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Large-scale plasma lipidomic profiling identifies lipids that predict cardiovascular events in secondary prevention
Piyushkumar A. Mundra, Christopher K. Barlow, Paul J. Nestel, Elizabeth H. Barnes, Adrienne Kirby, Peter Thompson, David R. Sullivan, Zahir H. Alshehry, Natalie A. Mellett, Kevin Huynh, Kaushala S. Jayawardana, Corey Giles, Malcolm J. McConville, Sophia Zoungas, Graham S. Hillis, John Chalmers, Mark Woodward, Gerard Wong, Bronwyn A. Kingwell, John Simes, Andrew M. Tonkin, Peter J. Meikle, LIPID Study Investigators
Piyushkumar A. Mundra, Christopher K. Barlow, Paul J. Nestel, Elizabeth H. Barnes, Adrienne Kirby, Peter Thompson, David R. Sullivan, Zahir H. Alshehry, Natalie A. Mellett, Kevin Huynh, Kaushala S. Jayawardana, Corey Giles, Malcolm J. McConville, Sophia Zoungas, Graham S. Hillis, John Chalmers, Mark Woodward, Gerard Wong, Bronwyn A. Kingwell, John Simes, Andrew M. Tonkin, Peter J. Meikle, LIPID Study Investigators
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Clinical Research and Public Health Cardiology Metabolism

Large-scale plasma lipidomic profiling identifies lipids that predict cardiovascular events in secondary prevention

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Abstract

BACKGROUND. Plasma lipidomic measures may enable improved prediction of cardiovascular outcomes in secondary prevention. The aim of this study is to determine the association of plasma lipidomic measurements with cardiovascular events and assess their potential to predict such events. METHODS. Plasma lipids (n = 342) were measured in a retrospective subcohort (n = 5,991) of the LIPID study. Proportional hazards regression was used to identify lipids associated with future cardiovascular events (nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death) and cardiovascular death. Multivariable models adding lipid species to traditional risk factors were created using lipid ranking established from the Akaike information criterion within a 5-fold cross-validation framework. The results were tested on a diabetic case cohort from the ADVANCE study (n = 3,779). RESULTS. Specific ceramide species, sphingolipids, phospholipids, and neutral lipids containing omega-6 fatty acids or odd-chain fatty acids were associated with future cardiovascular events (106 species) and cardiovascular death (139 species). The addition of 7 lipid species to a base model (11 conventional risk factors) resulted in an increase in the C-statistics from 0.629 (95% CI, 0.628–0.630) to 0.654 (95% CI, 0.653–0.656) for prediction of cardiovascular events and from 0.673 (95% CI, 0.671–0.675) to 0.727 (95% CI, 0.725–0.728) for prediction of cardiovascular death. Categorical net reclassification improvements for cardiovascular events and cardiovascular death were 0.083 (95% CI, 0.081–0.086) and 0.166 (95% CI, 0.162–0.170), respectively. Evaluation on the ADVANCE case cohort demonstrated significant improvement on the base models. CONCLUSIONS. The improvement in the prediction of cardiovascular outcomes, above conventional risk factors, demonstrates the potential of plasma lipidomic profiles as biomarkers for cardiovascular risk stratification in secondary prevention. FUNDING. Bristol-Myers Squibb, the National Health and Medical Research Council of Australia (grants 211086, 358395, and 1029754), and the Operational Infrastructure Support Program of the Victorian government of Australia.

Authors

Piyushkumar A. Mundra, Christopher K. Barlow, Paul J. Nestel, Elizabeth H. Barnes, Adrienne Kirby, Peter Thompson, David R. Sullivan, Zahir H. Alshehry, Natalie A. Mellett, Kevin Huynh, Kaushala S. Jayawardana, Corey Giles, Malcolm J. McConville, Sophia Zoungas, Graham S. Hillis, John Chalmers, Mark Woodward, Gerard Wong, Bronwyn A. Kingwell, John Simes, Andrew M. Tonkin, Peter J. Meikle, LIPID Study Investigators

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Figure 2

Plasma lipid species associated with future cardiovascular events and cardiovascular death in the LIPID cohort (n = 5,991).

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Plasma lipid species associated with future cardiovascular events and ca...
Cox regression models of each lipid species against cardiovascular events (left) and cardiovascular death (right) were created, adjusting for 11 covariates (age, sex, total cholesterol, HDL-C, current smoking, nature of prior acute coronary syndrome, revascularization, diabetes history, stroke history, history of hypertension, and randomized treatment allocation). Hazard ratios per unit standard deviation and 95% CIs are shown. Bolded markers indicate significance (corrected P < 0.05 by Wald test). Colored markers indicate highly significant associations (blue, cardiovascular events, corrected P < 5.0E–4; red, cardiovascular death, corrected P < 1.0E–5 by Wald test). CE, cholesteryl ester; COH, cholesterol; Cer, ceramide; DG, diacylglycerol; dhCer, dihydroceramide; GM3, GM3 ganglioside; HexCer, monohexosylceramide; Hex2Cer, dihexosylceramide; Hex3Cer, trihexosylceramide; LPC, lysophosphatidylcholine; LPC(O), alkylphosphatidylcholine; LPE, lysophosphatidylethanolamine; LPI, lysophosphatidylinositol; PC, phosphatidylcholine; PC(O), alkylphosphatidylcholine; PC(P), alkenylphosphatidylcholine; PE, phosphatidylethanolamine; PE(O), alkylphosphatidylethanolamine; PE(P), alkenylphosphatidylethanolamine; PG, phosphatidylglycerol; PI, phosphatidylinositol; PS, phosphatidylserine; SM, sphingomyelin; TG, triacylglycerol.

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