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1 DECLARATION OF CONFLICT OF INTEREST None
2 Plasma Lipidomic Analysis of Stable and Unstable Coronary Artery Disease Peter J Meikle 1, Gerard Wong 1, Despina Tsorotes 1, Christopher K Barlow 1, Jacquelyn M Weir 1, Michael J Christopher 1, Gemma L MacIntosh 1, Benjamin Goudey 2, Justin Bedo 2, Linda Stern 3, Adam Kowolczyk 2, Izhak Haviv 1, Anthony J White 4, Anthony M Dart 4, Stephen J Duffy 4, Garry L Jennings 1, Bronwyn A Kingwell 1. 1 Baker IDI Heart and Diabetes Institute, 2 National ICT Australia (NICTA) and 3 Department of Computer Science and Software Engineering, University of Melbourne, 4 Department of Cardiovascular Medicine, Alfred Hospital
3 Traditional risk algorithms: estimate medium-term event rates in patients without overt atherosclerosis are limited in their ability to predict sudden events: - cardiac death or - non-fatal MI Better markers of vulnerable plaque are required
4 Most sudden coronary ischaemic events are secondary plaque rupture /erosion Stable Unstable Small lipid core Dense fibrous cap Large lipid core Thin fibrous cap A B Reilly et al., Texas Heart Journal, 2008
5 Lipids Positive Remodelling macrophages inflammatory mediators MMPs Collagen SMC Plaque rupture
6 Aims To use plasma lipid profiling to: provide insight into disease pathogenesis & evaluate the potential of lipid profiles to assess risk of future plaque instability
7 Study Design Patients with de-novo presentation of CAD disease Stable / unstable classification (Braunwald criteria): symptoms 12 lead ECG cardiac troponin I Stable CAD n=80 Unstable CAD n=60 Healthy controls n=80
8 Clinical & Biochemical Characteristics Characteristic Stable CAD (n= 60) Unstable CAD (n= 80) P Age (years) 66.2 ± ± Sex (% female) BMI, (kg/m 2 ) 27.9 ± ± Current smoker (%) Diabetes (%) Glucose, (mmol/l) 5.77 ( ) 6.10 ( ) hscrp, mg/l 2.20 ( ) 9.15 ( ) < Troponin I (mg/l) 0 (0-0.13) 1.03 (0-88.6) < Data are mean ± standard deviation or median (range)
9 Plasma Lipids Characteristic Stable CAD (n= 60) Unstable CAD (n= 80) P Total cholesterol, mmol/l) 4.3 ( ) 4.0 ( ) LDL cholesterol, (mmol/l) 2.49 ( ) 2.28 ( ) HDL cholesterol, (mmol/l) 1.01 ( ) 1.1 ( ) Triglycerides, (mmol/l) 1.41 ( ) 1.28 ( ) Data are mean ± standard deviation or median (range)
10 Lipidomics of Coronary Artery Disease (Lipid analysis: API-4000 Q/TRAP ) Lipid extraction 10mL plasma Single phase CHCl 3 / MeOH / H 2 O BuOH / MeOH / H 2 O Multiple reaction monitoring (MRM) 2 x HPLC ESI-MS/MS experiments Quantified using deuterated standards 305 lipid species in total 67,000 measurements
11 Univariate Analysis Logistic regression (standardised to inter-quartile range) Unstable CAD against Stable CAD Adjust for age and sex, BMI, SBP and statin use 305 lipid species 50 associated with unstable CAD (vs. Stable CAD, p<0.01)
12 Binary Logistic Regression of Lipid Classes Against CAD class Total dhcer Total Cer Total MHC Total DHC Total THC Total GM3 Total SM Total PC Total oddpc Total PE Total PG Total PI Total PC(O) Total PC(P) Total PE(O) Total PE(P) Total LPC Total LPC(O) Total LPE Total COH Total CE Total DG Total TG Unstable CAD vs. Stable CAD Adjusted (age, sex, BMI, SBP, Statin) Odds Ratio
