Normalizing untargeted periconceptional urinary metabolomics data: a comparison of approaches

Ana K. Rosen Vollmar, Nicholas J. W. Rattray, Yuping Cai, Álvaro J. Santos-Neto, Nicole C. Deziel, Anne Marie Z. Jukic, Caroline H. Johnson

Research output: Contribution to journalArticle

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.
LanguageEnglish
Article number198
Number of pages15
JournalMetabolites
Volume9
Issue number10
DOIs
Publication statusPublished - 21 Sep 2019

Fingerprint

Metabolomics
Urine
Creatinine
Specific Gravity
Discriminant Analysis
Discriminant analysis
Least-Squares Analysis
Quality Control
Density (specific gravity)
Dilution
Quality control
Kidney
Principal Component Analysis
Principal component analysis
Mass spectrometry
Cluster Analysis
Mass Spectrometry
Mothers
Pregnancy

Keywords

  • urinary dilution
  • normalization
  • pregnancy
  • creatinine
  • specific gravity
  • probabilistic quotient normalization

Cite this

Rosen Vollmar, A. K., Rattray, N. J. W., Cai, Y., Santos-Neto, Á. J., Deziel, N. C., Jukic, A. M. Z., & Johnson, C. H. (2019). Normalizing untargeted periconceptional urinary metabolomics data: a comparison of approaches. Metabolites, 9(10), [198]. https://doi.org/10.3390/metabo9100198
Rosen Vollmar, Ana K. ; Rattray, Nicholas J. W. ; Cai, Yuping ; Santos-Neto, Álvaro J. ; Deziel, Nicole C. ; Jukic, Anne Marie Z. ; Johnson, Caroline H. / Normalizing untargeted periconceptional urinary metabolomics data : a comparison of approaches. In: Metabolites. 2019 ; Vol. 9, No. 10.
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Rosen Vollmar, AK, Rattray, NJW, Cai, Y, Santos-Neto, ÁJ, Deziel, NC, Jukic, AMZ & Johnson, CH 2019, 'Normalizing untargeted periconceptional urinary metabolomics data: a comparison of approaches' Metabolites, vol. 9, no. 10, 198. https://doi.org/10.3390/metabo9100198

Normalizing untargeted periconceptional urinary metabolomics data : a comparison of approaches. / Rosen Vollmar, Ana K.; Rattray, Nicholas J. W.; Cai, Yuping; Santos-Neto, Álvaro J.; Deziel, Nicole C.; Jukic, Anne Marie Z.; Johnson, Caroline H.

In: Metabolites, Vol. 9, No. 10, 198, 21.09.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Normalizing untargeted periconceptional urinary metabolomics data

T2 - Metabolites

AU - Rosen Vollmar, Ana K.

AU - Rattray, Nicholas J. W.

AU - Cai, Yuping

AU - Santos-Neto, Álvaro J.

AU - Deziel, Nicole C.

AU - Jukic, Anne Marie Z.

AU - Johnson, Caroline H.

PY - 2019/9/21

Y1 - 2019/9/21

N2 - 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.

AB - 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.

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KW - normalization

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KW - creatinine

KW - specific gravity

KW - probabilistic quotient normalization

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