Estimation of Newborn Risk for Child or Adolescent Obesity: Lessons from Longitudinal Birth Cohorts
Author(s)
Type
Journal Article
Abstract
Objectives: Prevention of obesity should start as early as possible after birth. We aimed to build clinically useful equations
estimating the risk of later obesity in newborns, as a first step towards focused early prevention against the global obesity
epidemic.
Methods: We analyzed the lifetime Northern Finland Birth Cohort 1986 (NFBC1986) (N = 4,032) to draw predictive equations
for childhood and adolescent obesity from traditional risk factors (parental BMI, birth weight, maternal gestational weight
gain, behaviour and social indicators), and a genetic score built from 39 BMI/obesity-associated polymorphisms. We
performed validation analyses in a retrospective cohort of 1,503 Italian children and in a prospective cohort of 1,032 U.S.
children.
Results: In the NFBC1986, the cumulative accuracy of traditional risk factors predicting childhood obesity, adolescent
obesity, and childhood obesity persistent into adolescence was good: AUROC = 0?78[0?74–0.82], 0?75[0?71–0?79] and
0?85[0?80–0?90] respectively (all p,0?001). Adding the genetic score produced discrimination improvements #1%. The
NFBC1986 equation for childhood obesity remained acceptably accurate when applied to the Italian and the U.S. cohort
(AUROC = 0?70[0?63–0?77] and 0?73[0?67–0?80] respectively) and the two additional equations for childhood obesity newly
drawn from the Italian and the U.S. datasets showed good accuracy in respective cohorts (AUROC = 0?74[0?69–0?79] and
0?79[0?73–0?84]) (all p,0?001). The three equations for childhood obesity were converted into simple Excel risk calculators
for potential clinical use.
Conclusion: This study provides the first example of handy tools for predicting childhood obesity in newborns by means of
easily recorded information, while it shows that currently known genetic variants have very little usefulness for such
prediction.
estimating the risk of later obesity in newborns, as a first step towards focused early prevention against the global obesity
epidemic.
Methods: We analyzed the lifetime Northern Finland Birth Cohort 1986 (NFBC1986) (N = 4,032) to draw predictive equations
for childhood and adolescent obesity from traditional risk factors (parental BMI, birth weight, maternal gestational weight
gain, behaviour and social indicators), and a genetic score built from 39 BMI/obesity-associated polymorphisms. We
performed validation analyses in a retrospective cohort of 1,503 Italian children and in a prospective cohort of 1,032 U.S.
children.
Results: In the NFBC1986, the cumulative accuracy of traditional risk factors predicting childhood obesity, adolescent
obesity, and childhood obesity persistent into adolescence was good: AUROC = 0?78[0?74–0.82], 0?75[0?71–0?79] and
0?85[0?80–0?90] respectively (all p,0?001). Adding the genetic score produced discrimination improvements #1%. The
NFBC1986 equation for childhood obesity remained acceptably accurate when applied to the Italian and the U.S. cohort
(AUROC = 0?70[0?63–0?77] and 0?73[0?67–0?80] respectively) and the two additional equations for childhood obesity newly
drawn from the Italian and the U.S. datasets showed good accuracy in respective cohorts (AUROC = 0?74[0?69–0?79] and
0?79[0?73–0?84]) (all p,0?001). The three equations for childhood obesity were converted into simple Excel risk calculators
for potential clinical use.
Conclusion: This study provides the first example of handy tools for predicting childhood obesity in newborns by means of
easily recorded information, while it shows that currently known genetic variants have very little usefulness for such
prediction.
Date Issued
2012-11-28
Date Acceptance
2012-10-15
Citation
PLOS One, 2012, 7 (11)
ISSN
1932-6203
Publisher
Public Library of Science
Journal / Book Title
PLOS One
Volume
7
Issue
11
Copyright Statement
© 2012 Morandi 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.
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
License URL
Sponsor
Medical Research Council (MRC)
Grant Number
G1002084
Subjects
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
MULTIDISCIPLINARY SCIENCES
GENOME-WIDE ASSOCIATION
AMERICAN-CANCER-SOCIETY
BODY-MASS INDEX
ADULT OBESITY
WEIGHT-GAIN
METAANALYSIS
PREVALENCE
PREDICTION
VARIANTS
LOCI
Adolescent
Adult
Birth Weight
Body Mass Index
Child
Cohort Studies
European Continental Ancestry Group
Female
Finland
Humans
Logistic Models
Male
Middle Aged
Obesity
Risk
Young Adult
General Science & Technology
MD Multidisciplinary
Publication Status
Published
Article Number
e49919