Development of a lifestyle–diet quality index for primary schoolchildren and its relation to insulin resistance: the Healthy Lifestyle–Diet Index

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Abstract

Objective:

The aim of this work was to develop an index that evaluates the degree of adherence to existing dietary and lifestyle guidelines for primary school-aged children (Healthy Lifestyle–Diet Index (HLD-Index)) and examine its relationship with selected nutrient intake and insulin resistance (IR).

Methods:

Ten components were used to develop the HLD-Index. Scores from 0 to 4 were assigned to all components. The HLD-Index total score ranged between 0 and 40. A sample of 729 schoolchildren from Greece aged 10–12 years (The Healthy Growth Study) was used to evaluate the validation of the proposed index.

Results:

The overall mean±standard deviation of the HLD-Index score was 20±4.4. Higher HLD-Index scores were associated with lower proportion of children having intakes lower than Estimated Average Requirements by Institute of Medicine. On the basis of the cutoff point of 3.16 for homeostasis model assessment of IR, 20.9% of participants were found to be insulin resistant. After adjusting for potential confounders, logistic regression showed that a 1 unit increase in the score is associated with almost 8% lower odds for being insulin resistant. The cutoff point analysis revealed that score equal to or lower than 21 best discriminates children with IR from those without IR. On the basis of this cutoff point, the sensitivity of the HLD-Index was 70% and the corresponding specificity was 47%.

Conclusions:

The proposed HLD-Index could be used by public health policy makers and other health-care professionals to identify subgroups in the population with poor diet–lifestyle habits who are at increased probability for IR.

Introduction

Children's dietary habits and lifestyle patterns have been associated with an increased prevalence of risk factors for chronic diseases, such as obesity, high blood pressure, increased cholesterol levels and insulin resistance (IR) (Hampl et al., 1999; Devaney et al., 2004; Kranz et al., 2004; Muntner et al., 2004; Canete et al., 2007; Slinger et al., 2008; Szamosi et al., 2008). Unhealthy dietary habits and lifestyle patterns originating in early life seem to track into adulthood increasing the risk for developing chronic diseases in adult life (Magarey et al., 2001; Hesketh et al., 2004). Therefore, the identification of children who follow an unhealthy diet and lifestyle pattern is of critical importance from a public health point of view.

Assessment of diet–lifestyle quality is complicated because people consume meals as opposed to isolated nutrients or foods and dietary habits are related to lifestyle patterns (Muller et al., 1999; Feskanich et al., 2004; Manios et al., 2009). Several investigators have examined the impact of isolated nutrients/foods and physical activity/sedentary life on several health outcomes (that is, IR; Pereira et al., 2002; Lara-Castro and Garvey, 2004; Isganaitis and Lustig, 2005; Slinger et al., 2008; Szamosi et al., 2008; Bremer et al., 2009; Hirschler et al., 2009; Kennedy et al., 2009). Although these studies reveal the effect of specific foods/nutrients/food groups and certain parameters of physical activity/sedentary lifestyle on health status, they do not reflect the cumulative effect of the overall diet–lifestyle quality.

As a result, pattern analysis has emerged as an alternative approach for studying the potential impact of diet–lifestyle as a whole in relation to disease prevention (Jacques and Tucker, 2001). Development of composite indices is one of the existing methods to obtain dietary–lifestyle patterns (Waijers et al., 2007). These indices evaluate the degree of adherence to current dietary–lifestyle guidelines and they have become attractive because they capture the multidimensional nature of people's diet–lifestyle.

Up till date, a variety of such indices that evaluate the degree of adherence to dietary guidelines have been proposed in the literature (Bach et al., 2006; Panagiotakos et al., 2007; Waijers et al., 2007; Kosti et al., 2009). The majority of existing indices are based on either the Mediterranean dietary pattern (Bach et al., 2006) or other dietary guidelines for adults (that is, US Dietary Guidelines) (Waijers et al., 2007). However, only a limited number of these indices are appropriate for primary schoolchildren (Kennedy et al., 1995; Feskanich et al., 2004; Serra-Majem et al., 2004; Lazarou et al., 2009). In particular, the Healthy Eating Index (Kennedy et al., 1995), the Youth Healthy Eating Index (Feskanich et al., 2004), the Mediterranean Diet Quality Index in children and adolescents (Serra-Majem et al., 2004) and the E-KINDEX (Lazarou et al., 2009) are the indices that can be used to evaluate diet quality among children. However, a major barrier for the wide application of some of these indices in the day-to-day practice is the fact that the estimation of selected nutrients intake (that is, iron, total fat, saturated fat, cholesterol, sodium, linoleic acid, and so on) is involved in their calculation, making them complex and labor-intensive. Moreover, none of the aforementioned indices that can be applied to primary schoolchildren population has been developed to include lifestyle characteristics of children as an index component and focus on dietary quality only.

Hence, the primary objective of this work was to develop an index (that is, Healthy Lifestyle–Diet Index (HLD-Index)) that assesses the diet–lifestyle quality of primary schoolchildren based on existing international recommendations. The second objective was to assess whether higher values of HLD-Index are associated with lower probability of having insulin-resistant children, as IR is a nutrition- and lifestyle-related disorder.

Methods

Component selection for diet index development

The HLD-Index was developed by using 10 components. The first eight components measure the frequency consumption of fruits, vegetables, grains, dairy products, meat, fish/seafood, soft drinks and sweets. The other two components reflect the physical activity status of children through measuring the time children spend watching television (TV) or playing computer games (sedentary life) and the time children spend on moderate to vigorous physical activity (MVPA). These components were selected based on dietary recommendations of MyPyramid (www.mypyramid.gov) and the recommendations of American Academy of Pediatrics with regard to the TV viewing time (American Academy of Pediatrics, 2001; Strasburger, 2006).

Scoring system for the development of HLD-index

A five-point scoring system (that is, 0–4) was used to assign the appropriate score to each index component. As concerns fruits and vegetables intake, score 4 was assigned to frequency consumption equal to the recommended one or higher (that is, >3 and >4 servings per day for fruits and vegetables intake, respectively). With regard to the consumption of sweets and regular soft drinks that no intake is recommended, scores 4 and 0 were assigned to no or rare consumption (<1 serving per week) and to daily consumption, respectively. With regard to the consumption of all grains, dietary recommendations suggest almost 5–6 servings per day, where half of all grains should be whole grains. Therefore, a combined component reflecting the intake of total grains and the percent of total grains, that is, whole grains, was used. In particular, two individual components reflecting the servings of all grains (components 1) and the percent of all grains, that is, whole grains (component 2), were initially generated. Scores between 0 and 4 were assigned to component 1 based on the dietary recommendations. In addition, scores 1–4 were assigned to component 2 as follows: score 1 to <25%, score 2 to 25–50%, score 3 to 50–75% and score 4 to >75% whole grains. The product of scores assigned to these components resulted in a total score ranged between 0 and 16. Then, this total score was divided into five categories and scores 0–4 were assigned as follows: 0 (score 0), 1 and 2 (score 1), 3 and 4 (score 2), 6 and 8 (score 3) and 9, 12 and 16 (score 4). Similarly, scores 0–4 were assigned to components reflecting meat and dairy intake, as dietary guidelines suggest that dairy products should be preferably fat-free or low-fat and meat should be lean/beans. With regard to fish/seafood component, scores were assigned taking into account that guidelines suggest that children should have at least 2 servings per week of fish/seafood. Therefore, score 4 was assigned to 2–3 servings per week and score 0 was assigned to no or rare consumption of fish/seafood. In terms of component measuring children's MVPA, the highest score was assigned to children reported that spent at least 60 min/day in MVPA, as all guidelines recommend at least 1 h/day MVPA and the lowest score was assigned to no MVPA. Moreover, with regard to the component measuring the sedentary life of children, score 4 was assigned to children spent almost less than 1 h/day watching TV/video and score 0 was assigned to children spent more than 4 h/day watching TV. More details for the scores assigned to each component are presented in Table 1.

Table 1 The scoring system for the development of HLD-Indexa

The total score of the HLD-Index was obtained by summing the scores assigned to each index component. The values of this index range between 0 and 40. Higher values of the HLD-Index indicate greater adherence to dietary–lifestyle recommendations or otherwise greater adherence to a ‘healthy’ dietary–lifestyle pattern. Then, primary schoolchildren were divided into three groups using the tertiles of HLD-Index as follows: those considered as having (a) an ‘unhealthy diet–lifestyle pattern’ (1st tertile); (b) a ‘moderate healthy diet–lifestyle pattern’ (2nd tertile); and (c) a ‘healthy diet–lifestyle pattern’ (3rd tertile).

