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Background <p>Improving the accuracy of 10-year cardiovascular risk prediction beyond established algorithms like SCORE2 is a clinical priority. The plasma ratio of polyunsaturated to monounsaturated fatty acids (PUFA/MUFA) is an objective marker of dietary fat quality, which is linked to cardiovascular health. This study aimed to evaluate whether adding the PUFA/MUFA ratio to the SCORE2 model improves the prediction of major adverse cardiovascular events (MACE).</p> Methods <p>This prospective cohort study included 183,237 UK Biobank participants aged 50–69 years, free of cardiovascular disease or diabetes at baseline. The plasma PUFA/MUFA ratio was quantified using high-throughput nuclear magnetic resonance (NMR) spectroscopy. The cohort was randomly split into training (70%) and validation (30%) sets. The predictive performance of the original SCORE2 model was compared to an extended model including the PUFA/MUFA ratio, using Harrell’s C-index, Net Reclassification Improvement (NRI), and Integrated Discrimination Improvement (IDI).</p> Results <p>In the independent validation set (<i>N</i> = 54,971), a higher PUFA/MUFA ratio was associated with a lower risk of MACE. Adding the PUFA/MUFA ratio to the SCORE2 model resulted in a statistically significant increase in the C-index from 0.740 (95% CI: 0.736–0.743) to 0.744 (95% CI: 0.740–0.748) (<i>P</i> &lt; 0.001). The extended model also showed significant risk reclassification, with An NRI of 7.5% (95% CI: 3.5–11.4%) And An IDI of 0.025 (95% CI: 0.016–0.034). Both models were well-calibrated.</p> Conclusions <p>Incorporating the plasma PUFA/MUFA ratio into the SCORE2 algorithm provides a modest but statistically significant improvement in 10-year MACE risk prediction. As an objective biomarker of dietary fat quality, the PUFA/MUFA ratio shows promise as a supplementary tool for risk assessment, though its direct clinical impact requires further validation and consideration of cost-effectiveness.</p>

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The polyunsaturated-to-monounsaturated fatty acid ratio and cardiovascular risk prediction: a prospective cohort study of 183,237 adults

  • 期刊论文

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  • 发表日期：

    2025年10月14日

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  • Meiyan Sun,
  • Dandan Jiang,
  • Xueqin Long,
  • Xuemei Chen

摘要

Background

Improving the accuracy of 10-year cardiovascular risk prediction beyond established algorithms like SCORE2 is a clinical priority. The plasma ratio of polyunsaturated to monounsaturated fatty acids (PUFA/MUFA) is an objective marker of dietary fat quality, which is linked to cardiovascular health. This study aimed to evaluate whether adding the PUFA/MUFA ratio to the SCORE2 model improves the prediction of major adverse cardiovascular events (MACE).

Methods

This prospective cohort study included 183,237 UK Biobank participants aged 50–69 years, free of cardiovascular disease or diabetes at baseline. The plasma PUFA/MUFA ratio was quantified using high-throughput nuclear magnetic resonance (NMR) spectroscopy. The cohort was randomly split into training (70%) and validation (30%) sets. The predictive performance of the original SCORE2 model was compared to an extended model including the PUFA/MUFA ratio, using Harrell’s C-index, Net Reclassification Improvement (NRI), and Integrated Discrimination Improvement (IDI).

Results

In the independent validation set (N = 54,971), a higher PUFA/MUFA ratio was associated with a lower risk of MACE. Adding the PUFA/MUFA ratio to the SCORE2 model resulted in a statistically significant increase in the C-index from 0.740 (95% CI: 0.736–0.743) to 0.744 (95% CI: 0.740–0.748) (P < 0.001). The extended model also showed significant risk reclassification, with An NRI of 7.5% (95% CI: 3.5–11.4%) And An IDI of 0.025 (95% CI: 0.016–0.034). Both models were well-calibrated.

Conclusions

Incorporating the plasma PUFA/MUFA ratio into the SCORE2 algorithm provides a modest but statistically significant improvement in 10-year MACE risk prediction. As an objective biomarker of dietary fat quality, the PUFA/MUFA ratio shows promise as a supplementary tool for risk assessment, though its direct clinical impact requires further validation and consideration of cost-effectiveness.