Epidemiological studies are applicable to communicable and non-com.docx

Epidemiological studies are applicable to communicable and
non-communicable diseases. Childhood obesity is an area that is
receiving more attention in public health due to the multiple
morbidities that emerge as a result of this condition. Below are
links to a cross-sectional study and a case-control study.
Imagine that you are interested in conducting a case-control or
cross-sectional study proposal of childhood obesity vs. birth
weight (prenatal and early life influences). Both articles below
address prenatal influences on childhood obesity and birth
weight using different approaches.
Article 1 -attached
Article 2-attached
Using the information in the articles, answer the following
questions using AMA format.
1. How would you select cases and controls for this study and
how would you define exposure and outcome variables for a
case-control study design? What other factors would you control
for?
2. How would you design a proposal measuring the effect of
birthweight on childhood obesity for a cross-sectional study
design? What other factors would you control for?
BioMed CentralBMC Public Health
ss
Open AcceStudy protocol
Cross sectional study of childhood obesity and prevalence of
risk

factors for cardiovascular disease and diabetes in children aged
11–
13
Anwen Rees*1, Non Thomas1, Sinead Brophy2, Gareth Knox1
and
Rhys Williams2
Address: 1Cardiff School of Sport, University of Wales Institute
Cardiff, Wales, UK and 2School of Medicine, Swansea
University, Wales, UK
Email: Anwen Rees* - [email protected]; Non Thomas -
[email protected]; Sinead Brophy - [email protected];
Gareth Knox - [email protected]; Rhys Williams -
[email protected]
* Corresponding author
Abstract
Background: Childhood obesity levels are rising with estimates
suggesting that around one in
three children in Western countries are overweight. People from
lower socioeconomic status and
ethnic minority backgrounds are at higher risk of obesity and
subsequent CVD and diabetes.
Within this study we examine the prevalence of risk factors for
CVD and diabetes (obesity,
hypercholesterolemia, hypertension) and examine factors
associated with the presence of these
risk factors in school children aged 11–13.
Methods and design: Participants will be recruited from schools
across South Wales. Schools
will be selected based on catchment area, recruiting those with
high ethnic minority or deprived

catchment areas. Data collection will take place during the PE
lessons and on school premises. Data
will include: anthropometrical variables (height, weight, waist,
hip and neck circumferences, skinfold
thickness at 4 sites), physiological variables (blood pressure
and aerobic fitness (20 metre multi
stage fitness test (20 MSFT)), diet (self-reported seven-day food
diary), physical activity (Physical
Activity Questionnire for Adolescents (PAQ-A),
accelerometery) and blood tests (fasting glucose,
insulin, lipids, fibrinogen (Fg), adiponectin (high molecular
weight), C-reactive protein (CRP) and
interleukin-6 (IL-6)). Deprivation at the school level will be
measured via information on the
number of children receiving free school meals. Townsend
deprivation scores will be calculated
based on the individual childs postcode and self assigned
ethnicity for each participating child will
be collected. It is anticipated 800 children will be recruited.
Multilevel modeling will be used to
examine shared and individual factors associated with obesity,
stratified by ethnic background,
deprivation level and school.
Discussion: This study is part of a larger project which includes
interviews with older children
regarding health behaviours and analysis of existing cohort
studies (Millennium cohort study) for
factors associated with childhood obesity.
The project will contribute to the evidence base needed to
develop multi-dimensional
interventions for addressing childhood obesity.
Published: 24 March 2009

BMC Public Health 2009, 9:86 doi:10.1186/1471-2458-9-86
Received: 9 February 2009
Accepted: 24 March 2009
This article is available from:
http://www.biomedcentral.com/1471-2458/9/86
© 2009 Rees et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the
Creative Commons Attribution License
(http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
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BMC Public Health 2009, 9:86
Background
The World Health organisation (WHO) predicts that by
2015 approximately 2.3 billion adults will be overweight
and more than 700 million will be obese. In 2005, at least
20 million children under the age of 5 were overweight
[1]. The UK, like many other countries is experiencing a
massive growth in the collective weight gain of its popula-
tion. Obesity is a global problem that is rising at an
uncontrollable rate. Obesity can be caused by many fac-

tors, including genetics. However, the underlying cause
has been attributed to two modifiable behavioural factors:
food consumption and physical activity [2].
Obesity can lead to many other health complications,
including hypercholesterolemia and hypertension, and
this can lead to serious health consequences. CVD and
diabetes are two chronic diseases which are rapidly
increasing globally.
Even though the health consequences of obesity are most
commonly seen during adulthood, the underlying factors
of these diseases could originate during childhood. Evi-
dence is now emerging that obesity-driven type 2 diabetes
might become the most common form of diabetes in ado-
lescents within the next ten years [3]. It is therefore vital to
know exactly how early the health consequences and risk
factors for these serious diseases occur, and how early the
can be detected if they are to be addressed successfully.
In recent years, childhood obesity has become a world
wide issue with increasing poor dietary habits together
with inactivity being associated with rising levels of obes-
ity in children. Many studies including the Muscatine
Study [4,5] and the Bogalusa Heart Study [6] have con-
vincingly shown that overweight and obesity during ado-
lescence is a determinant of a number of CVD risk factors
in adulthood. Results from longitudinal studies such as
the Bogalusa Heart Study have shown that adolescent adi-
posity tracks moderately into adulthood, implying that
preventing obesity during childhood may be advanta-
geous to health in later life [7]. Patterns of fat distribution
have also been shown to influence CVD risk. It has been
discovered by many studies that abdominal obesity better
predicts CVD risk compared to overall obesity [8,9]. It
seems that the elevated risk of abdominal obesity is

