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Am J Public Health. 2009 February; 99(2): 348–354.
PMCID: PMC2622791

Association of Smoking in Adolescence With Abdominal Obesity in Adulthood: A Follow-Up Study of 5 Birth Cohorts of Finnish Twins

Suoma E. Saarni, MD, PhD,corresponding author Kirsi Pietiläinen, MD, PhD, Suvi Kantonen, BM, Aila Rissanen, MD, PhD, and Jaakko Kaprio, MD, PhD


Objectives. We studied the association of adolescent smoking with overweight and abdominal obesity in adulthood.

Methods. We used the FinnTwin16, a prospective, population-based questionnaire study of 5 consecutive and complete birth cohorts of Finnish twins born between 1975 and 1979 (N = 4296) and studied at four points between the ages of 16 and 27 years to analyze the effect of adolescent smoking on abdominal obesity and overweight in early adulthood.

Results. Smoking at least 10 cigarettes daily when aged 16 to 18 years increased the risk of adult abdominal obesity (odds ratio [OR]=1.77; 95% confidence interval [CI] = 1.39, 2.26). After we adjusted for confounders, the OR was 1.44 (95% CI = 1.11, 1.88), and after further adjustment for current body mass index (BMI), the OR was 1.34 (95% CI = 0.95, 1.88). Adolescent smoking significantly increased the risk of becoming overweight among women even after adjustment for possible confounders, including baseline BMI (OR = 1.74; 95% CI = 1.06, 2.88).

Conclusions. Smoking is a risk factor for abdominal obesity among both genders and for overweight in women. The prevention of smoking during adolescence may play an important role in promoting healthy weight and in decreasing the morbidity related to abdominal obesity.

Smoking and obesity are major causes of preventable death in developed countries.1 The life expectancy of obese smokers is reduced by as much as 13 years.2 Obesity-related excess mortality may mainly be caused by abdominal obesity.3 The association between smoking and obesity is complex: smoking has been associated with both low and high body mass index (BMI; weight in kilograms divided by height in meters squared) and also with adverse fat distribution. In most cross-sectional studies, adult smokers were leaner than were nonsmokers but had a larger waist circumference or smaller waist-to-hip ratio.48 Also, among smokers, a greater number of cigarettes smoked per day was related to waist circumference and BMI.5 In one large Australian study of women, smokers were found to be more likely to gain weight during the follow-up than were women who had never smoked.9 Age, duration of smoking, and socioeconomic status have been shown to modify the effect of smoking on body weight.4,8,10,11

Among adolescents, the results of studies of the association between smoking and BMI have been inconsistent. In some studies, the relation between smoking and lower body weight often observed in adults was found to be reduced or absent among youth.12,13 Adolescence is a critical age for the development of obesity14 and the establishment of health habits such as smoking, eating behaviors, and physical activity. Only a few studies dealing with the association between smoking and later abdominal obesity have spanned the age period from adolescence to adulthood. Those studies were all beset with methodologic problems, however, and no associations were found.1517

On the basis of previous studies of adults, it seems reasonable to assume that tobacco smoking is associated with changes in body weight and shape even though the possible biological mechanisms remain unclear. Because the time span from adolescence to adulthood is an important period in stabilizing health habits and in the development of obesity, we examined the effect of smoking during late adolescence on overweight and abdominal obesity in early adulthood. To examine the independent effects of smoking on subsequent measures of obesity, we controlled our analyses for several potential confounders.



The data for our study originated from the FinnTwin16, a population-based study of 5 consecutive and complete birth cohorts of Finnish twins born in 1975 to 1979 and identified through the Central Population Registry of Finland.18 We collected baseline data (wave T1) through questionnaires mailed within 60 days of the twins’ 16th birthdays. Follow-ups were at ages 17 and 18.5 years (waves T2 and T3, respectively) and in young adulthood (mean age = 24.4 years; range = 22–27 years; wave T4). Parents were sent their own questionnaire at the time of the first mailing. The response rates in all surveys were very high (80%–90%).

