title: Potential selection effects when estimating associations between the infancy peak or adiposity rebound and later body mass index in children.
abstract: This study aims to evaluate a potential selection effect caused by exclusion of children with non-identifiable infancy peak (IP) and adiposity rebound (AR) when estimating associations between age and body mass index (BMI) at IP and AR and later weight status.
  
  
  
  In 4744 children with at least 4 repeated measurements of height and weight in the age interval from 0 to 8 years (37 998 measurements) participating in the IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants)/I.Family cohort study, fractional polynomial multilevel models were used to derive individual BMI trajectories. Based on these trajectories, age and BMI at IP and AR, BMI values and growth velocities at selected ages as well as the area under the BMI curve were estimated. The BMI growth measures were standardized and related to later BMI z-scores (mean age at outcome assessment: 9.2 years).
  
  
  
  Age and BMI at IP and AR were not identifiable in 5.4% and 7.8% of the children, respectively. These groups of children showed a significantly higher BMI growth during infancy and childhood. In the remaining sample, BMI at IP correlated almost perfectly (r⩾0.99) with BMI at ages 0.5, 1 and 1.5 years, whereas BMI at AR correlated perfectly with BMI at ages 4-6 years (r⩾0.98). In the total study group, BMI values in infancy and childhood were positively associated with later BMI z-scores where associations increased with age. Associations between BMI velocities and later BMI z-scores were largest at ages 5 and 6 years. Results differed for children with non-identifiable IP and AR, demonstrating a selection effect.
  
  
  
  IP and AR may not be estimable in children with higher-than-average BMI growth. Excluding these children from analyses may result in a selection bias that distorts effect estimates. BMI values at ages 1 and 5 years might be more appropriate to use as predictors for later weight status instead.
authors.affiliation: Biometry and Data Management, Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany.
authors.forename: C
authors.initials: C
authors.lastname: Börnhorst
authors.affiliation: Unit of Epidemiology and Population Genetics, Institute of Food Sciences, National Research Council, Avellino, Italy.
authors.forename: A
authors.identifier: 0000-0002-9797-2943
authors.initials: A
authors.lastname: Siani
authors.affiliation: Research and Education Institute of Child Health, Strovolos, Cyprus.
authors.forename: M
authors.initials: M
authors.lastname: Tornaritis
authors.affiliation: Department of Pediatrics, University of Pécs, Pécs, Hungary.
authors.forename: D
authors.initials: D
authors.lastname: Molnár
authors.affiliation: Section for Epidemiology and Social Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
authors.forename: L
authors.initials: L
authors.lastname: Lissner
authors.affiliation: School of Health and Welfare, Halmstad University, Halmstad, Sweden.
authors.forename: S
authors.initials: S
authors.lastname: Regber
authors.affiliation: Department of Intercultural Communication and Management, Copenhagen Business School, Frederiksberg, Denmark.
authors.forename: L
authors.initials: L
authors.lastname: Reisch
authors.affiliation: Department of Public Health, Ghent University, Ghent, Belgium.
authors.forename: A
authors.identifier: 0000-0002-9287-6813
authors.initials: A
authors.lastname: De Decker
authors.affiliation: GENUD (Growth, Exercise, Nutrition and Development) Research Group, Faculty of Health Sciences, Universidad de Zaragoza, Instituto Agroalimentario de Aragón (IA2), Instituto de Investigación Sanitaria Aragón (IIS Aragón), Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBERObn), Zaragoza, Spain.
authors.forename: L A
authors.initials: LA
authors.lastname: Moreno
authors.affiliation: Biometry and Data Management, Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany.|Faculty of Mathematics and Computer Science, University of Bremen, Bremen, Germany.
authors.forename: W
authors.initials: W
authors.lastname: Ahrens
authors.affiliation: Biometry and Data Management, Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany.|Faculty of Mathematics and Computer Science, University of Bremen, Bremen, Germany.
authors.forename: I
authors.initials: I
authors.lastname: Pigeot
mesh_terms: D050154:Adiposity / Q000502:physiology*; D017677:Age Distribution; D015992:Body Mass Index; D002648:Child; D002657:Child Development; D002664:Child Nutritional Physiological Phenomena; D002675:Child, Preschool; D015331:Cohort Studies; D004032:Diet; D002149:Energy Intake / Q000502:physiology; D005060:Europe / Q000453:epidemiology; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008019:Life Style; D008297:Male; D009752:Nutritional Status / Q000502:physiology; D016017:Odds Ratio; D063766:Pediatric Obesity / Q000453:epidemiology*; D012307:Risk Factors; D015430:Weight Gain / Q000502:physiology*