Dataset Information

Proteome profiling of early gestational plasma reveals novel biomarkers of congenital heart disease.

ABSTRACT: Prenatal diagnosis of congenital heart disease (CHD) relies primarily on fetal echocardiography conducted at mid-gestational age-the sensitivity of which varies among centers and practitioners. An objective method for early diagnosis is needed. Here, we conducted a case-control study recruiting 103 pregnant women with healthy offspring and 104 cases with CHD offspring, including VSD (42/104), ASD (20/104), and other CHD phenotypes. Plasma was collected during the first trimester and proteomic analysis was performed. Principal component analysis revealed considerable differences between the controls and the CHDs. Among the significantly altered proteins, 25 upregulated proteins in CHDs were enriched in amino acid metabolism, extracellular matrix receptor, and actin skeleton regulation, whereas 49 downregulated proteins were enriched in carbohydrate metabolism, cardiac muscle contraction, and cardiomyopathy. The machine learning model reached an area under the curve of 0.964 and was highly accurate in recognizing CHDs. This study provides a highly valuable proteomics resource to better recognize the cause of CHD and has developed a reliable objective method for the early recognition of CHD, facilitating early intervention and better prognosis.

SUBMITTER: Yin YN

PROVIDER: S-EPMC10701625 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Proteome profiling of early gestational plasma reveals novel biomarkers of congenital heart disease.

Yin Ya-Nan YN Cao Li L Wang Jie J Chen Yu-Ling YL Yang Hai-Ou HO Tan Su-Bei SB Cai Ke K Chen Zhe-Qi ZQ Xiang Jie J Yang Yuan-Xin YX Geng Hao-Ran HR Zhou Ze-Yu ZY Shen An-Na AN Zhou Xiang-Yu XY Shi Yan Y Zhao Rui R Sun Kun K Ding Chen C Zhao Jian-Yuan JY

EMBO molecular medicine 20231016 12

Prenatal diagnosis of congenital heart disease (CHD) relies primarily on fetal echocardiography conducted at mid-gestational age-the sensitivity of which varies among centers and practitioners. An objective method for early diagnosis is needed. Here, we conducted a case-control study recruiting 103 pregnant women with healthy offspring and 104 cases with CHD offspring, including VSD (42/104), ASD (20/104), and other CHD phenotypes. Plasma was collected during the first trimester and proteomic an ...[more]

PMID: 37840432

Similar Datasets

Project description:BackgroundThis study aims to identify early metabolomic biomarkers of gestational diabetes mellitus (GDM) and evaluate their association with hepatic steatosis.MethodsWe compared maternal serum metabolomic profiles between women who developed GDM (n = 118) and matched controls (n = 118) during the first (10-14 gestational weeks) and second (24-28 gestational weeks) trimesters using ultra-performance liquid chromatography coupled with mass spectrometry. Mediation analysis was performed to evaluate the mediating role of metabolic dysfunction-associated steatotic liver disease (MASLD) in the relationship between metabolites and subsequent development of GDM. A refined prediction model was developed to predict GDM using established clinical factors and selected metabolites.ResultsSignificant alterations in circulating metabolites, including amino acids, bile acids, and phospholipids, were observed in the GDM group compared to controls during early pregnancy. Mediation analysis revealed that several metabolites, including glycocholic acid (proportion mediated (PM) = 31.9%), butanoyl carnitine (PM = 25.7%), and uric acid (PM = 22.4%), had significant indirect effects on GDM incidence mediated by hepatic steatosis. The refined prediction model composed of clinical factors and selected metabolites in the first trimester demonstrated higher performance in predicting GDM development than the established prediction model composed solely of clinical factors (AUC, 0.85 vs. 0.63, p < 0.001).ConclusionsWomen who developed GDM exhibited altered metabolomic profiles from early pregnancy, which showed a significant correlation with GDM, with MASLD as a mediator. Selected metabolomic biomarkers may serve as predictive markers and potential targets for early risk assessment and intervention in GDM.Research insightsWHAT IS CURRENTLY KNOWN ABOUT THIS TOPIC?: Gestational diabetes mellitus (GDM) is a common pregnancy complication with significant health risks. Early identification of women at high risk for GDM is crucial for timely intervention and improved outcomes. WHAT IS THE KEY RESEARCH QUESTION?: What alterations in circulating metabolites during early pregnancy are associated with subsequent GDM development? Does metabolic dysfunction-associated steatotic liver disease (MASLD) mediate the association between specific metabolites and GDM risk? WHAT IS NEW?: Significant alterations in bile acids, amino acids, phosphatidylethanolamines, and phosphatidylinositols were observed in early pregnancy sera of women who later developed GDM. MASLD significantly mediated the effects of several metabolites on GDM risk, with mediation proportions ranging from 9.7 to 31.9%. A refined prediction model composed of clinical factors and metabolites significantly improved the performance in predicting GDM development. HOW MIGHT THIS STUDY INFLUENCE CLINICAL PRACTICE?: These results provide new insights into early metabolic alterations associated with GDM development and highlight the potential mediating role of MASLD. This comprehensive metabolomic approach may contribute to the development of improved risk prediction models and targeted interventions for GDM prevention.

Dataset Information

Proteome profiling of early gestational plasma reveals novel biomarkers of congenital heart disease.

Publications

Proteome profiling of early gestational plasma reveals novel biomarkers of congenital heart disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets