O D Rangel-Huerta1,2, A Gomez-Fernández3, M J de la Torre-Aguilar3, A Gil4,5, J L Perez-Navero3, K Flores-Rojas3,6, P Martín-Borreguero7, M Gil-Campos8,9. 1. Department of Nutrition, University of Oslo, Oslo, Norway. 2. Norwegian Veterinary Institute, Oslo, Norway. 3. Department of Pediatrics, Reina Sofia University Hospital, University of Córdoba, IMIBIC, Córdoba, Spain. 4. Department of Biochemistry and Molecular Biology II, Institute of Nutrition and Food Technology "José Mataix", Centre for Biomedical Research, University of Granada, Granada, Spain. 5. CIBEROBN, Madrid, Spain. 6. Paediatric Metabolism Unit, CIBEROBN, Madrid, Spain. 7. Paediatric Mental Unit, Reina Sofia University Hospital, Córdoba, Spain. 8. Department of Pediatrics, Reina Sofia University Hospital, University of Córdoba, IMIBIC, Córdoba, Spain. mercedes_gil_campos@yahoo.es. 9. Paediatric Metabolism Unit, CIBEROBN, Madrid, Spain. mercedes_gil_campos@yahoo.es.
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.
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 autisticchildren 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 ASDchildren 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.
Authors: Anne M Evans; Claire O'Donovan; Mary Playdon; Chris Beecher; Richard D Beger; John A Bowden; David Broadhurst; Clary B Clish; Surendra Dasari; Warwick B Dunn; Julian L Griffin; Thomas Hartung; Ping- Ching Hsu; Tao Huan; Judith Jans; Christina M Jones; Maureen Kachman; Andre Kleensang; Matthew R Lewis; María Eugenia Monge; Jonathan D Mosley; Eric Taylor; Fariba Tayyari; Georgios Theodoridis; Federico Torta; Baljit K Ubhi; Dajana Vuckovic Journal: Metabolomics Date: 2020-10-12 Impact factor: 4.290
Authors: Cristina Piras; Michele Mussap; Antonio Noto; Andrea De Giacomo; Fernanda Cristofori; Martina Spada; Vassilios Fanos; Luigi Atzori; Ruggiero Francavilla Journal: Metabolites Date: 2022-01-23