Given the importance of nutrition therapy in diabetes management, we hypothesized that food intake differs between individuals with and without diabetes. We investigated this hypothesis in two large prospective studies including different countries and ethnic groups.
Study populations were the European Prospective Investigation into Cancer and Nutrition Study (EPIC) and the Multiethnic Cohort Study (MEC). Dietary intake was assessed by food frequency questionnaires, and calibrated using 24h-recall information for the EPIC Study. Only confirmed self-reports of diabetes at cohort entry were included: 6192 diabetes patients in EPIC and 13 776 in the MEC. For the cross-sectional comparison of food intake and lifestyle variables at baseline, individuals with and without diabetes were matched 1:1 on sex, age in 5-year categories, body mass index in 2.5 kg/m2 categories and country.
Higher intake of soft drinks (by 13 and 44% in the EPIC and MEC), and lower consumption of sweets, juice, wine and beer (>10% difference) were observed in participants with diabetes compared with those without. Consumption of vegetables, fish and meat was slightly higher in individuals with diabetes in both studies, but the differences were <10%. Findings were more consistent across different ethnic groups than countries, but generally showed largely similar patterns.
Although diabetes patients are expected to undergo nutritional education, we found only small differences in dietary behavior in comparison with cohort members without diabetes. These findings suggest that emphasis on education is needed to improve the current behaviors to assist in the prevention of complications.
Healthy nutritional habits are a core element of diabetes self-management (Stumvoll et al., 2005; American Diabetes Association, 2007). Dietary behavior is intended to help control blood glucose levels and thereby prevent long-term complications (Fox et al., 2004; Stumvoll et al., 2005). Although overall cardiovascular disease morbidity and mortality have declined in recent years, the proportion of cardiovascular disease attributable to diabetes has increased over time, emphasizing not only the importance of diabetes prevention, but also the importance of disease management (Fox et al., 2007). Keeping in mind the high prevalence of diabetes, this aspect becomes even more important from a public health perspective (Mokdad et al., 2001).
The most specific dietary recommendations for individuals with diabetes are made for nutrient composition of the diet, focusing on carbohydrate, dietary fat and cholesterol intake (American Diabetes Association, 1994, 2007; Mann et al., 2004). The American Diabetes Association provides meal planning tools, but also endorses the use of MyPyramid to generate healthful meal plans (Krebs-Smith and Kris-Etherton, 2007). MyPyramid is a food guide to a healthful diet for the general public in the United States. Thus, overall, dietary recommendations regarding food intake for diabetes management do not differ largely from recommendations for a healthy diet in the general public. However, in contrast to the general public, diabetes patients receive regular health care and are more likely to have participated in nutritional counseling.
When comparing dietary data between groups of individuals with different degrees of obesity misreporting is of particular concern. It is well-known that obesity is related to dietary misreporting, particularly underreporting (Lissner et al., 2007). Obesity is a strong risk factor for diabetes and a large proportion of individuals with diabetes are obese. Recent data from the US (1999–2004) show that the prevalence of obesity in individuals with diabetes is 81% (Gregg et al., 2007), which is larger than in the general population. A comparison of food intake between individuals with and without diabetes might therefore be biased by measurement error due to underreporting related to the obesity status.
To our knowledge, whether and to what extent food intakes differ between individuals with and without diabetes has not been investigated in population-based studies. Accordingly, we investigated this topic in a large prospective study in Europe covering 10 countries (Riboli et al., 2002), and a large prospective study in the United States covering five different ethnic groups (Kolonel et al., 2000). We designed two nested cross-sectional studies using body mass index (BMI) as one matching factor for individuals with and without diabetes in an effort to minimize measurement error due to misreporting by obese subjects. We hypothesized that cohort participants with diabetes would be more likely to follow dietary recommendations for healthy eating than those without diabetes (American Diabetes Association, 2007; Krebs-Smith and Kris-Etherton, 2007).
Material and methods
The European prospective investigation into cancer and nutrition (EPIC Study)
Study design. The EPIC Study is an ongoing multi-center prospective cohort study to investigate associations between diet and other lifestyle factors with chronic diseases (Riboli et al., 2002). In brief, between 1992 and 2000 more than 500 000 study participants within the age range of 35–70 years were recruited in 10 European countries. Individuals signed informed consent. Approval for this study was obtained from the ethical review boards of the International Agency for Research on Cancer and from all local institutions where subjects had been recruited. Participants completed comprehensive questionnaires at enrollment, and information on weight and height were obtained by measurement from each study participant.
Self-reports of diabetes obtained at baseline were confirmed by additional sources of information, which include the following, depending on the available options in the different countries: repeated self-report during active follow-up, use of diabetes-specific medication, linkage to diabetes registries, patient records, contact with a medical practitioner and an HbA1c-value above 6% (values based on registry information, Malmø only). Only confirmed cases at baseline were included in this analysis (‘cases’). Participants with no self-report of diabetes at baseline and those without evidence for having had prevalent diabetes occurring during follow-up were considered diabetes-free (‘controls’) for this analysis. Of note, if no second source of information on diabetes self-reported at baseline was available, the true status of the participant was considered indeterminate and these individuals were excluded from this analysis.
Study population. The EPIC centers taking part in this effort (Italy: Florence, Varese, Ragusa, Turin, Naples; Spain: Navarra, San Sebastian; The Netherlands: Bilthoven, Utrecht; Germany: Heidelberg, Potsdam; Sweden: Malmø, Umea; Denmark: Aarhus, Copenhagen) contributed 7048 cases of self-reported diabetes mellitus to the EPIC Study. No separation between type 1 and type 2 diabetes was made. A total of 5542 self-reports could be confirmed and an additional 870 diabetes cases at baseline were identified (who had no positive self-report or were missing this variable), leading to 6412 participants with confirmed diabetes available for this analysis. The additional cases were found in connection with the verification of incident diabetes cases, which was done for different projects within EPIC. A total of 483 421 participants were available as controls. All participants without information on dietary intake or missing questionnaire or with implausible energy intake (top or bottom 1% of the ratio of energy intake to energy requirement) were excluded (n=14 553). For this study, individuals with and without diabetes were matched 1:1 on sex, age in 5-year categories, BMI in 2.5 kg/m2 categories and country. A total of 6192 prevalent diabetes cases could be matched to individuals without diabetes with these criteria.
Dietary assessment. In the EPIC Study, dietary intake during the previous 12 months was assessed at baseline by means of country-specific instruments (Riboli and Kaaks, 1997). In addition, a highly standardized reference dietary measurement was taken from an 8% age-stratified random sample of the cohort, using a computerized 24-h dietary recall (Slimani et al., 2000, 2002). Food and nutrient intake was analyzed as predicted by regression calibration (Ferrari et al., 2002, 2004; Nöthlings et al., 2008b).
Multiethnic cohort study (MEC)
Study design. The MEC was initiated to investigate diet and cancer risk among five major ethnic groups (African-Americans, Japanese-Americans, Native Hawaiians, Latinos and Caucasians) in Hawaii and Southern California (Kolonel et al., 2000). Between 1993 and 1996 more than 215 000 men and women were enrolled. With completion of a 26-page questionnaire participants consented to participate. A follow-up questionnaire inquiring about the occurrence of diseases including diabetes was administered ∼5 years after baseline. Between 2001 and 2006 a biorepository of blood and urine specimens was created. At that time a medication questionnaire including information on diabetes-specific drugs was administered.
Self-reports of diabetes at baseline were considered confirmed (‘cases’) if the participant repeatedly reported having diabetes in the follow-up questionnaire or to be taking diabetes-specific medication in the medication questionnaire. Participants without a self-report of diabetes at baseline and at follow-up or indication of diabetes-specific medication use were considered subjects without diabetes (‘controls’) for this analysis. Participants with inconsistent information in baseline and follow-up questionnaires were considered neither as case nor as control for this analysis.
Study population. For this analysis, we excluded all MEC participants not belonging to one of the five major ethnic groups (n=13 991), with implausible energy intake (Nöthlings et al., 2005) (n=8264), without information on diabetes (n=1) or BMI (n=2543), with implausible BMI outside the range of 15–50 kg/m2 (n=708). Follow-up information (either follow-up questionnaire or medication questionnaire) was available for 83% of the remaining cohort participants. A total of 11% of those reported a diabetes diagnosis at baseline. The self-report was considered confirmed by follow-up information for 13 794 subjects (81%). In total 89% of participants without a self-report of diabetes at baseline (n=125 953) were considered confirmed subjects without diabetes during follow-up. A study with 1:1 matching on sex, age in 5-year categories, BMI in 2.5 kg/m2 categories, region and ethnicity was set up. A total of 13 776 participants with diabetes could be matched to individuals without diabetes with the criteria.
