young women.

METHODS

A cross-sectional study was conducted using baseline data from UK Biobank. Logistic regression analyses were used to explore the association between smoking and obesity; defined as body mass index (BMI) >30 kg/m2. Smoking was examined in terms of smoking status, amount smoked, duration of smoking and time since quitting and we adjusted for the potential confounding effects of age, sex, socioeconomic deprivation, physical activity, alcohol consumption, hypertension and diabetes.

RESULTS

The study comprised 499,504 adults aged 31 to 69 years. Overall, current smokers were less likely to be obese than never smokers (adjusted OR 0.83 95% CI 0.81-0.86). However, there was no significant association in the youngest sub-group (≤40 years). Former smokers were more likely to be obese than both current smokers (adjusted OR 1.33 95% CI 1.30-1.37) and never smokers (adjusted OR 1.14 95% CI 1.12-1.15). Among smokers, the risk of obesity increased with the amount smoked and former heavy smokers were more likely to be obese than former light smokers (adjusted OR 1.60, 95% 1.56-1.64, p<0.001). Risk of obesity fell with time from quitting. After 30 years, former smokers still had higher risk of obesity than current smokers but the same risk as never smokers.

CONCLUSION

Beliefs that smoking protects against obesity may be over-simplistic; especially among younger and heavier smokers. Quitting smoking may be associated with temporary weight gain. Therefore, smoking cessation interventions should include weight management support.

Anthropometric measures and bladder cancer risk: a prospective study in the EPIC cohort

Anthropometric measures have been related to risk of several cancers. For bladder cancer, however, evidence is sparse. Comparability of existing studies is hampered by use of different obesity-measures, inadequate control for smoking, and few female cases. This study examined associations between height, weight, waist and hip circumference, waist-hip ratio, waist-height ratio, body mass index (BMI), recalled weight at age 20 and bladder cancer, and investigated effect modification by age, tumor aggressiveness and smoking. The study was conducted in the European Prospective Investigation into Cancer and Nutrition cohort, in 390,878 participants. Associations were calculated using Cox Proportional Hazards Models. During follow-up, 1,391 bladder cancers (1,018 male; 373 female) occurred. Height was unrelated to bladder cancer in both genders. We found a small but significant positive association with weight [1.04 (1.01-1.07) per 5 kilo], BMI [1.05 (1.02-1.08) per 2 units], waist circumference [1.04 (1.01-1.08) per 5 cm], waist-hip ratio (1.07 (1.02-1.13) per 0.05 unit] and waist-height ratio [1.07 (1.01-1.13) per 0.05 unit] in men. Stratification by smoking status confined associations in men to former smokers. In never smokers, we found no significant associations, suggesting residual confounding by smoking. Results did not differ with tumor aggressiveness and age. Residual analyses on BMI/waist circumference showed a significantly higher disease risk with BMI in men (p = 0.01), but no association with waist circumference. In conclusion, in this large study, height was unrelated to bladder cancer, whereas overweight was associated with a slightly higher bladder cancer risk in men. This association may, however, be distorted by residual confounding by smoking.

Smoking and long-term risk of type 2 diabetes: the EPIC-InterAct study in European populations

OBJECTIVE

The aims of this study were to investigate the association between smoking and incident type 2 diabetes, accounting for a large number of potential confounding factors, and to explore potential effect modifiers and intermediate factors.

RESEARCH DESIGN AND METHODS

The European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct is a prospective case-cohort study within eight European countries, including 12,403 cases of incident type 2 diabetes and a random subcohort of 16,835 individuals. After exclusion of individuals with missing data, the analyses included 10,327 cases and 13,863 subcohort individuals. Smoking status was used (never, former, current), with never smokers as the reference. Country-specific Prentice-weighted Cox regression models and random-effects meta-analysis were used to estimate hazard ratios (HRs) for type 2 diabetes.

RESULTS

In men, the HRs (95% CI) of type 2 diabetes were 1.40 (1.26, 1.55) for former smokers and 1.43 (1.27, 1.61) for current smokers, independent of age, education, center, physical activity, and alcohol, coffee, and meat consumption. In women, associations were weaker, with HRs (95% CI) of 1.18 (1.07, 1.30) and 1.13 (1.03, 1.25) for former and current smokers, respectively. There was some evidence of effect modification by BMI. The association tended to be slightly stronger in normal weight men compared with those with overall adiposity.

CONCLUSIONS

Former and current smoking was associated with a higher risk of incident type 2 diabetes compared with never smoking in men and women, independent of educational level, physical activity, alcohol consumption, and diet. Smoking may be regarded as a modifiable risk factor for type 2 diabetes, and smoking cessation should be encouraged for diabetes prevention.

Weight gain prior to entry into a weight-loss intervention study among overweight and obese breast cancer survivors

PURPOSE

Changes in cancer therapy, in addition to changes in obesity prevalence, suggest the need for a current assessment of weight gain patterns following breast cancer diagnosis. The aim of this study was to evaluate factors associated with weight gain among breast cancer survivors prior to enrolling into a behavioral weight loss intervention.

METHODS

Anthropometric measures and data on weight-related factors were collected at baseline on 665 breast cancer survivors. Postdiagnosis weight gain was determined between entry into the trial and previous diagnosis up to 5 years. Multivariate logistic regression analyses were used to evaluate the association between weight gain and influencing factors.

RESULTS

The mean weight gain was 4.5 % body weight (standard deviation = 10.6); 44 % of women experienced ≥5 % body weight gain. The risk of weight gain was inversely associated with age (adjusted odds ratio (ORadj) = 0.97, 95 % confidence interval (95 % CI) 0.95-0.99), Hispanic ethnicity (ORadj = 0.30, 95 % CI 0.13-0.68), and overweight (ORadj = 0.11, 95 % CI 0.05-0.23) or obese (ORadj = 0.03, 95 % CI 0.02-0.07) status at diagnosis and positively associated with time elapsed since diagnosis (ORadj = 1.19/year, 95 % CI 1.04-1.36). Women prescribed aromatase inhibitors were 46 % less likely to gain weight compared to women prescribed selective estrogen-receptor modulators (ORadj = 0.54, 95 % CI 0.31-0.93). The risk of weight gain was positively associated with smoking at diagnosis (ORadj = 2.69, 95 % CI 1.12-6.49) although this was attributable to women who subsequently quit smoking.

CONCLUSIONS

Postdiagnosis weight gain is common and complex and influenced by age, ethnicity, weight, smoking status, time elapsed since diagnosis, and endocrine-modulating therapy.

IMPLICATIONS FOR CANCER SURVIVORS

Weight gain continues to be a concern following a diagnosis of breast cancer. Factors influencing this weight gain include age, ethnicity, weight, smoking status, time elapsed since diagnosis, and endocrine-modulating therapy. Effective weight management strategies are needed for this population of women.

Effects of smoking cessation on β-cell function, insulin sensitivity, body weight, and appetite

OBJECTIVE

To stop smoking is commonly associated with significant weight gain, but the mechanisms for this are poorly understood. We assessed the effects of smoking cessation on body weight, insulin sensitivity, β-cell function, and appetite.

SUBJECTS AND METHODS

Twenty-seven long-term smokers (n=27; nine females/18 males, 28±1 years, 22.9±0.6 kg/m(2)) attending an ambulatory smoking cessation program in a community hospital in Vienna, Austria were examined at baseline (Visit A; still smoking) and after a minimum of 3 months of smoking abstinence (Visit B; n=14); relapsed smokers were not followed up. Participants underwent 3-h oral glucose tolerance tests and body composition measurements at each study visit. Fasting (QUICKI) and dynamic (oral glucose insulin sensitivity (OGIS)) insulin sensitivity and β-cell secretion (insulinogenic index 140 (IGI40)) were calculated. Food intake was quantified with a free choice buffet. Fasting plasma concentrations of neuropeptide-Y (NPY), peptide-YY (PYY), glucagon-like peptide 1 (GLP1), leptin, ghrelin, and visfatin were measured.

RESULTS

AFTER 3 MONTHS' SMOKING ABSTINENCE, BODY WEIGHT, AND FAT MASS WERE INCREASED (+4 AND +22% RESPECTIVELY, P0.05) AND FASTING INSULIN SENSITIVITY DETERIORATED (QUICKI: post, 0.37±0.02 vs baseline, 0.41±0.2; P<0.05), while OGIS remained unchanged throughout. IGI40 increased by 31% after >3 months' smoking abstinence (P<0.01). Carbohydrate ingestion increased after stopping smoking (P<0.05). NPY fasting levels were increased after >3 months (P<0.05), PYY, GLP1, leptin, ghrelin, and visfatin were unchanged.

CONCLUSION

Smoking cessation is associated with transient metabolic changes including increased β-cell secretion in response to glucose and fasting insulin resistance. These alterations may be associated with or contribute to the body weight gain after smoking cessation.

Smoking cessation and subsequent weight change

INTRODUCTION

People who quit smoking tend to gain more weight over time than those who continue to smoke. Previous research using clinical samples of smokers suggests that quitters typically experience a weight gain of approximately 5 kg in the year following smoking cessation, but these studies may overestimate the extent of weight gain in the general population. The existing population-based research in this area has some methodological limitations.

METHODS

We assessed a cohort of individuals born in Dunedin, New Zealand, between 1972-1973 at regular intervals from age 15 to 38. We used multiple linear regression analysis to investigate the association between smoking cessation at ages 21 years to 38 years and subsequent change in body mass index (BMI) and weight, controlling for baseline BMI, socioeconomic status, physical activity, alcohol use, and parity (women).

RESULTS

Smoking status and outcome data were available at baseline and at follow-up for 914 study members. People who smoked at age 21 and who had quit by age 38 had a BMI on average 1.5 kg/m(2) greater than those who continued to smoke at age 38. This equated to a weight gain of approximately 5.7 kg in men and 5.1 kg in women above that of continuing smokers. However, the weight gain between age 21 and 38 among quitters was not significantly different to that of never-smokers.