related to the visceral fat that is stored around the internal
organs [10].
Identifiable Risk Factors
Many risk factors have been recognised as contributors
towards the development of CVD. These include
unhealthy diets, physical inactivity, obesity, hypertension
and hypercholesterolemia [11-13]. More recently other
risk factors have been shown to contribute towards the
development of CVD, notably, elevated concentrations of
fibrinogen (Fg), C-reactive protein (CRP), interleukin-6
(IL-6); and reduced levels of adiponectin (high molecular
weight). Fibrinogen is the main coagulation protein in
plasma and thus promotes activities such as platelet aggre-
gation and increased blood viscosity. Elevated fibrinogen
levels have been found in obese individuals [14] and thus
may have an indirect effect on CVD through levels of adi-
posity. Fg is also thought to be responsible for changes in
other acute phase proteins, notably CRP. These are
released as a result of active inflammation (an underlying
cause of CVD), moreover, it appears that the inflamma-
tory cytokine IL-6 is the underlying stimulator of these
acute phase proteins. Adiponectin is a protein hormone
that regulates the metabolism of lipids. It is the most
abundant hormone released from fat cells and has been
suggested to be anti-inflammatory [15]. Concentrations
of this protein have been found to be low in obese indi-
viduals, thus contributing to the pathogenesis of CVD and
increased inflammation [16]. It can therefore be seen that
CVD risk may not solely be due to one factor but a com-
bination of many which may originate through behav-
ioural and lifestyle factors.
Socioeconomic status, ethnicity and other factors
It has also emerged that CVD risk may differ between peo-

ple of differing socioeconomic status (SES), this is true for
both developed, and developing countries. In developed
countries, epidemiological evidence illustrates that SES is
inversely linked with CVD morbidity and mortality [17];
however, evidence of this relationship in developing
countries, is sparse. This may suggest that behavioural fac-
tors have a higher influence on disease risk factor profiles
that genetic factors [18-20].
As previously highlighted, there is growing evidence that
the risk for CVD originates during childhood, indeed,
research has found that a very low or very high birth
weight is associated with increased risk of CVD [21]. Fur-
thermore, there is evidence that birth weight varies
according to ethnic background and thus individuals with
Asian or African origins could be at a higher genetic risk of
developing obesity and CVD [22-24].
This protocol outlines the methods to examine lifestyle
and behavioural factors associated with childhood obes-
ity, diabetes and CVD risk in school children aged 11–13
years.
Methods and design
Aims and objectives
The aim of the proposed project is to examine the preva-
lence of risk factors for CVD and diabetes, and environ-
mental determinants associated with these risk factors
among children aged 11–13.
Page 2 of 6

Primary objective
To estimate the prevalence of obesity, hypercholesterolae-
mia and hypertension stratified by, ethnic minority
group, socioeconomic status, and school.
Secondary objective
To examine determinants associated with increased risk
factors for CVD and diabetes such as low fitness levels,
high fat diet, low physical activity levels, birth weight, and
family history of CVD and diabetes.
Design
Recruitment
The study population will be recruited from schools in
both the Swansea and Cardiff areas of South Wales.
Recruitment of the schools will be based upon high ethnic
minority catchment area. Subsequently, a member of the
team will attend year 7 (aged 11–12) and year 8 (aged 12–
13) school assemblies to give a short presentation
explaining the basics of the study. An explanation of the
protocol will be provided, along with the advantages of
participating and the feedback that will be provided fol-
lowing the completion of the testing. Each child will be
provided with an envelope containing an information let-
ter, consent and assent forms, and a short questionnaire.
They will be asked to read through these with their parents
and if they wish to take part in the study, they will be
asked to provide both their assent and their parents/
guardians consent. This questionnaire includes questions
about birth weight and family history of diseases such as
diabetes and CVD. Parents are asked to help their child
complete the questionnaire. Once these have been com-
pleted testing will begin, however, every child will be
asked to verbally confirm his or her consent prior to each
session, and reminded of their right to withdraw from the
study at any stage. Ethics approval for this study was

granted by the Local NHS Research Ethics Committee.
Testing procedures
Following the collection of these completed documents,
all information will be inputted onto an excel spread-
sheet. To help recruit further participants, leaflets will be
distributed to parents/guardians via children to inform
them of the importance of an active and healthy lifestyle,
and these will continued to be distributed throughout the
first week of testing. All testing procedures will take place
during the Physical Education (PE) lessons and on school
premises.
Anthropometrical data
Data collected are outlined in Table 1. For all anthropo-
metrical variables (height, weight, skinfold, neck, waist
and hip circumference), participants will wear PE clothing
(shorts and t-shirt) with no footwear. Body weight will be
measured to the nearest 0.1 kg using calibrated electronic
weighing scales (Seca 770, Digital Scales, Seca Ltd, Bir-
mingham, UK). Participants will be asked to step onto the
scales and wait for 3 seconds whilst the scales determine
the correct weight. Stature will be measured using a port-
able stadiometer (Seca Stadiometer, Seca Ltd, Birming-
ham, UK). Participants will be asked to stand with their
backs straight, feet flat and arms hanging loosely by their
sides. Stature will be measured as the maximum distance
from the floor to the vertex of the head and recorded to
the nearest millimetre. Body mass index (BMI) will then
be calculated using the BMI formula of dividing the par-
ticipant's weight by their height squared [25]. This will be
recorded and compared to national guidelines [26] to
establish whether a participant falls into a healthy weight,
underweight, overweight or obese category. Biceps, tri-
ceps, subscapular and suprailiac skinfold measurements
will be taken on the right side of the body using

Harpenden skinfold callipers (John Bull, British Indica-
tors Ltd, West Sussex, UK). Participants will be asked to
stand with a relaxed arm for both the triceps and sub-
scapular measurement, a relaxed arm with palm facing
forward for the bicep measurement and with their arm
Table 1: Data collected
Individual Level Data Data
Questionnaire Name, date of birth, birth weight, school, medical
history of child, GP; family history of obesity – related
conditions;
parental height, weight, waist circumference, postcode and
DOB.
Anthropometrical data Height, weight (BMI), Waist, hip and
neck circumference, percentage body fat as assessed by skinfold
thickness
Physiological measurements Blood pressure, 20 metre shuttle
run
Blood tests Cholesterol, fasting triglyceride, insulin, glucose,
fibrinogen, leptin, adiponectin
Dietary questionnaire Seven day food diary
Physical activity Self reported activity levels on Physical
Activity Questionnaire, Accelerometer.
Ethic group Caucasian, South Asian, African or other.
School Level Data
Deprivation Proportion registered for free school dinners.
Page 3 of 6

