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Mapping Metabolite and ICD-10 Associations.

Egon Taalberg1,2, Kalle Kilk1,2

  • 1Department of Biochemistry, Institute of Biomedicine and Translational Medicine, University of Tartu, 50411 Tartu, Estonia.

Metabolites
|May 20, 2020
PubMed
Summary

Investigating metabolic biomarkers reveals that while many show potential, their accuracy (AUC-ROC) can be affected by comorbidities. Understanding these disease interferences is key for reliable biomarker development.

Keywords:
AUC-ROCICD-10biomarkercomorbiditymetabolomicssensitivityspecificity

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Area of Science:

  • Biochemistry
  • Medical Diagnostics
  • Bioinformatics

Background:

  • The development of novel metabolic biomarkers for medical applications is ongoing but faces challenges.
  • A significant hurdle is understanding how comorbidities impact biomarker performance and reliability.

Purpose of the Study:

  • To evaluate the performance of 49 metabolites as potential biomarkers across various diseases.
  • To investigate the influence of comorbidities on biomarker efficacy using diagnostic codes.
  • To map disease-metabolite associations to identify potential interferences in biomarker research.

Main Methods:

  • Targeted liquid chromatography-mass spectrometry (LC/MS) was used to quantify 49 metabolites in serum samples from 1011 volunteers.
  • Biomarker performance was assessed using the area under the curve of receiver operator characteristics (AUC-ROC).
  • Evaluations were conducted against 105 diagnosis codes from the International Classification of Diseases, 10th Revision (ICD-10), and interferences were analyzed.

Main Results:

  • The highest AUC-ROC values for individual metabolites and ICD-10 code combinations reached a moderate range (around 0.7).
  • Most identified potential metabolic biomarkers maintained their potential independently of control group composition or comorbidities.
  • The specific AUC-ROC values showed variability influenced by the presence of comorbidities.

Conclusions:

  • Metabolic biomarkers show promise, but their diagnostic accuracy can be modulated by patient comorbidities.
  • Understanding and mapping disease-metabolite associations is crucial for mitigating interference in metabolic biomarker research.
  • Further research is needed to refine biomarker selection and interpretation in the context of complex disease states.