Tobacco kills nearly six million people each year world-wide and unless action is taken, the annual death toll could increase to more than eight million by 2030 [
Smokers gain weight after they quit smoking mainly because of the removal of nicotine’s effects on the central nervous system [
Limited information has been reported in relation to maintenance or reduction of weight after smoking cessation, even though up to one out of five ex-smokers actually reduce or maintain weight after smoking cessation [
Two more recent Danish population-based studies followed smokers over a period of five years, giving us the opportunity to explore the topic of weight reduction after smoking cessation.
The aim of this study was to identify predictors for weight reduction after long-term smoking cessation. Furthermore, we wanted to determine the proportion of ex-smokers with weight reduction or maintenance after smoking cessation and the median weight reduction over a five-year period.
The study population is based on data from two population-based cohorts, the Inter99 study and the Helbred2006 study. The two studies included persons living in the south-western area of Copenhagen randomly selected from the Danish Civil Registration System and took place at Research Centre for Prevention and Health, Rigshospitalet-Glostrup. A written consent form was obtained from all participants. Both studies were approved by the Ethical Committee of Copenhagen County. The Helbred2006 study was approved by the Danish Data Protection Agency and the Inter99 study was approved by the Danish Health and Medicines Authority.
The Inter99 study is the largest randomized lifestyle intervention study in Denmark (1999–2006) including 61,301 persons aged 30–60 years. The aim of the Inter99 study was to assess the effect of a preventive strategy including individualized risk assessment; multifactorial nonpharmacological intervention based on tailored information, motivation, and support; and a programme for maintenance [
The aim of the Helbred2006 study (2006–2008) including 7,931 persons aged 18–69 years was to investigate prevalence and predictors of lifestyle-related chronic diseases such as coronary heart disease, diabetes, musculoskeletal disorders, asthma, allergy, chronic lung diseases, and mental disorders. Out of the almost eight thousand invited, 3,471 persons attended the baseline visit; participation rate was 44.7% (same definition as above); 773 were daily smokers at baseline. Participation rate at 5-year follow-up visit (2011) was 66.5%. Further details about the Helbred2006 cohort are described by Thuesen et al. [
All participants in both studies completed a questionnaire about health, lifestyle, and sociodemographic factors at inclusion. Anthropometric measurements (e.g., weight and height) performed by trained research staff were obtained.
A total of 3,577 (2,804 + 773) daily smokers were included in both studies at baseline. This paper is based on the 262 daily smokers in the Inter99 study and the 55 in the Helbred2006 study (total
Weight reduction was defined as ≥1 kg lower weight at five-year follow-up than at baseline. Weight maintenance was defined as having the same or <1 kg lower weight at five-year follow-up than at baseline.
Weight gain was defined as increased weight at the five-year-follow-up compared to baseline.
Tobacco consumption was measured in grams of tobacco in the following way: one cigarette/gram of pipe tobacco = 1 gram, one cheroot/cigarillo = 3 grams, and one cigar = 5 grams. Light smokers were defined as smoking ≤15 cigarettes daily and heavy smokers >15 cigarettes daily, a common cut-point in tobacco related studies.
Height was measured without shoes to the nearest cm. Weight was measured without shoes and overcoat to the nearest kg and body mass index (BMI) was calculated (kg/m2). BMI was divided into three categories: normal weight (<24.9), overweight (25–29.9), and obese (>30 kg/m2).
Sociodemographic variables were self-reported: socioeconomic status (SES) defined by the length of completed vocational training/academic education (low: <2 years/medium: 2–4 years/high: >4 years).
Lifestyle variables were self-reported at baseline. Dietary quality score, a three-classed variable, was generated for each of the four food-groups (fish, vegetable, fruit, and fat) from a 52-item food frequency questionnaire. Summation of the four variables resulted in a score ranging from zero to eight. Subsequently participants were categorized into three classes: healthy dietary habits (score 7–9), average dietary habits score 4–6), and unhealthy dietary habits (score 1–3). The dietary quality score has previously been validated (see also the discussion section) [
Proportions or medians were used to describe the characteristics of the study population. Proportions were compared across subgroups using the Pearson chi-square or the Fisher exact test and the Kruskal-Wallis test was used to compare quantitative variables.
