Technical Report: A Comprehensive Comparison between Different Quantification Versions of Nightingale Health’s 1H-NMR Metabolomics Platform

Metabolites, vol. 13, pp. 1181

Abstract

1H-NMR metabolomics data is increasingly used to track health and disease. Nightingale Health, a major supplier of 1H-NMR metabolomics, has recently updated the quantification strategy to further align with clinical standards. Such updates, however, might influence backward replicability, particularly affecting studies with repeated measures. Using data from BBMRI-NL consortium (~28,000 samples from 28 cohorts), we compared Nightingale data, originally released in 2014 and 2016, with a re-quantified version released in 2020, of which both versions were based on the same NMR spectra. Apart from two discontinued and twenty-three new analytes, we generally observe a high concordance between quantification versions with 73 out of 222 (33%) analytes showing a mean ρ > 0.9 across all cohorts. Conversely, five analytes consistently showed lower Spearman’s correlations (ρ < 0.7) between versions, namely acetoacetate, LDL-L, saturated fatty acids, S-HDL-C, and sphingomyelins. Furthermore, previously trained multi-analyte scores, such as MetaboAge or MetaboHealth, might be particularly sensitive to platform changes. Whereas MetaboHealth replicated well, the MetaboAge score had to be retrained due to use of discontinued analytes. Notably, both scores in the re-quantified data recapitulated mortality associations observed previously. Concluding, we urge caution in utilizing different platform versions to avoid mixing analytes, having different units, or simply being discontinued.

Authors 5

1. Daniele Bizzarri Aachen Leiden Computational Biology Center Department of Biomedical Data Science Molecular Epidemiology

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab., Department of Intelligent Systems, TU Delft, 2628 XE Delft, The Netherlands

   Leiden Computational Biology Center, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands

   Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands

2. Marcel J. T. Reinders Aachen Leiden Computational Biology Center Department of Biomedical Data Science

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab., Department of Intelligent Systems, TU Delft, 2628 XE Delft, The Netherlands

   Leiden Computational Biology Center, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands

3. Marian Beekman Aachen Molecular Epidemiology Department of Biomedical Data Science

   Leiden University Medical Center

   Affiliation as printed

   Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands

4. P. Eline Slagboom Aachen Molecular Epidemiology Department of Biomedical Data Science

   Leiden University Medical Center · Max Planck Institute for Biology of Ageing

   Affiliation as printed

   Max Planck Institute for the Biology of Ageing, 50931 Cologne, Germany

   Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands

5. Erik B. van den Akker corresponding Aachen Leiden Computational Biology Center Department of Biomedical Data Science Molecular Epidemiology

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab., Department of Intelligent Systems, TU Delft, 2628 XE Delft, The Netherlands

   Leiden Computational Biology Center, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands

   Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands