Indices of Insulin Resistance in Children Born after Intracytoplasmic Sperm Injection (ICSI): Biochemical and Metabolomics Analyses

Program: Abstracts - Orals, Featured Poster Presentations, and Posters
Session: MON 596-630-Pediatric Endocrinology
Monday, June 17, 2013: 1:45 PM-3:45 PM
Expo Halls ABC (Moscone Center)

Poster Board MON-604
Alexandra Gkourogianni*1, Ioanna Kosteria1, Aristeidis Telonis2, Alexandra Margeli3, Ioannis Papassotiriou3, Maria Konsta1, Dimitrios Loutradis4, Georgios Mastorakos5, Maria I. Klapa2, Christina Kanaka-Gantenbein1 and George P. Chrousos1
1University of Athens School of Medicine, Athens, Greece, 2Institute of Chemical Engineering Sciences, Foundation for Research and Technology-Hellas (FORTH/ICE-HT), Patras, Greece, 3Agia Sophia Children's Hospital, Athens, Greece, 4Division of In Vitro Fertilization, Greece, 5Athens Medical School, Athens, Greece
Background: The Intracytoplasmic Sperm Injection (ICSI) procedure, introduced in 1992 primarily for the treatment of male infertility, bypasses several natural selection barriers raising serious concerns about genetic, epigenetic and developmental risks to the ICSI offspring. Recently, metabolomics has provided a sensitive high-throughput monitoring tool of the metabolic state, considered substantial for in-depth systemic studies of human physiology.

Aims:To assess the metabolic profile of children born after ICSI in comparison to naturally conceived (NC) controls using standard biochemical metabolic markers and metabolomics.

Subjects and Methods: Metabolic profiles of plasma samples from 10 ICSI and 10 NC pre-pubertal female children matched for age and parity were analysed. Along with anthropometric (height, weight, BMI, blood pressure, WHR, etc) and standard laboratory parameters (glucose, lipids, insulin, etc), we measured the metabolic profiles of the subjects using GC-MS (Gas Chromatography-Mass spectrometry).

After appropriate normalization, the profiles of 72 metabolites were analyzed using multivariate statistical algorithms as implemented in TM4/MeV and ΧLSTAT software. The identified metabolic differences between the two groups were visualized by positioning the metabolites, the concentration of which was identified as significantly discriminatory, on the reconstructed inter-organ metabolic network.

Results: Partial least squares discriminant analysis (PLS-DA) of the metabolic parameters indicated that the two groups can be distinguished based on their 72 metabolite profiles. The separation was markedly augmented when the two datasets were compared with respect to both the metabolomics and the standard biochemical markers. Significance analysis for microarrays (SAM) indicated 38 metabolites and 2 standard biochemical markers with significantly different circulating concentrations in the ICSI group, most of them having been associated earlier with obesity, insulin resistance, and metabolic syndrome.

Conclusions: The results support an increased risk for insulin resistance in children conceived by ICSI, even before any standard biochemical abnormalities become evident. They demonstrate the usefulness of metabolomics in providing a high resolution perspective of the metabolic state, enabling the determination of characteristic metabolic profiles in complex physiological conditions.

(1) Kanaka-Gantenbein, C.,  G.P. Chrousos et al.,  Prog Brain Res, 2010; 182: 161-74. (2) Kanani H, Klapa MI et al., J Chromatogr B Analyt Technol Biomed Life Sci. 2008; 15;871(2):191-201.

Nothing to Disclose: AG, IK, AT, AM, IP, MK, DL, GM, MIK, CK, GPC

*Please take note of The Endocrine Society's News Embargo Policy at

Sources of Research Support: Supported by a Grant of the Hellenic Endocrine Society