Beer consumption and the ‘beer belly’: scientific basis or common belief?



The term ‘beer belly’ expresses the common belief that beer consumption is a major determinant of waist circumference (WC). We studied the gender-specific associations between beer consumption and WC (partially in relation to body weight and hip circumference (HC) change).


Within the European Prospective Investigation into Cancer and Nutrition (EPIC)–Potsdam study (7876 men, 12 749 women), cross-sectional associations were investigated applying general linear models. Prospective analyses of baseline beer consumption and an 8.5-year WC change were assessed using multivariate general linear models and polytomous logistic regression. To test the site-specific effect of beer consumption on WC, an adjustment for concurrent changes in body weight and HC was carried out. In addition, the relationship between change in beer consumption and change in WC was studied.


A positive association in men and no association in women were seen between beer consumption and WC at baseline. Men consuming 1000 ml/d beer were at 17% higher risk for WC gain compared with very light consumers. Significantly lower odds for WC gain (odds ratio=0.88; 95% confidence interval 0.81, 0.96) were found in beer-abstaining women than in very-light-drinking women. The adjustment for concurrent body weight and HC change diminished effect estimates notably, explaining most of the association between beer and change in WC. Decreasing beer consumption was related to higher relative odds for WC loss, although not statistically significant.


Beer consumption leads to WC gain, which is closely related to concurrent overall weight gain. This study does not support the common belief of a site-specific effect of beer on the abdomen, the beer belly.


With a beer consumption of about 112 l per capita in 2006 (Statistisches Bundesamt Deutschland, 2007), beer is a commonly consumed alcoholic beverage in Germany. It is believed that beer consumption is associated with increased waist circumference (WC), particularly in men—a phenomenon popularly referred to as ‘beer belly’. This belief might have found support by cross-sectional research, reporting abdominal obesity as being associated with beer consumption (Slattery et al., 1992; Duncan et al., 1995; Dallongeville et al., 1998; Bobak et al., 2003; Wannamethee et al., 2005). In contrast, prospective studies have been rather inconsistent, reporting positive associations (Vadstrup et al., 2003; Vernay et al., 2004), no associations (Halkjaer et al., 2004; Koppes et al., 2005) or even marginally negative associations among men (Halkjaer et al., 2006).

Abdominal obesity is one of the most potent cardiovascular risk factors, making it of interest to understand whether beer consumption increases the risk of this site-specific fat patterning. The European Prospective Investigation into Cancer and Nutrition (EPIC)–Potsdam study is a prospective cohort study that has assembled data at baseline and during follow-up, raising the possibility of studying the association between beer consumption and WC, as a measure of abdominal fat accumulation, using cross-sectional, prospective and change-in-exposure–change-in-outcome analyses. To extract the potential site-specific effect of beer consumption on WC, we also considered body weight and hip circumference (HC).

Participants and methods

Study population

The EPIC–Potsdam study is part of the large-scale prospective EPIC study (Riboli et al., 2002). Between 1994 and 1998, 27 548 men and women from the general population of Potsdam and adjacent communities, aged 35–65 years, were recruited. All participants provided written informed consent. Study approval was given by the Ethics Committee of the Medical Association of the Federal State of Brandenburg, Germany. For the present analysis, 7876 men and 12 749 women with complete information on beer consumption, WC and covariates were analysed. For the to change-in-beer-consumption to change-in-WC analysis, participants with missing information on beer consumption at follow-up were excluded, leaving a subsample of 19 941 participants (7614 men).

