184 Am J Clin Nutr 2012;95: Printed in USA. Ó 2012 American Society for Nutrition

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1 Fruit and vegetable consumption and prospective weight change in participants of the European Prospective Investigation into Cancer and Nutrition Physical Activity, Nutrition, Alcohol, Cessation of Smoking, Eating Out of Home, and Obesity study 1 4 Anne-Claire Vergnaud, Teresa Norat, Dora Romaguera, Traci Mouw, Anne M May, Isabelle Romieu, Heinz Freisling, Nadia Slimani, Marie-Christine Boutron-Ruault, Françoise Clavel-Chapelon, Sophie Morois, Rudolf Kaaks, Birgit Teucher, Heiner Boeing, Brian Buijsse, Anne Tjønneland, Jytte Halkjær, Kim Overvad, Marianne Uhre Jakobsen, Laudina Rodríguez, Antonio Agudo, Maria-José Sánchez, Pilar Amiano, José María Huerta, Aurelio Barricarte Gurrea, Nick Wareham, Kay-Tee Khaw, Francesca Crowe, Philippos Orfanos, Androniki Naska, Antonia Trichopoulou, Giovanna Masala, Valeria Pala, Rosario Tumino, Carlotta Sacerdote, Amalia Mattiello, H Bas Bueno-de-Mesquita, Fränzel JB van Duijnhoven, Isabel Drake, Elisabet Wirfält, Ingegerd Johansson, Göran Hallmans, Dagrun Engeset, Tonje Braaten, Christine L Parr, Andreani Odysseos, Elio Riboli, and Petra HM Peeters ABSTRACT Background: Fruit and vegetable consumption might prevent weight gain through their low energy density and high dietary fiber content. Objective: We assessed the association between the baseline consumption of fruit and vegetables and weight change in participants from 10 European countries participating in the European Prospective Investigation into Cancer and Nutrition study. Design: Diet was assessed at baseline in 373,803 participants by using country-specific validated questionnaires. Weight was measured at baseline and self-reported at follow-up in most centers. Associations between baseline fruit and vegetable intakes (per 100 g/d) and weight change (g/y) after a mean follow-up of 5 y were assessed by using linear mixed-models, with age, sex, total energy intake, and other potential confounders controlled for. Results: After exclusion of subjects with chronic diseases at baseline and subjects who were likely to misreport energy intakes, baseline fruit and vegetable intakes were not associated with weight change overall. However, baseline fruit and vegetable intakes were inversely associated with weight change in men and women who quit smoking during follow-up. We observed weak positive associations between vegetable intake and weight change in women who were overweight, were former smokers, or had high prudent dietary pattern scores and weak inverse associations between fruit intake and weight change in women who were.50yofage,wereofnormal weight, were never smokers, or had low prudent dietary pattern scores. Conclusions: In this large study, higher baseline fruit and vegetable intakes, while maintaining total energy intakes constant, did not substantially influence midterm weight change overall but could help to reduce risk of weight gain in persons who stop smoking. The interactions observed in women deserve additional attention. Am J Clin Nutr 2012;95: INTRODUCTION Obesity is associated with an increased risk of several diseases including coronary vascular disease, diabetes, and several cancers (1). The increasing prevalence of overweight and obesity has been attributed to quantitative and qualitative changes in the diet (ie, higher energy density, more fat, and added sugars in foods, 1 From the Department of Epidemiology and Public Health, Imperial College London, London, United Kingdom (A-CV, TN, DR, TM, ER, and PHMP); the Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands (AMM and PHMP); the National Institute for Public Health and the Environment, Bilthoven, Netherlands (AMM, HBB-d-M, and FJBvD); the International Agency for Research on Cancer, Lyon, France (IR, HF, and NS); the Institut National de la Santé et de la Recherche Médicale, ERI 20, EA 4045, Villejuif, France (M-CB-R, FC-C, and SM); the Institut Gustave Roussy, Villejuif, France (M-CB-R, FC-C, and SM); the Division of Clinical Epidemiology, German Cancer Research Center, Heidelberg, Germany (RK and BT); the Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbruecke, Germany (HB and BB); the Danish Cancer Society, Institute of Cancer Epidemiology, Copenhagen, Denmark (A Tjønneland and JH); the Department of Epidemiology, School of Public Health, Aarhus University, Aarhus, Denmark (KV and MUJ); the Department of Cardiology, Centre for Cardiovascular Research, Aalborg Hospital, Aarhus University Hospital, Aalborg, Denmark (KV and MUJ); the Public Health and Participation Directorate, Health and Health Care Services Council, Asturias, Spain (LR); the Unit of Nutrition, Environment and Cancer, Catalan Institute of Oncology, Institute of Biomedical Research of Bellvitge, Barcelona, Spain (AA); the Andalusian School of Public Health, the Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Granada, Spain (M-JS); the Public Health Division of Gipuzkoa, CIBERESP, Basque Regional Health Department, Spain (PA); the Epidemiology Department, Murcia Health Council, CIBERESP, Murcia, Spain (JMH); the Navarre Public Health Institute, CIBERESP, Pamplona, Spain (ABG); the Clinical Gerontology Unit, University of Cambridge, Cambridge, United Kingdom (NW and K-TK); the Cancer Research UK Epidemiology Unit, University of Oxford, Oxford, United Kingdom (FC); the Hellenic Health Foundation, Athens, Greece (PO, AN, and A Trichopoulou); the Department of Hygiene Epidemiology and Medical Statistics, University of Athens Medical School, Athens, Greece (PO, AN, and A Trichopoulou); the Molecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute, Florence, Italy (GM); the Nutritional Epidemiology Unit, IRCCS Foundation, 184 Am J Clin Nutr 2012;95: Printed in USA. Ó 2012 American Society for Nutrition

2 FRUIT AND VEGETABLE INTAKE AND WEIGHT GAIN 185 greater saturated fat intake, and reduced intakes of complex carbohydrates, dietary fiber, fruit, and vegetables) in addition to reduced physical activity at work and during leisure time (2). In addition to their potential effect on the reduction of risk of several major chronic diseases (3, 4), consumption of fruit and vegetables might prevent excessive weight gain (5 11). Their low energy density and high content of water and fiber could enhance satiation signals (12, 13). Fruit and vegetables share some similar nutritional characteristics, and people with high consumption of National Cancer Institute, Milan, Italy (VP); the Cancer Registry and Histopathology Unit, Civile - M.P. Arezzo Hospital, ASP 7, Ragusa, Italy (RT); the Centro di Prevenzione Oncologica Piemonte, Torino Italy (CS); the Department of Clinical and Experimental Medicine, Federico II University, Naples, Italy (AM); the Department of Gastroenterology and Hepatology, University Medical Center Utrecht, Utrecht, Netherlands (HBB-d-M); the Department of Clinical Sciences in Malmö/Nutrition Epidemiology, Lund University, Malmo, Sweden (ID and EW); the Departments of Odontology (IJ) and Public Health and Clinical Medicine, Nutritional Research (GH), Umeå University, Umea, Sweden; the Department of Community Medicine, University of Tromsø, Tromsø, Norway (DE and TB); the Institute of Basic Medical Sciences, Department of Biostatistics, University of Oslo, Oslo, Norway (CLP); and the EPOS-Iasis, Nicosia, Cyprus (AO). 2 The project Physical Activity, Nutrition, Alcohol, Cessation of Smoking, Eating Out of Home, and Obesity received funding from the European Union in the framework of the Public Health Programme (project ). In addition, the work was financially supported by the European Commission: Public Health and Consumer Protection Directorate ; the Research Directorate-General 2005; the Ligue contre le Cancer, Institut Gustave Roussy, Société 3M, Mutuelle Générale de l Education Nationale, and the Institut National de la Santé et de la Recherche Médicale (France); German Cancer Aid, the German Cancer Research Center, and the Federal Ministry of Education and Research (Germany); the Danish Cancer Society (Denmark); the Health Research Fund of the Spanish Ministry of Health, the participating regional governments and institutions (Spain); Cancer Research UK, the Medical Research Council, the Stroke Association, the British Heart Foundation, the Department of Health, the Food Standards Agency, and the Wellcome Trust (United Kingdom); the Greek Ministry of Health and Social Solidarity, the Hellenic Health Foundation, and the Stavros Niarchos Foundation (Greece); the Italian Association for Research on Cancer and the National Research Council (Italy); the Dutch Ministry of Public Health, Welfare and Sports), the Netherlands Cancer Registr Dutch Prevention Funds, the LK Research Funds, Dutch Zorg Onderzoek Nederland, the World Cancer Research Fund, and Statistics Netherlands (Netherlands); the Swedish Cancer Society, the Swedish Scientific Council, and the Regional Government of Skane (Sweden); and the Norwegian Cancer Society (Norway). The coordination of the European Prospective Investigation into Cancer and Nutrition is financially supported by the European Commission (Directorate General for Health and Consumer Affairs) and the International Agency for Research on Cancer. 3 Address reprint requests to T Norat, Imperial College London, Department of Epidemiology and Public Health, Medical Building, Room 509 Norfolk Place, St Mary s Campus, London W2 1PG, United Kingdom. t.norat@imperial.ac.uk. 4 Address correspondence to A-C Vergnaud, Imperial College London, Department of Epidemiology and Public Health, Medical Building, Room VC8 Norfolk Place, St Mary s Campus, London, W2 1PG, United Kingdom. a.vergnaud@imperial.ac.uk. 5 Abbreviations used: DiOGenes, Diet, Obesity, and Genes; EI:BMR, ratio of reported energy intake to predicted basal metabolic rate; EPIC, European Prospective Investigation into Cancer and Nutrition; M2, second model; PANACEA, Physical Activity, Nutrition, Alcohol, Cessation of Smoking, Eating Out of Home and Obesity. Received May 24, Accepted for publication October 5, First published online December 14, 2011; doi: /ajcn fruit also tend to have a high consumption of vegetables. However, fruit and vegetables are not consumed in the same occasion and, therefore, might not replace the same type of food in the diet. Vegetables are mainly consumed during meals as a side dish or instead of staple foods or meat, whereas fruit can be consumed instead of energy-dense snacks or desserts. Second, fruit and vegetables do not involve the same cooking process; vegetables are more often cooked, dressed in sauce, or fried than are fruit. On the basis of these differences and others as yet unknown, it is appropriate to distinguish fruit and vegetables when studying the relation with overweight and obesity. Several prospective observational studies and intervention studies have shown an inverse association between fruit and vegetable consumption and weight change (7, 14 27). However, results were inconsistent, and a recent review concluded that fruit and nonstarchy vegetables were not associated with amounts of subsequent excess weight gain and obesity (28). Individuals who eat higher quantities of fruit and vegetables also tend to eat less meat, especially processed meat, saturated fat, and refined carbohydrates, all of which have been positively associated with weight gain (11, 29, 30). Therefore, it remains unclear whether the associations previously observed were fully attributable to fruit and vegetable intake itself or to an underlying dietary pattern. In addition, most previous observational studies were performed in small- to medium-size national samples (7, 10, 18, 21 23, 26, 27, 31 35). Homogenous dietary intakes and limited sample sizes could have reduced their power to observe an association, especially when studying specific subgroups of participants. One previous study (DiOGenes 5 ) performed in a heterogeneous population, which included 6 centers of the EPIC study, showed an inverse association between fruit and vegetable intakes and annual weight gain (14). The EPIC-PANACEA project includes participants from 16 additional centers of the EPIC study (23 in total). Because heterogeneity was observed in the DiOGenes subsample, we aimed to investigate whether higher intakes of fruit and vegetables in the whole EPIC population was related to subsequent midterm changes in body weight, with physical activity levels, dietary patterns, and other lifestyle factors taken into account. SUBJECTS AND METHODS Study population The EPIC study is a multicenter, prospective cohort study that investigates the role of metabolic, dietary, lifestyle, and environmental factors in the development of cancer and other chronic diseases. Briefly, between 1992 and 2000, 521,448 apparently healthy volunteers aged between 25 and 70 y were recruited in 23 centers from 10 European countries (Denmark, France, Germany, Greece, Italy, Netherlands, Norway, Spain, Sweden, and the United Kingdom). In France, Norway, Utrecht (Netherlands) and Naples (Italy), only women were included. Individuals were selected from the general population with few exceptions. The French cohort was based on state-school employees. The Utrecht and Florence (Italy) cohorts included women invited for a local population-based breast cancer screening program. Components of the Italian and Spanish cohorts included members of local blood donor associations. In Oxford (United Kingdom), one-half of the cohort was recruited from lacto-ovo vegetarians and vegans

3 186 VERGNAUD ET AL (36). Approval for this study was obtained from the ethical review boards of the International Agency for Research on Cancer and from all local institutions. Details of the study design have been previously published (36, 37). After individuals with missing information on dietary or lifestyle variables, unavailable information on weight or height, extreme values on anthropometric data, pregnancy, and those in the lowest and highest 1% of the ratio of reported total energy intake: energy requirement were excluded, 497,735 individuals were available for the PANACEA analyses at baseline. Furthermore, 8226 participants died before the follow-up assessment, 23,957 were not contacted at follow-up yet, and 3991 participants emigrated. In the 461,561 participants who received the follow-up questionnaire, we further excluded 85,679 participants who did not provide their weight (18.6%), 13 participants who did not have a follow-up date, and 2,066 participants with extreme anthropometric measures at follow-up. Thus, 373,803 participants (103,455 men and 270,348 women) were included in the current study [for more details, see previously published flowchart (30)]. Anthropometric assessment Two weight measures were available for each center. For most centers, body weight and height were measured at baseline by using similar, standardized procedures. The exceptions were in France, Norway, and the health conscious group of the Oxford center where self-reported anthropometric values were used. Weight and height were measured to the nearest 0.1 kg and 0.1 or 0.5cm, respectively, with subjects wearing no shoes. For the current study, baseline weight was adjusted to reduce heterogeneity because of protocol differences in clothing worn during measurements (38). At follow-up, weight was self-reported by a questionnaire except in Norfolk (United Kingdom) and Doetinchem (Netherlands) where it was measured. The accuracy of self-reported anthropometric measures was improved by using prediction equations derived from participants with both measured and self-reported measures at baseline (39). BMI was calculated as weight in kilograms divided by height in meters squared. Because follow-up times were different across centers (from 2 y in Heidelberg, Germany, to 11 y in Varese, Italy), our main outcome was annual weight change [in g/y; ie, weight at follow-up minus weight at baseline divided by time of follow-up (y)]. Dietary assessment Usual dietary intakes at baseline were measured by using country-specific validated questionnaires. Most centers adopted a self-administered dietary questionnaire of food items (36). Nutrient intakes were calculated by using the EPIC Nutrient Database, which is a standardized food-composition table (40). Daily intakes of fresh fruit [cooked or raw citrus fruit, apples and pears, grapes, stone fruit, melons, berries, bananas, pineapples, kiwis (nuts, olives, and fruit juices were not included)] and vegetables [cooked or raw leafy vegetables, fruiting vegetables, root vegetables, cabbages, mushrooms, grains and pod vegetables, onion and garlic, stalk vegetables and sprouts, and mixed salads and vegetables (legumes, potatoes, and other tubers were not included)] were estimated in grams and kilocalories. Spearman s correlation coefficient between total plasma carotenoids and total fruit and vegetable intakes recorded by food-frequency questionnaire was consistent with other studies (r = 0.38,P, 0.05) (41). Because results that used grams, residuals on energy (42), or the percentage of energy coming from fruit and vegetables were similar, only results that used the consumption in grams are presented. To correct for any systematic underestimation or overestimation of dietary intakes between study centers, a dietary calibration study was conducted (43). A random sample of ;36,900 men and women (7.4% of the sample) completed a detailed computerized 24-h dietary recall, and nutrient intake was calculated by using a common food-composition database. Assessment of other covariates Standard questionnaires were used at baseline to collect information on the sociodemographic characteristics and lifestyle variables of participants (36), such as smoking status (never smoker, former smoker, and current smoker), educational level (highest attained educational level as follows: primary school, technical school, secondary school, and university degree), physical activity (with consideration of the combination of physical activity at work and nonworking activities of cycling and sport, classified in 4 categories as follows: inactive, moderately inactive, moderately active, and active) (44), and menopausal status in women (premenopausal, peri-menopausal, and postmenopausal). Smoking status was also collected during follow-up at the same time as anthropometric data, which permitted smoking status modification during follow-up in our analysis (stable current smoker, stable former smoker, stable never smoker, quitter, start smoking) to be taken into account. Participants with missing values for physical activity (11.6%), educational level (3.8%), smoking status (2.1%) or change in smoking status (11.9%) were classified as a separate category. Statistical analyses The population s main characteristics according to vegetable or fruit intake tertiles were assessed by using ANOVA or chi-square tests as appropriate. Because there was a significant interaction between both vegetable and fruit intakes and sex (both P, 0.001), all analyses were performed separately for men and women. The association between fruit and vegetable consumption (g/d) and annual weight change (g/y) was estimated by using a multilevel mixed-effects linear regression model with dietary consumption values on a continuous scale. Because of a significant interaction according to center, random effects on both intercept and slope according to center were modeled (fruit and vegetable, vegetable, or fruit intakes as appropriate). The linearity of the associations was checked by using random coefficient linear regression spline models (data not presented). We fitted several multivariable-adjusted models with potential confounders (as fixed effects) controlled for. The first model was adjusted for age at recruitment and an indicator of consumption (1 = consumers and 0 = nonconsumers of fruit and vegetables, vegetables, or fruit as appropriate). M2 was further adjusted for BMI at baseline, follow-up time, educational level, physical activity level, change in smoking status, total energy intake, energy intake from alcohol, plausibility of total energy intake reporting and fruit (for vegetable analysis) or vegetable (for fruit analysis) intake. Participants were classified as underreporters (EI:BMR,1.14), plausible reporters (EI:BMR = ) or overreporters (EI:BMR.2.10) by using cutoff points proposed by

4 FRUIT AND VEGETABLE INTAKE AND WEIGHT GAIN 187 Goldberg (45). Results were similar when energy from fat and energy from nonfat sources, instead of total energy intake, were adjusted and were, therefore, not presented. Finally, a third multivariable model was further adjusted for dietary patterns derived from maximum likelihood factor analysis as previously described (30). For this model, fruit and vegetable intakes were not mutually adjusted for each other. The prudent pattern distinguished participants with high intakes of vegetables, legumes, fruit, pasta and rice, and vegetable oils from participants with high intakes of processed meat, potatoes, margarines, coffee and tea, and beer and cider, and the fresh meat pattern distinguished participants with high intakes of red meat, poultry, and vegetable oils from participants with high intakes of cakes. To account for systematic and random errors in the measurement of dietary intakes between centers, we repeated the analyses by using calibrated vegetable and fruit data obtained from country- and sex-specific calibration models. Dietary intakes obtained from the 24-h dietary recall were regressed on dietary intakes obtained from the main dietary questionnaire, with adjustment for age, BMI at baseline, total energy, energy that came from alcohol, and study center (46). Data were weighted by the day of the week and the season of the year in which the 24-h dietary recall was collected. Nonconsumers in the main dietary questionnaires were excluded from the regression calibration models, and zero was kept as the predicted value. The SE of the coefficient was estimated by using bootstrap sampling (10 loops) (46). could not be used in stratified analyses because the numbers of participants who took part in the calibration substudy were too low in some subcategories. To explore if the results were confounded by changes in diet because of illness or by potential underreporting or overreporting of diet, analyses were also performed by excluding participants with chronic diseases at baseline (ie, diabetes, cancer, myocardial infarction, or stroke; n = 29,046) and participants who were likely to misreport energy intakes according to the Goldberg criteria (n = 90,765 underreporters and 20,237 overreporters) (45), which resulted in a subsample of 233,755 participants for this subanalysis. Sensitivity analyses that excluded participants with incident cancer (n = 9144) or participants who started or quit smoking during follow-up (n = 23,296) were also performed. We explored the potential effect modification by age, BMI at baseline, change of smoking status, physical activity, level of education, follow-up time, and prudent pattern score by including interaction terms between each variable and vegetable or fruit intake in the models. Variables were selected a priori. P values for