crossed horizontally across their chest for the suprailiac
measurement. In order to take the skinfold measure-
ments, the researcher will grasp the skin at the necessary
site and place the callipers around the skin. The researcher
will remain to hold the skin whilst the callipers are held
for 3 seconds and the reading determined. Duplicate
measurements will be taken at each site and the average
recorded. The sum of these four skinfold measurements
will then be calculated and recorded. Neck, waist and hip
circumference will be measured on each participant and
recorded to the nearest millimetre using a standard flexi-
ble measuring tape (Rosscraft, UK). These will provide an
index of fat distribution [27]. Waist circumference will be
measured at the narrowest part of the trunk whilst the hip
circumference will be measured around the maximum
protrusion of the buttocks [28].
Physiological measurements
Systolic and diastolic blood pressure (BP) will be meas-
ured using an Omron M6 automatic BP monitor (Omron
Healthcare UK Ltd, Milton Keynes, UK). Blood pressure
(BP) will be taken after each participant has sat quietly for
10 minutes. All BP measurements will be taken by the PR
to ensure consistency. The cuff will be positioned tightly
on the upper left arm and the machine started. BP will be
taken three times and the average of the second and third
readings will be recorded for analysis [29].
Aerobic fitness will be measured using the 20 metre multi
stage fitness test (20 MSFT). This field test has been vali-
dated as a predictor of maximal aerobic power in young
people [30] and has been found to be a consistent and
reliable field test of aerobic fitness [31]. The test requires
the participants to run between two cones that have been
set 20 m apart while keeping a pace with a pre-recorded

auditory signal. The initial running pace is set at 8.5 km/
hr and increases by 0.5 km/hr with each subsequent level.
Each level is announced by the tape which is produced by
the National Coaching Foundation. Participants will
receive verbal encouragement from the researchers
throughout the test. Once participants have reached their
maximal effort and withdrawn from the test, the number
of shuttles will be recorded.
Measures of deprivation and ethnicity
The school will be asked to complete a form providing
information on the number of children receiving free
school meals. This will be used as a measure of depriva-
tion at the school. Each child will also be asked to report
their postcode. Subsequently, a Townsend score of fifths
of deprivation will be assigned to each individual child
based on the postcode of their home address. Each child
will be requested to self assign their ethnicity and this will
be recorded during the anthropometrical data collection
sessions.
Physical activity
Each participant will be asked to complete the physical
activity questionnaire for adolescents (PAQ-A). The ques-
tionnaire is a seven day recall on physical activity; a vali-
dated questionnaire that has been used widely in research
[32,33]. The questionnaire will be administered and com-
pleted during the same session as the BP. Instructions will
be explained to participants prior to starting the test and
they will be encouraged to complete the questionnaire
alone. It will take approximately 20 minutes to complete
the questionnaire.
Dietary intake
Daily food intake will be assessed using a validated, self
reported seven day food diary [34]. Participants will be

asked to complete this diary at home and record what was
consumed for breakfast, lunch and dinner and snacks. It
will take approximately 5 minutes to complete per day. A
short dietary questionnaire will also be included, and this
will complement the food diary. The food diary will be
analysed by Health Options Ltd (Health options Ltd,
Cirencester, Gloucester, UK). Average daily kilojoules,
percentage of total fat, saturated fat, carbohydrate, protein
and fibre will be calculated.
Lipids and lipoproteins
Venous blood samples will take place during the morning
between 9 am and 10.30 am, following an overnight fast
and after the participants have sat quietly for 30 minutes.
Blood samples will be taken by qualified phlebotomists
and a health professional (nurse or doctor) will be present
at all times. The sampling will take place in the school
gymnasium or a suitable area that will be divided into
cubicles as necessary by screens. Participants will be called
in alphabetical order and accompanied by one of the
researchers. Whilst participants are waiting their turn, a
suitable DVD will be shown. Immediately following sam-
pling, the participants will be provided with breakfast in
the school canteen.
Blood samples will be drawn to measure levels of glucose,
insulin, lipids, fibrinogen (Fg), adiponectin (high molec-
ular weight), C-reactive protein (CRP) and interleukin-6
(IL-6).
Feedback
On completion of all data collection, feedback will be
provided to the school and participants on both an indi-
vidual and group basis. Each participant will receive a full
breakdown of their results with suitable advice to help
modify their lifestyle if needed. Both parents and family

doctor are informed of any abnormal findings. Group
feedback will be presented to the headmaster and PE staff.
Page 4 of 6
Analysis Plan
A multi-level analytical approach will be used to examine
the relationship between outcomes (obesity, hyperten-
sion, hypercholesterolemia), and individual and group
level determinant variables. This approach overcomes
common methodological barriers associated with con-
ventional regression analysis in epidemiology, where cor-
relation among individuals sharing the same local
environment is not accounted for. Multilevel modelling
allows for the examination of variability in outcomes
between individuals as well as between higher level units.
The overall levels of obesity, hypertension and hypercho-
lesterolemia will be examined both on the crude level,
and stratified by ethnic background and deprivation. Fac-
tors associated with obesity, and separately with hyperten-
sion and hypercholesterolemia will be assessed using
multilevel modelling accounting for shared environment
at the school level and neighbourhood level. Factors
examined will include; birth weight, family history of
obesity -related conditions, fitness tests, risk factors in the
blood (insulin, glucose, fibrinogen, leptin, adipondectin),
dietary analysis, deprivation, school, physical activity lev-
els, ethnic group, fitness tests, percentage body fat, waist/
hip ratio.
Sample size

This study aims to recruit five schools with participation
approximating 150–200 children in each school (n =
800). A total sample size of 800+ will give prevalence esti-
mates of obesity within 3% accuracy. Data collected in
this study will be combined and compared with the 400
children collected using the same methods in Car-
marthenshire in 2000 and 2006–2007. This will provide a
total sample size of 1200 children.
Handling of missing and incomplete data
The fitness levels and sex of the participants in each school
will be compared with the class averages. This will provide
an estimate of the generalisability of the findings in each
school according to the class they represent. Where partic-
ipants are found to differ from the class average, weighted
scores will be presented along with crude prevalence fig-
ures.
Missing data for individuals will be imputed using meas-
ures on individuals with comparable scores in other
known values. For example, waist measurement may be
imputed from a different participant with the same BMI
and height. Results based on data without and with
imputed fields will be presented.
Discussion
This study will provide estimates of obesity levels by eth-
nic group and socio-economic status for children aged
11–13 years. It will offer an evidence base to identify those
children at high risk of obesity and future health problems
in order to inform and target interventions to address
childhood obesity.
Abbreviations
BMI: Body Mass Index; CVD: Cardiovascular Disease;

PAQ: Physical Activity Questionnaire; CRP: C-reactive
protein; IL-6: Interlerleukin-6; MSFT: Multistage fitness
test; Fg: fibrinogen; SES: socioeconomic status; BP: Blood
Pressure.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
NT and AR designed and wrote the original proposal. This
as been modified and adapted by SB, GK and RW.
Acknowledgements
This work has been funded by a grant from the Welsh Office for
Research
and Development.
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http://www.biomedcentral.com/AbstractBackgroundMethods
and designDiscussionBackgroundIdentifiable Risk
FactorsSocioeconomic status, ethnicity and other
factorsMethods and designAims and objectivesPrimary
objectiveSecondary objectiveDesignRecruitmentTesting
proceduresAnthropometrical dataPhysiological
measurementsMeasures of deprivation and ethnicityPhysical
activityDietary intakeLipids and lipoproteinsFeedbackAnalysis
PlanSample sizeHandling of missing and incomplete
dataDiscussionAbbreviationsCompeting interestsAuthors'

contributionsAcknowledgementsReferencesPre-publication
history
Early Life Course Risk Factors for Childhood Obesity: The
IDEFICS Case-Control Study
Karin Bammann1,2*, Jenny Peplies2, Stefaan De Henauw3,
Monica Hunsberger4, Denes Molnar5,
Luis A. Moreno6, Michael Tornaritis7, Toomas Veidebaum8,
Wolfgang Ahrens2, Alfonso Siani9, on behalf
of the IDEFICS consortium
1 Institute for Public Health and Nursing Research (IPP),
University of Bremen, Bremen, Germany, 2 BIPS Institute for
Prevention Research and Epidemiology, Bremen,
Germany, 3 Department of Public Health, Ghent University,
Ghent, Belgium, 4 Department of Public Health and Community
Medicine, University of Gothenburg,
Gothenburg, Sweden, 5 Department of Pediatrics, University of
Pécs, Pécs, Hungary, 6 Growth, Exercise, Nutrition and
Development (GENUD) Research Group, University
of Zaragoza, Zaragoza, Spain, 7 Research and Education
Foundation of Child Health, Strovolos, Cyprus, 8 Department of
Chronic Diseases, National Institute for Health
Development, Tallinn, Estonia, 9 Unit of Epidemiology and
Population Genetics, Institute of Food Sciences, National
Research Council, Avellino, Italy
Abstract

Background: The early life course is assumed to be a critical
phase for childhood obesity; however the significance of single
factors and their interplay is not well studied in childhood
populations.
Objectives: The investigation of pre-, peri- and postpartum risk
factors on the risk of obesity at age 2 to 9.
Methods: A case-control study with 1,024 1:1-matched case-
control pairs was nested in the baseline survey (09/2007–05/
2008) of the IDEFICS study, a population-based intervention
study on childhood obesity carried out in 8 European countries
in pre- and primary school settings. Conditional logistic
regression was used for identification of risk factors.
Results: For many of the investigated risk factors, we found a
raw effect in our study. In multivariate models, we could
establish an effect for gestational weight gain (adjusted OR =
1.02; 95%CI 1.00–1.04), smoking during pregnancy (adjusted
OR = 1.48; 95%CI 1.08–2.01), Caesarian section (adjusted OR
= 1.38; 95%CI 1.10–1.74), and breastfeeding 4 to 11 months
(adjusted OR = 0.77; 95%CI 0.62–0.96). Birth weight was
related to lean mass rather than to fat mass, the effect of
smoking
was found only in boys, but not in girls. After additional
adjustment for parental BMI and parental educational status,
only
gestational weight gain remained statistically significant. Both,
maternal as well as paternal BMI were the strongest risk
factors in our study, and they confounded several of the
investigated associations.
Conclusions: Key risk factors of childhood obesity in our study
are parental BMI and gestational weight gain; consequently
prevention approaches should target not only children but also

adults. The monitoring of gestational weight seems to be of
particular importance for early prevention of childhood obesity.
Citation: Bammann K, Peplies J, De Henauw S, Hunsberger M,
Molnar D, et al. (2014) Early Life Course Risk Factors for
Childhood Obesity: The IDEFICS Case-
Control Study. PLoS ONE 9(2): e86914.
doi:10.1371/journal.pone.0086914
Editor: Amanda Bruce, University of Missouri-Kansas City,
United States of America
Received August 12, 2013; Accepted December 16, 2013;
Published February 13, 2014
Copyright: � 2014 Bammann et al. This is an open-access
article distributed under the terms of the Creative Commons
Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium,
provided the original author and source are credited.
Funding: This work was done as part of the IDEFICS Study
(www.idefics.eu). We gratefully acknowledge the financial
support of the European Community within
the Sixth RTD Framework Programme Contract No. 016181
(FOOD). The funders had no role in study design, data
collection and analysis, decision to publish, or
preparation of the manuscript.
Competing Interests: The authors have declared that no
competing interests exist.
* E-mail: [email protected]
Introduction
Excess body fat is one of the major health concerns in

childhood
populations [1,2]. Although the energy imbalance resulting from
high energy intake and low energy expenditure can contribute to
the development of overweight and obesity in children, there is
no
general consensus about its importance as the main driver of
weight gain [3]. In recent years, factors early in the life course
emerged as possible determinants of early overweight and
obesity
[4,5]. These can lead to epigenetic changes which are associated
with the programming of metabolic outcomes in the offspring.
Different pre-, peri- and postnatal risk factors for childhood
obesity
have been investigated and there is an increasing need to
disentangle the contribution of factors like maternal weight,
gestational weight gain, glycemic control, smoking and alcohol
use
during pregnancy, birth weight, breast feeding on the adiposity
of
the offspring, for which partly inconsistent results have been
obtained [5]. While high birth weight was consistently shown to

be
a risk factor for obesity, the situation is less clear with low
birth
weight that might or might not play a role in the development of
subsequent overweight and obesity [6]. Likewise, the impact of
breastfeeding on childhood obesity is still a matter of research
[7].
Although a protective effect of breastfeeding for subsequent
obesity of the offspring has been demonstrated in several
studies,
this result might at least partly be due to confounding: Amir and
Donath show in their studies that maternal smoking and obesity
lead to a lesser probability of breastfeeding [8,9], both,
maternal
PLOS ONE | www.plosone.org 1 February 2014 | Volume 9 |
Issue 2 | e86914
http://creativecommons.org/licenses/by/4.0/
smoking and maternal obesity, being themselves strong risk
factors
for childhood overweight [5]. Correspondingly, it is suspected
by

