The Urinary Metabolome of Healthy Newborns

Abstract

The knowledge of normal metabolite values for neonates is key to establishing robust cut-off values to diagnose diseases, to predict the occurrence of new diseases, to monitor a neonate's metabolism, or to assess their general health status. For full term-newborns, many reference biochemical values are available for blood, serum, plasma and cerebrospinal fluid. However, there is a surprising lack of information about normal urine concentration values for a large number of important metabolites in neonates. In the present work, we used targeted tandem mass spectrometry (MS/MS)-based metabolomic assays to identify and quantify 136 metabolites of biomedical interest in the urine from 48 healthy, full-term term neonates, collected in the first 24 h of life. In addition to this experimental study, we performed a literature review (covering the past eight years and over 500 papers) to update the references values in the Human Metabolome Database/Urine Metabolome Database (HMDB/UMDB). Notably, 86 of the experimentally measured urinary metabolites are being reported in neonates/infants for the first time and another 20 metabolites are being reported in human urine for the first time ever. Sex differences were found for 15 metabolites. The literature review allowed us to identify another 78 urinary metabolites with concentration data. As a result, reference concentration values and ranges for 378 neonatal urinary metabolites are now publicly accessible via the HMDB.

Monitoring Methylmalonic Aciduria by NMR Urinomics

“…Since then, several compilations of reference ranges for urine metabolites have been published, with a very good book being published by N. Blau et al [ 8 ]; and there are extensive Internet resources which are also worth being mentioned [ 48 , 49 , 50 ]. Valuable reference concentrations in urine for infants and children [ 51 , 52 ], and neonates and infants [ 24 , 51 , 53 ], have been recently published. In most cases ranges of normal concentrations previously published represent compilations of single samples (one shot) from a large number of control cases.…”

“…Orotic Acid (Orot) 0.029 (0.007-0.066) n = 18/95 Case 5 (2-11 yo) [MMAA] 0.066 (0.066-0.066) n = 1/5 Case 5-No Diet (2-11 yo) 0.027 (0.007-0.061) n = 17/90 Case 5-Diet (2-11 yo) Orotidine (Orotid) 0.082 (0.035-0.047) n = 19/95 Case 5 (2-11 yo) [MMAA] 0.097 (0.097-0.097) n = 1/5 Case 5-No Diet (2-11 yo) 0.081 (0.035-0.132) n = 18/90 Case 5-Diet (2-11 yo) L-Carnitine (Car) 0.247 (0.147-0.333) n = 6/9 Case 1 (7 do-9 mo) [MMUT] 0.0 n = 0/3 Case 1-No Diet (7 do-4 mo) 0.247 (0.147-0.333) n = 6/6 Case 1-Diet (19 do-9 mo) 0.538 (0.156-1.334) n = 34/38 (6 do-1 1 2 yo) [MMUT] 0.0 n = 0/4 Case 2-No Diet (11 do-1 mo) 0.538 (0.156-1.334) n = 34/34 Case 2-Diet (3 mo-1 1 2 yo) 0.218 (0.096-0.456) n = 5/11 Case 3 n = 0/9 Case 4-No Diet (4y9mo-4y11mo) 0.299 (0.068-0.782) n = 17/18 Case 4-Diet (5yo-5 1 2 yo) 0.221 (0.024-0.667) n = 13/28 Case 5 (2-11 yo) [MMAA] 0.0 n = 0/5 Case 5-No Diet (2-11 yo) 0.221 (0.024-0.667) n = 13/23 Case 5-Diet (2-11 yo) 0.009 ± 0.007 (1 do) [53]. Propionylcarnitine (PrCar) 0.715 (0.156-1.390) n = 6/9 Case 1 (7 do-9 mo) [MMUT] 0.0 n = 0/3 Case 1-No Diet (7 do-4 mo) 0.715 (0.156-1.390) n = 6/6 Case 1-Diet (19 do-9 mo) 0.003 (0.001-0.008) n = 34/38 Case 2 (6 do-1 1 2 yo) [MMUT] 0.0 n = 0/4 Case 2-No Diet (11 do-1 mo) 0.922 (0.253-2.266) n = 34/34 Case 2-Diet (3 mo-1 1 2 yo) 0.361 (0.182-0.612) n = 5/11 Case 3 n = 0/9 Case 4-No Diet (4y9mo-4y11mo) 0.099 (0.036-0.245) n = 17/18 Case 4-Diet (5yo-5 1 2 yo) 0.128 (0.044-0.199) n = 11/28 Case 5 (2-11 yo) [MMAA] 0.0 n = 0/5 Case 5-NoDiet (2-11 yo) 0.128 (0.044-0.199) n = 11/23 Case 5-Diet (2-11 yo)<1.20[8]; 0.002 (1 do)[53].…”

