ArticleComputational and structural biotechnology journal2022
Machine learning approach identifies meconium metabolites as potential biomarkers of neonatal hyperbilirubinemia.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Beyond just correlation: causal machine learning for the microbiome, from prediction to health policy with econometric tools.Frontiers in microbiology · 2025Pooled it
- Machine learning-based genome-wide association analysis to construct a clinical decision model for severe neonatal jaundice.Translational pediatrics · 2026Article
- Article
- Current progress and future prospects of machine learning in the diagnosis of neonatal encephalopathy: a narrative review.Translational pediatrics · 2025Review
- Effect ofFrontiers in nutrition · 2025Article
- Automatic deep learning method for analysis and prediction of neonatal hyperbilirubinemia in magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2024Article
- Analysis of the intestinal microbiota and profiles of blood amino acids and acylcarnitines in neonates with hyperbilirubinemia.BMC microbiology · 2024Article
- Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy.World journal of gastroenterology · 2022Article
- A metabolomic-based biomarker discovery study for predicting phototherapy duration for neonatal hyperbilirubinemia.Translational pediatrics · 2022Article
- Diagnostic and predictive values of serum metabolic profiles in sudden sensorineural hearing loss patients.Frontiers in molecular biosciences · 2022Article
- Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The gut microbiota plays an important role in the early stages of human life. Our previous study showed that the abundance of intestinal flora involved in galactose metabolism was altered and correlated with increased serum bilirubin levels in children with jaundice. We conducted the present study to systematically evaluate alterations in the meconium metabolome of neonates with jaundice and search for metabolic markers associated with neonatal jaundice. Methods: We included 68 neonates with neonatal hyperbilirubinemia, also known as neonatal jaundice (NJ) and 68 matched healthy controls (HC), collected meconium samples from them at birth, and performed metabolomic analysis via liquid chromatography-mass spectrometry. Results: Gut metabolites enabled clearly distinguishing the neonatal jaundice (NJ) and healthy control (HC) groups. We also identified the compositions of the gut metabolites that differed significantly between the NJ and HC groups; these differentially significant metabolites were enriched in aminyl tRNA biosynthesis; pantothenic acid and coenzyme biosynthesis; and the valine, leucine and isoleucine biosynthesis pathways. Gut branched-chain amino acid (BCAA) levels were positively correlated with serum bilirubin levels, and the area under the receiver operating characteristic curve of the random forest classifier model based on BCAAs, proline, methionine, phenylalanine and total bilirubin reached 96.9%, showing good potential for diagnostic applications. Machine learning-based causal inference analysis revealed the causal effect of BCAAs on serum total bilirubin and NJ. Conclusions: Altered gut metabolites in neonates with jaundice showed that increased BCAAs and total serum bilirubin were positively correlated. BCAAs proline, methionine, phenylalanine are potential biomarkers of NJ.
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