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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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When are metabolic ratios superior to absolute quantification? A statistical analysis.

Sarah E Hoch1, Ivan I Kirov2, Assaf Tal3

  • 1Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Israel.

NMR in Biomedicine
|March 9, 2017
PubMed
Summary

Magnetic resonance spectroscopy (MRS) metabolite ratios can be uncertain due to variability. This study provides guidelines for choosing between metabolite ratios and absolute quantification for accurate classification and optimal sample size in research and clinical settings.

Keywords:
MRSROCmagnetic resonance spectroscopymetabolite ratiosquantificationreceiver operator characteristicsample size

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Area of Science:

  • Neuroimaging
  • Biochemistry
  • Statistical Analysis

Background:

  • Metabolite levels in magnetic resonance spectroscopy (MRS) are frequently reported as ratios, not absolute concentrations.
  • Inter-subject variability in denominator metabolites introduces uncertainty into these ratios, complicating clinical interpretation and research design.

Purpose of the Study:

  • To derive the probability distribution function for the ratio of two normally distributed random variables.
  • To analytically compare metabolite ratios versus absolute quantification regarding sample size and diagnostic accuracy (Area Under the Curve).
  • To provide clear guidelines for selecting the optimal quantification method in clinical and research MRS.

Main Methods:

  • Derivation of the probability distribution function for the ratio of two normally distributed variables.
  • Analytical comparison of ratios and absolute quantification using sample size and Area Under the Receiver Operator Characteristic Curve (AUC).
  • Application of the derived methods to clinical MRS data from mild traumatic brain injury and multiple sclerosis patients.

Main Results:

  • The choice between metabolite ratios and absolute quantification is not straightforward and depends on specific parameters.
  • In certain scenarios, using ratios may reduce the required sample size for studies.
  • Absolute quantification can be more advantageous for individual clinical assessments.

Conclusions:

  • Guidelines are provided for choosing between metabolite ratios and absolute quantification based on population means and standard deviations.
  • The optimal method depends on whether the goal is clinical classification or research-based sample size optimization.
  • Reliable estimation of metabolite population parameters is key to making an informed decision.