Dietary assessment. Usual dietary intake was assessed at baseline using a comprehensive specific quantitative food frequency questionnaire (QFFQ) (Kolonel et al., 2000; Stram et al., 2000). The QFFQ asked about the consumption of over 180 food items. Before calculating food group intake, the food mixtures from the QFFQ were disaggregated to the ingredient level using a customized recipe database.
Harmonized food groups were calculated for the dietary databases of both studies as reported earlier (Nöthlings et al., 2008a). For the present analysis, 14 food groups were used. Of note, information on diet soft drinks was only available for the MEC and not for the EPIC Study.
Statistical analysis. Mean intakes (95% confidence interval) of food groups and nutrients were calculated and compared within each study and stratum by country or ethnicity. However, to avoid comparing questionnaire differences rather than true differences in intake, ratios of mean intake of individuals with diabetes to mean intake of individuals without were computed separately for each study or stratum. These ratios were then considered comparable across studies or ethnic groups.
Selected study participant characteristics are shown in Table 1. Mean age in MEC was about 5 years higher than in EPIC.
Food and nutrient intakes for individuals with and without diabetes are shown in Table 2. Because of the large sample sizes, most variables indicated statistically significant differences in means although small on an absolute scale. In the EPIC Study, the largest differences in intake (>10%) were seen for the food groups such as soft drinks (higher intake in individuals with diabetes) and sweets, wine and juice (lower intake in individuals with diabetes). In the MEC, the largest differences were seen for soft drinks and dairy (higher in individuals with diabetes) and wine, beer, sweets and juice (lower in individuals with diabetes). For the MEC, we were able to separate regular and diet soft drinks, showing that subjects with diabetes on average consumed about 2.6 times as many diet soft drinks than individuals without diabetes, whereas the consumption of regular soft drinks was less than half that of individuals without diabetes (data not shown).
Both in EPIC and MEC, the analysis was repeated using all self-reported cases of diabetes at baseline. Results were altered only marginally (data not shown).
Ratios of mean food intakes for individuals with and without diabetes stratified by country and ethnicity are shown in Table 3. Consumption of soft drinks was higher in individuals with diabetes than individuals without from Denmark (44%) and The Netherlands (18%) in the EPIC Study, but lower in participants with diabetes for the remaining EPIC countries. Consumption of soft drinks was consistently higher in subjects with diabetes for all ethnic groups in the MEC, which could be attributed to a much higher consumption of diet soft drinks (data not shown). For sweets, intake was consistently lower in subjects with diabetes across countries and ethnic groups. The lower consumption of juice by individuals with diabetes was most prominent for participants from Sweden (44%), and consistently lower for all ethnic groups in the MEC (range 17 (Japanese-Americans) to 10% (African-Americans). The difference in consumption of alcoholic beverages was much larger and consistent for the MEC. Quite consistently across all ethnic groups, individuals with diabetes consumed lower amounts of alcoholic beverages.
Comparing dietary habits between cohort participants with and without diabetes, largest differences were observed in the consumption of soft drinks, sweets, juice and alcoholic beverages. Individuals with diabetes consumed larger amounts of soft drinks (especially diet soft drinks) and lower amounts of sweets, juice and alcoholic beverages. We observed only small differences in the consumption of vegetables, fish and meat with slightly higher consumption in individuals with diabetes compared with those without. Findings were more consistent across different ethnic groups than across different countries, but generally showed largely similar patterns.
Our study has several limitations. First, the confirmation of diabetes status in the EPIC Study at baseline was not systematically conducted for all participants; only those with self-report who could be confirmed were included. A number of false-negative self-reports were detected through verification processes for incident cases (in connection with different studies in EPIC) and added to the study population with the attempt to reduce misclassification of disease status as much as possible. However, the sample must nevertheless be considered a convenience sub-sample of the study population. For the MEC, we used a prospective confirmation procedure, which precluded all participants with diabetes at baseline who died during the first 5 years of follow-up or who did not participate in the follow-up surveys. This might have led to some bias because of the selective exclusion of more severe cases of diabetes mellitus with related co-morbidities. Of note, in both studies, the same analysis including all participants with self-reports at baseline showed similar results.
Second, we were only able to separate diet soft drinks from regular soft drinks in the MEC, but not the EPIC Study. Regular soft drinks are a major source of added sugars. Indeed, we showed for the MEC, that consumption of dietary soft drinks was higher in individuals with diabetes.
Also, our dietary assessment was based on food frequency questionnaires, which generally are not optimal to estimate absolute food intakes, but rather provide information on ranking. However, we compared intakes assessed within strata (that is, countries or ethnic groups) with identical questionnaires and only compared relative intakes across strata with different questionnaires, attempting to minimize the impact of food frequency questionnaire measurement error.
Social desirability bias because of reporting of dietary intake could have occurred since the diabetes patients probably were counseled for their dietary behavior as opposed to their healthy counterparts. Indeed, the lack of information about the actual advice individuals received limits the interpretability of our findings. Nevertheless, misreporting due to status of obesity was controlled for by matching of participants according to categories of BMI. Furthermore, using calibrated and density-based food intake data might have accounted for some measurement error because of selective over- or underreporting for particular food groups. Of note, body weight and height were measured in EPIC, but self-reported in MEC. The latter could have led to some bias because of underreporting of body weight in individuals with diabetes who probably are especially aware that the advice is to maintain a healthy weight. Last, we were not able to distinguish between type 1 and type 2 diabetes for both studies consistently. From an etiological point of view, type 1 and type 2 diabetes are two different diseases. However, medical nutrition therapy is advised for both types of diabetes to prevent complications and we would not expect our findings to be different. Given the age distribution of the two cohorts, however, we expect that well over 90% of the diabetes patients in the study had type 2 diabetes.
Our study has several strengths. Both cohorts are among the largest population-based prospective epidemiologic studies, ensuring a large number of participants with diabetes at baseline. Coverage of different countries and ethnic groups ensured to inclusion of different dietary habits. The confirmation of self-reports ensured that only true cases of diabetes were included in the analysis. The matching on categories of BMI promises to control for some of the bias by misreporting due to obesity for this cross-sectional analysis.
Few population-based studies so far have compared lifestyle behaviors, especially dietary behaviors, between individuals with diabetes and without diabetes. In the NHANES survey, individuals with diabetes have been shown to be no more likely to adhere to healthy lifestyles than individuals without diabetes, which included the consumption of fruit and vegetables (King et al., 2009). Indeed, a comparison of surveys at different time periods showed a decrease in adherence to healthy behaviors in the population with diabetes over time, giving increasing concern for higher rates of complications. A study in the United States showed that the population with diabetes consumed less regular soft drinks and sweets, but larger amounts of artificially sweetened soft drinks and sweets than the population without diabetes (Fitzgerald et al., 2008). Intake of other food groups was similar between groups in this study, confirming the findings in the current study. It is surprising that although nutritional counseling of diabetes patients most likely has occurred in different contexts and populations, only small effects with respect to food intake could be observed. A possible explanation could also be that diabetes patients originally had worse habits, which contributed to the development of diabetes in the first place, and made already a substantial change in the desired direction before inclusion into this study—assuming the individuals without diabetes had better dietary habits. The food groups largely targeted by patients with diabetes were related to short-chain carbohydrates, that is, sweets and soft drinks. This finding was consistent across the different strata investigated, potentially reflecting different dietary habits, but also different health care systems. Alcohol intake apparently was of concern in the United States-based study, but not as consistently in different countries in Europe, which might be attributable to different recommendations and regulations related to alcohol consumption in general in the different countries rather than disease-specific recommendations.
The importance of adopting healthy lifestyle habits in terms of food intake or physical activity has been emphasized by studies of risk factors for cardiovascular disease incidence or mortality in populations with diabetes. We showed previously that a high intake of fruit and vegetables was inversely associated with cardiovascular disease mortality in a large cohort of diabetes patients in Europe (Nöthlings et al., 2008b). Other studies have shown inverse associations for alcohol intake (Solomon et al., 2000), consumption of nuts (Li et al., 2009), or consumption of fish (Hu et al., 2003) and positive associations for saturated fat or cholesterol intake (Tanasescu et al., 2004), or selected food groups like eggs (Trichopoulou et al., 2006). However, such studies focusing on food groups are scarce. Studies of cancer survivors or patients with cardiovascular diseases have also indicated that few patients maintain substantial life-style and nutritional changes after diagnosis (Bellizzi et al., 2005; Caan et al., 2005; Coups and Ostroff, 2005; Iestra et al., 2006).
Of note, some of the larger heterogeneity across EPIC countries compared with MEC ethnic groups can probably be explained by the different dietary assessment instruments. In the EPIC Study, country-specific questionnaires were used, to best cover country-specific consumption habits. To make dietary intakes comparable across countries, intakes after regression calibration were used for this analysis. In the MEC, a QFFQ was used, which was identical for all ethnic groups.
Although diabetes patients are likely to undergo some nutritional education to optimize their diet, we found only small differences in dietary behavior in comparison to cohort members without diabetes. These findings suggest that emphasis on education is needed to improve the current behaviors to assist in the prevention of complications.
American Diabetes Association (1994). Nutrition recommendations and principles for people with diabetes mellitus. Diabetes Care 17, 519–522.
American Diabetes Association (2007). Nutrition recommendations and interventions for diabetes: a position statement of the American diabetes association. Diabetes Care 30 (Suppl 1), S48–S65.
Bellizzi KM, Rowland JH, Jeffery DD, McNeel T (2005). Health behaviors of cancer survivors: examining opportunities for cancer control intervention. J Clin Oncol 23, 8884–8893.
Caan B, Sternfeld B, Gunderson E, Coates A, Quesenberry C, Slattery ML (2005). Life after cancer epidemiology (LACE) study: a cohort of early stage breast cancer survivors (United States). Cancer Causes Control 16, 545–556.
Coups EJ, Ostroff JS (2005). A population-based estimate of the prevalence of behavioral risk factors among adult cancer survivors and noncancer controls. Prev Med 40, 702–711.
Ferrari P, Slimani N, Ciampi A, Trichopoulou A, Naska A, Lauria C et al. (2002). Evaluation of under- and overreporting of energy intake in the 24-h diet recalls in the European Prospective Investigation into Cancer and Nutrition (EPIC). Public Health Nutr 5, 1329–1345.
Ferrari P, Kaaks R, Fahey MT, Slimani N, Day NE, Pera G et al. (2004). Within- and between-cohort variation in measured macronutrient intakes, taking account of measurement errors, in the European Prospective Investigation into Cancer and Nutrition study. Am J Epidemiol 160, 814–822.
Fitzgerald N, Damio G, Segura-Perez S, Perez-Escamilla R (2008). Nutrition knowledge, food label use, and food intake patterns among Latinas with and without type 2 diabetes. J Am Diet Assoc 108, 960–967.
Fox CS, Coady S, Sorlie PD, Levy D, Meigs JB, D’Agostino Sr RB et al. (2004). Trends in cardiovascular complications of diabetes. Jama 292, 2495–2499.
Fox CS, Coady S, Sorlie PD, D′Agostino Sr RB, Pencina MJ, Vasan RS et al. (2007). Increasing cardiovascular disease burden due to diabetes mellitus: the Framingham Heart Study. Circulation 115, 1544–1550.
Gregg EW, Cheng YJ, Narayan KM, Thompson TJ, Williamson DF (2007). The relative contributions of different levels of overweight and obesity to the increased prevalence of diabetes in the United States: 1976–2004. Prev Med 45, 348–352.
Hu FB, Cho E, Rexrode KM, Albert CM, Manson JE (2003). Fish and long-chain omega-3 fatty acid intake and risk of coronary heart disease and total mortality in diabetic women. Circulation 107, 1852–1857.
Iestra J, Knoops K, Kromhout D, de Groot L, Grobbee D, van Staveren W (2006). Lifestyle, mediterranean diet and survival in European post-myocardial infarction patients. Eur J Cardiovasc Prev Rehabil 13, 894–900.
King DE, Mainous III AG, Carnemolla M, Everett CJ (2009). Adherence to healthy lifestyle habits in US adults, 1988–2006. Am J Med 122, 528–534.
Kolonel LN, Henderson BE, Hankin JH, Nomura AM, Wilkens LR, Pike MC et al. (2000). A multiethnic cohort in Hawaii and Los Angeles: baseline characteristics. Am J Epidemiol 151, 346–357.
Krebs-Smith SM, Kris-Etherton P (2007). How does MyPyramid compare to other population-based recommendations for controlling chronic disease? J Am Diet Assoc 107, 830–837.
Li TY, Brennan AM, Wedick NM, Mantzoros C, Rifai N, Hu FB (2009). Regular consumption of nuts is associated with a lower risk of cardiovascular disease in women with type 2 diabetes. J Nutr 139, 1333–1338.
Lissner L, Troiano RP, Midthune D, Heitmann BL, Kipnis V, Subar AF et al. (2007). OPEN about obesity: recovery biomarkers, dietary reporting errors and BMI. Int J Obes (Lond) 31, 956–961.
Mann JI, De Leeuw I, Hermansen K, Karamanos B, Karlstrom B, Katsilambros N et al. (2004). Evidence-based nutritional approaches to the treatment and prevention of diabetes mellitus. Nutr Metab Cardiovasc Dis 14, 373–394.
Mokdad AH, Bowman BA, Ford ES, Vinicor F, Marks JS, Koplan JP (2001). The continuing epidemics of obesity and diabetes in the United States. Jama 286, 1195–1200.
Nöthlings U, Wilkens LR, Murphy SP, Hankin JH, Henderson BE, Kolonel LN (2005). Meat and fat intake as risk factors for pancreatic cancer: the multiethnic cohort study. J Natl Cancer Inst 97, 1458–1465.
Nöthlings U, Murphy SP, Wilkens LR, Boeing H, Schulze MB, Bueno-de-Mesquita HB et al. (2008a). A food pattern that is predictive of flavonol intake and risk of pancreatic cancer. Am J Clin Nutr 88, 1653–1662.
Nöthlings U, Schulze MB, Weikert C, Boeing H, van der Schouw YT, Bamia C et al. (2008b). Intake of vegetables, legumes, and fruit, and risk for all-cause, cardiovascular, and cancer mortality in a European diabetic population. J Nutr 138, 775–781.
Riboli E, Kaaks R (1997). The EPIC Project: rationale and study design. European prospective investigation into cancer and nutrition. Int J Epidemiol 26 (Suppl 1), S6–S14.
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.
Slimani N, Ferrari P, Ocke M, Welch A, Boeing H, Liere M et al. (2000). Standardization of the 24-h diet recall calibration method used in the European prospective investigation into cancer and nutrition (EPIC): general concepts and preliminary results. Eur J Clin Nutr 54, 900–917.
Slimani N, Kaaks R, Ferrari P, Casagrande C, Clavel-Chapelon F, Lotze G et al. (2002). European prospective investigation into cancer and nutrition (EPIC) calibration study: rationale, design and population characteristics. Public Health Nutr 5, 1125–1145.
Solomon CG, Hu FB, Stampfer MJ, Colditz GA, Speizer FE, Rimm EB et al. (2000). Moderate alcohol consumption and risk of coronary heart disease among women with type 2 diabetes mellitus. Circulation 102, 494–499.
Stram DO, Hankin JH, Wilkens LR, Pike MC, Monroe KR, Park S et al. (2000). Calibration of the dietary questionnaire for a multiethnic cohort in Hawaii and Los Angeles. Am J Epidemiol 151, 358–370.
Stumvoll M, Goldstein BJ, van Haeften TW (2005). Type 2 diabetes: principles of pathogenesis and therapy. Lancet 365, 1333–1346.
Tanasescu M, Cho E, Manson JE, Hu FB (2004). Dietary fat and cholesterol and the risk of cardiovascular disease among women with type 2 diabetes. Am J Clin Nutr 79, 999–1005.
Trichopoulou A, Psaltopoulou T, Orfanos P, Trichopoulos D (2006). Diet and physical activity in relation to overall mortality amongst adult diabetics in a general population cohort. J Intern Med 259, 583–591.
This study was supported by the German Research Foundation, the cluster of excellence ‘inflammation-at-interfaces’ and an EFSD/sanofi-aventis grant. The sponsor did not have any influence on the contents of the manuscript.
The authors declare no conflict of interest.
About this article
Cite this article
Nöthlings, U., Boeing, H., Maskarinec, G. et al. Food intake of individuals with and without diabetes across different countries and ethnic groups. Eur J Clin Nutr 65, 635–641 (2011). https://doi.org/10.1038/ejcn.2011.11
- food intake
- medical nutrition therapy
- secondary prevention
Preventing Chronic Disease (2018)
Development of a marmalade for patients with type 2 diabetes: Sensory characteristics and acceptability
Food Science and Technology International (2018)
Performance of an easy-to-use prediction model for renal patient survival: an external validation study using data from the ERA-EDTA Registry
Nephrology Dialysis Transplantation (2018)
Compliance with Nutritional and Lifestyle Recommendations in 13,000 Patients with a Cardiometabolic Disease from the Nutrinet-Santé Study