Dataset Information

Cytokine levels and associations with symptom severity in male and female children with autism spectrum disorder.

ABSTRACT: Autism spectrum disorders (ASDs) are complex, pervasive, and heterogeneous neurodevelopmental conditions with varying trajectories, significant male bias and largely unknown etiology. However, an understanding of the biological mechanisms driving pathophysiology is evolving. Immune system aberrations, as identified through cytokine profiles, are believed to have a role in ASD. Altered cytokine levels may facilitate identification of ASD subtypes as well as provide biological markers of response to effective treatments. Research exploring the relationship between cytokine profiles and ASD symptoms is, however, in its infancy. The objective of this study was to explore relationships between cytokine levels and the severity of ASD and other clinical traits.Multiplex assay techniques were used to measure levels of 27 cytokines in plasma samples from a cohort of 144 children diagnosed with ASD.Overall, results showed a significant negative association between platelet-derived growth factor (PDGF)-BB, and the severity of ASD symptoms. Furthermore, a significant interaction with sex suggested a different immune profile for females compared to males. ASD symptom severity was negatively associated with levels of 4 cytokines, IL-1?, IL-8, MIP-1?, and VEGF, in females, but not in males.Results of the present study suggest that an altered cytokine response or profile is associated with the severity of ASD-related symptoms, with sex a potential modifier of this relationship. Further research in larger populations which recognizes the importance of sex comparisons and longitudinal assessments are now required to extend and further describe the role of the immune system in ASD.

SUBMITTER: Masi A

PROVIDER: S-EPMC5712192 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Cytokine levels and associations with symptom severity in male and female children with autism spectrum disorder.

Masi Anne A Breen Edmond J EJ Alvares Gail A GA Glozier Nicholas N Hickie Ian B IB Hunt Anna A Hui Jennie J Beilby John J Ravine David D Wray John J Whitehouse Andrew J O AJO Guastella Adam J AJ

Molecular autism 20171202

<h4>Background</h4>Autism spectrum disorders (ASDs) are complex, pervasive, and heterogeneous neurodevelopmental conditions with varying trajectories, significant male bias and largely unknown etiology. However, an understanding of the biological mechanisms driving pathophysiology is evolving. Immune system aberrations, as identified through cytokine profiles, are believed to have a role in ASD. Altered cytokine levels may facilitate identification of ASD subtypes as well as provide biological m ...[more]

PMID: 29214007

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Project description:BackgroundAutism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition. The degree to which the brain development in ASD deviates from typical brain development, and how this deviation relates to observed behavioral outcomes at the individual level are not well-studied. We hypothesize that the degree of deviation from typical brain development of an individual with ASD would relate to observed symptom severity.MethodsThe developmental changes in anatomical (cortical thickness, surface area, and volume) and diffusion metrics (fractional anisotropy and apparent diffusion coefficient) were compared between a sample of ASD (n?=?247) and typically developing children (TDC) (n?=?220) aged 6-25. Machine learning was used to predict age (brain age) from these metrics in the TDC sample, to define a normative model of brain development. This model was then used to compute brain age in the ASD sample. The difference between chronological age and brain age was considered a developmental deviation index (DDI), which was then correlated with ASD symptom severity.ResultsMachine learning model trained on all five metrics accurately predicted age in the TDC (r?=?0.88) and the ASD (r?=?0.85) samples, with dominant contributions to the model from the diffusion metrics. Within the ASD group, the DDI derived from fractional anisotropy was correlated with ASD symptom severity (r?=?-?0.2), such that individuals with the most advanced brain age showing the lowest severity, and individuals with the most delayed brain age showing the highest severity.LimitationsThis work investigated only linear relationships between five specific brain metrics and only one measure of ASD symptom severity in a limited age range. Reported effect sizes are moderate. Further work is needed to investigate developmental differences in other age ranges, other aspects of behavior, other neurobiological measures, and in an independent sample before results can be clinically applicable.ConclusionsFindings demonstrate that the degree of deviation from typical brain development relates to ASD symptom severity, partially accounting for the observed heterogeneity in ASD. Our approach enables characterization of each individual with reference to normative brain development and identification of distinct developmental subtypes, facilitating a better understanding of developmental heterogeneity in ASD.

Dataset Information

Cytokine levels and associations with symptom severity in male and female children with autism spectrum disorder.

Publications

Cytokine levels and associations with symptom severity in male and female children with autism spectrum disorder.

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