Evidence map›Paper›PMID 35883459›Full record

ArticleBiomolecules2022

Characteristics of Normalization Methods in Quantitative Urinary Metabolomics-Implications for Epidemiological Applications and Interpretations.

Tianqi Li, Tuulia Tynkkynen, Andrei Ihanus, Siyu Zhao, Ville-Petteri Mäkinen, Mika Ala-Korpela

Abstract read
In one paragraph

Article in Biomolecules, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. A Urinary Three-Metabolite Signature Enables Noninvasive Identification of Patients with High-Risk Ovarian Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Urinary metabolites and fatigue in a population-based metabolomics study: an exploratory analysis.Metabolomics : Official journal of the Metabolomic Society · 2026
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. 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

6 authors.

Tianqi LiSystems Epidemiology, Faculty of Medicine, University of Oulu, 90014 Oulu, Finland.
Tuulia TynkkynenSystems Epidemiology, Faculty of Medicine, University of Oulu, 90014 Oulu, Finland.ORCID 0000-0002-8586-7345
Andrei IhanusSystems Epidemiology, Faculty of Medicine, University of Oulu, 90014 Oulu, Finland.
Siyu ZhaoSystems Epidemiology, Faculty of Medicine, University of Oulu, 90014 Oulu, Finland.
Ville-Petteri MäkinenSystems Epidemiology, Faculty of Medicine, University of Oulu, 90014 Oulu, Finland.ORCID 0000-0002-7262-2656
Mika Ala-KorpelaSystems Epidemiology, Faculty of Medicine, University of Oulu, 90014 Oulu, Finland.ORCID 0000-0001-5905-1206

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A systematic comparison is presented for the effects of seven different normalization schemes in quantitative urinary metabolomics. Morning spot urine samples were analyzed with nuclear magnetic resonance (NMR) spectroscopy from a population-based group of 994 individuals. Forty-four metabolites were quantified and the metabolite-metabolite associations and the associations of metabolite concentrations with two representative clinical measures, body mass index and mean arterial pressure, were analyzed. Distinct differences were observed when comparing the effects of normalization for the intra-urine metabolite associations with those for the clinical associations. The metabolite-metabolite associations show quite complex patterns of similarities and dissimilarities between the different normalization methods, while the epidemiological association patterns are consistent, leading to the same overall biological interpretations. The results indicate that, in general, the normalization method appears to have only minor influences on standard epidemiological regression analyses with clinical/physiological measures. Multimetabolite normalization schemes showed consistent results with the customary creatinine reference. Nevertheless, interpretations of intra-urine metabolite associations and nuanced understanding of the epidemiological associations call for comparisons with different normalizations and accounting for the physiology, metabolism and kidney function related to the normalization schemes.

Indexed as

MetabolomicsBiomarkersCreatinineHumansMagnetic Resonance SpectroscopyBiomarkersCreatininebiomarkersdisease riskepidemiologykidney functionmetabolomicsNMRnormalizationurine

Identifiers

PMID35883459
PMCPMC9313036

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Registered trials

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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.