ESPE Abstracts (2019) 92 P1-47

Fat, Metabolism and Obesity

How Early is 'extreme Early-Onset Obesity'? Results of Comprehensive Growth Curve Analysis to Identify Genetic Obesity Disorders Based on Age of Onset of Obesity

Ozair Abawi1,2, Lotte Kleinendorst1,2,3, Lizette Blankers1,2, Mieke van Haelst3, Bibian van der Voorn1,2,4, Erica van den Akker1,2


1Obesity Center CGG, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands. 2Dept. of Pediatrics div. of Endocrinology, Erasmus MC-Sophia, University Medical Center Rotterdam, Rotterdam, Netherlands. 3Dept. of Clinical Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam/University of Amsterdam, Amsterdam, Netherlands. 4Dept. of Internal Medicine, div. of Endocrinology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands

Background: Early-onset obesity is associated with genetic obesity disorders. According to the Endocrine Society guideline for paediatric obesity, genetic screening is indicated in selected cases with age of onset (AoO) of obesity <5 years. However, this cut-off value lacks evidence. Identifying genetic obesity is vital as treatment for leptin-melanocortin pathway disorders becomes available. We aimed to determine whether AoO of obesity is predictive for genetic causes in children with obesity and to identify the optimal cut-off value.

Methods: In this prospective observational study, patients visiting a specialized paediatric obesity center were included. All included patients underwent genetic testing (obesity gene panel and microarray analysis). Genetic obesity disorders were grouped based on presence or absence of intellectual disability (ID). We compared AoO in genetic obesity patients to AoO in patients with lifestyle-induced severe obesity, which are patients without a somatic diagnosis (i.e., no genetic, endocrine, cerebral or medication-induced obesities) in whom lifestyle factors played the main role. AoO of obesity was determined by assessment of patients' growth charts. Obesity was defined <2yrs as weight-for height >+3SDS for WHO median, and >2yrs as BMI-for-age >+2.3SDS. Performance of AoO (positive likelihood ratio; LR+, area under the curve; AUC) and optimal cut-off by Youden's J were determined.

Results: In total, n=84 patients were included: 34 with genetic obesity (16 with ID and 18 without) and 50 with lifestyle-induced obesity. At screening, median age was 11.6 years (IQR 7.78–14.8); mean BMI +3.8SDS (SD 1.2). Mean number of growth measurements <5yrs was 11 (SD 5). Median AoO was 0.7 years (IQR 0.4–1.1) in genetic obesity without ID, 2.4 years (IQR 1.2–6.2) in genetic obesity with ID and 3.1 years (IQR 1.8–4.7) in lifestyle-induced obesity. AoO ≤1.0 years was the most optimal cut-off (sensitivity 53%, specificity 96%, LR+ 13.2) compared to the cut-off <5 years (sensitivity 82%, specificity 22%, LR+ 1.1). AoO performed well in identifying patients with genetic obesity without ID (AUC 0.903, P<0.001), but not for genetic obesity with ID (AUC 0.555, P=0.735).

Conclusion: Age of onset of obesity, with optimal cut-off value ≤1.0 years, is an appropriate predictor to identify which children with obesity without ID should undergo genetic screening and is more discriminative than the cut-off value <5 years stated in the guideline. However, for genetic obesity with ID, AoO of obesity was not discriminative compared to children with severe lifestyle-induced obesity referred to a specialized pediatric obesity center.

Volume 92

58th Annual ESPE meeting

Vienna, Austria
19 Sep 2019 - 21 Sep 2019

European Society for Paediatric Endocrinology 

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