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Geographic and socioeconomic diversity of food and nutrient intakes: a comparison of four European countries

  • Elly Mertens
  • Anneleen Kuijsten
  • Marcela Dofková
  • Lorenza Mistura
  • Laura D’Addezio
  • Aida Turrini
  • Carine Dubuisson
  • Sandra Favret
  • Sabrina Havard
  • Ellen Trolle
  • Pieter van’t Veer
  • Johanna M. Geleijnse
Open Access
Original Contribution

Abstract

Purpose

Public health policies and actions increasingly acknowledge the climate burden of food consumption. The aim of this study is to describe dietary intakes across four European countries, as baseline for further research towards healthier and environmentally-friendlier diets for Europe.

Methods

Individual-level dietary intake data in adults were obtained from nationally-representative surveys from Denmark and France using a 7-day diet record, Italy using a 3-day diet record, and Czech Republic using two replicates of a 24-h recall. Energy-standardised food and nutrient intakes were calculated for each subject from the mean of two randomly selected days.

Results

There was clear geographical variability, with a between-country range for mean fruit intake from 118 to 199 g/day, for vegetables from 95 to 239 g/day, for fish from 12 to 45 g/day, for dairy from 129 to 302 g/day, for sweet beverages from 48 to 224 ml/day, and for alcohol from 8 to 15 g/day, with higher intakes in Italy for fruit, vegetables and fish, and in Denmark for dairy, sweet beverages and alcohol. In all countries, intakes were low for legumes (< 20 g/day), and nuts and seeds (< 5 g/day), but high for red and processed meat (> 80 g/day). Within countries, food intakes also varied by socio-economic factors such as age, gender, and educational level, but less pronounced by anthropometric factors such as overweight status. For nutrients, intakes were low for dietary fibre (15.8–19.4 g/day) and vitamin D (2.4–3.0 µg/day) in all countries, for potassium (2288–2938 mg/day) and magnesium (268–285 mg/day) except in Denmark, for vitamin E in Denmark (6.7 mg/day), and for folate in Czech Republic (212 µg/day).

Conclusions

There is considerable variation in food and nutrient intakes across Europe, not only between, but also within countries. Individual-level dietary data provide insight into the heterogeneity of dietary habits beyond per capita food supply data, and this is crucial to balancing healthy and environmentally-friendly diets for European citizens.

Keywords

Diet Foods Nutrients Dietary guidelines Europe SUSFANS 

Introduction

Poor dietary habits are the second-leading risk factor for deaths and disability-adjusted life-years (DALYs) globally, accounting for 10.3 million deaths and 229.1 million DALYs in 2016 [1]. Low intakes of whole grains, fruit and vegetables, and nuts and seeds, and high intakes of alcohol and sodium ranked among the leading risk factors for early death and disability in European populations. However, as westernisation of diets progressed, diets high in red and processed meat, followed by diets high in sugar-sweetened beverages and low in milk are becoming a growing public health concern.

Dietary patterns are shaped by cultural, environmental, technological and economic factors, and they have become more similar over time owing to a general rise in living standards and globalisation of the food sector [2, 3]. Also in Europe there is a growing similarity of diets, in which traditional diets of Northern and Mediterranean countries are converging towards a more Western diet, viewed by the increased share of fruit and vegetables in Northern countries and the increased share of animal-based products in Mediterranean countries [4, 5, 6]. Increase in animal-based products and excessive caloric intake have been thought as a key factor in nutrition transition, which warrants the need for public health action to promote healthier food patterns consistent with traditional cultural preferences, hence the development of food-based dietary guidelines.

Food-based dietary guidelines are evidence-based integrated messages aimed at the general population for maintaining health and the prevention of non-communicable diseases [7, 8]. Promoting the intake of whole grains, fruit and vegetables, low-fat dairy and fish, and limiting the intake of red and processed meat, sugar-sweetened food products, alcohol and salt is covered by most national food-based dietary guidelines [9], although recommended quantities may differ. Monitoring food consumption patterns and assessing adherence to dietary guidelines in a nationally representative sample is especially regarded as a key instrument for evaluating the effectiveness of public health action towards a healthier diet.

In recent years, public health policies and actions have increasingly acknowledged the climate burden of food production and consumption, hence the need to address the food-climate connection, as outlined in the SUSFANS project (Metrics, Models and Foresight for European SUStainable Food And Nutrition Security) [10]. Production and technological changes in the food system will, however, not be sustainable without a change in food consumption patterns. The SUSFANS project, therefore, elaborates on the status-quo of diets and the design of optimised diets that are environmentally Sustainable, Healthy, Affordable, Reliable and Preferred (SHARP). This paper is a first step to study European food consumption patterns in terms of food groups and nutrients using national dietary survey data carried out at the individual level in four countries. Intakes of food groups and nutrients were compared with current food-based dietary guidelines and nutrient reference values, overall and in relevant population subgroups.

Populations and methods

Data sources

Individual-level dietary intake data from national dietary surveys representative for different European regions, i.e. Denmark (Scandinavia) [11], Czech Republic (Central East Europe) [12], Italy (Mediterranean) [13] and France (Western Europe) [14], were collated for adult population aged ≥ 18 years within the SUSFANS project [10]. These four countries were chosen to capture the wide range of foods and agricultural commodities, including their extreme intakes, that are incorporated in the diverse European food consumption patterns.

Survey characteristics

Survey characteristics are shown in Table 1. National representativeness was ensured using random sampling based on civil registration systems in Denmark [11], national census data in Czech Republic [12] and France [14], and national census data with telephone books in Italy [13] that served as sampling frame, and followed by appropriate weighing for socio-demographic parameters, as applied in Denmark [11, 15] and France [14]. Surveys were organised throughout the whole year, covering the four seasons of the year, and have dietary data on week- and weekend-days.

Table 1

Dietary surveys in four European countries, i.e. Denmark, Czech Republic, Italy and France, including adult population only

 

Denmark

Czech Republic

Italy

France

Survey characteristics, including adult population only

 Survey, year

The Danish National Survey on Diet and Physical Activity 2005–2008

National Food Institute, Technical University of Denmark (DTU)

Czech National Food Consumption Survey 2003–2004 (SISP04)

National Institute of Public Health

Italian National Food Consumption Survey INRAN-SCAI 2005–2006

National institute for Research on Food and Nutrition

Individual and National Study on Food Consumption INCA-2 2006–2007

Agence Française de Sécurité Sanitaires des Aliments (AFSSA)

 Population

18–75 years

18–90 years

18–98 years

18–79 years

 Method of dietary assessment a

7-day diet record on consecutive days

24-h recall on two non-consecutive days

3-day diet record on consecutive days

7-day diet record on consecutive days

Baseline characteristics of the study sample, including adult population only, n (%)

 Sample size (response rate)

2025 (54%)

1869 (54%)

2831 (33%)

2624 (60%)

 Age, 18–64 years

1739 (85.9%)

1666 (89.1%)

2313 (81.7%)

2276 (86.7%)

 Gender, men

777 (44.7%)

793 (47.6%)

1068 (46.2%)

936 (41.1%)

 Educational level, low

248 (14.2%)

345 (20.7%)

692 (31.7%)

1039 (45.8%)

 Overweight status, BMI ≥ 25

739 (43.2%)

864 (51.9%)

828 (35.8%)

871 (38.7%)

BMI Body Mass Index

aIncluded in the present study were for Czech Republic both day, for Denmark and France two randomly selected days, and for Italy the first and the last day of the national dietary survey

Method of dietary assessment

In the four study countries, dietary intake was assessed over two to seven 24-h periods, either consecutively for 3–7 days using a diet record, as applied in Denmark, Italy and France [11, 13, 14], or non-consecutively spaced over a 3–5 months sampling period using two replicates of 24-h recall, as applied in Czech Republic [12]. In the present analyses, dietary intake from two random days has been reported. To this end, two non-consecutive days were sampled in Denmark, Italy and France, whereas all available days were used in Czech Republic.

Food and nutrient intakes

Intakes of food groups and nutrients were calculated for each subject from the mean of the selected two days, and were standardised for energy using the density method. Densities were calculated as the absolute value divided by total energy intake, and multiplied by 2000 kcal. Harmonised food groups, including similar foods, have been elaborated using the ‘Exposure Hierarchy’ of the food classification and description system FoodEx2 developed and revised in 2015 by the European Food Safety Authority (EFSA) [16, 17]. A main challenge to encounter when grouping the foods was the level of food disaggregation; disaggregation of foods into ingredients was only considered as necessary for composite/prepared foods provided that the food itself was not included in FoodEx2, but its ingredients are. Nutrient intakes were calculated from dietary sources only, i.e. excluding dietary supplements, using country-specific food composition tables [18, 19, 20, 21, 22, 23, 24]. Intakes of added sugar, plant and animal protein were calculated based on food selection. Added sugar was defined as the total sugar intake minus sugars naturally occurring in fruits, vegetables and dairy. Plant protein was defined as protein derived from cereals, legumes, nuts and seeds, and others (including potatoes, vegetables, fruits, etc.). Animal protein was defined as protein derived from meat and meat products, fish and fish products, egg and egg products, milk and milk products (including cream, cheese and butter). None of the data excluded under- and over-reporting, however, misreporting was identified using Goldberg equation [25] and adopted by Black [26] (Online Resource 1).

Dietary quality

Foods

To evaluate European populations’ energy-standardised food group intakes, references values were set for the food groups that are important for disease risk reduction based on an inventory of the current food-based dietary guidelines of European countries. Minimum values were set for foods that are beneficial for health, such as fruits and vegetables, and maximum values for foods that are unfavourable for health, such as red and processed meat (see Box 1). Reference values were derived using the 2015 Dutch food-based dietary guidelines [8] as reference point, complemented by the food-based dietary guidelines of the four countries [27, 28, 29, 30] in which the less restrictive reference values were chosen.

