Preliminary evaluation of a novel nine-biomarker profile for the prediction of autism spectrum disorder

Autoři: Afaf El-Ansary aff001;  Wail M. Hassan aff004;  Maha Daghestani aff001;  Laila Al-Ayadhi aff003;  Abir Ben Bacha aff007
Působiště autorů: Central Laboratory, Center for Female Scientific and Medical Colleges, King Saud University, Riyadh, Saudi Arabia aff001;  Therapeutic Chemistry Department, National Research Centre, Dokki, Cairo, Egypt aff002;  Autism Research and Treatment Center, King Saud University, Riyadh, Saudi Arabia aff003;  Department of Biomedical Sciences, University of Missouri- Kansas City School of Medicine, Missouri, United States of America aff004;  Zoology Department, Science College, King Saud University, Riyadh, Saudi Arabia aff005;  Department of Physiology, Faculty of Medicine, King Saud University, Riyadh, Saudi Arabia aff006;  Biochemistry Department, Science College, King Saud University, Riyadh, Saudi Arabia aff007;  Laboratory of Plant Biotechnology Applied to Crop Improvement, Faculty of Science of Sfax, University of Sfax, Tunisia aff008
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article



Autism spectrum disorder (ASD) is a complex group of heterogeneous neurodevelopmental disorders the prevalence of which has been in the rise in the past decade. In an attempt to better target the basic causes of ASD for diagnosis and treatment, efforts to identify reliable biomarkers related to the body’s metabolism are increasing. Despite an increase in identifying biomarkers in ASD, there are none so far with enough evidence to be used in routine clinical examination, unless medical illness is suspected. Promising biomarkers include those of mitochondrial dysfunction, oxidative stress, energy metabolism, and apoptosis.

Methods and participants

Sodium (Na+), Potassium (K+), glutathione (GSH), glutathione-s-transferase (GST), Creatine kinase (CK), lactate dehydrogenase (LDH), Coenzyme Q10, and melatonin (MLTN) were evaluated in 13 participants with ASD and 24 age-matched healthy controls. Additionally, five ratios, which include Na+/K+, GSH:GST, CK:Cas7, CoQ10: Cas 7, and Cas7:MLTN, were tested to measure their predictive values in discriminating between autistic individuals and controls. These markers, either in absolute values, as five ratios, or combined (9 markers + 5 ratios) were subjected to a principal component analysis and multidimensional scaling (MDS), and hierarchical clustering, which are helpful statistical tools in the field of biomarkers.


Our data demonstrated that both PCA and MDS analysis were effective in separating autistic from control subjects completely. This was also confirmed through the use of hierarchical clustering, which showed complete separation of the autistic and control groups based on nine biomarkers, five biomarker ratios, or a combined profile. Excellent predictive value of the measured profile was obtained using the receiver operating characteristics analysis, which showed an area under the curve of 1.


The availability of an improved predictive profile, represented by nine biomarkers plus the five ratios, inter-related different etiological mechanisms in ASD and would be valuable in providing greater recognition of the altered biological pathways in ASD. Our predictive profile could be used for the diagnosis and intervention of ASD.

Klíčová slova:

Autism – Autism spectrum disorder – Biomarkers – Glutathione – Glutathione chromatography – Melatonin – Mitochondria – principal component analysis


1. American Psychiatric Association (2013): Diagnostic and Statistical Manual of Mental Disorders, 5th ed. Arlington, VA: American Psychiatric Publishing.

2. Xu Guifeng, Strathearn Lane, Liu Buyun, Bao Wei, 2018. Corrected prevalence ofautism spectrum disorder among US children and adolescents. JAMA 28, 4–5.

3. Baio J, Wiggins L, Christensen DL, Maenner MJ, Daniels J, Warren Z, et al. (2018): Prevalence of autism spectrum disorder among children aged 8 years—Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2014. MMWR Surveill Summ 67:1–23.

