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Association of Dietary Inflammatory Index with anthropometric indices in children and adolescents: the weight disorder survey of the Childhood and Adolescence Surveillance and Prevention of Adult Non-communicable Disease (CASPIAN)-IV study

Published online by Cambridge University Press:  03 December 2018

Zahra Aslani
Affiliation:
Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran 1417653761, Iran Students’ Scientific Research Center, Tehran University of Medical Sciences, Tehran 1417755331, Iran
Mostafa Qorbani*
Affiliation:
Non-communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj 3149779453, Iran Chronic Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran 1411713137, Iran
James R. Hébert
Affiliation:
Cancer Prevention and Control Program and Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA Connecting Health Innovations LLC, University of South Carolina, Columbia, SC 29201, USA
Nitin Shivappa
Affiliation:
Cancer Prevention and Control Program and Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA Connecting Health Innovations LLC, University of South Carolina, Columbia, SC 29201, USA
Mohammad Esmaeil Motlagh
Affiliation:
Department of Pediatrics, Ahvaz Jundishapur University of Medical Sciences, Ahvaz 6135715794, Iran
Hamid Asayesh
Affiliation:
Department of Medical Emergencies, Qom University of Medical Sciences, Qom 3713649373, Iran
Armita Mahdavi-Gorabi
Affiliation:
Department of Basic and Clinical Research, Tehran Heart Center, Tehran University of Medical Sciences, Tehran 1411713138, Iran
Roya Kelishadi*
Affiliation:
Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-communicable Disease, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
*
*Corresponding authors: M. Qorbani, email mqorbani1379@yahoo.com; R. Kelishadi, email kelishadi@med.mui.ac.ir
*Corresponding authors: M. Qorbani, email mqorbani1379@yahoo.com; R. Kelishadi, email kelishadi@med.mui.ac.ir
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Abstract

This study aimed to assess the relationship between the Dietary Inflammatory Index (DII®), a validated tool for evaluating diet-associated inflammation, and anthropometric indices in children and adolescents. This multicentre survey was conducted on 5427 school students selected via multistage cluster sampling from thirty provinces of Iran. This survey was conducted under the framework of the weight disorders survey, which is part of a national surveillance programme entitled Childhood and Adolescence Surveillance and Prevention of Adult Non-communicable Diseases-IV. For calculating the DII scores, twenty-five dietary factors were obtained from a validated 168-item FFQ. Height, weight, wrist circumference, neck circumference (NC), waist circumference (WC) and hip circumference (HC) were measured. BMI z-score, waist circumference:hip circumference ratio (WHR), waist circumference:height ratio (WHtR) and parental BMI were computed. Linear regression models were used to evaluate the association of DII and anthropometric indices. Significant trends were observed across quartiles of DII score for all anthropometric indices in all participants (P <0·05), except for WHR and WHtR. After adjustment for potential confounders, the multiple linear regression analysis for each anthropometric index revealed that participants in the highest DII quartile had higher BMI z-score, WC, HC and parental BMI compared with those in the first (or lowest) quartile. In summary, we found that a pro-inflammatory diet was associated with higher BMI z-score, wrist circumference, NC, WC, HC and parental BMI. The large sample size of the present study may influence the statistical significance of observed associations. Hence, the findings should be clinically interpreted with caution.

Type
Full Papers
Copyright
© The Authors 2018 

Childhood obesity is emerging as a global problem and concern( Reference Lobstein, Baur and Uauy 1 Reference Wang, Beydoun and Liang 3 ). The results of a worldwide survey demonstrated that almost one-fifth of children were above normal weight (Wt)( Reference Ng, Fleming and Robinson 4 ). In 2010, the prevalence of childhood obesity in Europe, Southeast Asia and the Americas was 10, 5 and 15 %, respectively( Reference Wang and Lobstein 5 ). Prevalence rates are reported from Iran( Reference Kelishadi, Ardalan and Gheiratmand 6 ) similar to many other developing countries( Reference Kelishadi 7 ). Childhood obesity represents a range of disability and is a risk factor for many chronic disorders of adulthood such as osteoarthritis, cardiovascular and gall bladder diseases, as well as diabetes( Reference Tsiros, Coates and Howe 8 Reference Wang, McPherson and Marsh 10 ).

In obesity, concentrations of biomarkers of low-grade systemic inflammation such as C-reactive protein (CRP), TNF-α, IL-6 and IL-8 are increased( Reference Das 11 Reference Kim, Park and Kawada 15 ). Several studies revealed positive correlation between inflammatory biomarkers and anthropometric indices of obesity( Reference Kim, Park and Kawada 15 Reference Caminiti, Armeno and Mazza 25 ). A positive association between CRP and BMI or waist circumference (WC) is seen in several observational surveys( Reference Kim, Park and Kawada 15 , Reference Ford 18 Reference Huffman, Whisner and Zarini 23 ). Moreover, this association was observed between CRP and waist circumference:height ratio (WHtR)( Reference Malshe and Udipi 24 , Reference Caminiti, Armeno and Mazza 25 ).

Components of diet can affect systemic inflammation. Results of surveys indicate that high consumption of whole grains, olive oil, fruits, vegetables and fish, and low consumption of butter and red meat and low/moderate intake of wine (Mediterranean dietary pattern) are accompanied by reduced levels of inflammation. By contrast, the Western dietary pattern (rich in refined grains, red meat and high-fat dairy products) has been associated with high levels of inflammatory biomarkers( Reference Estruch, Martínez-González and Corella 26 Reference King, Egan and Geesey 28 ). Numerous studies have illustrated that nutrients such as fibre( Reference Ma, Griffith and Chasan-Taber 29 ), complex carbohydrates( Reference Kitabchi, McDaniel and Wan 30 ), moderate consumption of alcohol( Reference Avellone, Di Garbo and Campisi 31 ), n-3 PUFA( Reference Ferrucci, Cherubini and Bandinelli 32 ), vitamin C( Reference Wannamethee, Lowe and Rumley 33 ), vitamin E( Reference Bertran, Camps and Fernandez-Ballart 34 ), Mg( Reference King, Mainous III and Geesey 35 ) and β-carotene( Reference Erlinger, Guallar and Miller 36 ) have some anti-inflammatory effects, while SFA and sugar tend to have pro-inflammatory effects( Reference Huang, Sjögren and Ärnlöv 37 , Reference Gu and Lambert 38 ).