We excluded respondents with known illnesses (i.e., diabetes mellitus, systemic lupus erythematosus, inflammatory bowel disease, celiac disease, hyper- or hypothyroidism, malignancies, mobility disorders, and eating disorders) and those taking medications known to affect weight (e.g., insulin, thyroxin, and antipsychotic medications) from the analyses (n = 302). We also excluded respondents with missing data on weight, height, waist circumference, or smoking (n = 158). Thus, our final data set included 2278 women and 2018 men.

Twins’ zygosity was determined as described in previous studies by using questions included in the baseline questionnaire.19 Participants included 1326 monozygotic, 1353 same sex dizygotic, 1478 opposite sex dizygotic, and 139 unknown zygosity twins. Pairwise analyses were conducted when both twins had responded and no data were missing. We found 415 pairs discordant for abdominal obesity at wave T4.


Smoking behavior.

Adolescent smoking was categorized as follows: (1) never smokers (smoked fewer than 50 cigarettes in their lifetime), (2) former smokers, (3) occasional smokers (current smokers other than daily smokers), (4) daily smokers smoking fewer than 10 cigarettes per day, and (5) daily smokers smoking 10 or more cigarettes per day. We primarily used the smoking status at age 18 years (wave T3); if this data point was missing, we used answers from waves T2 and T1. We had several reasons for this: first, we assumed the smoking habits to be most stabilized at wave T3 and therefore to most reliably reflect the smoking behavior during our follow-up. Also, at earlier ages, the participants were probably more ambivalent about their smoking status than at wave T3. This supposition was based on our observation that at wave T1, 80% of those reporting to be former smokers had smoked fewer than 50 cigarettes in their lifetime. Second, the number of cigarettes smoked daily (fewer than 10 or 10 or more) was not asked at wave T1. Third, during data collection, it became illegal for children and adolescents younger than 18 years to smoke, which might have affected how the participants younger than 18 years answered questions concerning tobacco use. Also, smoking status remained reasonably stable from waves T1 to T3. The κ values comparing all smoking categories across different time points were approximately 0.5, and most of the changes were within categories of smokers (occasional and daily smokers). The κ value when comparing never versus ever smokers at waves T1 and T3 was 0.64.

Anthropometric measures.

Waist circumference was self-reported by the participants by using a tape measure supplied by mail in the wave-T4 survey. We used the World Health Organization20 cutoffs for abdominal obesity of 80 cm for women and 94 cm for men. Height and weight were self-reported, and body mass index (BMI; weight in kilograms divided by height in meters squared) was calculated on the basis of these values. We categorized the respondents as being normal weight, overweight, or obese at waves T1 and T2 by using the International Obesity Task Force reference for adolescent obesity, with the cutoffs created by using specific percentiles linked to adult cutoffs based on UK data.21,22 The BMI categories for waves T3 and T4 were defined as less than 25 kg/m2 for normal weight, 25.00 to 29.99 kg/m2 for overweight, and 30 kg/m2 or greater for obese.

Confounding Factors

We included selected potential confounding factors known to be associated with smoking or the outcome measures (BMI and abdominal obesity) in the analyses.

To control for dietary behavior, we used measures assessed at wave T1: the skipping of breakfast, type of milk and type of spread on bread, and cola drinking. Skipping breakfast has been associated with other risk behaviors23,24 and with an increased risk of weight gain.25 Eating breakfast was assessed by using 3 response categories: having breakfast every morning, having breakfast about 3 to 4 mornings per week, and having breakfast once per week or less often. Respondents were asked which type of milk (no milk, skim milk, 1% low-fat milk, 1.9% low-fat milk, or 3.5% fat whole milk), and which type of spread (no spread, low-fat, margarine, butter-margarine, butter, or other) they used. Type of milk and spread on bread have been shown to be good indicators of saturated fat intake.26 Cola drinking was assessed as the number of 12-ounce (0.33 L) bottles consumed per day. Because cola drinking was quite rare and the distribution was highly skewed, it was used as a dichotomized variable in the analyses. The use of sugar-sweetened and possibly even artificially sweetened27 soft drinks has been shown to be associated with weight gain and obesity.28

Physical activity was categorized in 3 classes on the basis of questions concerning the frequency of physical activity at leisure outside school repeated in identical form in surveys from wave T1 to T3. Those who reported exercising 4 to 5 times per week or more in all 3 questionnaires composed the “exerciser” group; the “passive” group comprised those reporting exercising 1 to 2 times a month or fewer in all 3 questionnaires; and the remainder composed the “intermediate” group.29

The age of smoking initiation (experimenting) was asked at wave T1. Parents’ BMI at age 20 years was calculated by using heights and weights recalled in the parental survey.

