Development and Validation of Apolipoprotein AI-Associated Lipoprotein Proteome Panel for the Prediction of Cholesterol Efflux Capacity and Coronary Artery Disease. (1st February 2019)

• Record Type: Journal Article
• Authors: Jin, Zhicheng; Collier, Timothy S; Dai, Darlene L Y; Chen, Virginia; Hollander, Zsuzsanna; Ng, Raymond T; McManus, Bruce M; Balshaw, Robert; Apostolidou, Sophia; Penn, Marc S; Bystrom, Cory
• Is Part Of: Clinical chemistry. Volume 65:Number 2(2019)
• Page Start: 282
• Page End: 290
• Publication Date: 2019-02-01
• Subjects: Clinical chemistry -- Periodicals; Pharmaceutical chemistry -- Periodicals; Biochemistry -- Periodicals; Biochimie -- Périodiques; Diagnostics biologiques -- Périodiques; Biochemistry; Clinical chemistry; Pharmaceutical chemistry; Biochemistry; Laboratory Techniques and Procedures; Klinische chemie; Periodicals; 616.075605
• DOI: 10.1373/clinchem.2018.291922
• Languages: English
• ISSNs: 0009-9147

Abstract

Abstract: BACKGROUND: Cholesterol efflux capacity (CEC) is a measure of HDL function that, in cell-based studies, has demonstrated an inverse association with cardiovascular disease. The cell-based measure of CEC is complex and low-throughput. We hypothesized that assessment of the lipoprotein proteome would allow for precise, high-throughput CEC prediction. METHODS: After isolating lipoprotein particles from serum, we used LC-MS/MS to quantify 21 lipoprotein-associated proteins. A bioinformatic pipeline was used to identify proteins with univariate correlation to cell-based CEC measurements and generate a multivariate algorithm for CEC prediction (pCE). Using logistic regression, protein coefficients in the pCE model were reweighted to yield a new algorithm predicting coronary artery disease (pCAD). RESULTS: Discovery using targeted LC-MS/MS analysis of 105 training and test samples yielded a pCE model comprising 5 proteins (Spearman r = 0.86). Evaluation of pCE in a case–control study of 231 specimens from healthy individuals and patients with coronary artery disease revealed lower pCE in cases ( P = 0.03). Derived within this same study, the pCAD model significantly improved classification ( P < 0.0001). Following analytical validation of the multiplexed proteomic method, we conducted a case–control study of myocardial infarction in 137 postmenopausal women that confirmed significant separation of specimen cohorts in both the pCE ( P = 0.015) and pCAD ( P = 0.001) models. CONCLUSIONS: Development of a proteomic pCE provides a reproducible high-throughput alternative to traditional cell-based CEC assays. The pCAD model improves stratification of case and control cohorts and, with further studies to establish clinical validity, presents a new opportunity for the assessment of cardiovascular health.