1. Introduction
Obesity has become a public health problem worldwide [
1] and adolescence is a vulnerable period for the development of obesity [
2]. In recent decades, increase in the prevalence of adolescent obesity has been reported in many developing countries [
3,
4]. According to national cutoffs, the prevalence of obesity among Iranian adolescents was 4.8% in 2003–2004, which rose to 15% in 2008–2011 [
5]. In adolescents, obesity could be a risk factor for both short- and long-term health consequences [
6], which include metabolic syndrome, cardiovascular disease and diabetes at later ages [
7]. However, not all obese adolescents exhibit these cardio-metabolic phenotypes. Evidence shows that metabolically healthy obese (MHO) individuals have lower incidence of cardiovascular disease and type II diabetes when compared to their metabolically unhealthy obese (MUO) counterparts [
8].
Among risk factors of obesity, special attention is paid to dietary patterns, as a controllable environmental factor [
9]. Different foods are used together and interactions between nutrients are possible [
10]. The examination of overall diet quality has been used in order to reflect the complexity of food intake patterns and dietary exposure. Two methods are used to evaluate dietary patterns: (1) dietary patterns based on statistical modeling of available dietary data, like cluster analysis and factor analysis; (2) predetermined dietary patterns based on previous knowledge of a healthy diet, like indices of diet quality [
11]. The US Departments of Agriculture and Health and Human Services have issued dietary recommendations, the dietary guidelines for Americans (DGA), to help reduce the risk of cardiovascular disease and other chronic diseases [
12]. Dietary guidelines for Americans adherence index (DGAI), healthy eating index 2005 (HEI-2005) and healthy eating index 2010 (HEI-2010) are three indices for evaluating diet quality; the first two indices (DGAI and HEI-2005) were developed according to the sixth version of DGA (DGA-2005) and HEI-2010 was developed according to the seventh version of the DGA (DGA-2010). DGA-2005 was published with new concepts of diet, emphasizing the important aspects of diet quality, such as whole grain, various types of vegetables and specific types of fat, and introducing the new concept of “discretionary calories” [
11]. Some features of DGA-2005 have been changed in DGA-2010; renaming the meat-beans and milk groups to “protein foods” and “dairy groups”, respectively; vegetable subgroups now provide more achievable intake recommendations, increased amounts of sea foods are recommended, and a limit on calories from solid fats and added sugars is included [
13].
Standard quantitative dietary guidelines are not available in Iran, although evidently the use of these indices can be beneficial for nutritional assessment. In previous studies the association between diet quality indices and obesity has been investigated in adolescents. It had been shown that an increased HEI-1995 score was not associated with waist circumference (WC), but was negatively associated with BMI [
14]. In addition, no significant relationship was found between Mediterranean diet score (MDS) and WC [
15]. Also, MHO adolescents had higher total HEI-2005 scores compared with their MUO counterparts [
8]. Comparing results from these studies is not easy because of differences in diet quality indices, study populations and statistical analysis methods.
To our knowledge, no study has hither examined adherence to multiple indices based on DGA recommendations in an adolescent population or to determine which index can better demonstrate the risk of obesity associated phenotypes. Thus, the aim of the present study was (1) to evaluate adherence of Tehranian adolescents to DGAI, HEI-2005 and HEI-2010; (2) to assess the relationship between each of these indices with obesity and obesity associated phenotypes and (3) to determine whether HEI-2010, compared to 2005 indices, demonstrates the relationship with obesity-associated phenotypes better, a comparison which would provide valuable comparative data on how existing diet quality indices relate to obesity status in adolescents.
3. Results
Of 722 participants, 45.7% were boys and 54.3% were girls, mean age 14.5 ± 2.9 and 14.9 ± 2.9 years, respectively. There was no significant difference in percentage of adolescents with central obesity, overweight, general obesity and MUO between genders. Only the prevalence of MHO was significantly higher in girls (
Table 1).
Table 2 shows percentage of unhealthy metabolic factors in obese adolescents. MUO individuals have consistently higher metabolically unhealthy risk factors.
The mean score of DGAI, HEI-2005 and HEI-2010 in the present study was 9.9 ± 1.9 (ranged 4.50–16.25), 69.6 ± 8.7 (ranged 41.1–91.0) and 71.8 ± 9.1 (ranged 44.2–92.2), respectively.
Table 3 shows the participant characteristics according to quartile categories of diet quality indices, those in the highest quartile category of DGAI score were significantly more likely to be girls than boys (32% vs. 22%,
p < 0.001). Based on HEI-2005, older participants were more likely to obtain higher diet quality scores. After adjustment for sex and age, those in the highest quartile category of HEI-2010 were found to be more physically active than those in the lowest. There was no significant difference between other characteristics of participants and diet quality scores.
The dietary intakes of participants across quartile categories of diet quality indices are given in
Table 4. After adjustment for sex, age and energy intake, the percent of energy intake from total fat, trans fatty acids, saturated fatty acid, cholesterol and sodium intake dropped significantly moving from the first to the last quartile of diet quality indices, except for HEI-2005 which showed no significant change with cholesterol intake. The percent of energy intake from carbohydrate and protein, fruit, vegetable and meat intakes increased significantly throughout the quartiles of diet quality indices. However, there were no significant differences between the highest and the lowest quartile category of DGAI in grain and dairy product intakes, the highest and the lowest quartile category of HEI-2005 in whole grain and dairy product intakes and the highest and the lowest quartile category of HEI-2010 in total fiber and grain intakes.
Adjusted mean values of BMI, WC and cardio metabolic risk factors across quartile categories of diet quality indices are given in
Table 5. After adjustment for sex, age, energy intake and physical activity, mean values of BMI and WC showed a significant decreasing trend, according to quartiles of HEI-2010. Also, there was a direct association between DGAI and HEI-2005 scores and HDL-C concentration; and an inverse association between HEI-2005 score and fasting blood glucose and triglyceride concentrations. No significant relationship was observed between BMI, WC and other cardio metabolic risk factors and indices scores.
Table 6 shows ORs for different types of obesity (including central obesity, general obesity, metabolically healthy obesity and metabolically un-healthy obesity) as dichotomous variables. After adjustment for age, sex, energy intake and physical activity, being in the highest quartile category of HEI-2010 reduced the risk of central obesity by 37% (OR: 0.63; 95% CI 0.44–0.95;
Ptrend = 0.04) and general obesity by 38% (OR: 0.62; 95% CI 0.38–0.93;
Ptrend = 0.03). No significant difference was observed in odds of different types of obesity according the quartile categories of DGAI and HEI-2005 scores.
4. Discussion
In the current study, we compared the association of DGAI, HEI-2005 and HEI-2010 with risk of different types of obesity. Our findings indicate that participants who had high adherence with HEI-2010 had a lower risk of general and central obesity. Considering the limited research available, examining the relationship between diet quality indices and obesity in adolescents, the present study provides useful information for establishing more accurate dietary guidelines in our country.
Compared with American children and adolescents, Iranian adolescents have higher diet quality (based on HEI-2005: 55.9 vs. 69.6 and based on HEI-2010: 49.8 vs. 71.8) [
32]. It is worth noting that the prevalence of obesity (BMI ≥ 95th percentile of the BMI-for-age) in Iranian adolescents was less than that of American children and adolescents (15.2 vs. 16.9) [
6].
Regarding the impact of dietary intakes on obesity, several studies have investigated the relationship between diet quality indices and obesity in children and adolescents. Linardakis and colleagues have shown that BMI decreased with increase in HEI-1995 scores (
Ptrend = 0.04), whereas no significant relationship was observed for WC [
14]. According to Jennings and colleagues, in 9 to 10 years old British children, higher Diet Quality Index (DQI) and Healthy Diet Indicator (HDI) scores were associated with lower WC; DQI was also associated with lower BMI; however, there was no significant relationship between MDS and weight status [
33]. Likewise, another study also reported no association between MDS and risk of high WC in 12 to 17 years adolescents [
15]. Also, compared with metabolically abnormal obesity, metabolically healthy obese adolescents had higher HEI-2005 scores [
8]. In Iranian adolescents, there were no significant associations between HEI-1995 score and BMI or abdominal obesity. Also, those in the highest tertile of mean adequacy ratio had higher BMI and WC [
34].
Findings from studies in adults are also contradictory. An inverse association was observed between HEI-1995 and risk of overweight, obesity and abdominal obesity in the third National Health and Nutrition Examination Survey (NHANES III) [
35,
36]. In addition, it has been shown that HEI-1995, Recommended Foods Score (RFS-24 hour recall) and Dietary Diversity Score were negative predictors of BMI [
37]. Also, Nurses’ findings of the Health Study (NHS) suggest that individuals with closer adherence to the HEI-2005 tended to have lower BMI [
38]. However, in Iranian adults, adherence to HEI-2005, MDS, and DQI could not predict BMI and WC after 6.7 years of follow-up [
39]. In a cross-sectional analysis of Americans, the DGAI score was inversely related to WC [
12], whereas, in Iranian adults, this relationship has not been observed [
40]. Furthermore, in a prospective study DGAI score predicted obesity risk in men, but not in women [
41].
Based on our findings, indices can represent diet quality of adolescents to some extent, such that by increasing diet quality score, percent of calorie intake from fat, saturated fatty acids and trans fatty acids decreased and intakes of fruits and vegetables and meat increased, indicating partially the validity of these three indices in our population.
Among the indices examined in this study, only HEI-2010 had an inverse relationship with the risk of general and central obesity; BMI and WC decreased significantly across the quartiles of this index. Generally, it is accepted that obesity is the result of positive energy balance [
42]; the ability of diet quality indices in predicting general and central obesity depends on how these indices control changes in energy balance, and also on the structure and method of scoring in each index [
37]. HEI-2005 and HEI-2010 are based on an energy density approach that focuses on intakes of food groups and nutrients per 1000 kcal energy intake [
24,
25]. Although this approach results in balance among food groups intakes, it does not consider each individual’s needs for energy and extra energy consumption. In other words, these indices do not allocate negative scores to intakes of energy above the energy requirement. However, it should be noted that scoring for various components of food has changed in HEI-2010, compared to HEI-2005, e.g., if a person consumes whole grains >1.5 ounce per 1000 kcal per day, based on HEI-2005 and HEI-2010 he/she gets a score of 5 and 10, respectively [
24,
25]. Also, in HEI-2005, if total grain intake was >3 ounces per 1000 kcal per day, a score of 5 is given; however, in HEI-2010, if refined grains intake was <1.8 ounce per 1000 kcal per day, the person is scored 10 [
24,
25], indicating that HEI-2010 places more emphasis on reducing consumption of refined grains and increasing consumption of whole grains. It should be noted that previous studies have shown that the main source of energy in Iranians was grains (especially the refined type) [
43]. Moreover, HEI-2010 considers the ratio of “(PUFA + MUFA)/SFA” as a one item scored 10, instead of 2 items (the amount of oil and saturated fatty acid consumed) that their scores of which are 20 [
25]. The difference in association between these two indices and obesity status may be attributable to differences in the maximum scores of the components and it seems that energy-rich food sources (like whole grains, refined grains and fats) have been scored better in HEI-2010.
In DGAI, food group intakes recommendations are based on energy level requirements [
23]. Items of this index have a maximum value of 1.0. For four food groups that are considered energy dense (starchy vegetables, meat and beans, cereals and milk and dairy products), this index imposed an overconsumption penalty for those who exceeded the recommended intake by ≥0.5 servings by assigning only 0.5 points [
23], indicating that if a person consumes more than the amount required from these for four food groups, consuming hence excess energy, of a total 20 points of this index, he/she gets a maximum 2 points as a penalty. Therefore, it seems that DGAI does not consider extra energy intake in calculating the diet quality score.
According to our findings in all diet quality indices higher diet quality scores are associated with lower energy density. Due to differences in genetic predisposition, environmental factors and specific characteristics of dietary pattern in different ethnicities, it is better that diet quality indices be revised in each population for better match with the health targets [
40].
To address the several potential limitations of this research, first, the cross-sectional design of the present study cannot demonstrate causal relations and only generates hypotheses regarding diet quality and weight status. Secondly, an important limitation to consider when interpreting our results was the use of FFQ for collecting the dietary data; despite its common use for characterizing habitual intakes, its weakness in the quantification of nutrient intake is well-recognized. However, being easy to complete and analyze, FFQs are the primary source for data collection in large epidemiologic surveys, being more informative of habitual intake than data on intake on a few specific days [
44]. In addition, analysis of the data for both sexes was not possible due to the low sample size. Moreover, because the Iranian FCT is incomplete and provides limited data on nutrient content of raw foods and beverages, the American FCT was used for analyzing an Iranian diet. Since there are no standard dietary guidelines accessible for Iranian populations, the diet quality of adolescents was assessed based on DGA-2005 and DGA-2010, which have been developed for American populations.
The population-based analysis and conducting of the study in a developing country under nutrition transition are the main strengths of the present study. In addition, measurement and control of many known confounders can also be considered strengths of our study.