Father's socioeconomic status was determined on the basis of questions concerning occupation, employment, and education. Socioeconomic status was classified into 6 categories (upper-level employee, lower-level employee, manual worker, self-employed or other, farmer, and unclassified) according to the criteria of the Finnish Classification of Socio-economic Groups.30 Educational attainment and employment status at wave T3 were used to classify the socioeconomic status into 4 categories (high school/university, vocational school, employed, and unemployed/other).

Validity Assessment

We assessed the validity of the self-reported BMI, waist circumference, and height measures in a subsample of 566 twins. Those participants had participated in another study on the consequences of adolescent alcohol use with a median of 650 days after the self-report. Height was measured without shoes on a stadiometer, and weight was measured in light clothes on a calibrated beam balance. Waist circumference was measured while the participants were standing, halfway between the iliac crest and the lowest rib, at the end of a light expiration. The intraclass correlations were high. The intraclass correlation for measured versus self-reported BMI was 0.89, and the mean difference was 0.93 kg/m2 (95% confidence interval [CI] = 0.79 kg/m2, 1.07 kg/m2). The intraclass correlation for measured versus self-reported height was 0.99, and the mean difference was 0.24 cm (95% CI = 0.14 cm, 0.35 cm). The intraclass correlation for measured versus self-reported waist circumference was 0.75, and the mean difference was 2.48 cm (95% CI = 0.96 cm, 3.00 cm).

Statistical Methods

The odds ratios for abdominal obesity and becoming overweight in adulthood were obtained from logistic regression models. We chose the binary outcomes for their relevance in clinical settings and in identifying high-risk patients. The potential gender interaction was tested by using a likelihood-ratio test between models with and without an interaction term (gender × smoking). A statistically significant gender interaction was found concerning the risk of becoming overweight, but this was not the case for abdominal obesity. Therefore, risk of becoming overweight was analyzed for men and women separately, and abdominal obesity was examined in gender-adjusted models. All regression analyses were run in 2 sets: (1) a robust model and (2) a model adjusted for confounding factors selected by using the stepwise procedure of Stata.31 The dietary variables were handled as a cluster in the stepwise analysis. The risk of abdominal obesity was also adjusted for BMI at wave T4 in addition to the stepwise-selected confounders. Correspondingly, the risk of becoming overweight was also adjusted for baseline BMI.

Because socioeconomic status has been shown to be associated with smoking behavior and to modify the effect of smoking on body weight,32 we tested for possible interactions between socioeconomic status and smoking on adult outcomes (BMI, abdominal obesity) by comparing models with and without the interaction term (smoking × socioeconomic status) using a likelihood ratio test. No statistically significant interactions were found in the models mentioned (all P values > .48).

The effect of the twin-sampling design on standard errors was taken into account in the individual-level analyses by computing robust standard errors with use of the cluster option in Stata. All statistical analyses were carried out by using Stata.31

The analyses were continued by studying the association between smoking and abdominal obesity within twin pairs discordant for abdominal obesity by using conditional logistic regression.31 If the association was also found within discordant twin pairs, this suggested that there was a causal association between smoking and abdominal obesity or that they were both caused by environmental influences unshared by the twins. Thus, if the association between abdominal obesity and smoking was found only in individual-level analyses, but not within discordant twin pairs, i.e., the smoking twin pair did not have a higher risk of abdominal obesity than did the nonsmoking twin pair, this indicated that the association was caused by shared familial and genetic factors affecting both smoking and weight changes.31,33


Smoking Behavior

About one half of the men and women had never smoked, and about 12% were former smokers in adolescence (Table 1). Smoking at least 10 cigarettes daily was more prevalent among men than among women (15.5% versus 9.4%). Among the men, 18.5% were classified as exercisers, and among the women, 11.7% were; 5% to 6% of both genders were physically inactive in adolescence. About 12% of the men and 15% of the women reported eating breakfast only once per week or less often. About one third of the women and one fifth of the men used fat-free milk, and 15.2% of the men and 5.9% of the women consumed cola drinks.

