Abstract
Introduction
Urine is an ideal matrix for metabolomics investigation due to its non-invasive nature of collection and its rich metabolite content. Despite the advancements in mass spectrometry and 1H-NMR platforms in urine metabolomics, the statistical analysis of the generated data is challenged with the need to adjust for the hydration status of the person. Normalization to creatinine or osmolality values are the most adopted strategies, however, each technique has its challenges that can hinder its wider application. We have been developing targeted urine metabolomic methods to differentiate two important respiratory diseases, namely asthma and chronic obstructive pulmonary disease (COPD).
Objective
To assess whether the statistical model of separation of diseases using targeted metabolomic data would be improved by normalization to osmolality instead of creatinine.
Methods
The concentration of 32 metabolites was previously measured by two liquid chromatography-tandem mass spectrometry methods in 51 human urine samples with either asthma (n = 25) or COPD (n = 26). The data was normalized to creatinine or osmolality. Statistical analysis of the normalized values in each disease was performed using partial least square discriminant analysis (PLS-DA). Models of separation of diseases were compared.
Results
We found that normalization to creatinine or osmolality did not significantly change the PLS-DA models of separation (R2Q2 = 0.919, 0.705 vs R2Q2 = 0.929, 0.671, respectively). The metabolites of importance in the models remained similar for both normalization methods.
Conclusion
Our findings suggest that targeted urine metabolomic data can be normalized for hydration using creatinine or osmolality with no significant impact on the diagnostic accuracy of the model.
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Acknowledgements
The authors would like to thank Dr. Paul Lee, College of Medicine, for providing help with osmolality measurements. We would also like to thank Drs. Cockcroft and Marciniuk for access to their patients to collect the urine samples, and to Ms. Joan Dietz, RN for gaining consent and collecting clinical data.
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TH did the experimental work, compiled the data and generated graphs using excel. HA analyzed a subset of patient samples. MMK prepared the manuscript and statistically analyzed the data using SIMCA. AE supervised TH and MMK, facilitated the equipment for measurements, revised the manuscript and proposed reviewers for the submission. DJA conceived the idea of the work, contributed to the writing and revision of the manuscript, supervised TH, HA and MMK and statistically analyzed the data using graph prism.
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The authors declare that they have no conflict of interest and no conflict of financial interest.
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Collection of human urine samples from consenting participants was approved by the University of Saskatchewan Biomedical Research Ethics Board and the University of Alberta and St. Joseph’s Healthcare Hamilton Health Research Ethics Boards, (Bio#1389).
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Khamis, M.M., Holt, T., Awad, H. et al. Comparative analysis of creatinine and osmolality as urine normalization strategies in targeted metabolomics for the differential diagnosis of asthma and COPD. Metabolomics 14, 115 (2018). https://doi.org/10.1007/s11306-018-1418-9
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DOI: https://doi.org/10.1007/s11306-018-1418-9