“…Orotidine (Orotid) 35 (21-55) n = 19/95 Case 5 (2-11 yo) [MMAA] 32 n = 1/5 Case 5-No Diet (2-11 yo) 36 (21-55) n = 18/90 Case 5-Diet (2-11 yo) l-Carnitine (Car) 346 (189-838) n = 6/9 Case 1 (7 do-9 mo) [MMUT] 0 n = 0/3 Case 1-No Diet (7 do-4 mo) 346 (189-838) n = 6/6 Case 1-Diet (19 do-9 mo) 520 (162-1028) n = 34/38 Case 2 (6 do-1 1 2 yo) [MMUT] 0 n = 0/4 Case 2-No Diet (11 do-1 mo) 520 (162-1082) n = 34/34 Case 2-Diet (3 mo-1 1 2 yo) 153 (95-226) n = 5/11 Case 3 0/9 Case 4-No Diet (4y9mo-4y11mo) 165 (40-342) n = 17/18 Case 4-Diet (5yo-5 1 2 yo) 109 (15-429) n = 13/28 Case 5 (2-11 yo) [MMAA] 0 n = 0/5 Case 5-No Diet (2-11 yo) 109 (15-429) n = 13/23 Case 5-Diet (2-11 yo) 5.0 (0.7-16.4) (19-67 yo) [58]; 2.01 (0.79-5.67) (1 do) [53]. 1186) n = 6/9 Case 1 (7 do-9 mo) [MMUT] 0 n = 0/3 Case 1-No Diet (7 do-4 mo) 700 (206-1186) n = 6/6 Case 1-Diet (19 do-9 mo) 902 (404-1624) n = 34/38 Case 2 (6 do-1 1 2 yo) [MMUT] 0 n = 0/4 Case 2-No Diet (11 do-1 mo) 902 (404-1624) n = 34/34 Case 2-Diet (3 mo-1 1 2 yo) 262 (187-356) n = 5/11 Case 3 0/9 Case 4-No Diet (4y9mo-4y11mo) 55 (21-106) n = 17/18 Case 4-Diet (5yo-5 1 2 yo) 65 (40-124) n = 11/28 Case 5 (2-11 yo) [MMAA] 0 n = 0/5 Case 5-No Diet (2-11 yo) 65 (40-124) n = 11/23 Case 5-Diet (2-11 yo) 0.07 (0.01-0.20) (19-67 yo) [58];0.03 (0.01-0.07) (1 do)[53].…”

Emerging Biomarkers for Prediction and Early Diagnosis of Necrotizing Enterocolitis in the Era of Metabolomics and Proteomics

“…Plasma/serum and urine metabolomics and proteomics have been studied in healthy neonates as well as in various neonatal conditions including, but not limited to, perinatal asphyxia, patent ductus arteriosus, inborn errors of metabolism, sepsis, and NEC (97)(98)(99)(100). Current data regarding the detection of surrogate biomarkers for prediction, diagnosis, and prognosis of NEC derived from experimental and clinical studies using metabolomics and proteomics approach is limited ( Table 2).…”

NMR-based metabolomics with enhanced sensitivity

“…There has been a progressive increase in indepth metabolic studies for biomarkers specic to many human diseases such as neurological 1 and cardiovascular disease, [2][3][4] cancer, 5 and in-born errors of metabolism (IEM) diseases. 6 NMR-based metabolomics relies mainly on one-dimensional 1 H NMR spectroscopy. The signicant limitations of 1D NMR include inherently modest sensitivity and substantial signal overlap.…”