ESPE Abstracts (2015) 84 FC12.3

ESPE2015 Free Communications Obesity - Clinical (6 abstracts)

Re-Classification of Childhood Obesity by Steroid Metabolomic Disease Signature

Aneta Gawlik a , Michael Shmoish b , Michaela Hartmann c , Ewa Malecka-Tendera a , Stefan Wudy c & Ze’ev Hochberg d

aSchool of Medicine in Katowice, Department of Pediatrics, Pediatric Endocrinology and Diabetes, Medical University of Silesia, Katowice, Poland; bBioinformatics Knowledge Unit, Lorry I. Lokey Interdisciplinary Center for Life Sciences and Engineering, Technion – Israel Institute of Technology, Haifa, Israel; cSteroid Research and Mass Spectrometry Unit, Division of Pediatric Endocrinology and Diabetology, Center of Child and Adolescent Medicine, Justus-Liebig University, Giessen, Germany; dFaculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel

Context: Analysis of steroids by gas chromatography – mass spectrometry (GC-MS) defines a subject’s ‘steroidal fingerprint’. Here, we clustered steroidal fingerprints to classify childhood obesity by ’steroid metabolomic signatures’.

Methods: Urinary samples of 87 children (44 F) age 8.5–18.0 with obesity (BMI >97%) underwent solid phase extraction, enzymatic hydrolysis and derivatization. 31 steroids metabolites were quantified by GC-MS and quantities were Z-transformed based on sex and age. MetaboAnalyst 3.0, a software tool designed for metabolic data analysis, provided five unique k-means clusters. Steroidal signatures and clinical data of patients in each cluster were analysed, and ANOVA was utilized to biochemically and clinically characterize each cluster.

Results: Cluster 1 subjects (n=39, 21F) have normal steroid metabolome and is clinically unique only in that 28% of males have gynecomastia. Cluster 2 (n=20, 11F) show mild, nonspecific elevation of C19- and C21-steroids. Females show resistance to PCO (9% vs 24, 43, 40% in Clusters 1, 3, 5, resp.), but have hirsutism (45% vs 14 and 40% in Clusters 1 and 5, resp.). Cluster 3 (n=7, F only), all with partial or full PCOS, show relative 21-hydroxylase insufficiency. Cluster 4 (n=4, M only), show markedly elevated steroids and shift to 11-oxidized metabolites suggesting an imbalance in the 11ß-HSD system, insulin resistance (P<0.001), high GGTP levels (P=0.0015), and high systolic BP (P=0.027); half of them present features of liver steatosis in ultrasonography. Cluster 5 (n=17, 5F) have elevated DHEA and 17OH-pregnenolone metabolites, suggesting 3ß-HSD insufficiency but no clinically unique phenotype.

Conclusions: 1. We define a novel concept of ‘steroid metabolomic signature’ based on high throughput urinary steroidal GCMS data. 2. Clustering by software designed for metabolic data analysis re-classified childhood obesity into five entities with their unique steroid metabolomic signatures, which require further definition and may need cluster-specific therapy.

Article tools

My recent searches

No recent searches.