Evidence map›Paper›PMID 31546636›Full record

ArticleMetabolites2019

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

Abstract read
In one paragraph

Article in Metabolites, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed.

  1. Article
  2. Review
  3. Early changes in urineMetabolomics : Official journal of the Metabolomic Society · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Vitamin DEuropean journal of nutrition · 2024
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ana K Rosen VollmarDepartment of Environmental Health Sciences, Yale School of Public Health, New Haven, CT 06510, USA. ana.rosenvollmar@yale.edu.ORCID 0000-0002-8009-0258
Nicholas J W RattrayDepartment of Environmental Health Sciences, Yale School of Public Health, New Haven, CT 06510, USA. nicholas.rattray@strath.ac.uk.ORCID 0000-0002-3528-6905
Yuping CaiDepartment of Environmental Health Sciences, Yale School of Public Health, New Haven, CT 06510, USA. ping.cai@yale.edu.
Álvaro J Santos-NetoDepartment of Environmental Health Sciences, Yale School of Public Health, New Haven, CT 06510, USA. alvarojsn@iqsc.usp.br.ORCID 0000-0003-1719-8458
Nicole C DezielDepartment of Environmental Health Sciences, Yale School of Public Health, New Haven, CT 06510, USA. nicole.deziel@yale.edu.ORCID 0000-0002-5751-9191
Anne Marie Z JukicEpidemiology Branch, National Institute of Environmental Health Sciences, Durham, NC 27709, USA. jukica@niehs.nih.gov.
Caroline H JohnsonDepartment of Environmental Health Sciences, Yale School of Public Health, New Haven, CT 06510, USA. caroline.johnson@yale.edu.ORCID 0000-0002-5298-1299

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
NCATS NIH HHS UL1 TR001863Yale School of Public Health Doctoral Fellowship
6 · The paper itself

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.

Indexed as

creatininenormalizationpregnancyprobabilistic quotient normalizationspecific gravityurinary dilution

Identifiers

PMID31546636
PMCPMC6835889

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.