METHODS FOR DIAGNOSING AN AUTISTIC SPECTRUM DISORDER

Abstract

The present invention provides methods for diagnosing an autistic spectrum disorder (ASD), comprising detecting the concentration of an amino acid adduct in a sample obtained from a subject, wherein said amino acid adduct is a glycated amino acid adduct, an oxidised amino acid adduct, or a nitrated amino acid adduct. Methods of the invention further comprise comparing the concentration of the amino acid adduct in the sample with the concentration of the same amino acid adduct in a reference standard; and identifying the presence or absence of a concentration difference of said amino acid adduct in the sample relative to the reference standard; wherein the presence or absence of a concentration difference correlates with the presence or absence of ASD. Diagnostic algorithms for use in methods of the invention are also provided.

Claims

1. A method for diagnosing an autistic spectrum disorder (ASD), said method comprising: a. detecting the concentration of an amino acid adduct in a sample obtained from a subject, wherein said amino acid adduct is a glycated amino acid adduct, an oxidised amino acid adduct, or a nitrated amino acid adduct; b. comparing the concentration of the amino acid adduct in the sample with the concentration of the same amino acid adduct in a reference standard; and c. identifying the presence or absence of a concentration difference of said amino acid adduct in the sample relative to the reference standard; wherein the presence or absence of a concentration difference correlates with the presence or absence of ASD.

2. A method for determining prognosis of an autistic spectrum disorder (ASD), said method comprising: a. detecting the concentration of an amino acid adduct in a sample obtained from a subject, wherein said amino acid adduct is a glycated amino acid adduct, an oxidised amino acid adduct, or a nitrated amino acid adduct; b. comparing the concentration of the amino acid adduct in the sample with the concentration of the same amino acid adduct in a reference standard; and c. identifying the presence or absence of a concentration difference of said amino acid adduct in the sample relative to the reference standard; wherein the presence or absence of a concentration difference correlates with a good prognosis or a poor prognosis.

3. A method for diagnosing an autistic spectrum disorder (ASD), said method comprising: a. detecting the concentration of an amino acid adduct in a sample obtained from a subject, wherein said amino acid adduct is a glycated amino acid adduct, an oxidised amino acid adduct, or a nitrated amino acid adduct; and b. classifying the health of the subject based on the concentration of the amino acid adduct detected in the sample with a diagnostic algorithm, wherein the diagnostic algorithm is trained on the corresponding concentration for the same amino acid adduct obtained from a population of subjects having known disease status, and thereby diagnosing the presence or absence of ASD in the subject.

4. A method for treating an autistic spectrum disorder (ASD), said method comprising: a. requesting the performance or obtaining the results of a method of claim 1 or 3; and b. administering to a subject diagnosed with ASD a therapy for ASD.

5. A method for identifying a therapy suitable for treating ASD, said method comprising: a. providing an isolated sample from a subject administered a candidate therapy; b. detecting the concentration of an amino acid adduct in said sample, wherein said amino acid adduct is a glycated amino acid adduct, an oxidised amino acid adduct, or a nitrated amino acid adduct; c. determining the relative concentration change of the amino acid adduct by comparing the concentration of the amino acid adduct detected in step (b) with the concentration of the amino acid adduct in an isolated sample from the subject prior to administration of the candidate therapy; and wherein the candidate therapy is suitable for treating ASD when a concentration change is detected after administering the candidate therapy; and wherein the candidate therapy is not suitable for treating ASD when a concentration change is not detected after administering the candidate therapy.

6. A method for monitoring the efficacy of an ASD therapy, said method comprising: a. providing an isolated sample from a patient administered said therapy; b. detecting the concentration of an amino acid adduct in said sample, wherein said amino acid adduct is a glycated amino acid adduct, an oxidised amino acid adduct, or a nitrated amino acid adduct; c. determining the relative concentration change of the amino acid adduct by comparing the concentration of the amino acid adduct detected in step (b) with the concentration of the amino acid adduct in an isolated sample from the subject at an earlier timepoint; and confirming the presence of efficacy when a concentration change is detected; and confirming the absence of efficacy when a concentration change is not detected.

7. The method according to claim 1-2 or 5-6, wherein steps (b) and/or (c) of claim 1 or 2 or step (c) of claim 5 or 6 are conducted with/using a diagnostic algorithm, preferably wherein the diagnostic algorithm is configured to diagnose the presence or absence of ASD based on the concentration of the amino acid adduct detected in the sample, wherein the diagnostic algorithm is trained on the corresponding concentration for the same amino acid adduct in one or more (preferably a plurality of) reference standards, or preferably wherein the diagnostic algorithm is configured to classify the health of the subject based on the concentration of the amino acid adduct detected in the sample, wherein the diagnostic algorithm is trained on the corresponding concentration for the same amino acid adduct obtained from a population of subjects having known disease status.

