Artificial intelligence and the analysis of multiplatform metabolomics data for the detection of intrauterine growth restriction
Artificial intelligence and the analysis of multiplatform metabolomics data for the detection of intrauterine growth restriction
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DOI：
10.1371/journal.pone.0214121
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发表时间：
2019-04-18
期刊：
影响因子：
3.7
通讯作者：
Graham, Stewart F.
中科院分区：
文献类型：
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作者：
Bahado-Singh, Ray Oliver;Yilmaz, Ali;Graham, Stewart F.
目的探讨胎儿宫内生长受限(IUGR)的发病机制，并将人工智能(AI)技术应用于多平台，即基于核磁共振(NMR)和质谱学(MS)的代谢组学分析预测IUGR。材料与方法对40例IUGR(出生体重及出生体重)和40名正常对照的脐血进行了基于MS和核磁共振的代谢组学分析。测试了基于相关性的特征选择(CFS)、偏最小二乘回归(PLS)和学习矢量量化(LVQ)三种变量选择算法的诊断性能。对于每个选定的代谢物集合，以及由三种选择算法中常见的代谢物组成的小组，建立了支持向量机(SVM)模型，其中使用10次交叉验证进行参数选择。计算受试者工作特征曲线下面积(AUC)、诊断IUGR的灵敏度和特异度。代谢物集浓缩分析(MSEA)用于确定脐血中IUGR的直接结果是哪些代谢途径受到干扰。结果所有选定的代谢物及其重叠集在优化的支持向量机模型中的准确度在0.78-0.82之间，具有统计学意义。利用数据集中所有代谢物的模型的AUC=0.91，灵敏度为0.83，特异度为0.80。CFS和OL(肌酐、C2、C4、LysoPC。A.C16.1，LysoPC。A.C20.3，LysoPC。A.C28.1、PC.aa.C24.0)的敏感性和特异性最高，分别为0.87和0.87。MSEA显示IUGR患者的代谢途径发生了显著改变。异常代谢途径包括：超长脂肪酸的β氧化、支链脂肪酸的氧化、磷脂的生物合成、赖氨酸的降解、尿素循环和脂肪酸代谢。IUGR患者存在明显的肝功能障碍和能量生成途径障碍。
ObjectiveTo interrogate the pathogenesis of intrauterine growth restriction (IUGR) and apply Artificial Intelligence (AI) techniques to multi-platform i.e. nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) based metabolomic analysis for the prediction of IUGR.Materials and methodsMS and NMR based metabolomic analysis were performed on cord blood serum from 40 IUGR (birth weight < 10th percentile) cases and 40 controls. Three variable selection algorithms namely: Correlation-based feature selection (CFS), Partial least squares regression (PLS) and Learning Vector Quantization (LVQ) were tested for their diagnostic performance. For each selected set of metabolites and the panel consists of metabolites common in three selection algorithms so-called overlapping set (OL), support vector machine (SVM) models were developed for which parameter selection was performed busing 10-fold cross validations. Area under the receiver operating characteristics curve (AUC), sensitivity and specificity values were calculated for IUGR diagnosis. Metabolite set enrichment analysis (MSEA) was performed to identify which metabolic pathways were perturbed as a direct result of IUGR in cord blood serum.ResultsAll selected metabolites and their overlapping set achieved statistically significant accuracies in the range of 0.78-0.82 for their optimized SVM models. The model utilizing all metabolites in the dataset had an AUC = 0.91 with a sensitivity of 0.83 and specificity equal to 0.80. CFS and OL (Creatinine, C2, C4, lysoPC. a. C16.1, lysoPC. a. C20.3, lysoPC. a. C28.1, PC.aa.C24.0) showed the highest performance with sensitivity (0.87) and specificity (0.87), respectively. MSEA revealed significantly altered metabolic pathways in IUGR cases. Dysregulated pathways include: beta oxidation of very long fatty acids, oxidation of branched chain fatty acids, phospholipid biosynthesis, lysine degradation, urea cycle and fatty acid metabolism.ConclusionA systematically selected panel of metabolites was shown to accurately detect IUGR in newborn cord blood serum. Significant disturbance of hepatic function and energy generating pathways were found in IUGR cases.