Literature DB >> 32370805

Feel4Diabetes healthy diet score: development and evaluation of clinical validity.

Eeva Virtanen1, Jemina Kivelä2,3, Katja Wikström2,4, Christina-Paulina Lambrinou5, Pilar De Miguel-Etayo6, Nele Huys7, Katalin Vraukó-Tóth8, Luis A Moreno6, Natalya Usheva9, Nevena Chakarova10, Sándorné A Rado8, Violeta Iotova11, Konstantinos Makrilakis12, Greet Cardon7, Stavros Liatis12, Yannis Manios5, Jaana Lindström2.   

Abstract

BACKGROUND: The aim of this paper is to present the development of the Feel4Diabetes Healthy Diet Score and to evaluate its clinical validity.
METHODS: Study population consisted of 3268 adults (63% women) from high diabetes risk families living in 6 European countries. Participants filled in questionnaires at baseline and after 1 year, reflecting the dietary goals of the Feel4Diabetes intervention. Based on these questions the Healthy Diet Score was constructed, consisting of the following components: breakfast, vegetables, fruit and berries, sugary drinks, whole-grain cereals, nuts and seeds, low-fat dairy products, oils and fats, red meat, sweet snacks, salty snacks, and family meals. Maximum score for each component was set based on its estimated relative importance regarding T2DM risk, higher score indicating better quality of diet. Clinical measurements included height, weight, waist circumference, heart rate, blood pressure, and fasting blood sampling, with analyses of glucose, total cholesterol, HDL-cholesterol, LDL-cholesterol, and triglycerides. Analysis of (co) variance was used to compare the Healthy Diet Score and its components between countries and sexes using baseline data, and to test differences in clinical characteristics between score categories, adjusted for age, sex and country. Pearson's correlations were used to study the association between changes from baseline to year 1 in the Healthy Diet Score and clinical markers. To estimate reproducibility, Pearson's correlations were studied between baseline and 1 year score, within the control group only.
RESULTS: The mean total score was 52.8 ± 12.8 among women and 46.6 ± 12.8 among men (p <  0.001). The total score and its components differed between countries. The change in the Healthy Diet Score was significantly correlated with changes in BMI, waist circumference, and total and LDL cholesterol. The Healthy Diet Score as well as its components at baseline were significantly correlated with the values at year 1, in the control group participants.
CONCLUSION: The Feel4Diabetes Healthy Diet Score is a reproducible method to capture the dietary information collected with the Feel4Diabetes questionnaire and measure the level of and changes in the adherence to the dietary goals of the intervention. It gives a simple parameter that associates with clinical risk factors in a meaningful manner. TRIAL REGISTRATION: Clinicaltrials.gov NCT02393872. Registered March 20, 2015.

Entities:  

Keywords:  Diet; Diet score; Evaluation; Intervention; Risk factors; Type 2 diabetes; Validity

Mesh:

Year:  2020        PMID: 32370805      PMCID: PMC7201941          DOI: 10.1186/s12902-020-0521-x

Source DB:  PubMed          Journal:  BMC Endocr Disord        ISSN: 1472-6823            Impact factor:   2.763


Background

Type 2 diabetes (T2DM) is a chronic disease that develops as a result of interactions between genetic and environmental factors. In addition to obesity and sedentary lifestyle, the composition of the diet is recognized as one of the important modifiable factors in the development of T2DM [1]. Lifestyle interventions focusing on dietary modification and increasing physical activity have been shown to prevent the development of T2DM in people at high risk [2]. To evaluate the effectiveness of a preventive intervention, it is thus important to be able to measure the achieved changes in lifestyle using validated methods. One of the aims in the multinational Feel4Diabetes project was to identify families at increased T2DM risk and provide them interventions to decrease the risk in a cluster-randomized study setting in 6 European countries (Belgium, Finland, Greece, Spain, Hungary, Bulgaria) [3]. The high-risk families living in the intervention areas were subject to the interventions delivered in the school and community setting between 2016 and 2018. In addition, they were offered a possibility to take part in a more intensive lifestyle counselling, consisting of group and individual sessions and SMS-intervention focusing specifically on preventing the development of T2DM. To measure the effect of the Feel4Diabetes intervention on dietary behaviours, questions on dietary intake were included in the questionnaire which the parents filled in at baseline and after 1 and 2 years [3, 4]. The dietary questions were formulated to focus specifically on the intake of the food items that had been chosen as the goals in the Feel4Diabetes dietary intervention. To test the questionnaire reliability, a total of 191 parents completed the questionnaires twice, with a 1–2 week interval, and based on the intra-class correlation coefficients (ICC) of test-retest the developed questionnaires showed acceptable reliability [4]. Based on the dietary questions, the Feel4Diabetes Healthy Diet Score was compiled, in order to simplify the information from the questionnaire in a single parameter that can be used as a marker of dietary compliance. The aim of this paper is to present the development of the Feel4Diabetes Healthy Diet Score and to evaluate its clinical validity.

Methods

Study population and measurements

Participants were 3268 adults (63% women) from high diabetes risk families living in the Feel4Diabetes study areas in 6 European countries. A high-risk family was defined as at least one of the parents having (relatively) high score in the Finnish Diabetes Risk Score FINDRISC [5]. FINDRISC is a validated tool to identify people at risk of developing T2DM in the future, consisting of eight questions (age, body mass index (BMI), waist circumference, physical activity, consumption of fruit and vegetables, blood pressure medication, high blood glucose measured at any point, and family history of diabetes). The FINDRISC Score ranges from 0 to 26, with score 12 or higher indicating increased T2DM risk. The mean age of the study participants was 42 ± 7 years, and 82% of them were married or lived with a spouse. The average years of education completed were 14–15 years, and 67% of the participants worked full or part-time during the intervention period. The recruitment of the participants, as well as the procedures and measurements, has been explained in detail earlier [3]. In brief, the adults who consented to take part in the clinical study were invited to a study visit. The measurements included height, weight, waist circumference, heart rate, blood pressure, and fasting blood sampling, with analyses of glucose, total cholesterol, HDL-cholesterol, LDL-cholesterol, and triglycerides. Body mass index (BMI) was calculated as weight (kg) divided by height (m) squared. Waist-to-height ratio (WHtR) was calculated dividing waist circumference (m) by height (m). Participants also filled in a questionnaire including dietary questions, which were used to develop components of the Score (see Additional file 1). The measurements were repeated after 1 year.

Feel4Diabetes healthy diet score

Dietary goals set in Feel4Diabetes intervention were used as the basis for the Feel4Diabetes Healthy Diet Score. There were a total of 12 Feel4Diabetes intervention goals that were related to food choices or food behaviour, and were selected as the main components of the Healthy Diet Score (Table 1). These components were breakfast, vegetables, fruit and berries, sugary drinks, whole-grain cereals, nuts and seeds, low-fat dairy products, oils and fats, red meat, sweet snacks, salty snacks, and family meals. Excluded intervention goals were related to physical activity and sedentary behaviour, and thus not included as part of the Healthy Diet Score.
Table 1

Scoring of the Healthy Diet Score

Component and F4D dietary goalContentCategoriesScore

1. Breakfast

Eating (healthy and balanced) breakfast daily

Weekdays and weekend days5 times on weekdays7
3–4 times on weekdays4
1–2 times on weekdays1
I don’t eat breakfast0
2 times during weekend3
1 time during weekend2
I don’t eat breakfast0
Total score10

2. Vegetables

≥ 5 servings of vegetable per day

Raw and cooked vegetables, cooked legumes5 servings per day10
4 servings per day8
3 servings per day6
2 servings per day4
1 servings per day2
<  1 serving per day0

3. Fruits and berries

≥ 3 servings of fruit & berries per day

Fruit and berries3 or more servings per day10
1–2 servings per day8
5–6 servings per week6
3–4 servings per week4
1–2 servings per week2
<  1 serving per week0

4. Sugary drinks

<  1 serving of sugary drinks per day

Soft drinks with sugar, juices with sugar, beer, cider, wine, spirits<  1 serving per day10
1–2 servings per day8
3–4 servings per day6
5–6 servings per day4
≥ 7 servings per day0

5. Whole-grain cereals

≥ 4 servings of whole-grain foods/cereals per day

Whole-grain bread, porridge, whole-grain cereals≥ 4 servings per day10
3 servings per day7
2 servings per day4
1 serving per day1
<  1 serving per day0

6. Nuts and Seeds

≥ 3 servings of nuts & seeds a week

Nuts and seeds≥ 3 servings per week6
1–2 servings per week3
<  1 time per week0

7. Low-fat Dairy

≥ 1 serving of low-fat dairy products per day

Low-fat dairy < 2% fat

Full-fat dairy > 2% fat

≥ 1 servings per day and no full-fat dairy products6
≥ 1 servings per day and ≥ 1 servings of full-fat dairy products3
<  1 serving per day and ≤ 1 servings of full-fat dairy products0

8. Oils and fats

daily use of olive or rapeseed oil or soft margarine including cooking fats and spreads

Favorable fats:

vegetable oils, margarine, soft and reduced-fat margarine, oil based dressings

Avoidable fats:

butter, butter mixtures, I don’t use any

Cooking (option to choose several):
Daily use of vegetable oils and margarines and no use of butter4
Daily use of vegetable oils and margarines and some usage of butter2
Daily use or no usage of vegetable oils or margarines and higher usage of butter or no usage of daily fats0
Bread spreads (option to choose only one):4
Daily use of vegetable oil, Soft margarine 70–80% or fat0
Reduced-fat margarine 28–60% fat
Daily use of butter-vegetable oil mixture, butter or I do not use fat spread on bread8
Total score

9. Red meat

≤ 2 servings of red and/or processed meat a week

Red and processed meat≤ 2 servings per week10
3 servings per week8
4 servings per week6
5 servings per week4
6 servings per week2
≥ 7 servings per week0

10. Sweet snacks

≤ 1 serving of sweet snacks per week

Biscuits, ice cream, cakes, pastries etc.≤ 1 servings per week6
2 servings per week5
3–4 servings per week3
5–6 servings per week1
≥1 servings per day0

11. Salty snacks

≤ 1 serving of salty snacks/fast food per week

Hamburger, chips, pizza, savory pastries etc.≤ 1 servings per week6
2 servings per week5
3–4 servings per week3
5–6 servings per week1
≥1 servings per day0

12. Family meals

Meals eaten with others

Breakfast, lunch and dinner with a friend, colleague, or with a family memberBreakfast
≥ 7 times per week2
5–6 times per week1
≤ 4 times per week0
Lunch
≥ 7 times per week2
5–6 times per week1
≤ 4 times per week0
Dinner
≥ 7 times per week4
5–6 times per week2
≤ 4 times per week0
Total score0–8
Total Healthy Diet ScoreAll components (1–12)0–100
Scoring of the Healthy Diet Score 1. Breakfast Eating (healthy and balanced) breakfast daily 2. Vegetables ≥ 5 servings of vegetable per day 3. Fruits and berries ≥ 3 servings of fruit & berries per day 4. Sugary drinks <  1 serving of sugary drinks per day 5. Whole-grain cereals ≥ 4 servings of whole-grain foods/cereals per day 6. Nuts and Seeds ≥ 3 servings of nuts & seeds a week 7. Low-fat Dairy ≥ 1 serving of low-fat dairy products per day Low-fat dairy < 2% fat Full-fat dairy > 2% fat 8. Oils and fats daily use of olive or rapeseed oil or soft margarine including cooking fats and spreads Favorable fats: vegetable oils, margarine, soft and reduced-fat margarine, oil based dressings Avoidable fats: butter, butter mixtures, I don’t use any 9. Red meat ≤ 2 servings of red and/or processed meat a week 10. Sweet snacks ≤ 1 serving of sweet snacks per week 11. Salty snacks ≤ 1 serving of salty snacks/fast food per week 12. Family meals Meals eaten with others Scoring of the components was done according to the 14 diet-related questions in the Feel4Diabetes questionnaire. Each component reflected one or two questions about intake frequencies of each particular food group or food behaviour. Components consisting of two questions were vegetables and oils and fats. Vegetables included questions about vegetable and legume consumption, and oils and fats included questions about bread spreads and daily use of different oils and fats. The maximum score for each component was set based on its estimated relative importance with regards to T2DM risk. A maximum score of 10 was given to breakfast, vegetables, fruit and berries, sugary drinks (including juices and soft drinks containing sugar and also high energy beverages beer, cider, wine and spirits), whole-grain cereals, and red meat. A maximum score of 8 was given to the consumption of oils and fats and frequency of family meals (including breakfast, lunch and dinner eaten in the company of others, defined as a friend, colleague or family member). The rest of the components, sweet snacks, salty snacks, nuts and seeds, and low-fat dairy, got a maximum score of 6. A higher score indicated higher or more frequent consumption, except for sugary drinks, red meat, sweet snacks and salty snacks where higher scores indicated lower consumption. Total score, calculated as the sum of the component scores, was ranging from 0 to 100, higher score indicating better quality of diet and maximum score indicating full achievement of the Feel4Diabetes dietary goals.

Descriptive and statistical analyses

Data was analyzed with IBM SPSS Statistics for Windows Version 25.0. The Healthy Diet Score components were calculated for each country and men and women separately using baseline data. Score component values, as country means for men and women, are presented as bar charts. The Feel4Diabetes Healthy Diet Score was divided into quarters. A multivariate analysis of covariance was used to test differences in clinical characteristics between the Score categories. In the model, clinical characteristics were used as dependent variables and score categories as the independent variable. The model was adjusted for age, sex and country and results are presented as marginal estimated means, standard deviations, and p-values. To estimate the clinical validity of the Feel4Diabetes Healthy Diet Score, the changes in the Score and changes in clinical markers between baseline and first year follow-up were studied using Pearson’s correlation. To estimate reproducibility, Pearson’s correlations were studied between baseline and first year follow-up scores within the control group participants only. Results from both analyses using Pearson’s correlation are presented as correlation coefficients and p-values.

Results

The distribution of the Healthy Diet Score in the total population at the baseline is depicted in Fig. 1.
Fig. 1

Distribution of the Healthy Diet Score in study population

Distribution of the Healthy Diet Score in study population The mean total score was 52.8 ± 12.8 among women and 46.6 ± 12.8 among men (p <  0.001). The mean score values by sex and country are presented in Fig. 2. There were significant differences in the total score, as well as its components, between countries.
Fig. 2

Feel4Diabetes Healthy Diet Score components by country and sex for all participants. a higher score indicating lower consumption

Feel4Diabetes Healthy Diet Score components by country and sex for all participants. a higher score indicating lower consumption The total score was highest among Finnish (56.8 ± 11.9) and Spanish (52.9 ± 11.4), and lowest in Hungarian (47.8 ± 12.7) and Bulgarian (47.9 ± 11.5) adults. Of the components, score value was generally low for vegetables and high for salty snacks and breakfast. The baseline clinical characteristics of all study participants according to Healthy Diet Score, adjusted for age, sex and country are presented in Table 2. The Score was directly associated with HDL-cholesterol (p = 0.022), and inversely associated with LDL-cholesterol (p = 0.003) and triglyceride (p = 0.043) concentrations as well as heart rate (p = 0.012).
Table 2

Baseline clinical characteristics of all participants by Healthy Diet Score quarters (EMM, SE and ANCOVA p-value)

1. quarter 0–422. quarter 43–513. quarter 52–604. quarter 61–100p-value
BMI (Kg/m2)28.9 ± 0.228.7 ± 0.228.1 ± 0.228.2 ± 0.20.054
Waist circumference (cm)95.3 ± 0.595.0 ± 0.694.0 ± 0.594.4 ± 0.60.325
WHtR0.57 ± 0.00.57 ± 0.00.56 ± 0.00.56 ± 0.00.113
SBP (mmHg)118 ± 1119 ± 1118 ± 1119 ± 10.779
DBP (mmHg)79 ± 179 ± 179 ± 179 ± 10.882
Heart rate (bpm)73 ± 072 ± 072 ± 071 ± 00.012*
total Cholesterol (mmol/L)5.12 ± 0.045.02 ± 0.045.03 ± 0.044.96 ± 0.040.058
LDL-cholesterol (mmol/L)3.21 ± 0.043.10 ± 0.043.10 ± 0.043.02 ± 0.040.003*
HDL-cholesterol (mmolL)1.36 ± 0.041.36 ± 0.041.38 ± 0.041.42 ± 0.040.022*
TG (mg/dL)1.29 ± 0.041.31 ± 0.031.23 ± 0.031.17 ± 0.040.043*
Glucose (mmol/L)5.24 ± 0.045.33 ± 0.055.31 ± 0.055.37 ± 0.050.240

*p <  0.05, ANCOVA adjusted for age, sex and country. BMI Body mass index, WHtR Waist-to-Height ratio, SBP Systolic blood pressure, DBP Diastolic blood pressure, TG Triglycerides, EMM Estimated marginal means, SE Standard error

