Validation of a salivary miRNA diagnostic test for autism spectrum disorder

项目概要/摘要 自闭症谱系障碍（ASD）是一个连续的神经发育特征，包括缺陷， 沟通和社会互动，以及限制性的，重复性的兴趣和行为。ASD是一个 越来越多的公共卫生问题，2014年约有1/45的美国儿童被诊断患有ASD，是2014年的10倍。 在过去的40年里，流行率有所上升。ASD对社会和经济的影响很大， 据估计，仅在美国每年就有超过2860亿美元的负担。虽然与ASD的单一直接链接 诊断尚未确定，研究已经确定了遗传，表观遗传，神经，激素， 以及影响ASD患者预后的环境因素。为了有效地治疗患有 ASD，及时发现对于实施早期治疗方案至关重要。利用这些知识 由于ASD的预先存在的因素，医生可以在患者还年轻时开始治疗，即使孩子已经 没有开始表现出典型的ASD症状。研究表明，早期治疗的结果更好的功能 ASD症状的改善和减轻。这些模型、药物和项目已被证明是 有效地控制ASD的症状，并可能完全将一些患者从ASD谱系中排除。 不幸的是，目前ASD的诊断方法对幼儿不是很准确; 诊断ASD是三年前的事了，其中大约一半是假阳性。发展精准 因此，ASD的诊断生物标志物将代表对患者护理的有价值的补充。运动智能是 开发一种方法来诊断ASD通过测量大脑相关的微核糖核酸（miRNA）， 口水miRNA通过外泌体和其他微泡的细胞外转运是一种既定的表观遗传学机制。 细胞改变附近细胞基因表达的机制。微泡被挤出到 细胞外空间，在那里它们可以停靠并进入邻近细胞，并改变基因表达。这些 微泡存在于各种体液如唾液中。这使得Motion Intelligence能够 通过收集唾液来测量可能来自中枢神经系统的遗传物质。 该方法最小化了与死后脑组织分析相关的许多限制（例如， 缺氧性脑损伤、RNA降解、死后间隔、濒死状态）或外周白细胞（相关性 的表达变化，痛苦的抽血）。因此，细胞外miRNA 唾液中的定量提供了一种有吸引力的和微创的生物标志物鉴定技术， ASD儿童这项I期研究将包括一项前瞻性临床试验， ASD儿童及年龄和性别匹配的正常儿童唾液中miRNAs的表达模式 发展来自一半受试者的数据将用作训练数据集，以创建相关的算法。 miRNA生物标志物，另一半将用于验证研究，以确定这种方法的有效性。 算法

Project Summary/Abstract Autism spectrum disorder (ASD) is a continuum of neurodevelopmental characteristics that includes deficits in communication and social interaction, as well as restrictive, repetitive interests and behaviors. ASD is an increasing public health concern, with about 1 in 45 American children diagnosed with ASD in 2014, a 10-fold increase in prevalence over the past 40 years. The effect of ASD on both society and the economy is a large burden, estimated at more than $286 billion per year in the U.S. alone. While a single direct link to ASD diagnosis has not been determined, studies have identified genetic, epigenetic, neurological, hormonal, and environmental factors that affect outcomes for patients with ASD. In order to effectively treat patients with ASD, timely detection is crucial for implementation of early treatment options. Using knowledge of these preexisting factors for ASD, doctors can begin treatment while the patient is still young, even if the child has not begun to exhibit typical ASD symptoms. Studies suggest that earlier treatment results in better functional outcomes and reductions in symptoms of ASD. These models, medications and programs have proven to be effective in managing the symptoms of ASD, and may remove some patients from the ASD spectrum entirely. Unfortunately, current diagnostic methods for ASD are not very accurate for young children; the average age of diagnosing ASD is three years old, and about half of those are false positives. Development of accurate diagnostic biomarkers for ASD would thus represent a valuable addition to patient care. Motion Intelligence is developing an approach to diagnose ASD by measuring brain-related micro ribonucleic acids (miRNAs) in saliva. Extracellular transport of miRNA via exosomes and other microvesicles is an established epigenetic mechanism for cells to alter gene expression in nearby cells. The microvesicles are extruded into the extracellular space, where they can dock and enter neighboring cells, and alter gene expression. These microvesicles are present in various bodily fluids, such as saliva. This has enabled Motion Intelligence to measure genetic material that may have originated from the central nervous system simply by collecting saliva. This method minimizes many of the limitations associated with analysis of post-mortem brain tissue (e.g., anoxic brain injury, RNA degradation, post-mortem interval, agonal state), or peripheral leukocytes (relevance of expression changes, painful blood draws) employed in previous studies. Thus, extracellular miRNA quantification in saliva provides an attractive and minimally invasive technique for biomarker identification in children with ASD. This Phase I study will include a prospective clinical trial that will characterize the expression pattern of salivary miRNAs in children with ASD and age- and gender-matched controls with typical development. Data from half of the subjects will be used as a training dataset to create an algorithm of relevant miRNA biomarkers, and the other half will be used in a validation study to determine the efficacy of this algorithm.