Exposure assessment

Information on consumption of alcoholic beverages (ml/d) covering the year before baseline was obtained from a self-administered food frequency questionnaire. Information on alcoholic beverages was reassessed at the third follow-up (approximately 6 years after recruitment, Figure 1). As men and women differed considerably in the amount of beer consumption, a gender-specific categorization of beer consumption was chosen with four categories for women (no beer, >0 to <125 ml/d (=very light), 125 to < 250 ml/d (=light) and 250 or more ml/d (=moderate)) and five categories for men (no beer, >0 to <250 ml/d (=very light), 250 to <500 ml/d (=light), 500 to <1000 ml/d (=moderate) and 1000 ml/d (=heavy)). This categorization is based on the usual bottle size of beer in Germany (0.5 l), which was also the reference unit in the food frequency questionnaire. The ‘very light’ consumption category was chosen as the reference category in the present analysis (>0–<125 ml/d for women, >0<250 ml/d for men) because the ‘no beer’ category contained only 7% of the men and it has been previously concluded that the non-drinker category is not an appropriate reference group in alcohol analysis (Wannamethee and Shaper, 1997). As beer consumption varied over follow-up time, we also studied change in beer consumption. A change in beer consumption between baseline and third follow-up was defined as the change of at least one category. Thus, change in beer consumption was classified as stable (same category at both examinations), increase or decrease.

Figure 1

Study design. Time points when waist circumference (WC), body weight and hip circumference (HC), as well as beer consumption, were assessed. Analysis strategies ( → ) (1) cross-sectional analysis (baseline beer consumption in relation to WC at baseline); (2) longitudinal analysis (baseline beer consumption in relation to WC at follow-up and change in WC between baseline and follow-up); (3) change-in-beer-consumption to change-in-WC analysis.

Outcome assessment

Baseline anthropometry was measured by trained interviewers. Body weight was obtained without shoes in light clothing to the nearest of 0.1 kg, WC was measured to the nearest of 0.1 cm midway between the iliac crest and the lower ribs, and HC was measured over the buttocks (Haftenberger et al., 2002). Follow-up information on anthropometry (Figure 1) was obtained by asking each participant to self-measure their body weight without clothes, and measure their WC midway between the iliac crest and the lower ribs, and HC at the widest point over the buttocks.

The WC change was computed as the difference between follow-up and baseline WC, divided by the individual follow-up time (in years) and multiplied by the mean follow-up time of 8.5 years. The same was carried out for change in body weight and change in HC used as covariates in analyses. The relative validity of self-reported circumferences was assessed in a separate validation study among 68 men and women. These participants measured their WC according to the enclosed instructions of the follow-up questionnaire. Thereafter, a trained interviewer repeated the measurement, which was considered as the reference measurement. The reference-to-self-measured difference was computed for each participant with a mean of =−1.82 cm and a standard deviation (s.d.) of 3.88. In this validation study, the reference-to-self-measured difference was found to be not significantly correlated with age or body mass index in women, but was inversely correlated with body mass index in men (r=−0.44, P<0.05). No systematic dependence of the reference-to-self-measured difference on absolute values of WC was observed. Using the formula, ±1.645 × s.d. the 90% tolerance interval (−8.2 to 4.6 cm) of the reference-to-self-measured difference was computed. This interval determines the range of WC change values that cannot be assumed a priori to be true changes. WC changes within this interval could be because of measurement error in the self-measurements. We used this interval to classify WC change into WC loss WC (<−8.2 cm), stable WC (−8.2 cm to 4.6 cm) and WC gain (>4.6 cm) categories.


All analyses were stratified by gender. First, general linear models were used to compute adjusted mean values of WC at baseline, at follow-up and the change in WC for each beer consumption category. As spline regression models did not reveal a significant deviation from linearity, tests for linear trend were carried out by using the median of each beer consumption category.

Second, to address the multilevel end-point WC change in one statistical model, polytomous logistic regression (PLR) was applied. In contrast to ordinal logistic regression, which models the cumulative risk estimate, PLR allows computing odds ratio (OR) for each level of a multilevel outcome. Age (years), physical activity (h/week), WC at baseline (cm), number of cigarettes, alcohol (g/d) from other alcoholic beverages (wine, fruit wine, sparkling wine, spirits, non-alcoholic beer and aperitif) and non-beer energy intake (EI) were fitted as continuous variables into the models; whereas categorical variables, such as change in smoking status, education, menopausal status, incident diseases (diabetes type 2, stroke, myocardial infarction and cancer) or change in menopausal status, were fitted as indicator variables. To estimate whether WC change due to beer consumption was independent of change in body weight or HC, PLR models were additionally adjusted for these variables in seperate models (Model 2, Model 3).

