DOI: 10.1542/peds.2013-1665; originally published online March 17, 2014; 2014;133;e884Pediatrics

Moreno, Michael Sjöström and Manuel J. CastilloMarcos, Marcela González-Gross, Anthony Kafatos, Christina Breidenassel, Luis A. Manios, Laurent Beghin, Dénes Molnar, Angela Polito, Kurt Widhalm, Ascensión

Germán Vicente-Rodriguez, Magdalena Cuenca-García, Luis Gracia-Marco, Yannis Francisco B. Ortega, Jonatan R. Ruiz, Idoia Labayen, David Martínez-Gómez,

GeneticsHealth Inequalities in Urban Adolescents: Role of Physical Activity, Diet, and

  

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located on the World Wide Web at: The online version of this article, along with updated information and services, is

 

of Pediatrics. All rights reserved. Print ISSN: 0031-4005. Online ISSN: 1098-4275.Boulevard, Elk Grove Village, Illinois, 60007. Copyright © 2014 by the American Academy published, and trademarked by the American Academy of Pediatrics, 141 Northwest Pointpublication, it has been published continuously since 1948. PEDIATRICS is owned, PEDIATRICS is the official journal of the American Academy of Pediatrics. A monthly

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Health Inequalities in Urban Adolescents: Role ofPhysical Activity, Diet, and Genetics

WHAT’S KNOWN ON THIS SUBJECT: Individuals living inMediterranean countries have historically had a lower risk ofcardiovascular disease. Important changes in diet and lifestyle havetaken place in these countries in recent years, and it is unknownhow these changes might influence current cardiovascular health.

WHAT THIS STUDY ADDS: Fitness and fatness levels indicate thaturban adolescents from southern Europe are less healthy thanthose from central northern Europe. The extent to which thesedifferences might be explained by physical activity, diet, andgenetics is analyzed and discussed in this article.

abstractOBJECTIVE: Coordinated European projects relying on standardizedmethods are needed to identify health inequalities across Europe. Thisstudy aimed to compare fitness, fatness, and cardiometabolic risk be-tween urban adolescents from the south and center-north of Europeand to explore whether physical activity (PA) and other factors mightexplain these differences.

METHODS: The Healthy Lifestyle in Europe by Nutrition in Adolescencecross-sectional project comprised 3528 adolescents from the south (4 cit-ies) and central-north (6 cities) of Europe, 1089 of whom provided bloodsamples for analysis. Fitness (strength, speed-agility, and cardiorespiratoryfitness), total and abdominal fatness (anthropometry and bioelectrical im-pedance), and cardiometabolic risk (z scores including fitness, fatness,blood lipids, insulin resistance, and blood pressure) were assessed. Theanalyses were adjusted for socioeconomic factors, objectively measuredPA (accelerometry), total energy intake and diet quality, and genetic var-iants of the FTO rs9939609 polymorphism.

RESULTS: Adolescents from southern Europe were less fit and fatteraccording to all markers (P , .001). Differences in cardiometabolicrisk scores were not consistent. Adolescents from the south were lessactive and this would largely explain the differences observed in speed-agility and cardiorespiratory fitness. Differences in total and abdominalfatness could not be explained by PA, energy intake, diet quality, or FTOrs9939609 polymorphism.

CONCLUSIONS: Fitness and fatness levels indicate that urban adoles-cents from the south are less healthy than those from central-northern Europe. Our data suggest that differences in PA mightexplain differences in important health-related fitness components,yet factors explaining the differences in fatness encountered remainunknown. Pediatrics 2014;133:e884–e895

AUTHORS: Francisco B. Ortega, PhD,a,b Jonatan R. Ruiz,PhD,a,b Idoia Labayen, PhD,c David Martínez-Gómez, PhD,d

Germán Vicente-Rodriguez, PhD,e,f Magdalena Cuenca-García, PhD,g Luis Gracia-Marco, PhD,e,h Yannis Manios,PhD,i Laurent Beghin, PhD,j Dénes Molnar, MD, PhD,k

Angela Polito, PhD,l Kurt Widhalm, MD, PhD,m AscensiónMarcos, PhD,n Marcela González-Gross, PhD,o AnthonyKafatos, MD, PhD,p Christina Breidenassel, PhD,q,o Luis A.Moreno, MD, PhD,e,r,s Michael Sjöström, MD, PhD,b andManuel J. Castillo, MD, PhD,g on behalf of the HELENAproject groupaPROFITH (PROmoting FITness and Health through physicalactivity) research group, Department of Physical Education andSports, Faculty of Sport Sciences, and gDepartment of MedicalPhysiology, Faculty of Medicine, University of Granada, Granada,Spain; bDepartment of Biosciences and Nutrition, KarolinskaInstitutet, Huddinge, Sweden; cDepartment of Nutrition and FoodScience, University of the Basque Country, Vitoria, Spain;dDepartment of Physical Education, Universidad Autónoma deMadrid, Spain; eGENUD (Growth, Exercise, NUtrition andDevelopment) Research Group, and rFaculty of Health Science,Department of Physiotherapy and Nursing, Universidad deZaragoza, Zaragoza, Spain; fFaculty of Health and Sport Science,Department of Physiotherapy and Nursing, Universidad deZaragoza, Huesca, Spain; hCHERC (Children’s Health and ExerciseResearch Centre), College of Life and Environmental Sciences,Sport & Health Sciences, St. Luke’s Campus, University of Exeter,Exeter, United Kingdom; iDepartment of Nutrition and Dietetics,Harokopio University, Athens, Greece; jUnité U995 Institut Nationalde la Santé et de La Recherche Médicale & Institut de MédecinePrédictive et Thérapeutique, Faculté de Médecine, Université LilleNord de France, France; Centre d’Investigation Clinique CIC-1403Centre Hospitalier & Universitaire de Lille, France; kDepartmentof Paediatrics, Medical Faculty, University of Pécs. József A. u. 7.,Pécs, Hungary; lAgricultural Research Council - Research Centeron Food and Nutrition – (C.R.A. NUT formerly INRAN), Rome, Italy;mDivision of Clinical Nutrition and Prevention, Department ofPediatrics, Medical University of Vienna, Vienna, Austria;nImmunonutrition Research Group, Department of Metabolismand Nutrition, Instituto del Frio, Institute of Food Science,Technology and Nutrition, Spanish National Research Council,Madrid, Spain; oImFINE Research Group. Department of Healthand Human Performance, Facultad de Ciencias de la ActividadFísica y del Deporte, Universidad Politécnica de Madrid, Madrid,Spain; pPreventive Medicine and Nutrition Unit, University ofCrete, School of Medicine, Heraklion, Crete, Greece; qInstitut fürErnährungs- und Lebensmittelwissenschaften—Humanernährung, Rheinische Friedrich-Wilhelms Universität,Bonn, Germany; and sDepartment of Preventive Medicine, Facultyof Medicine, University of Sao Paulo, Brazil

(Continued on last page)

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Mediterranean countries such asSpain, Italy, andGreeceshareeconomic,environmental, cultural, and socialfactors thatmake them likely to differ inhealth status from other countries lo-cated in central and northern Europe.For example, data from the EuropeanAssociation for the Study of Obesity(www.easo.org) indicate that theprevalence of childhood overweight/obesity is higher in southern Euro-pean countries than in the center andthe north. Although this information isvaluable, the data are based on studiesconducted at different times and usingdifferent methods, so accurate com-parisons between geographic areasare difficult.1 In addition, informationconcerning adiposity levels (fat mass)is called for to provide useful comple-mentary information to the BMI datacurrently available.

Another key health marker in childhoodand adolescence is physical fitness.2,3

The main health-related physical fitness(hereafter fitness) components are car-diorespiratoryfitness,muscular strength,and speed-agility. Similarly, some authorshave extensively reviewed the existingdata on this subject collected in dif-ferent areas in the world.4 As notedearlier, however, comparisons betweenstudies conducted at different times byusing different methods should betreated with caution. Consequently,whether there are any real differencesin fitness between youth across differ-ent European geographic areas is cur-rently unknown but may turn out tohave important implications for publichealth.

Likewise, we have not found any studiesexploring differences in traditional car-diometabolic risk factors, such as tri-glycerides, high-density lipoprotein (HDL)cholesterol, glucose, insulin, and bloodpressure, either in children or adoles-cents from different parts of Europe.

To understand health inequalities inEurope more fully, there is a need for

studies collecting data at the same timein different areas of the continent, byusing standardized protocols of as-sessment. Within the context of the EU-funded Healthy Lifestyle in Europeby Nutrition in Adolescence (HELENA)project, our study included adolescentsfrom 10 European cities (9 countries)and provided an excellent opportunityto investigate differences in healthmarkers between south and center-north of Europe. Our main aim in thisstudy was to explore whether fitness,fatness, and cardiometabolic risk fac-tors differ between urban adolescentsfrom southern and central-northernEurope. Because previous findings inother studies conducted under theaegis of the HELENA project showed thatobjectively measured physical activity(PA) levels were lower in urban ado-lescents from the south compared withthose from the center-north of Europe,5

a second aim of the current study wasto investigate the extent to which PAlevels might explain geographic differ-ences in fitness, fatness, and car-diometabolic risk factors in urbanEuropean adolescents. Finally, we alsoexplored the role of other potentialfactors such as energy intake, dietquality, and variants of the FTO gene inthe differences observed.

METHODS

Study Design and Sample

Details of the methods and samplingtechniques used in the HELENA cross-sectional project have been publishedelsewhere.6–8 A total of 3528 adoles-cents aged from 12.5 to 17.5 yearsparticipated in the study,9 one-third ofwhom (1089) were randomly selectedfor blood analysis.9 The sampling sizewas calculated with a confidence levelof 95% and6 0.3 error in the worst ofsituations for the parameter BMI (forcomplete description about samplesize calculation, see the HELENA meth-odologic study7). Data collection took

place between 2006 and 2008. To makemaximum use of the data, all partic-ipants with valid data on the mainstudy outcomes were included. Conse-quently, sample sizes vary amongmodels, as indicated in Tables 1 and 2.

