Russian Psychological Issues
JournalsTopicsAuthorsEditor's Choice Manuscript SubmissionAbout PsyJournals.ruContact Us

  Previous issue (2022. Vol. 20, no. 1)

Autism and Developmental Disorders

Publisher: Moscow State University of Psychology and Education

ISSN (printed version): 1994-1617

ISSN (online): 2413-4317


License: CC BY-NC 4.0

Published since 2003

Published quarterly

Free of fees
Open Access Journal


Autism Spectrum Disorders — in Search of Mechanistic Biomarkers 847


Rabbani N.
PhD, Reader of Experimental Systems Biology, Warwick Medical School and Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research; University of Warwick, Coventry, Great Britain

Thornalley P.J.
PhD, Direttore del Centro per la Ricerca sul Diabete, Diabetes Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University; Qatar Foundation, Doha, Qatar

Autism spectrum disorders are a group of neuropsychiatric conditions of increasing prevalence. They are initially detected in early development of children. Diagnosis is currently made on the basis of clinical behaviour and cognition. Improvements in accuracy, timeliness and access to diagnosis to help manage the condition is high on the agenda of the autistic communities. A blood test may help for early-stage detection of autism spectrum disorders to focus support where required — particularly when symptoms are most challenging. This article discusses briefly the scientific basis of diagnosis of autism spectrum disorders and recent emergence of candidate blood tests for autism. We conclude that further validation and improvements in understanding of autism spectrum disorders are required to provide the scientific basis and classifier characteristics for accurate and reliable diagnosis by clinical chemistry blood test.

Keywords: blood test, diagnosis, proteomics, autistic adults, delayed diagnosis


For Reference

  1. American Psychiatric Association: Diagnostic And Statistical Manual Of Mental Disorders [DSM-5]. Washington, DC: American Psychiatric Publishing, 2013.
  2. Anwar A, Abruzzo P.M., Pasha S., Rajpoot K., Bolotta A., Ghezzo A., Marini M., Posar A., Visconti P., Thornalley P.J., Rabbani N. Advanced glycation endproducts, dityrosine and arginine transporter dysfunction in autism — a source of biomarkers for clinical diagnosis. Molecular Autism, 2018, vol. 9:3.
  3. Bourgeron T. Current knowledge on the genetics of autism and propositions for future research. Comptes Rendus Biologies [Biology Reports], 2016, vol. 339, pp. 300—307.
  4. Diémé B., Mavel S., Blasco H., Tripi G., Bonnet-Brilhault F., Malvy J., Bocca C., Andres C.R., Nadal-Desbarats L., Emond P. Metabolomics Study of Urine in Autism Spectrum Disorders Using a Multiplatform Analytical Methodology. Journal of Proteome Research, 2015, vol. 14, pp. 5273—5282.
  5. Hallmayer J., Cleveland S., Torres A. et al. Genetic heritability and shared environmental factors among twin pairs with autism. Archives of General Psychiatry, 2011, vol. 68, pp. 1095—1102.
  6. Howsmon D.P., Kruger U., Melnyk S., James S.J., Hahn J. Classification and adaptive behavior prediction of children with autism spectrum disorder based upon multivariate data analysis of markers of oxidative stress and DNA methylation. PLOS Computational Biology, 2017, vol. 13. doi:10.1371/journal. pcbi.1005385
  7. Howsmon D.P., Vargason T., Rubin R.A., Delhey L., Tippett M., Rose S., Bennuri S.C., Slattery J.C., Melnyk S., James S.J., Frye R.E., Hahn J. Multivariate techniques enable a biochemical classification of children with autism spectrum disorder versus typically-developing peers: A comparison and validation study. Bioengineering & translational medicine, 2018, vol. 3, pp. 156—165.
  8. Hull L., Petrides K.V., Allison C., Smith P., Baron-Cohen S., Lai M.-C., Mandy W. “Putting on My Best Normal”: Social Camouflaging in Adults with Autism Spectrum Conditions. Journal of Autism and Developmental Disorders, 2017, vol. 47, pp. 2519—534.
  9. Jenkinson C.P., Göring H.H.H., Arya R., Blangero J., Duggirala R., DeFronzo R.A. Transcriptomics in type 2 diabetes: Bridging the gap between genotype and phenotype. Genomics Data, 2016, vol. 8, pp. 25—6.
  10. Melnyk S., Fuchs G.J., Schulz E., Lopez M., Kahler S.G., Fussell J.J., Bellando J., Pavliv O., Rose S., Seidel L., Gaylor D.W., James S.J. Metabolic Imbalance Associated with Methylation Dysregulation and Oxidative Damage in Children with Autism. Journal of Autism and Developmental Disorders, 2012, vol. 42, pp. 367—377.
  11. Momeni N., Bergquist J., Brudin L., Behnia F., Sivberg B., Joghataei M.T., Persson B.L. A novel bloodbased biomarker for detection of autism spectrum disorders. Translational Psychiatry, 2012, vol. 2. doi:10.1038/tp.2012.19
  12. Nava C., Rupp J., Boissel J.-P., Mignot C., Rastetter A., Amiet C., Jacquette A., Dupuits C., Bouteiller D., Keren B., Ruberg M., Faudet A., Doummar D., Philippe A., Périsse D., Laurent C., Lebrun N., Guillemot V., Chelly J., Cohen D., Héron D., Brice A., Closs E.I., Depienne C. Hypomorphic variants of cationic aminoacid transporter 3 in males with autism spectrum disorders. Amino Acids, 2015, vol. 47, pp. 2647—2658.
  13. Scherer S.W., Dawson G. Risk factors for autism: translating genomic discoveries into diagnostics. Human Genetics, 2011, vol. 130, pp. 123—148.
  14. Thornalley P.J. Quantitative screening of protein glycation, oxidation, and nitration adducts by LCMS/MS: protein damage in diabetes, uremia, cirrhosis, and Alzheimer’s disease. In Dalle-Donne I., Scaloni A., Butterfield D.A. (eds.) Redox Proteomics. Hoboken: Wiley, 2006. Pp. 681—28.
  15. Zwaigenbaum L., Penner M. Autism spectrum disorder: advances in diagnosis and evaluation. The BMJ, 2018, vol. 361, k1674.

© 2007–2022 Portal of Russian Psychological Publications. All rights reserved in Russian

Publisher: Moscow State University of Psychology and Education

Catalogue of academic journals in psychology & education MSUPE

Creative Commons License Open Access Repository     Webometrics Ranking of Repositories

RSS Psyjournals at Youtube ??????.???????