Metallomic profiling and natural copper isotopic signatures of childhood autism in serum and red blood cells.

Researchers tested blood samples from autistic children and found differences in metal levels compared to other children. They found lower zinc-to-copper ratios and differences in other metals like chromium and manganese. Using a computer program, they could identify autistic children with 94% accuracy based on copper measurements. This research suggests blood metal testing might help identify autism earlier.

This study examined metal levels in blood samples from autistic children compared to controls using advanced mass spectrometry techniques. Researchers found significant differences in several metals including chromium, manganese, cobalt, magnesium, and arsenic between groups. A notably lower zinc-to-copper ratio was observed in autistic children. The study also measured copper isotopic composition, finding strong associations with autism.

Using machine learning algorithms, researchers achieved 94.4% accuracy in distinguishing autistic from non-autistic samples based on copper measurements. The findings suggest potential biomarkers for early autism diagnosis and provide insights into possible metallomics-related mechanisms in autism development.

Results suggest potential for developing blood-based biomarkers for autism screening, though replication in larger samples is needed. The metallomics differences may inform understanding of autism pathophysiology and potential therapeutic targets related to metal metabolism.

Sample size not reported, limiting generalizability. Cross-sectional design cannot establish causation. Single study without replication. Unclear if findings represent cause, consequence, or correlation with autism.

Excessive exposure to metals directly threatens human health, including neurodeve lopment. Autism spectrum disorder (ASD) is a neurodevelopmental disorder, leaving great harms to children themselves, their families, and even society. In view of this, it is critical to develop reliable biomarkers for ASD in early childhood. Here we used inductively coupled plasma mass spectrometry (ICP-MS) to identify the abnormalities in ASD-associated metal elements in children blood.

Multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) was applied to detect isotopic differences in copper (Cu) for further assessment on account of its core role in the brain. We also developed a machine learning classification method for unknown samples based on a support vector machine (SVM) algorithm. The results indicated significant differences in the blood metallome (chromium (Cr), manganese (Mn), cobalt (Co), magnesium (Mg), and arsenic (As)) between cases and controls, and a significantly lower Zn/Cu ratio was observed in the ASD cases. Interestingly, we found a strong association of serum copper isotopic composition (δCu) with autistic serum.

SVM was successfully applied to discriminate cases and controls based on the two-dimensional Cu signatures (Cu concentration and δCu) with a high accuracy (94.4%). Overall, our findings revealed a new biomarker for potential early diagnosis and screening of ASD, and the significant alterations in the blood metallome also helped to understand the potential pathogenesis of ASD in terms of metallomics.