Baseline clinical characteristics of all participants by Healthy Diet Score quarters (EMM, SE and ANCOVA p-value) *p <  0.05, ANCOVA adjusted for age, sex and country. BMI Body mass index, WHtR Waist-to-Height ratio, SBP Systolic blood pressure, DBP Diastolic blood pressure, TG Triglycerides, EMM Estimated marginal means, SE Standard error The change in Healthy Diet Score from baseline to year 1, amongst all participants, was significantly and inversely correlated with changes in BMI, waist circumference, waist-to-height ratio and total and LDL cholesterol (Table 3). Increase in component scores for vegetables, fruit and berries, sweet snacks, salty snacks, and red meat correlated with a decrease of body weight indices, and increased intake of vegetables also with a decrease of total cholesterol and triglycerides. Higher consumption of whole-grains correlated only with a decrease of total and LDL-cholesterol. Improvement in breakfast habits correlated with a reduction in BMI, but improvement in family meal practices did not correlate with any clinical changes. Sugary drinks correlated only with a change in systolic blood pressure, but the correlation was not in the expected direction. Also for the change in sweet and salty snacks score components an inverse correlation with HDL-cholesterol was discovered.
Table 3

Pearson’s correlation for changes in the Healthy Diet Score components and clinical markers between baseline and first year for all participants

BMI (kg/m2)WC (cm)WHtRSBP (mmHg)DBP (mmHg)Heart rate (bpm)TC (mmol/l)LDL (mmol/l)HDL (mmol/l)Glucose (mmol/l)TG (mmol/l)
Total score− 0.143**− 0.116**− 0.123**nsnsns−0.127**− 0.114**nsnsns
Breakfast−0.061*nsnsnsnsnsnsnsnsnsns
Vegetables−0.077**−0.080**−0.070**nsnsns−0.052*nsnsns−0.050*
Fruit and berries−0.064*−0.069**− 0.061*nsnsnsnsnsnsnsns
Sugary drinksnsnsns0.053*nsnsnsnsnsnsns
Whole-grainnsnsnsnsnsns−0.065*−0.062*nsnsns
Nuts and seedsns−0.052*−0.052*nsnsnsnsnsnsnsns
Low-fat dairynsnsnsnsnsnsnsnsnsnsns
Oils and fats−0.052*nsnsnsnsnsnsnsnsnsns
Red meat−0.089**−0.077**− 0.078**nsnsnsnsnsnsnsns
Sweet snacks−0.120**−0.098**− 0.103**nsnsnsnsns−0.061*nsns
Salty snacks−0.061*−0.073**− 0.070**nsnsnsnsns−0.058*nsns
Family mealsnsnsnsnsnsnsnsnsnsnsns

*p <  0.05 and **p <  0.01. ns non-significant

BMI Body mass index, WC Waist circumference, WHtR Waist-to-Height ratio, SBP Systolic blood pressure, DBP Diastolic blood pressure, TC total cholesterol, LDL LDL-cholesterol, HDL HDL-cholesterol, TG Triglycerides

Pearson’s correlation for changes in the Healthy Diet Score components and clinical markers between baseline and first year for all participants *p <  0.05 and **p <  0.01. ns non-significant BMI Body mass index, WC Waist circumference, WHtR Waist-to-Height ratio, SBP Systolic blood pressure, DBP Diastolic blood pressure, TC total cholesterol, LDL LDL-cholesterol, HDL HDL-cholesterol, TG Triglycerides The Healthy Diet Score, as well as its components at baseline, were significantly correlated with the values at year 1 in the analysis including only the control group participants (Table 4). The Correlation coefficients varied from 0.479 (p <  0.001) for fats and oils to 0.795 (p <  0.001) for breakfast. For the total score, the correlation coefficient between baseline and year 1 was 0.755 (p <  0.001).
Table 4

Correlation for the Healthy Diet Score components between baseline and first year for controls

Score componentPearson’s correlationp-value
Total Score0.755**<  0.001
Breakfast0.795**<  0.001
Vegetables0.486**<  0.001
Fruit and berries0.704**<  0.001
Sugary drinks0.622**<  0.001
Whole-grain cereals0.565**<  0.001
Nuts and seeds0.602**<  0.001
Low-fat dairy0.517**<  0.001
Oils and fats0.479**<  0.001
Red meat0.626**<  0.001
Sweet snacks0.624**<  0.001
Salty snacks0.558**<  0.001
Family meals0.616**<  0.001

**p<0.01

Correlation for the Healthy Diet Score components between baseline and first year for controls **p<0.01