13 Binary Logistic Regression of Phosphatidylcholines Against CAD class PC 24:0 PC 28:0 PC 30:0 PC 32:0 PC 32:1 PC 32:2 PC 32:3 PC 34:0 PC 34:1 PC 34:2 PC 34:3 PC 34:4 PC 34:5 PC 36:1 PC 36:2 PC 36:3 PC 18:1/18:3 PC 16:0/20:4 PC 36:5 PC 36:6 PC 38:2 PC 38:3 PC 38:4 PC 38:5 PC 18:2/20:4 PC 16:0/22:6 PC 38:7 PC 40:5 PC 40:6 PC 40:7 Unstable CAD vs. Stable CAD Adjusted (age, sex, BMI, SBP, Statin) Odds Ratio
14 Binary Logistic Regression of Phosphatidylinositol Against CAD Class PI 32:0 PI 32:1 PI 34:0 PI 34:1 PI 36:0 PI 36:1 PI 36:2 PI 36:3 PI 36:4 PI 38:2 PI 38:3 PI 38:4 PI 38:5 PI 38:6 PI 40:4 PI 40:5 PI 40:6 phospholipase C Diacylglycerol Cell signalling via protein kinase C Unstable CAD vs. Stable CAD Adjusted (age, sex, BMI, SBP, Statin) Phosphatidylinosoitol phospholipase A2 Arachidonic acid (20:4) Prostaglandin PG2 and eicosanoids Odds Ratio
15 Binary Logistic Regression of Total PC and Total PE Against CAD Class Total PC Total PE Total PC(O) Total PE(O) Total PC(P) Total PE(P) Total LPC myeloperoxidase H 2 O Cl - Odds Ratio Plasmalogen (alkenylphospholipids) Total LPE Lysophospholipid + 2-chloroaldehyde phospholipase A Arachidonic acid (20:4) Odds Ratio Prostaglandin PG2 and eicosanoids Unstable CAD vs. Stable CAD Adjusted (age, sex, BMI, SBP, Statin)
16 Multivariate Analysis Goal Computational model for classification of unstable vs. stable CAD (maximum discrimination, minimum number of features) 3 stage approach Feature selection (ReliefF) ability of parameters to classify (rank) redundancy minimisation Classifier training (L2-Regularised Logistic Regression) determine optimum weighting (1/3 of cohort) Classification stage (2/3 of cohort) cross-validation framework (3-fold x 1000 repeats)
17 Multivariate analysis Output Receiver operator characteristic analysis (AUC) Probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one % Accuracy Accuracy measures the percentage of test instances correctly classified by the classification model
18 Area Under the Curve (C statistic) Risk Factors Age Sex BMI SBP Smoker CAD History Diabetes Cholesterol HDL Triglycerides hscrp
19 Accuracy Risk Factors Age Sex BMI SBP Smoker CAD History Diabetes Cholesterol HDL Triglycerides hscrp
20 Ranked features in multivariate models (% incorporation) Rank Risk factors only model % incorporation* Lipids and risk factor model % incorporation 1 CRP 98.2 PC 34: SBP 77.0 PC 18:1/18: Smoker 38.8 CRP Cholesterol 20.0 TG 14:0/16:0/18: CAD History 19.6 PC 28: BMI 14.5 SBP HDL 12.9 SM 18: Triglycerides 7.0 CE 14: Diabetes 6.8 PC(O-32:2) Sex 4.6 oddpc 33: Age 0.6 SM 18: PC(O-34:4) PC 18:2/20: PE(O-38:5) PC 40: PE(P-16:0/18:2) oddpc 31: PC 38: CE 18: DHC 16:0 46.6
21 Conclusion Demonstrated potential prognostic value for unstable CAD Improvement over conventional risk markers Likely that many of the changes in plasma lipids precede the development of plaque instability Mechanisms require elucidation Prospective studies required for validation
22 Acknowledgments Baker IDI Heart and Diabetes Institute Metabolomics Lab Peter Meikle Gerard Wong Chris Barlow Jacqui Weir Desi Tsorotes Natalie Mellett Clinical Diabetes and Epidemiology Jonathan Shaw Diana Magliano Narelle Grantham Baker IDI Heart and Diabetes Institute Melissa Barber (GeneBank) Izhak Haviv (Bioinformatics) Department of Cardiovascular Medicine, Alfred Hospital Tony White Stephen Duffy Tony Dart James Shaw Melbourne University NICTA Adam Kowalczyk Justin Bedo Benjamin Goudey Department of Computer Science Linda Stern
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