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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| Veröffentlicht in: | PloS one 2012-11, Vol.7 (11), p.e49919-e49919 |
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| creator | Morandi, Anita Meyre, David Lobbens, Stéphane Kleinman, Ken Kaakinen, Marika Rifas-Shiman, Sheryl L Vatin, Vincent Gaget, Stefan Pouta, Anneli Hartikainen, Anna-Liisa Laitinen, Jaana Ruokonen, Aimo Das, Shikta Khan, Anokhi Ali Elliott, Paul Maffeis, Claudio Gillman, Matthew W Järvelin, Marjo-Riitta Froguel, Philippe |
| description | 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 |
| doi_str_mv | 10.1371/journal.pone.0049919 |
| format | Article |
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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0049919</identifier><identifier>PMID: 23209618</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adolescent ; Adolescent obesity ; Adolescents ; Adult ; Age ; Birth Weight ; Body mass ; Body Mass Index ; Calculators ; Cardiovascular disease ; Child ; Child development ; Child health ; Childbirth & labor ; Childhood obesity ; Children ; Clinical medicine ; Cohort Studies ; Colorectal cancer ; Diabetes ; Disease prevention ; Epidemics ; Epidemiology ; Female ; Finland - epidemiology ; Genetic aspects ; Genetic counseling ; Genetic diversity ; Genetic polymorphisms ; Genetic variance ; Gynecology ; Health sciences ; Humans ; Logistic Models ; Male ; Maternal behavior ; Mathematical analysis ; Mathematical models ; Medicine ; Middle Aged ; Neonates ; Newborn infants ; Nutrition ; Obesity ; Obesity - epidemiology ; Obesity - prevention & control ; Obstetrics ; Pediatrics ; Predictions ; Prevention ; Public health ; Risk ; Risk analysis ; Risk factors ; Social behavior ; Sociodemographics ; Teenagers ; Type 2 diabetes ; Whites ; Young Adult</subject><ispartof>PloS one, 2012-11, Vol.7 (11), p.e49919-e49919</ispartof><rights>COPYRIGHT 2012 Public Library of Science</rights><rights>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.</rights><rights>2012 Morandi et al 2012 Morandi et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-6f714c406b9b1077808426b683646fe4e0562ceeee73bdcf9ad336096e6a5e3a3</citedby><cites>FETCH-LOGICAL-c692t-6f714c406b9b1077808426b683646fe4e0562ceeee73bdcf9ad336096e6a5e3a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3509134/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3509134/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79569,79570</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23209618$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Morandi, Anita</creatorcontrib><creatorcontrib>Meyre, David</creatorcontrib><creatorcontrib>Lobbens, Stéphane</creatorcontrib><creatorcontrib>Kleinman, Ken</creatorcontrib><creatorcontrib>Kaakinen, Marika</creatorcontrib><creatorcontrib>Rifas-Shiman, Sheryl L</creatorcontrib><creatorcontrib>Vatin, Vincent</creatorcontrib><creatorcontrib>Gaget, Stefan</creatorcontrib><creatorcontrib>Pouta, Anneli</creatorcontrib><creatorcontrib>Hartikainen, Anna-Liisa</creatorcontrib><creatorcontrib>Laitinen, Jaana</creatorcontrib><creatorcontrib>Ruokonen, Aimo</creatorcontrib><creatorcontrib>Das, Shikta</creatorcontrib><creatorcontrib>Khan, Anokhi Ali</creatorcontrib><creatorcontrib>Elliott, Paul</creatorcontrib><creatorcontrib>Maffeis, Claudio</creatorcontrib><creatorcontrib>Gillman, Matthew W</creatorcontrib><creatorcontrib>Järvelin, Marjo-Riitta</creatorcontrib><creatorcontrib>Froguel, Philippe</creatorcontrib><title>Estimation of newborn risk for child or adolescent obesity: lessons from longitudinal birth cohorts</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>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.