Multiple logistic regression analysis was performed to determine the associations between weight reduction and the explanatory variables. The analyses included BMI, tobacco consumption, lifestyle variables (diet, physical activity, and alcohol), sociodemographic variables (SES, sex, and age), and cohort. Possible interactions between cohort and the remaining explanatory variables were investigated, but no such interactions were found in any of the models.
To identify a set of predictive explanatory variables, a reduced model was defined using the Allen-Cady modified backwards selection procedure [
To evaluate the discriminatory power of the models, the area (AUC) under the receiver operating characteristic curve (ROC) was determined for the logistic regression models.
To assess the robustness of the results, the analysis based on the reduced regression model was repeated using the outcome variable “no weight gain” (maintained or reduced weight).
Statistical analyses were performed using SAS version 9.2 (SAS Institute, Cary, NC, USA). The level of significance was set to 5%.
Of those who were daily smokers at baseline 8.9 percent had been abstinent from smoking for at least 12 months at the five-year follow-up (317 out of 3,577). A total of 262 (82.6%) of the ex-smokers had gained weight at five-year follow-up, 41 (13%) had reduced weight by ≥1 kg, and 14 (4%) had maintained their weight.
The 41 ex-smokers who had reduced weight had a significantly higher BMI and lighter intensity of smoking at baseline than those who did not reduce weight. There were no significant differences regarding age, sex, SES, cohort, or lifestyle (Table
Baseline characteristic of ex-smokers with long-term abstinence at five-year follow-up (
Baseline variables | Weight reduction min. 1 kg |
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|
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No (%) | Yes (%) | ||
276 (87) | 41 (13) | ||
Body mass index kg/m2 |
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Obese BMI ≥ 30 | 31 (11.2) | 12 (29) | |
Overweight BMI 25–29.9 | 107 (38.8) | 20 (49) | |
Normal weight BMI 18,5–24.9 | 138 (50.0) | 9 (22) | |
Tobacco consumption |
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Light smoker < 15 g tobacco/day | 139 (50.6) | 29 (70.7) | |
Heavy smoker ≥ 15 g tobacco/day | 136 (49.5) | 12 (29.3) | |
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Cohort |
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Inter1999 | 228 (82.6) | 34 (82.9) | |
Health2006 | 48 (17.4) | 7 (17.1) | |
Socioeconomic status (length of education) | 0.41 | ||
High (>4 years) | 27 (10.9) | 7 (17.9) | |
Middle (2–4 years) | 180 (72.6) | 25 (65.1) | |
Low (<2 years) | 41 (16.5) | 7 (17.9) | |
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Sex | 0.68 | ||
Male | 151 (54.7) | 21 (51.2) | |
Female | 125 (45.3) | 20 (48.8) | |
Age groups | 0.85 | ||
18–39 years | 40 (14.5) | 5 (12.2) | |
40–49 years | 120 (43.5) | 17 (41.5) | |
50–69 years | 116 (42) | 19 (46.3) | |
Diet score | 0.53 | ||
Healthy (7–9) | 26 (9.5) | 6 (16.6) | |
Average (4–6) | 201 (74.2) | 30 (73.1) | |