Box 1

A set of food-based dietary guidelines for European countries, including their exposure definition and reference values, developed for the SUSFANS project

 

Exposure definition

Reference valuesa

Foods to increase

  

 Fruit

All kind of fruits (including fresh, dried, tinned or canned fruit products, but excluding fruit juice)

≥ 200 g/day

 Vegetables

All kind of vegetables (including fresh, dried, tinned or canned vegetable products, but excluding potatoes, vegetable juices and vegetables from soup, sauces and ready-to-eat products)

≥ 200 g/day

 Legumes

Kidney beans, pinto beans, white beans, black beans, garbanzo beans (chickpeas), lima beans, split peas, lentils, and edamame (green soybeans)

≥ 135 g/week (≥ 19 g/day)

 Nuts and seeds

Walnuts, almonds, hazel, cashew, pistachio, macadamia, Brazil, pecan, pine nuts, flax seeds, sesame seeds, sunflower seeds, pumpkin seeds, poppy seeds, and peanut

≥ 15 g/day

 Dairy products

Food products produced from the milk of mammals, including milk, yoghurt, fresh uncured cheese, quark, custard, milk puddings, excluding cheese and butter

≥ 300 g/day

 Fish

All kind of fish and fish products

≥ 150 g/week (≥ 21 g/day)

Foods to decrease

  

 Red and processed meat

Red meat: all mammalian muscle meat, including beef, veal, pork, lamb, mutton, horse and goat, excluding rabbit meat; Processed meat: meat transformed through salting, curing, fermentations, smoking or other processed to enhance flavour or improve preservation (e.g. meat products as sandwich filling, ready-to-eat minced meat, sausages, etc.)

≤ 500 g/week (≤ 71 g/day)

 Cheese

All types of cheese formed by coagulation of milk protein casein

≤ 150 g/week (≤ 21 g/day)

 Sugar-sweetened beverages

Cold beverages with added sugars (sucrose, fructose or glucose), for example fruit juices, fruit nectars, soft drinks, ice teas, vitamin-water or sports drinks with added sugars

≤ 500 ml/week (≤ 71 ml/day)

A lcohol (Ethanol)

Ethanol content calculated from all kind of alcoholic beverages

≤ 10 g/day

Foods to replaceb

  

 Whole grains

Whole grains (bran, germ and endosperm in their natural proportion) from cereals, pasta, bread, breakfast cereals and other grain sources

Replace white grains by whole grains

 White meat

Meat from all kind of poultry, including rabbit meat

Replace red and processed meat by white meat

 Soft margarine and oils

Soft margarine: soft-solid fats made from vegetables oils; Oils: liquid fats at room temperature derived from plants or fish

Replace butter and hard margarines by soft margarine and oils

aReference values were derived from current food-based dietary guidelines, using the 2015 Dutch food-based dietary guidelines [8] as reference point, complemented by the food-based dietary guidelines of the four countries [34, 35, 36, 37] in which the less restrictive reference values was chosen (Quantitative guideline)

b‘Foods to replace’ represent food groups for which insufficient convincing evidence was available to set a fixed cut-off point, however replacement of those food products by a healthier alternative is recommended (Qualitative guideline)

Nutrients

To evaluate European populations’ energy-standardised nutrient intakes, nutrient density of the diet was quantified using Nutrient Rich Diet (NRD) score [31, 32], i.e. overall summary estimate of nutrient intakes based on the principles of the Nutrient Rich Food Index [33, 34]. The NRD algorithm was calculated as:
$${\text{NRD}}~X \cdot Y=~\mathop \sum \limits_{i}^{{i=X}} \frac{{{Q_{{\text{nutrient}}~i}}}}{{{\text{DR}}{{\text{V}}_i}}} \times 100 - ~\mathop \sum \limits_{j}^{{j=Y}} \frac{{{Q_{{\text{nutrient}}~j}}}}{{{\text{MR}}{{\text{V}}_j}}} \times 100$$
where X is the number of qualifying nutrients, Y is the number of disqualifying nutrients, Q nutrient i or j is the average daily intake of nutrient i or j, DRV is the dietary reference value of qualifying nutrient i and MRV j is the maximum recommended value of the nutrient to limit j. DRVs are defined using reference values from EFSA [35], i.e. average requirement (AR), and adequate intake (AI) if AR cannot be set, and MRVs using reference values of World Health Organisation [36, 37] and Food and Agriculture Organisation [38].

In the present analyses, NRD9.3 and NRD15.3 were used. The NRD9.3, including nine nutrients for which intake should be promoted (protein, dietary fibre, calcium, iron, potassium, magnesium, and vitamin A, C and E) and three nutrients for which intake should be limited (saturated fat (SFA), added sugar, and sodium), standardised for 2000 kcal/day diet and capped nutrient intake at 100% of DRV was primarily chosen, based on its validation among US populations [33, 34]. To capture more nutrients that are potentially relevant for European populations, we also used its extended version, i.e. NRD15.3 that additionally included mono-unsaturated fatty acids, zinc, vitamin D and B-vitamins (B1, B2, B12, folate), but excluded magnesium. A sub-score on the intake of qualifying nutrients is represented in NRD9 and NRD15, and that of disqualifying nutrients in NRDX.3, while the total score, i.e. NRD9.3 and NRD15.3, is a combination of both.

Estimating the dietary quality of European populations’ diets

Percentages of the population that adhere to food-based dietary guidelines and percentages of the population with inadequate nutrient intakes were estimated using the AR cut-point method [39], without correction for within subject variability. This percentage would be interpreted as proxy figures for adherence and inadequacy, because of different survey’s methodologies. When the DRV of the nutrient under study was defined as an AI (dietary fibre, potassium, magnesium, vitamin D, E and B12), this percentage of populations with intake below AI was only applicable for comparison between countries and population subgroups. Dietary intakes were characterised in the overall country-specific population of adults aged ≥ 18 years and in relevant population subgroups by age, gender, educational level, and overweight status. Subgroups by age included younger and middle-aged adults (18–64 years) and elderly (≥ 65 years). Younger and middle-aged adult populations were additionally stratified by gender, educational level using three categories, i.e. primary or lower secondary degree (‘low’), higher secondary degree (‘intermediate’) and university or post-university degree (‘high’), and overweight status using two categories, i.e. BMI < 25 and ≥ 25 kg/m2.

As the information available consisted only of summarised data (i.e. mean and standard deviation of the energy-standardised dietary intake under study and sample size), analysis of variance test was performed to check whether there were differences in mean intake of food groups and nutrients between countries and within countries by population subgroups of age, gender, educational level and overweight status. Bonferroni post hoc test was used for multiple comparisons. A two sided p value below 0.0001 was considered as statistically significant. Statistical analyses were performed with SAS version 9.3 (SAS Institute Inc.).

Results

Baseline characteristics

Age and gender distribution were comparable between countries, with 80–90% of the population aged 18–64 years and 40–48% being men. Distribution of educational level varied markedly between countries; a low proportion of low-educated subjects in Denmark (15%) and a high proportion in France (46%); but proportion of the high-educated subjects was the lowest in Czech Republic (8%) and varied between 23–33% for Denmark, Italy and France. Approximately half of the Czech population (52%) was overweight, BMI ≥ 25 kg/m2, whereas overweight in Denmark (44%), France (39%) and Italy (36%) was less prevalent.

Foods

Table 2 shows the energy-standardised intakes of food groups and general adherence to food-based dietary guidelines in four European adult populations, aged ≥ 18 years. Stratified intakes by age, gender, educational level and overweight status are shown in Table 3.

Table 2

Energy-standardised food group intakes and the adherence to their corresponding food-based dietary guidelines in four European populations, aged ≥ 18 years

 

Cut-offs

Denmark (n = 2025)

Czech Republic (n = 1869)

Italy (n = 2831)

France (n = 2624)

 

Mean

Median

(P25; P75)

%adh

Mean

Median

(P25; P75)

%adh

Mean

Median

(P25; P75)

%adh

Mean

Median

(P25; P75)

%adh

Foods to increase

                 

 Fruit, g/day

≥ 200

174*

133

(36.0; 255)

35%

118*

83

(12.0; 171)

20%

199*

163

(76; 275)

40%

140*

95

(0.0; 210)

26%

 Vegetables, g/day

≥ 200

147*

112

(63; 184)

21%

95*

74

(39.0; 127)

10%

239*

206

(138; 300)

53%

187*

157

(84; 254)

37%

 Legumes. g/day

≥ 19

6.5

1.6

(0.0; 6.7)

10%

7.5

0.0

(0.0; 3.0)

12%

11.0

0.0

(0.0; 2.4)

19%

16.5*

0.0

(0.0; 0.8)

18%

 Nuts and seeds, g/day

≥ 15

2.2

0.0

(0.0; 0.0)

5%

2.6

0.0

(0.0; 0.0)

7%

0.5*

0.0

(0.0; 0.0)

1%

1.7

0.0

(0.0; 0.0)

3%

 Dairy products, g/day

≥ 300

302*

248

(113; 422)

41%

134

94

(31.0; 192)

12%

129

116

(8.0; 20)

8%

199*

152

(55; 290)

24%

 Fish, g/day

≥ 21

18.0

5.5

(0.0; 24.1)

28%

11.7

0.0

(0.0; 0.0)

17%

44.6*

6.5

(0.0; 77)

42%

34.3*

4.3

(0.0; 54)

43%

Foods to decrease

                 

 Red and processed meat, g/day

≤ 71

94

85

(51; 127)

39%

88

82

(46.0; 125)

42%

84

77

(39.2; 119)

51%

93

82

(40.5; 133)

43%

 Cheese, g/day

≤ 21

29.3

24.3

(11.3; 42.0)

44%

20.9*

13.2

(0.0; 33.0)

63%

53*

47.2

(16.2; 76)

28%

30.1

24.0

(2.9; 45.6)

46%

 Sweet beveragesa, ml/day

≤ 71

224*

127

(0.0; 305)

40%

108

0.0

(0.0; 144)

63%

47.5*

0.0

(0.0; 65)

76%

121

6.0

(0.0; 171)

56%

 Alcohol (ethanol), g/day

≤ 10

14.6*

7.3

(0.0; 22.6)

56%

10.3

4.4

(0.0; 16.0)

66%

8.2

0.1

(0.0; 13.7)

67%

9.3

0.1

(0.0; 14.5)

67%

Foods to replace

                 

 Cereals, total, g/day

26.1*

16.9

(6.7; 35.0)

48.2

32.5

(11.0; 72)

46.6

38.3

(0.6; 73)

38.8*

16.05

(0.0; 57)

 Cereals, whole grains, g/day

0.4

0.0

(0.0; 0.0)

0.1

0.0

(0.0; 0.0)

0.8

0.0

(0.0; 0.0)