</description><subject>Adolescent</subject><subject>Adolescent obesity</subject><subject>Adolescents</subject><subject>Adult</subject><subject>Age</subject><subject>Birth Weight</subject><subject>Body mass</subject><subject>Body Mass Index</subject><subject>Calculators</subject><subject>Cardiovascular disease</subject><subject>Child</subject><subject>Child development</subject><subject>Child health</subject><subject>Childbirth & labor</subject><subject>Childhood obesity</subject><subject>Children</subject><subject>Clinical medicine</subject><subject>Cohort Studies</subject><subject>Colorectal cancer</subject><subject>Diabetes</subject><subject>Disease prevention</subject><subject>Epidemics</subject><subject>Epidemiology</subject><subject>Female</subject><subject>Finland - epidemiology</subject><subject>Genetic aspects</subject><subject>Genetic counseling</subject><subject>Genetic diversity</subject><subject>Genetic polymorphisms</subject><subject>Genetic variance</subject><subject>Gynecology</subject><subject>Health sciences</subject><subject>Humans</subject><subject>Logistic Models</subject><subject>Male</subject><subject>Maternal behavior</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Medicine</subject><subject>Middle Aged</subject><subject>Neonates</subject><subject>Newborn infants</subject><subject>Nutrition</subject><subject>Obesity</subject><subject>Obesity - epidemiology</subject><subject>Obesity - prevention & control</subject><subject>Obstetrics</subject><subject>Pediatrics</subject><subject>Predictions</subject><subject>Prevention</subject><subject>Public health</subject><subject>Risk</subject><subject>Risk analysis</subject><subject>Risk factors</subject><subject>Social behavior</subject><subject>Sociodemographics</subject><subject>Teenagers</subject><subject>Type 2 diabetes</subject><subject>Whites</subject><subject>Young 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C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>ProQuest Central (New)</collection><collection>ProQuest One Academic (New)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest Health & Medical Research Collection</collection><collection>ProQuest One Academic Middle East (New)</collection><collection>ProQuest One Health & Nursing</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Applied & Life Sciences</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Morandi, Anita</au><au>Meyre, David</au><au>Lobbens, Stéphane</au><au>Kleinman, Ken</au><au>Kaakinen, Marika</au><au>Rifas-Shiman, Sheryl L</au><au>Vatin, Vincent</au><au>Gaget, Stefan</au><au>Pouta, Anneli</au><au>Hartikainen, Anna-Liisa</au><au>Laitinen, Jaana</au><au>Ruokonen, Aimo</au><au>Das, Shikta</au><au>Khan, Anokhi Ali</au><au>Elliott, Paul</au><au>Maffeis, Claudio</au><au>Gillman, Matthew W</au><au>Järvelin, Marjo-Riitta</au><au>Froguel, Philippe</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimation of newborn risk for child or adolescent obesity: lessons from longitudinal birth cohorts</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2012-11-28</date><risdate>2012</risdate><volume>7</volume><issue>11</issue><spage>e49919</spage><epage>e49919</epage><pages>e49919-e49919</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>23209618</pmid><doi>10.1371/journal.pone.0049919</doi><tpages>e49919</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2012-11, Vol.7 (11), p.e49919-e49919 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1350901856 |
| source | Public Library of Science (PLoS) Journals Open Access; MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Adolescent Adolescent obesity Adolescents Adult Age Birth Weight Body mass Body Mass Index Calculators Cardiovascular disease Child Child development Child health Childbirth & labor Childhood obesity Children Clinical medicine Cohort Studies Colorectal cancer Diabetes Disease prevention Epidemics Epidemiology Female Finland - epidemiology Genetic aspects Genetic counseling Genetic diversity Genetic polymorphisms Genetic variance Gynecology Health sciences Humans Logistic Models Male Maternal behavior Mathematical analysis Mathematical models Medicine Middle Aged Neonates Newborn infants Nutrition Obesity Obesity - epidemiology Obesity - prevention & control Obstetrics Pediatrics Predictions Prevention Public health Risk Risk analysis Risk factors Social behavior Sociodemographics Teenagers Type 2 diabetes Whites Young Adult |
| title | Estimation of newborn risk for child or adolescent obesity: lessons from longitudinal birth cohorts |
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