Differences in the serum metabolic profile to identify potential biomarkers for cyanotic versus acyanotic heart disease
| Autor: | Neeraj Sinha, Suman Vimal, Gauranga Majumdar, Surendra K Agarwal, Renuka Ranjan, Balraj Mittal, Surabhi Yadav |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Advanced and Specialized Nursing
medicine.medical_specialty Growth retardation Heart disease business.industry General Medicine Hypoxia (medical) medicine.disease Malnutrition Internal medicine Potential biomarkers Failure to thrive medicine Cardiology Radiology Nuclear Medicine and imaging medicine.symptom Differential diagnosis Cardiology and Cardiovascular Medicine business Safety Research Metabolic profile |
| Zdroj: | Perfusion. 38:124-134 |
| ISSN: | 1477-111X 0267-6591 |
| DOI: | 10.1177/02676591211042559 |
| Popis: | Background: Growth retardation, malnutrition, and failure to thrive are some of the consequences associated with congenital heart diseases. Several metabolic factors such as hypoxia, anoxia, and several genetic factors are believed to alter the energetics of the heart. Timely diagnosis and patient management is one of the major challenges faced by the clinicians in understanding the disease and provide better treatment options. Metabolic profiling has shown to be potential diagnostic tool to understand the disease. Objective: The present experiment was designed as a single center observational pilot study to classify and create diagnostic metabolic signatures associated with the energetics of congenital heart disease in cyanotic and acyanotic groups. Methods: Metabolic sera profiles were obtained from 35 patients with cyanotic congenital heart disease (TOF) and 23 patients with acyanotic congenital heart disease (ASD and VSD) using high resolution 1D 1H NMR spectra. Univariate and multivariate statistical analysis were performed to classify particular metabolic disorders associated with cyanotic and acyanotic heart disease. Results: The results show dysregulations in several metabolites in cyanotic CHD patients versus acyanotic CHD patients. The discriminatory metabolites were further analyzed with area under receiver operating characteristic (AUROC) curve and identified four metabolic entities (i.e. mannose, hydroxyacetone, myoinositol, and creatinine) which could differentiate cyanotic CHDs from acyanotic CHDs with higher specificity. Conclusion: An untargeted metabolic approach proved to be helpful for the detection and distinction of disease-causing metabolites in cyanotic patients from acyanotic ones and can be useful for designing better and personalized treatment protocol. |
| Databáze: | OpenAIRE |
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