State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
Department of Child Health Care, Maternity and Child Healthcare Hospital of Nanshan District, 1 Wanxia Road, Nanshan District, Shenzhen, 518067, China.
Chemosphere. 2023 Jul;330:138700. doi: 10.1016/j.chemosphere.2023.138700. Epub 2023 Apr 17.
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.
过量接触金属直接威胁人类健康，包括神经发育。自闭症谱系障碍（ASD）是一种神经发育障碍，给儿童本身、他们的家庭甚至整个社会都带来了巨大的危害。鉴于此，在儿童早期开发可靠的 ASD 生物标志物至关重要。在这里，我们使用电感耦合等离子体质谱（ICP-MS）来识别儿童血液中与 ASD 相关的金属元素的异常。多接收电感耦合等离子体质谱（MC-ICP-MS）被应用于检测铜（Cu）的同位素差异，以进一步评估其在大脑中的核心作用。我们还基于支持向量机（SVM）算法为未知样本开发了一种机器学习分类方法。结果表明，病例组和对照组之间的血液金属组（铬（Cr）、锰（Mn）、钴（Co）、镁（Mg）和砷（As））存在显著差异，ASD 病例的 Zn/Cu 比值明显较低。有趣的是，我们发现血清铜同位素组成（δCu）与自闭症血清之间存在很强的关联。SVM 成功地应用于基于二维 Cu 特征（Cu 浓度和 δCu）对病例和对照组进行区分，具有很高的准确性（94.4%）。总的来说，我们的研究结果揭示了一种用于 ASD 潜在早期诊断和筛查的新生物标志物，血液金属组的显著变化也有助于从金属组学的角度理解 ASD 的潜在发病机制。