the interaction term were calculated by using F tests, and group-specific coefficients were presented when statistically significant interactions were detected. Center-specific analyses were performed, and heterogeneity across center was evaluated by using random-effect meta-analyses (I 2 ). All statistical analyses were performed with SAS 9.2 software (SAS Institute Inc) or STATA 9.0 software (StataCorp LP). RESULTS Characteristics of the participants by sex and tertiles of fruit and vegetable intakes are summarized in Table 1. Compared with participants in the first tertiles of vegetable and of fruit intakes, participants in the highest tertiles were older, less physically active, less likely to quit smoking during follow-up, more likely to report a previous illness, and had lower intakes of meat and alcohol. Participants in the highest tertiles also had a higher BMI at baseline and gained less weight, except for women, according to vegetable intake tertiles. Overall, men consumed less fruit and vegetables than did women and reported a higher weight gain on average (data not shown). The adjusted increase in annual weight change (g/y) in men and women per 100 g baseline fruit and vegetable consumption/d before and after diet calibration is shown in Table 2. In men, weight change was inversely associated with fruit and vegetable, vegetable, or fruit intakes in age-adjusted models. This relation disappeared after adjustment for other potential confounders (M2) by using either uncalibrated or calibrated data. In women, a weak positive association between fruit and vegetable or vegetable intake and weight change (M2) was observed. Each 100-g increase in daily vegetable consumption was associated with a 14-g annual weight increase (95% CI: 4, 23 g; P = 0.005). The association was stronger when calibrated data were used (44 g; 95% CI: 7, 81; P = 0.02). No association was observed for fruit intake. Results were similar after additional adjustment for dietary pattern scores [in model 3, which used uncalibrated data: in men, b (95% CI) = 25 (212, 2) for vegetables and 4 (21, 8) for fruit; in women, b (95% CI) = 14 (4, 25) for vegetables and 2 (21, 6) for fruit]; after additional adjustment for meat intake [in men, b (95% CI) = 22 (28, 3) for vegetables and 0 (24, 4) for fruit; in women, b (95% CI) = 13 (4, 23) for vegetables and 21 (24, 2) for fruit] or after adjustment for the covariables used in the previous DiOGenes analysis that included 6 centers of the EPIC study (age, sex, duration of follow-up, baseline weight and height, level of education, change of smoking status, physical activity, and categories of alcohol intake and, in women, postmenopausal status and postmenopausal hormone use) [in men, b (95% CI) = 22 (28, 3) for vegetables and 22 (26, 2) for fruit; in women, b (95% CI) = 14 (4, 23) for vegetables and 1 (22, 3) for fruit]. The exclusion of participants with incident cancer or smoking-status modification during follow-up or the exclusion of participants 65 y of age did not substantially change these results. To explore if the results were confounded by changes in diet because of illness or potential underreporting or overreporting of diet, we excluded from the analyses the participants with chronic diseases and those likely to misreport energy intakes at baseline (45). A weak inverse association between baseline vegetable intake and weightchangebecamesignificantinmen(table 3). Each 100- g increase in daily vegetable consumption was associated with a 10-g lower annual weight change (95% CI: 217, 23; P = 0.007). However, this association disappeared when calibrated data were used. In women, the small positive association between baseline vegetable intake and weight change lost significance. When only participants with implausible energy intakes were excluded, results were similar except for the weak positive association between vegetable intake and weight change observed in women, which remained significant (data not presented). Compared with participants who reported a plausible energy intake, underreporters had a higher BMI at baseline, a higher weight change during follow-up when baseline BMI was controlled for, and underreporters were more likely to have an antecedent of chronic disease (data not presented). In addition, underreporters were more likely to have at least a university degree, to be physically inactive, and to be a never smokers if men, whereas the opposite was observed in women.

5 188 VERGNAUD ET AL TABLE 1 Characteristics of the population by sex and fruit and vegetable tertiles (n = 373,803) Men Women Tertiles of total vegetable consumption Tertiles of total fruit consumption Tertiles of total vegetable consumption Tertiles of total fruit consumption Intakes (g) 1 80 (0, 116) 158 (117, 219) 331 (220, 2310) 57 (0, 103) 156 (104, 232) 351 (233, 3357) 100 (0, 143) 192 (144, 255) 349 (256, 2498) 94 (0, 151) 213 (152, 279) 382 (280, 4174) Age (y) Postmenopausal women (%) BMI at inclusion (kg/m 2 ) Weight change (g/y) 2, Participants with at least a university degree (%) 4 Physically inactive participants (%) 4, Current smokers (%) Participants quitting smoking during follow-up (%) 4 Previous illness (%) 4, Follow-up time (y) Total energy intake (kcal/d) Daily intakes (g) 7 Potatoes and other tubers Legumes Dairy products Cereals Meat Fish and shellfish Eggs Added fat Sugar and confectionery Cakes Nonalcoholic beverage Alcoholic beverage Values are medians; minimum, maximum values in parentheses. 2 Continuous variable. All values are means 6 SDs. P, (ANOVA). 3 Corrected by using the Oxford formula. 4 Categorical variable. P, (chi-square test). 5 Inactive and moderately inactive participants combined. 6 Diabetes, cancer, myocardial infarction, or stroke 7 Values are means 6 SEs and adjusted for total energy. P, (ANOVA).