Validation of HLD in a population-based sample

Sampling. The ‘Healthy Growth Study’ is a cross-sectional study initiated in May 2007 and completed in June 2009. The population under study comprised of schoolchildren aged 10–12 years attending the 5th and 6th grades from primary schools located in municipalities within the wider region of Athens. The sampling of schools was random, multistage and stratified by parental educational level and the total population of students attending schools within these municipalities. An appropriate number of schools was randomly selected from each of these municipalities in relation to the population of schoolchildren registered in the 5th and 6th grade in each municipality, based on data obtained from the Greek Child Institute. All 19 primary schools that were invited to participate in the Healthy Growth Study until June 2008 responded positively. Full medical examination (that is, anthropometrics and body composition measurements, blood collection, clinical examination, and so on) and questionnaire data were obtained by a subgroup of pupils whose parents signed a relative consent form. Signed parental consent forms for full measurements were collected for 754 out of 1236 children (response rate 61%). From the total number of positive responses, complete medical examination data became available for 729 children. More details with regard to sampling procedure are presented elsewhere (Moschonis et al., 2010).

Approval to conduct the study was granted by the Greek Ministry of National Education and the Ethical Committee of Harokopio University of Athens.

Dietary intake assessment. Dietary intake data were obtained for two consecutive weekdays and one weekend day using 24-h recalls. Food intake data were analyzed using the Nutritionist V diet analysis software (version 2.1, 1999, First Databank, San Bruno, CA, USA), which was extensively amended to include traditional Greek foods and recipes, as described in Food Composition Tables and Composition of Greek Cooked Food and Dishes (University of Crete, 1991; Trichopoulou, 2004). Furthermore, the databank was updated with nutritional information of chemically analyzed commercial food items widely consumed by children in Greece. The distribution of usual intakes was estimated by using the National Research Council method, which attempts to remove the effects of day-to-day variability (within subject) in dietary intakes (Institute of Medicine, 2000). Estimated average requirements (EARs) cut-point method was used to calculate the proportion of the population with usual intakes less than EARs (Institute of Medicine, 2000). The food-grouping scheme was designed for all foods or entries (core and recipe) appearing in Nutritionist V. Forty-seven food groups were initially established, based on similar source characteristics and nutrient content. Composite food items, such as recipes, were de-composed and were assigned to food groups according to primary ingredients. A similar methodology for the extraction of food groups has been previously reported in studies with not only smaller sample size, but also only one 24-h recall available (Nicklas et al., 2003). Examples of foods included in the food groups have been documented previously (Nicklas et al., 1990).

Other characteristics that were recorded

Children's TV/video viewing and computer games playing time was assessed by children's report with regard to their TV/video viewing time and time playing computer games during a usual weekday and a usual weekend. The mean daily TV/video viewing and computer games playing time was calculated using the following equation: daily TV/video viewing and computer games playing hours=((weekday TV/video viewing and computer games playing hours × 5)+weekend TV/video viewing and computer games playing hours)/7. Moreover, assessment of children's MVPA was performed by using a standardized activity interview, based on a valid structured questionnaire (Manios et al., 1998) completed by a member of the research team.

Waist circumference was measured at the level of umbilicus to the nearest 0.1 cm with an inelastic tape. Identification of pubertal development was also assessed (Tanner and Whitehouse, 1962). Pubertal stage (Tanner stage) was recorded by a researcher of the same sex as the child, after brief observation. Breast development in girls and genital development in boys was used for pubertal classification. Finally, children were asked to report how many hours they sleep in a usual weekday and a usual weekend. Then, the mean sleep duration of children was calculated.

A structured interview was conducted with both parents, to collect additional information with regard to demographic characteristics, such as educational level (years of education) and nationality. Furthermore, parents were asked to bring with them their child's birth certificate from which birth weight was obtained. Finally, the gender and age of participants were also recorded from investigators.

After a 12-h overnight fast, venous blood samples (10 ml of whole blood) were obtained from each child. Plasma glucose was determined in duplicate using commercially available enzymatic colorimetric assays (Sigma Diagnostics, St Louis, MO, USA) on an automated ACE analyzer (Schiapparelli Biosystems Inc., Boca Raton, FL, USA). Serum insulin was determined in duplicate by immunofluorescence using an automated immunoassay analyzer AIA-600 II (Tosoh Corporation, Tokyo, Japan). IR was measured through homeostasis model assessment (HOMA-IR) (Matthews et al., 1985). This index was calculated using fasting glucose (GF) and fasting insulin (IF), as follows:

HOMA-IR >3.16 (Keskin et al., 2005) was used as a cut-off point to define insulin-resistant schoolchildren.

Statistical analysis

Descriptive measures such as mean, standard deviation, 25th and 75th percentiles, and minimum and maximum value were calculated to describe the distribution of the HLD-Index. The rest of the continuous variables are presented as mean±standard deviation and categorical variables are summarized as relative frequencies (%). Associations between categorical variables were tested by using the χ2 test. The associations between the continuous and binary variables (that is, sex) were evaluated through Student's t-test or Mann–Whitney test when scores were normally or skewed distributed, respectively. Comparisons between continuous variables (that is, intake of several nutrients) and the tertiles of the HLD-Index were performed using the one-way analysis of variance, after testing for equality of variances or the Kruskal–Wallis test, as appropriate. Bonferroni correction was used to account for increase in type I error owing to multiple comparisons.

Simple and multiple logistic regression models were used to assess the association between the HLD-Index score (either as a quantitative variable or as a categorical variable with three categories based on its tertiles) and schoolchildren's IR. Participants’ gender, age, waist circumference, total energy intake, sleep duration, nationality, birth weight and Tanner stage, as well as parental educational status were used as potential confounders. The results are presented as odds ratios and 95% confidence interval. The c-statistic was calculated to evaluate the diagnostic ability of the multiple logistic regression model. Moreover, correct classifications rates, that is, the percent of individuals correctly classified into their true category (that is, insulin resistant or non-insulin resistant) were also calculated.

Cut-off point analysis was used to identify the optimal value of the HLD-Index that differentiates insulin-resistant from non-insulin-resistant children. The threshold was defined by the largest distance from the diagonal line of the receiver operating characteristic curve (sensitivity × (1−specificity)) (Ma and Hall, 1993). Using the cut-off point obtained from the analysis mentioned above, the sensitivity (95% confidence interval) and specificity (95% confidence interval) of the index were calculated.

All reported P-values were based on two-sided hypotheses and compared with a significant level of 5%. All statistical calculations were performed using the STATA software, version 8.0 (STATA Corp., College Station, TX, USA).

Results

Table 2 illustrates descriptive characteristics of the HLD-Index among all schoolchildren of the Healthy Growth Study and among boys and girls, separately. The overall mean±standard deviation of the HLD-Index score was 20±4.4 and this score was normally distributed (P=0.908). No statistical difference was seen in the HLD-Index between boys and girls (P=0.235).

Table 2 Descriptive characteristics of Healthy Lifestyle–Diet Index in a Greek sample of children aged 10–12 years; the Healthy Growth Study (N=729)

Table 3 illustrates the mean±standard deviation of total energy intake, selected nutrients intake, the percent of population with lower intakes than EAR for particular nutrients by the tertiles of the index and the correlation coefficients of these nutrients with the HLD-Index score. It was detected that higher HLD-Index score was associated with lower proportion of children having intakes lower than EAR. Moreover, the mean intake of fiber, calcium and vitamin K was significantly higher among schoolchildren in 3rd tertile of the index compared with the rest of children. On the other hand, no significant difference was detected in total, monosaturated and polysaturated fat, carbohydrate and protein intake across the tertiles of index (Table 3). However, saturated fat intake was significantly lower among children with higher HLD-Index score (3rd tertile).

Table 3 Mean of selected nutrients intake and the percent of population with intakes lower than EARs (Institute of Medicine, 2000) by the tertiles of the total HLD-Index; the Healthy Growth Study (N=729)

Among the total of schoolchildren, the mean±standard deviation of HOMA-IR is 2.35±1.47 (median: 1.98 and 25th, 75 percentiles: 1.32, 2.92, respectively). Moreover, based on the cut-off point of 3.16 for HOMA-IR, it was found that 20.9% of participants are insulin resistant. Logistic regression showed that a unit increase in the HLD-Index score was associated with almost 7% lower odds of being insulin resistant even after adjusting for potential confounders. Finally, the likelihood of being insulin resistant was almost 60% lower among participants with high HLD-Index score (3rd tertile) compared with those belonging to the 1st tertile (Table 4).

Table 4 The association between insulin resistance (dependent variables) and the HLD-Index (independent variable); the Healthy Growth Study (N=729)

The discriminating ability of the final model was high as the c-statistic was 0.789 (95% confidence interval: 0.740–0.839). The correct classification rate of the estimated model was 74% for insulin resistant and 70% for non-insulin resistant (overall correct classification rate: 71%). The cut-off point analysis revealed that score equal to or lower than 21 best identifies insulin-resistant from non-insulin-resistant children. On the basis of these cut-off points, the sensitivity of the HLD-Index was 70% and the corresponding specificity was 47%.

Discussion

In this study, an index to assess the degree of adherence to existing dietary–lifestyle recommendations for primary schoolchildren (HLD-Index) was developed. The components of the HLD-Index comprise the frequency of consumption of particular foods/food groups as well as the assessment of time spent on TV watching/playing video games and on MVPA. There is no specific estimation of nutrient intake that would make the calculation of the total score more complex, and labor-intensive. To the best of our knowledge, the only composite indices that aim to evaluate the overall diet quality and are applicable to primary schoolchildren are the Healthy Eating Index, the Youth Healthy Eating Index, the Mediterranean Diet Quality Index in children and adolescents and E-KINDEX (Kennedy et al., 1995; Feskanich et al., 2004; Serra-Majem et al., 2004; Lazarou et al., 2009). However, some of these indices are not easy to be widely applied because the estimation of specific nutrients’ intake is required for their calculation. Moreover, lifestyle characteristics have not been included as index components in none of the aforementioned existing indices that are applicable to this particular age group. Therefore, by using HLD-Index, nutrition and other health-care professionals may obtain an indicative yet valuable ‘snapshot’ of schoolchildren's diet–lifestyle quality by asking them or their parents a small number of questions.

The proposed index was applied to a validation sample of 729 primary schoolchildren from Greece to examine the validation of index in terms of nutrients intake and to examine whether this index is associated with IR. The findings of the current work showed that the HLD-Index is a good instrument to assess diet–lifestyle quality of primary schoolchildren, because it was found that higher values of HLD-Index are strongly associated with higher intakes of selected nutrients and lower percentages of children with intakes <EAR for nutrients that are necessary to support normal growth and optimum health of children. The validation of Healthy Eating Index and Youth Healthy Eating Index using the Growing Up Today Study population also revealed similar trends for the relation between the index scores with energy and nutrients intake (Feskanich et al., 2004).

Moreover, a strong inverse association of the proposed index with IR was observed. To the best of our knowledge, only two previous works conducted by Kelishadi et al. (2009a, 2009b) have examined the association between the overall diet of children, through the use of the Healthy Eating Index and IR. The results of this study are in agreement with those reported from these two studies, indicating that the degree of adherence to specific dietary recommendation for schoolchildren is inversely associated with IR.

However, there are some potential limitations in the study used to examine the validation of proposed index. First, as a cross-sectional study, it is not appropriate for cause–effect relationships. Second, by using indirect methods of IR identification (HOMA-IR), some underestimation of IR prevalence is possible, although previous studies have reported high correlation between those and frequently sampled intravenous glucose tolerance test, which is the gold standard. With regard to physical activity data, a valid parental proxy questionnaire was used, although it is widely accepted that the use of accelerometry would provide more valid information. Likewise, children's TV viewing time was self-reported and the validity of their report was not examined.

Further implementation of the HLD-Index in prospective data from different populations with various dietary and lifestyle habits is considered necessary to evaluate its predictive validity against several health outcomes. Moreover, additional studies should be conducted to compare the predictive validity of the proposed index with that of existing indices. Further modifications of the HLD-Index may improve its predictive validity for several nutritional- and lifestyle-related diseases.

In conclusion, the HLD-Index assesses the degree of adherence to existing international dietary and lifestyle guidelines for primary schoolchildren. It was found that higher scores of this index were associated with better diet and lifestyle quality and reduced prevalence of IR. The HLD-Index could be used by health-care professionals and public health policy makers at community level to identify subgroups in the population with poor dietary and lifestyle habits who are potentially at higher risk for IR and who might benefit from the development and implementation of preventive actions.

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Acknowledgements

We would like to thank Sofia Tanagra, Anthi Naoumi, Maria Kantilafti, Aliki-Eleni Farmaki, Odysseas Androutsos, Maria Lymperi, Nora Karatsikaki, Konstantina Yfanti, Konstantinos Koutsikas, Louisa Damianidi, Despoina-Rodopi Gkakni, Sofia Micheli, Maria Nikolidaki, Ariadni Lidoriki, Vasiliki Iatridi, Maria Spyridonos, Panagiotis Kliasios, Konstantina Maragkopoulou, Fanouria Chlouveraki, Eleni Zompoulia Papadopoulou, Elpida Voutsadaki, Eirini Tsikalaki, Kelaidi Michailidou and Sofia Komninou for their contribution to the completion of the study.

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Manios, Y., Kourlaba, G., Grammatikaki, E. et al. Development of a lifestyle–diet quality index for primary schoolchildren and its relation to insulin resistance: the Healthy Lifestyle–Diet Index. Eur J Clin Nutr 64, 1399–1406 (2010) doi:10.1038/ejcn.2010.172

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Keywords

  • primary schoolchildren
  • dietary indices
  • dietary patterns
  • insulin resistance syndrome

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