4. Lukmanji S, Manji SA, Kadhim S, Sauro KM, Wirrell EC, Kwon CS, Jetté N. The co-occurrence of epilepsy and autism: A systematic review. Epilepsy Behav. 2019Sep;98(Pt A):238–248. doi: 10.1016/j.yebeh.2019.07.037 31398688

5. Kerns CM, Kendall PC, Berry L, Souders MC, Franklin ME, Schultz RT, et al. Traditional and atypical presentations of anxiety in youth with autism spectrum disorder. J Autism Dev Disord. 2014;44:2851–2861. doi: 10.1007/s10803-014-2141-7 24902932

6. Lasheras I, Seral P, Latorre E, Barroso E, Gracia-García P, Santabárbara J. Microbiota and gut-brain axis dysfunction in autism spectrum disorder: Evidence for functional gastrointestinal disorders. Asian J Psychiatr. 2019 Nov 12;47:101874. doi: 10.1016/j.ajp.2019.101874 31785441

7. PoChen C, Shur-FenGau S,ChunLee C Toward differential diagnosis of autism spectrum disorder using multimodal behavior descriptors and executive functions. Computer Speech & Language. 2019; 56: 17–35

8. Gabis LV. Chapter 4 - Autism spectrum disorder: A clinical path to early diagnosis, evaluation, and intervention. In: Neuroprotection in Autism, Schizophrenia and Alzheimer's Disease 2020, Edited by: Illana Gozes and Joseph Levine, PP79-100. Academic press.

9. Preeti K DP, Srinath DS, Seshadri DS, Girimaji DS, Kommu DJ. Lost time-Need for more awareness in early intervention of autism spectrum disorder. Asian J Psychiatr.2017;25:13–15. doi: 10.1016/j.ajp.2016.07.021 28262133

10. Dawson G. Recent advances in research on early detection, causes, biology, and treatment of autism spectrum disorders. Curr Opin Neurol. 2010;23:95–96. doi: 10.1097/WCO.0b013e3283377644 20216345

11. Fontil L, Sladeczek IE, Gittens J, Kubishyn N, Habib K. From early intervention to elementary school: A survey of transition support practices for children with autism spectrum disorders. Res Dev Disabil. 2019 May;88:30–41. doi: 10.1016/j.ridd.2019.02.006 30851481

12. El-Ansary A, Hassan WM, Qasem H, Das UN. Identification of Biomarkers of Impaired Sensory Profiles among Autistic Patients. PLoS One. 2016 Nov 8;11(11):e0164153. doi: 10.1371/journal.pone.0164153 27824861

13. Hassan WM, Al-Ayadhi L, Bjørklund G, Alabdali A, Chirumbolo S, El-Ansary A. The Use of Multi-parametric Biomarker Profiles May Increase the Accuracy of ASDPrediction. J Mol Neurosci. 2018 Sep;66(1):85–101 doi: 10.1007/s12031-018-1136-9 30112624

14. Beger RD, Dunn W, Schmidt MA, Gross SS, Kirwan JA, Cascante M, et al. Metabolomics enables precision medicine: “A White Paper, Community Perspective.” Metabolomics 2016;12:149. doi: 10.1007/s11306-016-1094-6 27642271

15. O'Neill J, Bansal R, Goh S, Rodie M, Sawardekar S, Peterson BS. Parsing the Heterogeneity of Brain Metabolic Disturbances in Autism Spectrum Disorder. Biol Psychiatry. 2019 Jun 21. pii: S0006-3223(19)31449-0.

16. Shoffner J, Hyams L, Langley GN, Cossette S, Mylacraine L, Dale J. et al. Fever plus mitochondrial disease could be risk factors for autistic regression. Journal of child neurology 2010; 25(4): 429–434 doi: 10.1177/0883073809342128 19773461

17. Mierau SB, Neumeyer AM. Metabolic interventions in Autism Spectrum Disorder.Neurobiol Dis. 2019;132:104544. doi: 10.1016/j.nbd.2019.104544 31351171

18. Fraguas D, Díaz-Caneja CM, Pina-Camacho L, Moreno C, Durán-Cutilla M, Ayora M, González-Vioque E, de Matteis M, Hendren RL, Arango C, Parellada M. Dietary Interventions for Autism Spectrum Disorder: A Meta-analysis. Pediatrics. 2019 Nov;144(5). pii: e20183218. doi: 10.1542/peds.2018-3218 Epub 2019 Oct 4. Review.31586029.