Having received complete informationabout the aims and methods of thestudy, all the parents/guardians signeda consent form, and all the adolescentsgave their assent to participate in thestudy. The study was undertaken fol-lowing the ethical guidelines of theDeclaration of Helsinki 1961 (revisedEdinburgh 2000) and the current leg-islation concerning clinical research inhumans in each of the participatingcountries. The protocol was approvedby human research review committeesat the centers involved.10

Definition of the South andCenter-North of Europe

Four of the 10 cities participating in theHELENA project were part of 3 Mediter-ranean countries: Athens and Heraklion(both island cities) in Greece, Rome inItaly, and Zaragoza in Spain. These 4cities were classified as belonging tothe south of Europe and were com-pared with the other 6 cities, whichbelong to higher latitudes, namely,the center-north of Europe: Dortmundin Germany, Ghent in Belgium, Lille inFrance, Pécs in Hungary, Stockholmin Sweden, and Vienna in Austria (Fig 1).

As reported in the methodologic studyof the HELENA project,7 we sampledEuropean cities of .100 000 inhab-itants. The geographic distribution wasnot random and not represented bystrata but it was decided according tothe following criteria: representationof territorial units (countries) ofEurope according to geographic loca-tion (north, south, east, west), culturalreference and socioeconomic situa-tion, and selection of a territorial unit(city) in the country, which is repre-sentative of the average level of

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demography, cultural, social, and eco-nomic markers. The cities were equiv-alent and comparable between countries.Gender ratio, mean age, and BMI weresimilar between nonparticipating andparticipating adolescents within eachstudy center and in the overall sample.11

This sample should not, however, beconsidered representative of ruralareas, whole countries, or whole Eu-ropean regions.

Physical Fitness

Detailed information onfitness protocols,operational manual, training workshops,and standardized instructions to partic-ipants, together with the fitness charac-teristicsofthesample,havebeenpublishedelsewhere.12,13 In brief, cardiorespiratoryfitness was assessed by the 20-metershuttle-run test,14 and maximum oxy-gen consumption (VO2max, mL/kg/min)was estimated by using the equationreported by Léger et al.14 Muscular fit-ness was assessed by means of the

handgrip strength15–17 and standinglong-jump16,18 tests. Speed-agility wasassessed with the 4310-meter shuttle-run test.19 These physical fitness testshave proved in other studies to be validand reliable in young people.12,20–22

Physical Examination,Anthropometrics, and BodyComposition

The anthropometric methods followedin general in the HELENA project havebeen described elsewhere.23 In brief,we measured height, weight, waistcircumference, and 6 skinfolds (tri-ceps, biceps, subscapular, suprailiac,thigh, and calf) using standard proto-cols. BMI was calculated as bodyweight (kilograms) divided by squaredheight (meters) and classified intoweight status categories.24,25 Body-fatpercentage was calculated by theequation described by Slaughteret al,26 and fat mass expressed inkilograms was calculated. In addition,a tetra-polar bioelectrical impedance

device (BIA 101 Akern, Italy) was used,and fat mass (kilograms) was calcu-lated as described elsewhere.23,27 Pu-bertal status was assessed duringa medical examination by a physician/pediatrician according to the methodsdescribed by Tanner and White-house.28

Other Cardiometabolic Risk Factors

Adetailed description of blood-samplingprocedures has been published else-where.9 In brief, serum concentrationsof cardiovascular risk factors weremeasured from fasting blood samplesin centralized laboratories. Serum totalcholesterol, HDL cholesterol, trigly-cerides, glucose, and insulin weremeasured using standard protocols.The homeostasis model assessmentindex was calculated.29

Systolic and diastolic blood pressurewas measured with an automaticoscillometric device (Omron M6, TheNetherlands). The adolescents sat ona chair quietly for 5 minutes before themeasurements were made on the ex-tended right arm. Two measurementswere taken 5 minutes apart, the lowestvalue being recorded in mm Hg. Meanarterial pressure was calculated.30

We computed 2 well-established car-diometabolic risk scores (gender- andage-specific z scores) to be used in themain analyses, as proposed by Andersenet al31 and Martínez-Vizcaino et al.32,33

See the biomarkers included in eachscore in the legend to Table 2. For sen-sitivity analyses, we selected these 2 riskscores, which largely differ from eachother in the risk factors included (eg,markers of total versus abdominal fat-ness, homeostasis model assessmentindex versus fasting insulin, etc, for theAndersen’s versus Martinez-Vizcainoscores, respectively).

Socioeconomic Status

The Family Affluence Scale (FAS)describes family expenditure and

TABLE 1 Characteristics of the Whole Study Sample and Stratification by Geographic Location inEurope

All South of Europe Center-North of Europe Pa

n Mean SEM n Mean SEM n Mean SEM

Age, y 3528 14.7 0.0 1291 14.5 0.0 2237 14.8 0.0 ,.001Height, cm 3528 165.8 0.2 1291 163.7 0.2 2237 167.0 0.2 ,.001Wt, kg 3528 59.1 0.2 1291 59.2 0.3 2237 59.1 0.3 .743

n Freq. % n Freq. % n Freq. %

Gender:female

3528 1845 52.3 1291 690 53.4 2237 1155 51.6 .31

Tannerstages

3116 978 2138 ,.001

I 12 0.4 5 0.5 7 0.3II 178 5.7 64 6.5 114 5.3II 698 22.4 224 22.9 474 22.2IV 1312 42.1 301 30.8 1011 47.3V 916 29.4 384 39.3 532 24.9

FAS 3475 1257 2218 ,.001Low 461 13.3 239 19.0 222 10.0Medium 1974 56.8 822 65.4 1152 51.9High 1040 29.9 196 15.6 844 38.1

Maternaleducation

3315 1224 2091 .01

Primary 281 8.5 125 10.2 156 7.5Secondary 1917 57.8 708 57.8 1209 57.8University 1117 33.7 391 31.9 726 34.7

a Differences in the characteristics of the study sample between the south and center-north of Europe were analyzed byanalysis of variance for continuous variables and by x2 tests for categorical variables.

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consumption based on 4 items: ownbedroom, number of cars in the family,numberof PCs in thehome, and Internetaccess.34 The FAS is an indicator of af-fluence.35,36 Maternal education wasself-reported by the mothers and usedas an additional indicator of socioeco-nomic status.37–39

Objectively Measured PA

A detailed description of the assess-ment of PA in the HELENA project hasbeen published elsewhere.5,40 Briefly,adolescents were asked to wear an ac-celerometer (GT1M, Actigraph, Pensacola,FL) for 7 consecutive days during wak-ing hours except when engaged inwater-based activities. The time sam-pling interval (epoch) was set at15 seconds. Moderate-to-vigorous PA

(MVPA) and vigorous PA were definedby the cutoff points of $2000 counts/minute and $4000 counts/minute, re-spectively.5,31,40–42 Sedentary time wasdefined using the standard cutoff pointof #100 counts/minute.5,43–45 AveragePA was computed as the total numberof counts divided by total wearing timein minutes and expressed as counts/minutes. At least 3 days of recordingwith a minimum of $8 hours of regis-tration per day were necessary for theadolescent to be included in the study.5

Nutritional Assessment

Every participant was asked to fill ina 24-hour dietary recall by using theHELENA-DIAT software on 2 non-consecutive days within a period of 2weeks.46 In the analyses, we used

a surrogate of the amount (ie, totalenergy intake) and a surrogate ofquality of the diet, the Diet Quality Indexfor Adolescents (DQI-A).47,48 The DQI-Aconsists of 3 principles forming thebasis of a healthy diet: dietary equilib-rium, dietary diversity, and dietaryquality.48,49 A final DQI-A score was cal-culated, with a higher score indicatinga better-quality diet. The validity of theHELENA-DIAT and the DQI-A has beenconfirmed elsewhere.49,50

Genetic Factors

For the purpose of the current study,wechose 1 of the genes most closelyassociated with adiposity levels, theobesity-associated gene (FTO) andparticularly the FTO rs9939609 poly-morphism.51 FTO rs9939609 genotypingwas done centrally (at the Institut Pas-teur de Lille, France) by using an Illuminasystem (Illumina, Inc, San Diego, CA),Golden-Gate technology (GoldenGateSoftware, Inc, San Francisco, CA).52,53

The genotyping success rate was 100%.Overall, 2% of the sample was double-genotyped and the concordance ratewas 99.9%.54 The genotype distributionrespected the Hardy-Weinberg equilib-rium (P = .56).

Statistical Analysis

Binary logistic regression was usedto analyze the likelihood of beingoverweight/obeseversusnormalweight(underweight excluded) according togeographical location (the south com-pared with the center-north of Europe).Differences in fitness, fatness and car-diometabolic risk scores between ado-lescents were analyzed by analysis ofcovariance (ANCOVA). All the modelswere adjusted for a set of potentialconfounders:age, gender, height (exceptwhen BMI was the outcome), and so-cioeconomic factors (FAS and mother’seducational level).

To test whether differences in MVPAbetween adolescents from the south

TABLE 2 Differences in Fitness, Fatness, and Cardiometabolic Risk Scores in Urban AdolescentsFrom the South and Center-North of Europe

South of Europe Center-North ofEurope

P a

n Mean SEM n Mean SEM

FitnessUpper-body muscular strength: hand grip, kg 1144 30.1 0.2 1964 31.0 0.1 ,.001Lower-body muscular strength: Standing

long jump, cm1139 154.4 0.8 1949 169.0 0.6 ,.001

Speed/agility: 4310-m shuttle run, sb 1038 12.4 0.0 1942 12.1 0.0 ,.001Cardiorespiratory fitness: VO2max (mL/kg/min) 948 40.0 0.2 1713 41.3 0.2 ,.001

FatnessTotal adiposity: BMI 1223 22.1 0.1 2090 21.0 0.1 ,.001Total adiposity: at mass, kg: skinfolds 1129 16.4 0.3 2015 13.4 0.2 ,.001Total adiposity: at mass, kg:

bioelectrical impedance1216 14.0 0.2 2031 11.6 0.2 ,.001

Abdominal adiposity: waist circumference, cm 1208 73.8 0.2 2045 71.3 0.2 ,.001Cardiometabolic risk scoresScore 1, Andersen et al31c 280 0.11 0.04 472 20.03 0.03 .007Score 2, Martinez-Vizcaino et al32,33c 379 0.08 0.03 596 20.05 0.03 .003

LifestyleModerate-to-vigorous PA, min/d 792 52.8 0.8 1307 61.5 0.6 ,.001Vigorous PA, min/d 792 16.4 0.5 1307 20.3 0.3 ,.001Average PA, counts/min 792 396.0 5.2 1307 457.4 4.0 ,.001Sedentary time, min/d 792 556.3 3.0 1307 534.2 2.3 ,.001Energy intake, Kcal/d 746 2108.0 27.6 1466 2220.7 19.2 ,.001Diet quality indexd 746 61.1 0.6 1796 49.1 0.4 ,.001

VO2max, estimated from the 20-m shuttle run test by using the Leger’s equation.14a ANCOVA models were adjusted for age, gender, height, and socioeconomic factors (FAS and maternal education). BMI wasnot adjusted for height because it is already part of the index (ie, weight divided by squared height).b In this test the lower the score (in seconds) the higher the performance.c The cardiometabolic risk score 1 is an average value computed from the gender- and age-specific z scores of sum of 4skinfolds, HOMA index, systolic blood pressure, triglyceride, total cholesterol/HDL ratio, and cardiorespiratory fitness(VO2max; this score was inverted multiplying by –1)31; the score 2 is an average value computed from the gender- andage-specific z scores of waist circumference, fasting insulin, triglyceride/HDL cholesterol ratio, and mean arterial pres-sure.32,33d In this variable, the higher the index the higher the quality.

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and center-north of Europe might ex-plain differences in health outcomes,we tested the differences using ANCOVAwith andwithout additional adjustmentfor MVPA. To make different variablesvisually comparable and on the samescale, they were all transformed intogender- and age-specific z scores:(value – mean) / SD. In exploratoryanalyses, we additionally adjusted forother potential confounders as in-dicated in the Results section. Overall,there were no significant interactionsaccording to gender, and thus the datafrom the main analyses were pre-sented for boys and girls together. Allstatistical analyses were made by us-ing the SPSS software (version 20.0.0,IBM SPSS Statistics, IBM Corporation),the a error being set at 5%.

RESULTS

Characteristics of the study sample areshown in Table 1. Adolescents from the

south performed worse than thosefrom the center-north of Europe in allthe fitness tests studied (P, .001), andthey also had higher levels of total andabdominal fatness (Table 2). Both car-diometabolic risk scores were higherin adolescents from the south than inthose from the center-north (P, .001;Table 2), yet mixed findings were ob-served when risk factors were ana-lyzed separately (see SupplementalTable 3). Compared with adolescentsfrom the center-north of Europe, ado-lescents from the south of Europe hadhealthier values in total cholesterol,triglycerides, and triglycerides/HDLratio, yet unhealthier values in HDLcholesterol and diastolic blood pres-sure, with no differences in the rest ofvariables studied. Adolescents fromthe south were less active and moresedentary than those from the center-north of Europe (P , .001; Table 2), aspreviously reported.5 Adolescents fromthe south showed a lower energy in-

take and better-quality diet than theirpeers from the center-north of Europe(P , .001; Table 2), as previouslyreported.55 All the analyses were ad-justed for age, gender, height, and so-cioeconomic factors: FAS and maternaleducational level. Adjustment for pu-bertal status instead of age did notmodify the results (data not shown).Figure 2 shows that the prevalence ofoverweight/obesity was markedlyhigher in adolescents of both gendersfrom southern compared with central-northern Europe (31% vs 21%).

The role of MVPA in the differencesfound in fitness, fatness, and car-diometabolic risk scores is shown inFigs 3, 4, and 5, respectively. Differ-ences in muscular strength could notbe explained by MVPA, although thedifferences observed in speed-agilitywere moderately attenuated after ad-justment for MVPA (P reduced from,.001 to .02), and the differences in

FIGURE 1The southern and central-northern European cities sampled in the HELENA study.

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cardiorespiratory fitness disappearedafter adjustment for MVPA (P reducedfrom ,.001 to .56). The differences in

cardiorespiratory fitness were alsoattenuated after adjustment for sed-entary time, although they remained

significant (P reduced from ,.001 to.01; data not shown). Differences inspeed-agility were moderately attenu-ated after further adjustment for totalfatness (fat mass, P reduced from,.001 to .1), and differences in car-diorespiratory fitness disappeared af-ter additional adjustment for totalfatness (either BMI or fat mass, P re-duced from ,.001 to ..5).

