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

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.

Keywords: Atherosclerosis; Cardiology; Cardiovascular disease; Metabolism.

Figure 1. Consort diagram for lipidomic profiling of the LIPID study and ADVANCE study.

In the LIPID trial, 9,014 participants were randomized to receive pravastatin treatment or placebo. Lipidomic profiling was performed on all participants with baseline samples available (n = 5,991). Of these, 1,359 experienced a cardiovascular event and 708 experienced cardiovascular death. In the ADVANCE study, from 7,376 available baseline samples, a case cohort (n = 3,779) was selected for lipidomic profiling. This consisted of n = 3,154 randomly selected participants and all additional cases of cardiovascular events, renal events, and all-cause mortality (n = 625). Of these, n = 698 experienced a cardiovascular event and 355 experienced cardiovascular death.

Figure 2. Plasma lipid species associated with future cardiovascular events and cardiovascular death in the LIPID cohort (n = 5,991).

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, G_M3 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.

Figure 3. C-statistic for models to predict cardiovascular events and cardiovascular death on the LIPID cohort (n = 5,991).

Cox regression models were used to determine the improvement in C-statistic to predict cardiovascular events (A) and cardiovascular death (B) by the addition of lipids to the base model consisting of 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). Analysis was performed within a 5-fold cross-validated framework (repeated 200 times). The plots show the average C-statistic (solid line) and 95% CI (dashed lines). Zero features denotes the base model. CE = cholesteryl ester; Cer(d18:0), dihydroceramide; Cer, ceramide; GM3, G_M3 ganglioside; LPC, lysophosphatidylcholine; LPC(O), lysoalkylphosphatidylcholine; LPI, lysophosphatidylinositol; PC, phosphatidylcholine; PC(O), alkylphosphatidylcholine; PC(P), alkenylphosphatidylcholine; PE, phosphatidylethanolamine; PE(P), alkenylphosphatidylethanolamine; PI, phosphatidylinositol; PS, phosphatidylserine; SM, sphingomyelin; TG, triacylglycerol.

Figure 4. Association of lipid species that are selected in the predictive models with cardiovascular events and cardiovascular death in the LIPID subcohort (n = 5,991) and ADVANCE case cohort (n = 3,779).

Cox regression analysis (LIPID subcohort, circles) or weighted Cox regression analysis (ADVANCE case/cohort, triangles) was performed on those lipid species selected for the optimum models to predict cardiovascular events (A) and cardiovascular death (B) in the LIPID data set. The models were adjusted 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) for the analysis of the LIPID subcohort or 10 covariates (age, sex, total cholesterol, HDL-C, current smoking, history of macrovascular disease, coronary artery bypass graft or percutaneous transluminal coronary angioplasty, diabetes duration, current antihypertensive treatment, and statin treatment) for the analysis of the ADVANCE case cohort. Hazard ratios per unit standard deviation and 95% CIs are shown. CE, cholesteryl ester; LPI, lysophosphatidylinositol; PC, phosphatidylcholine; PC(O), alkylphosphatidylcholine; PC(P), alkenylphosphatidylcholine; PE, phosphatidylethanolamine; PI, phosphatidylinositol; SM, sphingomyelin.

Figure 5. Branched-chain amino acid catabolism to produce odd-chain and branched-chain fatty acids.

Starting from the branched-chain amino acids (isoleucine and valine), transamination by a mitochondrial branched-chain aminotransferase followed by decarboxylation with a branched-chain α-ketoacid dehydrogenase complex leads to short branched and odd acyl-CoAs that are subsequently utilized in a similar fashion as acetyl-CoA in de novo lipogenesis.