CONCLUSIONS

The amount of long-term weight gained after quitting smoking is likely to be lower than previous estimates based on research with clinical samples. On average, quitters do not experience greater weight gain than never-smokers.

Nicotine alters food-cue reactivity via networks extending from the hypothalamus

Obesity and smoking constitute two of the main causes of preventable deaths in the developed countries today. Many smokers motivate consumption as a means to control their body weight because smoking cessation increases the risk to gain weight. Although it is well established that nicotine reduces feeding in animals and that smoking is associated with reduced body weight in quasi-experimental studies of humans, acute nicotine effects are mixed and little is known about the brain networks supporting these effects. Thus, we investigated 26 normal-weighted never-smokers who received either nicotine (2 mg) or placebo gums following a double-blinded randomized cross-over design. We used functional magnetic resonance imaging (fMRI) to investigate reactivity to palatable food cues after both overnight fasting and following a standardized caloric intake (75 g oral glucose tolerance test (OGTT)). Participants viewed food or low-level control pictures in a block design and rated their current appetite after each block. Nicotine had a small- to medium-sized effect on subjective appetite and significantly altered food-cue reactivity in a region sensitive to caloric intake that extended from the right hypothalamus to the basal ganglia. During placebo sessions, the OGTT reduced functional coupling of this region with a 'salience network' (ie, amygdala, ventromedial prefrontal cortex) in processing of food pictures. Furthermore, nicotine reduced coupling with the nucleus accumbens and the OGTT reduced coupling with an 'interoceptive network' (ie, insula, operculum) instead. We conclude that locally restricted acute effects of nicotine in the hypothalamic area have profound effects on food-processing networks.

Prevalence rate of Metabolic Syndrome in a group of light and heavy smokers

BACKGROUND

Smoking is an important cause of morbidity and mortality worldwide. It is widely accepted as a major risk factor for metabolic and cardiovascular disease. Smoking reduces insulin sensitivity or induces insulin resistance and enhances cardiovascular risk factors such as elevated plasma triglycerides, decreases high-density lipoprotein cholesterol and causes hyperglycemia. Several studies show that smoking is associated with metabolic abnormalities and increases the risk of Metabolic Syndrome. The aim of this study was to estimate the prevalence of the metabolic syndrome in a group of light and heavy smokers, wishing to give up smoking.

METHODS

In this cross-sectional study all the enrolled subjects voluntary joined the smoking cessation program held by the Respiratory Pathophysiology Unit of San Matteo Hospital, Pavia, Northern Italy.All the subjects enrolled were former smokers from at least 10 years and had no cancer or psychiatric disorders, nor history of diabetes or CVD or coronary artery disease and were not on any medication.

RESULTS

The subjects smoke 32.3 ± 16.5 mean Pack Years. The prevalence of the metabolic syndrome is 52.1%: 57.3% and 44.9% for males and females respectively. Analysing the smoking habit influence on the IDF criteria for the metabolic syndrome diagnosis we found that all the variables show an increasing trend from light to heavy smokers, except for HDL cholesterol. A statistical significant correlation among Pack Years and waist circumference (R = 0.48, p < 0.0001), Systolic Blood Pressure (R = 0.18, p < 0.05), fasting plasma glucose (R = 0.19, p < 0.005) and HDL cholesterol (R = -0.26, p = 0.0005) has been observed.

CONCLUSIONS

Currently smoking subjects are at high risk of developing the metabolic syndrome.Therapeutic lifestyle changes, including smoking cessation are a desirable Public health goal and should successfully be implemented in clinical practice at any age.

Combined impact of lifestyle factors on prospective change in body weight and waist circumference in participants of the EPIC-PANACEA study

Background

The evidence that individual dietary and lifestyle factors influence a person’s weight and waist circumference is well established; however their combined impact is less well documented. Therefore, we investigated the combined effect of physical activity, nutrition and smoking status on prospective gain in body weight and waist circumference.

Methods

We used data of the prospective EPIC-PANACEA study. Between 1992 and 2000, 325,537 participants (94,445 men and 231,092 women, aged between 25–70) were recruited from nine European countries. Participants were categorised into two groups (positive or negative health behaviours) for each of the following being physically active, adherent to a healthy (Mediterranean not including alcohol) diet, and never-smoking for a total score ranging from zero to three. Anthropometric measures were taken at baseline and were mainly self-reported after a medium follow-up time of 5 years.

Results

Mixed-effects linear regression models adjusted for age, educational level, alcohol consumption, baseline body mass index and follow-up time showed that men and women who reported to be physically active, never-smoking and adherent to the Mediterranean diet gained over a 5-year period 537 (95% CI −706, −368) and 200 (−478, −87) gram less weight and 0.95 (−1.27, −0.639) and 0.99 (−1.29, −0.69) cm less waist circumference, respectively, compared to participants with zero healthy behaviours.

Conclusion

The combination of positive health behaviours was associated with significantly lower weight and waist circumference gain.