{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T17:52:44Z","timestamp":1792086764897,"version":"4.4.0"},"reference-count":39,"publisher":"Oxford University Press (OUP)","funder":[{"DOI":"10.13039\/100000030","name":"Centers for Disease Control and Prevention","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000030","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Glycoprotein acetylation (GlycA) is a nuclear magnetic resonance\u2013derived biomarker of systemic inflammation. GlycA shows statistical interaction with small HDL particle number (s-HDL-P) in relation to coronary artery disease (CAD). This study examines the confounding effects of GlycA-related glycoproteins, hypertriglyceridemia, and s-HDL-P on CAD risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Plasma samples from 752 participants in the CATHGEN biorepository were size-fractionated by asymmetric flow field-flow fractionation and analyzed by targeted proteomics and lipidomics. Fraction concentrations were converted to HDL particle number using a volumetric approach based on apolipoprotein A-I (APOA1) concentration, HDL size, and lipoprotein lipid composition. Correlations between GlycA, glycoproteins, total triglyceride (TG), HDL composition and HDL particle number, and their associations with CAD were assessed using multivariate least-squares fit and nominal logistic regression analysis, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Acute-phase inflammation-related glycoproteins (GlycA-proteins) correlated strongly with GlycA. However, the size-distribution profiles of GlycA-proteins were consistent with circulation as free dimers\/trimers, and their lack of overlap with APOA1 profiles suggested no detectable HDL binding. In contrast, apolipoproteins and lipid transfer proteins showed size-profile shifts relative to APOA1 consistent with HDL binding. s-HDL-P showed an inverse association with GlycA, most significantly at low TG levels. In CAD models, significant GlycA \u00d7 s-HDL-P interaction emerged only when TG was included as a covariate. CAD correlated with lipoprotein lipids, apolipoproteins, and lipid transfer proteins that influence lipoprotein remodeling.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>GlycA-proteins circulate freely without direct HDL binding. The empirical GlycA \u00d7 s-HDL-P interaction is consistent with indirect coupling between systemic inflammation and altered lipoprotein remodeling associated with CAD risk.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/clinchem\/hvag129","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T11:27:06Z","timestamp":1788521226000},"source":"Crossref","is-referenced-by-count":0,"title":["Association between Glycoprotein Acetylation\u2013Indicated Inflammation and Small HDL Particle Number in Coronary Artery Disease"],"prefix":"10.1093","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6221-6240","authenticated-orcid":false,"given":"Anna A","family":"Ivanova","sequence":"first","affiliation":[{"name":"Clinical Chemistry Branch, Division of Laboratory Sciences, Centers for Disease Control and Prevention , Atlanta, GA ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David M","family":"Schieltz","sequence":"additional","affiliation":[{"name":"Clinical Chemistry Branch, Division of Laboratory Sciences, Centers for Disease Control and Prevention , Atlanta, GA ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael S","family":"Gardner","sequence":"additional","affiliation":[{"name":"Clinical Chemistry Branch, Division of Laboratory Sciences, Centers for Disease Control and Prevention , Atlanta, GA ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bryan A","family":"Parks","sequence":"additional","affiliation":[{"name":"Clinical Chemistry Branch, Division of Laboratory Sciences, Centers for Disease Control and Prevention , Atlanta, GA ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric C","family":"Leszczynski","sequence":"additional","affiliation":[{"name":"Department of Kinesiology, Michigan State University , East Lansing, MI ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark A","family":"Sarzynski","sequence":"additional","affiliation":[{"name":"University of South Carolina Arnold School of Public Health, , Columbia, SC ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Virginia B","family":"Kraus","sequence":"additional","affiliation":[{"name":"Duke Molecular Physiology Institute, Department of Medicine, Duke University School of Medicine, Duke University , Durham, NC ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William E","family":"Kraus","sequence":"additional","affiliation":[{"name":"Duke Molecular Physiology Institute, Department of Medicine, Duke University School of Medicine, Duke University , Durham, NC ,","place":["United 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