Early-life childhood obesity risk prediction: A Danish register-based cohort study exploring the predictive value of infancy weight gain.
childhood obesity
infancy weight gain
overweight
prediction
risk factors
Journal
Pediatric obesity
ISSN: 2047-6310
Titre abrégé: Pediatr Obes
Pays: England
ID NLM: 101572033
Informations de publication
Date de publication:
10 2021
10 2021
Historique:
revised:
06
03
2021
received:
15
12
2020
accepted:
13
03
2021
pubmed:
31
3
2021
medline:
21
12
2021
entrez:
30
3
2021
Statut:
ppublish
Résumé
Information on postnatal weight gain is important for predicting later overweight and obesity, but it is unclear whether inclusion of this postnatal predictor improves the predictive performance of a comprehensive model based on prenatal and birth-related predictors. To compare performance of prediction models based on predictors available at birth, with and without information on infancy weight gain during the first year when predicting childhood obesity risk. A Danish register-based cohort study including 55.041 term children born between January 2004 and July 2011 with birthweight >2500 g registered in The Children's Database was used to compare model discrimination, reclassification, sensitivity and specificity of two models predicting risk of childhood obesity at school age. Each model consisted of eight predictors available at birth, one additionally including information on weight gain during the first 12 months of life. The area under the receiving operating characteristic curve increased from 0.785 (95% confidence interval (CI) [0.773-0.798]) to 0.812 (95% CI [0.801-0.824]) after adding weight gain information when predicting childhood obesity. Adding this information correctly classified 30% more children without obesity and 21% with obesity and improved sensitivity from 0.42 to 0.48. Specificity remained unchanged at 0.91. Adding infancy weight gain information improves discrimination, reclassification and sensitivity of a comprehensive prediction model based on predictors available at birth.
Sections du résumé
BACKGROUND
Information on postnatal weight gain is important for predicting later overweight and obesity, but it is unclear whether inclusion of this postnatal predictor improves the predictive performance of a comprehensive model based on prenatal and birth-related predictors.
OBJECTIVES
To compare performance of prediction models based on predictors available at birth, with and without information on infancy weight gain during the first year when predicting childhood obesity risk.
METHODS
A Danish register-based cohort study including 55.041 term children born between January 2004 and July 2011 with birthweight >2500 g registered in The Children's Database was used to compare model discrimination, reclassification, sensitivity and specificity of two models predicting risk of childhood obesity at school age. Each model consisted of eight predictors available at birth, one additionally including information on weight gain during the first 12 months of life.
RESULTS
The area under the receiving operating characteristic curve increased from 0.785 (95% confidence interval (CI) [0.773-0.798]) to 0.812 (95% CI [0.801-0.824]) after adding weight gain information when predicting childhood obesity. Adding this information correctly classified 30% more children without obesity and 21% with obesity and improved sensitivity from 0.42 to 0.48. Specificity remained unchanged at 0.91.
CONCLUSION
Adding infancy weight gain information improves discrimination, reclassification and sensitivity of a comprehensive prediction model based on predictors available at birth.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e12790Informations de copyright
© 2021 World Obesity Federation.
Références
Woo Baidal JA, Locks LM, Cheng ER, Blake-Lamb TL, Perkins ME, Taveras EM. Risk factors for childhood obesity in the first 1,000 days: a systematic review. Am J Prev Med. 2016;50(6):761-779. https://doi.org/10.1016/j.amepre.2015.11.012.
Ziauddeen N, Roderick PJ, Macklon NS, Alwan NA. Predicting childhood overweight and obesity using maternal and early life risk factors: a systematic review. Obes Rev. 2018;19(3):302-312. https://doi.org/10.1111/obr.12640.
Weng SF, Redsell SA, Nathan D, Swift JA, Yang M, Glazebrook C. Estimating overweight risk in childhood from predictors during infancy. Pediatrics. 2013;132(2):e414-e421. https://doi.org/10.1542/peds.2012-3858.
Robson JO, Verstraete SG, Shiboski S, Heyman MB, Wojcicki JM. A risk score for childhood obesity in an urban Latino cohort. J Pediatr. 2016;172:29-34. https://doi.org/10.1016/j.jpeds.2016.01.055.
Santorelli G, Petherick ES, Wright J, et al. Developing prediction equations and a mobile phone application to identify infants at risk of obesity. PLoS One. 2013;8(8):e71183. https://doi.org/10.1371/journal.pone.0071183.
Druet C, Stettler N, Sharp S, et al. Prediction of childhood obesity by infancy weight gain: an individual-level meta-analysis. Paediatr Perinat Epidemiol. 2012;26(1):19-26. https://doi.org/10.1111/j.1365-3016.2011.01213.x.
Hosmer DW, Lemeshow S, Sturdivant RX. In: Hosmer DW, ed. Applied Logistic Regression. 3rd ed. Hoboken: Wiley; 2013.
Morandi A, Meyre D, Lobbens S, et al. Estimation of newborn risk for child or adolescent obesity: lessons from longitudinal birth cohorts. PLoS One. 2012;7(11):e49919.
Høstgaard AM, Pape-Haugaard L. Reusable data in public health databases-problems encountered in Danish Children's Database. Stud Health Technol Inform. 2012;180:609-613. https://doi.org/10.3233/978-1-61499-101-4-609.
Sundhedsdatastyrelsen. Dokumentation af registre: Børnedatabasen [Documentation of registries: The Children's Database]; 2018. https://www.esundhed.dk/Dokumentation/DocumentationExtended?id=20 (Accessed August 24, 2018)
Sundhedsstyrelsen. Monitorering Af Vaekst Hos 0-5-Årige Børn: Vejledning Til Sundhedsplejersker Og Praktiserende Laeger [Monitoring Growth in 0-5 Year Old Children: Guidance to Health Visitors and General Practitioners]; 2015.
Thygesen LC, Daasnes C, Thaulow I, Brønnum-Hansen H. Introduction to Danish (nationwide) registers on health and social issues: structure, access, legislation, and archiving. Scand J Public Health. 2011;39(7 suppl):12-16. https://doi.org/10.1177/1403494811399956.
Bliddal M, Broe A, Pottegård A, Olsen J, Langhoff-Roos J. The Danish Medical birth register. Eur J Epidemiol. 2018;33(1):27-36. https://doi.org/10.1007/s10654-018-0356-1.
Jensen VM, Rasmussen AW. Danish education registers. Scand J Public Health. 2011;39(7 suppl):91-94. https://doi.org/10.1177/1403494810394715.
Baadsgaard M, Quitzau J. Danish registers on personal income and transfer payments. Scand J Public Health. 2011;39(7 suppl):103-105. https://doi.org/10.1177/1403494811405098.
Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ. 2000;320(7244):1240-1243. https://doi.org/10.1136/bmj.320.7244.1240.
Sundhedsstyrelsen. Vejledning Om Forebyggende Sundhedsydelser Til Børn Og Unge [Guidance on Preventive Health Services for Children and Adolescents]; 2011.
Hendriksen JMT, Geersing GJ, Moons KGM, de Groot JAH. Diagnostic and prognostic prediction models. J Thromb Haemost. 2013;11(suppl 1):129-141. https://doi.org/10.1111/jth.12262.
Moons KGM, Kengne AP, Woodward M, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart. 2012;98(9):683-690. https://doi.org/10.1136/heartjnl-2011-301246.
UNESCO Institute for Statistics. International Standard Classification of Education ISCED 2011; 2012.
Ullits LR, Ejlskov L, Mortensen RN, et al. Socioeconomic inequality and mortality - a regional Danish cohort study. BMC Public Health. 2015;15:490. https://doi.org/10.1186/s12889-015-1813-3.
World Health Organization. Body mass index - BMI; 2018. http://www.euro.who.int/en/health-topics/disease-prevention/nutrition/a-healthy-lifestyle/body-mass-index-bmi (Accessed February 24, 2018)
Kramer MS, Platt RW, Wen SW, et al. A new and improved population-based Canadian reference for birth weight for gestational age. Pediatrics. 2001;108(2):E35. https://doi.org/10.1542/peds.108.2.e35.
World Health Organization. WHO Anthro (version 3.2.2, January 2011) and macros; 2011. http://www.who.int/childgrowth/software/en/
Zheng M, Lamb KE, Grimes C, et al. Rapid weight gain during infancy and subsequent adiposity: a systematic review and meta-analysis of evidence. Obes Rev. 2018;19(3):321-332. https://doi.org/10.1111/obr.12632.
Harrell FE. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. New York: Springer; 2001. doi:https://doi.org/10.1007/978-3-319-19425-7
Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidimiology. 2010;21(1):128-138. https://doi.org/10.1097/EDE.0b013e3181c30fb2.
Cook NR, Ridker PM. The use and magnitude of reclassification measures for individual predictors of global cardiovascular risk. Ann Intern Med. 2009;150(11):795-802.
Graversen L, Sørensen TIA, Gerds TA, et al. Prediction of adolescent and adult adiposity outcomes from early life anthropometrics. Obesity. 2015;23(1):162-169. https://doi.org/10.1002/oby.20921.
R Core Team. R: A Language and Environment for Statistical Comput Secur; Vienna, Austria: R Foundation for Statistical Computing; 2018.
Gerds TA, Ozenne B. riskRegression: Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks; 2018.
Kundu S, Aulchenko YS, Janssens CJ. Predict ABEL: Assessment of Risk Prediction Models; 2012.
The National Committee on Health Research Ethics. Act on research ethics review of health research projects; 2018. http://en.nvk.dk/rules-and-guidelines/act-on-research-ethics-review-of-health-research-projects (Accessed April 2, 2019)
Gluckman PD, Hanson MA. Developmental and epigenetic pathways to obesity: An evolutionary-developmental perspective. Int J Obes. 2008;32:S62-S71. https://doi.org/10.1038/ijo.2008.240.
Heerwagen MJR, Miller MR, Barbour LA, Friedman JE. Maternal obesity and fetal metabolic programming: a fertile epigenetic soil. Am J Phys Regul Integr Comp Phys. 2010;299(3):R711-R722. https://doi.org/10.1152/ajpregu.00310.2010.
Hernandez PA, Graham CH, Master LL, Albert DL. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography (Copenhagen). 2006;29(5):773-785.
Butler ÉM, Derraik JGB, Taylor RW, Cutfield WS. Childhood obesity: how long should we wait to predict weight? J Pediatr Endocrinol Metab. 2018;31(5):497-501. https://doi.org/10.1515/jpem-2018-0110.
Bentley F, Swift JA, Cook R, Redsell SA. “I would rather be told than not know” - A qualitative study exploring parental views on identifying the future risk of childhood overweight and obesity during infancy. BMC Public Health. 2017;17(1):1-10. https://doi.org/10.1186/s12889-017-4684-y.
Steyerberg EW. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. New York: Springer; 2009.
Hlatky MA, Greenland P, Arnett DK, et al. Criteria for Evaluation of Novel Markers of Cardiovascular Risk. Circulation. 2009;119(17):2408-2416. https://doi.org/10.1161/CIRCULATIONAHA.109.192278.
Ahrens W, Pigeot I, Pohlabeln H, et al. Prevalence of overweight and obesity in European children below the age of 10. Int J Obes. 2014;38(S2):S99-S107. https://doi.org/10.1038/ijo.2014.140.
Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49(12):1373-1379. https://doi.org/10.1016/s0895-4356(96)00236-3.
Tripepi G, Jager KJ, Dekker FW, Zoccali C. Selection bias and information bias in clinical research. Nephron Clin Pract. 2010;115(2):c94-c99. https://doi.org/10.1159/000312871.
Larsen LM, Hertel NT, Mølgaard C, Christensen RD, Husby S, Jarbøl DE. Prevalence of overweight and obesity in Danish preschool children over a 10-year period: A study of two birth cohorts in general practice. Acta Paediatr. 2012;101(2):201-207. https://doi.org/10.1111/j.1651-2227.2011.02551.x.
Sjöberg Brixval C, Johansen A, Rasmussen M, Due P. Overvaegt Blandt Børn i Region Hovedstaden i Perioden 2002-2014 [Obesity Among Children in the Capital Region in the Period 2002-2014]; 2017.
Pearson S, Hansen B, Sørensen TIA, Baker JL. Overweight and obesity trends in Copenhagen school children from 2002 to 2007. Acta Paediatr. 2010;99(11):1675-1678. https://doi.org/10.1111/j.1651-2227.2010.01897.x.
Butler ÉM, Derraik JGB, Taylor RW, Cutfield WS. Prediction models for early childhood obesity: applicability and existing issues. Horm Res Paediatr. 2018;90(6):358-367. https://doi.org/10.1159/000496563.
Gluckman PD, Hanson MA, Cooper C, Thornburg KL. Effect of in utero and early-life conditions on adult health and disease. N Engl J Med. 2008;359(1):61-73. https://doi.org/10.1056/nejmra0708473.
Gillman MW. Developmental origins of health and disease. N Engl J Med. 2005;353(17):1848-1850. https://doi.org/10.1056/NEJMe058187.
Brisbois TD, Farmer AP, McCargar LJ. Early markers of adult obesity: a review. Obes Rev. 2012;13(4):347-367. https://doi.org/10.1111/j.1467-789X.2011.00965.x.