一种根据母亲的生物标记，预测自闭症的算法准确率为100%

一种根据母亲的生物标记，预测自闭症的算法准确率为100%

New technology may be able to predict a certain type of autism with 100% accuracy using biomarkers in the mothers’ blood plasma, suggests a study published in Molecular Psychiatry. The innovation is based on a machine-learning algorithm that identifies key antibodies thought to be linked with maternal autoantibody-related autism spectrum disorder (MAR ASD), which accounts for around 20% of all autism diagnoses, and could become an important screening tool for autism in the future.

一项发表在《分子精神病学》杂志上的研究表明，利用母亲血浆中的生物标记物，新技术可能能够100%准确地预测某种类型的自闭症。这项创新是基于一种机器学习算法，该算法可以识别被认为与母亲自身抗体相关的自闭症谱系障碍(MAR ASD)相关的关键抗体，这种抗体约占所有自闭症诊断的20%，并可能成为未来自闭症筛查的重要工具。

MAR ASD is thought to result from the production of antibodies that are reactive with the developing brain of the unborn fetus, called autoantibodies. These autoantibodies react with specific proteins involved in brain development, and how much these autoantibodies react with the brain likely alters how challenging the resulting condition is. Therefore, screening for each may have predictive value for how likely a child will be to develop MAR ASD.

自闭症谱系障碍被认为是由于产生了抗体，这种抗体与未出生胎儿的大脑发育反应，被称为自身抗体。这些自身抗体与参与大脑发育的特定蛋白质发生反应，这些自身抗体与大脑发生反应的程度可能会改变由此产生的情况的挑战性。因此，对每一种类型进行筛查可能对儿童发展为MAR ASD的可能性有预测价值。

To test this hypothesis, researchers from UC Davis MIND Institute took plasma samples from 450 mothers with children with MAR ASD and 342 mothers with children without MAR ASD. The goal was to use machine learning to identify patterns of autoantibody reactivity that are strongly associated with ASD, and any that are specific for ASD. The mothers were sourced from the CHARGE study, which aims to understand the genetic and environmental factors involved in autism and has been running since 2003.

为了验证这一假设，加州大学戴维斯分校心理研究所的研究人员从450名有自闭症儿童的母亲和342名没有自闭症儿童的母亲身上提取了血浆样本。其目标是使用机器学习来识别与自闭症谱系障碍密切相关的自身抗体反应模式，以及任何自闭症谱系障碍特异性抗体反应模式。这些母亲来自CHARGE研究，该研究旨在了解自闭症的遗传和环境因素，自2003年以来一直在进行。

Jack Dunhill

The researchers discovered three main patterns associated with MAR ASD, including one that significantly increased the chances of the child being on the autism spectrum, called CRMP1 reactivity.

研究人员发现了与自闭症谱系障碍相关的三种主要模式，其中一种是CRMP1反应性，它显著增加了儿童患自闭症的几率。

“For example, if the mother has autoantibodies to CRIMP1 and GDA (the most common pattern), her odds of having a child with autism is 31 times greater than the general population, based on this current dataset. That’s huge,” said Professor Judy Van de Water, lead author of the study, in a statement. “There’s very little out there that is going to give you that type of risk assessment.”

例如，根据目前的数据集，如果母亲有对CRIMP1和GDA(最常见的模式)的自身抗体，她的孩子患有自闭症的几率是普通人群的31倍。这项研究的主要作者朱迪·范·德·沃特教授在一份声明中说。“几乎没有什么东西能给你提供那种风险评估。”

This is the first report of machine learning identifying biomarkers that are associated with MAR-ASD with 100% accuracy.

这是第一个关于机器学习以100%的准确率识别与MAR-ASD相关的生物标记物的报告。

Recognition of these autoantibody patterns could be integral for screening either before or during pregnancy, offering insight into the development of the child for parent preparation.

对这些自身抗体模式的识别对于怀孕前或怀孕期间的筛查是必不可少的，为父母准备对孩子的发育提供洞察力。

“We can envision that a woman could have a blood test for these antibodies prior to getting pregnant. If she had them, she’d know she would be at very high risk of having a child with autism. If not, she has a 43% lower chance of having a child with autism as MAR autism is ruled out,” said Van de Water.

“我们可以设想，女性可以在怀孕前验血检测这些抗体。如果她有自闭症，她就会知道她的孩子很有可能患有自闭症。如果没有，她的孩子患自闭症的几率会降低43%，因为MAR自闭症被排除在外。”Van de Water说。

Whilst some of the reactive mechanisms are characterized by the authors, understanding the role of all the predictive autoantibodies could be important for not just screening, but therapeutics too. However, the study remains as an early proof-of-concept and will need extensive trials before the technology sees clinical use.

虽然一些反应机制是作者的特点，但了解所有预测性自身抗体的作用不仅对筛查很重要，对治疗也很重要。然而，这项研究仍然是一个早期的概念验证，在这项技术得到临床应用之前还需要广泛的试验。