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~52 MONICA Data ' Centre, Department of Epidemiology and Health Promotion, National PubHc Health Institute, Nlannerheimintle 166, 00300 Helstnki, Finland. A Molarius K Kuulasmaa The Netherlands Institute for Health Sciences, Erasmus University Medical School, Rotterdam, The Netherlands. A Moladus Department of Chronic Disease and Environmental Epidemialogy, National Institute of Public Health and the Environment, Bilthoven, The Netherlands. J C Seidell Department of Statistics, University of Newcastle, New South Wales, Australia. A J Dobson Department of Health and Social Security, Institute of Health Studies~ Barcelona, Spain. S Sans WHO MONICA Proiect* A list of participants is published as appendix I Correspondence to: /vls A Molarius. Accepted for publication November 1996 Journal of Epidemiolo~y and Coramuni~y Health 1997;51:252-200 Smoking and relative body weight: an international perspective from the WHO MONICA Project WHO MONICA Project* prepared by A Molarius, J C Seidell, K Kuulasmaa, A ~[ Dobson, S Sans Abstract Study objective--To investigate the mag- nitude and consistency of the associations between smoking and body mass index (BMI) in different populations. Design~A cross sectlonal study. Setting and participants--About 69000 men and women aged 35-64 years from 42 populations participating in the first WHO MONICA survey in the early and mid 1980s. Main results--Compared to never smokers, regular smokers had sig- nificantly (p<0.05) lower median BMI in 20 (men) and 30 (women) out of 42 popu- lations (range --2.9 to 0.Skglm~). There was no population in which smokers had a significantly higher BMI than never smokers. Among men, the association be- tween leanness and smoking was less ap- parent in populations with relatively low proportions of regular smokers and high proportions of ex-smokers. Ex-smokers had significantly higher BMI than never smokers in I0 of the male populations but in women no consistent pattern was ob- served. Adjusu,nent for socioeconomic sta- tus did not affect these results. Conclusions~Although in most popu- lations the association between smoking and BMI is slmHar, the magnitude of this association may be affected by the pro- portions of smokers and ex-smokers in these populations. (J Epidemiol Gommunicy Health 1997;51:252-260) Ntmierous epidemiological studies have shown a consistent inverse relationship between smoking and body weight---smokers weigh relatlvely less than non-smokers,~-n and stopping smoking often leads to weight gain.I-3 ~ 7 ~0 t2-~4 It has been shown that this is mainly because smoking increases energy expenditure)~ Moreover, the inverse re- lationship between smoking and relative body weight becomes stronger with age,4 which ~ be explained by longer duration of smoking)16 Among smokers a U-:shaped relationship be- tween the number of cigarettes smoked and relative body weight has been found in several studies--those smoking 10-20 cigarettes per day being the leanest.~-~7°~71s Although this seems paradoxical given the metabolic effects of smoking, it has been suggested that heavy smokers may weigh more because of clustering of other unhealthy habits such as high intake of saturated fat, heavy use of alcohol, and little exercise. Indeed, a study in Finland found that a change in the association between smoking and body weight had occurred in the 1980s~ smoking was no longer associated with leanness in this population but rather it was positively related to BMI, especially among younger middle aged men.l~ Most studies of the relationship between smoking and relative body weight have looked at single populations or cohorts. Therefore we considered it important to examine whether associations are similar in populations with different histories of smoking habits and changes in body weight. We investigated this among men and women in 42 populations participating the WHO MONICA Project. Given the findings of the Finnish study on changes in the relationship between smoking and relative body weight, it could be hy- pothesised that the "classical" inverse as- sociation between smoking and relative body weight might hold in populations with a high prevalence of smoldng and comparatively few anti-smoking activities, while a "new" positive association between smoking and relative body weight may be more typical in populations with a previously high but currently falling prevalence of smoking due to and-smoking programmes. While our data do not allow us to test this hypothesis directly, we will mainly focus on determining whether there are popu- lations with the "new" association to warrant pursuing such a hypothesis. Methods The WHO MONICA Proiect was designed to measure trends in the incidence in and mor- tality from cardiovascular disease, and to assess the extent to which these trends are related to changes in known risk factors in 49 study populations in 26 countries. Risk factors in the WHO MONICA Project are monitored through up to three independent cross sectional population surveys,m° The surveys included random samples of at least 200 people in each gender and 10 year age group, for the age range 35-64 years, and optionally 25-34 years. This study presents data from the baseline surveys. The survey periods range from May 1979 to ~.~ February 1989 and are mosdy concentrated in the early and mid 1980s. In this study, only the age range from 35-64 years is considered. The overall participation rates for the surveys ~.~..~

Smoking and body weight in MONIGA participation rates, and survey periods have been described in more detail elsewhere.21 Height and body weight were measured with participants standing without shoes and heavy outer garments. Body mass index (BMI) was calculated as weight divided by height squared (kg/m2) as a measure for relative weight. BMI categories were formed according to the WHO guidelines,22 except for using 21 kg/m2 instead of the WHO recommendation of 18 kg/m2 as ' a cut off point for the leanest category. This cut off point was selected to ensure a sufficient number of subjects in each category and be- cause of its use in some other studies.23 The subjects were classified as follows: • Lean persons--BMI less than 21 kg/m2 • Persons of normal weight--BMI equal to or more than 21 but less than 25 kg/m2 • Overweight persons--BMI equal to or more than 25 but less than 30 kg/m2 • Obese persons--BMI equal to or more than 30 kg/m2. Data on smoking were obtained with a stand- ard questionnaire.24 In the analysis respondents were classified as follows: • Regular cigarette smokers, those reporting smoking cigarettes every day. They were fur- ther classified in concordance with several other studies2~so as (a) light to moderate smokers, those smoking 1-19 cigarettes per day, and 03) heavy smokers, those smoking 20 or more cigarettes per day. • Other current smokers, those reporting smoking cigarettes occasionally, or at least lg of pipe tobacco per week, or at least one cigar per week. • F.x-smokers, those reporting smoking ci- garettes regularly in the past but not cur- rently. • Never smokers, those who were not current smokers and had never smoked cigarettes regularly. The age group of the subject was obtained from the sampling frame at the time of sample selection. Tertiles of years of schooling within each population were used as a measure of socioeconomic status (SES). Years of schooling were obtained by asking--"How many years did you spent at school or in full-time study?". Terdles of years of schooling were calculated for men and women in each 10 year age group separately. KEY POINTS • Cigarette smokers are leaner than never smokers in most of the populations studied - and more so in women than men. • In some populations there was no as- sociation between smoking and body weight. In these populations, among men, there were fewer smokers and more ex-smokers than in populations in which smokers were leaner than never smokers. • Ex-smoking men weighed on average more than never smokers, whereas in women no consistent pattern was found. The quality of data on weight, height, smoking behaviour, and years of schooling has been cent- rally assessed. Any population ~rith an un- satisfactory quality of data or response rate lower than 50% for any of the items has been omitted from this study. This left 42 populations, except for analyses involving years of schooling, where only a subset of 34 populations with full data was included. STATISTICAL METHODS In the first phase of data analysis, population level (ecological) data were analysed to estimate the strength of association between smoking and relative body weight. Pearson correlation coefficients between the proportions of regular cigarette smokers and the means and centiles of BMI were calculated for men and women for each 10 year age group. Correlations of age standardised values are given for the age group 35-64. Age standardised values were calculated using the world standard population,z~ as the reference population with weights 12, I1, and 8 for the 10 year age groups 35-44, 45-54, and 55-64 respectively. In the second phase, individual data were used to examine the consistency and magnitude of the relation between smoking and BMI at the individual level. All analyses were carried out separately for men and women. Two types of analyses were performed--firstly, comparing medians or means of BMI between different categories of smoking, and secondly, comparing proportions of regnalar smokers between differ- ent categories of BMI within populations. Differences were reported to be statistically sig- nificant if the p value was less than 0.05. To compare the levels of BMI between smok- ing categories, medians instead of means of BMI were used because of the distributions of BMI were skewed to the right. Confidence intervals for the differences in median BMIs in categories of smokers, compared with the never smoker category, were calculated using the Normal ap- proximation as described by White et al.~6 Linear regression was used to control for potential confounding by SES. Mean BMIs and differ- ences in mean BMIs in relation to smoking category were calculated u.sing the general linear model (GLM) procedure of SAS statistical soft- ware,zr adjusting for age group and population as categorical covariates. To assess the con- founding effect of SES, regression analyses were performed both with and without adjusting for population specific textiles of years of schooling. Confidence intervals for the estimates were cal- culated from the standard errors of the re- gression coefficients assuming that the sampling distributions of the coefficients were normal. The results of the linear regression were also used to give an overall estimate of the differences in the mean BMIs between smoking categories, summarising the results across all populations. In addition, the same overall estimates were calculated using non-parametric methods to confirm that the estimates based on the re- gression analysis did not differ from the es- timates based on medians.

254 Molarius, Seidell, Kuulasmaa, et al Table 1 Number of subjects, age standardised proportion CA) of regular cigarette smokers, and age standardised p~vaIence of obesity (BMI> 30 kgl m2) in first MONICA population survey~ Men and women aged 35-64 years Population Country Abbreviation No 3moker~ % Obes~ % No 8moker~ % Obese% Newcasde Australia AUS-NEW 1218 34 15 1241 24 16 Perda Australia AUS-PER 631 33 9 661 22 11 Ghent Belgium BEL-GHE 539 43 11 495 25 15 Luxembourg Province Belgium BEI~LUX 989 43 13 959 18 18 Beijing China CHN-BEI -619 51 3 641 16 9 Czech Republic Czech Rcp. CZE-CZE 948 44 21 990 21 32 Glostmp Denmark DEN-GLO 1456 45 I1 1361 44 10 Kuopio Province Finland FIN-KUO 968 34 18 981 l0 19 North Karelia Finland FTN-NKA 1125 30 17 1212 9 24 Turku/Lulmaa Finland FTN-TUL 1194 30 19 1270 17 17 Lille France FRA-LIL 641 39 14 530 i i 19 Strasbourg France FRA-STR 666 34 22 714 14 23 Toulouse France FRA-TOU 678 36 9 645 17 11 Augsburg rural Germany GER-AUR 846 30 20 857 12 22 Augsburg urban Germany GER-AUU 712 36 18 679 18 15 Bremen Germany GER-BRE 633 45 14 656 29 18 Cottbu~ County Germany GER-COT 460 31 17 543 11 23 Halle County Germany GER-HAC 816 38 18 859 14 27 Karl-Marx-Stadt County Germany GER-KMS 813 37 14 926 15 19 Rest of DDR-MONICA Germany GER-RDM 763 37 17 822 24 21 R_hein-Neckar Region Germany GER-RHN 1170 31 13 1266 23 12 Iceland Iceland ICE-ICE 657 26 11 704 40 11 Area Brianza Italy ITA-BRI 618 44 11 639 18 15 Frinli Italy ITA-FRI 719 35 16 724 26 19 Karmas Lithuania LTU-KAU 728 38 22 735 4 45 Auckland New Zealand NEZ-AUC 1018 29 8 567 25 9 Tarnobrzeg Voivodship Poland POL-TAR 1250 58 13 1472 11 32 Warsaw Poland POD-WAR 1309 59 18 1337 33 26 Bucharest Romania ROM-BUC 524 38 20 632 15 31 Moscow control Russia RUS-MOC 770 48 13 645 12 33 Moscow intervention Russia RUS-MOI 1163 46 12 1~34 9 35 Novosibi~sk control Russia RUS-NOC 1061 59 15 1054 3 44 Novosibksk intetv. Russia RUS-NOI 601 53 13 646 3 43 Catalonia Spain SPA-CAT 993 47 9 994 7 24 Gothenburg Sweden SWE-GOT 517 33 7 557 34 9 Northern Sweden Sweden SWE-NSW 640 24 11 611 26 14 Ticino - Switzerland SWI-TIC 781 38 20 769 24 15 Vaud/Fribourg Switzerland SWI-VAF 627 32 . 13 568 21 13 Belfast UK LrNK-BEL 927 34 11 925 33 14 ~oOW .x ~X-~L~ 502 52 n 480 50 16 rd USA USA-STA 427 40 10 516 36 15 Novi Sad Yugoslavia YUG-NOS 592 49 17 555 27 29 To compare the prevalence of regular cigarette • smoking between BMI categories, age stand- ardised proportions of regular cigarette smokers were calculated for the age group 35-64 using the same method for age standarclisation as described above. The differences in the pro- portions of smokers between BMI categories within populations were tested by fitting a lo- gistic regression model with regular cigarette smoking as the binary dependent variable and Table 2 Pearson correlation coefficients between the Froportion (°,6) of regular cigarette smokers and mean and centiles of body mass index (BMI) for 42 populat~ns in the first MONIGA survey Age ~roup r (95% Gl) r (95% CI) 35-44 --0.07 (-0.36,0.24) --0.45 (--0.66,--0.17) 45-54 --0.37 (--0.61,-0.08) -0.65 (--0.79,- 0.43) 55-64 --0.30 (--0.55, 0.01) --0.63 (--0.79,-0.41) Age standardised 35-64 -0.25 (-0.52, 0.05) -0.59 (-0.76,-0.35) Median 35-44 0.00 (-0.30, 0.30) -0.46 (-0.67,-0.18) 45-54 -0.34 (-0.59,-0.04) -0.62 (-0.78,-0.39) 55-64 -0.30 (-0.55, 0.00) -0.64 (-0.79,-0.41) Age standardised 35-64 --0.22 (--0.49, 0.09) --0.57 (-- 0.75,--0.33) 10th centile 35-44 --0.16 (--0.44, 0.15) -0.47 (--0.68,--0.19) 45-54 --0.54 (--0.73,--0.29) --0.63 (--0.79,-0.41) 55-64 -0.50 (-0.70,--0.23) -0.58 (--0.75,--0.33) Age standardised - 35-64 -0.43 (-0.65, -0.14) -0.56 (-0.74,-0.31) 90th centile 35-44 0.04 (-0.27, 0.34) -0.37 (-0.61,-0.08) 45-54 --0.22 (--0.49, 0.09) -0.58 (- 0.75,--0.33) 55-64 --0.10 (-0.39, 0.21) -0.60 (--0.76,-- 0.36) Age standardised 35-64 -0.08 (-0.37, 0.23) -0.54 (-0.72,- 0.28) age group as the independent variable, with and without adjustment for indicator variables for BMI categories. To estimate the overall difference in the age standardised proportions of regular cigarette smokers between BMI categories, the mean of the dLfferences and a 95% confidence interval for this mean were calculated, summarising the results across all study populations. The normal weight catego~ (BMI=21.0-24.9 kg/m2) was used as the reference category when comparing proportions of regular smokers. The confidence intervals were calculated from standard errors of the means using t distribution with the number of populations minus one for the degrees of freedom. Results Table 1 gives the number ofsubiects, age stand- ardised proportion of regular cigarette smokers and age standardised prevalence of obesity (BMI >_. 30 kg/mz) in each population. The table shows considerable variation both in the pre- valence of regular smoking and obesity across the study populations. The prevalence of reg- ular cigarette smoking ranged from 24%-59% in men and from 3%-50% in women. In gen- eral, among men the prevalence of smoking was highest in some eastern European (Poland, Russia) populations and lowest in some Nordic (Sweden, Iceland) populations. " Among women, however, smoking was relatively more common in some western European popu-

Smoking and body weight in MONIGA 255 RUS-NOI RUS-NOC POL-TAR RUS-MOC ROM-BUC UNK.-GLA LTU-KAU POL-WAR BEL-GHE FRA-LIL RUS-MOI CZE-CZE FRA-STR YUG-NOS GER-HAC USA-STA ITA-BR! CHN-BEI ,. SWI-VAF .o_ SWI-TIC GEN-KMS --, FIN-TUL GER-RDM UNK-BEL SPA-CAT AUS-.PER ITA-FRI GER--.COT ICE-ICE BEL-LUX GER-AUU AUS--NEW NEZ-AUC F|N-NKA DEN..-GLO SWE-GOT FIN-KUO GER-RHN GER-AUR FRA-TOU SWE-NSW GER-BRE • .-4 -3 -2 -1 1 BMI (kg/m2| Women 35-64 y -2.40 (-3.10, -1.85) POL-WAR [ ~ I -2.87 (-3.56, -2.10) -1.99 (-2.96, -1,36) CZE-CZE ~ } -2.77 (-3.90, -1.81) -1.93 (-2.54, -1.27) GER-AUR ~ -2.55 (-3.62, -0.79) -1.82 (-2.49, -1.28) GER-HAC -2.47 (-3.39, -0.70) -1.78 (-2.61, -0.82) BEL-GHE -2.19 (-3.39, -0.76) -1.76 (-2.59, -0.83) RUS-NOI , -2.13 (-7.55, 5.091 -1.64 (-2.83, -0.81) RUS-NOC 1 -2.09 (-5.88, 1.48) -1.63 (-2.30, -1.22) FRA-STR --F---- -2.06 (-2.85, -0.77) -1.59 (-2.43, -0.02) YUG-NOS ~ -2.00 (-2.94, -0.84) -1.46 (-2.25, -0.63) RUS-MOI ---+---- -1.97 (-3.01, -0.69) -1.42 (-1.92, -0.90) FIN-NKA ~ -1.94 (-3.30, -0.61) -1.29 ( -1.85, -0.33) SPA-CAT ~ -1.88 (-3.63, -0.87) -1.03 (-1.82, ,-0.32) ~ LTU-KA.U I -1.74 (-5.38, 3.51) -0.97 (-1.86, 0.02) '~ ¢ SWE-NSW ~ -1.65 (-2.70,-0.64) -0.92 (1.60, 0.141 ¢ o POL-TAR ~ -1.66 (-3.19, -0.611 -0.92 (-1.65, -0.32) ~. '~ FRA-TOU ~ -1.48 (-2.27, -0.26) -0.67 (-1.20, -0.10) ~ UNK-BEL ---P.-- -1.33 (-1.93, -0.36) -0.59 (-1.75, 0.53) ~ GER-RDM ~ -1.24 (-2.24, -0.63) -0.55 (-1.20, 0.22) ~ USA-STA ~ -1.22 (-2.11, -0.09) (-1.47, 0.62) ~ DEN-GLO -+- -1.12 (-1.66, -0.57) -0.66 -0.52 (-1.33, 0.49) E3 AUS--NEW .----F.-- -1.08 (-2.17, -0.13) -0.45 (-1.21, 0.43) GER-RHN ~ -1.04 (-1.64, -0.47) -0,34 (-1.09, 0.23) FIN-KUO ~ - ' -0.95 (-2.71, 0.37) -0.33 (-0.94, 0.35) NF..Z-AUC --+-- -0.94 (-1.60, .-0.06) -0.30 (-0.88, 0.48) ITA-FRI ~ -0.92 (-2.25, 0 22) --0.29 (-0.87, 0.38) UNK-GLA ~ - ~--0.88 (-2.25, 0.2 I) -0.27 (-0.68, 0.26) GER-BRE ~ -0.86 (-1.93,-0.16) -0.16 (-1.33, 0.52) SWE-GOT ~ -0.74 (-1.76, -0.09) --.0.11 (-0.83, 0.45) FRA-LIL -----+- -0.72 (-2.33, 0.24) 0.01 (-0.50, 0.73) ITA.-BRI ~ - -0.65 (-1.62, 0.47) 0.04 (-0.92, 0.60) RUS-MOC ------.t- -- -0.58 (-2.49, 1.29) 0.28 (-0.16, 0.81) GER-AUU ~ -- -0.57 (-1.44, 0.65) 0.45 (-0.61, 1.52) AUS-PER -- -- -0.07 (-0.91, 1.11) 0.47 (--0.76, 1.23) GER-COT ..... -0.06 (-1.63, 1.93) BMI (kg/m2) Figure 1 Difference in median BMI between regular cigarette smokers and never smokers in the first MONICA survey. Left, men aged 35-64; right, women aged 35-64. lations and less common in eastern Europe. There were more female than male smokers only in Iceland (where 22% of men smoked pipes or cigars) and in Sweden. The prevalence of obesity ranged from 3%-22% in men and from 9%-45% in women and was relatively more common in populations with a low pre- valence of smoking, especially among women. Table 2 presents Pearson correlation co- efficients between the proportion of regular cigarette smokers and BMI. These are eco- logical correlations where each population rep- resents one observation. For women, smoking was significantly inversely related to BMI for all four measures~10th centile (leanness), mean and median Bh4I (average weight) or 90th centile (obesity). For men, the age standardised prevalence of smoking was significantly in- versely related to the 10th centile only. For both men and women the weakest correlations were observed in the age group 35-44 years. Figure 1 shows differences in median BMI between never smokers and regular cigarette smokers. In almost all populations smokers were leaner than never smokers--the difference was statistically significant in 20 out of 42 populations for men and in 30 out of 42 popu- lations for women. The differences ranged from -2.4 to 0.5 kg/mz in men and from -2.9 to --0.1 kg/mz in women. When translated into kg for average heights of 1.72 m and 1.60 m for men and women respectively, they cor- respond to the range from -7.1 to 1.5 kg for men and from -7.4 to -0.3 kg for women. The largest differences were observed in popu- lations with relatively high smoking rates (eg in some eastern European populations). To elucidate further the difference between the populations where the smokers were con- siderably leaner than never smokers in com- parison to populations where they were not, we compared the proportion of regular smokers in the 14 populations with the largest differ- ences in BMI to the 14 populations with the smallest differences in BMI between smokers and never smokers with a non-parametric (Wil- coxon rank sum) test (table 3). Among men, there were significantly more regular smokers in the populations with the largest differences in BMI than in the populations with the smallest differences. In addition, the proportions of ex- smokers were statistically significantly lower in these populations. For women, however, there were fewer smokers in the group of populations with the largest differences in BMI than in the populations with the smallest differences but the difference in smoking prevalehces was not statistically significant. The prevalence of ex-

2~6 Molarius, Seidell, Kuulasmaa, et al Table 3 Proportions of regular smokers and ex-smokers in 14 populations with the largest difference in BMI be~oeen. smokers and never smokers compared with 14 populations with the smallest difference. First MONIGA survgy, men and women aged 35-64 Range for difference in BMI between Median % of p value Median % of p ~aoIue No smokem attd never smokers (kg/ra~) regular smokers ex-smok~es Largest difference -2.4, - 1.3 47 23 14 <0.001 0.03 Smallest difference -0.5, 0.5 33 29 14 Women Largest difference - 2.9, - 1.8 14 7 14 0.07 0.02 Smallest difference -- 1.1, -0.1 22 10 14 smokers was significantly lower in the popu- lations with large differences in BMI. Figure 2 shows the difference in median BMI between never smokers and ex-smokers. smokers had higher BMI than never smokers in 37 (and significantly so in 10) out of 42 populations among men, whereas for women there were differences in both directions but few were statistically significant. No systematic differences in BMI were observed between heavy and light smokers in most populations (data not shown). Regression analysis was used to examine the potential confounding effects of SES using population specific tertiles of years of schooling as an indicator. The unadjusted (for SES) ana- Men 35-64 ITA-BRI -.--H- RUS-NOI .--H- FRA-STR "-~ SWI-VAF BEL-GHE --P- LTU-KAU -- NEZ-AUC POL-WAR -- ITA-FRI -- SWE--GOT -- GER-HAC -- USA-STA ------ ROM-BUC RUS-MOI .H- AUS-NEW +- FRA-LIL -H,-- ICE-ICE ++~ BEL-LUX e- SWI-TIC .o_ SPA-CAT ~-~ ~ RUS-MOC -~-- ~ . SWE-NSW -++- RUS.-NOC -H--- o. POL-TAR ' -',-- YUG-NOS UNK-BEL GER-AUU -~- GER-AUR GER-RDM GER-KMS UNK-.GLA FRA-TOU .+- CZE-CZE GER-COT i DEN-GLO ' -+- FIN-TUL GER-BRE CHN-BEI AUS-PER --+- FIN-KUO GER-RHN -4- FIN-NKA -4- I,l,l,l,l,l,l,l, -4-3-2-1 0 1 2 BMI (kg/m q then for a subset of 34 populations, for which data on years of schooling were available, and then the SES adjusted analysis was performed for the 34 populations (table 4). The results were very similar whether adjusted for tertiles of years of schooling or not, indicating that SES had hardly any confounding effect on this association. The mean BMI in the never smoking cat- egory was 26.6 g/m2 for men and 26.8 kg/m2 population. In men, regular cigarette smokers were on average 0.9 kg/m~ leaner than never smokers, which implies that a male smoker of average height of 1.72 m weighed 2.7 kg less Women 35-64 y -0.35 (-1.39, 0.26) SPA-CAT i -3.28 (-4.81, -0.49) -0.34 (-1.50, 0.47) ITA-BRI -1.93 (-3.01, 0.77) -0.23 (-0.65, 0.56) FIN-KUO -1.42 ( -2.46, -0,34) -0.12 (-1.07, 0.69) GER-AUR -1.30 (-2.44, 0,33) -0.05 (-0.99, 0.99) SWI-TIC -1.25 (-2,30, 0.07) 0.02 (-0.72, 0.86) FRA-STR -1.21 (-3,39, 0.61) 0.04 (-0.58, 0.58) FIN-NKA -1.09 (-2.20, 0.41) 0.06 (-0,81, 0.61) RUS-MOC -0.98 (-2.64, 2.88) 0.09 (-0.76, 0.89) ITA-FRI -0.93 (-2,77, 0.88) 0.10 (-0.85, 0.81) GER--HAC -0.89 (-2,18, 2.32) 0.16 (-0.72, 0.87) GER-RHN -0.82 (-1,93, 0.10) 0.18 (-0.77, 1.59) BEL-GHE -0,79 (-1.93, 2.05) 0.25 (-1.51, 1.55) SWI-VAF -0.74 (-2,25, 1.59) 0.40 (-0.15, 1.08) A RUS--MOI !-0.65 (-2,95, 2.58) 0.49 (-0.05, 1.14) ~ FRA-TOU -0.64 (1.47, 0.68) 0.50 (-0.36, 1.33) ~ GER-AUU -0.58 (-1.98, 0.26) 0.51 (-0.26, 1.77) Lo POL-TAR -0.50 (-3,24, 1.38) 0.51 (-0.37, 1.45) ~'~ POL-WAR -0.44 (-1,44, 0.78) 0.51 (-0.33, 1.56) ~ ,'-" 8EL-LUX -0.42 (-1,44, 0.53) 0.56 (-0.19, 1.44) ._m ._o CZE-CZE -0.42 {-2,84, 1.65) 0.56 (-0.04, 1.33) '~ _,~ AUS-NEW -0.28 (-1.28, 1.06) 0.58 (-0.53, 1.45) F: ~.. AUS-PER -0.23 (-1.31, 1.10) 0.60 (-0.67, 1.42) "- 0 UNK-BEL -.0.20 (-1.25, 0.90) 0.62 (-0.08, 1.38) ~ SWE-GOT -0.16 (-1.19, 0.96) 0.68 (--0.35, 1.92) c: FIN-TUL -0.13 (-1.34, 0.77) 0.76 (-0.20, 1.56) P GER-KMS --0.05 (-2,16, 1.91) 0.78 (--0.04, 1.43) ~ SWE-NSW 0.01 (-2.02, 1.13) 0.78 (0.07, 1.28) Pi RUS-NOI 0.03 (-3.12, 5.53) 0.78 (-0.35, 1.71) ICE-ICE 0.14 (-0.95, 1.57) 0.80 (0.06, 1.50) DEN-GLO 0.25 (-0.61, 1.48) 0.80 (-0.86, 1.64) GER-RDM 0.26 (-0.89, 1.68) 0.81 (0.38, 1.48) GER-BRE ---- -- 0.29 (-1.65, 1.34) 0.87 (0.19, 1.75) NEZ-AUC .~ 0.46 (-0.66, 1.61) 0.92 (-0.07, 2.16) YUG-NOS ~ 0.63 (-2.18, 3.36) 0.93 (0.31, 1.66) LTU-KAU I 0.64 (-4.24, 5.87) 1.06 (0.31, 1.83) UNK--GLA -- ~ 0.67 (-1.29, 2.17) 1.12 (-0.05, 2.03) USA-STA ~ ~ 0.70 (-0.89, 2.75) 1.26 (-1.86, 3.35) GER-COT - ----t----- 1.82 (-0.48, 4.23) 1.27 (0.31, 1.85) ROM-BUC [ 1.99 (-5.62, 4.16) 1.32 (0.57, 2.02) FRA-LIL I 2.25 (-0.85, 4.64) 1.46 10.96, 2.05) RUS-NOC I 2.77 (-1.70, 4.64) 1.47 (0.81, 2.10} CHN-BEI I 3.17 (-6.16, 17.98) -4-3-2-10 1 2 3 4 5 BMI (kg/m~) Fig~tre 2 Difference in median 83 fI between ex-smokers and never smokers in the first MONICA sumey. Lej~, men aged 35-64; Hglzt, women aged 35-64.