Unhealthy (1–3) | 44 (16.2) | 5 (12.2) | |
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Physical activity (leisure time) | 0.84 | ||
Active | 211 (77) | 31 (75.6) | |
Insufficiently active | 63 (23) | 10 (24.4) | |
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Alcohol units per week | 0.42 | ||
>14 | 82 (33.3) | 10 (26.3) | |
8–14 | 56 (22.7) | 7 (18.4) | |
≤7 | 105 (42.7) | 20 (52.6) | |
|
Baseline characteristics of ex-smokers at five year follow-up with a weight reduction of minimum 1 kg (
Baseline variables |
|
Weight loss (%) | Weight reduction (kg) |
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Median (upper-lower quartile) | Mean | Median (upper-lower quartile) | Mean | |||
Body Mass Index kg/m2 | 0.06 | |||||
Obese BMI ≥ 30 | 12 | 4.98 (2.8–10.9) | 8.04 | 4.45 (2.6–10.9) | 7.37 | |
Overweight BMI 25–29.9 | 20 | 5.37 (2.7–6.9) | 6.08 | 4.15 (2.1–5.7) | 4.93 | |
Normal weight BMI 18,5–24.9 | 9 | 3.25 (2.4–4.9) | 3.61 | 2.30 (1.6–2.9) | 2.37 | |
Tobacco consumption |
|
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Light ≤ 15 g tobacco/day | 29 | 3.25 (2.6–5.9) | 4.59 | 2.50 (1.9–4.4) | 3.50 | |
Heavy > 15 g tobacco/day | 12 | 6.18 (4.8–14.6) | 9.79 | 5.10 (3.7–15.4) | 8.92 | |
Cohort |
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Inter99 | 34 | 4.96 (2.8–7.2) | 6.69 | 3.65 (2.2–5.9) | 5.61 | |
Health2006 | 7 | 2.92 (1.4–5.7) | 3.29 | 1.90 (1.2–4.3) | 2.53 | |
Socioeconomic Status (length of education) | 0.24 | |||||
High (>4 years) | 7 | 7.16 (2.7–13.7) | 7.49 | 4.70 (2.1–13.8) | 7.16 | |
Middle (2–4 years) | 25 | 3.59 (2.6–5.7) | 5.21 | 3.00 (1.9–4.4) | 3.99 | |
Low (<2 years) | 7 | 5.73 (4.0–7.8) | 7.73 | 4.20 (3.6–6.1) | 6.69 | |
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Sex | 0.28 | |||||
Male | 21 | 4.55 (2.8–7.8) | 6.44 | 3.70 (2.2–6.1) | 5.92 | |
Female | 20 | 4.76 (2.6–6.4) | 5.76 | 2.95 (1.9–4.6) | 4.21 | |
Age groups | 0.54 | |||||
18–39 years | 5 | 4.91 (2.0–6.6) | 4.72 | 2.50 (1.6–5.5) | 3.80 | |
40–49 years | 17 | 4.61 (3.5–6.6) | 6.40 | 3.60 (3.0–5.9) | 5.49 | |
50–69 years | 19 | 3.25 (2.6–6.2) | 6.22 | 2.30 (1.9–4.7) | 5.06 | |
Diet score | 0.30 | |||||
Healthy (7–9) | 6 | 3.61 (2.0–5.7) | 3.73 | 2.40 (1.4–4.2) | 2.67 | |
Average (4–6) | 30 | 4.25 (2.8–6.6) | 6.24 | 3.55 (2.1–5.8) | 5.33 | |
Unhealthy (1–3) | 5 | 5.00 (4.9–8.1) | 8.18 | 4.10 (2.5–8.1) | 6.54 | |
Physical activity (leisure time) | 0.72 | |||||
Active | 31 | 3.95 (2.6–6.6) | 5.85 | 5.20 (1.9–5.5) | 4.89 | |
Insufficiently active | 10 | 4.96 (3.1–8.1) | 6.92 | 3.40 (2.3–8.1) | 5.69 | |
Alcohol units per week | 0.29 | |||||
>14 | 10 | 2.90 (2.0–5.7) | 4.77 | 2.15 (1.6–4.3) | 3.65 | |
8–14 | 7 | 7.16 (2.7–13.7) | 7.53 | 4.70 (2.1–13.8) | 7.17 | |
≤7 | 21 | 4.76 (2.8–6.0) | 6.38 | 3.50 (2.2–4.4) | 5.12 | |
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Ex-smokers with |
41 | 4.61 (2.6–6.6) | 6.11 | 3.50 (2.1–5.5) | 5.09 |
In the reduced multiple logistic regression model, BMI and tobacco consumption were the only predictors of the outcome (Table
Predictors of ex-smokers’ weight reduction after five years. Model 1 including all tested predictors. Model 4 including final predictors.
Baseline variables | Model 1 incl. all explanatory variables ( |
Model 4 ( |
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Estimate | CI 95% |
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Overall | Estimate | CI 95% |
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Overall | |
OR | test | OR | test | |||||
Heavy smoker | 0.37 | (0.16–0.86) |
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0.34 | (0.16–0.72) |
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Light smoker (ref.) | 1.00 | 1.00 | ||||||
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Obese | 7.13 | (2.46–20.69) |
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7.38 | (2.76–19.71) |
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Overweight | 2.72 | (1.10–6.73) |
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3.10 | (1.34–7.16) |
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Normal weight (ref.) | 1.00 | 1.00 | ||||||
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Inter99 | 1.25 | (0.43–3.65) | 0.68 | 0.68 | — | — | — | |
Health2006 (ref.) | 1.00 | |||||||
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High socioeconomic status | 0.85 | (0.23–3.23) | 0.81 | 0.59 | — | — | — | |
Middle socioeconomic status | 0.60 | (0.21–1.77) | 0.36 | — | — | — | ||
Low socioeconomic status (ref.) | 1.00 | — | — | — | ||||
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Male | 1.37 | (0.59–3.18) | 0.47 | 0.47 | — | — | — | |
Female (ref.) | 1.00 | — | — | — | ||||
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Age (years) | 1.02 | (0.98–1.08) | 0.34 | 0.34 | — | — | — | |
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Healthy diet | 1.36 | (0.30–6.23) | 0.70 | 0.82 | — | — | — | |
Average diet | 0.94 | (0.28–3.22) | 0.93 | — | — | — | ||
Unhealthy diet (ref.) | 1.00 | — | — | — | ||||
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Active (physical activity) | 0.65 | (0.25–1.67) | 0.37 | 0.38 | — | — | — | |
Insufficiently active (ref.) | 1.00 | — | — | — | ||||
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>14 alcohol units/week | 0.68 | (0.27–1.67) | 0.40 | 0.47 | — | — | — | |
8–14 alcohol units/week | 0.56 | (0.29–1.59) | 0.28 | — | — | — | ||
≤7 alcohol units/week (ref.) | 1.00 | — | — | — | ||||
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ROC | AUC | CI 95% | AUC | CI 95% | ||||
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Predictive value | 0.72 | (0.63–0.80) | 0.75 | (0.67–0.83) |
Area under the ROC-curve (AUC).
The ROC curve analysis showed the predictive value (AUC) of the reduced model to be 0.72 (CI 95% 0.63–0.80), corresponding to a “fair” predictive value. The odds of reducing weight for heavy ex-smokers were reduced with two-thirds compared to light ex-smokers. Ex-smokers with obesity had more than seven times higher odds and overweight ex-smokers almost three times higher odds of weight reduction than those with normal weights (Table
The robustness of the results analysis of the reduced logistic regression model considering “no weight gain” (weight reduction/maintenance) as outcome thereby increasing the number of cases from 41 to 55 showed attenuated effect of obesity (OR 5.03 (CI 95% 1.90–13.32)) but not of overweight (OR 2.27 (CI 95% 1.02–5.00)). Heavy smokers at baseline had less than half the chance of weight reduction/maintenance compared to heavy smokers (OR 0.42 (CI 95% 0.20–0.89)).