Discussion

In this paper, we report the construction of the Feel4Diabetes Healthy Diet Score and present some findings to support its usability, reproducibility and clinical validity. The Healthy Diet Score is constructed based on the questionnaire that was developed for the Feel4Diabetes project [4]. A detailed description of the study population and design, as well as study results, are out of the scope of this paper. The Healthy Diet Score was created to standardize and simplify the Feel4Diabetes dietary questionnaire [4] data in a single parameter that allows rapid evaluation of the diet across this multi-national project. The Healthy Diet Score is comprised of components that represent the dietary goals of the Feel4Diabetes high-risk intervention, weighted based on their appraised importance as risk or protective factors for type 2 diabetes. The weights of the components were decided based on research literature. The most convincing evidence about the potential of dietary manipulation in reducing T2DM risk comes from randomized prevention trials. The Finnish Diabetes Prevention Study (DPS) was the first individually-randomized controlled trial to show that T2DM can be prevented by dietary and physical activity counselling [6]. Risk of developing T2DM was shown to be the lowest among the participants who consumed a diet with moderate fat and high fibre content [7]. In the Diabetes Prevention Program, another important trial showing the effectiveness of lifestyle intervention in T2DM prevention, the diet was focused on the reduction of total fat and energy [8]. However, as we wanted the Feel4Diabetes Healthy Diet Score to be based on food rather than nutrient intake, research evidence from dietary patterns [1, 9] and other observational studies [10-15] were also considered when the scoring for the components was created. Furthermore, we wanted the score to be sensitive for all beneficial changes in diet, e.g. increase in consumption of fruit from once per day to twice per day, even if the actual goal (three portions per day) is not achieved. This approach differs from the one used in many studies. For example in the DPS, the “success score” varied from 0 to 5, according to the number of intervention goals (total fat intake < 30% of total energy consumed, saturated fat < 10% of total energy, and dietary fibre 15 g/1000 kcal or more, physical activity 30 min per day, weight reduction 5% from baseline) [6]. Another example is the PREDIMED study, where the Mediterranean diet score was calculated as the sum of dichotomized goals [16]. In real-life implementation projects, achieved changes in diet are often smaller than in clinical trials. In fact, it is generally acknowledged that setting small, achievable short term goals, as well as focusing on gradually building on behaviours that already are familiar, increases the likelihood that the new way of eating becomes a permanent habit [17]. For the Feel4Diabetes Healthy Diet Score, we wanted to acknowledge all the changes in diet, even when the actual intervention goal was not achieved. The score was composed from all the relevant information available in the validated questionnaire, and all changes for the better increased the score. The Healthy Diet Score at baseline was normally distributed and thus demonstrated variation within the study population. However, the country means of the Score, as well as its components, differed significantly from each other. These differences appeared plausible, considering the differing dietary cultures in the participating countries [18, 19]. For example, in Finland whole grain rye bread and low-fat milk are staples, and this is also reflected in the Score components for whole-grain cereal and dairy products. The consumption of vegetables was much lower than recommended across the countries, clearly indicating a need for public health actions to tackle the problem. In the cross-sectional analysis, the Feel4Diabetes Healthy Diet Score category was significantly, albeit moderately associated with clinical risk factors such as HDL- and LDL-cholesterol and triglycerides, when age, sex, and country were adjusted for. There also appeared to be a trend between decreasing BMI and increasing score. This association did not reach statistical significance. In the analysis exploring the association between changes in the Score and its components as opposed to changes in clinical risk factors, several associations were discovered. These analyses combined offer proof for the clinical validity of our Healthy Diet Score. Many diet scores have been developed for different purposes, and they have been proved clinically useful, especially when combined with other non-invasive measurements of risk factors [20]. Lassale et al. who compared the performance of different scores advocate the use of easy-to-measure, concise food-based scores that are developed based on predefined rather than study population-based cut-off points and propose that such scores “would be most pragmatic for individual risk prediction and health promotion” [20]. This is indeed also our justification for the development of the Feel4Diabetes Healthy Diet Score. Finally, the reproducibility of the Healthy Diet Score was shown to be good, based on the analysis of correlation between baseline and 1 year score components in the control participants only. This analysis excluded intervention subjects, as they were, by definition, instructed on how to make beneficial changes in their diet. An important limitation of the present study is that we do not have a reference method (e.g. food diaries) for dietary data collection. However, the questionnaire reliability was tested in volunteers in each country before the study [4] and was shown to be acceptable.

Conclusions

The Feel4Diabetes Healthy Diet Score is a reproducible method to capture the dietary information collected with the Feel4Diabetes questionnaire and measure the level of and changes in the adherence to the dietary goals of the intervention. It gives a simple parameter that associates with clinical risk factors in a meaningful and plausible manner. The Healthy Diet Score was constructed to facilitate the analyses of the Feel4Diabetes study outcomes, but it could also serve as a simple and visual tool to measure dietary intake in e.g. health care services and public health initiatives. Additional file 1. Feel4Diabetes dietary questions used in the development of the Healthy Diet score.
  19 in total

Review 1.  Dairy Products, Dairy Fatty Acids, and the Prevention of Cardiometabolic Disease: a Review of Recent Evidence.

Authors:  Edward Yu; Frank B Hu
Journal:  Curr Atheroscler Rep       Date:  2018-03-21       Impact factor: 5.113

2.  Total and subtypes of dietary fat intake and risk of type 2 diabetes mellitus in the Prevención con Dieta Mediterránea (PREDIMED) study.

Authors:  Marta Guasch-Ferré; Nerea Becerra-Tomás; Miguel Ruiz-Canela; Dolores Corella; Helmut Schröder; Ramon Estruch; Emilio Ros; Fernando Arós; Enrique Gómez-Gracia; Miquel Fiol; Lluís Serra-Majem; José Lapetra; Josep Basora; Nerea Martín-Calvo; Olga Portoles; Montserrat Fitó; Frank B Hu; Lluís Forga; Jordi Salas-Salvadó
Journal:  Am J Clin Nutr       Date:  2017-02-15       Impact factor: 7.045

3.  The diabetes risk score: a practical tool to predict type 2 diabetes risk.

Authors:  Jaana Lindström; Jaakko Tuomilehto
Journal:  Diabetes Care       Date:  2003-03       Impact factor: 19.112

4.  Legume consumption is inversely associated with type 2 diabetes incidence in adults: A prospective assessment from the PREDIMED study.

Authors:  Nerea Becerra-Tomás; Andrés Díaz-López; Núria Rosique-Esteban; Emilio Ros; Pilar Buil-Cosiales; Dolores Corella; Ramon Estruch; Montserrat Fitó; Lluís Serra-Majem; Fernando Arós; Rosa Maria Lamuela-Raventós; Miquel Fiol; José Manuel Santos-Lozano; Javier Díez-Espino; Olga Portoles; Jordi Salas-Salvadó
Journal:  Clin Nutr       Date:  2017-03-24       Impact factor: 7.324

5.  Meal patterns across ten European countries - results from the European Prospective Investigation into Cancer and Nutrition (EPIC) calibration study.

Authors:  E Huseinovic; A Winkvist; N Slimani; M K Park; H Freisling; H Boeing; G Buckland; L Schwingshackl; E Weiderpass; A L Rostgaard-Hansen; A Tjønneland; A Affret; M C Boutron-Ruault; G Fagherazzi; V Katzke; T Kühn; A Naska; P Orfanos; A Trichopoulou; V Pala; D Palli; F Ricceri; M Santucci de Magistris; R Tumino; D Engeset; T Enget; G Skeie; A Barricarte; C B Bonet; M D Chirlaque; P Amiano; J R Quirós; M J Sánchez; J A Dias; I Drake; M Wennberg; Jma Boer; M C Ocké; Wmm Verschuren; C Lassale; A Perez-Cornago; E Riboli; H Ward; H Bertéus Forslund
Journal:  Public Health Nutr       Date:  2016-05-19       Impact factor: 4.022

6.  High-fibre, low-fat diet predicts long-term weight loss and decreased type 2 diabetes risk: the Finnish Diabetes Prevention Study.

Authors:  J Lindström; M Peltonen; J G Eriksson; A Louheranta; M Fogelholm; M Uusitupa; J Tuomilehto
Journal:  Diabetologia       Date:  2006-03-16       Impact factor: 10.122

Review 7.  Sugar-sweetened beverages and risk of metabolic syndrome and type 2 diabetes: a meta-analysis.

Authors:  Vasanti S Malik; Barry M Popkin; George A Bray; Jean-Pierre Després; Walter C Willett; Frank B Hu
Journal:  Diabetes Care       Date:  2010-08-06       Impact factor: 19.112

8.  Diet Quality Scores and Prediction of All-Cause, Cardiovascular and Cancer Mortality in a Pan-European Cohort Study.

Authors:  Camille Lassale; Marc J Gunter; Dora Romaguera; Linda M Peelen; Yvonne T Van der Schouw; Joline W J Beulens; Heinz Freisling; David C Muller; Pietro Ferrari; Inge Huybrechts; Guy Fagherazzi; Marie-Christine Boutron-Ruault; Aurélie Affret; Kim Overvad; Christina C Dahm; Anja Olsen; Nina Roswall; Konstantinos K Tsilidis; Verena A Katzke; Tilman Kühn; Brian Buijsse; José-Ramón Quirós; Emilio Sánchez-Cantalejo; Nerea Etxezarreta; José María Huerta; Aurelio Barricarte; Catalina Bonet; Kay-Tee Khaw; Timothy J Key; Antonia Trichopoulou; Christina Bamia; Pagona Lagiou; Domenico Palli; Claudia Agnoli; Rosario Tumino; Francesca Fasanelli; Salvatore Panico; H Bas Bueno-de-Mesquita; Jolanda M A Boer; Emily Sonestedt; Lena Maria Nilsson; Frida Renström; Elisabete Weiderpass; Guri Skeie; Eiliv Lund; Karel G M Moons; Elio Riboli; Ioanna Tzoulaki
Journal:  PLoS One       Date:  2016-07-13       Impact factor: 3.240

Review 9.  Food groups and risk of type 2 diabetes mellitus: a systematic review and meta-analysis of prospective studies.

Authors:  Lukas Schwingshackl; Georg Hoffmann; Anna-Maria Lampousi; Sven Knüppel; Khalid Iqbal; Carolina Schwedhelm; Angela Bechthold; Sabrina Schlesinger; Heiner Boeing
Journal:  Eur J Epidemiol       Date:  2017-04-10       Impact factor: 8.082

10.  Development and reliability of questionnaires for the assessment of diet and physical activity behaviors in a multi-country sample in Europe the Feel4Diabetes Study.

Authors:  Costas A Anastasiou; Evaggelia Fappa; Konstantina Zachari; Christina Mavrogianni; Vicky Van Stappen; Jemina Kivelä; Eeva Virtanen; Esther M González-Gil; Paloma Flores-Barrantes; Anna Nánási; Csilla Semánová; Roumyana Dimova; Natalya Usheva; Violeta Iotova; Greet Cardon; Yannis Manios; Konstantinos Makrilakis
Journal:  BMC Endocr Disord       Date:  2020-03-12       Impact factor: 2.763

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  4 in total

1.  Association of breakfast consumption frequency with fasting glucose and insulin sensitivity/b cells function (HOMA-IR) in adults from high-risk families for type 2 diabetes in Europe: the Feel4Diabetes Study.

Authors:  Kiriaki Apergi; Kalliopi Karatzi; Kyriakos Reppas; Eva Karaglani; Natalya Usheva; Natalia Giménez-Legarre; Luis A Moreno; Roumyana Dimova; Emese Antal; Kivelä Jemina; Greet Cardon; Violeta Iotova; Yannis Manios; Konstantinos Makrilakis
Journal:  Eur J Clin Nutr       Date:  2022-05-25       Impact factor: 4.016

2.  Frequency of family meals and food consumption in families at high risk of type 2 diabetes: the Feel4Diabetes-study.

Authors:  Lubna Mahmood; Esther M González-Gil; Peter Schwarz; Sandra Herrmann; Eva Karaglani; Greet Cardon; Flore De Vylder; Ruben Willems; Konstantinos Makrilakis; Stavors Liatis; Violeta Iotova; Kaloyan Tsochev; Tsvetalina Tankova; Imre Rurik; Sándorné Radó; Luis A Moreno; Yannis Manios
Journal:  Eur J Pediatr       Date:  2022-03-30       Impact factor: 3.860

3.  Parental insulin resistance is associated with unhealthy lifestyle behaviours independently of body mass index in children: The Feel4Diabetes study.

Authors:  Esther M González-Gil; Natalia Giménez-Legarre; Greet Cardon; Christina Mavrogianni; Jemina Kivelä; Violeta Iotova; Tsvetalina Tankova; Rurik Imre; Stavros Liatis; Konstantinos Makrilakis; Peter Schwarz; Patrick Timpel; Elisabeth Dupont; Pedro Couck; Yannis Manios; Luis A Moreno
Journal:  Eur J Pediatr       Date:  2022-03-26       Impact factor: 3.860

4.  Dietary Behavior and Compliance to Bulgarian National Nutrition Guidelines in Patients With Type 1 Diabetes With Longstanding Disease.

Authors:  Rouzha Pancheva; Lyubomir Dimitrov; Michal Gillon-Keren; Kaloyan Tsochev; Tatyana Chalakova; Natalya Usheva; Silviya Nikolova; Yoto Yotov; Violeta Iotova
Journal:  Front Nutr       Date:  2022-07-08
  4 in total

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