6 FRUIT AND VEGETABLE INTAKE AND WEIGHT GAIN 189 TABLE 2 Adjusted increase in annual weight change (g/y) for 100-g increase in vegetable and fruit consumption by sex before and after calibration in the EPIC (n = 373,803) 1 Men Women b (95% CI) P b (95% CI) P Total fruit and vegetable M1 29 (213, 26), (23, 4) 0.73 M2 22 (25, 1) (0, 7) 0.03 M1 217 (227, 26) (0, 21) 0.05 M2 25 (214, 4) (2, 19) 0.01 Total vegetable M1 213 (220, 27), (22, 16) 0.14 M2 23 (28, 3) (4, 23) M1 260 (2116, 24) (1, 77) 0.04 M2 213 (271, 44) (7, 81) 0.02 Total fruit M1 210 (215, 24), (26, 0) 0.07 M2 22 (26, 2) (26, 0) 0.03 M1 214 (225, 22) (29, 22) 0.44 M2 23 (211, 5) (214, 13) We performed multilevel linear mixed models with the random effect on the intercept and slope according to center. M1 was adjusted for age and an indicator of vegetable (or fruit) consumption. M2 was adjusted as in M1 plus for educational level, physical activity level, change in smoking status, BMI at baseline, follow-up time, energy intake, energy intake from alcohol, plausibility of total energy intake reporting, and fruit (for vegetable analysis) or vegetable (for fruit analysis) intake. EPIC, European Prospective Investigation into Cancer and nutrition; M1, model 1; M2, model 2. In stratified analysis (Table 4), the small positive association between baseline vegetable intake and weight change remained significant in overweight women, former smokers, and women in the 2 highest tertiles of the prudent dietary pattern. Weak inverse associations between fruit intake and weight change were observed in women aged.50 y, normal weight women, and women in the lowest tertile of the prudent dietary pattern. Finally, we observed inverse associations between baseline fruit and vegetable intakes and weight change in men and women who quit smoking during follow-up. The associations between both fruit and vegetable intakes at baseline and weight change varied across centers in women but not in men (M2; Figure 1). In women, vegetable intake was significantly positively related to weight change in the cohorts from France, Norfolk (United Kingdom), Amsterdam-Maastricht and Utrecht (Netherlands), Malmo and Umea (Sweden) and negatively related to weight change in Denmark. For fruit intake, a significant inverse association was observed in France, Utrecht, Potsdam (Germany), and Malmo and a significant positive association was observed in Norfolk. DISCUSSION In this large prospective study, baseline fruit and vegetable intakes were not associated with weight change after an average of 5 y of follow-up in men and women after partial correction for measurement error of diet and the exclusion of participants who were likely to have modified their diet before baseline or to have misreported energy intakes. However, a weak positive association between vegetable intake and weight change was observed in women who were overweight, former smokers, or those with a high prudent dietary pattern scores, whereas a weak inverse association between fruit intake and weight change was observed in women.50 y of age, of normal weight, never smokers, or with low prudent dietary pattern scores. Baseline fruit and vegetable intakes were inversely associated with weight change in men and women who quit smoking during follow-up. Several intervention studies previously suggested an inverse association between vegetable consumption and weight gain. In 30 overweight families, participants who were recommended to increase their fruit and vegetable consumption lost more weight after 1 y than did participants who were recommended to decrease their fat and sugar intakes (16). Higher weight losses were obtained when fruit and vegetable consumption was increased in conjunction with a low-energy, low-cholesterol, and saturated fat diet in 406 patients hospitalized for acute myocardial infarction (24) or in conjunction with a low-fat diet without energy restriction in 97 obese women (47). The addition of a fruit snack to the usual diet instead of an oat snack with similar energy and fiber TABLE 3 Adjusted increase in annual weight change (g/y) for 100-g increase in vegetable and fruit consumption by sex in the EPIC after exclusion of participants with previous chronic disease and implausible diet reporting (n = 233,755) 1 Men Women b (95% CI) P b (95% CI) P Total fruit and vegetable M1 213 (216, 29), (27, 2) 0.33 M2 25 (28, 21) (24, 6) 0.69 M1 221 (233, 29), (28, 15) 0.56 M2 28 (217, 2) (24, 15) 0.28 Total vegetable M1 222 (228, 215), (29, 14) 0.67 M2 210 (217, 23) (21, 22) 0.08 M1 271 (2133, 210) (219, 75) 0.24 M2 228 (282, 27) (27, 85) 0.10 Total fruit M1 210 (216, 24) (211, 22) M2 21 (25, 4) (211, 21) M1 215 (228, 22) (219, 11) 0.61 M2 21 (210, 7) (221, 4) The plausibility of diet misreporting was estimated by using the Goldberg criteria (45). We performed multilevel linear mixed models with the random effect on the intercept and slope according to center. M1 was adjusted for age and an indicator of vegetable (or fruit) consumption. M2 was adjusted as in M1 plus for educational level, physical activity level, change in smoking status, BMI at baseline, follow-up time, energy intake, energy intake from alcohol, plausibility of total energy intake reporting, and fruit (for vegetable analysis) or vegetable (for fruit analysis) intake. EPIC, European Prospective Investigation into Cancer and nutrition; M1, model 1; M2, model 2.

