1H-NMR metabolomics-based surrogates to impute common clinical risk factors and endpoints.

ABSTRACT:

Background

Missing or incomplete phenotypic information can severely deteriorate the statistical power in epidemiological studies. High-throughput quantification of small-molecules in bio-samples, i.e. 'metabolomics', is steadily gaining popularity, as it is highly informative for various phenotypical characteristics. Here we aim to leverage metabolomics to impute missing data in clinical variables routinely assessed in large epidemiological and clinical studies.

Methods

To this end, we have employed ∼26,000 ¹H-NMR metabolomics samples from 28 Dutch cohorts collected within the BBMRI-NL consortium, to create 19 metabolomics-based predictors for clinical variables, including diabetes status (AUC_{5-Fold CV} = 0·94) and lipid medication usage (AUC_5-Fold

SUBMITTER: Bizzarri D 

PROVIDER: S-EPMC8703237 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature