Large-scale plasma lipidomic profiling identifies lipids that predict cardiovascular events in secondary prevention.
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.
['Mundra|Piyushkumar A|PA|', 'Barlow|Christopher K|CK|', 'Nestel|Paul J|PJ|', 'Barnes|Elizabeth H|EH|', 'Kirby|Adrienne|A|', 'Thompson|Peter|P|', 'Sullivan|David R|DR|', 'Alshehry|Zahir H|ZH|', 'Mellett|Natalie A|NA|', 'Huynh|Kevin|K|', 'Jayawardana|Kaushala S|KS|', 'Giles|Corey|C|', 'McConville|Malcolm J|MJ|', 'Zoungas|Sophia|S|', 'Hillis|Graham S|GS|', 'Chalmers|John|J|', 'Woodward|Mark|M|', 'Wong|Gerard|G|', 'Kingwell|Bronwyn A|BA|', 'Simes|John|J|', 'Tonkin|Andrew M|AM|', 'Meikle|Peter J|PJ|', '|||']
D000368:Aged D015415:Biomarkers D002318:Cardiovascular Diseases D002319:Cardiovascular System D005260:Female D006801:Humans D050356:Lipid Metabolism D008055:Lipids D008297:Male D008875:Middle Aged D015999:Multivariate Analysis D009203:Myocardial Infarction D012189:Retrospective Studies D012307:Risk Factors D055502:Secondary Prevention
Atherosclerosis Cardiology Cardiovascular disease Metabolism