some authors that the association of breastfeeding and
childhood
overweight is a statistical artifact rather than a true causal
association [10].
In summary, there is still a considerable lack of research
regarding the effect of early life course factors on the risk of
childhood obesity.
The IDEFICS study is a large European multi-center interven-
tion study on childhood obesity. Prior to the intervention phase,
a
baseline survey was conducted in the control and intervention
regions to assess a wealth of factors related to diet- and
lifestyle-
related diseases in childhood. We used this data to set up a
matched case-control study on obesity. The aim of the paper is
to
investigate the impact of pre-, peri- and postnatal risk factors
on
the subsequent risk of obesity within this case-control study.
Materials and Methods
Ethics Statement

We certify that all applicable institutional and governmental
regulations concerning the ethical use of human volunteers were
followed during this research, and that the IDEFICS project
passed the Ethics Review process of the Sixth Framework
Programme (FP6) of the European Commission. Ethical
approval
was obtained from the relevant local or national ethics
committees
by each of the eight study centers, namely from the Ethics
Committee of the University Hospital Ghent (Belgium), the
National Bioethics Committee of Cyprus (Cyprus), the Tallinn
Medical Research Ethics Committee of the National Institutes
for
Health Development (Estonia), the Ethics Committee of the
University Bremen (Germany), the Scientific and Research
Ethics
Committee of the Medical Research Council Budapest
(Hungary),
the Ethics Committee of the Health Office Avellino (Italy), the
Ethics Committee for Clinical Research of Aragon (Spain), and
the Regional Ethical Review Board of Gothenburg (Sweden).

All
parents or legal guardians of the participating children gave
written informed consent to data collection, examinations,
collection of samples, subsequent analysis and storage of
personal
data and collected samples. Additionally, each child gave oral
consent after being orally informed about the modules by a
study
nurse immediately before every examination using a simplified
text. This procedure was chosen due to the young age of the
children. The oral consenting process was not further document-
ed, but it was subject to central and local training and quality
control procedures of the study. Study participants and their
parents/legal guardians could consent to single components of
the
study while abstaining from others. All procedures were
approved
by the above-mentioned Ethics Committees.
Study Sample
IDEFICS is a multi-center population-based intervention study
on childhood obesity that is carried out in selected regions of 8

European countries comprising Belgium, Cyprus, Estonia, Ger-
many, Hungary, Italy, Spain and Sweden. The study was set up
in
pre- and primary school settings in a control and an intervention
region in each of these countries. Two major surveys (baseline
and
follow-up) were conducted in pre-schools and primary school
classes (1
st
and 2
nd
grades at baseline). The baseline survey
(September 2007–May 2008) reached a response proportion of
51% and included 16 220 children aged 2 to 9 years. The
general
design of the IDEFICS study has been described elsewhere [11].
A
brief description of the study regions can be found in Bammann
et al. [12].
Anthropometric measurements were done during a physical
examination. Weight to the nearest 0.1 kg and foot-to-foot

bioelectrical resistance in Ohm was measured using an
electronic
scale TANITA BC 420 SMA (TANITA Europe GmbH,
Sindelfingen, Germany) with the children being in a fasting
status
and wearing only underwear. Standing height was measured
with
the children’s head in a Frankfort plane using a stadiometer
SECA
225 (seca GmbH & Co. KG., Hamburg, Germany) to the nearest
0.1 cm. As in the weight measurement, the children were
wearing
only underwear, all hair ornaments were removed and all braids
undone. Body mass index (BMI) was calculated as weight (kg)
divided by height squared (m). For obtaining the International
Task Force of Obesity (IOTF) category, BMI categories were
interpolated for continuous age as proposed [13,14]. Cubic
splines
were used for this interpolation. The resistance index (RI) was
calculated as squared height (cm
2
) divided by resistance (Ohm).

The RI was shown to be a good predictor for fat free mass in
children [15].
Within the baseline survey, a self-administered questionnaire
was filled in by the parents to gather information on the
children’s
behavior, parental attitudes and on the social microenvironment
of
the children (IDEFICS parental questionnaire). A further ques-
tionnaire on health-related and medical information was given
to
the parents in the course of the physical examination of the
child
(IDEFICS medical questionnaire). Questionnaires were
developed
in English, translated to the respective languages and back
translated to English to minimize any heterogeneity due to
translation problems. Different language versions were
available in
the centers, and help was offered to those parents who felt they
were not able to fill in the questionnaire by themselves. For
filling

in the IDEFICS medical questionnaire, the help of medical
personnel was offered directly at the survey sites.
Eligible for the IDEFICS case-control study on obesity were all
children from the baseline survey for whom a basic set of
anthropometric indicators and pregnancy information was
present
(N = 16,113). From these children, all children aged 4 to 8 years
that met the IOTF criteria for childhood obesity were identified
as
cases (N = 1,024). Controls were selected from the group of
IOTF
normal weighted children (N = 11,193) and matched 1:1 by
study
center, sex and age (sliding window +/20.5 years) to the cases.
For
all cases, controls with identical sex and study center and a
minimal distance with respect to age were selected. The
decision
for a 1:1 matched design was based on a sample size
calculation.
In our study, risk factors with prevalences among controls of at
least 10% leading to odds ratios of 1.6 and more can be detected

with a power of more than 90% (two-sided, p = 0.05).
Investigated Risk Factors
The investigated risk factors directly related to the child can be
grouped into prepartum, peripartum, and postpartum factors (see
Table 1). Further family-related risk factors included in the
present
analysis are familial clustering and parental social background.
All
information was assessed by the IDEFICS parental
questionnaire,
except for the occurrence of gestational diabetes that was taken
from the IDEFICS medical questionnaire. All investigated risk
factors are based on self-report.
Included prepartum factors were smoking during pregnancy,
gestational weight gain and gestational diabetes. The questions
relating to smoking during pregnancy and to gestational weight
gain were posed to biological mothers, only. Regarding smoking
during pregnancy, possible answer categories were ‘‘Never’’,
‘‘Rarely, at maximum once a month’’, ‘‘Several occasions a
week’’ or ‘‘Daily’’. We categorized ‘‘Rarely, at maximum once
a

month’’ or more often as smoking during pregnancy. Validity of
Early Life Course Factors for Childhood Obesity
Issue 2 | e86914
maternal recall of smoking during pregnancy and of gestational
weight gain is high even after several decades [16,17].
Included peripartum factors were birth weight and information
whether the child was delivered by Caesarian section. The
validity
of parental long-term recall of birth weight and of Caesarian
sections is good to excellent [16–18], and is not related to time
of
recall since birth [19].
Included postpartum factors were duration of breastfeeding and
introduction of solid foods. The early infant feeding of the child
was assessed by a table indicating different types of feeding
where
starting and ending age could be given by the parents. The exact
wording of the question was ‘‘What type of feeding with your

child
was used prior to being fully integrated into the usual household
diet?’’. Length of breastfeeding was calculated by subtracting
starting age from ending age of breastfeeding, either exclusive
or in
combination with other types of feeding. The introduction of
solid
foods was assessed using the question ‘‘At what age did you
first
introduce …?’’ followed by a table with 5 different foods and
the
possibility of giving the time of introduction as age of the child
in
month. Early introduction of solid food was defined as
introducing
cereals, meat, vegetables or fruits at a child’s age before 4
months.
Previous research has shown that the validity of maternal recall
for
duration of breastfeeding is very high, even after 10 years and
more [16,17,20,21]. Repeatability and validity for maternal
recall
of introduction of solid foods is shown to be higher than for

introduction of fluids other than breast milk, but considerably
lower than for breastfeeding with no clear direction of bias
towards
wrong recall of earlier or later dates [20,22].
The familial clustering of overweight and obesity was assessed
using self-reported parental BMI. The parental BMI was
calculated as weight (kg)/squared height (m
2
).
The parental social background was described using the
parental education and the age of mother at birth. For the
analyses, these were transferred country-by-country to Interna-
tional Standard Classification of Education (ISCED) levels
[12,23]
and the maximum ISCED level of both parents was calculated.
Statistical Analyses
To evaluate the impact of a putative risk factor, conditional
logistic regression models were fitted to the data. This allows
an
unbiased estimation of effects in matched case-control studies
with

regard to the matching variables. Within this approach, each
matched set forms a stratum. In the case of 1:1-matched pairs,
the
likelihood of being a case for N strata is then given by:
LC (b)~
XN
i~1
1
(1ze
b(xi(Case){xi(Control) ))
The estimation of the bs was done using Cox’ proportional
hazard model with maximum likelihood estimation [24]. 95%
confidence intervals (95% CI) were computed.
For all variables, the influence of the child’s sex and age group
(, = 6 years, .6 years) on the raw odds ratio (OR) was tested
using the Breslow-Day test for homogeneity of the odds ratios
[25].
We found all OR to be homogenous regarding age group. OR
Table 1. Risk factors investigated in the study.
Risk factor Hypotheses
Prepartum

Smoking during pregnancy Own causal effect (risk factor) e.g.
through fetal growth retardation and subsequent developmental
adaptations [46,47].
Possible confounder: Parental energy intake [47], parental
socioeconomic status [46].
Gestational weight gain Own causal effect (risk factor) e.g.
through shared genetic factors (on weight gain), shared
environment
(e.g. diet), fetal programming [48,49].
Possible confounder: Maternal BMI [49].
Gestational diabetes Own causal effect (risk factor) e.g. through
shared genetic factors, shared environment, fetal
programming [50].
Possible confounder: Maternal BMI.
Peripartum
Birth weight Own causal effect (risk factor) reflecting fetal
growth, programming for lean mass and fat distribution [32].
Possible artifact due to high correlation of BMI and lean body
mass [32].
Caesarian section Own causal effect (risk factor) e.g. through
differences in colonizing bacteria species [34].
Possible confounder: Maternal BMI, maternal smoking during
pregnancy, breastfeeding [34].
Postpartum

Breastfeeding (initiation and duration) Own causal effect
(protective factor) e.g. through nutritional programming
[51,52], moderation of genetic
effects [53], reduced risk of overfeeding [54].
Known confounder: parental obesity, maternal smoking during
pregnancy, parental socioeconomic
status; might completely remove the effect [55].
Association possibly artificial due to lower breastfeeding
success in obese and/or smoking mothers
[10,26].
Early introduction of solid foods Own causal effect (risk factor)
e.g. through nutritional programming [56].
Possible confounder: Parental socioeconomic status [57],
breastfeeding [58,59].
doi:10.1371/journal.pone.0086914.t001
Issue 2 | e86914
that were found to be heterogeneous over sexes are reported in
the
text.
For each of the investigated factors, we built multivariate

models adjusting for known or suspected confounders from the
literature (cf. Table 1). Since many of the investigated risk
factors
are correlated, we finally built a multivariate model containing
all
factors that were shown to be influential in the analyses. We
reported the Wald statistics to judge the relative importance of
the
single factors.
All statistical analyses were done with SAS 9.2 (SAS Institute,
Cary (NC), USA).
Raw ORs for parental BMI and parental ISCED level can be
found in the Appendix (Table S1).
Results
A basic description of the study sample is displayed in Table 2.
Roughly 50% of the case-control-pairs are male, 48% are 4–6
years, and 52% are 7–8 years old. The case-control pairs show
large differences with respect to country of origin, ranging from
39.7% from Italy to 3.9 from Belgium and 2.7% from Sweden.
The investigated early life course risk factors are shown in

Table 3. Maternal smoking during pregnancy carried a 50%
higher risk of the child being a case. Stratification by sex
revealed
that this elevated risk was present only in boys (OR = 2.15; 95%
CI 1.43–3.23), but not in girls (OR = 1.13; 95% CI 0.78–1.62).
The elevated obesity risk of maternal smoking during pregnancy
remained practically unchanged when adjusting for parental
BMI
and parental educational level. Gestational weight gain showed
a
linear dose-response relationship mounting in a twofold risk of
obesity if the mother gained 25 kg and more during pregnancy,
which was not explained by maternal BMI. Also, gestational
diabetes was associated with an excess risk of 32%. After
adjustment for maternal BMI the elevated risk disappeared
almost
completely (OR = 1.05; 95%CI 0.57–1.94).
The matched raw odds ratios for birth weight show a clear
linear dose-response relationship with childhood obesity
(OR = 0.58; 95%CI 0.39–0.86 for a birth weight ,2.5 kg up to

