期刊
JOURNAL OF CLINICAL MEDICINE
卷 11, 期 23, 页码 -出版社
MDPI
DOI: 10.3390/jcm11237154
关键词
biomarkers; autism spectrum disorder; exposomics; environmental exposures; metal exposures; diagnostic testing; neurodevelopmental disorders; hair assays; prognostic testing; dynamical methods
资金
- National Institute of Environmental Health Sciences
- National Institute of Mental Health [P30ES023515, R01ES026033, U2CES030859, R35ES030435, R01ES02951, R01MH122447]
- Swedish Research Council [R01ES032294]
- Knut and Alice Wallenberg Foundation
- Vinnova
- Formas
- Swedish Brain foundation (Hjaernfonden)
- Stockholm Brain Institute
- Autism and Asperger Association Stockholm
- Queen Silvia's Jubilee Fund
- Solstickan Foundation
- PRIMA Child and Adult Psychiatry
- Pediatric Research Foundation at Astrid Lindgren Children's Hospital
- Swedish Foundation for Strategic Research
- Jerring Foundation
- Swedish Order of Freemasons
- Kempe-Carlgrenska Foundation
- Sunnderdahls Handikappsfond
- Jeansson Foundation
- EU-AIMS (European Autism Intervention)
- Innovative Medicines Initiative Joint Undertaking
- Autism Speaks [115300]
- EU AIMS-2-TRIALS
Autism spectrum disorder (ASD) is a neurodevelopmental condition that is challenging to diagnose and treat at an early age. Researchers have developed a non-invasive biomarker using mass spectrometry analysis of hair samples, coupled with machine learning, which can predict ASD risk as early as 1 month of age.
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.
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