Normalizing untargeted periconceptional urinary metabolomics data : a comparison of approaches

Rosen Vollmar, Ana K. and Rattray, Nicholas J. W. and Cai, Yuping and Santos-Neto, Álvaro J. and Deziel, Nicole C. and Jukic, Anne Marie Z. and Johnson, Caroline H. (2019) Normalizing untargeted periconceptional urinary metabolomics data : a comparison of approaches. Metabolites, 9 (10). 198. ISSN 2218-1989

[img]
Preview
Text (Vollmar-etal-Metabolites-2019-Normalizing-untargeted-periconceptional-urinary-metabolomics-data)
Vollmar_etal_Metabolites_2019_Normalizing_untargeted_periconceptional_urinary_metabolomics_data.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo

Download (1MB)| Preview

    Abstract

    Metabolomics studies of the early-life exposome often use maternal urine specimens to investigate critical developmental windows, including the periconceptional period and early pregnancy. During these windows changes in kidney function can impact urine concentration. This makes accounting for differential urinary dilution across samples challenging. Because there is no consensus on the ideal normalization approach for urinary metabolomics data, this study’s objective was to determine the optimal post-analytical normalization approach for untargeted metabolomics analysis from a periconceptional cohort of 45 women. Urine samples consisted of 90 paired pre- and post-implantation samples. After untargeted mass spectrometry-based metabolomics analysis, we systematically compared the performance of three common approaches to adjust for urinary dilution—creatinine adjustment, specific gravity adjustment, and probabilistic quotient normalization (PQN)—using unsupervised principal components analysis, relative standard deviation (RSD) of pooled quality control samples, and orthogonal partial least-squares discriminant analysis (OPLS-DA). Results showed that creatinine adjustment is not a reliable approach to normalize urinary periconceptional metabolomics data. Either specific gravity or PQN are more reliable methods to adjust for urinary concentration, with tighter quality control sample clustering, lower RSD, and better OPLS-DA performance compared to creatinine adjustment. These findings have implications for metabolomics analyses on urine samples taken around the time of conception and in contexts where kidney function may be altered.