1.8

0.0

(0.0; 0.0)

 Pasta, total, g/day

5.2*

0.0

(0.0; 1.2)

39.9*

13.6

(0.0; 66)

52*

48.4

(29.8; 82)

10.3*

0.0

(0.0; 0.0)

 Pasta, whole grains, g/day

 

0.0*

0.0

(0.0; 0.0)

0.3*

0.0

(0.0; 0.0)

9.8*

0.0

(0.0; 0.0)

 Bread, total, g/day

149*

140

(94; 194)

122*

118

(83; 157)

109*

103

(60; 151)

98*

92

(51; 139)

 Bread, whole grains, g/day

52*

44.3

(22.4; 72)

7.9*

0.0

(0.0; 0.0)

41.4*

0.0

(0.0; 70)

16.3*

0.0

(0.0; 6.1)

 Breakfast cereals, total, g/day

11.8*

0.6

(0.0; 18.0)

2.9

0.0

(0.0; 0.0)

1.5

0.0

(0.0; 0.0)

5.3*

0.0

(0.0; 0.0)

 Breakfast cereals, whole grains, g/day

9.3*

0.0

(0.0; 12.1)

1.9*

0.0

(0.0; 0.0)

0.5*

0.0

(0.0; 0.0)

3.4*

0.0

(0.0; 0.0)

 Red meat, g/day

66*

57.1

(28.3; 93)

34.0*

28.4

(0.0; 55)

58

53

(0.0; 89)

58

45.6

(0.0; 91)

 Processed meat, g/day

27.3

19.4

(7.1; 37.2)

54*

44.5

(14.0; 80)

25.5

19.4

(0.0; 38.9)

34.7*

22.6

(0.0; 54)

 White meat, g/day

21.3

1.6

(0.0; 29.9)

22.5

0.0

(0.0; 41.0)

23.5

0.0

(0.0; 44.9)

31.5*

0.0

(0.0; 52)

 Butter and hard margarines, g/day

24.8*

22.7

(13.5; 33.8)

17.6*

15.5

(7.0; 25.0)

2.8*

0.0

(0.0; 3.8)

16.3*

13.7

(5.8; 24.0)

 Soft margarine and oils, g/day

1.9*

0.0

(0.0; 1.5)

15.0*

13.1

(7.0; 21.0)

34.8*

34.0

(26.3; 42.7)

11.2*

7.4

(0.4; 17.3)

Intake of food groups are standardised to a 2000 kcal/day diet

%adherence represents a proxy for the percentage of the population that adhere to food-based dietary guidelines

aSweet beverages instead of sugar-sweetened beverages due to a lack of detailed data on beverages

*Bonferroni p < 0.0001 test comparison for intake that was significantly different from all other three countries under study

Table 3

Energy-standardised food group intakes and the adherence to their corresponding food-based dietary guidelines in four European populations in subgroups by age, gender, educational level, and overweight status: main findings

 

Cut-offs

Subgroups by age

Subgroups by gendera

  

Younger and middle-aged adults

Elderly, ≥ 65 years

p value

Men

Women

p value

  

Mean

Median

(P25; P75)

%adh

Mean

Median

(P25; P75)

%adh

 

Mean

Median

(P25; P75)

%adh

Mean

Median

(P25; P75)

%adh

Denmark

 

(n = 1739)

(n = 286)

 

(n = 777)

(n = 962)

 

 Fruit, g/day

≥ 200

171

126

(32.2; 251)

34%

197

159

(81; 281)

40%

0.011

120

74

(0.5; 172)

21%

222

187

(74; 324)

47%

< 0.0001

 Vegetables, g/day

≥ 200

151

114

(64; 189)

22%

119

98

(54; 167)

16%

< 0.0001

117

95

(54; 146)

13%

185

141

(84; 231)

31%

< 0.0001

 Legumes, g/day

≥ 19

6.6

1.8

(0.0; 7.1)

10%

5.3

0.9

(0.0; 4.6)

10%

< 0.0001

5.9

1.3

(0.0; 5.6)

8%

7.3

2.2

(0.0; 8.6)

11%

< 0.0001

 Red and processed meat, g/day

≤ 71

95

87

(52; 128)

38%

83

73

(41.5; 108)

48%

0.001

109

100

(66; 143)

29%

82

75

(43.3; 114)

47%

< 0.0001

 Alcohol, g/day

≤ 10

13.8

6.4

(0.0; 21.5)

58%

20.5

15.0

(1.7; 29.8)

40%

< 0.0001

16.6

10.0

(0.0; 25.6)

50%

10.9

0.0

(0.0; 17.0)

66%

< 0.0001

Czech Republic

 

(n = 1666)

(n = 203)

 

(n = 793)

(n = 873)

 

 Fruit, g/day

≥ 200

115

79

(10.0; 167)

19%

143

118

(38.7; 218)

28%

0.006

66

39

(0.7; 93)

6%

160

128

(51; 224)

31%

< 0.0001

 Vegetables, g/day

≥ 200

95

75

(39.3; 128)

10%

94

70

(39.4; 122)

8%

0.874

78

61

(35.0; 106)

5%

111

87

(46.0; 151)

14%

< 0.0001

 Legumes, g/day

≥ 19

7.6

0.0

(0.0; 2.2)

11%

6.7

0.0

(0.0; 4.2)

13%

0.591

6.1

0.0

(0.0; 1.7)

10%

9.0

0.0

(0.0; 2.6)

12%

0.012

 Red and processed meat, g/day

≤ 71

89

81

(44.8; 125)

42%

83

79

(45.3; 118)

42%

0.253

108

103

(69; 142)

27%

71

64

(28.4; 103)

55%

< 0.0001

 Alcohol, g/day

≤ 10

10.7

5.1

(0.0; 17.0)

65%

7.4

0.0

(0.0; 9.4)

77%

0.002

15.8

12.5

(1.2; 23.5)

47%

6.1

0.0

(0.0; 8.6)

81%

< 0.0001

Italy

 

(n = 2313)

(n = 518)

 

(n = 1068)

(n = 1245)

 

 Fruit, g/day

≥ 200

185

153

(67; 257)

37%

257

222

(125; 333)

54%

< 0.0001

153

125

(50.4; 220)

28%

214

185

(88; 292)

45%

< 0.0001

 Vegetables, g/day

≥ 200

238

205

(134; 299)

52%

241

215

(149; 307)

55%

0.680

222

190

(126; 282)

47%

252

156

(145; 317)

56%

< 0.0001

 Legumes, g/day

≥ 19

10.7

0.0

(0.0; 2.9)

19%

12.4

0.0

(0.0; 0. 0)

19%

0.194

10.1

0.0

(0.0; 3.9)

19%

11.3

27.1

(0.0; 2.3)

19%

0.265

 Red and processed meat, g/day

≤ 71

85

77

(37.6; 120)

65%

75

68

(31.6; 111)

62%

0.015

88

81

(43.6; 122)

65%

82

74

(32.7; 119)

64%

< 0.0001

 Alcohol, g/day

≤ 10

7.8

0.1

(0.0; 12.7)

70%

10.0

2.6

(0.0; 16.5)

60%

0.0002

11.3

6.8

(0.0; 18.9)

57%

4.8

8.4

(0.0; 7.0)

80%

< 0.0001

France

 

(n = 2276)

(n = 348)

 

(n = 936)

(n = 1340)

 

 Fruit, g/day

≥ 200

129

77

(0.0 ; 198)

23%

209

174

(77; 309)

42%

< 0.0001

103

65

(0.0; 154)

17%

148

103

(0.0; 219)

28%

< 0.0001

 Vegetables, g/day

≥ 200

182

152

(80; 248)

36%

219

196

(110; 293)

46%

< 0.0001

152

128

(65; 204)

26%

202

173

(95; 272)

45%

< 0.0001

 Legumes, g/day

≥ 19

15.9

0.0

(0.0 ; 0.8)

17%

20.9

0.0

(0.0; 5.3)

20%

0.040

17.7

0.0

(0.0; 1.8)

19%

14.6

0.0

(0.0; 0.4)

16%

0.068

 Red and processed meat, g/day

≤ 71

94

84

(40.7; 134)

43%

90

79

(37.8; 133)

45%

0.316

101

92

(49.8; 143)

38%

88

77

(33.9; 127)

47%

< 0.0001

 Alcohol, g/day

≤ 10

9.0

0.0

(0.0; 13.8)

69%

11.2

5.2

(0.0; 18.2)

56%

0.008

13.5

6.6

(0.0; 21.1)

57%

5.8

0.0

(0.0; 7.3)

81%

< 0.0001

  

Subgroups by educational levela

Subgroup by overweight statusa

  

Low

Intermediate

High

p valueb

BMI < 25 kg/m2

BMI ≥ 25 kg/m2

p value

  

Mean

Median

(P25; P75)

%adh

Mean

Median

(P25; P75)

%adh

Mean

Median

(P25; P75)

%adh

Mean

Median

(P25; P75)

%adh

Mean

Median

(P25; P75)

%adh

 

Denmark

 

(n = 248)

 

(n = 943)

 

(n = 548)

  

(n = 972)

 

(n = 739)

  

 Fruit, g/day

≥ 200

152

94

(0.0; 234)

29%

159

115

(30.4; 233)

32%

214

167

(64; 305)

42%

< 0.0001

167

124

(33.1; 246)

34%

174

129

(23.5; 255)

33%

0.382

 Vegetables, g/day

≥ 200

126

96

(56; 152)

16%

150

118

(63; 185)

21%

184

137

(84; 238)

32%

< 0.0001

154

118

(66; 191)

23%

146

108

(63; 182)

21%

0.072

 Legumes, g/day

≥ 19

6.1

0.4

(0.0; 6.7)

10%

6.5

1.6

(0.0; 6.8)

10%

7.7

2.8

(0.0; 7.8)

11%

< 0.0001

6.4

1.9

(0.0; 6.9)

9%

6.9

1.5

(0.0; 7.4)

11%

0.055

 Red and processed meat, g/day

≤ 71

102

90

(58; 143)

39%

99

92

(58; 131)

33%

82

75

(44.5; 111)

46%

< 0.0001

94

86

(52; 126)

38%

99

90

(54; 134)

37%

0.072

 Alcohol, g/day

≤ 10

13.2

6.3

(0.0; 21.4)

58%

13.7

6.0

(0.0; 20.6)

59%

15.0

8.8

(0.0; 24.5)

52%

0.226

13.2

6.2

(0.0; 20.5)

58%

14.5

6.7

(0.0; 23.4)

57%

0.100

Czech Republic

 

(n = 345)

 

(n = 1194)

 

(n = 127)

  

(n = 802)

 

(n = 864)

  

 Fruit, g/day

≥ 200

89

61

(1.3; 141)

11%

122

82

(13.4; 173)

21%

121

96

(40.1; 179)

20%

0.0004

112

79

(19.1; 165)

19%

118

79

(5.9; 168)

19%

0.371

 Vegetables, g/day

≥ 200

90

71

(40.0; 123)

8%

94

74

(37.0; 126)

10%

120

85

(59; 160)

15%

0.002

96

77

(40.0; 126)

10%

95

73

(37.8; 128)

9%

0.807

 Legumes, g/day

≥ 19

8.9

0.0

(0.0; 3.0)

12%

7.3

0.0

(0.0; 2.0)

11%

7.3

0.0

(0.0; 2.7)

11%

0.524

7.3

0.0

(0.0; 2.3)

11%

7.9

0.0

(0.0; 2.1)

11%

0.588

 Red and processed meat, g/day

≤ 71

96

86

(47.4; 134)

42%

88

82

(44.3; 124)

41%

81

72

(43.4; 117)

48%

0.035

83

73

(40.0; 121)

48%

94

88

(50.2; 130)

37%

0.0002

 Alcohol, g/day

≤ 10

11.7

5.0

(0.0; 19.0)

61%

10.5

4.8

(0.0; 16.3)

66%

10.1

7.7

(0.0; 16.8)

61%

0.354

10.4

4.5

(0.0; 16.9)

65%

11.0

5.5

(0.0; 16.9)

64%

0.402

Italy

 

(n = 692)

 

(n = 985)

 

(n = 507)

  

(n = 1484)

 

(n = 828)

  

 Fruit, g/day

≥ 200

182

155

(69; 260)

38%

183

149

(65; 250)

36%

206

169

(83; 282)

41%

0.027

185

155

(68; 249)

37%

187

150

(68; 272)

37%

0.788

 Vegetables, g/day

≥ 200

242

206

(137; 296)

53%

238

205

(136; 300)

52%

232

202

(129; 287)

51%

0.534

229

200

(130; 288)

50%

254

213

(144; 323)

55%

0.0001

 Legumes, g/day

≥ 19

11.7

0.0

(0.0; 4.1)

22%

10.7

0.0

(0.0; 3.3)

19%

10.1

0.0

(0.0; 4.5)

17%

0.560

10.5

0.0

(0.0; 2.3)

19%

11.1

0.0

(0.0; 4.2)

19%

0.592

 Red and processed meat, g/day

≤ 71

88

81

(41.0; 122)

65%

85

77

(37.5; 119)

65%

83

77

(35.9; 121)

65%

0.332

84

77

(36.8; 118)

65%

86

78

(39.2; 124)

64%

0.433

 Alcohol, g/day

≤ 10

8.8

0.0

(0.0; 15.3)

66%

7.1

0.1

(0.0; 11.9)

72%

7.4

0.2

(0.0; 11.1)

74%

0.001

6.8

0.0

(0.0; 11.2)

73%

9.6

4.0

(0.0; 15.9)

62%

< 0.0001

France

 

(n = 1039)

 

(n = 495)

 

(n = 737)

  

(n = 1379)

 

(n = 871)

  

 Fruit, g/day

≥ 200

125

76

(0.0; 200)

24%

128

84

(0.0; 195)

21%

137

95

(13.9; 196)

23%

0.265

126

82

(0.0; 191)

22%

134

89

(0.0; 204)

24%

0.180

 Vegetables, g/day

≥ 200

181

152

(77; 248)

36%

179

144

(74; 245)

33%

183

156

(87; 249)

37%

0.892

175

146

(75; 242)

33%

188

158

(85; 254)

39%

0.036

 Legumes, g/day

≥ 19

19.5

0.0

(0.0; 1.3)

21%

13.2

0.0

(0.0; 0.4)

15%

12.5

0.0

(0.0; 0.5)

15%

0.0003

16.3

0.0

(0.0; 1.1)

19%

15.5

0.0

(0.0; 0.5)

16%

0.645

 Red and processed meat, g/day

≤ 71

102

91

(48.7; 144)

39%

90

79

(33.5; 129)

44%

84

74

(33.9; 123)

47%

< 0.0001

89

78

(35.7; 127)

44%

101

91

(48.7; 145)

40%

0.0001

 Alcohol, g/day

≤ 10

8.3

0.0

(0.0; 11.8)

73%

9.4

0.2

(0.0; 15.1)

66%

9.6

0.2

(0.0; 15.5)

67%

0.135

8.0

0.0

(0.0; 12.1)

73%

10.6

0.1

(0.0; 16.9)

64%

< 0.0001

Intake of food groups are standardised to a 2000 kcal/day diet

%adherence represents a proxy for the percentage of the population that adhere to food-based dietary guidelines

BMI Body Mass Index

aYounger and middle-aged adults, aged 18–64 years, were stratified by gender, educational level and overweight status

bp value for the overall comparisons between population subgroups

Foods to increase

Mean fruit and vegetable intake varied significantly between countries with lower intakes for Czech Republic (118 and 95 g/day, respectively) and higher intakes for Italy (199 and 239 g/day, respectively), and varied in the same direction between men and women within all four countries showing higher intakes for women. Higher fruit intake was also observed in all four countries for the elderly and for subjects with a higher educational level, but no differences by overweight status. Vegetable intake tended to be higher among elderly in Denmark and France, among higher educated subjects in Denmark and Czech Republic, and among overweight subjects in Italy and France. Mean intakes of legumes (6.5–16.7 g/day), and nuts and seeds (0.5–2.6 g/day) were generally low in all countries. Mean intake of dairy was higher in Denmark (302 g/day), while fish was higher in Italy (44.6 g/day) and France (34.4 g/day).

Foods to decrease

Mean intake of red and processed meat was generally high in all countries (84–94 g/day). Within-countries, red and processed meat intake was lower for the elderly and women in all four countries, and except in Italy for the higher educated subjects, and in Czech Republic and France for the non-overweight. Alcohol intake varied between countries with lower intakes in Italy (8.2 g/day) and higher intakes for Denmark (14.6 g/day), and varied within countries in the same direction by gender and overweight status with lower intakes for women and the non-overweight. Alcohol intake also tended to be lower for the young and middle-aged adults, except in Czech Republic where intake is lower for the elderly. For the higher-educated subjects, alcohol intake tended to be lower in Czech Republic and Italy, but higher in Denmark and France.

Foods to replace

Mean intakes of whole grains from cereals, pasta and bread were low in all countries, illustrated by the fraction of whole grains on total grains of ≤ 15% with one exception for wholegrain pasta in France. Although mean intake of total breakfast cereals per day was very low, the whole grain variants were primarily eaten. Intake of white meat was much lower than red and processed meat, in particular red and processed meat contributed to 70–80% of total meat intake comprising mainly of red meat in Denmark, Italy and France, and of processed meat in Czech Republic. Intakes of butter and hard margarines were only slightly higher than intakes of soft margarines and vegetable oils, except for Denmark where butter and hard margarines were predominantly chosen as fat source, and for Italy where vegetable oils were dominating.

Nutrients

Table 4 shows the energy-standardised nutrient intakes, their corresponding proxy prevalence figures for inadequate intakes, and the NRD scores in four European adult populations, aged ≥ 18 years. Low intakes were observed for dietary fibre (15.8–19.4 g/day) and vitamin D (2.4–3.0 µg/day) in all countries, and for potassium (2288–2939 mg/day), and magnesium (268–285 mg/day), except in Denmark. Intake of vitamin E was lower in Denmark (6.7 mg/day), and folate in Czech Republic (212 µg/day). Mean intakes were high for protein (67.1–83.5 g/day), and iron (9.1–12.4 mg/day) in all countries analysed. Remaining nutrients, including calcium, zinc, vitamin A, C, B1, B2, and B12, showed varying intake levels between countries. Of the three nutrients to limit, a large penalty was obtained from saturated fatty acids (11.1–15.1 E%) in all countries, and from estimated sodium intake (2797–4244 mg/day) except in Italy. Based on the NRD scores, it is apparent that the nutrient density of the diet was highest in Italy (NRD9.3 of 537, and NRD15.3 of 1051), followed by Denmark (NRD9.3 of 416, and NRD15.3 of 896) and France, and the lowest in Czech Republic (NRD9.3 of 327 and NRD15.3 of 787). Within countries, nutrient density of the diet tended to be higher for women in all four countries and for the higher-educated subject, except in Italy (Table 5).

Table 4

Energy-standardised nutrient intakes, prevalence of inadequate intake, and Nutrient Rich Diet scores in four European populations, aged ≥ 18 years

 

DRV

Denmark (n = 2025)

Czech Republic (n = 1869)

Italy (n = 2831)

France (n = 2624)

Mean

Median

(P25; P75)

%<DRV

Mean

Median

(P25; P75)

%<DRV

Mean

Median

(P25; P75)

%<DRV

Mean

Median

(P25; P75)

%<DRV

Unstandardised energy intake, kcal/day

2264*

2155

(1681; 2738)

2523*

2396

(1790; 3106)

2119*

2057

(1666; 2491)

1980*

1912

(1509; 2390)

Qualifying nutrients

 Protein, g/day

0.66 g/BW

68.7

67.6

(59.7; 77.1)

16%

67.1

66.1

(59.1; 73.8)

12%

79.0*

77.8

(70.5; 86.1)

1%

83.5*

81.4

(70.9; 93.4)

2.4%

 Protein, E%

13.9

13.8

(12.4; 15.2)

13.4

13.2

(11.8; 14.8)

15.6*

15.6

(14.1; 17.2)

 

16.7*

16.3

(14.2; 18.7)

 

 Animal protein, g/day

44.8*

43.2

(35.6; 52.8)

38.8*

37.5

(30.1; 45.8)

48.6*

47.1

(38.9; 56.8)

c

   

 Plant protein, g/day

20.3*

20.2

(16.9; 23.6)

23.9*

23.8

(20.1; 27.3)

30.3*

30.3

(26.5; 34)

c

   

 Dietary fibre, g/daya

25

19.4*

18.6

(14.5; 23.2)

81%

15.8*

15.1

(12.7; 18.3)

96%

18.1*

17.0

(14.0; 21.0)

88%

16.6*

15.7

(12.3; 19.5)

91%

 MUFA, g/daya

25.7*

25.5

(21.0; 30.0)

32.0*

31.8

(27.8; 36.4)

39.0*

38.7

(33.5; 44.1)

29.7*

28.9

(24.0; 34.2)

 MUFA, E%

10–20 E%

11.7*

11.6

(9.5; 13.6)

31%

14.4*

14.3

(12.5; 16.4)

8%

17.6*

17.4

(15.1; 19.9)

25%

13.4*

13.0

(10.8; 15.4)

23%

 Calcium, mg/day

750

983*

928

(705; 1189)

30%

660*

593

(424; 805)

69%

742*

708

(539; 897)

57%

899*

842

(649; 1066)

38%

 Iron, mg/day

M: 6; F: 7

9.1*

8.9

(7.7; 10.2)

8%

10.6*

10.1

(8.5; 12.1)

4%

11.1*

10.5

(9.0; 12.3)

2%

12.4*

11.2

(9.4; 13.8)

2%

 Potassium, mg/dayb

3500

3143*

3073

(2514; 3658)

69%

2288*

2199

(1895; 2573)

96%

2938

2834

(2420; 3326)

81%

2879

2763

(2326; 3287)

82%

 Magnesium, mg/dayb

M: 350; F: 300

322*

315

(270; 365)

54%

285

274

(241; 315)

75%

268*

254

(219 299)

80%

282

263

(230 ; 309)

77%

 Zinc, mg/day

M: 7.5; F: 6.2

9.5*

9.3

(8.1; 10.8)

10%

7.0*

6.7

(5.6; 8.0)

52%

11.0*

10.5

(9.1; 12.4)

3%

10.2*

9.6

(8.1; 11.8)

9%

Vitamin A, µg RE/day

M: 570; F490

1032*

851

(557; 1242)

23%

692*

450

(315; 631)

62%

854*

635

(467; 924)

34%

1200*

822

(552; 1279)

23%

Vitamin C, mg/day

M: 90; F: 80

102*

85

(57; 131)

50%

78*

63

(37; 103)

65%

126*

103

(66; 159)

38%

91*

76

(46; 119)

56%

Vitamin E, mg/dayb

M: 13; F: 11

6.7*

6.1

(5.1; 7.7)

95%

11.7*

11.1

(8.4; 14.4)

56%

12.7*

11.8

(9.7; 14.1)

53%

10.6*

9.4

(6.9; 13.2)

66%

Vitamin D, µg/dayb

15

3.0

1.9

(1.3; 2.7)

97%

2.9

2.1

(1.4; 3.2)

99%

2.4

1.5

(1.0; 2.4)

99%

2.6

1.7

(1.0; 3.0)

99%

Vitamin B1, mg/day

0.6

1.1

1.1

(0.9; 1.3)

3%

1.1

1.0

(0.9; 1.2)

2%

1.10

0.9

(0.8; 1.1)

53%

1.20

1.1

(0.9; 1.3)

0%

Vitamin B2, mg/day

M: 1.1; F: 0.9

1.47*

1.38

(1.13; 1.70)

20%

1.08*

0.99

(0.84; 1.20)

65%

1.40*

1.3

(1.1; 1.6)

16%

1.80*

1.7

(1.4; 2.1)

8%

Vitamin B12, µg/dayb

4

4.7

4.2

(3.1; 5.6)

45%

4.4

3.4

(2.5; 4.8)

64%

6.1

4.1

(3.1; 5.8)

48%

5.6

4.0

(2.9; 5.8)

50%

Folate, µg DFE/d

250

293

268

(214; 334)

41%

212*

182

(146; 242)

76%

350*

305

(254; 380)

23%

278

253

(203; 322)

49%

Disqualifying nutrients

MRV

   

%> MRV

   

%> MRV

   

%> MRV

   

%> MRV

 SFA, g/day

30.4

30.2

(25.0; 35.4)

30.6

30.4

(25.5; 35.1)

24.6*

24.2

(20.3; 28.3)

33.5*

33.4

(27.7; 39.1)

 SFA, E%/dayd

< 10 E%

13.8

13.7

(11.3; 16.1)

86%

13.8

13.7

(11.5; 15.8)

80%

11.1*

10.9

(9.1; 12.7)

62%

15.1*

15.0

(12.5; 17.6)

91%

 Added sugar, g/day

43.2*

36.4

(21.3; 57.2)

36.6

31.3

(18.8; 50.6)

38.6

35.2

(21.1; 52.5)

c

  

 Added sugar, E%d

< 10 E%

8.8*

7.4

(4.3; 11.6)

32%

7.3

6.3

(3.8; 10.1)

21%

7.7

7.0

(4.2; 10.5)

24%

c

  

b

 Sodium, mg/dayd

< 2400

3012*

2919

(2484; 3439)

80%

4244*

4153

(3576; 4800)

98%

1703*

1648

(1245; 2076)

13%

2797*

2668

(2228; 3223)

85%

Nutrient Rich Diet Scores

                 

 Sub-score NRD9

765

775

(710; 829)

715*

721

(643; 794)

781*

793

(730; 841)

759

767

(701; 826)

 Sub-score NRD15

1245

1259

(1192; 1310)

1175*

1182

(1097; 263)

1295*

1310

(1246; 1356)

1250

1262

(1191; 1324)

 Sub-score NRDX.3

349*

346

(300; 392)

388*

387

(347; 427)

244*

242

(215; 271)

c

  

 Total score NRD9.3

416*

427

(334; 507)

327*

328

(256; 400)

537*

547

(482; 600)

c

  

 Total score NRD15.3

896*

916

(823; 992)

787*

791

(704; 875)

1051*

1062

997; 1115

c

  

DRV dietary reference value, AR average requirement, AI adequate intake, RE retinol equivalents, DFE dietary folate equivalents, E% energy percentage, MUFA mono-unsaturated fatty acids, SFA saturated fatty acids, NRD Nutrient Rich Diet scores, including their sub-scores

Intakes of nutrients are standardised to a 2000 kcal/day diet

a%<AR represents a proxy for the percentage of the population that have an inadequate intake, i.e. intake lower than the dietary reference value

bNutrients where AR cannot be set, hence AI is defined

cCannot be computed

dPercentages shown for SFA, added sugar and sodium reflect the proportion of the population that have an excessive intake, i.e. intake higher than the reference value (Maximum Recommend Value)

*Bonferroni p < 0.0001 test comparison for intake that was significantly different from all other three countries under study

Table 5

Nutrient density of the diet, using Nutrient Rich Diet scores 9.3 and 15.3, in four European populations in subgroups by age, gender, educational level and overweight status

 

Subgroups by age

Subgroups by gendera

Younger and middle-aged adults

Elderly,≥ 65 years

p value

Men

Women

p value

Mean

Median

(P25; P75)

Mean

Median

(P25; P75)

Mean

Median

(P25; P75)

Mean

Median

(P25; P75)

Denmark

(n = 1739)

(n = 286)

 

(n = 777)

(n = 965)

 

 Sub-score NRD9

764

774

(708; 829)

772

787

(721; 833)

0.120

731

733

(679; 786)

796

808

(758; 853)

< 0.0001

 Sub-score NRD15

1243

1256

(1191; 1308)

1256

1275

(1198; 1325)

0.033

1215

1227

(1162; 1280)

1271

1284

(1226; 1328)

< 0.0001

 Sub-score NRDX.3

351

348

(301; 395)

333

336

(291; 382)

< 0.0001

355

353

(309; 400)

346

339

(297; 388)

0.011

 Total score NRD9.3

413

424

(327; 505)

439

424

(328; 505)

0.001

376

386

(295; 456)

450

465

(388; 537)

< 0.0001

 Total score NRD15.3

892

913

(817; 988)

923

940

(847; 1010)

0.003

860

876

(780; 944)

925

944

(859; 1021)

< 0.0001

Czech Republic

(n = 1666)

(n = 203)

 

(n = 793)

(n = 873)

 

 Sub-score NRD9

714

720

(641; 793)

729

728

(666; 807)

0.037

659

656

(597; 719)

763

777

(713; 821)

< 0.0001

 Sub-score NRD15

1174

1182

(1092; 1261)

1185

1181

(1114; 1269)

0.208

1119

1115

(1039; 1197)

1223

1235

(1157; 1297)

< 0.0001

 Sub-score NRDX.3

387

385

(345; 427)

396

395

(360; 430)

0.053

375

377

(333; 417)

398

397

(358; 436)

< 0.0001

 Total score NRD9.3

327

327

(253; 400)

333

342

(270; 401)

0.456

284

283

(216; 349)

366

373

(298; 440)

< 0.0001

 Total score NRD15.3

787

790

(703; 876)

789

792

(711; 873)

0.830

744

744

(665; 821)

826

836

(751; 910)

< 0.0001

Italy

(n = 2313)

(n = 518)

 

(n = 1068)

(n = 1245)

 

 Sub-score NRD9

777

790

(725; 837)

796

805

(759; 852)

< 0.0001

747

754

(692; 806)

803

814

(764; 856)

< 0.0001

 Sub-score NRD15

1293

1307

(1240; 1350)

1305

1321

(1272; 1360)

0.003

1264

1271

(1210; 1330)

1317

1329

(1278; 1367)

< 0.0001

 Sub-score NRDX.3

245

243

(215; 271)

242

240

(213; 269)

0.464

242

240

(212; 271)

247

245

(219; 272)

0.004

 Total score NRD9.3

533

541

(476; 598)

554

563

(509; 609)

< 0.0001

505

513

(443; 572)

556

565

(509; 614)

< 0.0001

 Total score NRD15.3

1048

1059

(991; 1115)

1064

1075

(1021; 1122)

0.002

1022

1032

(959; 1091)

1070

1079

(1024; 1127)

< 0.0001

France

(n = 2276)

(n = 348)

 

(n = 936)

(n = 1340)

 

 Sub-score NRD9

754

762

(696; 821)

785

787

(743; 841)

< 0.0001

717

723

(668; 775)

788

799

(743; 846)

< 0.0001

 Sub-score NRD15

1244

1256

(1182; 1319)

1278

1289

(1222; 1346)

< 0.0001

1208

1219

(1147; 1284)

1278

1289

(1228; 1346)

< 0.0001

 

Subgroups by educational levela

Subgroup by overweight statusa

p value

Low

Intermediate

High

p valueb

BMI < 25 kg/m 2

BMI ≥ 25 kg/m 2

Mean

Median

(P25; P75)

Mean

Median

(P25; P75)

Mean

Median

(P25; P75)

Mean

Median

(P25; P75)

Mean

Median

(P25; P75)

Denmark

(n = 248)

(n = 943)

(n = 548)

 

(n = 972)

(n = 739)

 

 Sub-score NRD9

746

754

(690; 814)

760

767

(705; 826)

791

803

(743; 844)

< 0.0001

769

779

(717; 829)

759

766

(702; 831)

0.054

 Sub-score NRD15

1221

1236

(1165; 1293)

1242

1254

(1193; 1306)

1271

1282

(1224; 1325)

< 0.0001

1250

1261

(1204; 1308)

1237

1249

(1177; 1309)

0.021

 Sub-score NRDX.3

356

356

(305; 404)

356

350

(304; 401)

334

334

(291; 370)

<0.0001

351

349

(305:392)

351

347

(295; 398)

1.000

 Total score NRD9.3

390

404

(292; 498)

405

414

(324; 492)

456

459

(392; 537)

< 0.0001

408

418

(316; 511)

408

418

(316; 511)

0.2448

 Total score NRD15.3

865

893

(767; 978)

887

905

(817; 978)

937

942

(869; 1013)

< 0.0001

887

908

(791; 990)

887

907

(791; 990)

0.165

Czech Republic

(n = 345)

  

(n = 1194)

  

(n = 127)

   

(n = 802)

  

(n = 864)

   

 Sub-score NRD9

695

684

(624; 780)

716

722

(644; 794)

740

744

(682; 802)

< 0.0001

719

725

(646; 795)

709

713

(633; 791)

0.036

 Sub-score NRD15

1153

1149

(1060; 1252)

1175

1181

(1098; 1259)

1217

1238

(1149; 1281)

< 0.0001

1175

1186

(1097; 1260)

1172

1178

(1091; 1261)

0.605

 Sub-score NRDX.3

378

378

(339; 421)

390

387

(346; 430)

384

381

(348; 413)

0.007

389

390

(347; 430)

385

382

(343; 424)

0.196

 Total score NRD9.3

317

307

(237; 387)

327

327

(254; 406)

356

360

(301; 403)

0.003

330

329

(258; 400)

324

323

(248; 399)

0.260

 Total score NRD15.3

775

775

(681; 862)

785

789

(706; 874)

833

847

(771; 904)

< 0.0001

786

791

(704; 876)

787

789

(703; 877)

0.872

Italy

(n = 692)

  

(n = 985)

  

(n = 507)

   

(n = 1484)

  

(n = 828)

   

 Sub-score NRD9

774

788

(718; 835)

776

789

(725; 834)

788

801

(734; 851)

0.005

779

792

(728; 838)

775

788

(720; 836)

0.245

 Sub-score NRD15

1291

1309

(1234; 1355)

1292

1304

(1242; 1353)

1300

1316

(1249; 1360)

0.140

1294

1308

(1244; 1355)

1291

1307

(1234; 1354)

0.414

 Sub-score NRDX.3

240

240

(211; 267)

246

243

(217; 273)

249

246

(220; 276)

0.001

248

245

(219; 273)

240

237

(209; 268)

< 0.0001

 Total score NRD9.3

534

545

(478; 603)

530

536

(474; 593)

539

550

(480; 603)

0.158

531

539

(475; 598)

535

545

(476; 597)

0.289

 Total score NRD15.3

1051

1065

(992; 1118)

1046

1056

(993; 1111)

1051

1064

(991; 1115)

0.439

1046

1058

(992; 1114)

1051

1064

(990; 1115)

0.206

France

(n = 1039)

  

(n = 495)

  

(n = 737)

   

(n = 1379)

  

(n = 871)

   

 Sub-score NRD9

749

760

(681; 822)

756

763

(702; 817)

761

764

(707; 825)

0.014

753

760

(696; 819)

758

766

(699; 827)

0.181

 Sub-score NRD15

1237

1252

(1166; 1319)

1247

1250

(1194; 1314)

1254

162

(1190; 1326)

0.002

1242

1256

(1177; 1316)

1249

1258

(1191; 1329)

0.110

BMI Body Mass Index, NRD Nutrient Rich Diet scores, including their sub-scores

For France, sub-score NRDX.3, total score NRD9.3 and 15.5 cannot be computed due to a lack of data on sugars

aYounger and middle-aged adults, aged 18–64 years, were stratified by gender, educational level and overweight status

bp value for the overall comparisons between population subgroups

Discussion

In this study, we found that dietary intakes varied markedly across the four European countries, irrespective of energy intake. Within countries, food intakes also varied markedly by socio-economic factors such as age, gender, and educational level, but less pronounced by anthropometric factors such as overweight status. However, the set of food-based dietary guideline was not met by a large part of the population and/or population subgroup by age, gender, educational level or overweight status.

When describing food group intakes, mean daily intakes of fruit and vegetables, sweet beverages, and alcohol varied most between countries, showing higher intakes of fruit and vegetables, and lower intakes of sweet beverages and alcohol in Italy. In addition, we observed in Italy and France a similar vegetable intake among the different levels of education, whereas in Denmark and Czech Republic higher intake of vegetables was observed among higher-educated subjects; which is in line with previous studies conducted in European populations [40, 41, 42]. This region-dependent tendency might be attributed to the long-standing cultural tradition of using vegetables in the Mediterranean diet, as consumed in Italy and France, and is often easily recognisable by all layers of the population. However, a comparison of population subgroups within-countries is often closely related to dietary preferences, beliefs and practices of that particular consumer group. Higher intake of fish, nuts and seeds along with lower intake of red and processed meat are, for example, generally seen among women and higher-educated subjects, which might be driven by their health considerations and awareness of climate change [43].

When describing nutrient intakes summarised by the NRD9.3 and 15.3, the higher scores were observed for Italy, which is mainly attributed to their lower penalty score, i.e. NRDX.3, for the disqualifying nutrients of SFA and sodium. Because of the interrelation between food groups and nutrients intake, our results on variation in nutrient intakes can be partly reflected by our results on variation in food group intake. Low penalty score in Italy is likely to be in correspondence with its lower intakes for important sources of SFA intake such as butter and hard margarines, red and processed meat, and dairy products; however, with the estimates of sodium intake, caution must be applied, as they are very likely to be under-estimated due to difficulties in quantifying sodium content in recipes and discretionary salt intake [44]. Moreover, when focussing on qualifying nutrients, higher sub-scores NRD9 and NRD15 were also observed for Italy, but intake for calcium, potassium and magnesium was lower when compared with Denmark; related to intake of dairy products and whole-grain products. It could, thus, be argued whether these summary estimates could be used solely to describe nutrient intakes, as they do not point out specific inadequate nutrient intakes.

In the context of the SUSFANS project, we prefer to describe dietary intakes in terms of foods rather than nutrients, since foods are the constituents of a dietary pattern and the common denominator for linking dietary intakes with health, environment, affordability, consumer’s preferences, etc. Diet-associated environmental impact, in particular, has been attracting a lot of interest, as current food production and consumption patterns have been recognised as a major human-induced driver of climate change [45]. Some European countries have, therefore, developed guidelines for diets that are both healthy and environmentally-friendly [46, 47, 48, 49]. Such recommendations mostly emphasise the reduction of greenhouse gas emissions through propagating a shift towards plant-based foods. However, given European dietary intakes, there is still much progress to be made in this respect, simply showed by a percentage of around 35% for the intake of plant protein as opposed to total protein for the countries we studied. Moreover, predominant food groups contributing to animal and plant protein intake have been associated with regional and cultural traditions around dietary habits. Meat intake is regarded as the most important contributor to animal protein in European diets, but with differences related to the amount and types of meat consumed, as also denoted by previous studies [50, 51]. With regard to plant protein, cereals and cereal products have been identified as the main contributor to plant protein in European diets [52], while joint contributions from vegetables, legumes and fruit varied between countries, as observed in the present study.

The present study provides further support for the application of individual-level dietary data to address the food-climate connection. Often diet-associated environmental impact was quantified using food availability data related to food production, but not to food consumption as such. Using individual-level reported dietary data might, therefore, be regarded as a useful tool in the connection between health and environment with foods as their common denominator. Cross-country comparison of individual-level dietary data is, however, challenged by the dietary surveys conducted with different survey characteristics and data collection methods that may influence the comparability of the results. First, sampling procedures used in the surveys reported in this study varied in terms of recruitment methods, household and individual representativeness, number of subjects per household and weighting factors used; however, they all aimed at including a nationally representative sample of at least all age-sex categories. It still remains a possibility that those who have agreed to participate form a group with a greater interest in health, hence more optimistic results.

Second, methods of dietary assessment used in the surveys reported were conducted differently, with regard to the methods used and in the manner in which the assessment was carried out. Replicates of 24-h recall as applied in Czech Republic showed a higher mean energy intake compared to diet records as applied in Denmark, Italy and France. This might be explained by factors related to the methods themselves, such as reliance on memory and portion size estimations [53, 54, 55], and/or characteristics of the populations. Standardising intake data to a 2000 kcal/day diet had, therefore, the largest impact on results of Czech Republic; lowering its mean dietary intakes under the assumption that energy intake is positively correlated with food group and nutrient intake. Standardisation for energy is one of the more practical ways of reducing part of the extraneous variation in dietary estimates [56], and enables to study the relative contribution of food groups and nutrients intake to the total diet, regardless of energy intake. In the European Food COnsumption VALidation project, it has been suggested to adjust for BMI instead when analysing and interpreting dietary data of nutritional monitoring surveys to reduce mean bias at population level [57]. Given that stratified analyses by overweight status showed no relevant differences in dietary intakes within a country, it is questionable whether BMI-adjusted values should be the main exposure of interest in the present study describing the heterogeneity of European diets.

