Translational Health Sciences, University of Bristol, Bristol, UK.
University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK.
Metabolomics. 2024 Jul 2;20(4):70. doi: 10.1007/s11306-024-02129-8.
Congenital heart disease (CHD) is the most common congenital anomaly, representing a significant global disease burden. Limitations exist in our understanding of aetiology, diagnostic methodology and screening, with metabolomics offering promise in addressing these.
To evaluate maternal metabolomics and lipidomics in prediction and risk factor identification for childhood CHD.
We performed an observational study in mothers of children with CHD following pregnancy, using untargeted plasma metabolomics and lipidomics by ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). 190 cases (157 mothers of children with structural CHD (sCHD); 33 mothers of children with genetic CHD (gCHD)) from the children OMACp cohort and 162 controls from the ALSPAC cohort were analysed. CHD diagnoses were stratified by severity and clinical classifications. Univariate, exploratory and supervised chemometric methods were used to identify metabolites and lipids distinguishing cases and controls, alongside predictive modelling.
499 metabolites and lipids were annotated and used to build PLS-DA and SO-CovSel-LDA predictive models to accurately distinguish sCHD and control groups. The best performing model had an sCHD test set mean accuracy of 94.74% (sCHD test group sensitivity 93.33%; specificity 96.00%) utilising only 11 analytes. Similar test performances were seen for gCHD. Across best performing models, 37 analytes contributed to performance including amino acids, lipids, and nucleotides.
Here, maternal metabolomic and lipidomic analysis has facilitated the development of sensitive risk prediction models classifying mothers of children with CHD. Metabolites and lipids identified offer promise for maternal risk factor profiling, and understanding of CHD pathogenesis in the future.
先天性心脏病（CHD）是最常见的先天性异常，代表着重大的全球疾病负担。我们对病因学、诊断方法和筛查的理解存在局限性，代谢组学在解决这些问题方面具有很大的应用前景。
评估母体代谢组学和脂质组学在预测和识别儿童 CHD 的风险因素方面的作用。
我们对妊娠后患有 CHD 的儿童的母亲进行了一项观察性研究，使用超高效液相色谱-高分辨率质谱（UHPLC-HRMS）进行非靶向血浆代谢组学和脂质组学分析。从儿童 OMACp 队列中分析了 190 例病例（结构性 CHD（sCHD）患儿的 157 位母亲；遗传性 CHD（gCHD）患儿的 33 位母亲）和来自 ALSPAC 队列的 162 位对照者。根据严重程度和临床分类对 CHD 诊断进行分层。使用单变量、探索性和有监督的化学计量学方法来识别区分病例和对照的代谢物和脂质，以及预测模型。
注释了 499 种代谢物和脂质，用于构建 PLS-DA 和 SO-CovSel-LDA 预测模型，以准确区分 sCHD 和对照组。表现最好的模型在 sCHD 测试组中的准确率为 94.74%（sCHD 测试组的敏感性为 93.33%；特异性为 96.00%），仅使用 11 种分析物。gCHD 也有类似的测试性能。在表现最好的模型中，有 37 种分析物对性能有贡献，包括氨基酸、脂质和核苷酸。
本研究通过母体代谢组学和脂质组学分析，成功开发了用于分类 CHD 患儿母亲的敏感风险预测模型。鉴定出的代谢物和脂质为未来的母体危险因素分析和 CHD 发病机制的研究提供了新的思路。