8. Use of a glycated amino acid adduct, an oxidised amino acid adduct, a nitrated amino acid adduct, or a combination thereof for: a. diagnosing ASD; b. determining prognosis of ASD; c. identifying a therapy suitable for treating ASD; d. monitoring efficacy of an ASD therapy; and/or e. use as a feature in an ASD diagnostic algorithm.

9. The method or use according to any one of the preceding claims, wherein the sample is selected from one or more of blood, blood plasma, blood plasma ultrafiltrate, urine, blood serum, synovial fluid and/or sputum.

10. The method or use according to any one of the preceding claims, wherein the amino acid adduct is one or more selected from N-carboxymethyl-lysine (CML), 3-deoxyglucosone-derived hydroimidazolone (3DG-H), N-carboxymethylarginine (CMA), glutamic semialdehyde (GSA), glyoxal-derived hydroimidazolone (G-H1), pyrraline, methylglyoxal-derived hydroimidazolone (MG-H1), N.sub.-fructosyl-lysine (FL), N.sub.-(1-carboxyethyl)lysine (CEL), -aminoadipic semialdehyde (AASA), and methylglyoxal-derived lysine dimer (MOLD).

11. The method or use according to any one of the preceding claims, wherein the amino acid adduct is at least two selected from N-carboxymethyl-lysine (CML), 3-deoxyglucosone-derived hydroimidazolone (3DG-H), N-carboxymethylarginine (CMA), dityrosine (DT), glutamic semialdehyde (GSA), glyoxal-derived hydroimidazolone (G-H1), pyrraline, methylglyoxal-derived hydroimidazolone (MG-H1), N.sub.-fructosyl-lysine (FL), N.sub.-(1-carboxyethyl)lysine (CEL), -aminoadipic semialdehyde (AASA), and methylglyoxal-derived lysine dimer (MOLD).

12. The method or use according to any one of the preceding claims, wherein: a. the concentration of one or more selected from: CML, CMA, and GSA is increased; and/or b. the concentration of one or more selected from: 3DG-H and G-H1 (free adduct) is decreased; when compared to a non-ASD reference standard, and indicates the presence of ASD.

13. The method or use according to any one of the preceding claims, wherein: a. the concentration of one or more selected from: CML, CMA, and GSA is the same or increased; and/or b. the concentration of one or more selected from: 3DG-H and G-H1 (free adduct) is the same or decreased; when compared to an ASD reference standard, and indicates the presence of ASD.

14. The method or use according to any one of the preceding claims, wherein: a. the concentration of two or more selected from: CML, CMA, DT, and GSA is increased; when compared to a non-ASD reference standard, and indicates the presence of ASD.

15. The method or use according to any one of the preceding claims, wherein: a. the concentration of two or more selected from: CML, CMA, DT, and GSA is the same or increased; when compared to an ASD reference standard, and indicates the presence of ASD.

16. The method or use according to any one of the preceding claims, wherein: a. the concentration of one or more selected from: CML, and CMA is increased; and/or b. the concentration of 3DG-H is decreased; when compared to a non-ASD reference standard, and indicates the presence of ASD.

17. The method or use according to any one of the preceding claims, wherein: a. the concentration of one or more selected from: CML, and CMA is the same or increased; and/or b. the concentration of 3DG-H is the same or decreased; when compared to an ASD reference standard, and indicates the presence of ASD.

18. The method or use according to any one of the preceding claims, wherein: a. the concentration of two or more selected from: CML, CMA, and DT is increased; when compared to a non-ASD reference standard, and indicates the presence of ASD.

19. The method or use according to any one of the preceding claims, wherein: a. the concentration of two or more selected from: CML, CMA, and DT is the same or increased; when compared to an ASD reference standard, and indicates the presence of ASD.

20. The method or use according to any one of the preceding claims, wherein the concentration of CML and/or CMA is increased when compared to a non-ASD reference standard, and indicates the presence of ASD.

21. The method or use according to any one of the preceding claims, wherein the concentration of CML and/or CMA is the same or increased when compared to an ASD reference standard, and indicates the presence of ASD.

22. The method or use according to any one of claims 12-21, wherein the sample is a blood sample.

23. The method or use according to any one of the preceding claims, wherein the concentration of GSA and/or pyrraline is increased; when compared to a non-ASD reference standard, and indicates the presence of ASD.

24. The method or use according to any one of the preceding claims, wherein the concentration of GSA and/or pyrraline is the same or increased; when compared to an ASD reference standard, and indicates the presence of ASD.

25. The method or use according to claim 23 or 24, wherein the sample is a urine sample.

26. The method or use according to any one of the preceding claims, wherein the amino acid adduct is detected by stable isotopic dilution analysis liquid chromatography-tandem mass spectrometry reaction monitoring (SRM) mass spectrometry, Western Blot, Enzyme-Linked Immunosorbent Assay (ELISA), liquid chromatography mass spectrometry (LC-MS), reverse phase mass spectrometry, surface enhanced laser desorption ionisation time-of-flight mass spectrometry (SELDI-TOF), matrix assisted laser desorption ionisation time-of-flight mass spectrometry (MALDI-TOF), liquid chromatography-tandem mass spectrometry, isotope dilution mass spectrometry, size permeation (gel filtration), ion exchange, affinity, high performance liquid chromatography, ultra performance liquid chromatography, one-dimensional gel electrophoresis (1-DE), and/or two-dimensional gel electrophoresis (2-DE).

27. The method or use according to any one of the preceding claims, wherein the amino acid adduct is detected by mass spectroscopy.

28. The method or use according to any one of the preceding claims, wherein the amino acid adduct is detected by liquid chromatography-tandem mass spectrometry.

29. The method or use according to any one of the preceding claims, wherein the autistic spectrum disorder is selected from one or more of autism, Asperger syndrome, pervasive developmental disorder not otherwise specified (PDD-NOS), and childhood disintegrative disorder.

30. The method or use according to any of the preceding claims, further comprising the step of recording on a suitable data carrier, the data obtained in the step of detecting the concentration of an amino acid adduct in a sample.

31. The method according to any one of the preceding claims, wherein the detected concentration of an amino acid adduct is entered into a diagnostic algorithm, and said diagnostic algorithm indicates whether ASD is present or absent.

32. A data carrier comprising the data obtained in the step of detecting the concentration of an amino acid adduct in a sample according to the method or use according to any one of the preceding claims.

33. A data carrier according to claim 32 for use in a method for diagnosing an autistic spectrum disorder.

34. A kit comprising reagents for detecting the concentration of an amino acid adduct in a sample, wherein said amino acid adduct is one or more selected from a glycated amino acid adduct, an oxidised amino acid adduct, a nitrated amino acid adduct or a combination thereof; and instructions for use of the same.

35. The kit according to claim 34, wherein said amino acid adduct is one or more selected from: N.sub.-fructosyl-lysine (FL), glyoxal-derived hydroimidazolone (G-H1), 3-deoxyglucosone-derived hydroimidazolone (3DG-H), N.sub.-carboxymethyl-lysine (CML), N.sub.-carboxymethylarginine (CMA), methylglyoxal-derived hydroimidazolone (MG-H1), pyrraline, methylglyoxal-derived lysine dimer (MOLD), N-formylkynurenine (NFK), -aminoadipic semialdehyde (AASA), glutamic semialdehyde (GSA) and 3-Nitrotyrosine (3-NT).

36. The kit according to claim 34 or 35, wherein said amino acid adduct is two or more selected from: N.sub.-fructosyl-lysine (FL), glyoxal-derived hydroimidazolone (G-H1), 3-deoxyglucosone-derived hydroimidazolone (3DG-H), N.sub.-carboxymethyl-lysine (CML), N.sub.-carboxymethylarginine (CMA), methylglyoxal-derived hydroimidazolone (MG-H1), pyrraline, methylglyoxal-derived lysine dimer (MOLD), dityrosine (DT), N-formylkynurenine (NFK), -aminoadipic semialdehyde (AASA), glutamic semialdehyde (GSA) and 3-Nitrotyrosine (3-NT).

37. The kit according to any one of claims 34-36, further comprising reagents for detecting the concentration of an amino acid in a sample, wherein said amino acid is one or more selected asparagine, glutamate, glutamine, proline, serine, threonine, tryptophan, valine, or a combination thereof; and optionally instructions for use of the same.

38. The kit according to any one of claims 34-37, further comprising reagents for detecting the concentration of creatinine in a sample.

39. The kit according to any one of claims 34-38, wherein the reagents are for detecting the concentration of an amino acid adduct and/or amino acid by selected reaction monitoring (SRM) mass spectrometry, Western Blot, Enzyme-Linked Immunosorbent Assay (ELISA), liquid chromatography mass spectrometry (LC-MS), reverse phase mass spectrometry, surface enhanced laser desorption ionisation time-of-flight mass spectrometry (SELDI-TOF), matrix assisted laser desorption ionisation time-of-flight mass spectrometry (MALDI-TOF), liquid chromatography-tandem mass spectrometry, isotope dilution mass spectrometry, size permeation (gel filtration), ion exchange, affinity, high performance liquid chromatography, ultra performance liquid chromatography, one-dimensional gel electrophoresis (1-DE), and/or two-dimensional gel electrophoresis (2-DE).

40. The kit according to any one of claims 34-39, wherein the reagents are for detecting the concentration of an amino acid adduct by liquid chromatography-tandem mass spectrometry.