MVPA, in contrast, had little or no in-fluence on the differences in either totalor abdominal fatness (Fig 4). Likewise,adjustment for sedentary time, averagePA, or vigorous PA instead of MVPA didnot alter the results (data not shown).The differences in the cardiometabolicrisk scores (Table 2) were no longersignificant when the analyses were re-stricted to the sample with PA data(Fig 5). Compliance rates for accel-erometers in the whole sample weresimilar between adolescents from thesouth and those from the center-north

FIGURE 2Prevalence of overweight/obesity in urban adolescents from the south and center-north of Europe. *TheOR shown is for the whole sample and the model was adjusted for age, gender, height, and socio-economic factors (FAS and maternal education). The dependent variable was coded as 1 = overweight/obesity, 0 = normal weight, with underweight participants being excluded from the analysis. Note thatthe figure visually shows the lack of interaction, the differences being equal in boys and girls (ie,roughly a 10-point difference). Gender interactions were formally tested, and significant interactionswere not found for any of the main outcomes; consequently, the rest of the results are presented forboys and girls together. CI, confidence interval; OR, odds ratio.

FIGURE 3Differences in health-related fitness components between urban adolescents from the south and the center-north of Europe with and without additionaladjustment for MVPA. VO2max, estimated from the 20-meter shuttle run test using Leger’s equations. All ANCOVA models were adjusted for age, gender, height,and socioeconomic factors (FAS and maternal education).

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of Europe (63.9% and 61.5%, re-spectively; 2.4% difference). However,when the analysis was restricted to thesubsample with valid data in all therisk factors included in the riskscores, compliance rates were lower in

adolescents from the south of Europethan in those from the center-north ofEurope (64.4% and 72.4%, respectively,8% difference). In addition, significantdifferences were observed in both riskscores between adolescents with

accelerometer data (they had healthiervalues) and those without accelerome-ter data in adolescents from the south(P , .01), but not in adolescents fromthe center-north of Europe (P . .2).These associationswere notmodified to

FIGURE 4Differences in total (BMIandbody-fat percentage)andabdominal fatness (waist circumference)betweenurbanadolescents fromthesouthandcenter-northofEuropewith andwithout additional adjustment forMVPA. All ANCOVAmodelswere adjusted for age, gender, height (except for BMI), and socioeconomic factors(FAS and maternal education).

FIGURE 5Differences in cardiometabolic risk scores between urban adolescents from the south and center-north of Europe with and without additional adjustment forMVPA. All ANCOVAmodelswereadjusted forage, gender, height, and socioeconomic factors (FASandmaternal education). Thecardiometabolic risk score1 is anaverage value computed from the gender- and age-specific z scores of the sum of 4 skinfolds, homoeostasis model assessment index, systolic blood pressure,triglyceride, total cholesterol/HDL ratio, and cardiorespiratory fitness (VO2max; this score was inverted by multiplying by –1)31; the score 2 is an average valuecomputed from the gender- and age-specific z scores of waist circumference, fasting insulin, triglyceride/HDL cholesterol ratio, and mean arterial pres-sure.32,33

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any marked extent for any of the con-founders considered.

In exploratory analyses, we examinedwhether other factors (ie, fitness, di-etetic, or genetic factors) might explainthe differences encountered in the adi-posity markers, which remained signif-icant even after further adjustment forall these variables (data not shown). Nosignificant difference (P . .2) was ob-served in the distribution of the FTOrs9939609 polymorphism genotype be-tween adolescents from the south(15.6%, 49.0%, and 35.5%, for AA, TA, andTT genotypes, respectively) and thecenter-north (17.7%, 43.9%, and 38.4%,respectively). Finally, we built a fullyadjusted model including the set of ba-sic confounders, FTO rs9939609 poly-morphism and 2 major lifestyle factors,PA (either variable) and diet (both totalenergy intake and diet quality includedsimultaneously), and the adolescentsfrom the south of Europe still proved tobe more fat according to all the mark-ers studied (data not shown).

DISCUSSION

Thisstudycontributes toourknowledgeof health inequalities across Europe,including 4 particularly important find-ings. First, urban adolescents fromthe south of Europe are less fit in termsofmuscular strength, speed-agility, andcardiorespiratory fitness and aremorefat, both in total and abdominal fatness,than their counterparts from thecenter-north. Second, mixed and in-consistent findings were observed incardiometabolic risk scores and fac-tors. Third, PA largely explained thedifferences observed in speed-agilityand cardiorespiratory fitness, andthese differences could also beexplained by fatness, suggesting thaturban adolescents in the south did notperform so well in cardiorespiratoryfitness and speed-agility tests due inpart to their lower PA and higher adi-posity levels. Fourth, the differences

observed in adiposity between urbanadolescents fromsouthernandcentral-northern Europe persisted even afteraccounting for the set of basic con-founders (age, gender, height, and so-cioeconomic factors) plus PA, energyintake and diet quality, and the FTOpolymorphism. It is important to bearin mind that this study describes thedifferences observed in the HELENAadolescents living in the 10 Europeancities mentioned, and the data are nottherefore representative of ruralareas, whole countries, or whole Eu-ropean regions.

Fitness

The fact that the differences observedin speed-agility and particularly car-diorespiratory fitness, a major healthmarker, could be explained by differ-ences in PA supports the importance ofPA as a stimulus of fitness in thepopulation, yet the cross-sectionalnature of the data does not allowstating cause–effect links. We have notfound other similar multicenter Euro-pean Union projects in adolescentscomparing fitness across regions ofEurope, which rules out comparisonswith previous studies. Nevertheless,some meta-studies have reviewed andcompared published data, and theirconclusions concur with ours. The fit-ness level of Spanish adolescents wascompared with that of adolescentsfrom other countries and revealed theformer were less fit than adolescentsfrom most of the other countriesstudied.56 Also in line with our results,Olds et al reported that children andadolescents from Portugal, Italy, andGreece showed worse fitness levelsthan those from other parts of theworld for which fitness data areavailable.4

Fatness

Currently available information onadiposity in adolescents from different

parts of Europe is limited to BMIand the prevalence of overweight/obesity. Our study contributes tothis knowledge by comparing totalbody fat and waist circumference inaddition to BMI. Our results stronglyand consistently support the con-clusion that adolescents from thesouth have higher levels of total andabdominal fatness compared withadolescents from the center-north ofEurope and that these results couldnot be explained by MVPA, sedentarytime, or fitness. Because it has beenreported elsewhere that vigorousrather than moderate PA is associ-ated with total and abdominal fat-ness,57–59 we also adjusted ourmodels for vigorous PA, but the dif-ferences in fatness between thesouth and center-north persisted, asthey did after additional adjustmentfor dietetic (total energy intake anddiet quality) or/and genetic variantslinked to fatness (FTO rs9939609polymorphism).

Cardiometabolic Risk

The 2 risk scores studied, afteradjusting for relevant confounders,revealed that urban adolescentsfrom southern Europe had a worsecardiometabolic profile than theirpeers from the center-north. Never-theless, once we focused on thesample of participants with accel-erometer data, these differenceswere no longer significant. Thismightbe due to the lower accelerometercompliance observed in urban ado-lescents from the south comparedwith those from the center-north ofEurope. It has been consistentlyshown in epidemiologic studies thatparticipants with higher compliancein accelerometer data have highersocioeconomic status,60–62 which inturn is associated with healthieroutcomes.37–39 In addition, once theanalyses were conducted on singlerisk factors composed in the 2 risk

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scores presented, mixed findingswere found, without a clearly su-perior profile in adolescents fromeither the south or center-north ofEurope. This makes us to concludethat no consistent differences wereobserved in cardiometabolic fac-tors. We have not found otherstudies comparing these risk fac-tors in a standardized fashion be-tween adolescents from differentregions of Europe, so comparisonswith previous data are not possi-ble.

Public Health Implications

It is important to put the currentfindings into a public health per-spective. A vital question for Euro-pean health policies is to what extentthe differences in fitness and fatnessidentified in this study might lead topoorer health in the future andhigher needs for health care, whichwill inevitably impinge on the eco-nomic budget. The World HealthOrganization—Europe statistics (http://data.euro.who.int/hfadb) for thecountries included in our studyprovide figures contrary to ours, forexample, age-standardized cardio-vascular disease mortality (age 0–64) per 100 000 persons in 2009 was32.9 in Spain, Italy, and Greece com-pared with 45.3 in France, Germany,Austria, Hungary, and Sweden, withno data available for Belgium. Simi-larly, age-standardized cancer mor-tality was 62.8 in the southcompared with 78.2 in the center-north of Europe. Finally, 2009 lifeexpectancy in the south was 2 yearslonger than in the center-north ofEurope, including Belgium (81.3compared with 79.3). The worsehealth status in adolescents fromsouthern Europe observed in thisstudy might somehow be compen-sated by other factors later in life,thus inverting their current health

status. Alternatively, the differencesobserved in adolescents today mighthave repercussions later in life,leading to unhealthier adults, a higherrisk of mortality, and shorter life ex-pectancy in southern Europe in thenear future.

Limitations and Strengths of theStudy

In addition to the nonrepresentativenessof rural areas and whole countriesalready mentioned, there are a num-ber of limitations that should beconsidered. We accounted for a com-plete set of potential confounders(age, height, socioeconomic factors,objectively measured PA, dieteticand genetic variants), but we did notstudy other dietetic, genetic, epige-netic, and environmental factors,the possible role of which remain tobe determined. In addition, we ob-served differences between bothgroups of adolescents in some of thefactors studied (eg, socioeconomicvariables, height, PA). Even if thesevariables were entered as covar-iates in the models, it has beensuggested that their potential con-founding effect cannot be fully (math-ematically) controlled for when usingANCOVA.63

The main strength of the study is thethorough standardization of themethods and collection of datathroughout all the cities involved,allowing us to make accurate com-parisons. In addition, the study includesa broad set of health indicators, in-cluding the main health-related fit-ness components assessed usingvalid and reliable tests,3,20–22 mea-surements of total and abdominalfatness using different methods suchas anthropometry (using accurateand valid fat equations in adolescents64),bioelectrical impedance (measuredin .3200 participants), and a signifi-cant number of cardiometabolic risk

factors (centrally analyzed bloodsamples). The inclusion of thesehealth indicators in a single study, thestandardization of the measure-ments, and the time frame imposedfor data collection make it one of themost comprehensive examinations ofhealth inequalities in adolescenceacross Europe. The objective mea-surement of PA by accelerometry, theinclusion of diet quality and geneticvariants, and the specific analysis oftheir role in the health indicator dif-ferences observed are additionalstrengths of this study.

