Estimation of Newborn Risk for Child or Adolescent Obesity: Lessons from Longitudinal Birth Cohorts
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. We analyzed the lifetime Northern Finland Birth C...
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| Vydané v: | PloS one Ročník 7; číslo 11; s. e49919 |
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| Hlavní autori: | , , , , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
Public Library of Science
28.11.2012
Public Library of Science (PLoS) |
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | 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.
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.
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.
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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| AbstractList | 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. 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. 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 [less than or equal to]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. 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. 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. 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.OBJECTIVESPrevention 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.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.METHODSWe 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.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.RESULTSIn 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.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.CONCLUSIONThis 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. ObjectivesPrevention 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.MethodsWe 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.ResultsIn 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.ConclusionThis 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. 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 [less than or equal to]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. 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. 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. 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. 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. 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. |
| Audience | Academic |
| Author | Ruokonen, Aimo Pouta, Anneli Maffeis, Claudio Meyre, David Elliott, Paul Järvelin, Marjo-Riitta Gaget, Stefan Rifas-Shiman, Sheryl L. Khan, Anokhi Ali Kleinman, Ken Vatin, Vincent Laitinen, Jaana Morandi, Anita Hartikainen, Anna-Liisa Gillman, Matthew W. Das, Shikta Lobbens, Stéphane Kaakinen, Marika Froguel, Philippe |
| AuthorAffiliation | 1 Unité Mixte de Recherche 8199, Centre National de Recherche Scientifique (CNRS) and Pasteur Institute, Lille, France Scientific Directorate, Bambino Hospital, Italy 7 Institute of Clinical Medicine/Obstetrics and Gynecology, University of Oulu, Oulu, Finland 2 Regional Centre for Juvenile Diabetes, Obesity and Clinical Nutrition, University of Verona, Verona, Italy 11 Centre for Environment and Health, School of Public Health, Imperial College, London, United Kingdom 13 Department of Genomics of Common Disease, School of Public Health, Imperial College, London, United Kingdom 6 Department of Children, Young People and Families, National Institute for Health and Welfare, Helsinki, Finland 8 Finnish Institute of Occupational Health, Helsinki, Finland 10 Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London, United Kingdom 3 Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Canada 9 Department of Clinical Sciences and C |
| AuthorAffiliation_xml | – name: 7 Institute of Clinical Medicine/Obstetrics and Gynecology, University of Oulu, Oulu, Finland – name: 12 Department of Life Course and Services, National Institute for Health and Welfare, Oulu, Finland – name: 3 Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Canada – name: 11 Centre for Environment and Health, School of Public Health, Imperial College, London, United Kingdom – name: 8 Finnish Institute of Occupational Health, Helsinki, Finland – name: Scientific Directorate, Bambino Hospital, Italy – name: 5 Institute of Health Sciences and Biocenter, University of Oulu, Oulu, Finland – name: 2 Regional Centre for Juvenile Diabetes, Obesity and Clinical Nutrition, University of Verona, Verona, Italy – name: 13 Department of Genomics of Common Disease, School of Public Health, Imperial College, London, United Kingdom – name: 9 Department of Clinical Sciences and Clinical Chemistry, University of Oulu, Oulu, Finland – name: 4 Obesity Prevention Program, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, United States of America – name: 10 Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London, United Kingdom – name: 6 Department of Children, Young People and Families, National Institute for Health and Welfare, Helsinki, Finland – name: 1 Unité Mixte de Recherche 8199, Centre National de Recherche Scientifique (CNRS) and Pasteur Institute, Lille, France |
| Author_xml | – sequence: 1 givenname: Anita surname: Morandi fullname: Morandi, Anita – sequence: 2 givenname: David surname: Meyre fullname: Meyre, David – sequence: 3 givenname: Stéphane surname: Lobbens fullname: Lobbens, Stéphane – sequence: 4 givenname: Ken surname: Kleinman fullname: Kleinman, Ken – sequence: 5 givenname: Marika surname: Kaakinen fullname: Kaakinen, Marika – sequence: 6 givenname: Sheryl L. surname: Rifas-Shiman fullname: Rifas-Shiman, Sheryl L. – sequence: 7 givenname: Vincent surname: Vatin fullname: Vatin, Vincent – sequence: 8 givenname: Stefan surname: Gaget fullname: Gaget, Stefan – sequence: 9 givenname: Anneli surname: Pouta fullname: Pouta, Anneli – sequence: 10 givenname: Anna-Liisa surname: Hartikainen fullname: Hartikainen, Anna-Liisa – sequence: 11 givenname: Jaana surname: Laitinen fullname: Laitinen, Jaana – sequence: 12 givenname: Aimo surname: Ruokonen fullname: Ruokonen, Aimo – sequence: 13 givenname: Shikta surname: Das fullname: Das, Shikta – sequence: 14 givenname: Anokhi Ali surname: Khan fullname: Khan, Anokhi Ali – sequence: 15 givenname: Paul surname: Elliott fullname: Elliott, Paul – sequence: 16 givenname: Claudio surname: Maffeis fullname: Maffeis, Claudio – sequence: 17 givenname: Matthew W. surname: Gillman fullname: Gillman, Matthew W. – sequence: 18 givenname: Marjo-Riitta surname: Järvelin fullname: Järvelin, Marjo-Riitta – sequence: 19 givenname: Philippe surname: Froguel fullname: Froguel, Philippe |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/23209618$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | COPYRIGHT 2012 Public Library of Science 2012 Morandi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2012 Morandi et al 2012 Morandi et al |
| Copyright_xml | – notice: COPYRIGHT 2012 Public Library of Science – notice: 2012 Morandi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2012 Morandi et al 2012 Morandi et al |
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| DOI | 10.1371/journal.pone.0049919 |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interests exist. Conceived and designed the experiments: AM DM PF. Performed the experiments: SL SG VV. Analyzed the data: AM KK SLR-S. Contributed reagents/materials/analysis tools: SL MK SG VV. Wrote the paper: AM. Cohort investigators: M-RJ CM MG KK SLR-S MK AP A-LH JL AR SD AAK. Supervised the study: PE M-RJ PF. Equally contributed as last authors: M-RJ PF. Equal corresponding authors: AM PF. |
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| Title | Estimation of Newborn Risk for Child or Adolescent Obesity: Lessons from Longitudinal Birth Cohorts |
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