Another important factor in estimating dietary intakes consistently is the number of days included in the dietary assessment to enable comparison between countries across Europe. In this study, dietary data were, therefore, standardised for the number of days, but have not been corrected for time-interval between the two selected record/recall days, hence not corrected for within-subject day-to-day variability. Correcting for within-subject day-to-day variability would have resulted in comparable means for dietary intakes compared to unadjusted data, though with a shrinkage of intake distributions which in turn would have decreased the percentage of the population above and below a cut-off point [58]. However, relying on consecutive days, including days spaced over a week time-interval, is likely to underestimate the within-subject day-to-day variation [59] because of the interdependence of days that captures some of the day-to-day variation in the between-subject variation [60, 61]. Thus, this day-interdependence would have resulted in a shrinkage of the observed intake distribution that is too much toward the group mean, hence an under-estimation of true percentage of the population above and below a cut-off when statistically correcting intake distributions. In addition, the use of country-specific food composition databases might affect the number of subjects whose intake was below the DRV. In particular, when using different food composition databases, potential systematic errors in estimating nutrient intake would be different between countries, and in all probability alternate with magnitude and direction. With increasing globalisation, however, the foods and mixed dishes available in different countries are not all grown/produced/prepared in the same manner and, therefore, using a country-specific composition database is likely to reflect nutrient intake more accurately.

Exclusion of under-reporters would have increased the prevalence of adherence to the food-based dietary guidelines and decreased the prevalence of inadequate nutrient intakes, and inclusion of supplementation use would have decreased the prevalence of nutrient inadequacy even further. The present study did estimate the percentage of under- and over-reporters (Online Resource 1), but did not estimate intakes excluding them, because some of the mis-reporters may truly be consuming a low- or a high-energy diet. Over the past decades, dietary supplementation use has increased in Europe with a clear north–south gradient [62], showing a high number of users in Denmark (Online Resource 1). Hence, it is likely that in countries with higher level of supplementation use, dietary supplementation might have contributed to improved total nutrient intakes, with its impact dependent on the supplementation formulation, the frequency of use, and the level of micronutrient intakes of those taking supplements. However, our interest is on nutrient intakes from foods only to find nutritional gaps that are most in need to improve the healthiness of dietary intake.

In conclusion, there is considerable variation in food and nutrient intakes across European countries. The present study indicated that the intake of food groups showed larger deviations from food-based dietary guidelines for the overall population and population subgroups of the countries we studied. In addition, results suggested inadequate nutrient intakes from foods for dietary fibre and vitamin D in all countries, and for potassium, magnesium, vitamin E and folate in specific regions. Individual-level dietary data in different European population and population subgroups are, therefore, needed for balancing diets for European citizen.

Moreover, individual-level dietary data from national surveys serve as a practical tool for describing the healthiness of diet in terms of foods and nutrients, but dietary data harmonisation remains challenging. Using a common food classification system is a first step in the alignment of surveys and necessary to enable cross-country comparisons for food group intakes. However, further steps, such as standardisation for energy, number of days, etc., are needed for harmonisation of dietary data. Besides the healthiness of dietary intake, these dietary surveys might also be important in shaping optimised diets where other factors, such as environmental impact, affordability and consumer preferences are incorporated. We aim, therefore, to support further engagement of key stakeholders from the food supply chain and policy-makers in the next stages for the design of SHARP diets.

Notes

Author contributions

JMG and PvtV initiated the topic of the paper. MD, LM, LD, AT, CD, SF, and ET were responsible for the data collection and data analysis. EM, AK and were responsible for data interpretation. EM drafted the manuscript, which was reviewed by all authors for intellectual content. All authors read and approved the final submission of the paper.

Funding

Financial support for this original contribution was obtained from funding from the European Union’s H2020 Programme under Grant Agreement number 633692 (SUSFANS: Metrics, models and foresight for European sustainable food and nutrition security) and from the Top Consortia for Knowledge and Innovation of the Dutch Ministry of Economic Affairs.

Compliance with ethical standards

Conflict of interest

The authors have no conflicts of interest.

Supplementary material

394_2018_1673_MOESM1_ESM.docx (14 kb)
Supplementary material 1 (DOCX 13 KB)

