TY - JOUR
AU - Correia, Gonçalo D S
AU - Takis, Panteleimon G
AU - Sands, Caroline J
AU - Kowalka, Anna M
AU - Tan, Tricia
AU - Turtle, Lance C.W.
AU - Ho, Antonia
AU - Semple, Malcolm G
AU - Openshaw, Peter J M
AU - Baillie, J. Kenneth
AU - Takáts, Zoltán
AU - Lewis, Matthew R
PY - 2022
DA - May
TI - 1H NMR Signals from Urine Excreted Protein Are a Source of Bias in Probabilistic Quotient Normalization
JO - Analytical Chemistry
EP - 6923
VL - 94
IS - 19
DO - https://doi.org/10.1021/acs.analchem.2c00466
AB - Normalization to account for variation in urinary dilution is crucial for interpretation of urine metabolic profiles. Probabilistic quotient normalization (PQN) is used routinely in metabolomics but is sensitive to systematic variation shared across a large proportion of the spectral profile (&gt;50%). Where 1H nuclear magnetic resonance (NMR) spectroscopy is employed, the presence of urinary protein can elevate the spectral baseline and substantially impact the resulting profile. Using 1H NMR profile measurements of spot urine samples collected from hospitalized COVID-19 patients in the ISARIC 4C study, we determined that PQN coefficients are significantly correlated with observed protein levels (r2 = 0.423, p &lt; 2.2 × 10-16). This correlation was significantly reduced (r2 = 0.163, p &lt; 2.2 × 10-16) when using a computational method for suppression of macromolecular signals known as small molecule enhancement spectroscopy (SMolESY) for proteinic baseline removal prior to PQN. These results highlight proteinuria as a common yet overlooked source of bias in 1H NMR metabolic profiling studies which can be effectively mitigated using SMolESY or other macromolecular signal suppression methods before estimation of normalization coefficients.
PB - ACS Publications
UR - https://www.research.ed.ac.uk/en/publications/d868a00d-0cce-4ccd-8c3d-81ff461b29be
KW - Coronavirus (COVID-19)
ER