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Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
Estimation of newborn risk for child or adolescent obesity: lessons from longitudinal birth cohorts
Anita Morandi1, David Meyre, Stéphane Lobbens
1Unité Mixte de Recherche 8199, Centre National de Recherche Scientifique (CNRS) and Pasteur Institute, Lille, France. anita.morandi@voila.fr
Insights
This study developed simple tools to predict childhood obesity risk in newborns using easily recorded information. These tools are crucial for early intervention against the growing global obesity epidemic.
Area of Science:
- Pediatrics
- Genetics
- Public Health
Background:
- Obesity prevention is critical, starting from early life.
- Early identification of obesity risk in newborns is needed for targeted interventions.
- The global obesity epidemic requires proactive strategies.
Purpose of the Study:
- To create clinically useful predictive equations for later obesity in newborns.
- To develop tools for early obesity risk assessment in infants.
- To aid in the prevention of childhood and adolescent obesity.
Main Methods:
- Analysis of the Northern Finland Birth Cohort 1986 (NFBC1986) with traditional risk factors and a genetic score.
- Development of predictive equations for childhood and adolescent obesity.
- Validation of predictive models in Italian and U.S. pediatric cohorts.
Main Results:
- Traditional risk factors accurately predicted childhood and adolescent obesity (AUROC 0.75-0.85).
- Adding a genetic score showed minimal improvement (≤1%) in prediction accuracy.
- Predictive equations demonstrated good accuracy in independent validation cohorts and were converted into risk calculators.
Conclusions:
- The study provides the first practical tools for predicting childhood obesity risk in newborns using readily available data.
- Currently known genetic variants have limited utility in predicting childhood obesity.
- These tools can facilitate early, focused interventions against childhood obesity.
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.
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