Stemina Biomarker Discovery, Inc, Madison, Wisconsin, USA.
Pathology and Laboratory Medicine, Genomics, Neurology, and Pediatrics Institutes, Cleveland Clinic, Cleveland, Ohio, USA.
Autism Res. 2020 Aug;13(8):1270-1285. doi: 10.1002/aur.2330. Epub 2020 Jun 18.
Autism spectrum disorder (ASD) is biologically and behaviorally heterogeneous. Delayed diagnosis of ASD is common and problematic. The complexity of ASD and the low sensitivity of available screening tools are key factors in delayed diagnosis. Identification of biomarkers that reduce complexity through stratification into reliable subpopulations can assist in earlier diagnosis, provide insight into the biology of ASD, and potentially suggest targeted interventions. Quantitative metabolomic analysis was performed on plasma samples from 708 fasting children, aged 18 to 48 months, enrolled in the Children's Autism Metabolome Project (CAMP). The primary goal was to identify alterations in metabolism helpful in stratifying ASD subjects into subpopulations with shared metabolic phenotypes (i.e., metabotypes). Metabotypes associated with ASD were identified in a discovery set of 357 subjects. The reproducibility of the metabotypes was validated in an independent replication set of 351 CAMP subjects. Thirty-four candidate metabotypes that differentiated subsets of ASD from typically developing participants were identified with sensitivity of at least 5% and specificity greater than 95%. The 34 metabotypes formed six metabolic clusters based on ratios of either lactate or pyruvate, succinate, glycine, ornithine, 4-hydroxyproline, or α-ketoglutarate with other metabolites. Optimization of a subset of new and previously defined metabotypes into a screening battery resulted in 53% sensitivity (95% confidence interval [CI], 48%-57%) and 91% specificity (95% CI, 86%-94%). Thus, our metabolomic screening tool detects more than 50% of the autistic participants in the CAMP study. Further development of this metabolomic screening approach may facilitate earlier referral and diagnosis of ASD and, ultimately, more targeted treatments. LAY SUMMARY: Analysis of a selected set of metabolites in blood samples from children with autism and typically developing children identified reproducible differences in the metabolism of about half of the children with autism. Testing for these differences in blood samples can be used to help screen children as young as 18 months for risk of autism that, in turn, can facilitate earlier diagnoses. In addition, differences may lead to biological insights that produce more precise treatment options. We are exploring other blood-based molecules to determine if still a higher percentage of children with autism can be detected using this strategy. Autism Res 2020, 13: 1270-1285. © 2020 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals LLC.
自闭症谱系障碍（ASD）在生物学和行为上具有异质性。ASD 的诊断往往延迟，且存在问题。ASD 的复杂性和现有筛查工具的低敏感性是导致诊断延迟的关键因素。通过分层为可靠的亚群，识别可减少复杂性的生物标志物，有助于早期诊断，深入了解 ASD 的生物学，并可能提示针对性的干预措施。对来自参加儿童自闭症代谢组学项目（CAMP）的 708 名年龄在 18 至 48 个月的禁食儿童的血浆样本进行了定量代谢组学分析。主要目标是确定有助于将 ASD 受试者分层为具有共享代谢表型（即代谢型）的亚群的代谢变化。在发现组的 357 名受试者中鉴定出与 ASD 相关的代谢型。在 CAMP 中的 351 名独立复制组的验证中，对代谢型的重现性进行了验证。通过灵敏度至少为 5%且特异性大于 95%，鉴定出了可区分 ASD 亚组与正常发育参与者的 34 种候选代谢型。34 种代谢型基于乳酸或丙酮酸、琥珀酸、甘氨酸、精氨酸、4-羟脯氨酸或α-酮戊二酸与其他代谢物的比值形成六个代谢簇。对新的和先前定义的代谢型的子集进行优化，得到 53%的灵敏度（95%置信区间[CI]，48%-57%）和 91%的特异性（95%CI，86%-94%）。因此，我们的代谢组学筛查工具可以检测到 CAMP 研究中超过 50%的自闭症参与者。进一步开发这种代谢组学筛查方法可能有助于更早地转介和诊断 ASD，并最终实现更有针对性的治疗。 要点总结：对自闭症儿童和正常发育儿童的血液样本中一组选定代谢物的分析，确定了大约一半自闭症儿童的代谢存在可重现的差异。对血液样本进行这些差异的检测可用于帮助筛查 18 个月大的儿童患自闭症的风险，进而有助于更早地诊断。此外，差异可能会产生生物学上的洞察力，从而产生更精确的治疗选择。我们正在探索其他基于血液的分子，以确定是否使用这种策略可以检测到更高比例的自闭症儿童。自闭症研究 2020，13：1270-1285。 © 2020 作者。自闭症研究由自闭症研究国际协会出版，由 Wiley Periodicals LLC 出版。