41. The kit according to any one of claims 34-40, further comprising standards for use in detecting the concentration of an amino acid adduct by liquid chromatography-tandem mass spectrometry.

Description

FIGURES

[0265] Embodiments of the invention will now be described, by way of example only, with reference to the following Figures and Examples.

[0266] FIG. 1A shows the structure of the early glycation adduct FL. (B) shows the structure of the nitration adduct 3-NT. (C) shows the structure of the advanced glycation endproducts CML, CEL, pyrraline, G-H1, MG-H1, 3DG-H, CMA, Glucosepane and MOLD. (D) shows the structure of the oxidation adducts DT, NFK, 3-NT, AASA and GSA.

[0267] FIG. 2 shows a scheme of the training and validation subject groups of diagnostic algorithms for detection of autistic spectrum disorder.

[0268] FIG. 3 shows demographic and clinical features of the autistic children group. Onset pattern was defined according to Ozonoff et. al. (Autism Journal: Official journal of the International Society for Autism Research. 2008; 1(6):320-8) Probability the subject has autism estimated from diagnostic algorithms derived from the experimental biomarker data (algorithm performance is shown in FIG. 14).

[0269] FIG. 4 shows glycation, oxidation and nitration adduct residue content of plasma protein. Data are median (lower-upper quartile); healthy controls, n=21, and ASD, n=27. Significance (Mann-Whitney U); *, ** and ***, P<0.05, P<0.01 and P<0.001 after Bonferroni correction of 14 applied.

[0270] FIG. 5 shows plasma glycation, oxidation and nitration free adduct content in plasma filtrate. Data are median (lower-upper quartile); healthy controls, n=21-31, and ASD, n=27-38. Significance (Mann-Whitney U); *, P<0.05 after Bonferroni correction of 15 applied.

[0271] FIG. 6 shows urinary glycation, oxidation and nitration free adduct content. Data are median (lower-upper quartile); healthy controls, n=21-31, and ASD, n=27-38. Significance (Mann-Whitney U); *, P<0.05 after Bonferroni correction of 15 applied.

[0272] FIG. 7 shows plasma amino acid metabolome content. Data are MeanSD or median (lower-upper quartile); healthy controls, n=21, and ASD, n=27. Significance: t-test for parametric data and Mann-Whitney U for non-parametric data. *, p<0.05, **, P<0.01 after Bonferroni correction of 20 applied.

[0273] FIG. 8 shows urinary amino acid metabolome content. Data are MeanSD or median (lower-upper quartile); healthy controls, n=21, and ASD, n=27. Significance: t-test for parametric data and Mann-Whitney U for non-parametric data. *, p<0.05, **, P<0.01 after Bonferroni correction of 20 applied.

[0274] FIG. 9 shows renal clearance values of glycation, oxidation and nitration free adducts. Data are meanSD or median (lower-upper quartile); healthy controls, n=21, and ASD, n=27. Significance: t-test for parametric data and Mann-Whitney U for non-parametric data. *, p<0.05, after Bonferroni correction of 14 applied. # ml/mg creatinine.

[0275] FIG. 10 shows renal clearance values of amino acids. Data are meanSD or median (lower-upper quartile); healthy controls, n=21, and ASD, n=27. Significance: t-test for parametric data and Mann-Whitney U for non-parametric data. *, p<0.05, after Bonferroni correction of 20 applied.

[0276] FIG. 11A provides a heatmap illustrating concentration changes of glycation, oxidation and nitration adducts and amino acid metabolome in plasma and urine. (B) A heatmap showing changes of the amino acid metabolome in plasma and urine. The key ( log.sub.2) shown in (A) applies likewise to (B).

[0277] FIG. 12 shows data distributions of biomarkers with significantly different changes in the ASD study group after Bonferroni correction. (A) Protein adduct residues CML, CMA and DT. (B) Plasma free adduct CMA. (C) Urine free adducts DT and GSA; and amino acids Asn, Pro, Ser and Val. (D) Renal clearance of CMA and Arg. Significance: one asterisk, two asterisks, and three asterisks indicate P<0.05, P<0.01, and P<0.001, respectively.

[0278] FIG. 13 shows receiver operating characteristic plots of diagnostic algorithms for detection of autistic spectrum disorder by protein glycation and oxidation adducts. (A) Algorithm-1, plasma protein adduct residues. AUROC=0.96. (B) Algorithm-2, plasma free amino acid adducts. AUROC=0.78. (C) Algorithm-3, plasma protein adduct residues and free adducts. AUROC=0.99. (D) Algorithm-4, urine free amino acid adducts. AUROC=0.78. ROC plots are representative results from one run of the classification experiment. A random outcome is AUROC=0.50.

[0279] FIG. 14 shows details of diagnostic algorithms developed for autistic spectrum disorder from plasma and urinary analytes. Algorithm outcomes for 2-fold cross-validation (10 randomized repeat trials for robustness) using SVMs (95% CI given in brackets).

[0280] FIG. 15 shows a schematic explanation for changes found in protein damage and amino acids in ASD. (A) Proposed mechanism for observed changes found in plasma protein glycation and oxidation adducts. (B) Transport of Arg and CMA across the renal tubular epithelium and proposed mechanism for increased renal CL (increased Arg and CMA reuptake). Key: grey filled arrows show processes; black-filled arrows show changes observed (A) and changes expected (B) in ASD.

[0281] FIG. 16 shows mass spectrometric multiple reaction monitoring detection of protein glycation, oxidation and nitration adducts and amino acids.

[0282] FIG. 17 shows correlation analysis of plasma protein glycation, oxidation and nitration adduct residues. Correlation coefficients; Spearman (P<0.01).

[0283] FIG. 18 shows correlation analysis of plasma protein glycation, oxidation and nitration free adducts. Correlation coefficients; Spearman (P<0.01).

[0284] FIG. 19 shows correlation analysis of plasma amino acids. Correlation coefficients; Spearman (P<0.01).

[0285] FIG. 20 shows correlation analysis of urinary protein glycation, oxidation and nitration free adducts. Correlation coefficients; Spearman (P<0.01).

[0286] FIG. 21 shows correlation analysis of urinary amino acids. Correlation coefficients; Spearman (P<0.01).

[0287] FIG. 22 shows a confusion matrix of algorithms to identify autistic spectrum disorder. The confusion matrices shown are representative results from one run of the classification experiment.

EXAMPLES

Materials & Methods

Subject Recruitment

[0288] A total of 69 children were recruited. Of these, 38 had a diagnosis of ASD (29 males and 9 females) and 31 were classified as Typically Developing (TD) children (23 males and 8 females)FIG. 2. The age of the two subject groups was not significantly different. Subject age was: ASD group, 7.6 years2.0 years, range 5-12 years and TD group, 8.62.0 years, range 5-12 years. All ASD subjects received a diagnosis of ASD by two child development experts at the Child Neurology and Psychiatry Unit of the Bellaria Hospital of Bologna (IRCCS Institute of Neurological Sciences), according to the Diagnostic and Statistical Manual of Mental Disorders V (DSM 5 criteria, Autism Diagnostic Observation Schedule (ADOS), Childhood Autism Rating Scale (CARS) and characteristics of onset pattern of ASD previously defined. Developmental and cognitive levels were assessed by Psychoeducational Profile-3 (PEP-3) and Leiter International Performance ScaleRevised (Leiter-R). For both ASD and TD subjects, exclusion criteria were: presence of inflammatory or infective disease and taking antioxidant supplements at the time of study. No subject underwent any surgery intervention in the four months prior to blood and urine collection. None of the ASD subjects had active epilepsy at the time of blood and urine sampling. Subjects with ascertained medical and neurological comorbidity were excluded, through a medical work up including electroencephalography (recorded during awake and sleep), cerebral magnetic resonance imaging, standard clinical and neurological examination, neurometabolic and genetic investigations (including comparative genomic hybridization array, molecular assay for Fragile X and MECP2). Subjects recruited for this study were not taking any medication. TD children were recruited in the local community, with no sign of cognitive, learning and psychiatric involvement. They were attending mainstream school and had not been subjected to stressful events. Dietary habits were assessed by a Food Questionnaire, built according to the guidelines issued by the Emilia-Romagna Health Authority. No ASD child was on a diet free of gluten or casein. Both patients and controls were on a typical Mediterranean diet, as defined by the prevalence of both simple and complex carbohydrates, use of olive oil, and plenty of fruit. The consumption of vegetables was less than desirable in both patients and controls, although vegetable intake was more limited in ASD patients. Demographic and clinical features of ASD are summarized in FIG. 3. All subjects were recruited at the Child Neurology and Psychiatry Unit of the Bellaria Hospital of Bologna, Bologna, Italy.

[0289] Thirty-eight children with ASD were recruited for this study. The distribution of severity of ASD in this subject group recruited was (number of cases): mild (6), moderate (6) and severe (26). The distribution of cognitive/developmental impairment was (number of cases): normal/borderline IQ (11), mild (3), moderate (12) and severe (12). The distribution of onset pattern of ASD was (number of cases): early (22), regressive (6) and mixed (10). The ADOS score ranged from 13 to 22 and the CARS total score from 31.5 to 48.5.

Blood and Urine Sampling

[0290] Blood was withdrawn in the morning from fasting children. Spot urine samples were the first ones in the morning. Blood samples were collected using ethylenediaminetetra-acetic acid (EDTA) as anticoagulant. Plasma and blood cells were separated immediately by centrifugation (2000 g, 10 min) and plasma samples stored at 80 C. until analysis and transferred between collaborating laboratories on dry ice.

Assay of Markers of Protein Glycation, Oxidation and Nitration

[0291] The content of glycated, oxidized and nitrated adduct residues in plasma protein was quantified in exhaustive enzymatic digests by stable isotopic dilution analysis liquid chromatography-tandem mass spectrometry (LC-MS/MS), with correction for autohydrolysis of hydrolytic enzymes. The concentrations of related glycated, oxidized and nitrated amino acid free adducts (glycated, oxidised and nitrated amino acids) in plasma and urine were determined similarly in plasma and urine ultrafiltrate, respectively. Ultrafiltrate of plasma (50 l) was collected by microspin ultrafiltration (10 kDa cut-off) at 4 C. Retained protein was diluted with water to 500 l and washed by 4 cycles of concentration to 50 l and dilution to 500 l with water over the microspin ultrafilter at 4 C. The final washed protein (100 l) was delipidated and hydrolysed enzymatically as described (Rabbani et. al. Biochem Soc Trans. 2014; 42(2):511-7; Ahmed et. al. Sci Rep. 2015; 5:9259). Ultrafiltrate of urine (50 l) was collected by microspin ultrafiltration (3 kDa cut-off) at 4 C.

[0292] Protein hydrolysate (25 l, 32 g equivalent) or ultrafiltrate (5 l) was mixed with stable isotopic standard analytes and analysed by LC-MS/MS using an Acquity UPLC system with a Xevo-TQS tandem mass spectrometer (Waters, Manchester, U.K.). Samples are maintained at 4 C. in the autosampler during batch analysis. The columns were: 2.150 mm and 2.1 mm250 mm, 5 m particle size Hypercarb (Thermo Scientific), in series with programmed switching, at 30 C. Chromatographic retention was used to resolve oxidized analytes from their amino acid precursors to avoid interference from partial oxidation of the latter in the electrospray ionization source of the mass spectrometric detector. Analytes were detected by electrospray positive ionization and mass spectrometry multiple reaction monitoring (MRM) mode where analyte detection response was specific for mass/charge ratio of the analyte molecular ion and major fragment ion generated by collision-induced dissociation in the mass spectrometer collision cell. The ionization source and desolvation gas temperatures were 120 C. and 350 C., respectively, cone gas and desolvation gas flow rates were 99 and 900 I/h and the capillary voltage was 0.60 kV. Argon gas (5.0103 mbar) was in the collision cell. For MRM detection, molecular ion and fragment ion masses and collision energies optimized to 0.1 Da and 1 eV, respectively, were programmedFIG. 16.

[0293] Analytes determined were: [0294] glycation adductsFL, and AGEs, CML, CEL, pyrraline, CMA, G-H1, MG-H1, 3DG-H, MOLD and GSP; [0295] oxidation adductsDT, NFK, AASA, GSA; [0296] nitration adduct, 3-NT; and [0297] all major amino acids.

[0298] Oxidation, nitration and glycation adduct residues are normalised to their amino acid residue precursors and given as mmol/mol amino acid modified; and related free adducts are given in nM. Chemical structures and biochemical and clinical significance of these analytes have been described elsewhere (Thornalley & Rabbani, Biochim Biophys Acta. 2014; 1840(2):818-29; and Ahmed et. al, Sci Rep. 2015; 5(9259):9251-7). Renal clearance (CL) of glycation, oxidation and nitration free adducts and unmodified amino acids was deduced from plasma and spot urine collections: CL (l/mg creatinine or ml/mg creatinine)=[Analyte]Urine (nmol/mg creatinine)/[Analyte]Plasma (pmol/ml or nmol/ml).

Machine Learning Analysis

[0299] The objective was to distinguish between children with ASD and healthy controls. In all cases, the diagnostic algorithms were trained on 50% of the cases and controls (training subset) before being used to predict the disease class for each sample in the remaining subjects (test set); 2-fold cross-validation. The outcome was to assign, for each test set sample, a set of probabilities corresponding to each of the ASD/control groupsthe group assignment being that for which the probability is highest. Test data were held separate from algorithm training; algorithm settings were not adjusted once analysis of the test set data beganthereby guarding against overfitting and hence providing a rigorous estimate of predictive performance.

[0300] Four algorithm types were tested for performance: Random Forests, logistic regression, ensemble classifier, and Support Vector Machines (SVMs).

[0301] During the algorithm training, the complete panel of protein glycation, oxidation and nitration adducts were used as features and developed algorithms for each analyte type: plasma protein adduct residues, plasma free adducts and urinary free adducts. For the latter two, unmodified amino acids were also included as features. The aim during the training was to select the set of features that accomplishes the highest performance. The machine learning experiments were initially explored using all metabolite features. Advantageously, subsequent selection of a subset of discriminant biomarker features improved the algorithm performance (see FIG. 14). For the biomarker (e.g. amino acid adduct) selection, a sequential feature selection approach was used. The biomarker feature selection and classifier selection were made on the basis of algorithm performance defined by classification accuracy, sensitivity, specificity, area under-the-curve of the receiver operating characteristic curve (AUROC), positive likelihood ratio, negative likelihood ratio, positive predictive value, negative predictive value, and F-measure. For each performance metric, the mean and 95% CI was determined and reported. The algorithm training and testing was repeated 10 times, without altering the algorithm parameters, with 50% data split, to test for algorithm's robustness against any bias towards data split. Computer programs were developed using Statistics and Machine Learning Toolbox of MATLAB (MathWorks, Inc., Natick, USA), with a linear kernel SVM and sequential minimal optimisation (SMO).

Statistical Analysis

[0302] Data are presented as meanSD for parametric distributions and median (lower-upper quartile) for non-parametric distributions. The test for normality of data distribution applied was the Kolmogorov-Smirnov test. Significance was evaluated by Student's t-test or by Mann-Whitney U-test for parametrically or non-parametrically distributed data, respectively. Bonferroni correction was made for analysis of multiple analytes without preconceived hypothesis. Correlation analysis was performed by the Spearman's rho method with continuous variables. For clinical categorical variables with 6 categories, Spearman correlation was performedassuming approximation to a continuous variable; for other categorical variables, significance of difference of biomarker data distributions between categories was assessed by one-way ANOVA for parametric data and Kruskal-Wallis H test. Data were analysed using SPSS, version 24.0.

[0303] For power analysis in the study design, the level of the irreversible oxidative damage marker DT in plasma protein was chosen. In healthy human subjects, plasma protein DT was 0.02870.0027 mmol/mol tyr (n=29) in previous studies. This study was designed to detect a 50% increase in plasma protein DT at the 0.01% significance level, for which 18 case and control samples were required. Post-hoc analysis revealed an 88% increase with P=0.00017, after Bonferroni correction of 14 with 27 cases and 21 controls, suggesting the study was adequately powered for this key target analyte.

Example 1

[0304] Children with Autistic Spectrum Disorder Recruited for this Study

[0305] Thirty-eight children with ASD were recruited for this study. The distribution of severity of ASD in this subject group recruited was (number of cases): mild (6), moderate (6) and severe (26). The distribution of cognitive/developmental impairment was (number of cases): normal/borderline IQ (11), mild (3), moderate (12) and severe (12). The distribution of onset pattern of ASD was (number of cases): early (22), regressive (6) and mixed (10). The ADOS score ranged from 13 to 22 and the CARS total score from 31.5 to 48.5.

Example 2

Plasma Protein Glycation, Oxidation and Nitration

[0306] In plasma protein, protein content of AGEsCML, MG-H1 and CMAwere increased in children with ASD, with respect to healthy controls; whereas plasma protein content of AGE, 3DG-H, was decreased in children with ASD, with respect to healthy controls. Plasma protein content of the oxidative damage adduct, DT, was increased in children with ASD, with respect to healthy controls. Advantageously, changes in CML, CMA and DT remained significant after Bonferroni correction for measurement of multiple analytes (FIG. 4). In correlation analysis, highly significant positive correlations (P<0.01, Spearman) were of CML with DT, G-H1 with MG-H1 and DT, MG-H1 with CMA, CMA with DT, and AASA with GSAFIG. 17. No correlation or association of glycation, oxidation and nitration adduct residues was found with demographic and clinical features. There was no significant difference of these variables between subject groups of different genders with and without ASD.

Example 3

Plasma Glycated, Oxidized and Nitrated Amino Acids and Amino Acid Metabolome

[0307] For glycated, oxidized and nitrated amino acid concentration in plasma, FL, G-H1 and NFK were decreased whereas CMA, AASA and GSA were increased in children with ASD, with respect to healthy controls. Advantageously, increase in CMA remained significant after Bonferroni correction (FIG. 5). In correlation analysis, highly significant positive correlations were of pyrraline with MG-H1 and 3DG-H, FL with CML, G-H1 and MG-H1, CEL with MG-H1 and CMA, MG-H1 with 3DG-H, and CMA with AASA. There were highly significant negative correlations of pyrraline with NFK, CMA with MOLD, and MOLD with AASAFIG. 18.

[0308] For the conventional amino acid metabolome, there were increases in arg, gln, glu and thr and decrease in trp in children with ASD, with respect to healthy controls. There were many highly significant positive correlations between plasma amino acid concentrationsFIG. 19. No correlation or association of glycation, oxidation and nitration free adducts and amino acids was found with demographic and clinical features. There was no significant difference of these variables between genders

Example 4

Urinary Glycated, Oxidized and Nitrated Amino Acids and Amino Acid Metabolome and Renal Clearance

[0309] For the urinary flux of glycated, oxidized and nitrated amino acids, children with ASD showed increased urinary excretion of CML, G-H1, CMA, MOLD, pyrraline, DT, NFK, AASA and GSA. Advantageously, urinary excretions of DT and GSA remained significant after Bonferroni correction (FIG. 6). For the urinary flux of unmodified amino acids, children with ASD showed increased urinary excretion of all amino acids except asp, cys, lys, phe and tyr. Advantageously, increases in urinary excretion of asn, pro, ser and val remained significant after Bonferroni correction (FIG. 8). There were several highly significant positive correlations between urinary excretions of glycation, oxidation and nitration adducts and amino acidssee FIGS. 20 and 21.

[0310] Renal clearance of CMA, GSP, DT, arg, glu, leu, phe and thr were decreased and renal clearance of NFK and trp were increased in children with ASD, with respect to healthy controls. Advantageously, decreases in renal clearance of arg and CMA remained significant after Bonferroni correction: CL.sub.arg decreased 32% and CL.sub.CMA decreased 50% in children with ASD, compared to healthy control; P<0.001 (FIGS. 9 and 10). No correlation or association of these glycation, oxidation and nitration free adduct and amino acid variables was found with demographic and clinical features. There was no significant difference of these variables between genders.

[0311] Changes of glycation, oxidation and nitration adducts and amino acid metabolome in plasma and urine are summarized in heat maps (FIGS. 11A and 11B). Data distributions of biomarker (amino acid adduct and amino acid) with significantly different change in the ASD study group after Bonferroni correction are given in FIG. 12.

Example 5

Development of Diagnostic Algorithms for Autistic Spectrum Disorder

[0312] To explore diagnostic utility of protein glycation, oxidation and nitration measurements for ASD, we analysed plasma and urinary amino acid analyte data by a machine learning approach. SVMs was the best-performing method out of the four algorithms that were investigated. Algorithm optimised from 2-fold cross-validation were as below. [0313] (i) Algorithm-1, developed from plasma protein glycation, oxidation and nitration adduct residue analytes.

[0314] It has the following features: CML, 3DG-H, CMA and DT. Classification accuracy was 88%, sensitivity 92%, specificity 84% and AUROC 0.94. A random outcome is 0.50. [0315] (ii) Algorithm-2, developed from plasma glycated, oxidized and nitrated amino acids and conventional amino acid metabolome.

[0316] It has the following features: CML and CMA. Classification accuracy was 75%, sensitivity 81% and specificity 67% and AUROC 0.80. [0317] (iii) Algorithm-3, developed from plasma protein glycation, oxidation and nitration adduct residues and plasma glycated, oxidized and nitrated amino acids and conventional amino acid metabolome combined.

[0318] It has the following features: plasma protein CML, 3DG-H, CMA and DT residues and plasma G-H1 and GSA free adducts. Classification accuracy was 89%, sensitivity 90%, specificity 87% and AUROC 0.95. [0319] (iv) Algorithm-4, developed from urinary glycated, oxidized and nitrated amino acids.

[0320] It has the following features: GSA and pyrraline free adducts. Classification accuracy was 77%, sensitivity 77%, specificity 76% and AUROC 0.79 (FIGS. 13, 14 and 22).

[0321] The diagnostic algorithms were used to deduce the probability of having ASD for each patient diagnosed with ASD by clinical symptoms (FIG. 3). The association and correlation of these probabilities with clinical features was explored. No significant association or correlation of these probabilities with clinical features (age, ADOS, total CARS, CARS hyperactivity and CARS body use scores, autism severity, cognitive/developmental impairment and ASD onset pattern) was found.

[0322] Without wishing to be bound by theory, the findings of the present inventors implicate a disturbance of metabolism of dicarbonyl precursors of advanced glycation endproducts (AGEs) and activation of dual oxidase (DUOX) in ASD. The initial evidence given herein suggests detection of the combination of plasma protein AGE and dityrosine (DT) concentrations may provide an optimal blood-based test for diagnosis of ASD. Decreased renal clearance of arginine and CMA is proposed to be linked to amino acid transporter dysfunction in ASD, building on increasing evidence of neuronal amino acid availability as a driver in ASD development.

[0323] All publications mentioned in the above specification are herein incorporated by reference. Various modifications and variations of the described methods and system of the present invention will be apparent to those skilled in the art without departing from the scope and spirit of the present invention. Although the present invention has been described in connection with specific preferred embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the described modes for carrying out the invention which are obvious to those skilled in biochemistry and biotechnology or related fields are intended to be within the scope of the following claims.