Smoking and body weight in MONIC~d 257 Table 4 Summary measures of BMI in relation ~ smoking category. Results fiom regress.ion analysis. First MONIC~t survey, men and women aged 35-64 Mean BMI (95% Cl) adjusted for age group and population Unadjusted for SES* Unadjusted for SESt Adjusted for SESt Men Never smokers - 26.6 (26.5,26.6) Difference between never smoker~ and Regular cigarette smokers --0.9 (-- 1.0,--0.8) Light smokers --0.9 (-- 1.0,--0.7) Heavy smokers -0.9 (- 1.0,-0.7) Ex-~moker~ 0.5 (0.4,0.6) Women Never smokers 26.8 (26.7,26.9) Difference between never smokers and Regular cigarette smokers -- 1.1 (- 1.3,-- 1.0) Light smokers -- 1.3 (-- 1.4,-- 1.1) Heavy smokers --0.8 (-- 1.0,-0.6) Ex-smokers -0.03 (--0.2,0.2) 26.6(26.5,26.7) 26.6(26.5,26.7) -0.9 (-1.0,-0.8) -1.0 (- 1.1,-0.9) -0.9 (-1.0,-0.8) -0.9 (- 1.1,-0.8) -0.9 (- 1.1,-o.s) -1.0 (-1.1,-0.9) 0.5 (0.4,0.6) 0.5 (0.4,0.6) 26.9 (26.9,27.0) 26.9 (26.8,26.9) -1.2 (-1.4,- 1.I) -1.2 (- 1.3,-1.0) --1.4 (-1.5,- 1.2) -1.3 (-1.5,-I.1) --0.9 (--1.1,-0.7) -0.9 (-1.1,-0.7) -0.05 (-0.3,0.2) 0.1 (-0.1,0.3) Socioeconomic status (SES) measured with population, gender, and age group specific tettiles of years of schooling * Based on data from 42 populations "i" Based on data from 34 populations than a never smoker of the same height. Male ex-smokers had 0.Skg/m2 higher BM.I than never smokers indicating that an ex-smoker of average height weighed 1.5 kg more than never smoker. In women, regular cigarette smokers were on average 1.i kg/m2 leaner than never smokers which implies a difference of 2.8 kg for a woman of average height of 1.60 m, but there was no significant difference between never and ex-smokers. For women, but not for men, light smokers had significantly lower Bi.Is than heavy smokers thus showing a U- shaped relationship between smoking and BML The overall estimates of the differences in BMI between smoking categories were also calculated using non-parametric methods. The estimates based on medians were very similar to those produced by the regression analysis. Only the median BMIs for never smokers (26.3 and 26.1 kg/m2 for men and women re- spectively) were somewhat lower than the means, especially for women, due to the skew- ness of the distributions. The age standardized proportion of regular smokers decreased consistendy with increasing BMI category (table 5). The difference between BMI categories was significant in 35 out of 42 populations among men and in 26 among women. In men the differences were larger than in women. Some exceptions to the general pattern were observed, for example among men in Auckland, Gothenburg, Toulouse, and northern Sweden there were more smokers in the obese than in the normal weight category, Table 5 Age standardised prevalence of regular cigarette smoking in relation to BMI category based on data from 42 populations. First MONIGA suwey, men and women aged 35-64 B2~fl category Proportion (°.,6) of smokers Men Lean (BMI<21.0) 61.8 (5624, 67.2) Normal weight (BMI=21.0-24.9) 45.6 (41.8, 49.3) Overweight (BM[= 25.0-29.9) 35.2 (32.8, 37.6) Obese (BMI>=30.0) 31.8 (29.5, 34.1) Women Lean (BMI<21.0) 30.0 (26.0, 34.0) Normal weight (BMI=21.0-24.9) 22.8 (19.3, 26.4) Overweight (BMI =25.0=29.9) 18.0 (14.8, 21.2) Obese (BMI> = 30.0) 13.9 (11.3, 16.5) but the exceptions were usually not statistically significant. On the basis of these results one could group the populations into two categories. In most populations for men and almost all for women the "classic" inverse association between smok- ing and BM_I was observed. In some popu- lations, there was no clear association. These include at least Auckland, Gothenburg, Tou- louse, and northern Sweden for men and per- haps Cottbus County and Perth for women. Discussion The association between smoking and relative body weight is an important health issue be- cause both smoking and increased body weight are independent risk factors for cardiovascular disease and quitting smoking is known to lead to weight gain. In addition, smoking is a po- tential confounder in the relationship between relative body weight and mortality.823 There- fore the recent suggestion that the relationship might be changing from a negative association to a positive one,16 especially among men, prompted us to explore this association in a wide range of populations. The data collected through the WHO MONICA project popu- lation surveys provided a unique opportunity to look at this relationship in a large number of populations from different parts of the world, based on common standardised survey methods for data collection and quality as- surance, and centralised data analysis. Our results show that the generally accepted funding that smokers weigh less than never smokers,t" still prevails in most populations. This was especially true for women. Also, a U- shaped relationship between BMI and number of cigarettes smoked was found among women but not among men, whereas earlier in- vestigations have generally found a stronger relationship in men.4916 ts "l~liS could be partly explained by the fact that we only used two categories for numbers of cigarettes smoked. Among men, in some of the. study popu- lations there was no association between smok- ing and BMI and in these populations there 0 03 o

258 were in general fewer smokers and more ex- smokers than in populations where smokers were considerably leaner than never smokers. This finding suggests that the magnitude of the inverse association between smoking and body weight may be related to the prevalence of smoking in the population. It also partly sup- ports the original hypothesis that the "classical" inverse association might no longer be found in populations with extensive anti-smoking ac- tivities and reduced prevalence of smoking, eg in Australia, Finland, Sweden, the USA. However, no statistically significant positive association was found in any of these popu- lations. Therefore it would be premature to draw any definitive conclusions about a change in the direction of the relationship, especially because this study was based on cross sectional data and reflects the situation in the early and mid 1980s. More recent data, covering a longer time period, will allow this hypothesis to be tested directly. One mechanism by which the change from inverse to positive correlation between smoking "and BMI observed in the Finnish study,t6 might act is through selection among smokers. As an increasing proportion of light smokers tend to quit