19. Chauhan A, Gu F, Essa MM, Wegiel J, Kaur K, Brown WT, Chauhan V. Brain region-specific deficit in mitochondrial electron transport chain complexes in children with autism. J Neurochem. 2011;117:209–220. doi: 10.1111/j.1471-4159.2011.07189.x 21250997

20. Khemakhem AM, Frye RE, El-Ansary A, Al-Ayadhi L, Bacha AB. Novel biomarkers of metabolic dysfunction in autism spectrum disorder: potential for biological diagnostic markers. Metab. Brain Dis. 2017;32;1983–1997. doi: 10.1007/s11011-017-0085-2 28831647

21. Nguyen HTN, Kato H, Masuda K, Yamaza H, Hirofuji Y, Sato H, Pham TTM, Takayama F, Sakai Y, Ohga S, Taguchi T, Nonaka K. Impaired neurite development associated with mitochondrial dysfunction in dopaminergic neurons differentiated from exfoliated deciduous tooth-derived pulp stem cells of children with autism spectrum disorder. Biochem Biophys Rep. 2018 21;16: 24–31. doi: 10.1016/j.bbrep.2018.09.004 30258988

22. Ford TC, Abu-Akel A, Crewther DP. The association of excitation and inhibition signaling with the relative symptom expression of autism and psychosis-proneness: Implications for psychopharmacology. Prog Neuropsychopharmacol Biol Psychiatry. 2019;88:235–242. doi: 10.1016/j.pnpbp.2018.07.024 Epub 2018 Jul 31. 30075170.

23. El-Ansary A. Data of multiple regressions analysis between selected biomarkers related to glutamate excitotoxicity and oxidative stress in Saudi autistic patients. Data Brief. 2016 Feb 15;7:111–6. doi: 10.1016/j.dib.2016.02.025 2016 Jun. 26933667; PubMed Central PMCID: PMC4764897.

24. Castora FJ. Mitochondrial function and abnormalities implicated in the pathogenesis of ASD. Prog Neuropsychopharmacol Biol Psychiatry. 2018 Dec 29; 92:83–108. doi: 10.1016/j.pnpbp.2018.12.015 [Epub ahead of print] Review. 30599156

25. Randall M, Egberts KJ, Samtani A, Scholten RJ, Hooft L, Livingstone N, Sterling-Levis K, Woolfenden S, Williams K. Diagnostic tests for autism spectrum disorder (ASD) in preschool children. Cochrane Database Syst Rev. 2018; 7: CD009044. doi: 10.1002/14651858.CD009044.pub2 30075057

26. Mannervik B. The isoenzymes of glutathione transferase. Adv Enzymol Relat Areas Mol Biol. 1985; 57:357–417. doi: 10.1002/9780470123034.ch5 3898742

27. Beutler E, Duron O, Kelly BM. Improved method for the determination of blood glutathione. J Lab Clin Med. 1963; 61:882–8. 13967893

28. Schumann G, Bonora R, Ceriotti F, Clerc-Renaud P, Ferrero CA, Férard G, et al. IFCC primary reference procedures for the measurement of catalytic activity concentrations of enzymes at 37 degrees C. Part 2. Reference procedure for the measurement of catalytic concentration of creatine kinase. Clin Chem Lab Med. 2002; 40(6):635–42. doi: 10.1515/CCLM.2002.110 12211662

29. Amador E, Dorfman LE, Wacker WE. Serum lactic dehydrogenase activity: an analytical assessment of current assays. Clin Chem. 1963;9(4):391–9.

30. Wacker WE, Ulmer DD, Vallee BL. Metalloenzymes and myocardial infarction: Malic and lactic dehydrogenase activities and zinc concentrations in serum. N Engl J Med. 1956; 255(10):449–56.

31. Kintz P, Cirimele V, Jeanneau T, Ludes B. Identification of testosterone and testosterone esters in human hair. J Anal Toxicol. 1999;23:352–56. doi: 10.1093/jat/23.5.352 10488923

32. Barderas MG, Laborde CM, Posada M, de la Cuesta F, Zubiri I, Vivanco F, Alvarez-Llamas G. (2011). Metabolomic profiling for identification of novel potential biomarkers in cardiovascular diseases. J Biomed Biotechnol. 2011;1–9, 11107243 doi: 10.1155/2012/728342

33. Park JC, Han SH, Lee H, Jeong H, Byun MS, Bae J, Kim H, Lee DY, Yi D, Shin SA, Kim YK, Hwang D, Lee SW, Mook-Jung I. Prognostic plasma protein panel for Aβ deposition in the brain in Alzheimer's disease. Prog Neurobiol. 2019 Dec;183:101690. doi: 10.1016/j.pneurobio.2019.101690 Epub 2019 Oct 9. 31605717.

