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. 2022 Dec 1;11(23):7154.
doi: 10.3390/jcm11237154.

Elemental Dynamics in Hair Accurately Predict Future Autism Spectrum Disorder Diagnosis: An International Multi-Center Study

Christine Austin  1   2 , Paul Curtin  1   2 , Manish Arora  1   2 , Abraham Reichenberg  2   3 , Austen Curtin  1   2 , Miyuki Iwai-Shimada  4 , Robert O Wright  2 , Rosalind J Wright  2   5 , Karl Lundin Remnelius  6 , Johan Isaksson  6   7 , Sven Bölte  6   8   9 , Shoji F Nakayama  4
Affiliations

Elemental Dynamics in Hair Accurately Predict Future Autism Spectrum Disorder Diagnosis: An International Multi-Center Study

Christine Austin et al. J Clin Med. .

Abstract

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.

Keywords: autism spectrum disorder; biomarkers; diagnostic testing; dynamical methods; environmental exposures; exposomics; hair assays; metal exposures; neurodevelopmental disorders; prognostic testing.

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Conflict of interest statement

Manish Arora, Christine Austin, Austen Curtin, and Paul Curtin are employees of Linus Biotechnology Inc, a start-up company of Mount Sinai Health System. They hold equity in the company. The company develops tools for the detection of autism spectrum disorder and related conditions. The following authors report no competing interests: Abraham Reichenberg, Austen Curtin, Miyuki Iwai-Shimada, Robert Wright, Rosalind Wright, Karl Lundin Remnelius, Johan Isaksson, Sven Bölte, and Shoji F. Nakayama.

Figures

Figure 1
Figure 1
Study design and analytical pipeline. (a) Participants were recruited in Japan (prospective national study), Sweden (cross-sectional national study), and New York (cross-sectional, single-clinical-center study). Clinical case ascertainment was undertaken using DSM-5 criteria (Autism Spectrum Disorder, ASD). Scalp hair strands were analyzed using laser ablation-inductively coupled plasma-mass spectrometry to generate 4 to 6 hourly profiles of elemental uptake. (b) Recurrence and cross-recurrence quantification analyses were used to quantify the dynamic nature of elemental assimilation. A machine learning algorithm was trained on 80% of the data and tested on a randomly selected 20% holdout set.
Figure 2
Figure 2
Elemental pathways associated with ASD diagnosis: (A) For each of 210 features measured in each elemental pathway, we constructed a discrete generalized linear model to test for associations with odds of diagnosis with ASD. Models were adjusted for age and sex, and p-values associated with diagnostic status were adjusted via false discovery rates (FDRs). x-axis plots the standardized effect estimate for the effect of ASD diagnosis; dots and corresponding lines reflect the effect estimate and associated 95% confidence interval for each feature. (B) Receiver operating characteristic [38] curve generated from predicting case status in the validation set (n = 97; 28 ASD). (C) Comparison of overall model performance with sex-stratified estimates of model performance. p-values reflect comparison of sex-stratified ROC curves relative to ROC curves in the overall model. (D) Comparison of overall model performance with age-stratified estimates of model performance. p-values reflect comparison of age-stratified ROC curves relative to ROC curves derived in the overall model.
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