Cardiovascular Disease Biomarker Discovery: Plasma and Serum Collection Protocols

Cardiovascular disease is the leading cause of death worldwide, causing about 17.9 million deaths a year despite major treatment advances in recent decades. Cardiovascular disease isn’t just one condition — it covers atherosclerosis, heart failure, arrhythmias, valve disease, and inflammatory heart conditions. This variety calls for precision medicine approaches tailored to each patient’s molecular profile.¹ Cardiovascular-metabolic conditions are one of the core areas covered by our respiratory & metabolic conditions biospecimen portfolio. Biomarker discovery is a critical path toward better precision medicine. It enables earlier disease detection, risk assessment, treatment monitoring, and insight into how disease progresses. But cardiovascular biomarker research faces real pre-analytical challenges: how blood is collected, processed, and stored has a major effect on whether biomarkers stay stable and detectable in downstream assays.

Cardiovascular disease involves many biological layers, from genetic risk and environmental factors to systemic inflammation, blood vessel dysfunction, clotting, and changes in heart muscle. Circulating biomarkers reflect these different processes. They include proteins released during heart muscle injury, inflammatory cytokines signaling systemic inflammation, clotting factors that predict clot risk, natriuretic peptides that signal stress on the heart, and metabolites that reveal changes in cell metabolism.² Each type of biomarker behaves differently and has its own collection requirements. Handle it wrong, and you can get artificial spikes or drops that mask the real biological signal. Standardized collection protocols are essential for getting measurements you can reproduce and compare across studies.

At Sanguine, our cardiovascular biospecimen expertise spans a full specimen menu built for different biomarker classes. Human Plasma collected in multiple anticoagulant formulations (EDTA, heparin, citrate, ACD) works with different analytical platforms and biomarker stability needs. