PDF(Pubmed)
Abstract：
One of the main challenges of newborn screening programs, which screen for inherited metabolic disorders, is cutting down on false positives (FPs) in order to avoid family stresses, additional analyses, and unnecessary costs. False positives are partly caused by an insubstantial number of robust biomarkers in evaluations. Another challenge is how to distinguish between diseases which share the same primary marker and for which secondary biomarkers are just as highly desirable. Focusing on pathologies that involve butyrylcarnitine (C4) elevation, such as short-chain acylCoA dehydrogenase deficiency (SCADD) and isobutyrylCoA dehydrogenase deficiency (IBDD), we investigated the acylcarnitine profile of 121 newborns with a C4 increase to discover secondary markers to achieve two goals: reduce the FP rate and discriminate between the two rare diseases. Analyses were carried out using tandem mass spectrometry with whole blood samples spotted on filter paper. Seven new biomarkers (C4/C0, C4/C5, C4/C5DC\\C6OH, C4/C6, C4/C8, C4/C14:1, C4/C16:1) were identified using a non-parametric ANOVA analysis. Then, the corresponding cut-off values were found and applied to the screening program. The seven new ratios were shown to be robust (p < 0.001 and p < 0.01, 0.0937 < ε2 < 0.231) in discriminating between FP and IBDD patients, FP and SCADD patients, or SCADD and IBDD patients. Our results suggest that the new ratios are optimal indicators for identifying true positives, distinguishing between two rare diseases that share the same primary biomarker, improving the predictive positive value (PPV) and reducing the false positive rate (FPR).
摘要：
新生儿筛查计划的主要挑战之一，筛查遗传性代谢紊乱，正在减少假阳性(FP)，以避免家庭压力，额外的分析，和不必要的成本。假阳性部分是由评估中大量稳健的生物标志物引起的。另一个挑战是如何区分共享相同的主要标志物和同样高度期望的次要生物标志物的疾病。关注涉及丁酰肉碱（C4）升高的病理，如短链酰基辅酶A脱氢酶缺乏症（SCADD）和异丁酰辅酶A脱氢酶缺乏症（IBDD），我们调查了121例C4增加的新生儿的酰基肉碱谱，以发现次要标志物以实现两个目标：降低FP发生率和区分两种罕见疾病。使用将全血样品点样在滤纸上的串联质谱法进行分析。七个新的生物标志物（C4/C0，C4/C5，C4/C5DC\\C6OH，使用非参数ANOVA分析鉴定C4/C6、C4/C8、C4/C14:1、C4/C16:1)。然后，找到相应的截止值并应用于筛查程序.这七个新的比率被证明在区分FP和IBDD患者方面是稳健的(p<0.001和p<0.01,0.0937<ε2<0.231)。FP和SCADD患者，或SCADD和IBDD患者。我们的结果表明，新的比率是识别真阳性的最佳指标，区分两种具有相同主要生物标志物的罕见疾病，提高预测阳性率（PPV），降低假阳性率（FPR）。
