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Abstract
Background Metabolic profiling (the extensive measurement of circulating metabolites across multiple biological pathways) is increasingly employed in clinical care. However, there is little evidence on the benefit of metabolic profiling as compared with established atherosclerotic cardiovascular disease (CVD) risk scores.
Methods UK Biobank is a prospective study of 0.5 million participants, aged 40–69 at recruitment. Analyses were restricted to 74 780 participants with metabolic profiling (measured using nuclear magnetic resonance) and without CVD at baseline. Cox regression was used to compare model performance before and after addition of metabolites to QRISK3 (an established CVD risk score used in primary care in England); analyses derived three models, with metabolites selected by association significance or by employing two different machine learning approaches.
Results We identified 5097 incident CVD events within the 10-year follow-up. Harrell’s C-index of QRISK3 was 0.750 (95% CI 0.739 to 0.763) for women and 0.706 (95% CI 0.696 to 0.716) for men. Adding selected metabolites did not significantly improve measures of discrimination in women (Harrell’s C-index of three models are 0.759 (0.747 to 0.772), 0.759 (0.746 to 0.770) and 0.759 (0.748 to 0.771), respectively) or men (0.710 (0.701 to 0.720), 0.710 (0.700 to 0.719) and 0.710 (0.701 to 0.719), respectively), and neither did it improve reclassification or calibration.
Conclusion This large-scale study applied both conventional and machine learning approaches to assess the potential benefit of metabolic profiling to well-established CVD risk scores. However, there was no evidence that metabolic profiling improved CVD risk prediction in this population.
- CARDIOVASCULAR DISEASES
- EPIDEMIOLOGY
- PRIMARY HEALTH CARE
Data availability statement
Data are available upon reasonable request. Data from the UK Biobank are available to researchers after registration at the UK Biobank server. The data cleaning and coding used to generate the findings of this study are available from the corresponding author on reasonable request.
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Data availability statement
Data are available upon reasonable request. Data from the UK Biobank are available to researchers after registration at the UK Biobank server. The data cleaning and coding used to generate the findings of this study are available from the corresponding author on reasonable request.
Footnotes
SL and BL are joint senior authors.
DJ and ET contributed equally.
Contributors DJ and ET had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. NI, SL and BL critically revised the manuscript and contributed important intellectual content. BL and SL are the guarantors of this work.
Funding This research used UK Biobank data assets made available by National Safe Haven as part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref MC_PC_20058).
Competing interests SL reports grants from the Medical Research Council (MRC) and research funding from the US Centers for Disease Control and Prevention Foundation (with support from Amgen) and from the World Health Organization during the conduct of the study. The Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU) receives research grants from industry that are governed by University of Oxford contracts that protect its independence and has a staff policy of not taking personal payments from industry; further details can be found at https://www.ndph.ox.ac.uk/files/about/ndph-independence-of-research-policy-jun-20.pdf. All other authors declared no conflict of interest.
Provenance and peer review Not commissioned; internally peer reviewed.
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