European Journal of Clinical Nutrition (2014), 1–9 © 2014 Macmillan Publishers Limited All rights reserved 0954-3007/14 www.nature.com/ejcn
ORIGINAL ARTICLE
Inflammation profile in overweight/obese adolescents in Europe: an analysis in relation to iron status M Ferrari1, M Cuenca-García2, J Valtueña1,3, LA Moreno4,5, L Censi1, M González-Gross3,6, O Androutsos7, CC Gilbert8, I Huybrechts9, J Dallongeville10, M Sjöström11, D Molnar12, S De Henauw13,14, S Gómez-Martínez15, ACF de Moraes4,16, A Kafatos17, K Widhalm18 and C Leclercq1 on behalf of the HELENA Study Group19 BACKGROUND/OBJECTIVES: The objectives of this study were to investigate the relationship between inflammatory parameters (CRP, c-reactive protein; AGP, α1-acid glycoprotein), iron status indicators (SF, serum ferritin; sTfR, soluble transferrin receptor) and body mass index (BMI) z-score, fat-free mass (FFM) and fat mass (FM) in European adolescents. Differences in intake for some nutrients (total iron, haem and non-haem iron, vitamin C, calcium, proteins) were assessed according to BMI categories, and the association of nutrient intakes with BMI z-score, FM and FFM was evaluated. METHODS: A total of 876 adolescents participating in the Healthy Lifestyle in Europe by Nutrition in Adolescence-Cross Sectional Study were included in the study sample. RESULTS: Mean CRP values (standard error; s.e.) were significantly higher in overweight/obese adolescents (1.7 ± 0.3 and 1.4 ± 0.3 mg/l in boys and girls, respectively) than in thin/normal-weight adolescents (1.1 ± 0.2 and 1.0 ± 0.1 mg/l in boys and girls, respectively) (P o0.05). For boys, mean SF values (s.e.) were significantly higher in overweight/obese adolescents (46.9 ± 2.7 μg/l) than in thin/normal-weight adolescents (35.7 ± 1.7 μg/l) (Po 0.001), whereas median sTfR values did not differ among BMI categories for both boys and girls. Multilevel regression analyses showed that BMI z-score and FM were significantly related to CRP and AGP (P o 0.05). Dietary variables did not differ significantly among BMI categories, except for the intake of vegetable proteins, which, for boys, was higher in thin/normal-weight adolescents than in overweight/obese adolescents (P o 0.05). CONCLUSIONS: The adiposity of the European adolescents was sufficient to cause chronic inflammation but not sufficient to impair iron status and cause iron deficiency. European Journal of Clinical Nutrition advance online publication, 10 September 2014; doi:10.1038/ejcn.2014.154
INTRODUCTION There is growing evidence that obesity is characterised by a chronic, low-grade, systemic inflammation1,2 that may have a causal role in the development of several diseases such as cardiovascular diseases, insulin resistance and type 2 diabetes, as well as metabolic disturbances such as metabolic syndrome,3,4 but the underlying mechanisms are unclear. The concept of obesityrelated inflammation is supported by previous studies that showed a correlation between levels of inflammatory mediators, acute-phase proteins and body weight.5 The relationship between c-reactive protein (CRP), an acute-phase reactant, and obesity has undergone intense investigation because its elevation was shown to be correlated to the increase of body weight and adiposity in both adults and adolescents.6,7 The regulation of cytokines by
adipose tissue for the synthesis of acute-phase proteins could be an explanation for the association between CRP and obesity.8 However, it is not known whether adipocytokines are related specifically to CRP or to other acute-phase proteins produced by the liver in obesity. Other inflammatory markers such as alpha 1-acid glycoprotein (AGP) were found to be increased in obese adult subjects.5,9 In some studies, overweight and obese children appear to be at a higher risk of iron deficiency than those having normal body weight,10–12 and similar findings have been reported in adults as well.11,13,14 A full understanding of the mechanisms that could explain the observed low iron status in obese subjects has not yet been reached. One proposed explanation is that obese children and adolescents are at a higher risk of iron deficiency because
1 CRA-NUT, Agricultural Research Council—Food and Nutrition Research Centre, Rome, Italy; 2Department of Medical Physiology, School of Medicine, Granada University, Granada, Spain; 3ImFine Research Group, Department of Health and Human Performance, Facultad de Ciencias de la Actividad Fisica y del Deporte-INEF, Universidad Politécnica de Madrid, Madrid, Spain; 4GENUD (Growth, Exercise, Nutrition and Development) research group, E.U. Ciencias de la Salud, Universidad de Zaragoza, Zaragoaza, Spain; 5Visiting Professor, School of Medicine of the University of São Paulo – Department of Preventive Medicine, São Paulo, Brazil; 6Department of Nutrition and Food Sciences-Nutritional Physiology, University of Bonn, Bonn, Germany; 7Department of Nutrition and Dietetics, Harakopio University, Athens, Greece; 8Department of Consumer & Sensory Sciences, Campden BRI, Gloucestershire, UK; 9International Agency for research on Cancer (IARC), Dietary Exposure Assessment group, Lyon, France; 10INSERM U744, Institut Pasteur de Lille, Univesité Lille Nord de France, Lille, France; 11Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden; 12Department of Paediatrics, University of Pécs, Pécs-József A.7, Hungary; 13Ghent University, Department of Public Health, University Hospital, Ghent, Belgium; 14University College Ghent, Department of Nutrition and Dietetics, Faculty of Health Care ‘Vesalius’, Ghent, Belgium; 15Immunonutrition Research Group, Department of Metabolism and Nutrition, Spanish Council for Scientific Research (CSIC), Madrid, Spain; 16School of Medicine of the University of São Paulo – Post-Graduate Program in Science, Department of Preventive Medicine, São Paulo/SP, Brazil; 17 Department of Social Medicine, Preventive Medicine and Nutrition Clinic, School of Medicine, University of Crete, Crete, Greece and 18Department of Paediatrics and Adolescents Medicine, Division of Clinical Nutrition, Private Medical University of Salzburg, Salzburg, Austria. Correspondence: Dr M Ferrari, Agricultural Research Council—Food and Nutrition Research Centre, Via Ardeatina, 546, Rome 00178, Italy. E-mail:
[email protected] 19 See Appendix. Received 17 May 2013; revised 27 June 2014; accepted 10 July 2014
Inflammation profile and iron status in overweight adolescents M Ferrari et al
2 their diets tend to be more imbalanced, particularly rich in carbohydrates and fat, which are poor in iron even in the presence of the excessive caloric intake. In an analysis of the US survey NHANES III, low nutrient density foods were found to contribute to more than 30% of the daily energy intake in children and adolescents, and micronutrient intake was inversely related to the intake of low nutrient density foods in this population group.15 Another possible mechanism is that low iron stores could be related to the greater iron requirements of obese subjects owing to their larger blood volume16 and higher basal iron losses estimated from formulae using the assumption that iron requirements are increased in states of weight increase such as obesity.17 However, iron requirements of obese subjects have not been directly measured. A more likely explanation of iron deficiency in obesity is the sequestration of iron through an inflammatorymediated mechanism with hepcidin proposed as a key mediator.18 Moschonis et al.12 observed that the percentages of body fat and visceral fat mass were positively associated with iron deficiency in both genders of schoolchildren aged 9–13 years and suggested that these associations might be because of the chronic inflammation induced by excess adiposity. Ferritin is an acutephase protein and its serum levels are plausibly elevated in states of chronic inflammation such as an excess of visceral fat accumulation.12 Some studies report elevated body mass index (BMI) associated with increased levels of serum