OR = 1.83; 95%CI 1.26–2.65 for a birth weight of .4 kg).
However, when we adjusted for the RI as an indicator of fat free
mass of the child, the effect of birth weight on the subsequent
obesity risk disappeared completely. Being born via Caesarian
section carried a higher obesity risk in our study (OR = 1.43;
95%CI 1.17–1.75). After adjustment for maternal BMI, maternal
weight gain during pregnancy and initiation of breastfeeding,
this
risk was reduced and no longer statistically significant (OR =
1.27;
95%CI 0.99–1.64). The initiation of any breastfeeding carries
an
unadjusted OR of 0.75 (95%CI 0.61–0.91), and is thus
protective
against subsequent obesity in childhood. However, parental
educational level, maternal BMI and maternal smoking during
pregnancy almost completely explained this effect; the adjusted
OR is near to unity (OR = 0.91; 95%CI 0.70–1.18). Likewise,
the
duration of breastfeeding shows unadjusted a u-shaped relation-

ship to obesity with a protective effect only for the two middle
categories (4–6 months, 7–11 months of non-exclusive
breastfeed-
ing). After adjustment for the afore-mentioned confounders,
only
7–11 moths of non-exclusive breast-feeding remained
statistically
significant protective (OR = 0.70; 95%CI 0.49–0.99). Early
introduction of solid foods shows an unadjusted OR of 1.43
(95% CI 1.02–2.01) that is reduced to a non-significant 1.33
(95%
CI 0.93–1.90) after adjustment for parental educational level
and
initiation of breast-feeding.
To assess the combined effect of all investigated factors, we
built
two multivariate models (Table 4). The first model included
gestational weight gain in kg, and design variables for smoking
during pregnancy, Caesarian section, early introduction of solid
foods and breastfeeding 4 to 11 months. The second model
additionally included the confounding factors parental BMI in
kg/

m
2
and parental educational level (unadjusted matched OR of
these confounders can be found in Table A of the appendix).
In the overall group, statistically significant risk factors for
obesity were Caesarian section (adjusted OR = 1.38; 95%CI
1.10–
1.74), gestational weight gain in kg (adjusted OR = 1.02; 95%CI
1.00–1.04), maternal smoking during pregnancy (adjusted
OR = 1.48; 95%CI 1.08–2.01) and breastfeeding 4 to 11 months
(adjusted OR = 0.77; 95%CI 0.62–0.96). After additional
adjust-
ment for parental BMI and parental educational level,
statistically
significant factors were maternal BMI in kg/m
2
(adjusted
OR = 1.16; 95%CI 1.11–1.20), paternal BMI in kg/m
2
(adjusted
OR = 1.11; 95%CI 1.07–1.16), and gestational weight gain in kg

(adjusted OR = 1.04; 95%CI 1.01–1.07).
Discussion
This paper investigated the impact of early life course risk
factors on the risk of subsequent childhood obesity. For many of
the investigated risk factors, we found a raw effect in our study.
Our results regarding maternal smoking are confirmed by
literature. In studies where maternal smoking was assessed, this
factor is quite consistently reported to be a marked risk factor
[26–
28]. Similarly, high gestational weight gain was previously
shown
to be a risk factor not only for overweight and obesity of the
child
[29], but also of the mother [30]. Sometimes this effect is
attributed to the influence of maternal adiposity [5], however in
our study, gestational weight gain showed an independent
effect,
also after control for parental BMI. We were well in accordance
with the literature, regarding high birth weight which was also a
risk factor in our study [6,26,31]. However, when we adjusted

for
fat-free mass, this effect was completely removed,
corroborating
Table 2. Description of the study sample.
Case-control pairs
N %
Sex
Girls 515 50.3
Boys 509 49.7
Age
4–6 years 494 48.2
7–8 years 530 51.8
Center
Italy 407 39.7
Cyprus 193 18.8
Hungary 135 13.2
Germany 81 7.9
Spain 77 7.5
Estonia 63 6.2

Belgium 40 3.9
Sweden 28 2.7
Total 1,024 100
Issue 2 | e86914
the hypothesis that birth weight is mainly influencing lean body
mass and not fat mass [32]. Since the BMI is correlated with fat
mass and with fat-free mass, one could argue that adjusting for
fat-
free mass leads generally to over-adjustment. Since the
difference
of fat-free mass between normal-weight and obese children is
much lower than that of fat mass [33], we argue that adjustment
for fat-free mass should not be able to mask any true effect on
fat
mass. Moreover, we checked for each of the other investigated
factors whether adjustment for fat-free mass influences the OR.

This was, apart from the effect for birth weight, not the case.
Caesarian section is another putative risk factor for childhood
obesity that came into focus rather recently [34]. We also did
find
an elevated risk for obesity in the offspring, also after
controlling
for gestational gain, maternal BMI and breastfeeding initiation.
However, in the final multivariate model Caesarian section was
not of particular importance, when judged by the Wald statistic,
and no longer statistically significant. The impact breastfeeding
exerts on overweight is controversial since it is influenced by
several other risk factors, as e.g. socioeconomic status, maternal
BMI and maternal smoking [35]. One of the rare RCTs showed
no effects of feeding breast milk on the prevalence of obesity
until
Table 3. IOTF obesity risk of early life course factors.
Raw OR OR adjusted for confounding factors
Controls Cases OR
a
95% CI OR

ab
95% CI Adjustment factors
Maternal smoking during pregnancy N % N % Maternal BMI,
parental educational
level
No 857 88.0 809 83.1 1.00 – 1.00 –
Yes 117 12.0 165 16.9 1.52 1.16–1.98 1.50 1.09–2.06
Gestational weight gain in kg N % N % Maternal BMI
,10 179 20.7 160 18.5 0.85 0.65–1.13 0.81 0.59–1.11
10–,15 434 50.1 414 47.8 1.00 – 1.00 –
15–,25 225 26.0 245 28.3 1.07 0.84–1.36 1.03 0.79–1.35
. = 25 28 3.2 47 5.4 2.00 1.16–3.45 2.11 1.14–3.89
Gestational diabetes N % N % Maternal BMI
No 999 97.6 991 96.8 1.00 – 1.00 –
Yes 25 2.4 33 3.2 1.32 0.79–2.22 1.05 0.57–1.94
Birth weight in kg N % N % Resistance index of the child
,2.5 73 7.6 44 4.5 0.58 0.39–0.86 1.00 0.55–1.82
2.5–4 836 86.8 830 85.3 1.00 – 1.00 –
.4 54 5.6 99 10.2 1.83 1.26–2.65 0.99 0.57–1.71

