一種根據(jù)母親的生物標記，預測自閉癥的算法準確率為100%

一種根據(jù)母親的生物標記，預測自閉癥的算法準確率為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.

一項發(fā)表在《分子精神病學》雜志上的研究表明，利用母親血漿中的生物標記物，新技術(shù)可能能夠100%準確地預測某種類型的自閉癥。這項創(chuàng)新是基于一種機器學習算法，該算法可以識別被認為與母親自身抗體相關(guān)的自閉癥譜系障礙(MAR ASD)相關(guān)的關(guān)鍵抗體，這種抗體約占所有自閉癥診斷的20%，并可能成為未來自閉癥篩查的重要工具。

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.

自閉癥譜系障礙被認為是由于產(chǎn)生了抗體，這種抗體與未出生胎兒的大腦發(fā)育反應，被稱為自身抗體。這些自身抗體與參與大腦發(fā)育的特定蛋白質(zhì)發(fā)生反應，這些自身抗體與大腦發(fā)生反應的程度可能會改變由此產(chǎn)生的情況的挑戰(zhàn)性。因此，對每一種類型進行篩查可能對兒童發(fā)展為MAR ASD的可能性有預測價值。

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.

為了驗證這一假設(shè)，加州大學戴維斯分校心理研究所的研究人員從450名有自閉癥兒童的母親和342名沒有自閉癥兒童的母親身上提取了血漿樣本。其目標是使用機器學習來識別與自閉癥譜系障礙密切相關(guān)的自身抗體反應模式，以及任何自閉癥譜系障礙特異性抗體反應模式。這些母親來自CHARGE研究，該研究旨在了解自閉癥的遺傳和環(huán)境因素，自2003年以來一直在進行。

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.

研究人員發(fā)現(xiàn)了與自閉癥譜系障礙相關(guān)的三種主要模式，其中一種是CRMP1反應性，它顯著增加了兒童患自閉癥的幾率。

“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，根據(jù)目前的數(shù)據(jù)集，如果母親有對CRIMP1和GDA(最常見的模式)的自身抗體，她的孩子患有自閉癥的幾率是普通人群的31倍。這項研究的主要作者朱迪·范·德·沃特教授在一份聲明中說。“幾乎沒有什么東西能給你提供那種風險評估。”

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

這是第一個關(guān)于機器學習以100%的準確率識別與MAR-ASD相關(guān)的生物標記物的報告。

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.

對這些自身抗體模式的識別對于懷孕前或懷孕期間的篩查是必不可少的，為父母準備對孩子的發(fā)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.

“我們可以設(shè)想，女性可以在懷孕前驗血檢測這些抗體。如果她有自閉癥，她就會知道她的孩子很有可能患有自閉癥。如果沒有，她的孩子患自閉癥的幾率會降低43%，因為MAR自閉癥被排除在外。”Van de Water說。

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.

雖然一些反應機制是作者的特點，但了解所有預測性自身抗體的作用不僅對篩查很重要，對治療也很重要。然而，這項研究仍然是一個早期的概念驗證，在這項技術(shù)得到臨床應用之前還需要廣泛的試驗。