Several dietary indices exist for evaluating the quality of an individual’s diet( Reference Puchau, Zulet and de Echávarri 39 Reference Guenther, Kirkpatrick and Reedy 41 ). One of these indices is the Dietary Inflammatory Index (DII®), which was first described in 2009( Reference Cavicchia, Steck and Hurley 42 ) and updated in 2014( Reference Shivappa, Steck and Hurley 43 ). The DII is used to evaluate the inflammatory potential of diet based on pro- and anti-inflammatory properties of specific food items, spices, macronutrients, micronutrients and flavonoids( Reference Shivappa, Steck and Hurley 43 ). In two observational studies, a significant association was demonstrated between DII score and anthropometric indices in adults( Reference Sokol, Wirth and Manczuk 44 , Reference Ruiz-Canela, Zazpe and Shivappa 45 ). Results of a cohort in the USA indicated the relationship between maternal prenatal and early childhood DII with mid-childhood WC and BMI z-score( Reference Sen, Rifas-Shiman and Shivappa 46 ).

Given that there is no study focusing on the association between the DII and anthropometric indices in children and adolescents in Middle Eastern countries, the present study was conducted to evaluate this relationship using data derived from the fourth survey of an Iranian national surveillance programme entitled the Childhood and Adolescence Surveillance and Prevention of Adult Non-communicable Diseases (CASPIAN-IV). The hypothesis of the present study is that high inflammatory potential of diet, indicated by higher DII score, is related to higher levels of anthropometric indices.

Methods

Ethical statement

The study protocol was approved by the Ethics Committee of Isfahan and Alborz Universities of Medical Sciences (code: 194049). The objectives of the study were explained to the students and their parents, and the informed consent form and oral assent were obtained from eligible parents and students who wished to participate. The study was conducted according to the guidelines of the Declaration of Helsinki (Seoul, 2008).

Study design and population

The present study is based on data obtained from the CASPIAN-IV study. This nationwide cross-sectional survey was performed among students aged 6–18 years, from urban and rural areas of thirty provinces of Iran in 2011–2012. Students were selected by multistage, cluster sampling. Stratification was done in each province according to the number of students (proportional to size) in each residential area (urban/rural) and education grade (elementary/intermediate/high school) with equal numbers of boys and girls. Cluster sampling with equal clusters (eighty-three clusters) was used in each province to reach the necessary sample size. Clusters were defined at the level of schools and ten students were selected in each cluster. The sampling frame was defined based on the information bank of Ministry of Education. In each province, schools were categorised by type and name of school and the number of students was added cumulatively in each province. A total of ten students were randomly selected from clusters when the clusters were defined in each province. Finally, eighty-three clusters were selected and a total of 25 000 students were selected. In each province approximately a quarter of clusters, a total of 6505 students, were selected randomly for dietary assessment. Among 6505 invited students for dietary assessment, students following special diets (104 students); those having a history of chronic diseases, for example, type 1 diabetes, the metabolic syndrome (MetS), CVD (212 students); those using medications such as glucose-, lipid- and blood pressure-lowering medications (eighty-eight students); and those with incomplete dietary data (674 subjects) were excluded from our analysis. A total of 5427 students were included in the present study. None of these students consumed fortified foods. The methodology of the present study was published previously( Reference Kelishadi, Ardalan and Qorbani 47 ).

Demographic information

Demographic variables were collected using a validated questionnaire provided based on the WHO Global School-based Student Health Survey( Reference Kelishadi, Motlagh and Bahreynian 48 , Reference Kelishadi, Majdzadeh and Motlagh 49 ). The data were obtained from all participants through conducting an interview with one of their parents in the sampled classes of the selected schools. The variables related to the family consisted of family history of chronic diseases (diabetes, hypertension, obesity and dyslipidaemia), level of parental education (the total years of schooling), having a family private car, type of home (rented/owned), dietary behaviours, physical activity (PA) and sedentary lifestyle behaviours.

Dietary assessment

Assessment of usual dietary intakes for students was conducted using a validated 168-item semi-quantitative FFQ( Reference Esfahani, Asghari and Mirmiran 50 , Reference Mirmiran, Esfahani and Mehrabi 51 ), which included a description of standard serving sizes. This questionnaire was validated for children and adolescents before (Cronbach’s α coefficient = 0·96)( Reference Kelishadi, Majdzadeh and Motlagh 52 ). The FFQ for children <10 years old was completed by the student’s parent; students ≥10 years old completed the form by themselves. Reports consisted of the standard portion size and frequency of consumption of each food item during the previous year on a daily, weekly, monthly or yearly basis, as appropriate. Frequencies of consumption of foods for each person were converted to daily frequencies and then the gram amount consumed daily for each food item was computed using household scale guide. For nutrient analyses, Nutritionist 4 software (First Databank; Hearst), including the United States Department of Agriculture (USDA) food composition data, was used. Other food items, which were not included in this database, were analysed by means of Iranian food composition table( Reference Azar and Sarkisian 53 ).

Anthropometric measurements

Expertly trained staff conducted all anthropometric measurements in the selected schools. Body Wt was evaluated to the nearest 0·1 kg without shoes and wearing only light clothing. Height (Ht) was assessed to the nearest 0·1 cm with each participant standing without shoes and the shoulders positioned against a stadiometer. Wt and Ht were used to calculate the BMI (BMI = Wt (kg)/Ht (m)2)( Reference Kelishadi, Marashinia and Heshmat 54 , Reference Khashayar, Heshmat and Qorbani 55 ). Parental Wt and Ht also were measured. BMI z-scores of the students for age and sex were computed based on WHO growth reference standards for children aged between 5 and 19 years.

Students’ WC was measured to the nearest 0·1 cm in the midpoint between of the lowest rib and the iliac crest, with the students standing and breathing out (i.e. expiring). Hip circumference (HC) was assessed to the nearest 0·1 cm at maximum level of the ileac crest without any pressure to body surface. Neck circumference (NC) was evaluated underneath the Adam’s apple at a comfortable position to the nearest 0·1 cm. Wrist circumference was assessed on the dominant arm to the nearest 0·1 cm. The participants were asked to hold their arm on a flat surface such as a table. The superior border of the tape measure was placed just distal to the prominences of radial and ulnar bones. All assessments were carried out using a non-elastic tape. Waist:hip ratio (WHR) and WHtR were calculated by dividing WC to HC and WC to Ht, respectively( Reference Kelishadi, Ardalan and Qorbani 47 ).

Socio-economic status

Principal components analyses were used to provide a summary measure of socio-economic status (SES) of students. Relevant questions entered in this analysis included type of home, parental education, parent’s job, possessing private car, school type (public/private) and having personal computer. These variables were incorporated as a unit index( Reference Caro and Cortés 56 ), and finally this index was categorised into three grades (low, moderate and high). Questions about SES were asked to the student’s parent for children <10 years, while students ≥10 years old completed the questionnaire independently.

Physical activity and screen time

The Physical Activity Questionnaire for Adolescents (PAQ-A) was used to evaluate the levels of PA in children and adolescents. In this questionnaire, the students’ 7-d recalls of sports or activities were assessed by a self-administrated questionnaire. The validity and reliability of the questionnaire has been tested in the Iranian paediatric population( Reference Kelishadi, Majdzadeh and Motlagh 52 ). The questions included sports or activities that caused the participants to sweat or feel tired in their legs or exercises that made them breathe with difficultly, for example, running, climbing and playing on rope. The questionnaire also collected information regarding PA during the physical education period, leisure time, lunch time and after school, in the evenings and during weekends( Reference Adeniyi, Okafor and Adeniyi 57 , Reference Kowalski, Crocker and Donen 58 ). The total score of PAQ-A questionnaire was 1–5, and PA was categorised as low (students with scores between 1 and 1·9) and high (students with scores between 2 and 5) levels( Reference Kowalski, Crocker and Donen 58 , Reference Copeland, Kowalski and Donen 59 ).

In the present study, the average number of hours per day in which the students spent watching personal computer, television and electronic games was used to estimate the screen time (ST) behaviour during the entire week. According to the international ST recommendations, this variable was categorised into two: low (<2 h/d) and high (2 h/d or more) grades( Reference Salmon, Campbell and Crawford 60 , Reference Bar-On, Broughton and Buttross 61 ). Parents of children <10 years of age completed questions about PA and ST behaviour of students, while students ≥10 years old completed their questionnaire independently.

Dietary Inflammatory Index

The DII® was developed to determine the inflammatory potential of the diet. Development of this index has been described completely elsewhere( Reference Shivappa, Steck and Hurley 43 ). Briefly, 1943 papers published from 1950 to 2010 that assessed the effect of whole foods, spices and nutrients (forty-five dietary factors) on specific inflammatory markers (IL-1β, IL-4, IL-6, IL-10, TNF-α and CRP) were reviewed and scored. Each dietary factor that was accompanied by a significant higher values of IL-1β, IL-6, TNF-α or CRP or lower values of IL-4 or IL-10 was considered a pro-inflammatory component (+1 score), and each dietary factor that was associated with significant lower amounts of IL-1β, IL-6, TNF or CRP or higher amounts of IL-4 or IL-10 was considered as an anti-inflammatory component (–1 score); 0 was obtained for items that had no significant effect on six inflammatory biomarkers.

First, global mean intake of each dietary factor was subtracted from dietary intake of it, and then this value was divided by world standard deviation intake of that factor as derived from the data sets of eleven countries (i.e. to obtain the z-score). To minimise the effect of ‘right skewing’, dietary factor-specific z-scores were converted to a proportion (i.e. with values from 0 to 1). Next, these values were multiplied by 2 and then 1 was subtracted to achieve a symmetrical distribution with a range of –1 to +1 and centred on 0. To obtain the factor-specific DII score of each food, the amount for each dietary factor was multiplied by the overall dietary factor-specific inflammatory effect score. Finally, all dietary factor-specific DII scores were summed to calculate the overall DII score for each student.

Data on twenty-five items of forty-five possible dietary factors that could be used to calculate the DII score were available in this study. Energy, carbohydrate, protein, total fat, cholesterol, SFA, vitamin B12 and Fe were the pro-inflammatory components, and MUFA, PUFA, fibre, folic acid, niacin, riboflavin, thiamin, vitamin A, vitamin C, vitamin D, vitamin E, vitamin B6, Zn, Se, Mg, β-carotene and caffeine were the anti-inflammatory items. The remaining components of DII were not collected as part of the FFQ.

Statistical analyses

Data analyses were performed using STATA® software (version 11). Means and standard deviations and percentages were used to illustrate quantitative and qualitative variables, respectively. DII score and intake of energy and macronutrients were presented as medians and interquartile ranges in both sexes of students. The independent-samples t test was performed for comparison of the means between two groups. One-way ANOVA was used to compare the trend of mean values of the anthropometric indices across DII quartiles. The Mann–Whitney U test was applied for comparing medians of energy, macronutrients and DII score in two groups of students. The percentages of categorical variables were compared using Pearson’s χ 2 test. For evaluating the association between anthropometric indices (dependent variables) and DII score (categorised and continuous independent variables), linear regression models were used. There was no adjustment for covariates in one of the models (model I), while age, sex, place of residence, ST and PA and SES were adjusted in the other model (model II). The first quartile of DII score was considered the referent. A cluster sampling procedure was applied in all analyses. All P values were corrected using the Benjamini–Hochberg correction method to control the false discovery rate due to the multiple comparison problem( Reference Benjamini and Hochberg 62 ). The adjusted P value <0·05 was considered as statistically significant after the multiple comparison correction.

Results

In this national survey, 5427 students (53·2 % boys) with a mean age of 12·61 (sd 3·23) years were included. The anthropometric indices and other characteristics of participants were presented by sex (Table 1). Among anthropometric indices Ht, wrist circumference, NC, WC and WHR were significantly higher in boys than in girls (P for all <0·001). Girls had significantly higher BMI z-score and HC compared with boys (P for all <0·001). The BMI of girls’ parents was slightly higher than boys’ parents (27·40 (sd 5·32) v. 27·06 (sd 5·05) kg/m2) (P = 0·01).

Table 1 Characteristics of the study population according to sex in the weight disorders survey of the Childhood and Adolescence Surveillance and Prevention of Adult Non-communicable Disease-IV study (Mean values and standard deviations)

NC, neck circumference; WC, waist circumference; HC, hip circumference; WHR, waist:hip ratio; WHtR, waist:height ratio; ST, screen time; SES, socio-economic status; PA, physical activity.

* P values for the comparisons between quantitative variables were derived using the independent-samples t test, while for the comparisons of categorical variables the χ 2 test was used.

Most participants lived in urban areas (71·4 %). In general, no significant association was found between sex and SES (P = 0·08). However, more boys had high PA levels than that observed in girls (62·5 v. 35·8 %) (P <0·001) (Table 1). Also, ST was significantly higher in boys than in girls (81·7 v. 77·1 %) (P <0·001).

Table 2 shows sex-specific means of DII, intake of energy and macronutrients. All differences among both sexes were not statistically significant.

Table 2 Dietary Inflammatory Index (DII) and intake of energy and macronutrients according to sex in the weight disorders survey of the Childhood and Adolescence Surveillance and Prevention of Adult Non-communicable Disease-IV study (Medians and interquartile ranges (IQR))

* The Mann–Whitney U test was used.

Table 3 presents means and standard deviations of anthropometric indices according to the DII quartiles. The lower and upper limits of the DII for each quartile are provided. Overall, higher means of BMI z-score, NC, wrist circumference, HC and WC were associated with higher DII score (P trend <0·001 in all comparisons). Anthropometric indices in boys had a significant escalating trend across DII quartiles for all parameters except for WHR and WHtR (P trend in all comparisons was significant). In girls higher DII score was associated with higher BMI z-score, HC and WC (P trend for all comparisons <0·05).