References

  1. 1.
    GBD 2016 Risk Factors Collaborators (2017) Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 390:1345–1422.  https://doi.org/10.1016/S0140-6736(17)32366-8 CrossRefGoogle Scholar
  2. 2.
    Traill WB, Mazzocchi M, Shankar B, Hallam D (2014) Importance of government policies and other influences in transforming global diets. Nutr Rev 72:591–604CrossRefGoogle Scholar
  3. 3.
    Global Panel on Agriculture and Food Systems for Nutrition (2016) Food systems and diets: Facing the challenges of the 21st century. Global Panel on Agriculture and Food Systems for Nutrition, LondonGoogle Scholar
  4. 4.
    Schmidhuber J, Traill WB (2006) The changing structure of diets in the European Union in relation to healthy eating guidelines. Public Health Nutr 9:584–595CrossRefGoogle Scholar
  5. 5.
    Balanza R, García-Lorda P, Pérez-Rodrigo C, Aranceta J, Bonet MB, Salas-Salvadó J (2007) Trends in food availability determined by the Food and Agriculture Organization’s food balance sheets in Mediterranean Europe in comparison with other European areas. Public Health Nutr 10:168–176CrossRefGoogle Scholar
  6. 6.
    Gerbens-Leenes P, Nonhebel S, Krol M (2010) Food consumption patterns and economic growth. Increasing affluence and the use of natural resources. Appetite 55:597–608CrossRefGoogle Scholar
  7. 7.
    Mozaffarian D, Ludwig DS (2010) Dietary guidelines in the 21st century—a time for food. JAMA 304:681–682CrossRefGoogle Scholar
  8. 8.
    Kromhout D, Spaaij C, de Goede J, Weggemans R (2016) The 2015 Dutch food-based dietary guidelines. Eur J C Nutr 70(8):869–878CrossRefGoogle Scholar
  9. 9.
    World Health Organisation (WHO) (2003) Food based dietary guidelines in the WHO European region. WHO, Copenhagen:Google Scholar
  10. 10.
    Rutten M, Achterbosch TJ, de Boer IJ, Cuaresma JC, Geleijnse JM, Havlík P, Heckelei T, Ingram J, Leip A, Marette S (2016) Metrics, models and foresight for European sustainable food and nutrition security: the vision of the SUSFANS project. Agric Syst.  https://doi.org/10.1016/j.agsy.2016.10.014 Google Scholar
  11. 11.
    Pedersen A, Fagt S, Groth MV, Christensen T, Biltoft-Jensen A, Matthiessen J, Andersen NL, Kørup K, Hartkopp H, Ygil K, Hinsch HJ, Saxholt E, Trolle E (2009) Danskernes kostvaner 2003–2008. In: DTU FødevareinstituttetGoogle Scholar
  12. 12.
    Ruprich JDM, Rehurkova I, Slamenikova E, Resova D (2006) Individual food consumption—the national study SISP04. CHFCH National Institute of Public Health, PragueGoogle Scholar
  13. 13.
    Leclercq CAD, Piccinelli R, Sette S, Le Donne C, Turrini A (2009) The Italian national food consumption survey INRAN-SCAI 2005-06: main results in terms of food consumption. Publ Health Nutr 12(12):2504–2532CrossRefGoogle Scholar
  14. 14.
    Agence Française de Sécurité Sanitaire des Aliments (AFSSA) (2009) Report of the 2006/2007 Individual and National Study on Food Consumption 2 (INCA 2). In: Synthèse de l’étude individuelle nationale des consommations alimentaires 2 (INCA 2), 2006–2007, pp 1–44Google Scholar
  15. 15.
    Matthiessen J, Stockmarr A, Biltoft-Jensen A, Fagt S, Zhang H, Groth MV (2014) Trends in overweight and obesity in Danish children and adolescents: 2000–2008-exploring changes according to parental education. Scand J Public Health 42:385–392.  https://doi.org/10.1177/1403494813520356 CrossRefGoogle Scholar
  16. 16.
    European Food Safety Authority (2015) The food classification and description system FoodEx2 (revision 2). EFSA Supp Publ 804:90Google Scholar
  17. 17.
    EFSA (Eurepean Food Safety Authority) (2011) Use of the EFSA comprehensive european food consumption database in exposure assessment. EFSA J 9:2097CrossRefGoogle Scholar
  18. 18.
    Møller ASE, Christensen AT, Hartkopp H (2005) Fødevaredatabanken version 6.0. Fødevareinformatik, Afdeling for Ernæring, DenmarkGoogle Scholar
  19. 19.
    Saxholt E, Christensen AT, Møller A, Hartkopp HB, Hess Ygil H, Hels OH (2008) Fødevaredatabanken, version 7. In: Fødevareinformatik, Afdeling for Ernæring, Fødevareinstituttet, Danmarks Tekniske UniversitetGoogle Scholar
  20. 20.
    Czech Centre for Food Composition Database (2016) Czech food composition database version 6.16. Institute of Agricultural Economics and Information, PragueGoogle Scholar
  21. 21.
    Food Research Institute (2016) Slovak food composition data bank. Department of Risk Assessment Food Composition Data Bank and Consumer’s Survey VUP Food Research Institute, BratislavaGoogle Scholar
  22. 22.
    Istituto Nazionale di Ricerca per gli Alimenti e la Nutrizione (INRAN) (2016) Banca Dati di Composizione degli Alimenti. Istituto Nazionale di Ricerca per gli Alimenti e la Nutrizione, RomaGoogle Scholar
  23. 23.
    Feinberg M (1995) Répertoire général des aliments (General Inventory of Foods). In: FJ-CLC (ed) Institut national de la recherche agronomique. Technique & Documentation—Lavoisier, ParisGoogle Scholar
  24. 24.
    Ireland J, dCL, Oseredczuk M et al (2008) French food composition table, version 2008. In: French Food Safety Agency (AFSSA)Google Scholar
  25. 25.
    Goldberg G, Black A, Jebb S, Cole T, Murgatroyd P, Coward W, Prentice A (1991) 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 45:569–581Google Scholar
  26. 26.
    Black AE (2000) 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 24:1119CrossRefGoogle Scholar
  27. 27.
    Ministry of Food Agriculture and Fisheries (2013) The official dietary guidelines (Danish: De officielle kostråd). Ministry of Food, Agriculture and Fisheries Glostrup, DenmarkGoogle Scholar
  28. 28.
    Czech Society for Nutrition (2012) Nutrition recommendations for Czech Republic (Czech: Výživová doporučení pro obyvatelstvo České republiky). Czech Society for Nutrition, PragueGoogle Scholar
  29. 29.
    Italian National Research Institute on Food and Nutrition (INRAN; CRA-NUT) (2003) Guidelines for healthy Italian food habits, 2003 (Italian: Linee guida per una sana alimentazione italiana. Revisione 2003). Italian National Research Institute on Food and Nutrition (INRAN; CRA-NUT), RomeGoogle Scholar
  30. 30.
    Programme National Nutrition Santé (PNNS) (2016) La Santé vient en mangeant Le guide alimentaire pour tous. ANSES Agence Nationale de Sécurité Sanitaire de l’alimentation, de l’environnement et du travail, Maisons-Alfort CedexGoogle Scholar
  31. 31.
    Van Kernebeek HRJ, Oosting SJ, Feskens EJM, Gerber PJ, De Boer IJM (2014) The effect of nutritional quality on comparing environmental impacts of human diets. J Clean Prod 73:88–99.  https://doi.org/10.1016/j.jclepro.2013.11.028 CrossRefGoogle Scholar
  32. 32.
    Roos E, Karlsson H, Witthoft C, Sundberg C (2015) Evaluating the sustainability of diets-combining environmental and nutritional aspects. Environ Sci Policy 47:157–166.  https://doi.org/10.1016/j.envsci.2014.12.001 CrossRefGoogle Scholar
  33. 33.
    Drewnowski A (2009) Defining nutrient density: development and validation of the nutrient rich foods index. J Am Coll Nutr 28:421 s-426 sCrossRefGoogle Scholar
  34. 34.
    Fulgoni VL 3rd, Keast DR, Drewnowski A (2009) Development and validation of the nutrient-rich foods index: a tool to measure nutritional quality of foods. J Nutr 139:1549–1554.  https://doi.org/10.3945/jn.108.101360 CrossRefGoogle Scholar
  35. 35.
    EFSA (European Food Safety Authority) (2010) Panel (EFSA Panel on Dietetic Products, Nutrition and Allergies), 2010. Scientific opinion on principles for deriving and applying dietary reference values. EFSA J 8:1458Google Scholar
  36. 36.
    World Health Organisation (WHO) (2012) Guideline: sodium intake for adults and children. WHO, GenevaGoogle Scholar
  37. 37.
    World Health Organisation (WHO) (2015) Guideline: sugars intake for adults and children. WHO, GenevaGoogle Scholar
  38. 38.
    Food and Agriculture Organisation (FAO) (2010) Fats and fatty acids in human nutrition. Report of an expert consultation. FAO Food Nutr Pap 91:1–166Google Scholar
  39. 39.
    Institute of Medicine (IOM) (2000) Dietary reference intakes: applications in dietary assesment. National Academy, Washington DCGoogle Scholar
  40. 40.
    De Irala-Estevez J, Groth M, Johansson L, Oltersdorf U (2000) A systematic review of socio-economic differences in food habits in Europe: consumption of fruit and vegetables. Eur J Clin Nutr 54:706CrossRefGoogle Scholar
  41. 41.
    Prättälä R, Hakala S, Roskam A-JR, Roos E, Helmert U, Klumbiene J, Van Oyen H, Regidor E, Kunst AE (2009) Association between educational level and vegetable use in nine European countries. Public Health Nutr 12:2174–2182CrossRefGoogle Scholar
  42. 42.
    Roos E, Talala K, Laaksonen M, Helakorpi S, Rahkonen O, Uutela A, Prättälä R (2008) Trends of socioeconomic differences in daily vegetable consumption, 1979–2002. Eur J Clin Nutr 62:823–833CrossRefGoogle Scholar
  43. 43.
    de Boer J, Schosler H, Aiking H (2014) “Meatless days” or “less but better”? Exploring strategies to adapt Western meat consumption to health and sustainability challenges. Appetite 76:120–128.  https://doi.org/10.1016/j.appet.2014.02.002 CrossRefGoogle Scholar
  44. 44.
    McLean RM (2014) Measuring population sodium intake: a review of methods. Nutrients 6:4651–4662CrossRefGoogle Scholar
  45. 45.
    Tukker AHG, Guinée J, Heijungs R, de Koning A, van Oers L et al (2006) Environmental Impact of Products (EIPRO) Analysis of the life cycle environmental impacts related to the final consumption of the EU25. In: European Commission Technical Report EUR 22284 EN. IPTS/ESTO, European Commission Joint Research Centre BrusselsGoogle Scholar
  46. 46.
    German Nutrition Society (2013) Ten guidelines for wholesome eating and drinking from the German Nutrition Society (German: Vollwertig essen und trinken nach den 10 Regeln der DGE). Deutsche Gesellschaft für Ernährungs e.V., BonnGoogle Scholar
  47. 47.
    The Swedish National Food Agency (Livsmedelsverket) (2017) Find your way to eat greener, not too much and to be active! (Hitta ditt sätt att äta grönare, lagom mycket och röra på dig!). Livesmedelsverket, UppsalaGoogle Scholar
  48. 48.
    Health Council of the Netherlands (2011) Guidelines for a healthy diet: the ecological perspective. Health Council of the Netherlands, The HagueGoogle Scholar
  49. 49.
    Macdiarmid J, Kyle J, Horgan G, Loe J, Fyfe C, Johnstone A, McNeill G (2011) Livewell: a balance of healthy and sustainable food choices. In: WWF-UKGoogle Scholar
  50. 50.
    Linseisen J, Kesse E, Slimani N, Bueno-De-Mesquita H, Ocké M, Skeie G, Kumle M, Iraeta MD, Gómez PM, Janzon L (2002) Meat consumption in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohorts: results from 24-hour dietary recalls. Public Health Nutr 5:1243–1258CrossRefGoogle Scholar
  51. 51.
    Kushi LH, Lenart EB, Willett WC (1995) Health implications of Mediterranean diets in light of contemporary knowledge. 2. Meat, wine, fats, and oils. Am J Clin Nutr 61:1416S–1427SGoogle Scholar
  52. 52.
    Halkjaer J, Olsen A, Bjerregaard L, Deharveng G, Tjønneland A, Welch A, Crowe F, Wirfält E, Hellstrom V, Niravong M (2009) Intake of total, animal and plant proteins, and their food sources in 10 countries in the European Prospective Investigation into Cancer and Nutrition. Eur J Clin Nutr 63:S16-S36CrossRefGoogle Scholar
  53. 53.
    Bingham S, Gill C, Welch A, Day K, Cassidy A, Khaw K, Sneyd M, Key T, Roe L, Day N (1994) Comparison of dietary assessment methods in nutritional epidemiology: weighed records v. 24 h recalls, food-frequency questionnaires and estimated-diet records. Br J Nutr 72:619–643CrossRefGoogle Scholar
  54. 54.
    Holmes B, Dick K, Nelson M (2008) A comparison of four dietary assessment methods in materially deprived households in England. Public Health Nutr 11:444–456CrossRefGoogle Scholar
  55. 55.
    De Keyzer W, Huybrechts I, De Vriendt V, Vandevijvere S, Slimani N, Van Oyen H, De Henauw S (2011) Repeated 24-hour recalls versus dietary records for estimating nutrient intakes in a national food consumption survey. Food Nutr Res 55:7307CrossRefGoogle Scholar
  56. 56.
    Willett WC, Howe GR, Kushi LH (1997) Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr 65:1220S–1228SCrossRefGoogle Scholar
  57. 57.
    Crispim SP, Geelen A, De Vries JH, Freisling H, Souverein OW, Hulshof PJ, Ocke MC, Boshuizen H, Andersen LF, Ruprich J (2012) Bias in protein and potassium intake collected with 24-h recalls (EPIC-Soft) is rather comparable across European populations. Eur J Nutr 51:997–1010CrossRefGoogle Scholar
  58. 58.
    Dodd KW, Guenther PM, Freedman LS, Subar AF, Kipnis V, Midthune D, Tooze JA, Krebs-Smith SM (2006) Statistical methods for estimating usual intake of nutrients and foods: a review of the theory. J Am Diet Assoc 106:1640–1650CrossRefGoogle Scholar
  59. 59.
    Larkin FA, Metzner HL, Guire KE (1991) Comparison of 3 consecutive-day and 3 random-day records of dietary-intake. J Am Diet Assoc 91:1538–1542Google Scholar
  60. 60.
    Tarasuk V, Beaton GH (1991) The nature and individuality of within-subject variation in energy-intake. Am J Clin Nutr 54:464–470CrossRefGoogle Scholar
  61. 61.
    Ellozy M (1983) Dietary variability and its impact on nutritional epidemiology. J Chron Dis 36:237–249CrossRefGoogle Scholar
  62. 62.
    Skeie G, Braaten T, Hjartåker A, Lentjes M, Amiano P, Jakszyn P, Pala V, Palanca A, Niekerk E, Verhagen H (2009) Use of dietary supplements in the European Prospective Investigation into Cancer and Nutrition calibration study. Eur J Clin Nutr 63:S226–S238CrossRefGoogle Scholar

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Elly Mertens
    • 1
  • Anneleen Kuijsten
    • 1
  • Marcela Dofková
    • 2
  • Lorenza Mistura
    • 3
  • Laura D’Addezio
    • 3
  • Aida Turrini
    • 3
  • Carine Dubuisson
    • 4
  • Sandra Favret
    • 4
  • Sabrina Havard
    • 4
  • Ellen Trolle
    • 5
  • Pieter van’t Veer
    • 1
  • Johanna M. Geleijnse
    • 1
  1. 1.Division of Human NutritionWageningen UniversityWageningenThe Netherlands
  2. 2.Center for Health, Nutrition and FoodNational Institute of Public HealthBrnoCzech Republic
  3. 3.Council for Agricultural Research and EconomicsResearch Centre for Food and NutritionRomeItaly
  4. 4.French Agency for Food, Environmental and Occupational Health and Safety (Anses)/Risk Assessment Department (DER)Maisons-Alfort CedexFrance
  5. 5.Division of Risk Assessment and NutritionNational Food Institute, Technical University of DenmarkSøborgDenmark

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