| Title: | Predictive value of metabolic profiling in cardiovascular risk scores: analysis of 75 000 adults in UK Biobank |
| Journal: | Journal of Epidemiology & Community Health |
| Published: | 12 Sep 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37699667/ |
| DOI: | https://doi.org/10.1136/jech-2023-220801 |
Publication 9586
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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.</p>
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.</p>
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.</p>
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.</p>
5 Authors
- Danyao Jin
- Eirini Trichia
- Nazrul Islam
- Sarah Lewington
- Ben Lacey
1 Application
| Application ID | Title |
|---|---|
| 31461 | Major risk factors for Cardiovascular Disease |
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