Predicting overweight and obesity in young adulthood from childhood body-mass index: comparison of cutoffs derived from longitudinal and cross-sectional data

Noora Kartiosuo, Rema Ramakrishnan, Stanley Lemeshow, Markus Juonala, Trudy L. Burns, Jessica G. Woo, David R. Jacobs, Stephen R. Daniels, Alison Venn, Julia Steinberger, Elaine M. Urbina, Lydia Bazzano, Matthew A. Sabin, Tian Hu, Ronald J. Prineas, Alan R. Sinaiko, Katja Pahkala, Olli Raitakari, Terence Dwyer

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Background: Historically, cutoff points for childhood and adolescent overweight and obesity have been based on population-specific percentiles derived from cross-sectional data. To obtain cutoff points that might better predict overweight and obesity in young adulthood, we examined the association between childhood body-mass index (BMI) and young adulthood BMI status in a longitudinal cohort. Methods: In this study, we used data from the International Childhood Cardiovascular Cohort (i3C) Consortium (which included seven childhood cohorts from the USA, Australia, and Finland) to establish childhood overweight and obesity cutoff points that best predict BMI status at the age of 18 years. We included 3779 children who were followed up from 1970 onwards, and had at least one childhood BMI measurement between ages 6 years and 17 years and a BMI measurement specifically at age 18 years. We used logistic regression to assess the association between BMI in childhood and young adulthood obesity. We used the area under the receiver operating characteristic curve (AUROC) to assess the ability of fitted models to discriminate between different BMI status groups in young adulthood. The cutoff points were then compared with those defined by the International Obesity Task Force (IOTF), which used cross-sectional data, and tested for sensitivity and specificity in a separate, independent, longitudinal sample (from the Special Turku Coronary Risk Factor Intervention Project [STRIP] study) with BMI measurements available from both childhood and adulthood. Findings: The cutoff points derived from the longitudinal i3C Consortium data were lower than the IOTF cutoff points. Consequently, a larger proportion of participants in the STRIP study was classified as overweight or obese when using the i3C cutoff points than when using the IOTF cutoff points. Especially for obesity, i3C cutoff points were significantly better at identifying those who would become obese later in life. In the independent sample, the AUROC values for overweight ranged from 0·75 (95% CI 0·70–0·80) to 0·88 (0·84–0·93) for the i3C cutoff points, and the corresponding values for the IOTF cutoff points ranged from 0·69 (0·62–0·75) to 0·87 (0·82–0·92). For obesity, the AUROC values ranged from 0·84 (0·75–0·93) to 0·90 (0·82–0·98) for the i3C cutoff points and 0·57 (0·49–0·66) to 0·76 (0·65–0·88) for IOTF cutoff points. Interpretation: The childhood BMI cutoff points obtained from the i3C Consortium longitudinal data can better predict risk of overweight and obesity in young adulthood than can standards that are currently used based on cross-sectional data. Such cutoff points should help to more accurately identify children at risk of adult overweight or obesity. Funding: None.

Original languageEnglish (US)
Pages (from-to)795-802
Number of pages8
JournalThe Lancet Child and Adolescent Health
Volume3
Issue number11
DOIs
StatePublished - Nov 2019

Bibliographical note

Funding Information:
AV reports grants from the Australian National Health and Medical Research Council and grants from the US National Heart, Lung, and Blood Institute. EMU reports grants from US National Institutes of Health during the conduct of the study. All other authors declare no competing interests.

Fingerprint Dive into the research topics of 'Predicting overweight and obesity in young adulthood from childhood body-mass index: comparison of cutoffs derived from longitudinal and cross-sectional data'. Together they form a unique fingerprint.

Cite this