Noninvasive screening for congenital heart defects using a serum metabolomics approach

Troisi, Jacopo; Cavallo, Pierpaolo; Richards, Sean; Symes, Steven A.; Colucci, Angelo; Sarno, Laura; Landolfi, Annamaria; Scala, Giovanni; Adair, David; Ciccone, Carla; Maruotti, Giuseppe Maria; Martinelli, Pasquale; Guida, Maurizio

doi:10.1002/pd.5893

Cited by 25 publications

(22 citation statements)

References 72 publications

(80 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Serum metabolites were extracted, purified, and derivatized using the MetaboPrep GC kit (Theoreo srl, Montecorvino Pugliano, Italy) as reported in Troisi et al [14][15][16][17][18], prior to GC-MS analysis. Briefly, 50 µL of serum were pipetted into a microcentrifuge tube containing the extraction solution and the internal standard (2-isopropyl malic acid).…”

Section: Metabolome Analysis By Gc-msmentioning

confidence: 99%

A Serum Metabolomic Signature for the Detection and Grading of Bladder Cancer

et al. 2021

Self Cite

View full text Add to dashboard Cite

Bladder cancer has a high incidence and is marked by high morbidity and mortality. Early diagnosis is still challenging. The objective of this study was to create a metabolomics-based profile of bladder cancer in order to provide a novel approach for disease screening and stratification. Moreover, the study characterized the metabolic changes associated with the disease. Serum metabolomic profiles were obtained from 149 bladder cancer patients and 81 healthy controls. Different ensemble machine learning models were built in order to: (1) differentiate cancer patients from controls; (2) stratify cancer patients according to grading; (3) stratify patients according to cancer muscle invasiveness. Ensemble machine learning models were able to discriminate well between cancer patients and controls, between high grade (G3) and low grade (G1-2) cancers and between different degrees of muscle invasivity; ensemble model accuracies were ≥80%. Relevant metabolites, selected using the partial least square discriminant analysis (PLS-DA) algorithm, were included in a metabolite-set enrichment analysis, showing perturbations primarily associated with cell glucose metabolism. The metabolomic approach may be useful as a non-invasive screening tool for bladder cancer. Furthermore, metabolic pathway analysis can increase understanding of cancer pathophysiology. Studies conducted on larger cohorts, and including blind trials, are needed to validate results.

show abstract

Section: Metabolome Analysis By Gc-msmentioning

confidence: 99%

A Serum Metabolomic Signature for the Detection and Grading of Bladder Cancer

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Extraction, purification, and derivatization of serum samples were performed using the MetaboPrep GC kit (Theoreo srl, Montecorvino Pugliano, Italy) as reported in Troisi et al [ 28 , 29 , 32 , 33 , 34 , 35 , 36 ]. This sample preparation kit results in untargeted metabolomic profiles.…”

Section: Methodsmentioning

confidence: 99%

“…The purpose of this mechanism is to have an EML score = 0 for a hypothetical sample where prediction for and against EC diagnosis were equal both in terms of number and weight. In contrast, an EML score > 0 indicates an EC-“positive” patient according to our model, while an EML score < 0 indicates a non-EC (CTRL) patient [ 28 , 29 , 32 , 33 , 34 , 35 , 36 ].…”

Section: Methodsmentioning

confidence: 99%

The Metabolomic Approach for the Screening of Endometrial Cancer: Validation from a Large Cohort of Women Scheduled for Gynecological Surgery

et al. 2022

Self Cite

View full text Add to dashboard Cite

Endometrial cancer (EC) is the most common gynecological neoplasm in high-income countries. Five-year survival rates are related to stage at diagnosis, but currently, no validated screening tests are available in clinical practice. The metabolome offers an unprecedented overview of the molecules underlying EC. In this study, we aimed to validate a metabolomics signature as a screening test for EC on a large study population of symptomatic women. Serum samples collected from women scheduled for gynecological surgery (n = 691) were separated into training (n = 90), test (n = 38), and validation (n = 563) sets. The training set was used to train seven classification models. The best classification performance during the training phase was the PLS-DA model (96% accuracy). The subsequent screening test was based on an ensemble machine learning algorithm that summed all the voting results of the seven classification models, statistically weighted by each models’ classification accuracy and confidence. The efficiency and accuracy of these models were evaluated using serum samples taken from 871 women who underwent endometrial biopsies. The EC serum metabolomes were characterized by lower levels of serine, glutamic acid, phenylalanine, and glyceraldehyde 3-phosphate. Our results illustrate that the serum metabolome can be an inexpensive, non-invasive, and accurate EC screening test.

show abstract

“…Metabolites 2022, 12, 100 2 of 16 This is particularly important, as disrupted metabolism related to the affected birth could impact long-term maternal health. Women with pregnancies affected by CHD have been reported with altered metabolic profiles and were associated with dysregulated metabolic pathways [7][8][9][10]. These markers were often utilized as predictive markers for fetuses with congenital heart defects.…”

Section: Congenital Heart Defects (Chds) Affect Approximately 1% Of A...mentioning

confidence: 99%

Metabolomics Signatures and Subsequent Maternal Health among Mothers with a Congenital Heart Defect-Affected Pregnancy

et al. 2022

View full text Add to dashboard Cite

Congenital heart defects (CHDs) are the most prevalent and serious of all birth defects in the United States. However, little is known about the impact of CHD-affected pregnancies on subsequent maternal health. Thus, there is a need to characterize the metabolic alterations associated with CHD-affected pregnancies. Fifty-six plasma samples were identified from post-partum women who participated in the National Birth Defects Prevention Study between 1997 and 2011 and had (1) unaffected control offspring (n = 18), (2) offspring with tetralogy of Fallot (ToF, n = 22), or (3) hypoplastic left heart syndrome (HLHS, n = 16) in this pilot study. Absolute concentrations of 408 metabolites using the AbsoluteIDQ® p400 HR Kit (Biocrates) were evaluated among case and control mothers. Twenty-six samples were randomly selected from above as technical repeats. Analysis of covariance (ANCOVA) and logistic regression models were used to identify significant metabolites after controlling for the maternal age at delivery and body mass index. The receiver operating characteristic (ROC) curve and area-under-the-curve (AUC) are reported to evaluate the performance of significant metabolites. Overall, there were nine significant metabolites (p < 0.05) identified in HLHS case mothers and 30 significant metabolites in ToF case mothers. Statistically significant metabolites were further evaluated using ROC curve analyses with PC (34:1), two sphingolipids SM (31:1), SM (42:2), and PC-O (40:4) elevated in HLHS cases; while LPC (18:2), two triglycerides: TG (44:1), TG (46:2), and LPC (20:3) decreased in ToF; and cholesterol esters CE (22:6) were elevated among ToF case mothers. The metabolites identified in the study may have profound structural and functional implications involved in cellular signaling and suggest the need for postpartum dietary supplementation among women who gave birth to CHD offspring.

show abstract

Noninvasive screening for congenital heart defects using a serum metabolomics approach

Cited by 25 publications

References 72 publications

A Serum Metabolomic Signature for the Detection and Grading of Bladder Cancer

A Serum Metabolomic Signature for the Detection and Grading of Bladder Cancer

The Metabolomic Approach for the Screening of Endometrial Cancer: Validation from a Large Cohort of Women Scheduled for Gynecological Surgery

Metabolomics Signatures and Subsequent Maternal Health among Mothers with a Congenital Heart Defect-Affected Pregnancy

Contact Info

Product

Resources

About