Third, the association between change-in-beer-consumption and change-in-WC was investigated using PLR, with the ‘stable’ beer consumption category as reference. This analysis was adjusted for age, change variables for smoking status and consumption of other alcoholic beverages, and baseline information on physical activity, education, non-alcoholic EI, beer consumption, WC, and for women, additionally for change in menopausal status.

Furthermore, we checked directly whether the part of the variation of change in WC that is not explained by change in body weight was associated with beer consumption. For this purpose, we calculated the residuals of the regression of change in WC on body weight change and repeated the PLR analyses.

All statistical analyses were conducted with SAS, Statistical Analysis System, 9.1, Cary, NC, USA.


The relative proportion of men and women with stable WC was 41 and 32%, respectively, whereas 57% of men and 67% of women displayed increasing WC. Among 57% of the men and 69% of the women, stable beer consumption was observed, whereas 30 and 22%, respectively, decreased their beer consumption. Table 1 shows the distribution of co-variables across categories of beer consumption by gender. In men, more u-shaped distributions were observed for change in HC and total EI. In women, non-beer EI and physical activity were stable across beer consumption categories. Total EI in women increased across beer consumption categories.

Table 1 General characteristics across beer consumption categories for men (n=7876) and women (n=12 749)

Tables 2 and 3 describe the association between beer consumption, WC and change in WC, weight and waist-to-hip ratio by gender. In men (Table 2), WC at baseline and WC at follow-up increased significantly with increasing beer consumption (Ptrend<0.0001). A rather u-shaped relationship between beer consumption and change in WC and weight was found. The lowest changes were observed in the category of light beer drinkers, and the largest increases were observed for beer abstainers and heavy-beer-drinking men.

Table 2 Adjusted arithmetic mean values of WC at baseline (cross-sectional), WC at follow-up and change in WC, WHR and body weight across the beer consumption categories in men (n=7876)
Table 3 Adjusted arithmetic mean values of WC at baseline (cross-sectional), WC at follow-up and change in WC, WHR and body weight across the beer consumption categories in women (n=12 749)

In women (Table 3), WC at baseline and at follow-up was not linearly related to beer consumption (Ptrend=0.21, P=0.30). However, in contrast to men, a positive association between beer consumption and change in WC (Ptrend<0.004; Table 3) was observed. Weight change increased in the light beer drinkers and decreased in the moderate drinkers again.

Table 4 describes the results of the multivariate PLR analyses. In men, lower relative odds for WC loss were observed for all categories of beer consumption and for beer abstainers compared with that in men consuming 1–<250 ml/d beer. Moderate consumers of beer had significantly reduced relative odds for WC loss (OR=0.49; confidence interval (CI) 0.28, 0.85) compared with those in the reference group. In contrast, heavy consumers (1000 ml/d+) had significantly increased relative odds for WC gain compared with very light drinkers (OR=1.17; CI 1.01, 1.35; Table 4, model 1). Stratification on baseline WC (<95 cm, 95 cm) showed that heavy beer consumption seemed to be more strongly related to WC gain in lean men (OR=1.31; 95% CI 1.06, 1.63) than in men with WC95 cm (OR=1.09; 95% CI 0.89, 1.33). However, tests for interaction were not significant (WC loss: P=0.94; WC gain: P=0.50).

Table 4 Prospective approach: OR and 95% CI obtained from the polytomous logistic regression for beer consumption in relation to change in waist circumference in men (n=7876) and women (n=12 749)

Beer-abstaining women showed significantly lower relative odds (OR=0.88; CI 0.81, 0.96) (Table 4, model 1) for WC gain compared with their very-light-drinking counterparts. As in men, stratification on baseline WC showed that leaner women (WC<80 cm) were still at significantly lower risk for WC gain (OR=0.85; 95% CI 0.75, 0.95) when abstaining from beer. In contrast, women with WC80 cm were not at a significantly higher risk for WC gain (OR=0.92; 95% CI 0.81, 1.03). Again, there was no statistical evidence of effect modification by baseline WC (WC loss: P=0.10; WC gain: P=0.78).

