Eating out, weight and weight gain. A cross-sectional and prospective analysis in the context of the EPIC-PANACEA study

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1 (2011) 35, & 2011 Macmillan Publishers Limited All rights reserved /11 ORIGINAL ARTICLE Eating out, weight and weight gain. A cross-sectional and prospective analysis in the context of the EPIC-PANACEA study A Naska 1,2, P Orfanos 1,2, A Trichopoulou 1,2, AM May 3,4, K Overvad 5,6, MU Jakobsen 5, A Tjønneland 7, J Halkjær 7, G Fagherazzi 8, F Clavel-Chapelon 8, M-C Boutron-Ruault 8, S Rohrmann 9, S Hermann 9, A Steffen 10, J Haubrock 10, E Oikonomou 1,2, V Dilis 1,2, M Katsoulis 1,2, C Sacerdote 11, S Sieri 12, G Masala 13, R Tumino 14, A Mattiello 15, HB Bueno-de-Mesquita 4, G Skeie 16, D Engeset 16, A Barricarte 17,18, L Rodríguez 19, M Dorronsoro 17,20, M-J Sánchez 17,21, M-D Chirlaque 17,22, A Agudo 23, J Manjer 24, E Wirfält 25, V Hellström 26, D Shungin 26,27, K-T Khaw 28, NJ Wareham 29, EA Spencer 30, H Freisling 31, N Slimani 31, A-C Vergnaud 32, T Mouw 32, D Romaguera 32, A Odysseos 33 and PHM Peeters 3,32 1 Hellenic Health Foundation, Athens, Greece; 2 Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine, University of Athens, Athens, Greece; 3 Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands; 4 National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands; 5 Department of Clinical Epidemiology, Aarhus University, Aalborg, Denmark; 6 Department of Cardiology, Aalborg Hospital, Aarhus University Hospital, Aalborg, Denmark; 7 Institute of Cancer Epidemiology, Danish Cancer Society, Copenhagen, Denmark; 8 INSERM, UMRS 1018, Centre for Research in Epidemiology and Population Health, Paris South University, Institut Gustave Roussy, Villejuif, France; 9 Nutritional Epidemiology Unit, Division of Clinical Epidemiology, German Cancer Research Center, Heidelberg, Germany; 10 Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Potsdam-Rehbrücke, Germany; 11 Center for Cancer Prevention (CPO-Piemonte), University of Torino, Torino, Italy; 12 Nutritional Epidemiology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy; 13 Molecular and Nutritional Epidemiology Unit, ISPO Cancer Research and Prevention Institute, Florence, Italy; 14 Cancer Registry and Histopathology Unit, Civile M.P.Arezzo Hospital, Ragusa, Italy; 15 Department of Clinical and Experimental Medicine, Federico II University, Naples, Italy; 16 Department of Community Medicine, University of Tromsø, Tromsø, Norway; 17 CIBER Epidemiología y Salud Pública, Barcelona, Spain; 18 Epidemiology, Prevention and Promotion Health Service, Public Health Institute of Navarra, Pamplona, Spain; 19 Public Health and Participation Directorate, Health and Health Care Services Council, Asturias, Spain; 20 Public Health Division of Gipuzkoa, Gipuzkoa, Spain; 21 Granada Cancer Registry, Andalusian School of Public Health, Granada, Spain; 22 Department of Epidemiology, Murcia Regional Health Authority, Murcia, Spain; 23 Unit of Nutrition Environment and Cancer, Catalan Institute of Oncology, Barcelona, Spain; 24 Department of Surgery, Malmø University Hospital, Lund University, Malmø, Sweden; 25 Department of Clinical Sciences in Malmö/Nutrition Epidemiology, Lund University, Malmö, Sweden; 26 Department of Public Health and Clinical Medicine, Umeå University, Umeå,Sweden; 27 Department of Odontology, Umeå University, Umeå, Sweden; 28 Clinical Gerontology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; 29 MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK; 30 Cancer Research UK Epidemiology Unit, University of Oxford, Oxford, UK; 31 Dietary Exposure Assessment Group, Nutrition and Metabolism Section, International Agency for Research on Cancer, Lyon, France; 32 Department of Epidemiology and Public Health, Imperial College, London, UK and 33 EPOS-Iasis Research and Development, Ltd, Nicosia, Cyprus Objective: The aim of this study was to examine the association of body mass index (BMI) and weight gain with eating at restaurants and similar establishments or eating at work among 10 European countries of the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Subjects: This study included a representative sample of randomly selected EPIC participants. Methods: Single 24-h dietary recalls with information on the place of consumption were collected using standardized procedures between 1995 and Eating at restaurants was defined to include all eating and drinking occasions at Correspondence: Dr A Trichopoulou, Department of Hygiene Epidemiology and Medical Statistics, School of Medicine, University of Athens, 75 Mikras Asias Street, Athens 11527, Greece. antonia@nut.uoa.gr Received 29 March 2010; revised 25 May 2010; accepted 9 June 2010; published online 27 July 2010

2 restaurants, cafeterias, bars and fast food outlets. Eating at work included all eating and drinking occasions at the workplace. Associations between eating at restaurants or eating at work and BMI or annual weight changes were assessed using sex-specific linear mixed-effects models, controlling for potential confounders. Results: In southern Europe energy intake at restaurants was higher than intake at work, whereas in northern Europe eating at work appeared to contribute more to the mean daily intake than eating at restaurants. Cross-sectionally, eating at restaurants was found to be positively associated with BMI only among men (b ¼þ0.24, P ¼ 0.003). Essentially no association was found between BMI and eating at work among both genders. In a prospective analysis among men, eating at restaurants was found to be positively, albeit nonsignificantly, associated with weight gain (b ¼þ0.05, P ¼ 0.368). No association was detected between energy intake at restaurants and weight changes, controlling for total energy intake. Conclusion: Among men, eating at restaurants and similar establishments was associated with higher BMI and possibly weight gain. (2011) 35, ; doi: /ijo ; published online 27 July Keywords: eating at restaurants; eating at work; body mass index; weight gain; EPIC-PANACEA Introduction Overweight and obesity are recognized as important public health challenges worldwide. 1,2 In Europe, 30 80% of adults and about 20% of children and adolescents are either overweight or obese and the trend is particularly alarming because the current annual rate of increase is much higher than that of the 1970s. 2 Although obesity is the result of an unbalanced energy intake and expenditure, mechanisms implicate genetic, environmental, socioeconomic, cultural and behavioral factors. Among cultural and behavioral factors, eating out of home has received increasing attention. There are several publications on eating out and obesity that make use of cross-sectional 3 15 and longitudinal data. 5,11,16,17 Results, however, have been inconsistent, probably because of different definitions used and variable study objectives. Eating out was either defined to include all food items prepared at out-of-home locations, irrespective of place of consumption; 3,7 10 or alternatively, all food items consumed at out-of-home locations, irrespective of place of preparation. 14,15,17 20 Furthermore some studies refer to eating out in general; 3,7 10,14,15,17 others focus on restaurant visits, 4,16 and several refer to fast food consumption in particular. 4 6,11,13,16 In terms of outcome, on the other hand, some studies assess associations of eating out with energy and fat intake; 3,10 others with body mass index (BMI) or body weight; 4,11,16 a few with the risk of being obese 14,15 and many with a combination of several of the above. 5 7,9,13,17 We have analyzed data collected among more than men and women in 10 European countries to examine, cross-sectionally, the associations of eating at restaurants and similar establishments or eating at work, in comparison to those not doing so, with BMI. Moreover, we have evaluated prospectively associations between eating at restaurants in conjunction with the corresponding energy intake with weight gain. This analysis was undertaken in the context of the EPIC-PANACEA (European Prospective Investigation into CancerFPhysical Activity, Nutrition, Alcohol, Cessation of Smoking, Eating Out of Home and Obesity) study. Materials Subjects EPIC is a large multicenter prospective cohort study covering about half a million individuals from 23 centers and regions in 10 western European countries (Denmark, France, Germany, Greece, Italy, The Netherlands, Norway, Spain, Sweden and the United Kingdom), designed with the aim to elucidate the role of dietary, biological, lifestyle and environmental factors in the etiology of cancer and other chronic diseases. Study participants were men and women from the general population, with the exception of France, Norway, Naples (Italy) and Utrecht (The Netherlands) where only women were recruited. All procedures have been conducted in accordance with the Declaration of Helsinki Principles and all participants signed an informed consent form before enrollment. Details on the recruitment and study design have been published Data collection at baseline followed standardized procedures that have also been described elsewhere A parallel data collection was undertaken in the context of the EPIC calibration study, which was set up to adjust for possible systematic over- or underestimation of dietary intake measurements and correcting for attenuation bias in relative risk estimates. For these purposes, a single 24-h dietary recall (24-HDR) was collected from a random sample of each EPIC cohort. In total, individuals from the participating countries provided one 24-HDR between 1995 (study initiated in France) and 2000 (study completed in Norway). The design and methods of the EPIC calibration study have been described. 27 This analysis makes use of dietary data collected through the EPIC calibration study. To construct the EPIC-PANACEA sample, we excluded 710 EPIC participants on account of pregnancy at baseline, nonavailability of dietary data and implausible energy intake, height or weight at baseline. 28

3 418 In addition, individuals with missing information in one or more of the variables of interest, or with follow-up time o6 months, or with annual weight change over 5 kg were excluded. To maintain the same age range in all the cohorts, we did not include individuals below 35 and over 74 years of age in this analysis (960 individuals). Thus, analysis relied on individuals, 8712 men and women. Centers or regions were grouped together by country with a single exception: the UK-Oxford group of participants consisting of lacto-ovo vegetarians and vegans was evaluated separately (the UK-health conscious group in contrast to the UK-general population). Data collection Dietary intake. The consumption of foods and beverages was recorded by a single 24-HDR, using the EPIC-SOFT standardized computerized software developed at the International Agency for Research on Cancer in collaboration with the EPIC centers. 29 EPIC-SOFT was administered by trained interviewers through face-to-face interviews, except in Norway where telephone interviews were carried out as a validated alternative approach. 30 Information was collected on all foods and beverages consumed during the period between waking up on the day of recall and waking up on the following day (interview day). For the calculation of energy and nutrient intakes, the EPIC Nutrient Database developed to harmonize nutrient databases across the EPIC participating countries was used. 31 Assessment of out-of-home eating. For each eating (and drinking) occasion recalled in the 24-HDRs, the place of consumption was reported. 19,20 In this analysis, eating at restaurants was defined to include all eating and drinking occasions at restaurants, cafeterias, bars or fast food outlets. In addition, eating at work was defined to include eating occasions at work or school because participants were all adults, including a number of schoolteachers. Eating at work is an ambiguous area in out-of-home eating, as it can include eating at the work canteen or acquiring an item from a shop or a vending machine, but it can also include, as it frequently does, eating or drinking something sourced from the household supplies. Our decision of considering eating at work separately in our analysis and not considering it together with eating at restaurants and similar establishments is dictated by this fact. All other eating and drinking occasions that were recalled to have been consumed at home or at friends houses (and to a far lesser extent at any other of the remaining locations, that is in a street, car/boat and other) were labeled as eating at home, reflecting essentially home eating. For the purpose of this analysis and under the assumption that individuals who did not report consuming any item out of home on the recall day are less likely to eat out frequently than those who reported doing so, three mutually exclusive groups were formed as follows: (1) participants who reported eating at restaurants as previously defined, whether or not they also reported eating at work (eating at restaurants); (2) participants who did not report any consumption at restaurants, but consumed at least one item at work (eating at work) and (3) participants who did not report any consumption at restaurants or at work (eating at home). Assessment of weight and height. Anthropometric data used for the cross-sectional analysis were collected on the day of the 24-HDRs and were mainly self-reported with some exceptions: in Umea (Sweden), height was self-reported and weight was measured; in Malmö (Sweden), only weight was measured and baseline measurements of height were used; in Denmark, Italy, The Netherlands and UK-Cambridge, the measured baseline weight and height were used because the time difference between the two data collections was o1 year (or o7 months for Denmark and The Netherlands). BMI was calculated as the ratio of weight in kilograms divided by the square of the height in meters and participants were initially classified into four categories according to WHO definitions: 1 underweight (BMIo18.5 kg m 2 ), acceptable weight (BMIX18.5 to o25 kg m 2 ), overweight (BMIX25 to o30 kg m 2 ) and obese (BMIX30 kg m 2 ). Because of the small number of individuals in the underweight group (197), the first two groups were merged (BMIo25 kg m 2 ). At follow-up, weight was also self-reported, except in Norfolk (the United Kingdom) and Doetinchem (The Netherlands), where individuals were invited for a second measurement of body weight. As the average follow-up times were different across the study centers (from 1.1 years in regions in France to 9.4 years in Varese, Italy), the annual weight change (kg per year), that is, weight at follow-up minus weight on the day of the 24-HDR interview divided by follow-up time (in years), was used in the prospective analysis. Assessment of potential confounders. Lifestyle, anthropometric and sociodemographic characteristics, including age, weight, height, school level, smoking status, physical activity and occupation, were recorded at baseline. 23,25 School level was classified as none or primary school completed; technical, professional or secondary school completed; and university degree. With respect to smoking status, participants were classified as never, former and current smokers. Physical activity was classified into four categories: inactive, moderately inactive, moderately active and active. 32,33 The information on participants physical activity collected in Umea (Sweden) and Norway was not comparable to the one collected in the other centers and was therefore not included in this analysis. These participants (9.6%) were classified in a separate category. Finally, occupation was used as a dichotomous variable (employed and not employed).

