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Dilution correction for dynamically influenced urinary analyte data.

Johannes Hertel1, Markus Rotter2, Stefan Frenzel1

  • 1Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany.

Analytica Chimica Acta
|August 26, 2018
PubMed
Summary

Urinary dilution correction needs analyte-specific, non-linear regression, not simple division. This approach accurately accounts for complex dependencies on urinary flow rate and improves metabolomic data analysis.

Keywords:
Dilution correctionMetabolomicsModel diagnosticsNon-linear regression techniquesNormalizationUrine analysis

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Area of Science:

  • Biochemistry
  • Metabolomics
  • Analytical Chemistry

Background:

  • Urinary analyte concentrations vary significantly due to sample dilution.
  • Current dilution correction methods (e.g., probabilistic quotient normalization) assume log-linearity, which is often invalid.
  • This limits the comparability of urinary concentration measures.

Purpose of the Study:

  • To evaluate the limitations of division-based urinary dilution correction methods.
  • To develop and validate a more accurate, analyte-specific normalization methodology.
  • To identify novel factors influencing urinary metabolomic data variance.

Main Methods:

  • Mathematical modeling of physiological processes affecting urinary analyte concentrations.
  • Simulations and analysis of multi-timepoint metabolomic data.
  • Development of a flexible non-linear regression approach for normalization.
  • Creation of normalization diagnostics for method selection.

Main Results:

  • Division-based normalization is insufficient for analytes not in steady-state due to non-linear dependencies on urinary flow rate.
  • The proposed analyte-specific non-linear regression method significantly outperforms division-based approaches.
  • Time since last urination was identified as a critical, previously neglected, source of variance.

Conclusions:

  • Urinary data normalization must be analyte-specific, especially for dynamically influenced analytes.
  • A novel regression-based methodology effectively removes dilution variance while respecting analyte kinetics.
  • Accurate normalization is crucial for reliable urinary metabolomic data analysis.