Table 3 Anthropometric measures according to quartiles (Q) of the Dietary Inflammatory Index (DII) in the weight disorders survey of the Childhood and Adolescence Surveillance and Prevention of Adult Non-communicable Disease-IV study (Mean values and standard deviations)

NC, neck circumference; WC, waist circumference; HC, hip circumference; WHR, waist:hip ratio; WHtR, waist:height ratio.

* Range of quartiles for total participants: Q 1 = (–4·42, –1·63), Q 2 = (–1·62, –0·05), Q 3 = (–0·04, 1·51) and Q 4 = (1·50, 4·26).

One-way ANOVA was used.

Table 4 presents results from the linear regression models fit to describe the association between anthropometric indices and quartiles of DII score as categorical and continuous variable. In the categorical form of DII quartile, in the multivariate model (after adjustment for potential confounders), students in the highest quartile of DII had higher BMI z-scores compared with those in the first quartile as reference (P <0·05). With respect to WC, individuals in the highest quartile of the DII had higher level of WC compared with those in the lowest quartile (P <0·05). Moreover, students in the highest quartile of DII had higher level of HC compared with the individuals in the first quartile (P <0·05). Students’ parents in the second, third and highest quartiles of the DII had higher BMI compared with those in the lowest quartile (P <0·05). When we considered DII quartile as a continuous variable, a significant association was observed between the DII quartile and WC, HC and parental BMI (P <0·05). In the crude and multivariate model, the association of WHR and WHtR with DII quartiles was not statistically significant.

Table 4 Associations between the Dietary Inflammatory Index (DII) and anthropometric measures in the weight disorders survey of the Childhood and Adolescence Surveillance and Prevention of Adult Non-communicable Disease-IV study (β Estimates and 95 % confidence intervals)

Ref., referent values; NC, neck circumference; WC, waist circumference; HC, hip circumference; WHR, waist to hip ratio; WHtR, waist to height ratio; ST, screen time; PA, physical activity; SES, socio-economic status.

* Statistically significant.

Linear regression was used.

Crude association.

§ DII quartile considered as continuous variable.

Adjusted for age, sex, place of residence, ST, PA and SES.

Discussion

In this nationwide study performed on Iranian students, the association between overall inflammatory potential of diet, estimated by DII score, and anthropometric indices was assessed. Higher DII scores were associated with higher BMI z-scores, wrist circumference, NC, WC and HC in all students and BMI of their parents. These associations were observed among boys, whereas higher DII score was associated with higher BMI z-score, WC and HC in girls. Results that included positive relationship between DII and BMI z-score of students, HC, WC and parental BMI persisted after adjusting for potential confounders, including age, sex, place of area, ST, PA and SES.

Higher DII score was associated with general obesity (assessed by BMI z-score) in the students. This finding might be associated with the consumption of more pro-inflammatory diets (e.g. fast foods, cookies and crackers), which tend to be energy dense( Reference Bowman, Gortmaker and Ebbeling 63 , Reference Borges, Marchioni and Levy 64 ). This result is line with the study of Ramallal et al. ( Reference Ramallal, Toledo and Martinez 65 ) that showed consumption of an anti-inflammatory diet (lower DII) was associated with lower BMI; with the corollary that participants with pro-inflammatory diet (higher DII) tended to be overweight or obese. The findings of the present study in children and adolescents are consistent with the results of a Spanish study conducted in 7236 adult participants( Reference Ruiz-Canela, Zazpe and Shivappa 45 ). An observational study on 430 young Americans, aged 21–35 years, indicated no significant association between DII and BMI( Reference Wirth, Hébert and Shivappa 66 ). This discrepancy may have resulted from the small sample size of that study. In line with the present study, a meta-analysis was performed on three cross-sectional studies, indicating higher protein (a pro-inflammatory dietary factor) consumption in obese v. non-obese subjects( Reference Kaartinen, Knekt and Kanerva 67 ). A cross-sectional study conducted in 2013–2014 found that high fat (a pro-inflammatory dietary factor) intake is associated with higher BMI, and individuals with normal BMI consumed higher amounts of fibre compared with the obese persons( Reference Little, Humphries and Patel 68 ).

The present finding on the association between DII and WC is consistent with a previous research conducted on older adults( Reference Ruiz-Canela, Zazpe and Shivappa 45 ). Similarly, a case–control study performed on Cuban-American population with and without type 2 diabetes and aged >30 years indicated a direct association between WC and CRP( Reference Huffman, Whisner and Zarini 23 ). Likewise, another observational study observed this positive relationship in participants with abdominal obesity( Reference Faam, Zarkesh and Daneshpour 69 ). It was hypothesised that visceral fat is the main anatomic site for secretion of IL-6( Reference Fontana, Eagon and Trujillo 70 ), and this inflammatory biomarker could stimulate CRP production by liver( Reference Weisberg, McCann and Desai 71 ). A cross-sectional study on 722 adolescents aged 10–19 years, indicated that participants in the highest quartile of healthy eating index-2010 had lower risk of central obesity than those in the lowest quartile( Reference Mohseni-Takalloo, Hosseini-Esfahani and Mirmiran 72 ). The results of a cohort study conducted with the aim of examining the association of Dietary Approaches to Stop Hypertension (DASH) and the MetS on 425 Iranian children and adolescents demonstrated higher adherence to DASH diet was associated with lower abdominal obesity( Reference Asghari, Yuzbashian and Mirmiran 73 ).

WHtR is one of the anthropometric indices associated with body fat( Reference Nambiar, Truby and Abbott 74 ). In a large cross-sectional study conducted with the aim of determination of the association between DII and anthropometric indices in Spain, the findings demonstrated a positive relationship between the inflammatory potential of diet and WHtR( Reference Ruiz-Canela, Zazpe and Shivappa 45 ), which is not in agreement with the outcome of the present study. In this study, the DII score was calculated from only twenty-five dietary factors; whereas in the previous report data for thirty-three food components were available for calculating the DII score. Therefore, this discrepancy may be the reason for the difference in the results. However, we have shown previously that there is no drop off in predictability in going from 44 to 27 parameters used for DII calculation( Reference Shivappa, Steck and Hurley 75 ). On the contrary to our study, in a cross-sectional survey conducted on British children and adolescents, the dietary glycaemic load in adolescents was independently associated with higher WHtR( Reference Murakami, McCaffrey and Livingstone 76 ). This dietary index was positively associated with carbohydrate (a pro-inflammatory dietary factor)( Reference Buyken, Dettmann and Kersting 77 ). This disagreement may be associated with missing data on twenty dietary factors in the DII calculation. However, it should be noted that the range of DII score (from –4·42 to 4·26) is typical of what we see in many of our studies( Reference Sokol, Wirth and Manczuk 44 Reference Sen, Rifas-Shiman and Shivappa 46 , Reference Wirth, Hébert and Shivappa 66 ). An observational study on 347 Italian children and preadolescents indicated high consumption of protein was associated with greater WHtR( Reference Del Mar Bibiloni, Tur and Morandi 78 ). It must be kept in mind that the nutritional requirements of children are different than those of adults, owing largely to the metabolic requirements for growth( Reference Goodhart and Shils 79 , Reference Hebert 80 ). In particular, we believe that it is important to point out that protein intake may be particularly important, especially for children during growth spurts and those who are involved in extreme athletic activities (e.g. weightlifting, marathon running)( Reference Petrie, Stover and Horswill 81 , Reference Dewey, Beaton and Fjeld 82 ). So even though protein is pro-inflammatory, the need for growth and development may outweigh its effect on inflammation. This is a matter separate and distinct from energy balance, which may be the main driving force in this population of children( Reference Shivappa, Hebert and Marcos 83 , Reference Sen, Rifas-Shiman and Shivappa 84 ).

No association was observed between DII and WHR. This lack of association may be due to the relatively small number of participants with abdominal obesity. Similarly, a survey on Finnish elderly aged ≥90 years indicated no association between WHR and levels of TNF-α, CRP and IL-6 in both sexes( Reference Lisko, Tiainen and Stenholm 85 ). On the contrary, in a study conducted on 377 adolescents and young adults in India, high WHR was associated with high levels of CRP( Reference Tsai and Tsai 86 ). A cross-sectional study that assessed the relationship between inflammatory biomarkers and obesity on young adults aged 18–30 years showed that WHR was positively associated with the levels of IL-6 and CRP( Reference Hermsdorff, Zulet and Puchau 87 ). In another observational study conducted on 1248 Taiwanese participants aged 65 years or older, a positive significant association was found between WHR and the levels of CRP( Reference Vikram, Misra and Dwivedi 88 ). The results confirmed that inflammatory biomarkers can be produced in visceral fat( Reference Fontana, Eagon and Trujillo 70 , Reference Matsuzawa 89 , Reference Berg and Scherer 90 ).

The results of the present study indicted the DII was positively associated with parental BMI. In an extensive study carried out among Korean households, the significant correlations were observed between the pattern of vitamin intake (B1, B2, B3, C and A), Fe, Ca and P in parents and their offsprings( Reference Lee and Park 91 ). In another study conducted on 294 families, the findings demonstrated the significant positive association between consumption of total carbohydrate, SFA, PUFA, energy and cholesterol in children and adolescents and their parents( Reference Laskarzewski, Morrison and Khoury 92 ). Vauthier et al. ( Reference Vauthier, Lluch and Lecomte 93 ) show that there is association between consumption of energy and macronutrients in parents and their children and adolescents in French families. Recently, a cross-sectional study carried out among 401 child/parent pairs in New Zealand represented an inverse association between parental diet quality and snack consumption pattern in their preadolescents( Reference Davison, Saeedi and Black 94 ). These findings reinforced the correlation between parent’s and children’s diet in one family.

Strengths

This national report covered a large group of Iranian children and adolescents as well as their parents. Using a validated dietary assessment instrument and PA questionnaire, applying an updated and validated tool for evaluating the inflammatory effect of diet and estimating the relationship between DII and anthropometric indices after adjustment for potentially confounding factors are the other strengths of the present study.

Limitations and other considerations

The present study was carried out on school students and their parents; hence, the results cannot be generalised to all Iranians or to children in other countries or those of different SES. Moreover, information bias could result from using the FFQ for dietary assessment. Therefore, misclassification is a potential problem common in epidemiologic reports. The questionnaires of children under 10 years of age were completed by one of their parents; this may bias the results. Another limitation is non-availability of data on twenty dietary factors, which may affect the results, primarily by underestimating the DII (as most missing dietary factors are anti-inflammatory though most are probably consumed only infrequently). So, these dietary factors may have no great effect on the DII scoring. Another consideration for the present study is its sample size. Although large sample size is one of the main strengths of this study, having such a large sample size may lead to observing statistically significant association between the DII score and a variety of outcomes. Therefore, we should consider this point when considering the clinical significance of results.

Conclusion

The present investigation observed a positive association between pro-inflammatory diet and greater indices of generalised and abdominal obesity in young children and adolescents. Prospective studies are warranted to confirm our findings.

Acknowledgements

This large observational study was performed with the cooperation of the Ministry of Health and Medical Education, Ministry of Education and Training, Child Growth and Development Research Center, Isfahan University of Medical Sciences and Alborz University of Medical Sciences. This study was supported by Isfahan University of Medical Sciences and Alborz University of Medical Sciences.

N. S. and J. R. H. supported the study by grant number R44DK103377 from the United States National Institute of Diabetes and Digestive and Kidney Diseases.

J. R. H. owns controlling interest in Connecting Health Innovations LLC (CHI), a company planning to license the right to his invention of the DII from the University of South Carolina to develop computer and smart phone applications for patient counseling and dietary intervention in clinical settings. N. S. is an employee of CHI.

M. Q., R. K. and M. E. M. formulated the research questions and designed the study. H. A. and A. M.-G. carried it out the interviews. M. Q. analysed the data and Z. A., J. R. H. and N. S. computed the DII score and contributed to writing the article.

The authors declare that there are no conflicts of interest.