smoking when smoking becomes regarded as socially undesirable behaviour, the group of smokers consists increasingly of heavy smokers, who on one hand have more difficulties in quitting,~7 and who on the other hand have higher BMIs than light smokers.1~9~7 The change in the association from inverse to posi- tive would therefore be only an ecological change at the population level since the relative body weight of the heavy smokers at individual level need not have cbanged. The lack of an inverse association between smoking and BMI is more often seen among younger men than among older men or women. This might be partly explained because the decline in body weight is a long term affect of smoking, whereas the slightly higher BMI observed in heavy smokers may be unrelated to the duration of smoking. This is, in fact, in agreement with the findings of the Finnish study where, in spite of the overall positive association, years of smoking was confirmed as a significant inverse predictor of BMI.~ The effect of duration of smoking on body weight can however be an indirect one; it is better recognised in older people whose weights have a bigger range than in the young. The reasons for higher BMI of heavy smokers remain unclear. Clustering of unhealthy habits,~ and use of smoking as a way to control body weight among obese people,4 have been suggested as potential explanations, but no studies have been conducted specifically to explore this phenomenon. When looking at the prevalence of smoking between different BMI categories, the most consistent inverse association was found in re- lation to leanness, especially among men. This is supported by earlier research,s and suggests that even if, in some populations, average body weight might be positively associated ~vith smoking, leanness remains inversely associated with cigarette smoking. Our data did not allow us to investigate the association between and duration of smoking. This might have further elucidated the differences between populations, because mean age of starting to smoke may differ among populations and this, too, could affect the distribution of BMI. Some studies have found ex-smokers to be heavier than never smokers,4 ~o whereas others have not. 3~ Our findings suggest that, among men, ex-smokers tend to have higher BMI than never smokers, but not among women and this finding is supported by one earlier study.~1 Also Flegal eta/,~4 found that male ex-smokers were heavier than never smokers, but among women only those ex-smokers who had stopped smok- ing less than 10 years ago were heavier. The category of occasional cigarette smokers, pipe, and cigar smokers was not compared with never smokers in this study because of the small number of observations. Socioeconomic status (SES) is a potential confounder in the relationship between smok- ing and body weight. Persons with lower SES tend to smoke more,9~s and to have higher BMIs,9~ ~s than those with higher SES, the latter especially among women. The as- sociations found in this study were not ex- plained by the effects of SES measured in textiles of years of schooling. This is consistent with the results of several other studies.3~9~ We did not measure such potential confounders as physical activity~ caloric intake, and alcohol use, but in several studies they have not been found to be actual confounders,~s for the BMI-smoking relationship. This work is one example how large inter- national multi-centre studies can be used to obtain an overview strengthened by stand- ardised methods of data collection and quality assurance. One should, however, be cautious in applying quantitative measures obtained by combining data from heterogenous popu- lations. Nevetxheless, the consistency of as- sociations observed among a large number of different populations gives considerably more weight to the findings than results based only on one cohort or study population which cannot be directly generalised to other populations. In summary, in populations of the WHO MONICA project covering a wide range of smoking habits and prevalence of overweight, men and women who smoked generally had lower BMIs than never smokers. Among men, the difference was more pronounced in popu- lations where smoking was relatively more com- mon. Heavy smokers did not generally have lower BMIs than light smokers. Among men, but not among women, those who had stopped smoking had higher BMIs than those who never smoked. These results confirm that smoking is associated with relative body weight in in- dividuals as well as in populations but that differences in smoking habits in a population can influence the magnitude of this association. Funding: MONICA Centres are funded predominantly by regional and national governments, research councils, and re- search charities. Coordination is the responsibility of the World Health Organization (WHO), assisted by local fund raising for congresses and workshops. WHO also supports the MONICA Data Centre (MDC) in Helsinki. Not covered by this general description is the ongoing generous support of the MDC by the National Public Health Institute of Finland, and a con- lribution to WHO from the National Heart, Lung, and Blood

Smoking and body weight in MONIGA Institute, National Institutes of Health, Bethesda, Maryland, USA for support of the MDC. Conflicts of interest: none. 1 K_hosla T, Lowe CR. Obesity and smoking habit*. BMJ 1971;4:10-13. 2 Gordon T, Katmel WB, Dawbcr TR, McGee D. Changes associated with quitting cigarette smoking: the Fram- ingham study. Am Heart y 1975;90:322-28. 3 Noppa H, Bengt*son C. Obesity in relation to smoking: a population study of women in G6tcborg, Sweden. Prey Med 1980;9:534-43. 4 ]'acobs DR Jr, Gottenborg S. Smoking and weight: the Minnesota Lipid Research Clinic. Am J Public Health 1981;71:391-96. 5 Albanes DJones DY, Micozzi MS, Mattson M. Associations between smoking and body weight in the U.S. popu- lation-analysis ofNHANES II..4mJPublic Health 1987; 77:439-44. 6 Kxomhout D, Saris W'H, Horst CH. Energy intake, energy ~xpenditure and smoking in relation to body lamest: the Zutphen study. Am y Clin Nutr 1988;47:668-74. 7 Shimokata H, Muller DC, Andres R. 