Search

Currently, there are no reliable biomarkers for autism diagnosis. The heterogeneity of autism and several co-occurring conditions are key challenges to establishing these. Here, we used untargeted mass spectrometry-based urine metabolomics to investigate metabolic differences for autism diagnosis and autistic traits in a well-characterized twin cohort (N = 105). We identified 208 metabolites in the urine samples of the twins. No clear, significant metabolic drivers for autism diagnosis were detected when controlling for other neurodevelopmental conditions. However, we identified nominally significant changes for several metabolites. For instance, phenylpyruvate (p = 0.019) and taurine (p = 0.032) were elevated in the autism group, while carnitine (p = 0.047) was reduced. We furthermore accounted for the shared factors, such as genetics within the twin pairs, and report additional metabolite differences. Based on the nominally significant metabolites for autism diagnosis, the arginine and proline metabolism pathway (p = 0.024) was enriched. We also investigated the association between quantitative autistic traits, as measured by the Social Responsiveness Scale 2nd Edition, and metabolite differences, identifying a greater number of nominally significant metabolites and pathways. A significant positive association between indole-3-acetate and autistic traits was observed within the twin pairs (adjusted p = 0.031). The utility of urine biomarkers in autism, therefore, remains unclear, with mixed findings from different study populations., Competing Interests: A.A., F.M., and K.T. declare no competing interests. S.B. declares no direct conflict of interest related to this article. He discloses that he has in the last three years acted as an author, consultant, or lecturer for Medice and Roche. He receives royalties for textbooks and diagnostic tools from Hogrefe, Kohlhammer, UTB and Liber. S.B. is a partner in NeuroSupportSolutions International AB., (Copyright: © 2024 Arora et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose of Review: Substantial research exists focusing on the various aspects and domains of early human development. However, there is a clear blind spot in early postnatal development when dealing with neurodevelopmental disorders, especially those that manifest themselves clinically only in late infancy or even in childhood., Recent Findings: This early developmental period may represent an important timeframe to study these disorders but has historically received far less research attention. We believe that only a comprehensive interdisciplinary approach will enable us to detect and delineate specific parameters for specific neurodevelopmental disorders at a very early age to improve early detection/diagnosis, enable prospective studies and eventually facilitate randomised trials of early intervention. In this article, we propose a dynamic framework for characterising neurofunctional biomarkers associated with specific disorders in the development of infants and children. We have named this automated detection 'Fingerprint Model', suggesting one possible approach to accurately and early identify neurodevelopmental disorders.

Autism spectrum disorder (ASD) is a neurodevelopmental condition diagnosed in approximately 2% of children. Reliance on the emergence of clinically observable behavioral patterns only delays the mean age of diagnosis to approximately 4 years. However, neural pathways critical to language and social functions develop during infancy, and current diagnostic protocols miss the age when therapy would be most effective. We developed non-invasive ASD biomarkers using mass spectrometry analyses of elemental metabolism in single hair strands, coupled with machine learning. We undertook a national prospective study in Japan, where hair samples were collected at 1 month and clinical diagnosis was undertaken at 4 years. Next, we analyzed a national sample of Swedish twins and, in our third study, participants from a specialist ASD center in the US. In a blinded analysis, a predictive algorithm detected ASD risk as early as 1 month with 96.4% sensitivity, 75.4% specificity, and 81.4% accuracy (n = 486; 175 cases). These findings emphasize that the dynamics in elemental metabolism are systemically dysregulated in autism, and these signatures can be detected and leveraged in hair samples to predict the emergence of ASD as early as 1 month of age. [ABSTRACT FROM AUTHOR]

No robust biomarkers have yet been identified for autism spectrum disorder (ASD) or autistic traits. Familial factors likely influence biomarkers such as protein concentrations. Comparing twins with ASD or high autistic traits to the less affected co‐twin allows estimating the impact of familial confounding. We measured 203 proteins in cerebrospinal fluid (n = 86) and serum (n = 127) in twins (mean age 14.2 years, 44.9% females) enriched for ASD and other neurodevelopmental conditions. Autistic traits were assessed by using the parent‐report version of the Social Responsiveness Scale‐2. In cerebrospinal fluid, autistic traits correlated negatively with three proteins and positively with one. In serum, autistic traits correlated positively with 15 and negatively with one. Also in serum, six were positively—and one negatively—associated with ASD. A pathway analysis of these proteins revealed immune system enrichment. In within twin pair analyses, autistic traits were associated with serum B‐cell activating factor (BAFF) only, whereas Cystatin B (CSTB) remained significantly associated with ASD. These associations did not remain significant when only considering monozygotic twins. For the remainder, the within‐pair analysis indicated familial confounding, including shared environment and genes, influencing both autism and protein levels. Our findings indicate proteins involved in immunity as putative biomarkers of autistic traits and ASD with partial genetic confounding. Although some results are in line with previous studies in general, further studies are needed for replication. [ABSTRACT FROM AUTHOR]

Background: Autism spectrum disorder (ASD) evolves from an interplay between genetic and environmental factors during prenatal development. Since identifying maternal biomarkers associated with ASD risk in offspring during early pregnancy might result in new strategies for intervention, we investigated maternal metabolic biomarkers in relation to occurrence of ASD in offspring using both univariate logistic regression and multivariate network analysis. Methods: Serum samples from 100 women with an offspring diagnosed with ASD and 100 matched control women with typically developing offspring were collected at week 14 of pregnancy. Concentrations of 62 metabolic biomarkers were determined, including amino acids, vitamins (A, B, D, E, and K), and biomarkers related to folate (vitamin B9) metabolism, lifestyle factors, as well as C-reactive protein (CRP), the kynurenine-tryptophan ratio (KTR), and neopterin as markers of inflammation and immune activation. Results: We found weak evidence for a positive association between higher maternal serum concentrations of folate and increased occurrence of ASD (OR per 1 SD increase: 1.70, 95% CI 1.22–2.37, FDR adjusted P = 0.07). Multivariate network analysis confirmed expected internal biochemical relations between the biomarkers. Neither inflammation markers nor vitamin D3 levels, all hypothesized to be involved in ASD etiology, displayed associations with ASD occurrence in the offspring. Conclusions: Our findings suggest that high maternal serum folate status during early pregnancy may be associated with the occurrence of ASD in offspring. No inference about physiological mechanisms behind this observation can be made at the present time because blood folate levels may have complex relations with nutritional intake, the cellular folate status and status of other B-vitamins. Therefore, further investigations, which may clarify the potential role and mechanisms of maternal blood folate status in ASD risk and the interplay with other potential risk factors, in larger materials are warranted. [ABSTRACT FROM AUTHOR]

Background: Effective multisensory processing develops in infancy and is thought to be important for the perception of unified and multimodal objects and events. Previous research suggests impaired multisensory processing in autism, but its role in the early development of the disorder is yet uncertain. Here, using a prospective longitudinal design, we tested whether reduced visual attention to audiovisual synchrony is an infant marker of later‐emerging autism diagnosis. Methods: We studied 10‐month‐old siblings of children with autism using an eye tracking task previously used in studies of preschoolers. The task assessed the effect of manipulations of audiovisual synchrony on viewing patterns while the infants were observing point light displays of biological motion. We analyzed the gaze data recorded in infancy according to diagnostic status at 3 years of age (DSM‐5). Results: Ten‐month‐old infants who later received an autism diagnosis did not orient to audiovisual synchrony expressed within biological motion. In contrast, both infants at low‐risk and high‐risk siblings without autism at follow‐up had a strong preference for this type of information. No group differences were observed in terms of orienting to upright biological motion. Conclusions: This study suggests that reduced orienting to audiovisual synchrony within biological motion is an early sign of autism. The findings support the view that poor multisensory processing could be an important antecedent marker of this neurodevelopmental condition. [ABSTRACT FROM AUTHOR]

Neurodevelopmental disorders affect a substantial minority of the general population. Their origins are still largely unknown, but a complex interplay of genetic and environmental factors causing disturbances of the central nervous system's maturation and a variety of higher cognitive skills is presumed. Only limited research of rather small sample size and narrow scope has been conducted in neurodevelopmental disorders using a twin-differences design. The Roots of Autism and ADHD Twin Study in Sweden (RATSS) is an ongoing project targeting monozygotic twins discordant for categorical or dimensional autistic and inattentive/hyperactive-impulsive phenotypes as well as other neurodevelopmental disorders, and typically developing twin controls. Included pairs are 9 years of age or older, and comprehensively assessed for psychopathology, medical history, neuropsychology, and dysmorphology, as well as structural, functional, and molecular brain imaging. Specimens are collected for induced pluripotent (iPS) and neuroepithelial stem cells, genetic, gut bacteria, protein-/monoamine, and electron microscopy analyses. RATSS's objective is to generate a launch pad for novel surveys to understand the complexity of genotype-environment-phenotype interactions in autism spectrum disorder and attention-deficit hyperactivity disorder (ADHD). By October 2013, RATSS had collected data from 55 twin pairs, among them 10 monozygotic pairs discordant for autism spectrum disorder, seven for ADHD, and four for other neurodevelopmental disorders. This article describes the design, recruitment, data collection, measures, collected pairs’ characteristics, as well as ongoing and planned analyses in RATSS. Potential gains of the study comprise the identification of environmentally mediated biomarkers, the emergence of candidates for drug development, translational modeling, and new leads for prevention of incapacitating outcomes. [ABSTRACT FROM PUBLISHER]

The author looks at the challenge of seeking behavioral biomarkers that detect signs of developmental disorders. Topic discussed include diseases that have unrecognized origins like single-gene mutations, diseases with genetic heterogeneity and genetically complex disorders, and the importance of combining methods from various fields in evaluating progression of developmental disorders through biomarkers.