Tesi etd-11102021-112257
Link copiato negli appunti
Tipo di tesi
Corso Ordinario Ciclo Unico 6 Anni
Autore
CAPODIFERRO, AGATA MARIA
URN
etd-11102021-112257
Titolo
1H NMR Based Metabolomics in Paediatric Acute-Onset Neuropsychiatric Syndrome (PANS) and Autism Spectrum Disorder (ASD): diagnostic and pathophysiological implications
Struttura
Cl. Sc. Sperimentali - Medicina
Corso di studi
SCIENZE MEDICHE - SCIENZE MEDICHE
Commissione
relatore ZUDDAS, ALESSANDRO
Membro Prof. EMDIN, MICHELE
Membro Prof. LIONETTI, VINCENZO
Membro Prof. PASSINO, CLAUDIO
Membro Prof. COCEANI, FLAVIO
Membro Prof. RECCHIA, FABIO ANASTASIO
Membro Prof. ANGELONI, DEBORA
Membro Prof. GIANNONI, ALBERTO
Membro Dott.ssa CASIERI, VALENTINA
Membro GAGLIANO, ANTONELLA
Membro Prof. EMDIN, MICHELE
Membro Prof. LIONETTI, VINCENZO
Membro Prof. PASSINO, CLAUDIO
Membro Prof. COCEANI, FLAVIO
Membro Prof. RECCHIA, FABIO ANASTASIO
Membro Prof. ANGELONI, DEBORA
Membro Prof. GIANNONI, ALBERTO
Membro Dott.ssa CASIERI, VALENTINA
Membro GAGLIANO, ANTONELLA
Parole chiave
- 1H NMR metabolomics
- ASD
- asparagine
- aspartate
- Autism Spectrum Disorder
- glucose
- glycine
- lactate
- Maternal Immune Activation
- MIA
- neuroimmunology
- neuroinflammation
- NMDA receptors
- Paediatric acute onset neuropsychiatric syndrome
- PANS
- pyruvate
- schizophrenia
- tryptophan
Data inizio appello
20/12/2021;
Disponibilità
parziale
Riassunto analitico
Introduction: Autism Spectrum Disorder (ASD) and Paediatric acute onset neuropsychiatric syndrome (PANS) are two distinct clinically heterogeneous neuropsychiatric disorders occurring in childhood. For both, the diagnosis is mainly clinical and, even though they differ in many clinical features (e.g. mean age of onset, main clinical symptoms, longitudinal course and treatments), they share some atypical biomarkers’ patterns underlying the challenging hypothesis of an autoimmune and/or dysreactive pathogenesis. Furthermore, PANS is a recent and controversial nosographic entity largely overlapping with other neuropsychiatric disorders (e.g. obsessive-compulsive disorder, Tourette syndrome, ADHD, affective disorders as anxiety), for whom the research for new specific biomarkers is strongly needed to confirm the specificity of PANS as autonomous clinical entity. In a previous study by our research group, a cohort of PANS patients underwent a metabolomic case-control study (vs healthy controls) in sera samples, by Nuclear Magnetic Resonance (1H-NMR) spectroscopy. The study showed the involvement of specific patterns of neurotransmission (tryptophan, glycine, histamine/histidine), as well as of markers suggesting a more general state of neuroinflammation and oxidative stress (glutamine, 2-Hydroxybutyrate and tryptophan-kynurenine pathway) in PANS. The present study extends the previous metabolomic research comparing the same PANS cohort and healthy control group with a group of ASD patients, in order to assess analogies and differences between the two clinical groups in their metabolomic profile, and evaluating if the panel of metabolomics biomarkers could be of diagnostic and/or pathophysiological interest for both the disorders.
Design: The current observational case-control study tested consecutive patients referred for PANS and ASD from June 2019 to May 2020. PANS and ASD diagnosis were confirmed according to the PANS working criteria (National Institute of Mental Health, 2010) and DSM-5 criteria, respectively. Healthy age and sex-matched subjects were recruited as controls.
Methods: Thirty-four outpatients referred for PANS (mean age 9.5 years; SD 2.9, 71% male), fifteen outpatients referred for ASD (mean age 9.07 years; SD 4.28, 100% male) and twenty-five neurotypical subjects matched for age and gender underwent physical and neuro-psychiatric evaluations, and metabolomic analysis from sera samples through 1H-NMR spectroscopy. Subsequently, Multivariate and Univariate statistical analyses, Receiver Operator Curves (ROC), Permutation Test, and Projection to Latent Structures (PLS) regression were among the tests performed on the dataset.
Results: The metabolomic profile of the ASD group segregated from that of controls by applying supervised models (R2X = 0.49, R2Y = 0.61, Q2 = 0.49, p-value = 0.0001). Similarly, separation of the samples in line with the presence of PANS or ASD diagnosis was observed (R2X = 0.41, R2Y = 0.51, Q2 = 0.3, p-value = 0.02), also confirmed when accounting for sex differences in each group. The significantly altered metabolites between the ASD group and controls were asparagine, aspartate, betaine, glycine, lactate, glucose and pyruvate, whereas arginine, aspartate, betaine, choline, creatine phosphate, glycine, pyruvate and tryptophan exhibited the greatest differences the PANS and ASD groups (p-value of <0.05). The PLS analysis showed a weak correlation between the PANS metabolomic profile and all the psychodiagnositic scales (PARS, CYBOCS, YGTSS, C-GAS, WISC-IV and USCRS; Y parameters) except for the PANSS scale (R2 = 0.7), while strong correlations were found between the ASD metabolomic profile and clinical scores at C-GAS (R2 = 8.8), WISC-IV (R2 = 0.8) and USCRS (R2 = 0.9) tests.
Conclusions: Overall, this study has provided data that could be of diagnostic and/or pathophysiological interest for both the disorders. In particular, we identified specific ASD metabolomic specifiers (e.g. glucose, lactate, pyruvate and aspartate) likely depending on disturbances in glucose metabolism occurring in ASD and having a potential pathogenic role (e.g. “of maintenance”) in this condition. However, further investigations are required to delineate the “metabolic phenotype” (e.g. insulin and/or glucagon resistant) of ASD and its relationship with other neuropsychiatric disorders (i.e. schizophrenia). Furthermore, we identified metabolomic similarities between PANS and ASD patients (e.g. progressive depletion of glycine, asparagine reduction) putatively related to NMDA receptor dysfunction and to neuroinflammation, even though the inflammatory burden could be underestimated in the ASD group. Finally, we confirmed for PANS the existence of metabolomic “fingerprints” (e.g. tryptophan, glycine) distinguishing PANS patients from the ASD group as well as from controls. These metabolites could play a role both as diagnostic predictors of the clinical phenotype and as molecules involved in the pathogenesis of the syndrome. In this way, metabolomics could contribute to sustain the research on PANS biomarkers bypassing the elusive “upper” pathogenesis (autoantibodies against CSN antigens) and focusing on pathophysiological mechanisms converging on “the common final pathway”.
Design: The current observational case-control study tested consecutive patients referred for PANS and ASD from June 2019 to May 2020. PANS and ASD diagnosis were confirmed according to the PANS working criteria (National Institute of Mental Health, 2010) and DSM-5 criteria, respectively. Healthy age and sex-matched subjects were recruited as controls.
Methods: Thirty-four outpatients referred for PANS (mean age 9.5 years; SD 2.9, 71% male), fifteen outpatients referred for ASD (mean age 9.07 years; SD 4.28, 100% male) and twenty-five neurotypical subjects matched for age and gender underwent physical and neuro-psychiatric evaluations, and metabolomic analysis from sera samples through 1H-NMR spectroscopy. Subsequently, Multivariate and Univariate statistical analyses, Receiver Operator Curves (ROC), Permutation Test, and Projection to Latent Structures (PLS) regression were among the tests performed on the dataset.
Results: The metabolomic profile of the ASD group segregated from that of controls by applying supervised models (R2X = 0.49, R2Y = 0.61, Q2 = 0.49, p-value = 0.0001). Similarly, separation of the samples in line with the presence of PANS or ASD diagnosis was observed (R2X = 0.41, R2Y = 0.51, Q2 = 0.3, p-value = 0.02), also confirmed when accounting for sex differences in each group. The significantly altered metabolites between the ASD group and controls were asparagine, aspartate, betaine, glycine, lactate, glucose and pyruvate, whereas arginine, aspartate, betaine, choline, creatine phosphate, glycine, pyruvate and tryptophan exhibited the greatest differences the PANS and ASD groups (p-value of <0.05). The PLS analysis showed a weak correlation between the PANS metabolomic profile and all the psychodiagnositic scales (PARS, CYBOCS, YGTSS, C-GAS, WISC-IV and USCRS; Y parameters) except for the PANSS scale (R2 = 0.7), while strong correlations were found between the ASD metabolomic profile and clinical scores at C-GAS (R2 = 8.8), WISC-IV (R2 = 0.8) and USCRS (R2 = 0.9) tests.
Conclusions: Overall, this study has provided data that could be of diagnostic and/or pathophysiological interest for both the disorders. In particular, we identified specific ASD metabolomic specifiers (e.g. glucose, lactate, pyruvate and aspartate) likely depending on disturbances in glucose metabolism occurring in ASD and having a potential pathogenic role (e.g. “of maintenance”) in this condition. However, further investigations are required to delineate the “metabolic phenotype” (e.g. insulin and/or glucagon resistant) of ASD and its relationship with other neuropsychiatric disorders (i.e. schizophrenia). Furthermore, we identified metabolomic similarities between PANS and ASD patients (e.g. progressive depletion of glycine, asparagine reduction) putatively related to NMDA receptor dysfunction and to neuroinflammation, even though the inflammatory burden could be underestimated in the ASD group. Finally, we confirmed for PANS the existence of metabolomic “fingerprints” (e.g. tryptophan, glycine) distinguishing PANS patients from the ASD group as well as from controls. These metabolites could play a role both as diagnostic predictors of the clinical phenotype and as molecules involved in the pathogenesis of the syndrome. In this way, metabolomics could contribute to sustain the research on PANS biomarkers bypassing the elusive “upper” pathogenesis (autoantibodies against CSN antigens) and focusing on pathophysiological mechanisms converging on “the common final pathway”.
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