Association between red and processed meat consumption and chronic diseases: the confounding role of other dietary factors



High consumption of meat has been linked with the risk for obesity and chronic diseases. This could partly be explained by the association between meat and lower-quality diet. We studied whether high intake of red and processed meat was associated with lower-quality dietary habits, assessed against selected nutrients, other food groups and total diet. Moreover, we studied whether meat consumption was associated with obesity, after adjustment for all identified associations between meat and food consumption.


The nationally representative cross-sectional study population consisted of 2190 Finnish men and 2530 women, aged 25–74 years. Food consumption over the previous 12 months was assessed using a validated 131-item Food Frequency Questionnaire. Associations between nutrients, foods, a modified Baltic Sea Diet Score and meat consumption (quintile classification) were analysed using linear regression. The models were adjusted for age and energy intake and additionally for education, physical activity and smoking.


High consumption of red and processed meat was inversely associated with fruits, whole grain and nuts, and positively with potatoes, oil and coffee in both sexes. Results separately for the two types of meat were essentially similar. In a linear regression analysis, high consumption of meat was positively associated with body mass index in both men and women, even when using a model adjusted for all foods with a significant association with meat consumption in both sexes identified in this study.


The association between meat consumption and a lower-quality diet may complicate studies on meat and health.


Consumption of red and processed meat (hereafter the general term 'meat' in this manuscript refers to red and processed meat, but excluding poultry) is increasing globally, most markedly in developing countries.1 Besides being an ecological concern, increased meat consumption may also predict an increased risk for chronic diseases2, 3 and mortality.4 One meta-analysis5 showed a link between the consumption of red meat or processed meat vs colorectal cancer, and another concluded that meat consumption predicted the risk of pancreatic cancer6. Both in population studies7, 8 and meta-analyses,3 high consumption of red and processed meat were associated with an increased risk for type II diabetes. Moreover, high consumption of processed meat predicted an increased risk for cardiovascular diseases.3

Although proteins are the most satiating of all macronutrients,9 high consumption of meat has often been associated with higher weight gain in population studies.10 In the European Prospective Investigation into Cancer and Nutrition cohort, every 250 g increase in daily meat portion predicted 2 kg weight gain over 5 years.11

One important question in interpreting the associations between meat and health is whether they are causal or whether they are at least partly explained by correlations between meat and the remaining diet. Vergnaud et al.11 reported that high meat consumption was associated with food choices that per se are predictors of weight gain.10 Another study, using the National Health and Nutrition Examination Survey (NHANES) III data,12 showed that higher consumption of red meat was associated with more frequent consumption of vegetables but less fruits.

Studies addressing meat consumption and health have used different approaches to adjust for dietary confounders. Vergnaud et al.11 used the most extensive adjustment by including other types of meat and a dietary pattern in the model. Also Pan et al.13 used a dietary score, which was based on gastrointestinal, trans-fatty acids and the ratio between polyunsaturated and saturated fatty acids. Other studies have used simpler dietary adjustments, such as adjusting for other types of meat4 or fruits and vegetables.6, 12

The above findings indicate that the association between meat consumption and the overall diet deserves more attention, in order to assist in planning research and analyses on meat and health. Consequently, the aim of the present cross-sectional population study was to test the hypothesis that high consumption of red and processed meat was associated with lower-quality dietary habits, assessed both against nutrients, other food groups and total diet. Moreover, as a post hoc analysis, we used this information to test how the inclusion of the identified dietary confounders affected the association between meat consumption and body mass index (BMI).

Participants and methods

Study participants

The cross-sectional study population consisted of men and women aged 25–74 years who participated in the National FINRISK Study in year 2007.14 A random sample of 10 000 people was drawn from the Finnish population register in five geographical areas. The sample was stratified by gender, 10-year age group and area. The participants were invited to a health examination and to fill in questionnaires on socio-demographic factors, physical activity, smoking and diet. A total of 6258 men and women participated (response rate 63%).

The same participants were contacted later in spring 2007 and asked to come to a health examination of obesity and obesity-related risk factors. The examination included more specific queries on diet, physical activity and medical history. A total of 5024 volunteered (participation rate was 80% of those invited). After exclusions of participants with a missing Food Frequency Questionnaire (FFQ) or background information data or who were pregnant, the sample size for the present study was 2530 women and 2190 men.

This study was conducted according to the guidelines of the Declaration of Helsinki. The participants signed an informed consent. The study protocols were approved by the Ethics Committee of Helsinki and Uusimaa Hospital District.

Food frequency questionnaire

Food consumption over the previous 12 months was assessed using a validated, self-administered 131-item FFQ updated for this study.15, 16 Participants recorded their average consumption of food items and prepared dishes in nine frequency categories ranging from 'never or seldom' to 'at least six times a day'. The participants could also report other frequently consumed foods not included on the list. The portion size was fixed for each food item and mixed dish (for example, by the glass or the slice).

The participants completed the FFQ at the study site, where a trained study nurse reviewed the questionnaire. A nutritionist entered the data into the study database. The average daily food, nutrient and energy intakes (EIs) were calculated using the national food composition (Fineli) database and in-house software.17 Exclusions were made due to incompletely filled FFQs (n=74). In addition, 48 participants were excluded as outliers if their daily EI corresponded to 0.5% at either end of the daily energy-intake distribution range.18

In the present study, 'red meat' consisted of beef, pork and lamb and 'processed meat' of sausages and other processed meat products. As the results were, for the major part, similar for red meat and processed meat, the results of this study are shown combining these two categories.

Baltic Sea Diet Score (BSDS)

BSDS is an overall dietary index, designed to be used in epidemiological studies in the Nordic countries.19 The score consists of nine variables, that is, six food groups and three nutrients. The food groups included fruits, vegetables, cereals, low-fat milk, fish (all regarded as healthy) and meat (regarded as unhealthy). The nutrients included total fat of the diet expressed as a percentage of total EI (E%) (unhealthy), the ratio of polyunsaturated fatty acids to saturated fatty acids and trans-fatty acids (healthy) and alcohol (as ethanol; unhealthy) consumption. We divided each component using sex-specific consumption quartiles as cutoffs, and points from 0 to 3 were assigned according to the predicted health impact of the component (for example, three points to the highest use of healthy and to the lowest use of unhealthy foods). Participants received one point if alcohol intake was moderate (<20 g/day ethanol for men and <10 g/day ethanol for women) and 0 if vice versa. As we investigated the association between meat consumption and an index for the overall dietary quality, having meat in the index would have artificially improved the associations. Therefore, in the present study, meat was removed from the score. The modified ('meatless') BSDS ranged from 0 to 22.

Assessment of covariates

Participants’ age was obtained from the population registry. Self-reported total years of education were categorized by tertiles (low, medium or high third). To adjust for the extension of the basic education system and increase in average school years over time, the classification was done by participants’ birth year. Physical activity and smoking status were determined by a self-administered questionnaire. Leisure time physical activity was assessed according to four categories: inactive (mainly reading, watching television or other light activities); moderately active (walking, cycling, gardening or other activity at least 4 h per week); active (brisk running, walking, cross-country skiing, swimming or other physically demanding activities at least 3 h per week); and highly active (competition sports and physically demanding exercises done several times per week). Because of the low number of highly active participants, the two highest levels were merged to one (active). Smoking was categorized in three classes: never, had quit smoking, and current smoker.

Anthropometric and body composition assessment

Specially trained nurses measured weight, height and waist circumference (WC) using standardized protocols.20 Body weight was measured to the nearest 0.1 kg, with all participants wearing light clothing and no shoes. Height was measured to the nearest 0.1 cm. WC was measured at the midpoint between the lower ribs and iliac crest to the nearest 0.5 cm using a measuring tape. Bioelectric impedance (TANITA TBF-300MA, Tanita Corporation of America, Inc., Arlington Heights, IL, USA) was used to assess body composition.

Statistical analysis

There were significant interactions between sex and meat consumption against consumption of other food groups (results not shown). Hence, the analyses were conducted separately for men and women. All analyses were performed with the R statistical computing program (version 2.13.0).21 A value of P<0.05 was considered significant. Descriptive characteristics were compared across meat consumption fifths by using one-way analysis of variance for continuous variables and chi-square test for categorical variables. Because food consumption distributions were skewed, they were logarithmically transformed.

Associations of meat consumption against macronutrient and sodium intakes, selected foods/food groups and modified BSDS were tested using linear regression (lm-procedure in Base-package, R).21 The foods were chosen to characterize the Finnish diet. We made additional analyses after replacing whole grain with total grains and low-fat and fat-free milk with total dairy. These substitutions did not change the results. As whole grain and low-fat milk products are more clearly regarded as healthy (compared with total grains or dairy), we kept these narrower food groups in the analyses. Nutrient intakes were adjusted to EI by using the residual method.22 We first fitted a simple model using each food at a time as dependent variable and the meat consumption as independent explanatory variable, adjusting the model for age (years) and EI (kJ). Then, we made a more complete model by additionally adjusting for education (three levels), PA (three levels) and smoking (three levels). Graphical modelling of all the above associations was done with splines (gam-procedure in mgcv-package, R).21

The association between BMI, WC, body fat percentage and meat consumption was analysed using linear regression. The simple model was only adjusted for age. The second model was additionally adjusted for education, physical activity, smoking status and foods that were found to be significantly associated with meat consumption for both sexes and analytical models in the earlier analyses. We did not adjust for EI, as this variable is in the causal pathway between food consumption and obesity. The results were essentially similar regardless of the indicator for obesity (BMI, WC or body fat-%), and only the results using BMI are shown. We also analysed the same associations against obesity (BMI>30 kg/m2) with logistic regression. The results of the linear and logistic regressions were essentially similar. Moreover, the results were also essentially similar when the individual food groups were replaced by the BSDS (results not shown).

To take into account possible misreporting of EI, the ratio of reported EI to predicted basal metabolic rate (EI:BMR) was calculated.23 Participants were classified as either under-reporters (EI:BMR1.14) or plausible reporters (EI:BMR>1.14).24, 25 We performed all analyses with and without under-reporters. As the results were essentially similar, we have included all participants in our analyses.


The age-adjusted results showed a linear association between high meat consumption (red and processed combined) and low education, leisure time physical inactivity, smoking and obesity in women (Table 1). The linear trend between higher meat consumption and younger age was significant also in men. The variation in meat consumption within the study population was remarkable: the median consumption in the highest fifth was 5–6 times larger than the median value in the lowest fifth.

Table 1 Characteristics of the participants by meat consumption fifths (mean values or percentages with 95% confidence intervals)

The energy-adjusted associations between intakes of selected nutrients and meat consumption are shown in Supplementary Table S1. Regardless of the model used to adjust for confounders, all associations were highly significant and similar in men and women. Consumption of read and processed meat was positively associated with the intake (g) of protein, total and sub-classes of fat and sodium. In contrast, the intake of carbohydrates, dietary fibre and sucrose were inversely associated with meat consumption (Supplementary Table S1).

Table 2 shows the food consumption in the fifths of meat (red and processed combined) consumption. The differences between a simple model (adjusted for age and EI) and a more complete model (additional adjustments for education, physical activity and smoking) were small. In both sexes, high consumption of meat was inversely associated with fruits, whole grain and nuts. These food groups are typically classified as healthy. Moreover, consumption of meat was positively associated with the consumption of potatoes, oil and coffee in both sexes. The above foods with a significant association with meat consumption in both sexes were chosen as confounders in the subsequent analysis of meat vs BMI (Table 2).

Table 2 Geometric means and 95% confidence intervals (CIs) of food consumption by fifths of meat consumption

The significant inverse association with the modified BSDS in both sexes verified that high consumption of meat was associated with a generally lower-quality diet (Table 2). The association between meat consumption and the modified BSDS, separately for women and men, across the entire range of meat consumption, is shown graphically in Supplementary Figure S1.

The only important difference between red and processed meat was observed for the association with vegetable consumption: the association between vegetables and red meat was positive in men and not significant in women, while the association for processed meat was inverse for both sexes (Supplementary Tables S2 and S3). The index for dietary quality (modified BSDS) had a clear and significant inverse association against both red and processed meat (Supplementary Tables S2 and S3).

The analyses of meat consumption vs BMI are shown in Table 3. For both sexes, the positive association between combined red and processed meat consumption and BMI was seen in the simple model (adjusted for age), as well as in the more complete model. In the latter, age, physical activity and smoking were also associated with BMI in men and women but education only in women. Moreover, in this model consumption of nuts and seeds were inversely associated with BMI; however, the association in men was weak.

Table 3 Association between combined red and processed meat consumption and BMI


High intake of especially processed but also red meat has been linked to increased mortality and morbidity.2, 3, 4, 5, 6, 7, 8 However, many of the population-based studies on meat and health have not adequately considered the potential associations between meat consumption and the remaining diet or other lifestyle factors. Our aim was to make a detailed analysis on associations between meat consumption and diet and thus to create new knowledge for adjustment of confounders in future studies on meat and health.

In our study, high consumption of combined red and processed meat was inversely associated with the modified ('meatless') BSDS. The score has been designed to indicate a healthy diet.19 Moreover, meat consumption was positively associated with the consumption of several single foods or food groups that typically are considered unhealthy. The positive association between meat consumption and sodium intake was also according to this hypothesis. Altogether, our findings indicate that high consumption of red and processed meat seems to be feature of a generally lower-quality diet, despite that a few of the positive associations were also seen against healthier foods (oil, for example).

Red meat and processed meat were similarly related to nutrient intakes and other dietary patterns. We thought initially that processed meat, compared with red meat, would show stronger associations with lower-quality dietary habits. This was not confirmed. Although statistically significant associations between meat and other food groups were more often observed for women than for men, the trends for associations between meat and diet in both sexes were rather similar. This was unexpected, as men eat more meat than women and their dietary habits are also different in many other ways.

Vergnaud et al.11 found that meat consumption was positively associated with potatoes, fish, egg, beverages and alcohol and inversely with vegetables, fruit, legumes, cereals, added fats and cakes. Comparison between their and our results is complicated, as the above results were only adjusted for EI and many of the food groups used were different from ours. Moreover, Vergnaud et al.11 did not separate whole and refined grains. Finally, their meat consumption also included poultry, which we analysed separately. They found one sex difference: sugar was positively associated with meat consumption in men but inversely in women.

In the NHANES III data,12 fruit consumption was inversely associated with the consumption of red meat. In contrast to our study, NHANES III data showed a positive association between red meat and vegetable consumption in both men and women. These analyses were used in describing the study population, and only age was used as a confounder.

We are not aware of any other studies on the relationships between meat and the remaining diet. However, in studies using data-based whole-diet analyses (cluster or factor analysis), meat typically loads in dietary patterns usually regarded as unhealthy. In a study from the United States,26 meat was classified as a component in the 'Western' pattern, together with refined grains, French fries, high-fat dairy, eggs and sweets and desserts. In a Finnish study,27 sausages were included in a 'traditional' eating pattern, together with, for example, rye, potatoes, milk, butter and coffee. These findings are in general agreement with ours. Comparison between the above and our study should be done with care, as actually data-based analyses are hypotheses-free and this is different from theory-based approaches, such as using predetermined scores (for example, the modified BSDS).

We used our data also to study the association between red and processed meat consumption and BMI and adjusted for the same food groups that we had identified to associate with meat consumption. Despite an apparently strong adjustment, the positive association between meat and obesity, seen in the simple model, was almost unaffected. With the cross-sectional design, we could only illustrate how using the information of our data on confounders affects the association between meat consumption and obesity. However, the results corroborate with several prospective cohort studies linking high consumption of meat with larger increases in weight or WC.11, 28, 29, 30, 31, 32

The way we adjusted for dietary variables, that is, by using associations identified in the same data set, suggests that high-meat consumption has a real effect on energy balance at least in women. However, we cannot rule out a remaining residual confounding. Moreover, the adjustments may be inadequate due to inaccurate assessment of food consumption, leaving additional space for residual confounding.

The strengths of our study are a large and representative study population, measured body weight and WC and a dietary analysis by a validated FFQ. The detailed dietary data allowed us to use robust adjustments to examine the associations and confounders of interest. The cross-sectional design is a weakness for the analysis of meat vs obesity. However, the design is appropriate for our main interest on the associations between meat and other dietary variables. We tried to control for under-reporting dietary intake by analysing the data with and without under-reporters.25 However, the results were essentially similar in both cases.

We found that in both men and women the combined consumption of red and processed meat was associated with lower-quality diet, described by using nutrients and food groups, as well as a diet score. The strong association between meat consumption and a lower-quality diet may complicate epidemiological studies on meat and health. Because of the multiple associations, we would recommend using, e.g., theory-based or data-driven diet scores in the attempt to control for these confounding effects.


  1. 1

    Kearney J . Food consumption trends and drivers. Philos Trans R Soc Lond B Biol Sci 2010; 365: 2793–2807.

    Article  Google Scholar 

  2. 2

    Aston LM, Smith JN, Powles JW . Impact of a reduced red and processed meat dietary pattern on disease risks and greenhouse gas emissions in the UK: a modelling study. BMJ Open 2012; 2: e001072.

