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Dynamical properties of elemental metabolism distinguish attention deficit hyperactivity disorder from autism spectrum disorder.

ABSTRACT: Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are neurodevelopmental conditions of overlapping etiologies and phenotypes. For ASD, we recently reported altered elemental metabolic patterns in the form of short and irregular zinc and copper cycles. Here, we extend the application of these biomarkers of prenatal and early postnatal elemental metabolism to distinguish between individuals diagnosed with ADHD and/or ASD and neurotypical controls. We recruited twins discordant for ADHD, ASD and other neurodevelopmental diagnoses from national twin studies in Sweden (N?=?74) diagnosed according to DSM-5 clinical consensus and standardized psychiatric instruments. Detailed temporal profiles of exposure to 10 metals over the prenatal and early childhood periods were measured using tooth biomarkers. We used recurrence quantification analysis (RQA) to characterize properties of cyclical metabolic patterns of these metals. Regularity (determinism) and complexity (entropy) of elemental cycles was consistently reduced in ADHD for cobalt, lead, and vanadium (determinism: cobalt, ??=?-0.03, P?=?0.017; lead, ??=?-0.03, P?=?0.016; and vanadium, ??=?-0.03, P?=?0.01. Entropy: cobalt, ??=?-0.13, P?=?0.017; lead, ??=?-0.18, P?=?0.016; and vanadium, ??=?-0.15, P?=?0.008). Further, we found elemental pathways and dynamical features specific to ADHD vs ASD, and unique characteristics associated with ADHD/ASD combined presentation. Dysregulation of cyclical processes in elemental metabolism during prenatal and early postnatal development not only encompasses pathways shared by ADHD and ASD, but also comprise features specific to either condition.

SUBMITTER: Austin C

PROVIDER: S-EPMC6760156 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Dynamical properties of elemental metabolism distinguish attention deficit hyperactivity disorder from autism spectrum disorder.

Austin Christine C Curtin Paul P Curtin Austen A Gennings Chris C Arora Manish M Tammimies Kristiina K Isaksson Johan J Willfors Charlotte C Bölte Sven S

Translational psychiatry 20190925 1

Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are neurodevelopmental conditions of overlapping etiologies and phenotypes. For ASD, we recently reported altered elemental metabolic patterns in the form of short and irregular zinc and copper cycles. Here, we extend the application of these biomarkers of prenatal and early postnatal elemental metabolism to distinguish between individuals diagnosed with ADHD and/or ASD and neurotypical controls. We recruited twin ...[more]

PMID: 31551411

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Project description:Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorders (ASD) are two of the most represented neurodevelopmental conditions in childhood. The diagnostic shift introduced by the DSM-5, allowing a combined diagnosis of ADHD and ASD, poses different clinical challenges related to diagnostic overshadowing, accuracy of clinical judgment and potential delay in an ASD diagnosis in children presenting with ADHD. Here we tried to disentangle the clinical phenotype and specificity of the two co-occurring conditions in relation to autism traits and empathy, by comparing children with ASD with and without comorbid ADHD with children presenting ADHD only and children with typical development. The child versions of the Autism Quotient (C-AQ) and Empathy Quotient (C-EQ) were administered to a total sample of 198 male children between 6 and 14 years old with age appropriate language skills and normal intelligence. Univariate analysis demonstrated no significant differences in the C-AQ total and subscale scores as well as the C-EQ between children with ASD and children with ASD + ADHD, while children with ADHD alone presented an intermediate phenotype between ASD and TD. Furthermore, a receiver operating characteristic (ROC) analysis was applied to discriminate among the different phenotypes. We found that the C-AQ and C-EQ were accurate at distinguishing with satisfactory reliability between: (a) ASD vs. non- ASD (N-ASD) groups comprising both ADHD and TD children (Area Under the Curve AUC 88% for C-AQ and 81% for C-EQ); (b) ASD and TD (AUC 92% for C-AQ and 95% for C-EQ); (c) ASD and ADHD (AUC 80% for C-AQ and 68% for C-EQ). Our data confirm the reliability of the C-AQ and C-EQ as behavioral markers to differentiate ASD (regardless of comorbid ADHD) from an ADHD condition and TD. Interestingly, in our sample an ADHD condition does not increase the severity of the clinical phenotype in terms of autism traits distribution and empathy, suggesting that the psychological measures detected by the two quantitative instruments are independent of ADHD traits. This evidence will contribute to the translational efforts in developing better tailored treatments and preventive strategies.

Project description:ImportanceClinical overlap between autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) is increasingly appreciated, but the underlying brain mechanisms remain unknown to date.ObjectiveTo examine associations between white matter organization and 2 commonly co-occurring neurodevelopmental conditions, ASD and ADHD, through both categorical and dimensional approaches.Design, setting, and participantsThis investigation was a cross-sectional diffusion tensor imaging (DTI) study at an outpatient academic clinical and research center, the Department of Child and Adolescent Psychiatry at New York University Langone Medical Center. Participants were children with ASD, children with ADHD, or typically developing children. Data collection was ongoing from December 2008 to October 2015.Main outcomes and measuresThe primary measure was voxelwise fractional anisotropy (FA) analyzed via tract-based spatial statistics. Additional voxelwise DTI metrics included radial diffusivity (RD), mean diffusivity (MD), axial diffusivity (AD), and mode of anisotropy (MA).ResultsThis cross-sectional DTI study analyzed data from 174 children (age range, 6.0-12.9 years), selected from a larger sample after quality assurance to be group matched on age and sex. After quality control, the study analyzed data from 69 children with ASD (mean [SD] age, 8.9 [1.7] years; 62 male), 55 children with ADHD (mean [SD] age, 9.5 [1.5] years; 41 male), and 50 typically developing children (mean [SD] age, 9.4 [1.5] years; 38 male). Categorical analyses revealed a significant influence of ASD diagnosis on several DTI metrics (FA, MD, RD, and AD), primarily in the corpus callosum. For example, FA analyses identified a cluster of 4179 voxels (TFCE FEW corrected P < .05) in posterior portions of the corpus callosum. Dimensional analyses revealed associations between ASD severity and FA, RD, and MD in more extended portions of the corpus callosum and beyond (eg, corona radiata and inferior longitudinal fasciculus) across all individuals, regardless of diagnosis. For example, FA analyses revealed clusters overall encompassing 12121 voxels (TFCE FWE corrected P < .05) with a significant association with parent ratings in the social responsiveness scale. Similar results were evident using an independent measure of ASD traits (ie, children communication checklist, second edition). Total severity of ADHD-traits was not significantly related to DTI metrics but inattention scores were related to AD in corpus callosum in a cluster sized 716 voxels. All these findings were robust to algorithmic correction of motion artifacts with the DTIPrep software.Conclusions and relevanceDimensional analyses provided a more complete picture of associations between ASD traits and inattention and indexes of white matter organization, particularly in the corpus callosum. This transdiagnostic approach can reveal dimensional relationships linking white matter structure to neurodevelopmental symptoms.

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Dynamical properties of elemental metabolism distinguish attention deficit hyperactivity disorder from autism spectrum disorder.

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Dynamical properties of elemental metabolism distinguish attention deficit hyperactivity disorder from autism spectrum disorder.

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