Metabolic profiling in children with autism spectrum disorder with and without mental regression: preliminary results from a cross-sectional case–control study

Abstract

Introduction

It is challenging to establish the mechanisms involved in the variety of well-defined clinical phenotypes in autism spectrum disorder (ASD) and the pathways involved in their pathogeneses.

Objectives

The aim of the present study was to evaluate the metabolomic profiles of children with ASD subclassified by mental regression (AR) phenotype and with no regression (ANR).

Methods

The present study was a cross-sectional case–control study. Thirty children aged 2–6 years with ASD were included: 15 with ANR and 15 with AR. In addition, a control group of 30 normally developing children was selected and matched to the ASD group by sex and age. Plasma samples were analyzed with a metabolomics single platform methodology based on liquid chromatography-mass spectrometry. Univariate and multivariate analysis, including orthogonal partial least squares-discriminant analysis modeling and Shared-and-Unique-Structures plots, were performed using MetaboAnalyst 4.0 and SIMCA-P 15. The primary endpoint was the metabolic signature profiling among healthy children and autistic children and their subgroups.

Results

Metabolomic profiles of 30 healthy children, 15 ANR and 15 AR were compared. Several differences between healthy children and children with ASD were detected, involving mainly amino acid, lipid and nicotinamide metabolism. Furthermore, we report subtle differences between the ANR and AR groups.

Conclusions

In this study, we report, for the first time, the plasmatic metabolomic profiles of children with ASD, including two different phenotypes based on mental regression status. The use of a liquid chromatography-mass spectrometry platform approach for metabolomics in ASD children using plasma appears to be very efficient and adds further support to previous findings in urine. Furthermore, the present study documents several changes related to amino acid, NAD+ and lipid metabolism that, in some cases, such as arginine and glutamate pathway alterations, seem to be associated with the AR phenotype. Further targeted analyses are needed in a larger cohort to validate the results presented herein.

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Fig. 1
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Data availability

The metabolomics datasets used and/or analyzed during the current study are included as Supplemental Material and any additional data is available from the corresponding author on reasonable request.

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Acknowledgements

We gratefully acknowledge the patients, controls and family members who participated in this study.

Funding

ODRH has received funding from the European Union Seventh Framework Programme (FP7-PEOPLE-2013-COFUND) under grant agreement n° 609020 - Scientia Fellows.

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Affiliations

Authors

Contributions

ODRH conducted the metabolomics data analyses, analyzed and interpreted the biochemical data and wrote the manuscript. AGF, MJTA, collected the plasma samples, interpreted the data. MGC and AG designed the study. JLPN, KFR, PMB were responsible of the clinical assessments and interpreted the data. All authors read and approved the manuscript.

Corresponding author

Correspondence to M. Gil-Campos.

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Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

The present study was a cross-sectional case–control study case–control study and was approved by the Clinical Research and Bioethics Committee at Reina Sofia University Hospital respecting the fundamental principles established in the Declaration of Helsinki of 1964.

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Informed consent from the children’s legal guardians was obtained.

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Rangel-Huerta, O.D., Gomez-Fernández, A., de la Torre-Aguilar, M.J. et al. Metabolic profiling in children with autism spectrum disorder with and without mental regression: preliminary results from a cross-sectional case–control study. Metabolomics 15, 99 (2019). https://doi.org/10.1007/s11306-019-1562-x

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Keywords

  • Autism spectrum disorders
  • Metabolomics
  • Metabolic profiling
  • Mental regression