    Article  Google Scholar 

  3. 3

    Micha R, Michas G, Mozaffarian D . Unprocessed red and processed meats and risk of coronary artery disease and type 2 diabetes—an updated review of the evidence. Curr Atheroscler Rep 2012; 14: 515–524.

    CAS  Article  Google Scholar 

  4. 4

    Rohrmann S, Overvad K, Bueno-de-Mesquita HB, Jakobsen MU, Egeberg R, Tjønneland A et al. Meat consumption and mortality—results from the European Prospective Investigation into Cancer and Nutrition. BMC Med 2013; 11: 63.

    Article  Google Scholar 

  5. 5

    Chan DSM, Lau R, Aune D, Vieira R, Greenwood DC, Kampman E et al. Red and processed meat and colorectal cancer incidence: meta-analysis of prospective studies. PLoS One 2011; 6: e20456.

    CAS  Article  Google Scholar 

  6. 6

    Larsson SC, Wolk A . Red and processed meat consumption and risk of pancreatic cancer: meta-analysis of prospective studies. Br J Cancer 2012; 106: 603–607.

    CAS  Article  Google Scholar 

  7. 7

    Männistö S, Kontto J, Kataja-Tuomola M, Albanes D, Virtamo J . High processed meat consumption is a risk factor of type 2 diabetes in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention study. Br J Nutr 2010; 103: 1817–1822.

    Article  Google Scholar 

  8. 8

    Fretts AM, Howard BV, McKnight B, Duncan GE, Beresford SAA, Mete M et al. Associations of processed meat and unprocessed red meat intake with incident diabetes: the Strong Heart Family Study. Am J Clin Nutr 2012; 95: 752–758.

    CAS  Article  Google Scholar 

  9. 9

    Brennan IM, Luscombe-Marsh ND, Seimon RV, Otto B, Horowitz M, Wishart JM et al. Effects of fat, protein, and carbohydrate and protein load on appetite, plasma cholecystokinin, peptide YY, and ghrelin, and energy intake in lean and obese men. Am J Physiol Gastrointest Liver Physiol 2012; 303: G129–G140.

    CAS  Article  Google Scholar 

  10. 10

    Fogelholm M, Anderssen S, Gunnarsdottir I, Lahti-Koski M . Dietary macronutrients and food consumption as determinants of long-term weight change in adult populations: a systematic literature review. Food Nutr Res 2012; 56; doi:10.3402/fnr.v56i0.19103.

    Article  Google Scholar 

  11. 11

    Vergnaud A-C, Norat T, Romaguera D, Mouw T, May AM, Travier N et al. Meat consumption and prospective weight change in participants of the EPIC-PANACEA study. Am J Clin Nutr 2010; 92: 398–407.

    CAS  Article  Google Scholar 

  12. 12

    Kappeler R, Eichholzer M, Rohrmann S . Meat consumption and diet quality and mortality in NHANES III. Eur J Clin Nutr 2013; 67: 598–606.

    CAS  Article  Google Scholar 

  13. 13

    Pan A, Sun Q, Bernstein AM, Schulze MB, Manson JE, Willett WC et al. Red meat consumption and risk of type 2 diabetes: 3 cohorts of US adults and an updated meta-analysis. Am J Clin Nutr 2011; 94: 1088–1096.

    CAS  Article  Google Scholar 

  14. 14

    Vartiainen E, Laatikainen T, Peltonen M, Juolevi A, Männistö S, Sundvall J et al. Thirty-five-year trends in cardiovascular risk factors in Finland. Int J Epidemiol 2010; 39: 504–518.

    Article  Google Scholar 

  15. 15

    Männistö S, Virtanen M, Mikkonen T, Pietinen P . Reproducibility and validity of a food frequency questionnaire in a case-control study on breast cancer. J Clin Epidemiol 1996; 49: 401–409.

    Article  Google Scholar 

  16. 16

    Kaartinen NE, Tapanainen H, Valsta LM, Similä ME, Reinivuo H, Korhonen T et al. Relative validity of a FFQ in measuring carbohydrate fractions, dietary glycaemic index and load: exploring the effects of subject characteristics. Br J Nutr 2012; 107: 1367–1375.

    CAS  Article  Google Scholar 

  17. 17

    Reinivuo H, Hirvonen T, Ovaskainen M-L, Korhonen T, Valsta LM . Dietary survey methodology of FINDIET 2007 with a risk assessment perspective. Public Health Nutr 2010; 13: 915–919.

    Article  Google Scholar 

  18. 18

    Meltzer HM, Brantsaeter AL, Ydersbond TA, Alexander J, Haugen M . Methodological challenges when monitoring the diet of pregnant women in a large study: experiences from the Norwegian Mother and Child Cohort Study (MoBa). Matern Child Nutr 2008; 4: 14–27.

    Article  Google Scholar 

  19. 19

    Kanerva N, Kaartinen NE, Schwab U, Lahti-Koski M, Männistö S . Adherence to the Baltic Sea diet consumed in the Nordic countries is associated with lower abdominal obesity. Br J Nutr 2012; 10: 1–9.

    Google Scholar 

  20. 20

    Tolonen H, Koponen P, Aromaa A . Recommendations for the Health Examination Surveys in Europe. The National Public Health Institute: Helsinki, Finland, 2008.

    Google Scholar 

  21. 21

    R Development Core Team R: A language and environment for statistical computing (Internet). (cited 3 February 2013). Available from:

  22. 22

    Willett W, Stampfer MJ . Total energy intake: implications for epidemiologic analyses. Am J Epidemiol 1986; 124: 17–27.

    CAS  Article  Google Scholar 

  23. 23

    FAO/WHO/UNU. Report of a joint expert consultation. Energy and Protein Requirements. World Health Organization: Geneva, Switzerland, 1985.

  24. 24

    Goldberg GR, Black AE, Jebb SA, Cole TJ, Murgatroyd PR, Coward WA et al. Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording. Eur J Clin Nutr 1991; 45: 569–581.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25

    Black AE . Critical evaluation of energy intake using the Goldberg cut-off for energy intake:basal metabolic rate. A practical guide to its calculation, use and limitations. Int J Obes Relat Metab Disord 2000; 24: 1119–1130.

    CAS  Article  Google Scholar 

  26. 26

    Fung TT, Schulze M, Manson JE, Willett WC, Hu FB . Dietary patterns, meat intake, and therisk of type 2 diabetes in women. Arch Intern Med 2004; 164: 2235–2240.

    Article  Google Scholar 

  27. 27

    Mikkilä V, Räsänen L, Raitakari OT, Pietinen P, Viikari J . Consistent dietary patterns identified from childhood to adulthood: the cardiovascular risk in Young Finns Study. Br J Nutr 2005; 93: 923–931.

    Article  Google Scholar 

  28. 28

    Rosell M, Appleby P, Spencer E, Key T . Weight gain over 5 years in 21,966 meat-eating, fish-eating, vegetarian, and vegan men and women in EPIC-Oxford. Int J Obes (Lond) 2006; 30: 1389–1396.

    CAS  Article  Google Scholar 

  29. 29

    Sánchez-Villegas A, Bes-Rastrollo M, Martínez-González MA, Serra-Majem L . Adherence to a Mediterranean dietary pattern and weight gain in a follow-up study: the SUN cohort. Int J Obes (Lond) 2006; 30: 350–358.

    Article  Google Scholar 

  30. 30

    Halkjaer J, Tjønneland A, Overvad K, Sørenson TIA . Dietary predictors of 5-year changes in waist circumference. J Am Diet Assoc 2009; 109: 1356–1366.

    Article  Google Scholar 

  31. 31

    Mozaffarian D, Hao T, Rimm EB, Willett WC, Hu FB . Changes in diet and lifestyle and long-term weight gain in women and men. N Engl J Med 2011; 364: 2392–2404.

    CAS  Article  Google Scholar 

  32. 32

    Romaguera D, Ängquist L, Du H, Jakobsen MU, Forouhi NG, Halkjær J et al. Food composition of the diet in relation to changes in waist circumference adjusted for body mass index. PLoS One 2011; 6: e23384.

    CAS  Article  Google Scholar 

Download references


The study was supported by the Academy of Finland (grants 136895 and 263836).

Author Contributions

MF and SM designed research; NK analyzed data; all the authors wrote the paper; and MF had primary responsibility for the final content.

Author information



Corresponding author

Correspondence to M Fogelholm.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information

Supplementary Information accompanies this paper on European Journal of Clinical Nutrition website

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Fogelholm, M., Kanerva, N. & Männistö, S. Association between red and processed meat consumption and chronic diseases: the confounding role of other dietary factors. Eur J Clin Nutr 69, 1060–1065 (2015).

Download citation

Further reading