ferritin (SF) and low transferrin saturation in both schoolchildren aged 9–13 years19 and adults.20 However, the question of whether these alterations are mostly owing to a metabolic iron deficiency related to an inflammatory state or to a true dietary iron deficiency remains unclear owing to the lack of data for soluble transferrin receptor (sTfR), a biochemical indicator of iron status that is not affected by inflammation.21 The present study reports the associations between obesity, inflammation and iron status in a group of European adolescents by using a battery of biochemical markers including sTfR, SF, CRP and AGP. In addition, the intake of relevant nutrients was assessed according to BMI categories, and the association with BMI z-score, fat mass (FM) and fat-free mass (FFM) was also evaluated. MATERIALS AND METHODS Data were derived from the HELENA–CSS (Healthy Lifestyle in Europe by Nutrition in Adolescence-Cross Sectional Study), a multi-centre study on lifestyle and nutrition among adolescents conducted in 10 European cities (Athens in Greece, Dortmund in Germany, Ghent in Belgium, Heraklion in Greece, Lille in France, Pecs in Hungary, Rome in Italy, Stockholm in Sweden, Vienna in Austria and Zaragoza in Spain) from 2006 to 2007. The main objective of the HELENA-CSS was to obtain reliable and comparable data in a sample of European adolescents (12.5–17.5 years) on a variety of nutrition and health parameters, such as body composition and iron status, via standardised procedures, where details on sampling procedures and the design of the study have been reported elsewhere.22 The study was conducted by following the ethical guidelines of the Declaration of Helsinki 1964 (revision of Edinburg 2000), Convention of Oviedo (1997), the Good Clinical Practice and following legislation about clinical research in humans in each of the participating countries. Informed written consent was obtained from participants and parents or guardians. A complete description of ethical issues and good clinical practice within the HELENACSS has been published elsewhere.23 Adolescents were recruited from selected schools using a multi-stage random cluster sampling procedure, taking into account the geographical distribution in each city, private/ public school ratio and number of classes by school. Participants were included in the HELENA study if they met the following inclusion criteria: subject was aged 12.5–17.5 years, schooling in one of the participating classes, informed consent form signed by the parents and/or legal guardian and not participating simultaneously in another similar research study. A total of 3528 adolescents were considered eligible for the HELENA analyses. Following the HELENA-CSS protocol, it was established that blood samples were randomly obtained in one-third of the population sample. A total of 876 adolescents were included in the current study with complete valid data on BMI, biochemical iron indicators (SF and sTfR), inflammatory European Journal of Clinical Nutrition (2014) 1 – 9
parameters (CRP and AGP) and Tanner stage. Among these, data on dietary intake for animal and vegetable proteins, iron, haem and non-haem iron, calcium and vitamin C were available for a total sample of 499 adolescents. Mean age, gender ratio and reported BMI were similar between nonparticipating and participating adolescents in the overall sample. No selection bias was identified as described by Beghin et al.24 who reported detailed information on participation rate.
Anthropometric measurements Weight, height and skinfold thickness of the adolescents were measured by trained researchers using standardised approaches.25 Weight was recorded in underwear and without shoes to the nearest 0.1 kg, using an electronic scale (Type SECA 861, Seca Hamburg, Germany). Height was measured barefoot in the Frankfort horizontal plane to the nearest 0.1 cm, using a telescopic height-measuring instrument (Type SECA 225, Seca Hamburg, Germany). Skinfold thickness was measured in triplicate with the use of a skinfold caliper (Holtain, Holtain Ltd., Crymych, UK) at the triceps, biceps, subscapular and suprailiac sites on the left side of the body26 and the mean value of the three measurements was calculated. Skinfold thickness measurements were used to estimate FFM and FM using the equations of Slaughter et al.27 The adolescents’ BMI was calculated as body weight in kilograms (kg) divided by the square of height in meters (m). The corresponding BMI z-scores, relative to the British 1990 Growth Chart References, were determined to obtain comparable values across both genders and all ages.28 The BMI z-score is the number of standard deviation units that a person’s BMI deviates from a mean or reference value. Age- and gender-standardised BMI cutoff points developed by the International Obesity Task Force were used to define thinness (o18 kg/m2), normal weight (18–24.9 kg/m2), overweight (25–29.9 kg/m2) and obesity (4 = 30 kg/m2).29 Pubertal stage was judged by a physician during a medical examination, according to the methods described by Tanner and Whitehouse.30 Degree of pubertal development ranged from 1 to 5 (stages), and classification was based on the development of the testes, scrotum and penis size in boys, and breast development and pubic hair in girls.
Iron and inflammation status assessment Blood sampling procedure, laboratory measures for iron status indicators (SF, sTfR), inflammatory parameters (CRP, AGP) and quality control approach have been described elsewhere.21 Briefly, SF, sTfR, CRP and AGP were measured using in-house sandwich enzyme-linked immunosorbent assay)31 in the Human Nutrition Laboratory of the National Research Institute on Food and Nutrition (INRAN, Rome, Italy). A commercially available control sample from Bio-Rad Liquichek Immunology Control Level 3 (Bio-Rad, Milan, Italy) was used to obtain a calibration curve on each plate.
Dietary data Dietary data for the present study were limited to total iron, haem and non-haem iron intake and some selected micronutrients known to affect iron absorption positively (animal protein and vitamin C) and negatively (vegetable proteins and calcium). The dietary intake assessment used in the HELENA-CSS has been described in detail elsewhere.32 Briefly, the HELENA-DIAT (HELENA-Dietary Assessment Tool) 24-h dietary recall software was used and the adolescents were instructed, in a standardised way, on how to fill in this recall as accurately as possible (using household measures for estimating portion sizes). Every adolescent was asked to fill in the HELENA-DIAT twice, on arbitrary days, within a time span of 2 weeks. A validation study by Vereecken et al.33 indicated that HELENA-DIAT showed good agreement with an interviewer-administrated 24-h recall. To calculate energy and nutrient intake, data collected with the HELENA-DIAT were linked to the German Food Code and Nutrient Data Base (BLS (Bundeslebensmittelschlüssel), version II.3.1, 2005). The usual dietary intake of nutrients was estimated by the Multiple Source Method https://nugo.dife.de/msm/.34 Because no differentiation is made between haem and non-haem iron in the German food composition table, the percentages for haem and nonhaem iron reported in the NEVO database (Dutch Food Composition Table) were multiplied by the iron content reported in the German food composition table. Dietary intakes of total iron, haem iron and non-haem iron, animal and vegetable proteins were expressed in grams per day (g/day) and calcium intake was expressed in milligrams per day. Dietary intakes of total iron, haem iron and non-haem iron were also examined © 2014 Macmillan Publishers Limited
Inflammation profile and iron status in overweight adolescents M Ferrari et al
3 after they were adjusted for energy intake, using the nutrient density method (unit per 1000 kcal).35
(StatSoft, Inc., Tulsa, OK, USA) and SPSS Statistics PASW for Windows version 18 (PASW Inc., Chicago, IL, USA) were used for statistical analysis.