4 Statistical analyses All statistical analyses were performed using the Stata statistical package (Stata/SE 8.0. for Windows; Stata Corporation, College Station, TX, USA) and were run separately for men and women. To allow for differences in data collections, we calculated mean energy intakes through weighted regression models adjusting for age continuously and applying a set of weights taking into account the day of the week (Monday to Thursday, Friday to Sunday) and the season (spring, summer, autumn, winter) recalled in the 24-HDRs. 27,34 For descriptive purposes, percentages of participants eating at restaurants or eating at work in categories of potential confounders, as well as BMI groups based on follow-up anthropometric measurements, were calculated by BMI groups. Cross-sectional analysis. Three-level (individuals within centers within countries), multivariate linear mixed-effects models were fitted using BMI on the day of the 24-HDR as the outcome variable and, alternatively, two dichotomous variables as the main predictors: (1) a variable contrasting those eating at restaurants (whether they were also eating at work) versus those not eating at restaurants (whether they were also eating at work) and (2) after excluding those eating at restaurants, a variable contrasting eating at work versus eating essentially exclusively at home. Random intercepts and slopesfwhen the interaction between the main predictor and center was found significant (Po0.05)Fwere included. In the case of nonsignificant interactions, random intercept mixed models were used assuming a common effect for eating at restaurants or eating at work. The covariance structure used in the mixed models was independent, allowing a distinct variance for each random effect within a random-effects equation. In these models all the aforementioned potential confounders were introduced in the described categories. Age (continuously, per year) and total energy intake (continuously, per 1 standard deviation increment) were also introduced in the models. Forrest plots were further displayed depicting country-specific estimates along with the overall estimate obtained from the mixed models. Prospective analysis. In the prospective analysis, the effect of eating at restaurants and, independently, of the energy consumed in restaurants on annual weight change was evaluated by properly modeling the relevant variables. Thus, one model term described the effect of eating at restaurants and consuming the mean energy intake (centered variable, that is, individual energy intake minus mean energy intake) versus not eating at restaurants, whereas another term described the effect of energy intake among individuals who reported eating at restaurants on the day of the recall. Thus, a variable, say Z1, is coded 1 if participants reported eating at restaurants and 0 if not (that is, the variable used in the cross-sectional analysis). The centered variable, say Z2, is always introduced in conjunction with Z1 (that is, Z1 Z2, even though Z2 does not appear on its own in the model) and aims to estimate the weight gain per increment in energy intake at restaurant(s) among participants who reported eating at restaurants. Accordingly, Z1 (and thus Z1 Z2) take the value of zero among individuals who did not report eating at restaurants; the regression coefficient b1 (of Z1) shows the effect of eating at restaurants and reporting energy intake equal to the mean versus not eating at restaurants (because Z1 Z2 becomes zero when Z2 becomes zero); and the regression coefficient b2 (of Z1 Z2) describes the effect of a certain increment of energy intake (set at 500 kcal) among individuals who reported eating at restaurant(s). 35,36 These model terms were introduced to the multivariate linear mixed-effects models described above to assess the association between eating at restaurants in conjunction with the corresponding energy intake with weight gain. The confounders considered in the cross-sectional analysis were also introduced in the prospective analysis, together with the participants BMI on the day of the recall and follow-up time. In subsequent subgroup analysis, participants were separated into two age groups (below and above 60 years). Analysis was further repeated after excluding under- (and over-) reporters identified using the Goldberg criteria 37 (3355 observations). Results Table 1 presents mean energy intake when eating at restaurants, eating at work and total energy intake, together with the mean BMI and annual weight change of male and female participants, by country. Overall, men appeared to eat proportionally more at restaurants or at workplace than women. In general, among both men and women in southern Europe energy intake at restaurants was higher than intake at the workplace. In central Europe, the two intakes were very similar, whereas in northern Europe eating at work appeared to contribute more to the energy intake than eating at restaurants. Among men, the contribution of eating at restaurants to daily energy intake ranged between 12% (Spain) and 4% (UK-health conscious group) and eating at work contributed between 15% (UK-general population) to 4% (Spain). Among women, contributions were smaller and eating at restaurants provided between 7% (UK-general population) and 3% (Norway) of the daily caloric intake. The contribution of energy intake at work to the total daily energy intake was relatively close to that at restaurants, with the exception of Spain (eating at restaurants provided 7% whereas eating at work only 1% of the daily energy intake) and Nordic countries (eating at restaurants was responsible for about 4% and eating at work for about 11% of the daily caloric intake). In Table 2 the proportional distribution of participants by eating occasion and BMI category on the day of the 24-HDR is presented across personal and lifestyle characteristics, as 419

5 420 Table 1 Mean energy intake (kcal) and contributions to total intake of eating at restaurants and eating at work as well as BMI (±s.d.) and annual weight change (±s.d.) of study participants, by country and gender (The EPIC-PANACEA study) Country Men Women Eating at Eating at BMI c Annual weight restaurants a work b change d Eating at Eating at BMI c Annual weight n Total restaurants a work b change d intake n Total intake Mean Mean Mean Mean (s.d.) Mean (s.d.) Mean Mean Mean Mean (s.d.) Mean (s.d.) Greece Spain Italy France 0 F F F F F F F F F Germany The Netherlands UK-GP UK-HC Denmark Sweden Norway Abbreviations: BMI, body mass index; EPIC-PANACEA, European Prospective Investigation into CancerFPhysical Activity, Nutrition, Alcohol, Cessation of Smoking, Eating Out of Home and Obesity; GP, general population; HC, health conscious. Mean energy intake adjusted for age (continuously) and weighted by season and day of recall. a Eating at restaurants defined to include all eating and drinking occasions at restaurants, bars, cafeterias or fast food outlets. b Eating at work defined to include eating and drinking occasions at workplace. c BMI calculated as kilogram per squared meter (kg m 2 ). d The annual weight change was calculated as weight at follow-up minus weight on the day of the 24-HDR interview divided by follow-up time (years). well as BMI categories at follow-up. The distribution of the participants included in this study by the characteristics indicated in Table 2 was essentially the same to the distribution of the overall study sample by the same characteristics. Although these data are not directly interpretable because of mutual confounding among several characteristics, comparisons of group frequencies show that eating at restaurants was reported more: among overweight or obese men than among their normal weight counterparts; among participants of higher education than among those of lower educational attainment (a pattern mostly driven by women, data not shown) and among current smokers. With respect to eating at work, normal weight women reported eating at work more frequently than overweight or obese ones. Figures 1a, b and 2a, b provide gender-specific estimates of associations under study at country level and overall. Results are presented by gender because there was significant gender by eating at restaurants interactions with respect to BMI (P-value for interaction ¼ 0.012). Figure 1a presents adjusted differences in BMI among male consumers at restaurants (as operationally defined) when compared to participants not reporting any consumption at restaurants. Overall, eating at restaurants was found to be positively associated with BMI among men after controlling for potential confounders (b ¼ þ 0.24, P-value of overall estimate ¼ 0.003, P-value for heterogeneity ¼ 0.141). We repeated the analysis after excluding data from Greece, where a strong positive association was detected and the association overall remained positive (b ¼ þ 0.13), although nonsignificant (P ¼ 0.151). The association remained positive and significant after excluding underreporters 37 (b ¼þ0.26, P ¼ 0.002). When repeating the analysis separating men aged up to 60 years from those aged 60 years and older (P-value for interaction ¼ 0.043), the association between BMI and eating at restaurants was positive, but significant only among older men (o60 years, b ¼ þ 0.13, P ¼ 0.217; X60 years, b ¼ þ 0.47, P ¼ 0.001). Among women (Figure 1b), the association of eating at restaurants and BMI was essentially null and nonsignificant (b ¼ 0.04, P ¼ 0.630). After controlling for potential confounders, no significant association was observed between eating at work and BMI among either men (b ¼ þ 0.01, P ¼ 0.889; Figure 2a) or women (b ¼ 0.09, P ¼ 0.268; Figure 2b). Figure 3 presents adjusted annual changes in body weight per increments of energy intake among men reporting eating at restaurants on the day of the recall. In particular, there is a weak positive but nonsignificant association (b1 ¼þ0.05, P ¼ 0.368) between annual changes in body weight and eating at restaurants with energy intake close to the average of the restaurant eaters. An increase of 500 kcal in energy intake at restaurants was marginally positively related to weight gain (b2 ¼ þ 0.01, P ¼ 0.836), after controlling for potential confounders, including total energy intake. Results concerning weight gain in relation to eating at restaurants among women and results concerning eating at work for either gender were unremarkable (data not shown).

6 421 Table 2 Proportional distribution of study participants by BMI (kg m 2 ) category on the day of the recall and eating occasion across personal and lifestyle characteristics as well as BMI categories at follow-up (The EPIC-PANACEA study) BMI o25 Normal weight 425 to o30 Overweight 430 obese Total n Eating at restaurants a Eating at work b n Eating at restaurants a Eating at work b n Eating at restaurants a Eating at work b n Eating at restaurants a Eating at work b Total Sex Men Women Age (years) o X School level None/primary Technical/professional/secondary University Smoking status Never Former Current Physical activity c Inactive Moderately inactive Moderately active Active Occupation Not employed F F F F Employed BMI categories at follow-up Normal weight F F Overweight Obese 16 F F Abbreviation: EPIC-PANACEA, European Prospective Investigation into CancerFPhysical Activity, Nutrition, Alcohol, Cessation of Smoking, Eating Out of Home and Obesity. a Percentage of individuals who reported consuming at least one item at restaurants, bars, cafeterias or fast food outlets, whether or not they also reported eating at work. b Percentage of individuals who reported consuming at least one item at workplace, but not at restaurants, bars, cafeterias or fast food outlets. c Information collected in Umea (Sweden) and Norway was not included in this analysis.

7 422 a Study ID Greece Spain Italy Germany Netherlands UK-GP UK-HC Denmark Sweden Overall(mixed model) p= (0.46, 1.36) 0.20 (-0.13, 0.52) (-0.67, 0.57) 0.02 (-0.35, 0.40) 0.00 (-0.53, 0.54) 0.49 (-0.50, 1.49) 0.26 (-2.00, 2.53) 0.09 (-0.49, 0.67) 0.10 (-0.32, 0.51) 0.24 (0.08, 0.41) Heterogeneity p=0.141 DIFFERENCE IN BMI FOR CONSUMERS AT RESTAURANTS vs OTHERS / MEN b Study ID Greece Spain Italy France Germany Netherlands UK-GP UK-HC Denmark Sweden Norway Overall(mixed model) p= (-0.99, 0.90) (-0.87, 0.21) 0.13 (-0.39, 0.65) 0.19 (-0.20, 0.59) (-0.79, 0.23) 0.03 (-0.42, 0.47) 0.25 (-0.93, 1.44) (-1.64, 1.34) 0.10 (-0.61, 0.82) (-0.50, 0.47) 0.44 (-0.23, 1.11) (-0.22, 0.13) Heterogeneity p=0.848 DIFFERENCE IN BMI FOR CONSUMERS AT RESTAURANTS vs OTHERS / WOMEN Figure 1 (a) In men, effect estimate (b) and 95% confidence intervals (CIs) of eating at restaurants versus not on BMI by country and overall (derived by mixedeffects linear regression model), controlling for potential confounders (age (continuously, per year), educational level (categorically, none or primary school completed; technical, professional or secondary school completed; and university degree), physical activity level (categorically, inactive, moderately inactive, moderately active, active and unknown), smoking status (categorically, never, former and current smokers), occupation (categorically, employed and not employed) and total energy intake (continuously, per 1 standard deviation increment)). (b) In women, effect estimate (b) and 95% CIs of eating at restaurants versus not on BMI by country and overall (derived by mixed-effects linear regression model), controlling for potential confounders (age (continuously, per year), educational level (categorically, none or primary school completed; technical, professional or secondary school completed; and university degree), physical activity level (categorically, inactive, moderately inactive, moderately active, active and unknown), smoking status (categorically, never, former and current smokers), occupation (categorically, employed and not employed) and total energy intake (continuously, per 1 standard deviation increment)).