References

1. Lobstein, T, Baur, L & Uauy, R (2004) Obesity in children and young people: a crisis in public health. Obes Rev 5, 485.Google Scholar
2. Olshansky, SJ, Passaro, DJ, Hershow, RC, et al. (2005) A potential decline in life expectancy in the United States in the 21st century. N Engl J Med 352, 11381145.Google Scholar
3. Wang, Y, Beydoun, MA, Liang, L, et al. (2008) Will all Americans become overweight or obese? Estimating the progression and cost of the US obesity epidemic. Obesity 16, 23232330.Google Scholar
4. Ng, M, Fleming, T, Robinson, M, et al. (2014) Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 384, 766781.Google Scholar
5. Wang, Y & Lobstein, T (2006) Worldwide trends in childhood overweight and obesity. Pediatr Obes 1, 1125.Google Scholar
6. Kelishadi, R, Ardalan, G, Gheiratmand, R, et al. (2008) Thinness, overweight and obesity in a national sample of Iranian children and adolescents: CASPIAN study. Child Care Health Dev 34, 4454.Google Scholar
7. Kelishadi, R (2007) Childhood overweight, obesity, and the metabolic syndrome in developing countries. Epidemiol Rev 29, 6276.Google Scholar
8. Tsiros, MD, Coates, AM, Howe, PR, et al. (2011) Obesity: the new childhood disability? Obes Rev 12, 2636.Google Scholar
9. Whitaker, RC, Wright, JA, Pepe, MS, et al. (1997) Predicting obesity in young adulthood from childhood and parental obesity. N Engl J Med 337, 869873.Google Scholar
10. Wang, YC, McPherson, K, Marsh, T, et al. (2011) Health and economic burden of the projected obesity trends in the USA and the UK. Lancet 378, 815825.Google Scholar
11. Das, U (2001) Is obesity an inflammatory condition? Nutrition 17, 953966.Google Scholar
12. Ross, R (1999) Atherosclerosis – an inflammatory disease. N Engl J Med 340, 115126.Google Scholar
13. Dandona, P, Aljada, A, Chaudhuri, A, et al. (2004) Endothelial dysfunction, inflammation and diabetes. Rev Endocr Metab Disord 5, 189197.Google Scholar
14. Lyon, CJ, Law, RE & Hsueh, WA (2003) Minireview: adiposity, inflammation, and atherogenesis. Endocrinology 144, 21952200.Google Scholar
15. Kim, C, Park, H, Kawada, T, et al. (2006) Circulating levels of MCP-1 and IL-8 are elevated in human obese subjects and associated with obesity-related parameters. Int J Obes (Lond) 30, 13471355.Google Scholar
16. Herder, C, Peltonen, M, Koenig, W, et al. (2006) Systemic immune mediators and lifestyle changes in the prevention of type 2 diabetes. Diabetes 55, 23402346.Google Scholar
17. Herder, C, Illig, T, Rathmann, W, et al. (2005) Inflammation and type 2 diabetes: results from KORA Augsburg. Gesundheitswesen 67, 115121.Google Scholar
18. Ford, ES. (2003) C-reactive protein concentration and cardiovascular disease risk factors in children. Circulation 108, 10531058.Google Scholar
19. Lambert, M, Delvin, EE, Paradis, G, et al. (2004) C-reactive protein and features of the metabolic syndrome in a population-based sample of children and adolescents. Clin Chem 50, 17621768.Google Scholar
20. Rutherford, JN, Mcdade, TW, Lee, NR, et al. (2010) Change in waist circumference over 11 years and current waist circumference independently predict elevated CRP in Filipino women. Am J Hum Biol 22, 310315.Google Scholar
21. Santos, A, Lopes, C, Guimaraes, J, et al. (2005) Central obesity as a major determinant of increased high-sensitivity C-reactive protein in metabolic syndrome. Int J Obes (Lond) 29, 14521456.Google Scholar
22. Shemesh, T, Rowley, K, Jenkins, A, et al. (2007) Differential association of C-reactive protein with adiposity in men and women in an Aboriginal community in northeast Arnhem Land of Australia. Int J Obes (Lond) 31, 103108.Google Scholar
23. Huffman, FG, Whisner, S, Zarini, GG, et al. (2010) Waist circumference and BMI in relation to serum high sensitivity C-reactive protein (hs-CRP) in Cuban Americans with and without type 2 diabetes. Int J Environ Res Public Health 7, 842852.Google Scholar
24. Malshe, SD & Udipi, SA (2017) Waist-to-height ratio in Indian women: comparison with traditional indices of obesity, association with inflammatory biomarkers and lipid profile. Asia Pac J Public Health 29, 411421.Google Scholar
25. Caminiti, C, Armeno, M & Mazza, CS (2016) Waist-to-height ratio as a marker of low-grade inflammation in obese children and adolescents. J Pediatr Endocrinol Metab 29, 543551.Google Scholar
26. Estruch, R, Martínez-González, MA, Corella, D, et al. (2006) Effects of a Mediterranean-style diet on cardiovascular risk factors: a randomized trial. Ann Intern Med 145, 111.Google Scholar
27. Johansson-Persson, A, Ulmius, M, Cloetens, L, et al. (2017) A high intake of dietary fiber influences C-reactive protein and fibrinogen, but not glucose and lipid metabolism, in mildly hypercholesterolemic subjects. Eur J Nutr 53, 3948.Google Scholar
28. King, DE, Egan, BM & Geesey, ME (2003) Relation of dietary fat and fiber to elevation of C-reactive protein. Am J cardiol 92, 13351339.Google Scholar
29. Ma, Y, Griffith, JA, Chasan-Taber, L, et al. (2006) Association between dietary fiber and serum C-reactive protein. Am J Clin Nutr 83, 760766.Google Scholar
30. Kitabchi, AE, McDaniel, KA, Wan, JY, et al. (2013) Effects of high-protein versus high-carbohydrate diets on markers of β-cell function, oxidative stress, lipid peroxidation, proinflammatory cytokines, and adipokines in obese, premenopausal women without diabetes. Diabetes Care 36, 19191925.Google Scholar
31. Avellone, G, Di Garbo, V, Campisi, D, et al. (2006) Effects of moderate Sicilian red wine consumption on inflammatory biomarkers of atherosclerosis. Eur J Clin Nutr 60, 4147.Google Scholar
32. Ferrucci, L, Cherubini, A, Bandinelli, S, et al. (2006) Relationship of plasma polyunsaturated fatty acids to circulating inflammatory markers. J Clin Endocrinol Metab 91, 439446.Google Scholar
33. Wannamethee, SG, Lowe, GD, Rumley, A, et al. (2006) Associations of vitamin C status, fruit and vegetable intakes, and markers of inflammation and hemostasis. Am J Clin Nutr 83, 567574.Google Scholar
34. Bertran, N, Camps, J, Fernandez-Ballart, J, et al. (2005) Diet and lifestyle are associated with serum C-reactive protein concentrations in a population-based study. J Lab Clin Med 145, 4146.Google Scholar
35. King, DE, Mainous III, AG, Geesey, ME, et al. (2005) Dietary magnesium and C-reactive protein levels. J Am Coll Nutr 24, 166171.Google Scholar
36. Erlinger, TP, Guallar, E, Miller, ER III, et al. (2001) Relationship between systemic markers of inflammation and serum β-carotene levels. Arch Intern Med 161, 19031908.Google Scholar
37. Huang, X, Sjögren, P, Ärnlöv, J, et al. (2014) Serum fatty acid patterns, insulin sensitivity and the metabolic syndrome in individuals with chronic kidney disease. J Intern Med 275, 7183.Google Scholar
38. Gu, Y & Lambert, JD (2013) Modulation of metabolic syndrome-related inflammation by cocoa. Mol Nutr Food Res 57, 948961.Google Scholar
39. Puchau, B, Zulet, , de Echávarri, AG, et al. (2009) Dietary total antioxidant capacity: a novel indicator of diet quality in healthy young adults. J Am Coll Nutr 28, 648656.Google Scholar
40. Haines, PS, Siega-Riz, AM & Popkin, BM (1999) The Diet Quality Index revised: a measurement instrument for populations. J Am Diet Assoc 99, 697704.Google Scholar
41. Guenther, PM, Kirkpatrick, SI, Reedy, J, et al. (2014) The Healthy Eating Index-2010 is a valid and reliable measure of diet quality according to the 2010 Dietary Guidelines for Americans. J Nutr 144, 399407.Google Scholar
42. Cavicchia, PP, Steck, SE, Hurley, TG, et al. (2009) A new dietary inflammatory index predicts interval changes in serum high-sensitivity C-reactive protein. J Nutr 139, 23652372.Google Scholar
43. Shivappa, N, Steck, SE, Hurley, TG, et al. (2014) Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutr 17, 16891696.Google Scholar
44. Sokol, A, Wirth, MD, Manczuk, M, et al. (2016) Association between the dietary inflammatory index, waist-to-hip ratio and metabolic syndrome. Nutr Res 36, 12981303.Google Scholar
45. Ruiz-Canela, M, Zazpe, I, Shivappa, N, et al. (2015) Dietary inflammatory index and anthropometric measures of obesity in a population sample at high cardiovascular risk from the PREDIMED (PREvencion con DIeta MEDiterranea) trial. Br J Nutr 113, 984995.Google Scholar
46. Sen, S, Rifas-Shiman, S, Shivappa, N, et al. (2017) Associations of prenatal and early life dietary inflammatory potential with childhood adiposity and cardiometabolic risk in Project Viva. Pediatr Obes 13, 19.Google Scholar
47. Kelishadi, R, Ardalan, G, Qorbani, M, et al. (2013) Methodology and early findings of the fourth survey of childhood and adolescence surveillance and prevention of adult non-communicable disease in Iran: the CASPIAN-IV study. Int J Prev Med 4, 14511460.Google Scholar
48. Kelishadi, R, Motlagh, ME, Bahreynian, M, et al. (2015) Methodology and early findings of the assessment of determinants of weight disorders among Iranian children and adolescents: the Childhood and Adolescence Surveillance and Prevention of Adult Noncommunicable Disease-IV Study. Int J Prev Med 6, 77.Google Scholar
49. Kelishadi, R, Majdzadeh, R, Motlagh, ME, et al. (2012) Development and evaluation of a questionnaire for assessment of determinants of weight disorders among children and adolescents: the CASPIAN-IV study. Int J Prev Med 3, 699705.Google Scholar
50. Esfahani, FH, Asghari, G, Mirmiran, P, et al. (2010) Reproducibility and relative validity of food group intake in a food frequency questionnaire developed for the Tehran Lipid and Glucose Study. J Epidemiol 20, 150158.Google Scholar
51. Mirmiran, P, Esfahani, FH, Mehrabi, Y, et al. (2010) Reliability and relative validity of an FFQ for nutrients in the Tehran Lipid and Glucose Study. Public Health Nutr 13, 654662.Google Scholar
52. Kelishadi, R, Majdzadeh, R, Motlagh, M-E, et al. (2012) Development and evaluation of a questionnaire for assessment of determinants of weight disorders among children and adolescents: the CASPIAN-IV study. Int J Prev Med 3, 699705.Google Scholar
53. Azar, M & Sarkisian, E (1980) Food Composition Table of Iran: National Nutrition and Food Research Institute. Tehran: Shaheed Beheshti University.Google Scholar
54. Kelishadi, R, Marashinia, F, Heshmat, R, et al. (2013) First report on body image and weight control in a nationally representative sample of a pediatric population in the Middle East and North Africa: the CASPIAN-III study. Arch Med Sci 9, 210217.Google Scholar
55. Khashayar, P, Heshmat, R, Qorbani, M, et al. (2013) Metabolic syndrome and cardiovascular risk factors in a national sample of adolescent population in the middle east and north Africa: the CASPIAN III study. Int J Endocrinol 2013, 702095.Google Scholar
56. Caro, DH & Cortés, D (2012) Measuring family socioeconomic status: an illustration using data from PIRLS 2006. IERI Monograph Series. Issues and Methodologies in Large-Scale Assessments 5, 933.Google Scholar
57. Adeniyi, AF, Okafor, NC & Adeniyi, CY (2011) Depression and physical activity in a sample of Nigerian adolescents: levels, relationships and predictors. Child Adolesc Psychiatry Ment Health 5, 16.Google Scholar
58. Kowalski, KC, Crocker, PR & Donen, RM (2004) The Physical Activity Questionnaire for Older Children (PAQ-C) and Adolescents (PAQ-A) Manual, vol. 87, pp. 138. Saskatchewan, Canada: College of Kinesiology, University of Saskatchewan.Google Scholar
59. Copeland, JL, Kowalski, KC, Donen, RM, et al. (2005) Convergent validity of the physical activity questionnaire for adults: the new member of the PAQ Family. J Phys Act Health 2, 216229.Google Scholar
60. Salmon, J, Campbell, KJ & Crawford, DA (2006) Television viewing habits associated with obesity risk factors: a survey of Melbourne schoolchildren. Med J Aust 184, 6467.Google Scholar
61. Bar-On, ME, Broughton, DD, Buttross, S, et al. (2001) Children, adolescents, and television. Pediatrics 107, 423426.Google Scholar
62. Benjamini, Y & Hochberg, Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol 57, 289300.Google Scholar
63. Bowman, SA, Gortmaker, SL, Ebbeling, CB, et al. (2004) Effects of fast-food consumption on energy intake and diet quality among children in a national household survey. Pediatrics 113, 112118.Google Scholar
64. Borges, CA, Marchioni, DML, Levy, RB, et al. (2018) Dietary patterns associated with overweight among Brazilian adolescents. Appetite 123, 402409.Google Scholar
65. Ramallal, RE, Toledo, JA, Martinez, N, et al. (2017) Inflammatory potential of diet, weight gain, and incidence of overweight/obesity: The SUN cohort. Obesity (Silver Spring) 25, 9971005.Google Scholar
66. Wirth, MD, Hébert, JR, Shivappa, N, et al. (2016) Anti-inflammatory dietary inflammatory index scores are associated with healthier scores on other dietary indices. Nutr Res 36, 214219.Google Scholar
67. Kaartinen, NE, Knekt, P, Kanerva, N, et al. (2016) Dietary carbohydrate quantity and quality in relation to obesity: a pooled analysis of three Finnish population-based studies. Scand J Public Health 44, 385393.Google Scholar
68. Little, M, Humphries, S, Patel, K, et al. (2016) Factors associated with BMI, underweight, overweight, and obesity among adults in a population of rural south India: a cross-sectional study. BMC Obes 3, 12.Google Scholar
69. Faam, B, Zarkesh, M, Daneshpour, MS, et al. (2014) The association between inflammatory markers and obesity-related factors in Tehranian adults: Tehran Lipid and Glucose Study. Iran J Basic Med Sci 17, 577582.Google Scholar
70. Fontana, L, Eagon, JC, Trujillo, ME, et al. (2007) Visceral fat adipokine secretion is associated with systemic inflammation in obese humans. Diabetes 56, 10101013.Google Scholar
71. Weisberg, SP, McCann, D, Desai, M, et al. (2003) Obesity is associated with macrophage accumulation in adipose tissue. J Clin Invest 112, 17961808.Google Scholar
72. Mohseni-Takalloo, S, Hosseini-Esfahani, F, Mirmiran, P, et al. (2016) Associations of pre-defined dietary patterns with obesity associated phenotypes in Tehranian adolescents. Nutrients 8, 505.Google Scholar
73. Asghari, G, Yuzbashian, E, Mirmiran, P, et al. (2016) Dietary Approaches to Stop Hypertension (DASH) dietary pattern is associated with reduced incidence of metabolic syndrome in children and adolescents. J Pediatr 174, 178184.Google Scholar
74. Nambiar, S, Truby, H, Abbott, RA, et al. (2009) Validating the waist-height ratio and developing centiles for use amongst children and adolescents. Acta Paediatr 98, 148152.Google Scholar
75. Shivappa, N, Steck, SE, Hurley, TG, et al. (2016) A population-based dietary inflammatory index predicts levels of C-reactive protein (CRP) in the SEASONS study. Public Health Nutr 17, 18251833.Google Scholar
76. Murakami, K, McCaffrey, TA & Livingstone, MB (2013) Dietary glycaemic index and glycaemic load in relation to food and nutrient intake and indices of body fatness in British children and adolescents. Br J Nutr 110, 15121523.Google Scholar
77. Buyken, AE, Dettmann, W, Kersting, M, et al. (2005) Glycaemic index and glycaemic load in the diet of healthy schoolchildren: trends from 1990 to 2002, contribution of different carbohydrate sources and relationships to dietary quality. Br J Nutr 94, 796803.Google Scholar
78. Del Mar Bibiloni, M, Tur, JA, Morandi, A, et al. (2015) Protein intake as a risk factor of overweight/obesity in 8- to 12-year-old children. Medicine (Baltimore) 94, e2408.Google Scholar
79. Goodhart, RS & Shils, ME (editors) (1980) Modern Nutrition in Health and Disease, 6th ed. Philadelphia, PA: Lea & Febiger.Google Scholar
80. Hebert, JR (1987) Growth monitoring: the “G” in GOBI FFF. In Child Health and Survival: The UNICEF GOBI FFF Program, pp. 1120 [R Cash, GT Keusch and J Lamstein, editors]. London: Croom Helm.Google Scholar
81. Petrie, HJ, Stover, EA & Horswill, CA (2004) Nutritional concerns for the child and adolescent competitor. Nutrition 20, 620631.Google Scholar
82. Dewey, KG, Beaton, G, Fjeld, C, et al. (1996) Protein requirements of infants and children. Eur J Clin Nutr 50, 119147.Google Scholar
83. Shivappa, N, Hebert, JR, Marcos, A, et al. (2017) Association between dietary inflammatory index and inflammatory markers in the HELENA study. Mol Nutr Food Res 61, 10.1002/mnfr.201600707.Google Scholar
84. Sen, S, Rifas-Shiman, SL, Shivappa, N, et al. (2016) Dietary inflammatory potential during pregnancy is associated with lower fetal growth and breastfeeding failure: results from project Viva. J Nutr 146, 728736.Google Scholar
85. Lisko, I, Tiainen, K, Stenholm, S, et al. (2012) Inflammation, adiposity, and mortality in the oldest old. Rejuvenation Res 15, 445452.Google Scholar
86. Tsai, H-J & Tsai, AC-H (2008) The association of plasma C-reactive protein levels with anthropometric and lipid parameters in elderly Taiwanese. Asia Pac J Clin Nutr 17, 651656.Google Scholar
87. Hermsdorff, HH, Zulet, MA, Puchau, B, et al. (2011) Central adiposity rather than total adiposity measurements are specifically involved in the inflammatory status from healthy young adults. Inflammation 34, 161170.Google Scholar
88. Vikram, NK, Misra, A, Dwivedi, M, et al. (2003) Correlations of C-reactive protein levels with anthropometric profile, percentage of body fat and lipids in healthy adolescents and young adults in urban North India. Atherosclerosis 168, 305313.Google Scholar
89. Matsuzawa, Y (2006) Therapy insight: adipocytokines in metabolic syndrome and related cardiovascular disease. Nat Rev Cardiol 3, 3542.Google Scholar
90. Berg, AH & Scherer, PE (2005) Adipose tissue, inflammation, and cardiovascular disease. Circ Res 96, 939949.Google Scholar
91. Lee, HA & Park, H (2015) Correlations between poor micronutrition in family members and potential risk factors for poor diet in children and adolescents using Korean National Health and Nutrition Examination Survey data. Nutrients 7, 63466361.Google Scholar
92. Laskarzewski, P, Morrison, JA, Khoury, P, et al. (1980) Parent–child nutrient intake interrelationships in school children ages 6 to 19: the Princeton School District Study. Am J Clin Nutr 33, 23502355.Google Scholar
93. Vauthier, JM, Lluch, A, Lecomte, E, et al. (1996) Family resemblance in energy and macronutrient intakes: the Stanislas Family Study. Int J Epidemiol 25, 10301037.Google Scholar
94. Davison, B, Saeedi, P, Black, K, et al. (2017) The association between parent diet quality and child dietary patterns in nine- to eleven-year-old children from Dunedin, New Zealand. Nutrients 9, 483.Google Scholar
Figure 0

Table 1 Characteristics of the study population according to sex in the weight disorders survey of the Childhood and Adolescence Surveillance and Prevention of Adult Non-communicable Disease-IV study (Mean values and standard deviations)

Figure 1

Table 2 Dietary Inflammatory Index (DII) and intake of energy and macronutrients according to sex in the weight disorders survey of the Childhood and Adolescence Surveillance and Prevention of Adult Non-communicable Disease-IV study (Medians and interquartile ranges (IQR))

Figure 2

Table 3 Anthropometric measures according to quartiles (Q) of the Dietary Inflammatory Index (DII) in the weight disorders survey of the Childhood and Adolescence Surveillance and Prevention of Adult Non-communicable Disease-IV study (Mean values and standard deviations)

Figure 3

Table 4 Associations between the Dietary Inflammatory Index (DII) and anthropometric measures in the weight disorders survey of the Childhood and Adolescence Surveillance and Prevention of Adult Non-communicable Disease-IV study (β Estimates and 95 % confidence intervals)