To investigate the dependence of WC change on body weight or HC change, an adjustment for concurrent change in weight and HC (Table 4, models 2 and 3) was carried out. Both adjustments diminished ORs notably toward the null value, and they were no longer statistically significant. However, after adjusting for HC change in women, the OR for WC gain among beer abstainers was on the border of significance (OR=0.91; CI 0.83, 1.00), which implies the possibility of a site-specific effect. The results from the residual analyses were not significant, thus supporting the observation that WC change is mostly attributable to body weight change.

The results from the analysis to determine the relationship between change-in-beer-consumption and change-in-WC, although not statistically significant, showed that men and women who decreased their beer consumption had higher relative odds for WC loss, OR=1.48 (95% CI 0.99, 2.10) and OR=1.20 (95% CI 0.81, 1.76), respectively, and lower relative odds for WC gain, OR=0.94 (95% CI 0.83, 1.05) and OR=0.94 (95% CI 0.85, 1.04). Conversely, when beer consumption was increased during follow-up, the risk for WC loss was decreased with OR=0.77 (95% CI 0.42, 1.39) for men and OR=0.81 (95% CI 0.46, 1.42) for women, whereas the risk for WC gain was increased, OR=1.13 (95% CI 0.98, 1.31) and OR=1.08 (95% CI 0.94, 1.23), respectively. Effect estimates diminished again when an adjustment for change in body weight and HC was made (data not shown).


This analysis showed the empirical basis for the common belief of a beer belly, as we found that beer drinking and WC were positively associated. However, our data provided only limited evidence for a site-specific effect of beer drinking on WC, and beer consumption seems to be rather associated with an increase in overall body fatness.

Similar to most other studies, we started with cross-sectional comparison. Our results support most of the cross-sectional findings of a positive association between beer consumption and WC (Dallongeville et al., 1998; Bobak et al., 2003; Wannamethee et al., 2005; Riserus and Ingelsson, 2007) and waist-to-hip ratio (Slattery et al., 1992; Duncan et al., 1995; Wannamethee et al., 2005; Riserus and Ingelsson, 2007) in men and are also in line with the absent association (Duncan et al., 1995; Bobak et al., 2003; Lukasiewicz et al., 2005) in women, but contradict some (Slattery et al., 1992; Dallongeville et al., 1998; Rosmond and Bjorntorp, 1999; Halkjaer et al., 2004) reported results. Beverage preferences or temporal sequence could be a reason for the diversity of the cross-sectional association, as the higher energy requirement of obese persons could be the reason for higher beer consumption. The longitudinal analyses showed a positive association for both genders. Our findings are supported by the French DESIR (Vernay et al., 2004) and the Copenhagen City Heart Study (Vadstrup et al., 2003). In women, this is additionally shown by two other studies (Kahn et al., 1997; Halkjaer et al., 2006). However, several studies report no association (Halkjaer et al., 2004; Koppes et al., 2005) or a marginally negative association, although not significant, between beer consumption and WC or WC change (Halkjaer et al., 2006) in men. These inconsistencies could be because of self-reported vs interviewer-measured anthropometry.

Furthermore, after adjustment for change in body weight, beer had no weight-independent effect on WC, which was also supported by results from the residual analysis. Changes in WC are largely explained by changes in body weight. Adjustment for change in HC also diminished the effect measures notably, more in men than in women, which implies no site-specific gain at the abdomen, but rather an increase in both WC and HC. Such concurrent changes have not been considered in the above-mentioned studies, a reason why these studies cannot rule out that the observed increase in WC was accompanied by increasing body weight and HC. Only one study has adjusted for a concurrent change in body mass index (Halkjaer et al., 2004), with WC gain remaining significantly associated with beer and spirits consumption in women. This result could be because of an insufficient adjustment for smoking, which is known to be a predictor of large WC (Shimokata et al., 1989). Even if there was an independent effect of beer consumption on WC change, the overall effect was small, with approximately 1.5 cm in 6 years.