7 190 VERGNAUD ET AL TABLE 4 Adjusted increase in annual weight change (g/y) for 100-g increase in vegetable and fruit consumption according to sex and interaction variables in the EPIC after exclusion of participants with previous chronic disease and implausible diet reporting (n = 233,755) 1 Total vegetables Total fruit b (95% CI) P P-interaction b (95% CI) P P-interaction Men Change in smoking status Current smokers 29 (223, 6) (1, 22) 0.04 Former smokers 210 (221, 2) (213, 4) 0.28 Never smokers 214 (226, 21) (26, 10) 0.67 Quitters 232 (258, 26) (246, 27) Start smoking 18 (216, 52) (224, 24) 1.00 Women Age y 14 (2, 26) (210, 1) y 6 (26, 18) (215, 25),0.001 BMI, ,25 kg/m 2 22(215, 10) (216, 26), kg/m 2 27 (15, 40), (26, 6) kg/m 2 10 (24, 24) (213, 4) 0.30 Change in smoking status,0.001,0.001 Current smokers 15 (0, 30) (213, 6) 0.44 Former smokers 20 (7, 33) (29, 5) 0.61 Never smokers 8 (24, 20) (212, 21) 0.01 Quitters 225 (247, 22) (262, 230),0.001 Start smoking 19 (25, 43) (210, 27) 0.36 Prudent pattern score tertiles First tertile 28 (225, 9) (226, 27),0.001 Second tertile 17 (3, 30) (27, 6) 0.95 Third tertile 20 (7, 33) (28, 2) The plausibility of diet misreporting was estimated by using the Goldberg criteria (45). We performed multilevel linear mixed models with the random effect on intercept and slope according to center by using uncalibrated data adjusted for age, indicator of vegetable (or fruit) consumption, educational level, physical activity level, change in smoking status, BMI at baseline, follow-up time, total energy intake, and energy coming from alcohol and fruit (for vegetable analysis) or vegetable (for fruit analysis) intake. P-interaction was tested by inserting an interaction term in the model. EPIC, European Prospective Investigation into Cancer and nutrition. contents resulted in higher weight loss after 10 wk in 49 overweight women (15). Inverse associations were observed in several prospective studies that examined the association between fruit (14, 18, 21 23, 26, 32), vegetables (7, 14, 18, 21, 23, 26, 32), or fruit plus vegetable intakes (14, 18, 21, 23, 33) and weight change. However, some others studies did not find a significant association between intakes of fruit (34, 35) or vegetables (31, 34) and weight change, and a recent meta-analysis showed no association between intakes of fruit and nonstarchy vegetables and subsequent excess weight gain or obesity (28). In the EPIC subsample of the DiOGenes project, a 100-g increase in baseline fruit and vegetable daily consumption was not significantly associated with weight change in men [b (95% CI) = 27 (215, 1)]. However, a 12 g (218, 25 g) lower annual weight change per 100-g increase in fruit and vegetable daily consumption was observed in women (14). Dietary patterns differences could account for the discrepancy observed in women between the 2 studies. In the DiOGenes population, 48% of women belonged to the lowest tertile of the prudent dietary pattern (rich in vegetables, legumes, fruit, pasta and rice, and vegetable oil and low in processed meat, potatoes, margarines, coffee and tea, and beer and cider) compared with only 18% in the extra centers added in PANACEA. In agreement with the DiOGenes results, we observed an inverse association between fruit and vegetable intakes and weight change in participants who quit smoking during follow-up. If confirmed in other populations, this finding may have important public health implications because weight gain after smoking cessation is a frequent reason for relapse (48). Several hypotheses could explain the unexpected positive association between baseline vegetable intake and weight gain observed in overweight women. First, overweight women tend to underestimate energy intake (49), especially when it comes from socially undesirable food groups such as added fat (50), which is more often consumed with vegetables (dressed in sauce, oil, or fried) than with fruit. In agreement with this hypothesis, the positive association between vegetable intake and weight change was more pronounced in the second and third tertiles of the prudent dietary pattern, which was highly correlated with vegetable fat (r = 0.67). Previous results from the EPIC-Norfolk cohort also suggested that self-reported fruit and vegetable intakes could be less reliable in obese individuals because dietary vitamin C was strongly associated with plasma vitamin C in normal-weight participants but not in obese participants (51). This misreporting could be too subtle to be captured by the Goldberg criterion, which excludes only outliers. Second, overweight women who reported high consumption of vegetables may actually be women

8 FRUIT AND VEGETABLE INTAKE AND WEIGHT GAIN 191 FIGURE 1. Adjusted increase in annual weight change (g/y) per 100-g increase in vegetable and fruit intakes in centers (n = 373,803). We performed generalized linear models by using uncalibrated data and adjusted for age, indicator of vegetable (or fruit) consumption, educational level, physical activity level, change in smoking status, BMI at baseline, follow-up time, total energy intake, energy that came from alcohol, plausibility of total energy reporting, and fruit (for vegetable analysis) or vegetable (for fruit analysis) intake. NL, Netherlands; UK, United Kingdom. engaged in dieting. Because vegetables are very low in energy density and highly satiating, they are often consumed in high quantity in numerous weight-loss diets. The positive association was also more pronounced in the