CONCLUSIONS

Our data clearly indicate that urbanadolescents from southern Europeare markedly less physically fit thantheir counterparts from the center-north. The lower level of speed-agilityand cardiorespiratory fitness observedin urban adolescents from the southseems to be largely explained bytheir lower engagement in PA. Thisfinding illustrates indirectly theimportance of PA for the health ofyoung people at a population level,yet because of the cross-sectionalnature of the data, cause–effectrelationships cannot be established.Our results also suggest that urbanadolescents from the south havehigher levels of adiposity than theirpeers from the center-north ofEurope, with these differences per-sisting after accounting for age,gender, socioeconomic factors,lifestyle factors (PA and diet), andobesity-related genetic variants(FTO gene). Further study is neededto identify the reasons urban ado-lescents from the south are morefat than their peers from central-northern Europe. Finally, this studydoes not provide evidence of con-sistent differences in other car-diometabolic risk factors between

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adolescents from the south andtheir peers from the center-north ofEurope, but it is plausible that thedifferences observed in fitness andfatness in adolescence might in-fluence cardiometabolic risk fac-tors in the long-term.

ACKNOWLEDGMENTSWe thank the children and adoles-cents who participated in the studyand their parents and teachersfor their collaboration. We also ac-knowledge the members involved infield work for their efforts, particu-

larly Anke Carstensen and PetraPickert for their work in the bloodsample laboratory. Finally, wethank Dr J. Trout (recently deceased)of the University of Granada ScientificTranslation Service for revising theEnglish text.

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(Continued from first page)

KEY WORDSphysical fitness, fatness, obesity, body-fat distribution, cardiovascular disease risk factors, adolescence

ABBREVIATIONSANCOVA—analysis of covarianceDQI-A—Diet Quality Index for AdolescentsFAS—Family Affluence ScaleHDL—high-density lipoproteinHELENA—Healthy Lifestyle in Europe by Nutrition in AdolescenceMVPA—moderate-to-vigorous physical activityPA—physical activityVO2max—maximum oxygen consumption

Dr Ortega conceptualized and designed the part of the study concerning fitness assessment, coordinated the collection of fitness data, carried out the statisticalanalyses, and drafted the initial manuscript; Dr Ruiz conceptualized and designed the part of the study concerning fitness assessment, substantially contributed tothe analysis and interpretation of the data, and critically reviewed the manuscript; Drs Labayen, Martínez-Gómez, and Cuenca-García substantially contributed tothe analysis and interpretation of the data and critically reviewed the manuscript; Dr Vicente-Rodríguez designed the data collection instruments, coordinated andsupervised data collection, and critically reviewed the manuscript; Dr Gracia-Marco actively participated in the data collection and critically reviewed themanuscript; Drs Manios, Beghin, Polito, Widhalm, Marcos, González-Gross, Kafatos, Breidenassel, Moreno, Sjöström, and Castillo conceptualized and designed thestudy, designed the data collection instruments, and coordinated and supervised data collection; Dr Molnar conceptualized and designed the study, designed thedata collection instruments, and coordinated and supervised data collection; and all authors approved the final manuscript as submitted.

The authors of this paper take sole responsibility for its content. See Supplemental Information for a complete list of the HELENA project members.

www.pediatrics.org/cgi/doi/10.1542/peds.2013-1665

doi:10.1542/peds.2013-1665

Accepted for publication Jan 22, 2014

Address correspondence to Francisco B. Ortega, PhD, Department of Physical Education and Sports, Faculty of Sport Sciences, University of Granada, Carretera deAlfacar s/n, Granada 18071, Spain. E-mail: ortegaf@ugr.es

PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275).

Copyright © 2014 by the American Academy of Pediatrics

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

FUNDING: The HELENA project was supported by the European Community Sixth RTD Framework Programme (contract FOOD-CT-2005-007034). The data for thisstudy were gathered under the aegis of the HELENA project, and further analysis was additionally supported by the Spanish Ministry of Economy andCompetitiveness (grants RYC-2010-05957 and RYC-2011-09011), the Spanish Ministry of Health: Maternal, Child Health and Development Network (grant RD08/0072),and the Fondo Europeo de Desarrollo Regional (MICINN-FEDER). The content of this paper reflects the authors’ views alone, and the European Community is notliable for any use that may be made of the information contained herein.

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

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DOI: 10.1542/peds.2013-1665; originally published online March 17, 2014; 2014;133;e884Pediatrics

Moreno, Michael Sjöström and Manuel J. CastilloMarcos, Marcela González-Gross, Anthony Kafatos, Christina Breidenassel, Luis A. Manios, Laurent Beghin, Dénes Molnar, Angela Polito, Kurt Widhalm, Ascensión

Germán Vicente-Rodriguez, Magdalena Cuenca-García, Luis Gracia-Marco, Yannis Francisco B. Ortega, Jonatan R. Ruiz, Idoia Labayen, David Martínez-Gómez,

GeneticsHealth Inequalities in Urban Adolescents: Role of Physical Activity, Diet, and

  

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