Background Characteristics of Participants at Age 18.5 Years: Survey Wave T3, FinnTwin16 Study, Finland

Overweight and Obesity

The prevalence of overweight remained low (about 6%) at ages 16 to 18.5 years (waves T1–T3) but rose steeply thereafter; 11.2% of women and 24.3% of men were overweight by wave T4 (Figure 1). The proportion of obese men and women at wave T4 (4%) was almost 6 times higher than at wave T1 (0.7%).

Prevalence of overweight and obesity (%) from ages 16 to 24 years among 2278 female and 2018 male participants: FinnTwin16 cohort, Finland.

Smoking and Risk of Becoming Overweight

In the unadjusted model, girls who smoked in adolescence had a higher risk of becoming overweight women than did girls who had never smoked. The risk was greatest among girls who smoked at least 10 cigarettes per day (odds ratio [OR] = 2.32; 95% CI = 1.51, 3.58). Adjustment for possible confounders and baseline BMI attenuated the effect (OR = 1.74; 95% CI = 1.06, 2.88). Among men, adolescent smoking had no effect on the risk of becoming overweight. Overweight in adulthood was thus most prevalent among girls who smoked 10 or more cigarettes daily, whereas no differences were found among boys (Figure 2). The participants in all smoking categories gained weight from age 18 to 24 years.

Prevalence of general overweight (a) and abdominal obesity (b) at age 24 years, by smoking status during adolescence: FinnTwin16 cohort, Finland.

Abdominal Obesity

Girls smoking at least 10 cigarettes daily during adolescence had an approximately 3.4 cm larger waist circumference as young adults than did girls who had never smoked. Adolescent smoking predicted abdominal obesity (i.e., waist circumference ≥ 80 cm for women, ≥ 94 cm for men) in adulthood among both men and women. The OR for abdominal obesity was highest among those smoking at least 10 cigarettes per day (OR = 1.77; 95% CI = 1.39, 2.26) in the model adjusted only for gender (Table 2). Heavy daily smoking (i.e., ≥ 10 cigarettes per day) remained a significant predictor of an increased risk of subsequent abdominal obesity after adjustments for potential confounders (Table 2). The risk was partly attributable to overall adiposity, because adjustment for current BMI broadened the confidence intervals so that statistical significance was not maintained.

Odds Ratios (ORs) for Abdominal Obesity at Age 24 Years, by Smoking Status in Adolescence: FinnTwin16 Study, Finland

Comparing those who started daily smoking between T1 and T3 with those who were daily smokers throughout adolescence did not reveal any significant differences between groups (data not shown). In the analyses concerning twin pairs discordant for abdominal obesity (n = 415), among all discordant pairs, the abdominally obese twin was statistically nonsignificantly more likely to have smoked at least 10 cigarettes daily (OR = 1.60; 95% CI = 0.92, 2.78). Also, the risk was increased for being a former smoker during adolescence (OR = 1.8; 95% CI = 1.10, 3.02). In the subgroup analyses of zygosity groups, among the same-sex dizygotic pairs, the abdominally obese twin was more likely to have smoked at least 10 cigarettes daily (OR = 3.2; 95% CI = 1.09, 9.42). The number of monozygotic discordant pairs was small, and no statistically significant effects were noted.


We showed in this population-based cohort of healthy young adults that smoking predicts abdominal obesity. Smoking at least 10 cigarettes daily during adolescence was associated with an increased risk of abdominal obesity and overall overweight among women. The risk of becoming abdominally obese was not fully explained by overall adiposity and other possible factors affecting central fat accumulation, as shown in the models adjusted for current BMI and many other possible confounders. The extra kilograms on the waistline among the heavy smokers are of clinical and public health importance because of the well-documented detrimental metabolic effects of central fat.3