Caesarian section N % N % Maternal BMI, gestational weight
gain, initiation of breastfeeding
No 662 70.4 596 62.7 1.00 – 1.00 –
Yes 279 29.6 355 37.3 1.43 1.17–1.75 1.27 0.99–1.64
Breastfeeding N % N % Maternal BMI, parental educational
level, maternal smoking during
pregnancy
No 252 24.6 310 30.3 1.00 – 1.00 –
Yes 772 75.4 714 69.7 0.75 0.61–0.91 0.91 0.70–1.18
Breastfeeding duration in months N % N % Maternal BMI,
parental educational
level, maternal smoking during
pregnancy
0 252 24.6 310 30.3 1.00 – 1.00 –
.0–3 171 16.7 181 17.7 0.86 0.66–1.12 1.03 0.73–1.44
4–6 311 30.4 276 27.0 0.71 0.56–0.90 0.89 0.66–1.21
7–11 218 21.3 170 16.6 0.62 0.47–0.81 0.70 0.49–0.99
. = 12 72 7.0 87 8.5 0.93 0.65–1.35 1.25 0.80–1.94
Early introduction of solid foods N % N % Parental educational
level, duration
of breastfeeding
No 955 93.3 931 90.9 1.00 – 1.00 –

Yes 69 6.7 93 9.1 1.43 1.02–2.01 1.33 0.93–1.90
a
Analyses were matched on sex, age and country.
b
Analyses were additionally adjusted for putative confounders
(see last column).
Matched odds ratios (OR) and 95% confidence intervals (95%
CI): OR with p,0.05 are printed in bold.
Issue 2 | e86914
the age of 15 [36], suggesting that the protective effects found
in
other studies were due to statistical artifacts. Similarly, the
RCT of
Kramer et al. on breastfeeding promotion in preterm babies in
Belarus showed no difference regarding obesity prevalence later
in
life in the two arms of the study [37]. Many previous studies
have
investigated either ever versus never-breastfeeding, while fewer

have studied the duration of breastfeeding. Moreover, duration
of
breast-feeding is frequently modeled as months of breastfeeding
assuming thus a strictly linear effect without any upper limit. In
our study, we categorized the duration of breastfeeding and
found
a u-shaped effect. Interestingly, a similar pattern was found in a
cohort study in Brazil [38] and in a secondary analyses of
several
cohorts examining the risk of duration of breastfeeding on
cardiovascular risk factors [39]. However in our study, the
effects
were no longer statistically significant when controlling for
confounding factors. As many other studies [5,40–42], we also
found a strong impact of parental weight status on the risk of
childhood obesity. In a full model that included all investigated
early life course risk factors for which we could establish an
effect,
gestational weight gain, smoking during pregnancy, Caesarian
section were found to be risk factors for later childhood obesity,
and breastfeeding 4 to 11 months was found to be protective.

However, after additional adjustment for parental BMI and
parental educational status, only gestational weight gain
remained
statistically significant. Both, maternal as well as paternal BMI
were the strongest risk factors in our study, and they
confounded
several of the investigated associations. The current study has
several limitations. It has to be kept in mind that the IDEFICS
regions are not representative of the respective countries of
Europe, let alone of the European population as a whole.
Moreover, due to the high number of Italian obese children in
the IDEFICS baseline survey (and the low number in Sweden
and
Belgium), the case-control sample was highly unbalanced with
regard to country. As country was a matching variable this
should
not bias the results. We did not test for different effects by
country,
as we did for sex and age group, since the sample size was too
small for most countries. For most of the associations that are

hypothesized to be of a physiologic nature, like e.g. the
association
between Caesarian section and obesity, generalizability should
not
be compromised by the choice of regions. However, the
association of social factors, like parental education, with
childhood obesity is strongly dependent on context and
therefore
of limited external validity [12].
The investigated risk factors all rely on parental self-report. The
validity of parental recall of most of the investigated factors is
quite
high, and we did not find much evidence for differential
misclassification, which means that recall bias leads to risk
attenuation rather than to distorted results. Recall bias might
play a role in the lacking effect for introduction of solid food,
as
this factors has probably lowest validity among all investigated
factors. Parental BMI is calculated using self-reported height
and
weight. Self-reported BMI is known to underestimate the true

BMI, especially in the obese [43]. Assuming a familial
clustering of
obesity this produces a likely differential misclassification for
parental BMI in our cases and controls. This misclassification
also
leads to attenuated Odds Ratios for paternal and maternal BMI,
since especially among cases the proportion of overweight and
obese parents is underestimated as opposed to the control group.
Our results show that parental BMI is of particular importance
for childhood obesity. The parental BMI is a factor or an
indicator
for very different causal pathways, belonging to categories of
shared genetics, a shared obesogenic environment or the process
known as early programming. Currently many obesity interven-
tion programs target children. However, reducing the prevalence
of parental overweight and obesity would not only help
preventing
childhood obesity, but would in general lead to an improved
health not only of the children, but also of their parents. Beyond
this, our results indicate that especially maternal weight gain

should be monitored closely during pregnancy. The mechanisms
and possible prevention targets of maternal diet [44,45] and
other
social and behavioral factors before and during pregnancy as
risk
factors for parental BMI and high gestational weight gain as
well
as for obesity in the offspring should be the subject of further
studies.
Supporting Information
Table S1 Distribution of continuous variables in the study
population.
(DOCX)
Author Contributions
Analyzed the data: KB JP. Contributed
reagents/materials/analysis tools:
KB JP SDH MH DM LAM MT TV WA AS. Wrote the paper:
KB JP
MH AS. Conceived and designed the study: KB DM LAM WA
AS.
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