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Published on: June 25, 2010
Resolving Early Targets and Metabolomic Profile of Congenital Heart Disease Through Tandem Mass Spectrometry
Jiayu Zhang1, Wei Jiang1, Die Li1
1Heart Center Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health Hangzhou China.
Insights
Metabolomics can help screen for congenital heart disease (CHD) in newborns. This study identified metabolic biomarkers and developed a screening model with potential for early diagnosis and improved health economics.
Area of Science:
- Biochemistry
- Genetics
- Neonatal Medicine
Background:
- Early diagnosis and intervention are crucial for congenital heart disease (CHD) prognosis.
- Current screening methods for neonatal CHD have limitations, leading to undetected cases.
- Metabolomics offers a promising, underutilized approach for efficient neonatal CHD screening.
Purpose of the Study:
- To investigate the potential of metabolomics in identifying biomarkers for neonatal CHD.
- To develop and evaluate machine learning models for screening CHD in neonates using metabolic data.
Main Methods:
- Analysis of metabolomic data from over 22,000 neonates using tandem mass spectrometry.
- Comparison of metabolic profiles between CHD, nonsignificant CHD, and normal groups.
- Construction of machine learning models based on identified metabolic biomarkers.
Main Results:
- Identified 46 distinguishing metabolic biomarkers in neonates with CHD.
- Found significantly lower levels of 17-hydroxyprogesterone in the CHD group.
- Developed a screening model with an area under the receiver operating characteristic curve of 0.745.
Conclusions:
- Neonates with CHD exhibit a distinct metabolic profile.
- The developed screening model shows significant potential for early neonatal CHD detection.
- The approach holds value from a health economics perspective for neonatal screening.
Background:
A good prognosis of congenital heart disease (CHD) depends on early diagnosis and intervention. Under the current screening conditions, a significant proportion still go undetected. Metabolomics, as a phenotype-correlated research methodology, remains underused in the study of CHD, which could provide the possibility to screen neonatal CHD efficiently.
Methods:
Data for the analysis are from >22 000 neonates captured in the Network Platform for CHD from April 2020 to November 2021 in 11 cities in China. After data matching and quality control, a total of 22 674 neonates were finally included and divided into the CHD group (n=1823), nonsignificant CHD group (n=17 968), and normal group (n=2748). Demographic and clinical characteristics and tandem mass spectrometry-based metabolic data for genetic and metabolic disease screening were gathered and compared for all groups. Machine learning models based on metabolic biomarkers were constructed to screen CHD in neonates.
Results:
After quality control, 22 539 neonates were ultimately included. Among them, 1823 were diagnosed with CHD, 17 968 were nonsignificant CHD, and 2748 were normal. A total of 46 distinguishing metabolic biomarkers were identified, and we found that the CHD group had significantly lower levels of 17-hydroxyprogesterone (CHD versus nonsignificant CHD, P<0.001, log2 fold change=-0.16; CHD versus normal, P<0.001, log2 fold change=-0.15). We constructed CHD and ventricular septal disease screening models based on metabolic biomarkers. The best fitting model achieved an area under the receiver operating characteristic curve of 0.745 (95% CI, 0.696-0.791).
Conclusions:
This study reveals the unique metabolic profile of neonates with CHD. The screening model demonstrates considerable potential in early neonatal CHD screening and reflects significant value from a health economics perspective.
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