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Lancet Child Adolesc Health. 2019 Aug 23. pii: S2352-4642(19)30204-4. doi: 10.1016/S2352-4642(19)30204-4. [Epub ahead of print]

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

Author information

1
Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.
2
The George Institute for Global Health, Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK; UNSW, Sydney, NSW, Australia.
3
College of Public Health, The Ohio State University, Columbus, OH, USA.
4
Department of Medicine, University of Turku, Turku, Finland; Division of Medicine, Turku University Hospital, Turku, Finland.
5
Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, IA, USA.
6
Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
7
Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
8
University of Colorado School of Medicine, Aurora, CO, USA.
9
Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia.
10
Department of Pediatric Cardiology, University of Minnesota Masonic Children's Hospital, Minneapolis, MN, USA.
11
The Heart Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA.
12
Department of Epidemiology, Tulane University Health Sciences Center, Tulane University, New Orleans, LA, USA.
13
The Royal Children's Hospital and Murdoch Children's Research Institute, Royal Children's Hospital, Melbourne, VIC, Australia.
14
Division of Public Health Sciences, Wake Forest University School of Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA.
15
Department of Pediatrics, University of Minnesota Masonic Children's Hospital, Minneapolis, MN, USA.
16
Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland; Paavo Nurmi Centre, Sports and Exercise Medicine Unit, Department of Health and Physical Activity, University of Turku, Turku, Finland; Centre for Population Health Research, Turku University Hospital, Turku, Finland.
17
Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland; Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland; Centre for Population Health Research, Turku University Hospital, Turku, Finland.
18
The George Institute for Global Health, Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK. Electronic address: terence.dwyer@georgeinstitute.ox.ac.uk.

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.

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