Previous Studies

Our results differ from those of the previous studies,1517,34,35 probably as the result of some methodologic differences. Neither the Amsterdam Growth and Health Study16,17,34 nor the Northern Finland Cohort Study15,35 found an association between adolescent smoking and later abdominal obesity. However, those studies used crude measures of smoking (i.e., weekly smoking versus nonsmoking at age 13–14 years), which may have reflected smoking habits not yet stabilized. We were able to differentiate between occasional and daily smokers, and could further take into account the number of cigarettes smoked daily. The results of cross-sectional studies of body weight and smoking behavior have been inconsistent,36,37 as have the results of longitudinal studies with no inclusion of indicators of abdominal obesity.9,12,36,38,39

In our data, the prevalence of overweight rose steeply from adolescence into young adulthood. The determinants of later obesity during this important age period are not well understood. Our results showed that smoking during adolescence was associated with future overweight among young women and with abdominal obesity among both women and men. Smoking is known to be associated with low education,4042 dietary behavior,23,24 and physical inactivity,29,43 all of which are documented risk factors for overweight. However, these confounders explained only a small amount of the association between smoking and overweight and later abdominal obesity. This, together with the dose-dependent effect of smoking and our findings from twin pairs discordant for abdominal obesity, supports the interpretation that a causal relation may exist between smoking and the development of obesity. The pathophysiologic mechanisms behind this possibly causal link remain obscure. However, one possible explanation may be that changes in glucocorticoid metabolism associated with smoking44 and possibly also with psychosocial stress associated with smoking45,46 are responsible for the central fat accumulation.47,48

Strengths and Weaknesses

The increase in the risk of abdominal obesity or overweight among the women was no longer statistically significant in the fully adjusted models. This may have been caused by insufficient statistical power. Another possible interpretation of our findings is that adolescent smoking is a proxy for other factors or is part of a cluster of factors predisposing to later abdominal obesity. There are 2 implications of these 2 explanations: first, adolescent smoking should be seen as a sign of increased metabolic hazards in clinical work. Second, counteracting smoking initiation may be a more effective tool against later metabolic morbidity than previously thought in both clinical and public health settings.

In this study, we adjusted for selected indicators of dietary behavior, physical exercise, parental BMI, and fathers’ and participants’ own socioeconomic status as potential confounders. Our inability to adjust for dietary habits in more detail (e.g., in a food-frequency questionnaire or food diary) can be seen as a limitation of our study. Even if dietary measures have several methodologic problems,49 this topic should be addressed in greater detail in future research. Our validated measurement of exercise can be seen as reasonably accurate.29 Inclusion of both parents’ BMI in the analysis is a special strength of our study because BMI has a strong genetic component.50

We used self-reported data, and our measurement validation showed good association with measured values. Self-reports of smoking status among Finns have previously been shown to be valid.51 Some underreporting of body size may occur, which would lead to an underestimation of the risk of abdominal obesity. Unfortunately, we had information on waist circumference only at young adulthood. Given the low prevalence of obesity and overweight in adolescence in this and other studies,52 it is unlikely that abdominal obesity is common at this young age or that it could have influenced smoking habits in adolescence.

We used a large population-based sample with high response rates. Although twins have slightly lower BMIs than did singletons in midadolescence,53 this is unlikely to affect the association between smoking and body composition. Further, determination of adult body size takes place from late adolescence to early adulthood. Our adolescents were assessed at each survey in adolescence within a very narrow age range, thus reducing variation caused by age. Unlike in previous studies, we adjusted for several confounders affecting abdominal obesity and smoking. We also used a more detailed measure of smoking behavior than did the earlier studies and were able to consider the level of daily cigarette consumption.


Given the greater risk of overweight and abdominal obesity among girls who smoked daily and the fact that adolescent smoking is often associated with preoccupation with weight,54 emphasizing the deleterious effect of smoking on abdominal fat accumulation could be effective in smoking prevention among young women. Counteracting smoking initiation may be a more effective tool against later morbidity than previously thought. These results need to be replicated in other well-conducted epidemiologic studies with longer follow-ups and possibly more-careful measurement of the risk factors and the outcome.

Our results show that adolescent smoking predicts abdominal obesity among young adults. Both abdominal obesity and smoking are major risk factors for metabolic dysfunction55 and cardiovascular disease.56 The prevention of smoking during adolescence could be a very effective tool for preventing later metabolic morbidity. However, it will be important to better understand the possible causal pathways between smoking and later abdominal obesity.