8 a Study ID 423 Greece Spain Italy Germany Netherlands UK-GP UK-HC Denmark Sweden Overall(mixed model) p= (-0.44, 0.79) (-0.82, 0.18) 0.43 (-0.67, 1.54) 0.14 (-0.29, 0.57) (-0.76, 0.54) 0.25 (-0.75, 1.25) 3.66 (1.36, 5.96) (-0.37, 0.35) (-0.57, 0.36) 0.01 (-0.17, 0.20) Heterogeneity p=0.107 DIFFERENCE IN BMI FOR CONSUMERS AT WORK ONLY (NOT RESTAURANTS) vs OTHERS / MEN b Study ID Greece Spain Italy France Germany Netherlands UK-GP UK-HC Denmark Sweden Norway Overall(mixed model) p= (-0.99, 0.67) (-1.15, 0.62) 0.51 (-0.22, 1.24) 0.03 (-0.32, 0.39) 0.11 (-0.41, 0.63) (-0.65, 0.36) (-1.65, 0.65) 1.36 (0.08, 2.65) (-0.57, 0.28) (-0.89, 0.00) (-0.74, 0.07) (-0.26, 0.07) Heterogeneity p=0.219 DIFFERENCE IN BMI FOR CONSUMERS AT WORK ONLY (NOT RESTAURANTS) vs OTHERS / WOMEN Figure 2 (a) In men, effect estimate (b) and 95% confidence intervals (CIs) of eating at work only versus eating at home (those eating at restaurants were excluded from the analyses) on BMI by country and overall (derived by mixed-effects linear regression model), controlling for potential confounders (age (continuously, per year), educational level (categorically, none or primary school completed; technical, professional or secondary school completed; and university degree), physical activity level (categorically, inactive, moderately inactive, moderately active, active and unknown), smoking status (categorically, never, former and current smokers) and total energy intake (continuously, per 1 standard deviation increment)). (b) In women, effect estimate (b) and 95% CIs of eating at work versus eating at home (those eating at restaurants were excluded from the analyses) on BMI by country and overall (derived by mixed-effects linear regression model), controlling for potential confounders (age (continuously, per year), educational level (categorically, none or primary school completed; technical, professional or secondary school completed; and university degree), physical activity level (categorically, inactive, moderately inactive, moderately active, active and unknown), smoking status (categorically, never, former and current smokers) and total energy intake (continuously, per 1 standard deviation increment)).

9 424 ID β1 β (-0.04, 0.25) Greece 0.09 (-0.02, 0.20) 0.10 (-0.10, 0.29) Spain (-0.11, 0.06) (-0.20, 0.01) Italy (-0.07, 0.05) (-0.24, 0.06) Germany (-0.11, 0.09) 0.03 (-0.11, 0.17) Netherlands 0.06 (-0.02, 0.14) 0.09 (-0.34, 0.53) UK-GP (-0.44, 0.12) 0.49 (-0.31, 1.29) UK-HC 0.37 (-0.74, 1.49) 0.27 (0.12, 0.41) Denmark 0.01 (-0.06, 0.08) (-0.34, 0.07) Sweden (-0.24, 0.20) p= (-0.05, 0.15) Overall (mixed model) p= (-0.05, 0.06) Heterogeneity p= Heterogeneity p=0.533 DIFFERENCE IN ANNUAL WC FOR AVERAGE CONSUMERS AT RESTAURANTS vs OTHERS DIFFERENCE IN ANNUAL WC FOR 500 Kcal INCREASE OF EATING AT RESTAURANTS AMONG CONSUMERS ONLY Figure 3 In men, effect estimate of eating at restaurants and reporting average energy intake (versus not eating at restaurant) (b1) and of energy intake at restaurants (per 500 kcal increase among restaurant eaters) (b2) and corresponding 95% confidence intervals (CIs) on annual weight change by country and overall (derived by mixed-effects linear regression model), controlling for potential confounders (age (continuously, per year), educational level (categorically, none or primary school completed; technical, professional or secondary school completed; and university degree), physical activity level (categorically, inactive, moderately inactive, moderately active, active and unknown), smoking status (categorically, never, former and current smokers), occupation (categorically, employed and not employed), BMI on the day of the dietary recall (continuously) and follow-up time (continuously, per year) and total energy intake (continuously, per 1 standard deviation increment)). Note: the effect of eating at restaurants and, independently, of the energy consumed in restaurants on annual weight change was evaluated by properly modeling relevant variables. Discussion In a large sample of men and women aged years and participating in the EPIC study, we calculated energy intake obtained from foods consumed at restaurants, bars, cafeterias or fast food outlets (eating at restaurants) or consumed at work using data collected in 10 European countries through highly standardized 24-HDRs. Crosssectionally, BMI was found to be positively associated with eating at restaurants among men particularly older men, after adjusting for sociodemographic and lifestyle factors. When the cross-sectional analysis was repeated after excluding data for Greek men, who reported higher BMI in comparison to men in the other study centers (even though the interaction by center was not statistically significant), the association remained positive albeit not significant. Essentially no association between BMI and eating at restaurants was observed among women, as well as between BMI and eating at work in both genders. In a prospective analysis among men, eating at restaurants was found to be positively, albeit nonsignificantly, associated with weight gain. No association was, however, detected between energy intake at restaurants and weight changes, controlling for total energy intake. In previous work using these data, but with a different definition of eating out, no association between BMI and out-of-home eating was detected. 