Over the past decade, the abundance of high-throughput omics approaches coupled with the use of machine learning techniques, has made it possible to investigate the full molecular complexity of... Show moreOver the past decade, the abundance of high-throughput omics approaches coupled with the use of machine learning techniques, has made it possible to investigate the full molecular complexity of health and aging. The primary forcus of this thesis was to study and improve biological aging prediction. To achieve this we developed, evaluated, and deployed state-of-the-art models predicting different aspects of human health risks by employing multiple omics measurement, with a particular attention given to 1H-NMR metabolomics. Availability, affordability, interpretability, and robustness of the 1H-NMR metabolomics platform by Nightingale Health makes it a powerful tool with implications in the risk prediction of common diseases. We explored this research line in epidemiological settings within the BBMRI-nl consortium, which incorporates 28 cohorts with various specific characteristics. Hence, we took advantage of the wide range of health statuses when examining the extensive BBMRI datasets, investigated specific subgroups such as elderly or night-working individuals respectively recruited for the Leiden Longevity Study (LLS) and LIFELINES, and even explored the potential complementarity and interaction of different omics (e.g., 1H-NMR metabolomics, DNA methylome) available within the subset known as BIOS Consortium. Show less
Bizzarri, D.; Reinders, M.J.T.; Beekman, M.; Slagboom, P.E.; BBMRI-NL; Akker, E.B. van den 2022
Background Missing or incomplete phenotypic information can severely deteriorate the statistical power in epidemiological studies. High-throughput quantification of small-molecules in bio-samples,... Show moreBackground 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 similar to 26,000 H-1-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 CV) = 0.90).Findings Subsequent application in independent cohorts confirmed that our metabolomics-based predictors can indeed be used to impute a wide array of missing clinical variables from a single metabolomics data resource. In addition, application highlighted the potential use of our predictors to explore the effects of totally unobserved confounders in omics association studies. Finally, we show that our predictors can be used to explore risk factor profiles contributing to mortality in older participants.Interpretation To conclude, we provide H-1-NMR metabolomics-based models to impute clinical variables routinely assessed in epidemiological studies and illustrate their merit in scenarios when phenotypic variables are partially incomplete or totally unobserved. Copyright (C) 2021 The Author(s). Published by Elsevier B.V. Show less