In men, non-drinkers and heavy drinkers of beer showed the largest increase in WC. Especially, beer abstainers differed substantially from light-drinking men, and are more similar to heavy beer drinkers. The strong WC and body weight gain in beer abstainers may be because of former beer drinkers in this group, and because of characteristics that increase their risk of WC and body weight gain. Although the prospective design should eliminate such exposure influence from the past, especially for alcoholic beverages, it is shown that former exposure can be of importance. As total EI was seen to be u-shaped and non-beer EI decreases across the beer consumption categories, calories from beer are partially compensated by lower non-beer EI and are partly additive, resulting in a higher total EI. In contrast, energy from beer was not compensated but additive in women. However, the level of beer consumption was different between men and women.

The approach to study the relationship between change in beer consumption and changes in WC and the resulting higher relative odds for WC loss due to reduction of beer consumption lends further credence to the hypothesis of beer being a determinant of WC. As in our study, this relationship was also not statistically significant in other studies when investigating changes in total alcohol use (Eck et al., 1995; Koh-Banerjee et al., 2003; Koppes et al., 2005) and changes in WC. A few other studies have focussed on change in alcohol use in relation to change in body weight instead of WC change. Some (Wannamethee and Shaper, 2003; Wannamethee et al., 2004), but not all (Sherwood et al., 2000), reports showed increased risks for weight gain for those starting (heavy) alcohol use.

As energy from beer is additive (partially) in our study, this could be a plausible mechanism for the positive association between gain in WC and body weight. Besides ethanol, the high carbohydrate content of beer contributes to the daily EI, with about 60 calories per 500 g (Souci et al., 2000). Wine too contains about 50 calories from carbohydrates per 500 g edible portion (Souci et al., 2000); however, wine is not consumed in such large amounts as is beer. Thus, the provision of calories from carbohydrates is typical for beer consumption. The role of ethanol to provide energy (7 kcal/g) is, in contrast to carbohydrates, questionable and remains insufficiently resolved in the literature (Klesges et al., 1994; Jequier, 1999; McCarty, 1999). Another suggested mechanism could be the activation of the hypothalamic–pituitary–adrenal axis because of ethanol, which is shown to be associated with increased abdominal fat (waist-to-hip ratio) and also with overall fat (body mass index) (Bjorntorp, 2001). However, it is not possible to investigate this hypothesis with our data at hand.

This study has a number of strengths. First, information on self-reported alcoholic beverages from the food frequency questionnaire showed high reproducibility and validity compared with 24 h recalls (Bohlscheid-Thomas et al., 1997). Second, we used several approaches to test the association between beer consumption and WC, and the results were consistent. We also distinguished between overall and site-specific gain in relation to beer consumption, which was not addressed in many previous publications. Finally, WC changes were classified using information from the validation study addressing self- vs interviewer measured WC.

We also faced limitations. First, some categories of beer consumption contained only a small number of study participants. Effect estimates from these groups should be interpreted cautiously. Second, as beer consumption was already positively associated with WC at baseline, we cannot rule out an underestimation of the impact of beer consumption on long-term WC change because the increase in WC may have occurred before the study began. Finally, the self-reported WC measurements may not completely be compensated by the tolerance interval derived from the validation study, because the study was relatively small.

In summary, these results suggest that beer consumption and absolute WC and WC change are associated. However, this association is driven by body weight change. We could not confirm the hypothesis of a site-specific effect of beer on WC, as HC changed comparably. In terms of public health relevance, it may be therefore important to focus on beer abstention to maintain body weight. In terms of the ‘beer belly belief’, an explanation could be that all the observed ‘beer bellies’ in the population result from the natural variation in fat patterning and not from the fact of drinking beer. In this way, the common belief of beer consumption and ‘beer belly’ development is not supported by our study.

Conflict of interest

The authors declare no conflict of interest.


  1. Bjorntorp P (2001). Do stress reactions cause abdominal obesity and comorbidities? Obes Rev 2, 73–86.

    CAS  Article  Google Scholar 

  2. Bobak M, Skodova Z, Marmot M (2003). Beer and obesity: a cross-sectional study. Eur J Clin Nutr 57, 1250–1253.

    CAS  Article  Google Scholar 

  3. Bohlscheid-Thomas S, Hoting I, Boeing H, Wahrendorf J (1997). Reproducibility and relative validity of food group intake in a food frequency questionnaire developed for the German part of the EPIC project. European Prospective Investigation into Cancer and Nutrition. Int J Epidemiol 26 (Suppl 1), S59–S70.

    Article  Google Scholar 

  4. Dallongeville J, Marecaux N, Ducimetiere P, Ferrieres J, Arveiler D, Bingham A et al. (1998). Influence of alcohol consumption and various beverages on waist girth and waist-to-hip ratio in a sample of French men and women. Int J Obes Relat Metab Disord 22, 1178–1183.

    CAS  Article  Google Scholar 

  5. Duncan BB, Chambless LE, Schmidt MI, Folsom AR, Szklo M, Crouse 3rd JR et al. (1995). Association of the waist-to-hip ratio is different with wine than with beer or hard liquor consumption. Atherosclerosis Risk in Communities Study Investigators. Am J Epidemiol 142, 1034–1038.

    CAS  Article  Google Scholar 

  6. Eck LH, Pascale RW, Klesges RC, Ray JA, Klesges LM (1995). Predictors of waist circumference change in healthy young adults. Int J Obes Relat Metab Disord 19, 765–769.

    CAS  PubMed  Google Scholar 

  7. Haftenberger M, Lahmann PH, Panico S, Gonzalez CA, Seidell JC, Boeing H et al. (2002). Overweight, obesity and fat distribution in 50- to 64-year-old participants in the European Prospective Investigation into Cancer and Nutrition (EPIC). Public Health Nutr 5, 1147–1162.

    CAS  Article  Google Scholar 

  8. Halkjaer J, Sorensen TI, Tjonneland A, Togo P, Holst C, Heitmann BL (2004). Food and drinking patterns as predictors of 6-year BMI-adjusted changes in waist circumference. Br J Nutr 92, 735–748.

    CAS  Article  Google Scholar 

  9. Halkjaer J, Tjonneland A, Thomsen BL, Overvad K, Sorensen TI (2006). Intake of macronutrients as predictors of 5-y changes in waist circumference. Am J Clin Nutr 84, 789–797.

    CAS  Article  Google Scholar 

  10. Jequier E (1999). Alcohol intake and body weight: a paradox. Am J Clin Nutr 69, 173–174.

    CAS  PubMed  Google Scholar 

  11. Kahn HS, Tatham LM, Heath Jr CW (1997). Contrasting factors associated with abdominal and peripheral weight gain among adult women. Int J Obes Relat Metab Disord 21, 903–911.

    CAS  Article  Google Scholar 

  12. Klesges RC, Mealer CZ, Klesges LM (1994). Effects of alcohol intake on resting energy expenditure in young women social drinkers. Am J Clin Nutr 59, 805–809.

    CAS  Article  Google Scholar 

  13. Koh-Banerjee P, Chu NF, Spiegelman D, Rosner B, Colditz G, Willett W et al. (2003). Prospective study of the association of changes in dietary intake, physical activity, alcohol consumption, and smoking with 9-y gain in waist circumference among 16 587 US men. Am J Clin Nutr 78, 719–727.

    CAS  Article  Google Scholar 

  14. Koppes LL, Twisk JW, Van Mechelen W, Snel J, Kemper HC (2005). Cross-sectional and longitudinal relationships between alcohol consumption and lipids, blood pressure and body weight indices. J Stud Alcohol 66, 713–721.

    Article  Google Scholar 

  15. Lukasiewicz E, Mennen LI, Bertrais S, Arnault N, Preziosi P, Galan P et al. (2005). Alcohol intake in relation to body mass index and waist-to-hip ratio: the importance of type of alcoholic beverage. Public Health Nutr 8, 315–320.

    Article  Google Scholar 

  16. McCarty MF (1999). The alcohol paradox. Am J Clin Nutr 70, 940–942.

    CAS  Article  Google Scholar 

  17. Riboli E, Hunt KJ, Slimani N, Ferrari P, Norat T, Fahey M et al. (2002). European Prospective Investigation into Cancer and Nutrition (EPIC): study populations and data collection. Public Health Nutr 5, 1113–1124.

    CAS  Article  Google Scholar 

  18. Riserus U, Ingelsson E (2007). Alcohol intake, insulin resistance, and abdominal obesity in elderly men. Obesity (Silver Spring) 15, 1766–1773.

    CAS  Article  Google Scholar 

  19. Rosmond R, Bjorntorp P (1999). Psychosocial and socio-economic factors in women and their relationship to obesity and regional body fat distribution. Int J Obes Relat Metab Disord 23, 138–145.

    CAS  Article  Google Scholar 

  20. Sherwood NE, Jeffery RW, French SA, Hannan PJ, Murray DM (2000). Predictors of weight gain in the Pound of Prevention study. Int J Obes Relat Metab Disord 24, 395–403.

    CAS  Article  Google Scholar 

  21. Shimokata H, Muller DC, Andres R (1989). Studies in the distribution of body fat III. Effects of cigarette smoking. JAMA 261, 1169–1173.

    CAS  Article  Google Scholar 

  22. Slattery ML, McDonald A, Bild DE, Caan BJ, Hilner JE, Jacobs Jr DR. et al. (1992). Associations of body fat and its distribution with dietary intake, physical activity, alcohol, and smoking in blacks and whites. Am J Clin Nutr 55, 943–949.

    CAS  Article  Google Scholar 

  23. Souci SW, Fachmann W, Kraut H (2000). Food Composition and Nutrition Tables. 6th ed. Stuttgart: Medpharm.

    Google Scholar 

  24. Statistisches Bundesamt Deutschland (2007). Entwicklung des Bierverbrauchs in Deutschland seit 1997. Wiesbaden.

  25. Vadstrup ES, Petersen L, Sorensen TI, Gronbaek M (2003). Waist circumference in relation to history of amount and type of alcohol: results from the copenhagen city heart study. Int J Obes Relat Metab Disord 27, 238–246.

    CAS  Article  Google Scholar 

  26. Vernay M, Balkau B, Moreau JG, Sigalas J, Chesnier MC, Ducimetiere P (2004). Alcohol consumption and insulin resistance syndrome parameters: associations and evolutions in a longitudinal analysis of the French DESIR cohort. Ann Epidemiol 14, 209–214.

    Article  Google Scholar 

  27. Wannamethee SG, Field AE, Colditz GA, Rimm EB (2004). Alcohol intake and 8-year weight gain in women: a prospective study. Obes Res 12, 1386–1396.

    Article  Google Scholar 

  28. Wannamethee SG, Shaper AG (1997). Lifelong teetotallers, ex-drinkers and drinkers: mortality and the incidence of major coronary heart disease events in middle-aged British men. Int J Epidemiol 26, 523–531.

    CAS  Article  Google Scholar 

  29. Wannamethee SG, Shaper AG (2003). Alcohol, body weight, and weight gain in middle-aged men. Am J Clin Nutr 77, 1312–1317.

    CAS  Article  Google Scholar 

  30. Wannamethee SG, Shaper AG, Whincup PH (2005). Alcohol and adiposity: effects of quantity and type of drink and time relation with meals. Int J Obes (Lond) 29, 1436–1444.

    CAS  Article  Google Scholar 

Download references


The EPIC–Potsdam study was supported by grants from German Cancer Aid, from the German Federal Ministry of Education and Research and the European Union.

Author information



Corresponding author

Correspondence to M Schütze.

Additional information

Contributors: MS, AK, HB and MS were responsible for the study design, concept and idea of the analysis. Data analysis and interpretation of the data was done by MS, HB and MS. The manuscript was drafted by MS and HB. All authors were involved into interpretation of the study results, critical review of the manuscript and contributed significantly to the content of the manuscript.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Schütze, M., Schulz, M., Steffen, A. et al. Beer consumption and the ‘beer belly’: scientific basis or common belief?. Eur J Clin Nutr 63, 1143–1149 (2009).

Download citation


  • beer
  • waist circumference
  • abdominal fat
  • cohort studies
  • longitudinal studies

Further reading