Data analysis
RESULTS Descriptive characteristics of the study sample by gender are shown in Table 1. Height, FFM and SF values were significantly higher in boys (P o 0.001) than girls, whereas FM was significantly higher in girls (P o0.001) than boys. All nutrient intakes reported in this study were also significantly higher in boys (except for vitamin C intake). Low SF values (o 15 μg/l) with and without correction were significantly higher in girls (30% and 26%, respectively) compared with boys (20% and 17%, respectively), and 7.5% of participants showed high sTfR levels (48.5 mg/l). Acute (CRP45 μg/l) and chronic (AGP41 g/l) inflammation was present in 5% and 28% of adolescents, respectively. No significant difference in sTfR concentrations was observed between adolescents with and without inflammation. Table 2 shows the values of iron (SF and sTfR) and inflammatory (CRP and AGP) indicators according to the BMI categories (thin/normal-weight and overweight/obese). Overweight/obese boys showed significantly higher mean values for SF and CRP than thin/normal-weight (P o 0.05) boys, whereas mean AGP and sTfR values were not different among the two categories. In girls, overweight/obese
Descriptive statistics were computed for all variables. The normality of the distribution of each variables was determined using the Kolmogorov– Smirnov test (P40.01). Variables with skewed distribution (SF, sTfR, CRP, AGP, FFM, FM and all dietary intake variables) were log-transformed to be used in parametric tests. SF levels and prevalence of low SF (o15 μg/l) were also calculated using correction factors of 0.77, 0.53 and 0.75 for participants with inflammation, respectively, in incubation (CRP45 mg/l), early convalescence (CRP45 mg/l and AGP41 g/l) and late convalescence (AGP41 g/l) phases.36 Gender differences were tested with T-test. The χ2test was used for comparing proportions. To examine the relationship among BMI z-score, FFM, FM, iron and inflammatory indicators and dietary intake parameters, multilevel analyses were performed.37 BMI z-score, FFM and FM were considered as the independent variables and SF, sTfR, CRP, AGP and dietary intakes parameters were considered as dependent variables, first, in the continuous form (BMI z-score, FFM and FM) and, second, as the dichotomous variables (BMI categories: thin/normal-weight and overweight/obese according to Cole et al.29). The study centre was included as a contextual variable in the random intercept multilevel model, and age and Tanner stage were entered as covariates. The level of significance was set as 0.05. Statistica for windows version 8.0
Table 1.
Descriptive characteristics of the study sample stratified by gender All N
Median
Age (years)
876
14.9
Body composition Height (cm) Weight (kg) BMI z-score Fat mass (kg) Fat-free mass (kg)
876 876 876 862 862
Biomarkers SF (μg/l) SF (μg/l)c sTfR (mg/l) CRP (mg/l) AGP (g/l)
Boys
Pa
Girls
N
Median
25–75th percentile
N
Median
25–75th percentile
13.9− 16
407
14.8
13.9 − 15.9
469
15
14 − 16
165.7 57.3 0.3 12.1 43.5
159.2 − 172.5 50.5 − 64.9 0.4 − 1.1 8.3 − 17.3 38.7 − 50.2
407 407 407 397 397
171.2 61 0.4 9.6 49.6
165 − 177.6 52.3 − 70.1 0.3 − 1.3 6.7 − 15.4 43.5 − 55.5
469 469 469 465 465
162.2 55.2 0.2 13.7 41
157.4 − 167 49.3 − 61.6 −0.4 − 1 10.3 − 18.1 37.6 − 44.2
876 876 876 876 876
27.7 25.5 5.6 0.3 0.8
16.1 − 46.4 14.5 − 39.2 4.5 − 6.9 0 − 1.2 0.6 − 1.1
407 407 407 407 407
33.9 30.2 5.7 0.4 0.8
18.9 − 52.3 17.4 − 46.2 4.6 − 6.9 0 − 1.2 0.6 − 1.1
469 469 469 469 469
24.4 21.9 5.6 0.3 0.8
Dietary intakes Total iron (g/day) Haem iron (g/day) Non-haem iron (g/day) Energy-adjusted total iron (mg/1000 kcal) Energy-adjusted haem iron (mg/1000 kcal) Energy-adjusted non-haem iron (mg/1000 kcal) Vitamin C (g/day) Animal proteins (g/day) Vegetable proteins (g/day) Calcium (mg/day)
499 499 499 499 499 499 499 499 499 499
13 1.5 11.3 5.4 0.7 4.8 90.5 53.2 35.5 793
10.8 − 15.2 1.1 − 2.1 9.4 − 13.3 5.0 − 6.0 0.5 − 0.9 4.3 − 5.2 63.2 − 127.8 40.9 − 69.4 30.1 − 45.4 526.3 − 1081
240 240 240 240 240 240 240 240 240 240
14.6 1.9 12.7 5.3 0.7 4.6 90.4 65.3 41.5 938.4
12.5 − 17.6 1.4 − 2.5 10.7 − 15.4 4.9 − 5.8 0.5 − 0.9 4.2 − 5.1 64.3 − 127.8 48.8 − 78.6 33.7 − 50 629.8 − 1268.4
259 259 259 259 259 259 259 259 259 259
11.6 1.3 10.1 5.5 0.6 4.9 91 45.7 32.4 700.2
Abnormal biomarkers valuesd Low SF (%) Low SF(%)c High sTfR (%) High CRP (%) High AGP (%) sTfR (mg/l) with inflammation (CRP45 mg/l and/or AGP41 g/l) sTfR (mg/l) without inflammation (CRP ⩽ 5 mg/l and/or AGP ⩽ 1 g/l)
193 224 66 43 244 630 246
22.0 25.2 7.5 4.9 27.8 5.7 5.5
70 81 28 20 119
17.2 19.9 6.9 4.9 29.3
123 143 38 23 125
26.2 30.5 8.1 4.9 26.6
25–75th percentile
4.6 − 7.1 4.3 − 6.8
0.979 o 0.001 o 0.001b 0.002 o 0.001b o 0.001b
14 − 39 o 0.001b 12.3 − 33.8 o 0.001b 4.4 − 7 0.292b 0 − 1.2 0.595b 0.6 − 1 0.492b 10 − 13.6 0.9 − 1.7 8.9 − 12.1 5.1 − 6.1 0.4 − 0.9 4.4 − 5.4 60.3 − 130 36.8 − 58.1 27.5 − 38 473.5 − 921
o 0.001b o 0.001b o 0.001b 0.007b 0.005b o 0.001b 0.957b o 0.001b o 0.001b o 0.001b 0.001b o 0.001b 0.490b 0.999b 0.458b 0.137e
Abbreviations: AGP, α1-acid glycoprotein; BMI, body mass index; CRP, c-reactive protein; SF, serum ferritin; sTfR, soluble transferrin receptor. Medians and quartiles (25–75th) are presented as untransformed data. aSignificantly different between boys and girls (P o0.05). bP-values correspond to the variables logarithmically transformed. cCorrection of data using correcton factors of 0.77, 0.53 and 0.75 for adolescents, respectively, in incubation (high CRP), early convalescence (high CRP and high AGP) and late convalescence (high AGP) phases.36 dLow SF = SFo 15 μg/l; high sTfR = sTfR48.5 mg/l; high CRP = CRP45 mg/l; high AGP = AGP41 g/l. eSignificantly different between with and without inflammation (P o0.05).
© 2014 Macmillan Publishers Limited
European Journal of Clinical Nutrition (2014) 1 – 9
Inflammation profile and iron status in overweight adolescents M Ferrari et al
4 adolescents presented higher values for both inflammatory indicators CRP and AGP (P o0.05) compared with thin/normalweight adolescents, whereas mean SF and sTfR values did not differ between the two categories. Multilevel regression analysis (Table 3) showed that BMI z-score and FM were positively associated with CRP and AGP (Po 0.001 and P o 0.05, respectively) for both boys and girls, whereas they were positively related to ferritin (P o0.001) only in boys. BMI z-score was positively related to sTfR only for girls (P o 0.05). Among iron-deficient adolescents (sTfR48.5 mg/l), no significant differences were observed between BMI categories of CRP, AGP and SF values. A raised CRP concentrations was observed in 20% and 11% of overweight/obese and thin/normal-weight adolescents, respectively, whereas AGP concentrations were raised in 53.3% of overweight/obese adolescents and 30.8% of thin/normal-weight participants; these differences between BMI categories were not significant (P = 0.397 for CRP and P = 0.108 for AGP) (Table 4). Haem, non-haem and total iron intake, as well as all the other dietary variables, did not differ significantly between BMI categories except for vegetable proteins in boys, where the thin/normal-weight adolescents had a higher intake (43.6 g/day)
than the overweight/obese adolescents (38.9 g/day) (P o0.05) (Table 5). Association between BMI z-score, FFM and FM and nutrient intake are shown in Table 6. For boys, BMI z-score was positively associated with calcium intake (Po 0.05) and FFM was negatively related to total iron intake and positively associated with animal protein (P o 0.05). In addition, FFM was positively related to calcium intake in both boys and girls (P o 0.05). DISCUSSION This study provides results that support the view that obesity in adolescents is an inflammatory state that increases acute-phase inflammatory proteins such as CRP and AGP. The results regarding the relationship between CRP and obesity are in line with several studies in which higher BMI was associated with higher CRP concentrations in adults,6 as well as in children.38 Few studies report an association between AGP and obesity, and these studies included only adults, either healthy39 or with metabolic syndrome.40 In the present study, higher AGP values were observed in overweight/obese adolescent girls than in thin/ normal adolescent girls. Moreover, a positive correlation was
Table 2.
Multilevel analysis examining iron and inflammation indicators (dependent variable) according to the BMI categories defined using ageand gender-specific criteria (Cole et al.)29 BMI categories
Boys (n = 407) Thin/normalweight (n = 312)
SF (μg/l) sTfR (mg/l) CRP (mg/l) AGP (g/l)
Girls (n = 469)
Overweight/obese (n = 95 )
Mean
s.e.
Mean
s.e.
35.7 6.0 1.1 0.8
1.7 0.2 0.2 0.0
46.9 5.7 1.7 0.9
2.7 0.2 0.3 0.1
Pa
Thin/normalweight (n = 383)
Overweight/obese (n = 86)
Mean
s.e.
Mean
s.e.
27.5 5.9 1.0 0.8
1.1 0.2 0.1 0.0
31.1 6.1 1.4 0.9
2.2 0.3 0.3 0.1
o0.001 0.153 0.002 0.097
Pa
0.552 0.301 0.002 0.034
Abbreviations: AGP, α1-acid glycoprotein; BMI, body mass index; CRP, c-reactive protein; s.e., standard error; SF, serum ferritin; sTfR, soluble transferrin receptor. Mean and s.e. are presented as untransformed data. aP-values correspond to the variables logarithmically transformed after adjusting for centre, age and Tanner stages.
Table 3. Multilevel regression analysis examining the association of iron and inflammatory indicators (dependent variables) with BMI z-score, fat-free mass and fat mass Pa
Boys
Pa
Girls
β
s.e.
β
BMI z-score SF (μg/l) sTfR (mg/l) CRP (mg/l) AGP (g/l)
0.055 − 0.002 0.049 0.015
0.014 0.006 0.011 0.005
(n = 407) 0.029 − 0.015 0.027 0.005
0.082 0.010 0.071 0.025
o0.001 0.736 o0.001 0.005
0.016 0.017 0.045 0.015
0.017 0.008 0.011 0.005
(n = 469) − 0.018 0.001 0.023 0.006
0.049 0.033 0.067 0.024
0.351 0.036 o0.001 0.001
Fat-free mass SF (μg/l) sTfR (mg/l) CRP (mg/l) AGP (g/l)
0.107 0.016 − 0.093 − 0.012
0.110 0.050 0.090 0.042
(n = 397) − 0.110 − 0.083 − 0.270 − 0.094
0.323 0.114 0.084 0.071
0.333 0.756 0.302 0.783
− 0.088 0.113 0.204 0.057
0.146 0.070 0.097 0.040
(n = 465) − 0.376 − 0.024 0.014 − 0.022
0.200 0.250 0.395 0.136
0.549 0.107 0.036 0.155
Fat mass SF (μg/l) sTfR (mg/l) CRP (mg/l) AGP (g/l)
0.090 − 0.008 0.085 0.022
0.026 0.012 0.021 0.010
(n = 397) 0.039 − 0.032 0.044 0.002
0.140 0.016 0.127 0.041
0.001 0.495 o0.001 0.030
0.017 0.035 0.108 0.038
0.045 0.022 0.029 0.012
(n = 465) − 0.071 − 0.008 0.050 0.013
0.104 0.077 0.165 0.062
0.712 0.107 o0.001 0.002
95% CI
s.e.
95% CI
Abbreviations: AGP, α1-acid glycoprotein; β, estimated value; BMI, body mass index; CI, confidence interval; CRP, c-reactive protein; s.e., standard error; SF, serum ferritin; sTfR, soluble transferrin receptor. All variables were log-transformed, except BMI z-score. aAfter adjusting for centre, age and Tanner stages.
European Journal of Clinical Nutrition (2014) 1 – 9
© 2014 Macmillan Publishers Limited
Inflammation profile and iron status in overweight adolescents M Ferrari et al
5 observed between AGP and BMI z-score and FM in both boys and girls. AGP is considered a specific marker of chronic inflammation that remains elevated for a longer period of time than CRP after clinical signs of inflammation have disappeared.41 Thurnham et al.36 emphasised the importance of the measurement of the two acute-phase proteins (CRP and AGP) to reveal the inflammation in community-level studies discriminating between incubation (CRP45 mg/l), early convalescence (CRP45 mg/l and AGP41 g/l) and late convalescence (AGP41 g/l) phases to investigate more in Table 4.
Inflammatory profile in iron-deficient adolescentsa (dependent variables) according to the BMI categories defined using the age- and gender-specific criteria (Cole et al.)29 Thin/normal weight (n = 52) Mean Biomarkers SF (μg/l) SF (μg/l)c CRP (mg/l) AGP (g/l) Abnormal biomarkers Low SF (% (n)) Low SFc (% (n)) High CRP (% (n)) High AGP (% (n))
20.1 16.4 2.0 0.9 valuesd 65.4 (34) 65.4 (34) 11.5 (6) 30.8 (16)
s.e. 4.5 2.5 0.6 0.1
Overweight/obese (n = 15) Mean
s.e.
29.4 19.9 3.4 1.1 46.7 53.3 20.0 53.3
Pb
7.6 5.3 1.2 0.2 (7) (8) (3) (8)
0.715 0.521 0.194 0.180 0.190 0.395 0.397 0.108
Abbreviations: AGP, α1-acid glycoprotein; BMI, body mass index; CRP, c-reactive protein; s.e., standard error; SF, serum ferritin. Mean and s.e. are presented as untransformed data. asTfR48.5 mg/l. bP-values correspond to the variables logarithmically transformed after adjusting for centre, age and Tanner stages. ccorrection of data using correcton factors of 0.77, 0.53 and 0.75 for adolescents, respectively, in incubation (high CRP), early convalescence (high CRP and high AGP) and late convalescence (high AGP) d phases.36 low SF = SFo15 μg/l; high CRP = CRP4 5 mg/l; high AGP = AGP4 1 g/l.
depth the influence of inflammation on biochemical markers. In boys, SF values were significantly higher in overweight/obese adolescents. In addition, BMI z-score and FM values were positively related to SF levels in boys. These results confirm that SF, an acutephase protein,42 may be increased by adipose-mediated inflammation and therefore not be a reliable indicator of iron stores in overweight/obese adolescents. In the study, the use of sTfR as an additional parameter to determine iron status was crucial, as it is unaffected by the acute-phase response and provides an appropriate indicator of iron status in obese adolescents.21 The correlation observed between obesity and inflammatory indicators does not imply a causal relationship, but is an important element to consider when interpreting the finding that an excess of adiposity may negatively affect the iron status.12 A possible physiological pathway could be that inflammatory condition associated with obesity may result in the expression of proinflammatory cytokines, such as IL-6 and TNF-α,43 which may result in the expression and release of hepcidin, a known inhibitor of iron absorption.44 Although hepcidin concentration was not measured in the present study, measurements of CRP and AGP clearly indicated that inflammation increased with greater adiposity, but no differences in sTfR concentrations were found between BMI categories for both boys and girls. In addition, the results derived from the multilevel regression analysis did not support the hypothesis that BMI and FM are predictors of iron deficiency as defined by high level of sTfR levels among boys, although a slightly significant association with BMI z-score was found among girls. On the other hand, Zimmermann et al.11 observed an inverse relationship between BMI z-score and body iron. They concluded that BMI was a significant predictor of sTfR and zinc protoporphyrin, indicating that greater BMI was associated with impaired iron status. More recently, Pinhas-Hamiel et al.45 showed that low iron levels were present in 38.8%, 12.1% and 4.4% of obese, overweight and normal-weight children, respectively. The literature on adolescents suggests that total body fat may be associated with a chronic low-grade systemic inflammation in apparently healthy subjects,43 although the mechanism of hepcidin expression related to inflammation is not clear. According to Cepeda-López et al.,46 low iron status in
Table 5.
Multilevel analysis examining nutrient intakes (dependent variables) according to the BMI categories defined using the age- and genderspecific criteria (Cole et al.)29 BMI categories
Boys (n = 240) Thin/normal weight (n = 200) Mean
Total iron (g/day) Haem iron (g/day) Non-haem iron (g/day) Energy-adjusted total iron (mg/1000 kcal) Energy-adjusted haem iron (mg/1000 kcal) Energy-adjusted non-haem iron (mg/1000 kcal)
s.e.
Girls (n = 259)
Overweight/obese (n = 40) Mean
Pa
s.e.
Thin/normal weight (n = 225)
Overweight/obese (n = 34)
Mean
s.e.
Mean
Pa
s.e.
15.6 2.1 13.4 5.6 0.7 4.8
0.4 0.1 0.4 0.1 0.1 0.1
14.0 1.9 12.3 5.4 0.7 4.7
0.8 0.2 0.7 0.2 0.1 0.2
0.057 0.550 0.166 0.598 0.895 0.937
12.0 1.5 10.6 5.7 0.7 5.0
0.2 0.2 0.2 0.1 0.1 0.1
11.7 1.6 10.2 5.6 0.7 4.9
0.5 0.2 0.5 0.2 0.1 0.2
0.404 0.233 0.266 0.557 0.213 0.304
Factors that increase iron absorption Vitamin C (g/day) Animal proteins (g/day)
105.9 68.4
7.7 4.3
100.3 68.5
11.2 5.5
0.789 0.925
105.8 51.1
5.1 3.8
92.2 52.4
11.1 4.7
0.450 0.844
Factors that decrease iron absorption Vegetable proteins (g/day) Calcium (mg/day)
43.6 931.7
1.9 142.5
38.9 1007.6
2.6 158.9
0.027 0.294
33.2 710.9
1.2 93.8
33.4 723.2
1.8 102.1
0.878 0.949
Abbreviations: BMI, body mass index; s.e., standard error. Mean and s.e. are presented as untransformed data. aP-values correspond to the variables logarithmically transformed after adjusting for centre, age and Tanner stages.
© 2014 Macmillan Publishers Limited
European Journal of Clinical Nutrition (2014) 1 – 9
Inflammation profile and iron status in overweight adolescents M Ferrari et al
6 Table 6.
Multilevel regression analysis examining the association of nutrient intakes (dependent variable) with BMI z-score, fat-free mass and fat
mass Boys
Girls Pa
β
s.e.
0.004 0.040 0.011 0.034 0.020 0.005 0.044
0.116 0.738 0.355 0.609 0.803 0.208 0.032
− 0.006 0.040 − 0.012 − 0.002 0.006 − 0.008 0.004
0.0092 0.0278 0.0101 0.014 0.000 0.009 0.087
(n = 259) − 0.024 − 0.015 − 0.032 − 0.029 − 0.011 − 0.021 − 0.013
0.012 0.095 0.007 0.026 0.023 0.005 0.021
0.540 0.151 0.218 0.888 0.479 0.211 0.672
(n = 238) − 0.303 − 0.538 − 0.265 − 0.152 0.010 − 0.025 0.146
− 0.016 0.152 0.037 0.237 0.257 0.178 0.437
0.030 0.272 0.138 0.665 0.034 0.138 0.000
− 0.026 0.017 − 0.028 0.140 0.070 − 0.019 0.152
0.079 0.235 0.086 0.120 0.073 0.054 0.072
(n = 258) − 0.183 − 0.447 − 0.198 − 0.097 − 0.073 − 0.126 0.010
0.130 0.480 0.142 0.376 0.213 0.087 0.294
0.741 0.943 0.747 0.246 0.338 0.720 0.037
(n = 238) − 0.066 − 0.090 − 0.063 − 0.043 − 0.031 − 0.031 − 0.175
0.010 0.098 0.017 0.062 0.037 0.037 0.065
0.154 0.936 0.264 0.720 0.860 0.860 0.259
− 0.011 0.140 − 0.033 − 0.004 0.018 − 0.025 0.004
0.024 0.072 0.026 0.036 0.023 0.017 0.023
(n = 258) − 0.058 − 0.003 − 0.085 − 0.075 − 0.027 − 0.057 −0.041
0.035 0.282 0.018 0.067 0.062 0.008 0.048
0.640 0.054 0.201 0.915 0.435 0.137 0.873
β
s.e.
BMI z-score Total ironb Haem ironb Non-haem ironb Vitamin C Animal proteins Vegetable proteins Calcium
− 0.016 − 0.008 − 0.010 0.007 0.002 − 0.009 0.023
0.0100 0.0247 0.0106 0.014 0.007 0.009 0.011
(n = 240) − 0.036 − 0.057 − 0.031 − 0.020 − 0.015 − 0.023 0.002
Fat-free mass Total ironb Haem ironb Non-haem ironb Vitamin C Animal proteins Vegetable proteins Calcium
− 0.159 − 0.193 − 0.114 0.043 0.134 0.077 0.291
0.073 0.175 0.076 0.099 0.063 0.051 0.074
Fat mass Total ironb Haem ironb Non-haem ironb Vitamin C Animal proteins Vegetable proteins Calcium
− 0.028 0.004 − 0.023 0.010 0.003 − 0.024 0.024
0.019 0.048 0.020 0.027 0.017 0.017 0.021
95% CI
Pa
95% CI
Abbreviations: β, estimated value; BMI, body mass index; s.e., standard error. All variables were log-transformed, except BMI z-score. aAfter adjusting for centre, age and Tanner stages. bAdjusted for energy intake (mg/1000 kcal).
overweight individuals may result from a combination of metabolic (reduced iron absorption or increased iron sequestration) and nutritional (low dietary iron intake) disorders. Aeberli et al.18 suggested that there is reduced iron availability for erythropoiesis in overweight children and that this is unlikely to be due to low dietary iron supply but rather due to hepcidinmediated reduced iron absorption and increased iron sequestration. Recent studies47,48 have shown that, in obese children, BMI reduction is associated with hepcidin reduction, potentially improving iron status and absorption. In the present study, the proportion of overweight/obese adolescents in incubation (high CRP) and convalescence phases (both AGP and CRP elevated) was 7% and 35%, respectively (results not shown in the tables). Therefore, in the convalescent period when iron absorption and mobilisation are less affected by hepcidin,49 the elevated sTfR concentrations may indicate iron deficiency associated with adiposity. Otherwise, Knowles et al.49 suggested higher sTfR values in convalescence group of children as a true indicator of higher erythropoietic requirements when the effect of hepcidin diminishes. In the present study, the prevalence of elevated AGP concentrations between BMI categories of iron-deficient adolescents indicate that more than half of overweight/obese participants were in the convalescence phase. In addition, a greater proportion of obese/overweight adolescents had elevated CRP values than thin/normal-weight participants. The results of this study do not confirm that overweight/obese adolescents have a lower dietary iron intake than thin/normal-weight subjects, in line with the findings of another study where iron intake was comparable between obese and normal-weight children.50 Cepeda-López et al.51 found that the risk of iron deficiency in obese Mexican children was two to four times higher as compared European Journal of Clinical Nutrition (2014) 1 – 9
with normal-weight individuals with the same quantities of iron intake. In boys, the intake of vegetable proteins was significantly higher in thin/normal-weight adolescents (P o0.05) than overweight/obese participants. In addition, vitamin C intake in overweight/obese adolescents was lower than in thin/normalweight adolescents among both boys and girls, but the difference was not statistically significant. On the other hand, Aeberli et al.18 reported no significant associations between intakes of bioavailable iron or factors enhancing iron absorption and overweight in Swiss children and adolescents. The main strength of the present study was that it obtained reliable data on inflammatory profile, body composition and iron status in a large sample of European adolescents under the same conditions and using the same methodologies. To our knowledge, this is the first study reporting, at the European level, data on the association between inflammation, iron status, dietary intake and obesity in adolescents. This study has certain limitations. First, the low percentage of adolescents with high sTfR levels (7,5%) makes it difficult to identify a possible association between iron deficiency and BMI from a statistical point of view. Second, the inability to determine hepcidin levels in the adolescent group was disadvantageous. At the time of data collection, the analysis of hepcidin analytical determination in blood samples had not been considered and it was not included in the protocol of the HELENA Study. Another limitation is the cross-sectional design, which does not allow us to draw conclusions on a possible a cause–effect relationship, and the fact that no data on iron supplements are reported, as this information was not available from the study. However, a minority of adolescents is expected to use ironcontaining supplements on a regular basis.52 © 2014 Macmillan Publishers Limited
Inflammation profile and iron status in overweight adolescents M Ferrari et al
7 In conclusion, in a multi-centre sample of European adolescents, BMI z-score and FM values were found to be positively associated with both acute-phase inflammatory levels CRP and AGP after controlling for confounders (centre, age and Tanner stage). In addition, overweight/obese adolescents showed elevated levels of CRP in both boys and girls and of AGP for girls. Such increased inflammatory status could increase their risk of iron deficiency. The observed data on iron status do not confirm such risk, as sTfR values were not increased in overweight/obese adolescents as compared with thin/normalweight participants, although a slightly significant association was found between sTfR and BMI z-score only for girls. Otherwise, among iron-deficient adolescents, the higher prevalence of chronic inflammation observed in obese/overweight participants may indicate that elevation of sTfR values may be an inflammation response. Overweight/obese adolescents do not differ in estimated intake of total, haem and non-haem iron intake and in intake of dietary factors that can affect iron absorption. Taken together, these findings support the hypothesis that a critical level of FM may be required to impair the iron status of adolescents. Additional studies are needed in adolescents to investigate the relationship between obesity-related inflammation and iron absorption mediated by hepcidin. CONFLICT OF INTEREST
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The authors declare no conflict of interest.
ACKNOWLEDGEMENTS The HELENA Study has taken place with the financial support of the European Community Sixth RTD Framework Programme (Contract FOOD-CT-2005–007034). Additional support was obtained from the Spanish Ministry of Education (AGL200729784-E/ALI; AP-2005-3827; AP-2008-03806). Jara Valtueña was financially supported by the Universidad Politécnica de Madrid. Augusto César F. de Moraes was given scholarship from São Paulo Research Foundation – FAPESP (proc. 2011/11137-1 and 2011/20662-2). Luis A. Moreno was given scholarship of visiting professor from Brazilian government by Science without Borders Program by CNPq (National Counsel of Technological and Scientific Development) and CAPES (Coordination of Improvement of Higher Education Personnel) (proc. 007/2012). The content of this article reflects only the authors’ views, and the European Community is not liable for any use that may be made of the information contained therein. We thank all adolescents who participated in the study. We also thank Lorenza Mistura for her assistance in the statistical analysis.
DISCLOSURE
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The content of this paper reflects only the authors’ view and the rest of HELENA Study members are not responsible for it. The writing group takes sole responsibility for the content of this article.
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REFERENCES
27
1 Day CP. From fat to inflammation. Gastroeneterology 2006; 130: 207–210. 2 Trayhurn P, Wood IS. Signalling role of adipose tissue: adipokines and inflammation in obesity. Biochem Soc Trans 2005; 33: 1078–1808. 3 Weisberg SP, McCann D, Desai M, Rosenbaum M, Leibel RL, Ferrante AW Jr. Obesity is associated with macrophage accumulation in adipose tissue. J Clin Invest 2003; 112: 1796–1808. 4 Xu H, Barnes GT, Yang Q, Tan G, Yang D, Chou CJ et al. Chronic inflammation in fat plays a crucial role in the development of obesity-related insulin resistance. J Clin Invest 2003; 112: 1821–1830. 5 van Dielen FMH, van’t Veer C, Schols AM, Soeters PB, Buurman WA, Greve JWM. Increased leptin concentrations correlate with increased concentrations of inflammatory markers in morbidly obese individuals. In J Obes 2001; 25: 1759–1766. 6 Visser M, Bouter LM, McQuillan GM, Wener MH, Harris TB. Elevated C-reactive protein levels in overweight and obese adults. JAMA 1999; 282: 2131–2135. 7 Visser M, Bouter LM, McQuillan GM, Wener MH, Harris TB. Low grade systemic inflammation in overweight children. Pediatrics 2001; 107: 1–6. 8 Maachi M, Piéroni L, Bruckert E, Jardel C, Fellahi S, Hainque B, Capeau J, Bastard J-P. Systemic low-grade inflammation is related to both circulating and
© 2014 Macmillan Publishers Limited
28 29
30
31
32 33
adipose tissue TNFa, leptin and IL-6 levels in obese women. In J Obes 2004; 28: 993–997. Benedek IH, Blouin RA, McNamara PJ. Serum protein binding and the role of increased alpha 1-acid glycoprotein in moderately obese male subjects. Br J Clin Pharmacol 1984; 18: 941–946. Nead KG, Halterman JS, Kaczorowski JM, Auinger P, Weitzman M. Overweight children and adolescents: A risk group for iron deficiency. Pediatrics 2004; 114: 104. Zimmermann MB, Zeder C, Muthayya S, Winichagoon P, Chaouki N, Aeberli I et al. Adiposity in women and children from transition countries predicts decreased iron absorption, iron deficiency and a reduced response to iron fortification. Int J Obes 2008; 32: 1098–1104. Moschonis G, Chrousos GP, Lionis C, Mougios V, Manios Y. Healthy Growth Study group. Association of total body and visceral fat mass with iron deficiency in preadolescents: the Healthy Growth Study. Br J Nutr 2012; 108: 710–719. Lecube A, Carrera A, Losada E, Hernández C, Simó R, Mesa J. Iron deficiency in obese postmenopausal women. Obesity 2006; 14: 1724–1730. Gillum RF. Association of serum ferritin and indices of body fat distribution and obesity in Mexican American men: the Third National Health and Nutrition Examination Survey. Int J Obes 2001; 25: 639–645. Kant AK. Reported consumption of low nutrient density foods by American children and adolescents, nutritional health correlates, NHANES III, 1988 to 1994. Arch Pediatr Adolesc Med 2003; 157: 789–796. Failla ML, Kennedy ML, Chen ML. Iron metabolism in genetically obese (ob/ob) mice. J Nutr 1988; 118: 46–51. Anonymous. Dietary reference intake for vitamin A, vitamin K, arsenic, borium, chromium, copper, iodine, iron, manganese, molybdenum, nickel, silicon, vanadium and zinc. National Academy Press: Washington, D.C., 2001, pp 290–393. Aeberli I, Hurrel RF, Zimmermann MB. Overweight children have higher circulating hepcidin concentrations and lower iron status but have dietary iron intakes and bioavailability comparable with normal weight children. In t J Obes 2009; 33: 1111–1117. Manios Y, Moschonis G, Chrousos GP, Lionis C, Mougios V, Kantilafti M et al. The double burden of obesity and iron deficiency on children and adolescents in Greece: the Healthy Growth Study. J Hum Nutr Diet 2013; 26: 470–478. Cheng HL, Bryant C, Cook R, O’Connor H, Rooney K et al. The relationship between obesity and hypoferraemia in adults: a systematic review. Obes Rev 2012; 13: 150–161. Beguin Y. Soluble transferring receptor for the evaluation of erythropoiesis and iron status. Clin Chim Acta 2004; 329: 9–22. Moreno LA, De Henauw S, González-Gross M, Kersting M, Molnár D, Gottrand F et al. Design and implementation of the healthy lifestyle in Europe by nutrition in adolescence Cross-Sectional Study. Int J Obes 2008; 32: S4–S11. Béghin L, Castera M, Manios Y, Gilbert CC, Kersting M, De Henauw S et al. Quality assurance of ethical issues and regulatory aspects relating to good practices in the HELENA Cross-Sectional Study. Int J Obes 2008; 32S: S12–S18. Béghin L, Huybrechts I, Vicente-Rodríguez G, De Henauw S, Gottrand F, Gonzales-Gross M et al. Main characteristics and participation rate of European adolescents included in the HELENA study. Arch Public Health 2012; 70: 14. Nagy E, Vicente-Rodriguez G, Manios Y, Béghin L, Iliescu C, Censi L, Dietrich S, Ortega FB et al. Harmonization process and reliability assessment of anthropometric measurements in a multicenter study in adolescents. Int J Obes 2008; 32S: S58–S65. Moreno LA, Rodríguez G, Guillén J, Rabanaque MJ, León JF, Ariño A. Anthropometric measurements in both sides of the body in the assessment of nutritional status in prepubertal children. Eur J Clin Nutr 2002; 56: 1208–1215. Slaughter MH, Lohman TG, Boileau RA, Horswill CA, Stillman RJ, van Loan MD et al. Skinfold equation for estimation of body fatness in children and youths. Hum Biol 1988; 60: 709–723. Cole TJ, Freeman JV, Preece MA. Body Mass Index References curves for the UK, 1990. Arch Dis Child 1995; 73: 25–29. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: International survey. BMJ 2000; 320: 1240–1243. Tanner JM, Whitehouse RH. Clinical longitudinal standards for height, weight, height velocity, weight velocity, and stages of puberty. Arch Dis Child 1976; 51: 170–179. Erhardt JG, Estes JE, Pfeiffer CM, Biesalski HK, Craft NE. Combined measurement of ferritin, soluble transferrin receptor, retinol binding protein, and C-reactive protein by an inexpensive, sensitive, and simple sandwich enzyme-linked immunosorbent assay technique. J Nutr 2004; 134: 3127–3132. Vereecken CA, Covents M, Matthys C et al. Young adolescent’s nutrition assessment on computer (YANA-C). Eur J Clin Nutr 2005; 59: 658–667. Vereecken CA, Covents M, Sichert-Hellert W, Alvira JM, Le Donne C, De Henauw S et al. Development and evaluation of a self-administered
European Journal of Clinical Nutrition (2014) 1 – 9
Inflammation profile and iron status in overweight adolescents M Ferrari et al
8 computerized 24-h dietary recall method for adolescents in Europe. Int J Obes 2008; 32S: S26–S34. Haubrock J, Hartting U, Souverein O, Boeing H. An improved statistical tool for estimating usual intake distributions: the Multiple Source Method (MSM). Arch Public Health 2010; 68: 14–15. Hallberg L. Bioavailable nutrient density: a new concept applied in the interpretation of food iron absorption data. Am J Clin Nutr 1981; 34: 2242–2247. Thurnham D, McCabe LD, Haldar S, Wieringa FT, Northrop-Clewes CA, McCabe GP. Adjusting plasma ferritin concentrations to remove the effects of subclinical inflammation in the assessment of iron deficiency: a meta analysis. Am J Clin Nutr 2010; 92: 546–555. Pardo A, Ruiz MA, San Martin R. How to fit and interpret multilevel models using SPSS. Psicothema 2007; 19: 308–321. Ford ES, Galuska DA, Gillespie C, Will JC, Giles WH, Dietz WH. C-reactive protein and body mass index in children: findings from the third National Health and Nutrition Examination Survey, 1988–1994. J Pediatr 2001; 138: 486–492. Benedek IH, Blovin RA, Mc Namara PJ. Serum protein binding and the role of increate alpha 1-acid glycoprotein in moderately obese male subjects. Br J Clin Pharmacol 1984; 18: 941–946. Bogdański P, Chyrek R, Pupek-Musialik D, Jabłecka A. Evaluation of selected acute phase proteins in patients with metabolic syndrome. Pol Merkur Lekarski 2006; 21: 12–14. Northrop-Clewes CA. Interpreting indicators of iron status during an acute phase response – lessons from malaria and HIV. Ann Clin Biochem 2008; 45: 18–32. Zimmermann MB, Hurrell RF. Nutritional iron deficiency. Lancet 2007; 370: 511–520. Wärnberg J, Nova E, Moreno LA, Romeo J, Mesana MI, Ruiz JR et al. Inflammatory proteins are related to total and abdominal adiposity in a healthy adolescent population: the AVENA Study. Am J Clin Nutr 2006; 84: 505–512.
44 Mena NP, Esparza A, Tapia V, Valdés P, Núñez MT. Hepcidin inhibits apical iron uptake in intestinal cells. Am J Physiol Gastrointest Liver Physiol 2008; 294: 192–198. 45 Pinhas-Hamiel O, Newfield RS, Koren I, Agmon A, Lilos P, Philip M. Greater prevalence of iron deficiency in overweight and obese children and adolescents. Int J Obes 2003; 27: 416–418. 46 Cepeda-López AC, Aeberli I, Zimmermann MB. Does obesity increase risk for iron deficiency? A review of the literature and the potential mechanism. Int J Vitam Nutr Res 2010; 80: 263–270. 47 Amato A, Santoro N, Calabrò P, Grandone A, Swinkels DW, Perrone L, del Giudice EM. Effect of body mass index reduction on serum hepcidin levels and iron status in obese children. Int J Obes 2010; 34: 1772–1774. 48 del Giudice EM, Santoro N, Amato A, Brienza C, Calabrò P, Wiegerinck ET et al. Hepcidin in obese children as a potential mediator of the association between obesity and iron deficiency. J Clin Endocrinol Metab 2009; 94: 5102–5107. 49 Knowles JM, Thurnham DI, Phengdy B et al. Impact of inflammation on biomarkers of iron status in a cross-sectional survey of Lao women and children. Brit J Nutr 2013; 110: 2285–2297. 50 Aeberli I, Kaspar M, Zimmermann MB. Dietary intake and physical activity of normal weight and overweight 6- to 14-year-old Swiss Children. Swiss Med Wkly 2007; 137: 424–430. 51 Cepeda-López AC, Osendarp SJ, Melse-Boonstra a, Aeberli I, Gonzalez-Salazar F, Feskens E et al. Sharply higher rates of iron deficiency in obese Mexican women and children are predicted by obesity-related inflammation rather than by differences in dietary iron intake. Am J Clin Nutr 2011; 93: 975–983. 52 Stang J, Story MT, Harnack L, Neumark-Sztainer D. Relationships between vitamin and mineral supplement use, dietary intake, and dietary adequacy among adolescents. J Am Diet Assoc 2000; 100: 905–910.
APPENDIX **Helena Study Group Co-ordinator: Luis A. Moreno Core Group members: Luis A. Moreno, Fréderic Gottrand, Stefaan De Henauw, Marcela González-Gross, Chantal Gilbert. Steering Committee: Anthony Kafatos (President), Luis A. Moreno, Christian Libersa, Stefaan De Henauw, Jackie Sáchez, Fréderic Gottrand, Mathilde Kesting, Michael Sjostrom, Dénes Molnár, Marcela González-Gross, Jean Dallongeville, Chantal Gilbert, Gunnar Hall, Lea Maes, Luca Scalfi. Project Manager: Pilar Meléndez 1. Universidad de Zaragoza (Spain): Luis A. Moreno, Jesús Fleta, José A. Casajús, Gerardo Rodríguez, Concepción Tomás, María I. Mesana, Germán Vicente-Rodríguez, Adoración Villarroya, Carlos M. Gil, Ignacio Ara, Juan Revenga, Carmen Lachen, Juan Fernández Alvira, Gloria Bueno, Aurora Lázaro, Olga Bueno, Juan F. León, Jesús Mª Garagorri, Manuel Bueno, Juan Pablo Rey López, Iris Iglesia, Paula Velasco, Silvia Bel. 2. Consejo Superior de Investigaciones Científicas (Spain): Ascensión Marcos, Julia Wärnberg, Esther Nova, Sonia Gómez, Esperanza Ligia Díaz, Javier Romeo, Ana Veses, Mari Angeles Puertollano, Belén Zapatera, Tamara Pozo. 3. Université de Lille 2 (France): Laurent Beghin, Christian Libersa, Frédéric Gottrand, Catalina Iliescu, Juliana Von Berlepsch. 4. Research Institute of Child Nutrition Dortmund, Rheinische Friedrich-Wilhelms-Universität Bonn (Germany): Mathilde Kersting, Wolfgang Sichert-Hellert, Ellen Koeppen. 5. Pécsi Tudományegyetem (University of Pécs) (Hungary): Dénes Molnar, Eva Erhardt, Katalin Csernus, Katalin Török, Szilvia Bokor, Mrs. Angster, Enikö Nagy, Orsolya Kovács, Judit Repásy. 6. University of Crete School of Medicine (Greece): Anthony Kafatos, Caroline Codrington, María Plada, Angeliki Papadaki, Katerina Sarri, Anna Viskadourou, Christos Hatzis, Michael Kiriakakis, George Tsibinos, Constantine Vardavas Manolis Sbokos, Eva Protoyeraki, Maria Fasoulaki. 7. Institut für Ernährungs- und Lebensmittelwissenschaften –Ernährungphysiologie. Rheinische Friedrich Wilhelms Universität (Germany): Peter Stehle, Klaus Pietrzik, Marcela González-Gross, Christina Breidenassel, Andre Spinneker, Jasmin
Al-Tahan, Miriam Segoviano, Anke Berchtold, Christine Bierschbach, Erika Blatzheim, Adelheid Schuch, Petra Pickert. 8. University of Granada (Spain): Manuel J. Castillo, Ángel Gutiérrez, Francisco B. Ortega, Jonatan R Ruiz, Enrique G. Artero, Vanesa España-Romero, David Jiménez-Pavón, Palma Chillón, Magdalena Cuenca-Garcia. 9. Istituto Nazionale di Ricerca per gli Alimenti e la Nutrizione (Italy): Davide Arcella, Elena Azzini, Emma Barrison, Noemi Bevilacqua, Pasquale Buonocore, Giovina Catasta, Laura Censi, Donatella Ciarapica, Paola D’Acapito, Marika Ferrari, Myriam Galfo, Cinzia Le Donne, Catherine Leclercq, Giuseppe Maiani, Beatrice Mauro, Lorenza Mistura, Antonella Pasquali, Raffaela Piccinelli, Angela Polito, Raffaella Spada, Stefania Sette, Maria Zaccaria. 10. University of Napoli ‘Federico II’ Dept of Food Science (Italy): Luca Scalfi, Paola Vitaglione, Concetta Montagnese. 11. Ghent University (Belgium): Ilse De Bourdeaudhuij, Stefaan De Henauw, Tineke De Vriendt, Lea Maes, Christophe Matthys, Carine Vereecken, Mieke de Maeyer, Charlene Ottevaere, Inge Huybrechts. 12. Medical University of Vienna (Austria): Kurt Widhalm, Katharina Phillipp, Sabine Dietrich. 13. Harokopio University (Greece): Yannis Manios, Eva Grammatikaki, Zoi Bouloubasi, Tina Louisa Cook, Sofia Eleutheriou, Orsalia Consta, George Moschonis, Ioanna Katsaroli, George Kraniou, Stalo Papoutsou, Despoina Keke, Ioanna Petraki, Elena Bellou, Sofia Tanagra, Kostalenia Kallianoti, Dionysia Argyropoulou, Katerina Kondaki, Stamatoula Tsikrika, Christos Karaiskos. 14. Institut Pasteur de Lille (France): Jean Dallongeville, Aline Meirhaeghe. 15. Karolinska Institutet (Sweden): Michael Sjöstrom, Patrick Bergman, María Hagströmer, Lena Hallström, Mårten Hallberg, Eric Poortvliet, Julia Wärnberg, Nico Rizzo, Linda Beckman, Anita Hurtig Wennlöf, Emma Patterson, Lydia Kwak, Lars Cernerud, Per Tillgren, Stefaan Sörensen. 16. Asociación de Investigación de la Industria Agroalimentaria (Spain): Jackie Sánchez-Molero, Elena Picó, Maite Navarro, Blanca Viadel, José Enrique Carreres, Gema Merino, Rosa Sanjuán, María Lorente, María José Sánchez, Sara Castelló.
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European Journal of Clinical Nutrition (2014) 1 – 9
© 2014 Macmillan Publishers Limited
Inflammation profile and iron status in overweight adolescents M Ferrari et al
9 17. Campden & Chorleywood Food Research Association (United Kingdom): Chantal Gilbert, Sarah Thomas, Elaine Allchurch, Peter Burgess. 18. SIK—Institutet foer Livsmedel och Bioteknik (Sweden): Gunnar Hall, Annika Astrom, Anna Sverkén, Agneta Broberg. 19. Meurice Recherche & Development asbl (Belgium): Annick Masson, Claire Lehoux, Pascal Brabant, Philippe Pate, Laurence Fontaine. 20. Campden & Chorleywood Food Development Institute (Hungary): Andras Sebok, Tunde Kuti, Adrienn Hegyi. 21. Productos Aditivos SA (Spain): Cristina Maldonado, Ana Llorente.
© 2014 Macmillan Publishers Limited
22. Cárnicas Serrano SL (Spain): Emilio García. 23. Cederroth International AB (Sweden): Holger von Fircks, Marianne Lilja Hallberg, Maria Messerer. 24. Lantmännen Food R&D (Sweden): Mats Larsson, Helena Fredriksson, Viola Adamsson, Ingmar Börjesson. 25. European Food Information Council (Belgium): Laura Fernández, Laura Smillie, Josephine Wills. 26. Universidad Politécnica de Madrid (Spain): Marcela González-Gross, Jara Valtueña, David Jiménez-Pavón, Ulrike Albers, Raquel Pedrero, Agustín Meléndez, Pedro J. Benito, Juan José Gómez Lorente, David Cañada, Alejandro Urzanqui, Juan Carlos Ortiz, Francisco Fuentes, Rosa María Torres, Paloma Navarro.
European Journal of Clinical Nutrition (2014) 1 – 9
