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Nutritional Epidemiology

Food Groups as Predictors for Short-Term Weight Changes in Men and Women of the EPIC-Potsdam Cohort¹

Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbruecke, Germany 14558 and ^* Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC

²To whom correspondence should be addressed. E-mail: schulzm{at}mailbox.sc.edu.

	ABSTRACT

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
This study examined the effect of food group intake on subsequent 2-y weight change. Food-frequency questionnaire–based food intake data of 17,369 nonsmoking subjects of the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort were examined in their relation to a subsequent weight change. Dietary data, collected from 1994 to 1998, were grouped into 24 food groups. Weight change per year follow-up was the outcome of interest; large weight gain was defined as 2 kg; small weight gain as 1 kg to <2 kg; large weight loss as -2 kg; small weight loss as -1 kg to > -2 kg and weight maintenance as ± 1 kg. For each food group, a separate polytomous logistic regression model with stable weight as the reference group was constructed, controlling for age, body mass index, previous weight change, and behavioral and lifestyle factors. Odds ratios (OR) and 95% confidence intervals (CI) estimated the increase in risk associated with each 100 g/d increment in food group intake. In women, consumption of high energy, high fat food groups significantly predicted large weight gain, e.g., fats (OR = 1.75; 95% CI, 1.01–3.06), sauces (OR = 2.12; 95% CI, 1.17–3.82) and meat (OR = 1.36; 95% CI, 1.04–1.79), and the consumption of cereals predicted large weight loss (OR = 1.43; 95% CI, 1.09–1.88). In men, intake of high energy, high sugar foods, i.e., sweets, was significantly predictive of large weight gain (OR = 1.48; 95% CI, 1.03–2.13). Our data show that a diet rich in high fat and high energy foods predicts short-term weight gain even if controlled for many potential confounding factors.

KEY WORDS: • food group intake • weight change • EPIC-Potsdam Study • humans

	INTRODUCTION

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
Obesity is a major public health problem in Western civilizations. Despite widespread recognition and strong evidence linking obesity to adverse health outcomes, the prevalence of obesity has increased steadily during the last decades (1 ,2 ). Principally, changes in body weight are caused by an imbalance between energy intake and energy expenditure. Although major research efforts have been made, the qualitative and quantitative contribution of dietary factors and physical activity as well as the effect of genetic factors to the observed energy imbalance and weight changes, respectively, remain unclear. The contribution of dietary factors and eating behavior to the development of weight change has been particularly difficult to identify. Although experimental studies are able to show that both energy intake and dietary fat influence body weight change (3 –6 ), observational studies often found inconsistent results concerning total energy/macronutrient intake (7 –11 ). In contrast to the energy/macronutrient intake approach, the influence of specific foods and food groups on subsequent weight change has also been under investigation. The few prospective analyses following this approach, however, also showed inconsistent results. Although some studies found evidence that high energy and high fat food groups such as sweets and meat are related to increases in body weight (12 –14 ), other investigators found an opposite or no relation between examined food groups and weight change (15 ,16 ). However, the food group intake approach may describe dietary behavior more comprehensively than the single nutrient intake approach because nutrients are consumed via foods and occur in combination. Furthermore, nutrient composition and energy density are frequently similar in foods of one food group and allow inferences on nutrient intake.

The goal of the present analysis was to identify food groups that are predictive of weight gain or weight loss in a cohort of German adults. In contrast to previous investigators, whose common analytical feature was modeling linear regressions, we applied a polytomous logistic regression analysis to estimate the effect of food group intake on weight change categories, i.e., weight gain and weight loss, with stable weight as the reference category.

	SUBJECTS AND METHODS

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
Study population

Subjects for this analysis were selected from the European Prospective Investigation into Cancer and Nutrition (EPIC)³ cohort in Potsdam, Germany (17 ,18 ). EPIC is a large-scale multicenter cohort study, which includes about half a million subjects from 10 European countries (19 ). The EPIC study center in Potsdam recruited 27,548 residents of Potsdam and adjacent communities between 1994 and 1998. For this analysis, men aged 24–69 and women aged 19–70 with complete data on body weight and disease status at baseline and the first follow-up examination were eligible (n = 24,950). Mean follow-up time was 2.2 y, ranging between 0.6 and 5.4 y.

Because smoking as well as smoking cessation is a strong factor for weight gain and smoking status at first follow-up was unknown, only nonsmokers at baseline who were never smokers or who had quit smoking >2 y ago were included in the analysis. Study participants using appetite-suppressing drugs, subjects with incident diseases (cancer, diabetes mellitus, myocardial infarction, stroke, chronic colitis, immobilizing fractures) and women who were pregnant or postpartum (i.e., either still breast-feeding or delivery within the last 12 mo) at baseline or at follow-up were excluded. Finally, body weight changes of 11,005 women and 6,364 men occurring between baseline and first follow-up examination were investigated with respect to reported baseline food group intake.

Approval for all study procedures was given by the Ethical Committee of the State of Brandenburg, Germany, and informed consent was obtained from all study participants.

Assessment of variables

    Body weight. Baseline body weight was measured without shoes and in light clothes on a calibrated scale to the nearest 0.1 kg by a trained technician. Weight at follow-up was self-reported by the participants (follow-up questionnaire). Absolute body weight change was calculated by subtracting baseline weight from follow-up weight. Different follow-up times were taken into account by calculating weight change per years of follow-up.

    Dietary intake and food groups. A self-administered, validated food-frequency questionnaire was the basic instrument for the assessment of habitual dietary intake at baseline (20 –23 ). The questionnaire consisted of 148 food items and included questions about the consumption of sauces and fat content of certain food items. Selected fresh fruits and vegetable intake was included according to season. Food items were summarized into 24 food groups after dietary data were assessed (see Table 1 ), (21 ) and absolute intakes in gram per day were calculated. Dietary changes occurring between baseline and follow-up were assessed once with the self-administered follow-up questionnaire. Subjects were asked whether they changed their dietary habits (profoundly, partly or not) after baseline.

Statistical analysis

Statistical analyses were performed separately for men and women. We used SAS for Windows V8 (SAS Institute, Cary, NC). Means and SD as well as frequency distributions of participant characteristics were calculated. Weight change per year was used to categorize subjects into five weight change categories, i.e., subjects with weight differences ranging from -1 kg/y to +1 kg/y were considered weight maintainers and represented the reference group, participants with small weight differences (-2 to -1 kg/y or +1 to +2 kg/y) were subjects with small weight loss and small weight gain, respectively, and participants with larger differences in body weight ( -2 kg/y and +2 kg/y) were regarded as those with large weight loss and large weight gain. The categorical variable "weight change" was the dependent variable, comprising now five categories (large weight gain, small weight gain, weight maintenance, small weight loss and large weight loss) and food group intake variables were the independent variables in the chosen statistical models. Differences in mean food group intake across weight change categories were tested using ANOVA. Polytomous logistic regression analysis was applied for the multilevel weight change response variable. Based on generalized logits, the CATMOD procedure was applied for estimating regression parameters. Regression models were fitted for each food group, which included empirically determined confounding factors significantly associated with weight change; these were age at baseline, baseline body weight and height, education, dietary changes (assessed at baseline as dietary changes before baseline and at the first follow-up as dietary changes after baseline), previous weight changes (i.e., previous weight gain if subject reported weight gain of at least 5 kg during the 2 y before baseline, previous weight loss if the subject reported a weight loss of at least 5 kg during the 2 y before baseline and weight cycling if the subjects reported both weight gain and weight loss of at least 5 kg), physical activity expressed as leisure time physical activity, life and health satisfaction, medications affecting body weight (use of corticoids, insulin, psychopharmacologic drugs, antiepileptic drugs and pharmacologic thyroid treatment) and prevalent diseases. In the women’s model, menopausal status was also included. Odds ratios (OR) and 95% confidence intervals (CI) were estimated. Results were considered significant at P < 0.05.

	RESULTS

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
General characteristics of men and women of the study population are presented in Table 2 . Mean follow-up time was 2.2 y in both sexes. The range for the follow-up times was 4.8 y with a minimum of 0.6 y and a maximum of 5.4 y. On average, men lost 0.2 kg (0.1 kg/y of follow-up) and women lost 0.4 kg (0.2 kg/y) after 2 y of follow-up.

In women, significant differences across categories were also observed for several food groups (data not shown); women who gained weight reported a higher intake of processed meat, meat, sauces, fats, eggs and sweets and more coffee/tea, milk/milk products and soft drinks compared with female weight maintainers. Women who lost weight reported to consume less cakes/biscuits, sweets and fats than women who maintained their body weight.

Results of polytomous logistic regression models for the association of each food group with each of the categories of weight change are presented in Tables 4 and 5 . Odds ratios and 95% CI were estimated after controlling for potential confounding factors, i.e., age, body mass index (BMI) and lifestyle factors (but no other food group), with stable body weight as reference. In men, large weight gain was significantly predicted only by consumption of sweets. For each 100 g/d increment in sweets intake, the likelihood of observing a large weight gain increased by 48% (OR 1.48; 95% CI, 1.03–2.13). Other food groups with similar, but not significant effects of higher intake on weight gain were salty snacks, eggs, nuts/seeds, cheese, fats, sauces and processed meat. In contrast, among women, large weight gain was significantly predicted by reported higher fat, sauce and meat consumption. Odds ratios were 1.36 for meat intake, 1.75 for fat intake and 2.12 for reported intake of sauces, for each 100 g/d increment. Food group intake with similar but nonsignificant effects on weight gain was observed for salty snacks, sweets, eggs, fish, and processed meat.

For several food groups, an effect on both weight gain and weight loss was observed. For example, in women, for cooking and spreading fats, a significant positive effect on the large weight gain category was accompanied by a significant inverse effect on the large weight loss category and an increased nut/seed intake decreased the likelihood of both large weight gain and loss.

For the remaining food groups we found no or only weak associations with weight change.

	DISCUSSION

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
We examined the relationship between food group intake and subsequent weight change over a 2-y period. We had generally assumed that intake of high energy, high fat food groups is predictive of weight change, based on the concept that total energy intake above energy expenditure, particularly caused by higher fat intake, is causally related to changes in body weight (24 ). The results of the present analyses mainly support these hypotheses. In particular, the reported intake of food groups that are characterized by a high content of energy and fat, namely, cooking and spreading fats, sauces, meat and sweets was found to be related to changes in body weight, i.e., higher intake of those food groups was predictive of weight gain, and lower intake was predictive of weight loss. Meat consumption was also found to be positively associated with changes in BMI over a 10-y period in a study of 79,000 men and women (13 ). The same study also reported an inverse association between weight gain and high vegetable consumption. This finding, however, could not be confirmed by our data. There were methodological differences between our study and the ones cited in terms of the food group intake data used for the analysis and the choice of the referent group, that is, their exposure of interest was stable behavior over time, which was determined by the assessment of dietary behavior at baseline and 10 y later; subjects were considered to have a stable behavior if they ranged in the same quintile for dietary intake at baseline and after 10 y. The reference group in that study was not made up of those who maintained body weight, as in our study, but those who were constantly in the lowest quintile for the specific food group.

Several other studies failed to show effects of the intake of single food items or groups of food items on weight change. In a cohort from two southeastern New England communities, none of the examined food groups except artificial sweeteners were significantly related to weight change over a 4-y period (16 ). In a 10-y prospective study in Finnish men, a so-called "healthy diet," that is, the consumption of fruit and vegetables (at least three times the day), margarine and skimmed milk, was related to an increase in body weight (15 ).

In addition, we observed a clear gender difference in associations of food group intake with weight change; in women, several high fat food groups were strongly related to weight gain or loss, whereas among men, the reported sweets intake was the only strong predictor of weight gain. These observations are similar to findings from other studies. Using multivariate linear regression analysis, French et al. (14 ) examined food groups as predictors of weight change in a cohort of 3500 men and women. They observed that the increased consumption of French fries, dairy products, sweets and meat over a 2-y period independently predicted increases in body weight in women, whereas in men, increased consumption of sweets and eggs were positively related to weight gain. Another analysis focusing on the relation between food intake and weight change in men of the Multiple Risk Factor Intervention Trial (12 ), showed that the lower the intake of food groups rich in energy and fat and the higher the consumption of low fat food groups, the greater the weight loss.

Unlike the previous studies on dietary intake and weight change, we applied a polytomous logistic regression analysis. This approach allowed us to test the effect of reported food group intake on weight change categories, that is, on weight gain and on weight loss separately, with stable weight as the reference group. The advantages of the polytomous logistic regression over the traditionally used separate logistic regression models include the increased power, the more parsimonious model and the fact that all available data are entered simultaneously in one model. Compared with the linear regression approach, which assumes a uniform function between food group intake and weight change over the entire scale of weight change, the polytomous logistic regression allows the examination of different degrees of the association. For example, our analytical approach allowed us to estimate simultaneously the effect of sweets intake on both large and small weight gain and on large and small weight loss. A clear disadvantage of the polytomous logistic approach, one it shares with all other categorical approaches, is the simplification of the weight change variable from a continuous into a categorized variable. However, we think that this was reasonable and even necessary to reduce the risk of misclassification of subjects due to measurement error associated with self-reported body weight.

In our study, the calculated weight change was based on an exact measured variable at baseline and a self-report at follow-up and therefore required careful attention as an outcome variable in the statistical analyses. It has been shown often that although self-reported body weight is highly correlated with measured body weight by technicians, there are substantial uncertainties due to underreporting in overweight subjects, overreporting in underweight subjects, rounding and last digit preferences (25 –27 ). These assumptions about measurement error are supported by the observations that in our study population, mean body weight decreased slightly whereas in other large cohorts, mean body weight increased during follow-up (11 ,14 ,28 ,29 ). That the mean body weight decreased in our study population might be attributed to underreporting of follow-up body weight or to a healthy cohort effect.

Our study has some limitations that should be mentioned. One refers to the period for which the dietary behavior was assessed. The dietary assessment was done at baseline and covers the eating behavior of the year before baseline. Information on quantitative and qualitative changes of dietary habits during follow-up was not available. To allow for this limitation at least in part, we took the information on dietary changes after baseline from the follow-up examination into account by adding a discrete variable (dietary changes yes or no) to the regression model.

Assessing previous diet is indeed predictive of measured weight change in prospective studies, whereas the cross-sectional analyses of food intake and body weight are highly inconsistent (30 –33 ). For example, in our study, fat intake in women was predictive of subsequent weight gain, whereas in a cross-sectional analysis, it was inversely related to body weight; a decreased consumption of sweets and cakes/biscuits was predictive of weight loss, but intake was not related to baseline body weight (data not shown). One possible explanation might be the temporal sequence of dietary changes and weight changes as well as their mutual interdependence. Dietary habits are assessed in various states of energy balance and thus at varying states of body weight development. If both diet and body weight are measured at the same time, the changes of dietary behavior as a consequence of changes in body weight and vice versa can distort the cross-sectional relation, whereas, prospectively, the expected relation between dietary intake and body weight development is observable. In a small prospective study of 271 women over a 12-y period, it was observed that dietary changes during the first 6 y of observation were related to anthropometric changes during the subsequent 6 y of observation (34 ). Another limitation is the self-reported follow-up body weight.

The strengths of the present study are its large sample size and the exclusion of smokers and recent quitters from the study population. Smoking habits are known to influence body weight and particularly recent smoking cessation is a strong predictor of weight gain (35 –39 ). Because we had no information on smoking status at follow-up and the scope of the present paper was the examination of food group intakes as predictors of weight gain and weight loss, analyses were done for nonsmokers only. Another strength of the present analysis was the consideration of many potential confounding factors. Even controlling for many potential confounders (e.g., initial BMI, age, previous weight change, dieting behavior, education and lifestyle factors), we observed a significant effect of reported food group intake on changes in body weight.

Our regression models did not control for total energy intake. Quantitative variation in dietary intake results from differences in body size, physical activity and metabolic efficiency. Because we included BMI as a measure of body size and leisure time physical activity in our regression models, we tried to reduce variation in food group intake that is simply the result of differences in total energy intake caused by the conditions mentioned.

However, because dietary habits are a combination of the intake of single food items and food groups, food pattern analysis using factor or cluster analysis would be appropriate methods for further investigations.

In conclusion, our data show that the reported intake of food items high in sugar predicted short-term weight change in men and that an increased intake of food groups high in fat predicted short-term weight change in women. Despite limitations of the present analysis, we suggest that the dietary factors identified in our study population represent important predictors of short-term weight gain and weight loss.

	FOOTNOTES

 
¹ The EPIC-Potsdam Study is supported by the German Cancer Aid (Grant no. 70–2488-Ha I) and the European Community (Grant no. SI2.195579 05F02).

³ Abbreviations: BMI, body mass index; CI, confidence interval; EPIC, European Prospective Investigation into Cancer and Nutrition; OR, odds ratio.

Manuscript received 21 August 2001. Initial review completed 2 October 2001. Revision accepted 28 February 2002.

	LITERATURE CITED

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 

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