34. Heuer LS, Croen LA, Jones KL, Yoshida CK, Hansen RL, Yolken R, Zerbo O, DeLorenze G, Kharrazi M, Ashwood P, Van de Water J. An Exploratory Examination of Neonatal Cytokines and Chemokines as Predictors of Autism Risk: The Early Markers for Autism Study. Biol Psychiatry. 2019 Aug 15;86(4):255–264. doi: 10.1016/j.biopsych.2019.04.037 31279535

35. El-Ansary A, Al-Daihan S, Al-Dbass A, Al-Ayadhi L. Measurement of selected ions related to oxidative stress and energy metabolism in Saudi autistic children. Clin Biochem. 2010;43:63–70. doi: 10.1016/j.clinbiochem.2009.09.008 Epub 2009 Sep 23. 19781542.

36. Guglielmi L, Servettini I, Caramia M, Catacuzzeno L, Franciolini F, D'Adamo MC, Pessia M. Update on the implication of potassium channels in autism: K(+) channelautism spectrum disorder. Front Cell Neurosci. 2015 Mar 2;9:34. doi: 10.3389/fncel.2015.00034 25784856

37. James S.J., Rose S., Melnyk S., Jernigan S., Blossom S., Pavliv O., Gaylor D.W., 2009. Cellular and mitochondrial glutathione redox imbalance in lymphoblastoid cells derived from children with autism. FASEB J. 23, 2374–2383. doi: 10.1096/fj.08-128926 19307255

38. Marí M, Morales A, Colell A, García-Ruiz C, Kaplowitz N, Fernández-Checa JC. Mitochondrial glutathione: features, regulation and role in disease. Biochim Biophys Acta. 2013 May;1830(5):3317–28. doi: 10.1016/j.bbagen.2012.10.018 23123815

39. Faber S, Fahrenholz T, Wolle MM, Kern JC 2nd, Pamuku M, Miller L, Jamrom J, Skip Kingston HM. Chronic exposure to xenobiotic pollution leads to significantly higher total glutathione and lower reduced to oxidized glutathione ratio in red blood cells of children with autism. Free Radic Biol Med. 2019; 134:666–677. doi: 10.1016/j.freeradbiomed.2019.02.009 30763613

40. Schlattner U, Tokarska-Schlattner M, Wallimann T. Mitochondrial creatine kinase in human health and disease. Biochim Biophys Acta 2006;1762(2):164–80 doi: 10.1016/j.bbadis.2005.09.004 16236486

41. Ostojic SM. Plasma creatine as a marker of mitochondrial dysfunction. Med Hypotheses. 2018;113:52–53. doi: 10.1016/j.mehy.2018.02.022 Epub 2018 Feb21. 29523294.

42. Cornelius N, Wardman JH, Hargreaves IP, Neergheen V, Bie AS, Tümer Z, Nielsen JE, Nielsen TT. Evidence of oxidative stress and mitochondrial dysfunction in spinocerebellar ataxia type 2 (SCA2) patient fibroblasts: Effect of coenzyme Q10 supplementation on these parameters. Mitochondrion. 2017; 34:103–114. doi: 10.1016/j.mito.2017.03.001 28263872

43. Parker W D, Parks J, Filley C M, Kleinschmidt-DeMasters B K. Electron transport chain defects in Alzheimer's disease brain. Neurology 1994; 44:1090–1096. doi: 10.1212/wnl.44.6.1090 8208407

44. Smale G, Nichols N R, Brady D R, Finch C E, Horton W E. Evidence for apoptotic cell death in Alzheimer's disease. Exp Neurol. 1995;133: 225–230. doi: 10.1006/exnr.1995.1025 7544290

45. Braam W, Keijzer H, Struijker Boudier H, Didden R, Smits M, Curfs L. CYP1A2 polymorphisms in slow melatonin metabolisers: a possible relationship with autism spectrum disorder? J Intellect Disabil Res. 2013; 57(11):993–1000. doi: 10.1111/j.1365-2788.2012.01595.x 22823064

46. Goyal N, Kashyap B, Kaur IR. Significance of IFN- IFN-IFN-IFN-of IFN-IFN- IFN-lisers: a possible relatiextrapulmonary tuberculosis. Scand J Immunol. 2016 May;83(5):338–44 doi: 10.1111/sji.12424 26946082

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