This population-based study found that almost one in six persons who had quit smoking for at least one year had lost or maintained their weight. A predictor of weight reduction was a high BMI at baseline, even when adjusted for lifestyle factors and socioeconomic status. Quitters with obesity had more than seven times higher odds than normal weight quitters to lose weight. Of those of the quitters who lost weight after smoking cessation, persons with obesity at baseline had the largest median weight loss of 4.45 kg (Table
A meta-analysis based on 62 studies found that 16 to 21% of ex-smokers had lost or maintained their weight [
Predictors of weight
Several studies have reported a high prequit tobacco consumption or high nicotine addiction to be positively associated with weight gain [
To our knowledge, only one previous study has investigated predictors of weight
As previous studies have found that persons with obesity experience the largest weight gain [
Unfortunately, several smoking cessation programs focusing on weight have resulted in lower quit rates [
For many smokers, the anticipation of weight gain can hinder smoking cessation [
The major weakness of this study is that out of many thousands of citizens included in two large population-based studies there were only few individuals/baseline-smokers who succeeded to quit smoking on long term and to lose weight. Results based on 41 persons must be interpreted with caution and analyses should be repeated in larger populations. Also, only a minority of persons with obesity are affected by these results.
A large weight gain might lead to smoking relapse, which would mean that those who put on large amounts of weight early in their quit attempt and relapsed were not represented by our data. People with obesity could be those with the lowest tolerance of weight gain and have high relapse rates early in the smoking cessation process, so only those who did not put on weight/lost weight are abstinent at long term. This selection-bias would highly influence our conclusions. However, no clear association between weight gain and the risk of relapse has been found [
Both cohorts were population-based and results therefore have a higher generalizability than results from randomized controlled trials on smoking cessation. We included a population of daily smokers to begin with to find the 371 persons with long-term abstinence from smoking, and compared to the existing literature we followed the cohorts for a long time. Information on weight and height was objectively measured by experienced health professionals. Analyses were consistent with or without inclusion of lifestyle- and sociodemographic factors and the cohort variable.
In two large Danish population-based cohorts we found that 13% had lost weight and 4% had maintained their weight after smoking cessation. A predictor of weight reduction was a high BMI, even when adjusted for lifestyle factors and socioeconomic status. Quitters with obesity had more than seven times higher odds than normal weight quitters to lose weight, and they had the largest median weight loss of 4.45 kg. The only other predictor of weight reduction was low tobacco consumption at baseline, whereas baseline lifestyle factors, sex, age, socioeconomic status, and cohort were not found to be associated with weight reduction. For many smokers, the anticipation of weight gain can hinder smoking cessation and many lay people and health professionals have the misperception that obesity is a larger danger to health than smoking. The benefits of quitting smoking will however mostly out-weight the risks of increased weight; almost 16 kg/m2 BMI units is required for to offset the detrimental effect of smoking. Results from this study might hopefully motivate smokers with obesity and overweight to quit and health professionals to give them full support.
The researchers are independent of the founders who had no influence on study design, conduct, analyses, or interpretation of results. This manuscript is based on Helle Øster Nielsen’s master thesis.
All authors state that they have no conflicts of interest and nothing to declare.
Charlotta Pisinger was involved in the design of the Inter99 study and development of the intervention. Helle Øster Nielsen, Susanne Rosthøj, and Charlotta Pisinger were all involved in design of the actual study. Helle Øster Nielsen analyzed data, assisted by Susanne Rosthøj. All discussed data analyses and interpretation and contributed to first version of the manuscript. Caroline Kuhlmann contributed to update of literature and revision of later versions of the manuscript. All critically revised the manuscript and approved the final version of the manuscript.
The authors thank the whole staff of the Inter99 study and the Helbred2006 study and all persons participating in these studies. The Inter99 study was initiated by Professor Torben Jorgensen, D.M.S. (principal investigator), Knut Borch-Johnsen, D.M.S. (principal investigator on the diabetes part), Troels Thomsen, Ph.D., and Hans Ibsen, D.M.S. The present steering committee of the Inter99 study are Professor Torben Jorgensen, D.M.S. (principal investigator), and Charlotta Pisinger, Ph.D. and M.P.H. The Helbred2006 study was initiated by Professor Torben Jorgensen, D.M.S. (principal investigator), Allan Linneberg. The Danish Health Foundation funded CP’s work with this paper.