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Hobbs Key persormel--K Jamrozik, P L Thompson, BK Armstrong University of Newcastle, Newcastle Principal Investigator---A Dobson Key personnel--H Alexander, R Heller Belgium Ghent State University, Ghent Principal Investlgator--G de Backer Key persormel--I De Cmene, P Van Onsem, L Van Parys Interuniversity Association for the Prevention of Cardiovascular Diseases, Brussels Principal Investigator--M Jeanjean Key personnel--C Brohet, H Kulberms, S Degre China Beijing Heart, Lung and Blood Vessel Research Institute, Beijing Principal Investigator--Wu Zhaosu Former Principal Invesdgator--Wu Ying-Kal Key personnel for risk factor surveys--Yao Chonghua, Zhang Ruisong Czech Republic Institute for Clinical and Experimental Medicine, Prague Principal Investlgamr--Z Skodovfi Key persormel--Z Pisa, L Berka, Z Cicha, R Emrovfi, J Pikhartovfi, P Voitisek, Wiesner Denmark Copenhagen University Hospital, Glostrup Principal Investigator--M Schroll Key personnel--M Kirchhoff, A Si~l, T genscn Finland National Public Health Institute, Helsinld Principal Investigator--~ Tuomflehto Former Principal Investigator--P Puska Key personnel for risk factor surveys--C-G Gref, H Korhonen, M ]'auhiainen France Coun~-y Coordinator--~ Richard National Institute of Health and Medical Re- search (U258), Paris Key personnel--A Bingham National Institute of Health and Medical Re- search (I'NSERM 326)~ Toulouse Principal Investigators--JP Cambou, J Ferrieres Key personnel--1-B Ruidavets Institute ofHygienc~Faculty ofMedicine~ Stras- bourg Principal Investigators--D Arveiler, P Schaffer Key personnel--I Escudero, V Baas Pasteur Institute and Study and Research Group on Myocardial Infarction, Lille Principal Investigators--P Amouyel, M Mon- taye-Faivre Former Principal Investigators--j'-L Salomez, M-C Nuttens Key personnel--N Marecaux, C Steclebout Germany GSF-Institute of Epidemiology, Neuherberg/ Munich Principal Investlgator--U Keil Key personnel--J Stieber, A DSring, B Filipiak, U H~rtel, HW Hense Centre for Epidemiology & Health Research, Berlin (from October 1990 Previously German Democratic Republic) Principal Investigators---W Barth, 1". Heinemann Key personnel--A Assmann, S B6thig, G Voigt, S Brasche, D Quietsch, E Classen

260 Bremer Institute for Prevention Research and Social Medicine, Bremen Principal Investigator--E Greiser Co-Principal Investigator~B Herman Key personnel--G Studemann Department of Clinical and Social Medicine of the University Medical Clinic, Heidelberg Principal Investigator--E Nussel Former Co-Principal Invesfigator--E Ostor- Key persormel--R Scheidt, W Morgenstem, M Stadler Iceland Heart Preventive Clinic, Reykjavik Principal Investigator--N Sigffsson Key persormel--II Gudmundsd6ttir, I Ste- f~nsd6ttir, Th Thorsteinsson, H Sigvaldason Italy Institute of Cardiology, Regional Hospital, Udine Principal Investigator---GA Ferugiio Key personnel--D Vanuzzo, L Pilotto, G Cig- nacco, M Scarpa, M Palmieri, M Spanghero, R Marini, G Zilio University of Milan, Institute of Occupational Health, Milan Principal Invesfigators--43C Cesana, M Ferratio Key personnel--R Sega, P Mocarelli, G DeVito Lthuania Kaunas Medical Academy Institute of Car- diology, Kaunas Principal Invesfigator--J Bluzhas Key personnel for risk factor survey---S Do- markiene, A Tamosiunas, R Reklaitiene New Zealand University of Auckland, Auckland Principal Invesfigator--R Beaglehole Key personnel--R Jackson, R Bonita, A Stewart, • D Mahon, W Bingiey Poland Unit of Clinical Epidemiology and Population Studies, School of Public Health, Jagiellonian University, Krakow Principal Investigator--A Pajak Former Principal InvesfigatornJ Sznaid Key personnel--E Kawalec, T Pazucha, M Nial- czewska, R Momwski, A Celinskl, U Zeman National Institute of Cardiology, Warsaw, De- partment of Cardiovascular Epidemiology and Prevention Principal Invesfigator--SL Rywik Key personnel~ Broda (coordinator), M Po- lakowska, P Kurjata, H Wagrowska Romania Medical Institute, Fundeni Hospital, Bucharest Principal Investigators--C Carp, I Orha Key personnel--E Apetrei, I Coman, M Tarlea Russian Federation State Research Centre for Preventive Medicine, MOSCOW Principal Investigator--TA Varlamova Key personnel--A Britov~ V Konstantinov~ L Pavlova, A AIex~indri, O Konstantinova Institute of Internal Medicine, Novosibirsk Principal Investigator--YuP Nikitin Key personnel--S Malyutina, I Shalaurova Spain Institute of Health Studies, Department of Health and Social Security, Barcelona Molarius, Seidell~ Kuulasmaa, et al Principal Investigators--S Sans, I Balaguer-Vin- tr6 Key personnel--LI Balanfi, G Paluzie, T Puig Sweden Department of Medicine, Ostra Hospital, teborg Principal Investigator--L Wilhelmsen Key personnel--S Johansson, S Piros, G Lappas, Ume~ University Hospital, Lule~-Boden Hos- pital and Kalix Hospital, Departments of Medi- cine Principal Investigator--K Asplund, F Hulatasaari Key personnel--B Stegmayr, V Lundberg Switzerland University Institute of Social and Preventive Medicine, Lausanne Principal Investigator--F Gutzwiller (ZC~ich) Key personnel--M Rickenbach, V Wietlisbach, F Barazzoni, D Hausser United Kingdom The Queen's University of Belfast, Belfast, Northern Ireland Principal Investigator--A Evans Key personnel--E McCrum, T Falconer, S Cash- man University of Dundee, Dundee, Scotland Principal Investigator--H Tunstall-Pedoe Former Co-Principal Investigator (Population Surveys)--WCS Smith Key personnel--R Tavendale, K Barrett, C Brown Former key personnel--I Crombie, M Kenicer USA Stanford Center for Research in Disease Pre- vention, Stanford, California Principal Investigator--SP Fortmann Key personnel--A Varady, M Hull, JW Farquhar Yugoslavia Health Centre "Novi Sad", Novi Sad Principal Investigator--M Piano/eric Former Principal Investigator--D Jakovljevic Key personnel--A Svircevic, M MJrilov, T Strasser II MONICA MANAGEMENT CENTRE--GENEVA World Health Organization, Geneva Responsible Officer--I Gyarfas Former Responsible Officers--Z Pisa, SRA Dod~, S B6thig Key personnel--I Martin, MJ Watson, M Hill Ill MONICA DATA CENTRE--HELSINKI National Public Health Institute, Helsinki, Fin- land Responsible Officer---K Kuulasmaa Former Responsible Officer--J Tuomilehto Key personnel--A-M Koivisto, A Molarius, V Moltchanov, E Ruokokoskl IV MONICA STEERING COMMITTEE A Evans (Chair), M Hobbs (Chair Publications SubCommittee), M Ferrario, H Tunstall-Pedoe CRapporteur), I Gyarfas, K Kuulasmaa, A Shatchkute (WHO, Copenhagen), Consultants--A Dobson, Z Pisa, and OD Wil- liams IV PREVIOUS STEERING COMMITTEE MEMBERS S Sans, F Gutzwiller, SP Fort_mann, A Menotti, P Puska, SL Rywik, U Keil, R Beaglehole, former chiefs of CVD/HOo Geneva, V Zaitsev, J Tuo- milehto Former Consultants--MJ Karvonen, R~ Prineas, M Feinleib, FH Epstein