Development and Validation of Apolipoprotein AI-Associated Lipoprotein Proteome Panel for the Prediction of Cholesterol Efflux Capacity and Coronary Artery Disease.

Autor:	Jin Z; Cleveland HeartLab, Inc., Cleveland, OH., Collier TS; Cleveland HeartLab, Inc., Cleveland, OH., Dai DLY; Proof Centre of Excellence, Vancouver, British Columbia, Canada., Chen V; Proof Centre of Excellence, Vancouver, British Columbia, Canada., Hollander Z; Proof Centre of Excellence, Vancouver, British Columbia, Canada., Ng RT; Proof Centre of Excellence, Vancouver, British Columbia, Canada., McManus BM; Proof Centre of Excellence, Vancouver, British Columbia, Canada., Balshaw R; Proof Centre of Excellence, Vancouver, British Columbia, Canada., Apostolidou S; Gynaecological Cancer Research Centre, Department of Women's Cancer, Institute for Women's Health, University College London, London, UK., Penn MS; Cleveland HeartLab, Inc., Cleveland, OH., Bystrom C; Cleveland HeartLab, Inc., Cleveland, OH; cbystrom@clevelandheartlab.com.
Jazyk:	angličtina
Zdroj:	Clinical chemistry [Clin Chem] 2019 Feb; Vol. 65 (2), pp. 282-290. Date of Electronic Publication: 2018 Nov 21.
DOI:	10.1373/clinchem.2018.291922
Abstrakt:	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. (© 2018 American Association for Clinical Chemistry.)
Databáze:	MEDLINE
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