This study was supported by grants from the Signe and Ane Gyllenberg Foundation, the National Institute on Alcohol Abuse and Alcoholism (grants AA-08315, AA-00145, and AA-12502), the European Union Fifth Framework Program (QLRT-1999-00916 and QLG2-CT-2002-01254), the Academy of Finland (grants 44069, 100499, 118555, and 201461), the Academy of Finland Centre of Excellence in Complex Disease Genetics, and Helsinki University Central Hospital.

We thank Tellervo Korhonen from the Department of Public Health, University of Helsinki, for valuable comments on the article. We also thank the anonymous referees for their careful help in improving the quality of this article.

Note. Funding sources had no involvement in planning or conducting the study.

Human Participant Protection

This study was approved by the ethical committees of Helsinki University and Indiana University.


1. World Health Report 2002. Geneva, Switzerland: World Health Organization; 2002
2. Peeters A, Barendregt JJ, Willekens F, Mackenbach JP, Al Mamun A, Bonneux L. Obesity in adulthood and its consequences for life expectancy: a life-table analysis. Ann Intern Med 2003;138(1):24–32 [PubMed]
3. Bigaard J, Frederiksen K, Tjonneland A, et al. Waist circumference and body composition in relation to all-cause mortality in middle-aged men and women. Int J Obes (Lond) 2005;29(7):778–784 [PubMed]
4. Akbartabartoori M, Lean ME, Hankey CR. Relationships between cigarette smoking, body size and body shape. Int J Obes (Lond) 2005;29(2):236–243 [PubMed]
5. Bamia C, Trichopoulou A, Lenas D, Trichopoulos D. Tobacco smoking in relation to body fat mass and distribution in a general population sample. Int J Obes Relat Metab Disord 2004;28(8):1091–1096 [PubMed]
6. Barrett-Connor E, Khaw KT. Cigarette smoking and increased central adiposity. Ann Intern Med 1989;111(10):783–787 [PubMed]
7. Canoy D, Wareham N, Luben R, et al. Cigarette smoking and fat distribution in 21,828 British men and women: a population-based study. Obes Res 2005;13(8):1466–1475 [PubMed]
8. Marti B, Tuomilehto J, Korhonen HJ, et al. Smoking and leanness: evidence for change in Finland. BMJ 1989;298(6683):1287–1290 [PMC free article] [PubMed]
9. Ball K, Brown W, Crawford D. Who does not gain weight? Prevalence and predictors of weight maintenance in young women. Int J Obes Relat Metab Disord 2002;26(12):1570–1578 [PubMed]
10. Molarius A, Seidell JC, Kuulasmaa K, Dobson AJ, Sans S. Smoking and relative body weight: an international perspective from the WHO MONICA Project. J Epidemiol Community Health 1997;51(3):252–260 [PMC free article] [PubMed]
11. Molarius A, Seidell JC. Differences in the association between smoking and relative body weight by level of education. Int J Obes Relat Metab Disord 1997;21(3):189–196 [PubMed]
12. Klesges RC, Ward KD, Ray JW, Cutter G, Jacobs DR, Jr, Wagenknecht LE. The prospective relationships between smoking and weight in a young, biracial cohort: the Coronary Artery Risk Development in Young Adults Study. J Consult Clin Psychol 1998;66(6):987–993 [PubMed]
13. Klesges RC, Zbikowski SM, Lando HA, Haddock CK, Talcott GW, Robinson LA. The relationship between smoking and body weight in a population of young military personnel. Health Psychol 1998;17(5):454–458 [PubMed]
14. Gordon-Larsen P, Adair LS, Nelson MC, Popkin BM. Five-year obesity incidence in the transition period between adolescence and adulthood: the National Longitudinal Study of Adolescent Health. Am J Clin Nutr 2004;80(3):569–575 [PubMed]
15. Laitinen J, Pietiläinen K, Wadsworth M, Sovio U, Järvelin MR. Predictors of abdominal obesity among 31-y-old men and women born in Northern Finland in 1966. Eur J Clin Nutr 2004;58(1):180–190 [PubMed]
16. Twisk JW, Kemper HC, van Mechelen W, Post GB, van Lenthe FJ. Body fatness: longitudinal relationship of body mass index and the sum of skinfolds with other risk factors for coronary heart disease. Int J Obes Relat Metab Disord 1998;22(9):915–922 [PubMed]
17. van Lenthe FJ, van Mechelen W, Kemper HC, Post GB. Behavioral variables and development of a central pattern of body fat from adolescence into adulthood in normal-weight whites: the Amsterdam Growth and Health Study. Am J Clin Nutr 1998;67(5):846–852 [PubMed]
18. Kaprio J, Pulkkinen L, Rose RJ. Genetic and environmental factors in health-related behaviors: studies on Finnish twins and twin families. Twin Res 2002;5(5):366–371 [PubMed]
19. Sarna S, Kaprio J, Sistonen P, Koskenvuo M. Diagnosis of twin zygosity by mailed questionnaire. Hum Hered 1978;28(4):241–254 [PubMed]
20. Han TS, van Leer EM, Seidell JC, Lean ME. Waist circumference action levels in the identification of cardiovascular risk factors: prevalence study in a random sample. BMJ 1995;311(7017):1401–1405 [PMC free article] [PubMed]
21. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 2000;320(7244):1240–1243 [PMC free article] [PubMed]
22. Vidmar A, Carlin J, Hesketh K, Cole T. Standardizing anthropometric measures in children and adolescents with new functions for egen. Stata Journal 2004;4(1):50–55
23. Wingard DL, Berkman LF, Brand RJ. A multivariate analysis of health-related practices: a nine-year mortality follow-up of the Alameda County Study. Am J Epidemiol 1982;116(5):765–775 [PubMed]
24. Keski-Rahkonen A, Kaprio J, Rissanen A, Virkkunen M, Rose RJ. Breakfast skipping and health-compromising behaviors in adolescents and adults. Eur J Clin Nutr 2003;57(7):842–853 [PubMed]
25. Purslow LR, Sandhu MS, Forouhi N, et al. Energy intake at breakfast and weight change: prospective study of 6,764 middle-aged men and women. Am J Epidemiol 2008;167(2):188–192 [PubMed]
26. Roos E, Ovaskainen M-L, Pietinen P. Validity and comparison of three saturated fat indices. Scand J Nutr 1995;39:55–59
27. Swithers SE, Davidson TL. A role for sweet taste: calorie predictive relations in energy regulation by rats. Behav Neurosci 2008;122(1):161–173 [PubMed]
28. Malik VS, Schulze MB, Hu FB. Intake of sugar-sweetened beverages and weight gain: a systematic review. Am J Clin Nutr 2006;84(2):274–288 [PMC free article] [PubMed]
29. Aarnio M, Winter T, Peltonen J, Kujala UM, Kaprio J. Stability of leisure-time physical activity during adolescence—a longitudinal study among 16-, 17- and 18-year-old Finnish youth. Scand J Med Sci Sports 2002;12(3):179–185 [PubMed]
30. Classification of Socio-economic Groups 1989. Helsinki: Helsinki Handbooks, Statistics Finland; 1989
31. STATA User's Guide Release 9.1. 8. College Station, TX: Stata Press; 2005
32. Laaksonen M, Rahkonen O, Karvonen S, Lahelma E. Socioeconomic status and smoking: analysing inequalities with multiple indicators. Eur J Public Health 2005;15(3):262–269 [PubMed]
33. Boomsma D, Busjahn A, Peltonen L. Classical twin studies and beyond. Nat Rev Genet 2002;3(11):872–882 [PubMed]
34. Bernaards CM, Twisk JW, Snel J, van Mechelen W, Kemper HC. In a prospective study in young people, associations between changes in smoking behavior and risk factors for cardiovascular disease were complex. J Clin Epidemiol 2005;58(11):1165–1171 [PubMed]
35. Laitinen J, Näyhä S, Kujala V. Body mass index and weight change from adolescence into adulthood, waist-to-hip ratio and perceived work ability among young adults. Int J Obes (Lond) 2005;29(6):697–702 [PubMed]
36. Potter BK, Pederson LL, Chan SS, Aubut JA, Koval JJ. Does a relationship exist between body weight, concerns about weight, and smoking among adolescents? An integration of the literature with an emphasis on gender. Nicotine Tob Res 2004;6(3):397–425 [PubMed]
37. Weitzman M, Cook S, Auinger P, et al. Tobacco smoke exposure is associated with the metabolic syndrome in adolescents. Circulation 2005;112(6):862–869 [PubMed]
38. Cooper TV, Klesges RC, Robinson LA, Zbikowski SM. A prospective evaluation of the relationships between smoking dosage and body mass index in an adolescent, biracial cohort. Addict Behav 2003;28(3):501–512 [PubMed]
39. Kvaavik E, Tell GS, Klepp KI. Predictors and tracking of body mass index from adolescence into adulthood: follow-up of 18 to 20 years in the Oslo Youth Study. Arch Pediatr Adolesc Med 2003;157(12):1212–1218 [PubMed]
40. Pierce JP. International comparisons of trends in cigarette smoking prevalence. Am J Public Health 1989;79(2):152–157 [PMC free article] [PubMed]
41. Saarni SE, Silventoinen K, Rissanen A, Sarlio-Lähteenkorva S, Kaprio J. Intentional weight loss and smoking in young adults. Int J Obes Relat Metab Disord 2004;28(6):796–802 [PubMed]
42. Green MP, McCausland KL, Xiao H, Duke JC, Vallone DM, Healton CG. A closer look at smoking among young adults: where tobacco control should focus its attention. Am J Public Health 2007;97(8):1427–1433 [PMC free article] [PubMed]
43. Escobedo LG, Marcus SE, Holtzman D, Giovino GA. Sports participation, age at smoking initiation, and the risk of smoking among US high school students. JAMA 1993;269(11):1391–1395 [PubMed]
44. Rohleder N, Kirschbaum C. The hypothalamic-pituitary-adrenal (HPA) axis in habitual smokers. Int J Psychophysiol 2006;59(3):236–243 [PubMed]
45. Cohen S, Doyle WJ, Baum A. Socioeconomic status is associated with stress hormones. Psychosom Med 2006;68(3):414–420 [PubMed]
46. Lahiri K, Rettig-Ewen V, Bohm M, Laufs U. Perceived psychosocial stress and cardiovascular risk factors in obese and non-obese patients. Clin Res Cardiol 2007;96(6):365–374 [PubMed]
47. Bjorntorp P, Rosmond R. Obesity and cortisol. Nutrition 2000;16(10):924–936 [PubMed]
48. Bjorntorp P. Do stress reactions cause abdominal obesity and comorbidities? Obes Rev 2001;2(2):73–86 [PubMed]
49. Maurer J, Taren DL, Teixeira PJ, et al. The psychosocial and behavioral characteristics related to energy misreporting. Nutr Rev 2006;64(2 Pt 1):53–66 [PubMed]
50. Schousboe K, Willemsen G, Kyvik KO, et al. Sex differences in heritability of BMI: a comparative study of results from twin studies in eight countries. Twin Res 2003;6(5):409–421 [PubMed]
51. Vartiainen E, Seppälä T, Lillsunde P, Puska P. Validation of self reported smoking by serum cotinine measurement in a community-based study. J Epidemiol Community Health 2002;56(3):167–170 [PMC free article] [PubMed]
52. Rasmussen F, Johansson M, Hansen HO. Trends in overweight and obesity among 18-year-old males in Sweden between 1971 and 1995. Acta Paediatr 1999;88(4):431–437 [PubMed]
53. Pietiläinen KH, Kaprio J, Rissanen A, et al. Distribution and heritability of BMI in Finnish adolescents aged 16y and 17y: a study of 4884 twins and 2509 singletons. Int J Obes Relat Metab Disord 1999;23(2):107–115 [PubMed]
54. Fulkerson JA, French SA. Cigarette smoking for weight loss or control among adolescents: gender and racial/ethnic differences. J Adolesc Health 2003;32(4):306–313 [PubMed]
55. Manson JE, Ajani UA, Liu S, Nathan DM, Hennekens CH. A prospective study of cigarette smoking and the incidence of diabetes mellitus among US male physicians. Am J Med 2000;109(7):538–542 [PubMed]
56. Mokdad AH, Marks JS, Stroup DF, Gerberding JL. Actual causes of death in the United States, 2000. JAMA 2004;291(10):1238–1245 [PubMed]

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