second and third tertiles of the prudent dietary pattern, in which dieters could be more frequent, and in former smokers, who could be more prone to dieting to compensate the weight gain after smoking cessation. Results from an analysis that included 6 cohorts from the EPIC study showed that the stronger inverse association between fat intake and weight change observed in women of UK Norfolk than in in other centers was mainly driven by women who were intentionally dieting (52). Repeated weight-loss attempts have been shown to be a strong predictor of subsequent large weight gain (53). We could not take into account weight-change history before inclusion in our analyses to confirm this hypothesis. Some other limitations of the current study should be mentioned. First, weight at follow-up was self-reported in most centers and, thus, was most likely underestimated. Therefore, the self-reported weight was corrected with the use of a prediction equation, which has previously been shown to improve the

9 192 VERGNAUD ET AL accuracy of BMI estimates (39). This equation did not take into account adiposity status, which has been shown to influence weight underreporting, especially in women (54). Therefore, some weight misreporting bias could have remained and contributed to the unexpected results obtained in overweight women. However, concordant results were observed in the 2 centers with both weights measured (United Kingdom Norfolk and Netherlands Doetinchem), which indicated that the observed associations were unlikely to be due to misreporting of weight only. Second, we only measured diet at baseline and were not able to consider changes in diet during follow-up. Previous studies reported that women kept their dietary pattern relatively stable after 2 y (55) but tended to increase their consumptions of healthy foods, which resulted in higher prudent pattern scores after 7 17 y (56). Participants with low fruit and vegetable intakes at baseline could have been more likely to be less healthy and, therefore, to increase their fruit and vegetable intakes during follow-up, which would have resulted in a potentially lower weight gain at followup. This possibility may have confounded the association toward the null, especially for long follow-up. In sensitivity analyses that excluded participants who were likely to have modified their diet because of a previous illness, the unexpected positive association observed between vegetable intake and weight change in women lost significance, and no interaction with follow-up time was observed. A recent study showed that an increase in fruit and vegetable intake was inversely associated with weight gain after 4 y (27). As the authors pointed out, the time sequence between the change in diet and change in weight is critical in observational studies, and reverse causation may remain even when diet is measured at both baseline and follow-up (27). Therefore, we could not rule out that residual confounding related to changes in diet during follow-up remained in our analyses. Finally, despite a high overall response rate at follow-up in the EPIC-PANACEA (80% response rate in the 461,561 participants contacted), nonresponse was more likely in participants who reported a poorer health and unhealthy lifestyle, especially a low BMI (,18.5) or high BMI (.25, and especially 30) (May et al; unpublished data). This selection bias could have limited the generalizability of our results and also contributed to the discordant results observed according to initial BMI. The main strengths of the current study were the very large sample size and the heterogeneity of the European population according to both dietary behaviors and obesity prevalence, which allowed the detection of small associations with dietary components and the exploration of a variety of interaction factors. Heterogeneity in dietary pattern seemed particularly important when the associations between fruit and vegetable intake and weight gain were analyzed because the direction and signification of associations in women differed according to their adherence to a prudent pattern. In addition, measurement error of diets has been partially corrected because of a randomized dietary calibration study (43). Finally, analyses were adjusted for underlying dietary pattern scores, which have shown to be more effective to account for the whole diet of participants rather than adjustment for individual nutrients when studying the association between alcohol and type II diabetes (57). In conclusion, our results indicate that higher baseline fruit and vegetable intakes, while keeping total energy intakes constant, does not appear to have a substantial impact on midterm weight change in adult populations. However, it could reduce risk of weight gain after smoking cessation. The interactions observed in women, especially according to BMI and dietary patterns, deserve additional attention. The authors responsibilities were as follows A-CV, TN, BB, and HB: participated in the statistical design, analysis, and writing of the manuscript; PHMP: is the principal investigator of the EPIC-Panacea project. ER is coordinator of the overall EPIC project; A-CV: was responsible of the data analysis and writing of the manuscript and also serves as a guarantor; and all authors: directly participated in the planning, execution, or analysis of the study and helped with the interpretation of data or writing of the manuscript and reviewed the manuscript. 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