Evidence map›Paper›PMID 40314364›Full record

ArticleJournal of the American Heart Association2025

Resolving Early Targets and Metabolomic Profile of Congenital Heart Disease Through Tandem Mass Spectrometry Screening in Neonates.

Jiayu Zhang, Wei Jiang, Die Li, Weijie Jia, Dingfeng Wu, Rulai Yang, Weize Xu, Qiang Shu

Abstract readMulticenter Study
In one paragraph

Article in Journal of the American Heart Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jiayu ZhangHeart Center Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health Hangzhou China.ORCID 0000-0001-9074-4780
Wei JiangHeart Center Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health Hangzhou China.ORCID 0000-0002-6685-7053
Die LiHeart Center Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health Hangzhou China.ORCID 0000-0003-2000-2372
Weijie JiaBinjiang Institute of Zhejiang University Hangzhou China.ORCID 0009-0007-5936-2515
Dingfeng WuHeart Center Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health Hangzhou China.ORCID 0000-0002-9287-122X
Rulai YangDepartment of Genetics and Metabolism Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health Hangzhou China.
Weize XuHeart Center Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health Hangzhou China.ORCID 0000-0003-1350-0613
Qiang ShuHeart Center Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health Hangzhou China.ORCID 0000-0002-4106-6255

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA 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.

methodsData 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.

resultsAfter 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,

conclusionsThis 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.

Indexed as

Heart Defects, CongenitalMetabolomicsNeonatal ScreeningTandem Mass SpectrometryBiomarkersChinaEarly DiagnosisFemaleHumansInfant, NewbornMachine LearningMaleBiomarkerscongenital heart disease screeninggenetic and metabolic disease screeningmetabolomics

Identifiers

PMID40314364
PMCPMC12184283

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.