ArticleJournal of clinical medicine2022
Elemental Dynamics in Hair Accurately Predict Future Autism Spectrum Disorder Diagnosis: An International Multi-Center Study.
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Exposure to heavy metals in utero and autism spectrum disorder at age 3: a meta-analysis of two longitudinal cohorts of siblings of children with autism.Environmental health : a global access science source · 2024Pooled it
- Loss of Fmr1 reorganizes the multi-elemental composition across tissues in Fragile X Syndrome mice.PloS one · 2026Article
- AI-Enabled Technologies and Biomarker Analysis for the Early Identification of Autism and Related Neurodevelopmental Disorders.Children (Basel, Switzerland) · 2025Review
- Rule-out test for autism using machine-learning analysis of molecular temporal dynamics in hair - a multicenter study.medRxiv : the preprint server for health sciences · 2025Article
- Metabolic network analysis of pre-ASD newborns and 5-year-old children with autism spectrum disorder.Communications biology · 2024Article
- Associations between Elemental Metabolic Dynamics and Default Mode Network Functional Connectivity Are Altered in Autism.Journal of clinical medicine · 2023Article
- Metal Profiles in Autism Spectrum Disorders: A Crosstalk between Toxic and Essential Metals.International journal of molecular sciences · 2022Review
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 4 countries.
Funding
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 (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.