19 Our finding of a positive association between eating at restaurants and BMI among men agrees to that of a large study in the United States among adults and using two 24-HDRs. 4 The feasibility of direct comparisons with other previous publications is however limited because of the variation in definitions of out-of-home eating and the obesity indicators used. In general, different effects of restaurant food compared to fast food intakes were reported, particularly among women,

10 and positive associations were more consistently detected between eating at fast food outlets and BMI or weight gain, 5,11,13,16,38,39 usually under the assumption that diets out of home are richer in energy and fat. 3,10 Other publications either failed to report any association 7,14 or observed an even inverse association between eating lunch out and the risk of being obese among women. 14 The advantages of this study are the large sample size; the coverage of several countries with standardized protocols; the use of a food composition database harmonized across the participating countries; the control for most sociodemographic and lifestyle factors potentially confounding associations and the use of a conclusive definition for eating out that includes all participants who consumed at least one energy-yielding item at restaurants, cafeterias and similar establishments. Weight and height used in our investigation were in many cases self-reported, introducing a possibility of misclassification that may attenuate the associations observed. It is further possible that overweight individuals selectively underreport snacking or eating at restaurants and similar establishments in an attempt to claim adherence to what are generally perceived as healthy dietary choices. 40,41 The use of a single 24-HDR that is less likely to reflect patterns of eating out is another limitation, but we are unable to correct for this because repeated measurements for the same individual were not available. Therefore, some intra-individual random variation most probably exists, but it is not expected to affect mean values for large groups of individuals. The inability, however, to correct mean estimates for within-person variation can result in exaggerated standard errors. The availability of a single 24-HDR may further result in an underestimation of potential associations, but it is unlikely that significant results would be generated when in reality these do not exist. 42 Other limitations are the relatively old study population, the comparison of data collected over a 5-year period in a background of increasing secular trend of out-of-home eating and the lack of temporal correspondence between 24-HDRs and some of the confounding variables. The collective impact of these limitations, however, is likely to be an underestimation of the reported associations. In conclusion, we found evidence that, among men, eating at restaurants was significantly associated with BMI and nonsignificantly with weight gain. Among women no similar patterns were observed. Moreover, we found no evidence that the amount of energy consumed at restaurants was associated with weight gain. It appears that eating out, except at work, may contribute to overweight at least among men, possibly because they are likely to be less attentive to issues of body weight and healthy eating and more prone to unaccounted excess energy intake. Conflict of interest The authors declare no conflict of interest. Acknowledgements This publication arises from the EPIC-PANACEA project, which has received funding from the European Union, in the framework of the Public Health Program (Project Number ). This work was further financially supported by the European Commission: Public Health and Consumer Protection Directorate ; Research Directorate-General 2005; Ligue contre le Cancer, Societé 3M, Mutuelle Générale de l Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center, Federal Ministry of Education and Research (Germany); Danish Cancer Society (Denmark); Health Research Fund (FIS) of the Spanish Ministry of Health, the participating regional governments and institutions and the ISCIII of the Spanish Ministry of Health (RETICC DR06/0020) (Spain); Cancer Research UK, Medical Research Council, Stroke Association, British Heart Foundation, Department of Health, Food Standards Agency, the Wellcome Trust (United Kingdom); Greek Ministry of Health and Social Solidarity, Hellenic Health Foundation and Stavros Niarchos Foundation (Greece); Italian Association for Research on Cancer, National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF) (The Netherlands); Swedish Cancer Society, Swedish Scientific Council, Regional Government of Skane (Sweden); Nordforsk (Norway). 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