Early detection of ureteropelvic junction obstruction in neonates with prenatal diagnosis of renal pelvis dilatation using 1 H NMR urinary metabolomics.
Aurélien ScalabreYohann ClémentFlorence GuillièreSophie AyciriexSégolène GaillardDelphine DemèdeAurore BoutyPierre LanteriPierre-Yves MurePublished in: Scientific reports (2022)
Renal pelvis dilatation (RPD) is diagnosed in utero on prenatal ultrasonography (US) and can resolve spontaneously. However, isolated RPD can also reflect ureteropelvic junction obstruction (UPJO), which requires surgical treatment to prevent progressive renal deterioration. The diagnosis of UPJO can only be confirmed after birth with repeat US and renal isotope studies. 1 H Nuclear Magnetic Resonance spectroscopy (NMR) was performed on urine of newborns with prenatally diagnosed unilateral RPD and healthy controls to identify specific urinary biomarkers for UPJO. The original combination of EigenMS normalization and sparse partial-least-squares discriminant analysis improved selectivity and sensitivity. In total, 140 urine samples from newborns were processed and 100 metabolites were identified. Correlation network identified discriminant metabolites in lower concentrations in UPJO patients. Two main metabolic pathways appeared to be impaired in patients with UPJO i.e. amino acid and betaine metabolism. In this prospective study, metabolic profiling of urine samples by NMR clearly distinguishes patients who required surgery for UPJO from patients with transient dilatations and controls. This study will pave the way for the use of metabolomics for the diagnosis of prenatal hydronephrosis in clinical routine.
Keyphrases
- pregnant women
- magnetic resonance
- high resolution
- end stage renal disease
- mass spectrometry
- ms ms
- gestational age
- amino acid
- minimally invasive
- low birth weight
- chronic kidney disease
- ejection fraction
- multiple sclerosis
- peritoneal dialysis
- cord blood
- coronary artery disease
- preterm infants
- atrial fibrillation
- subarachnoid hemorrhage
- percutaneous coronary intervention
- neural network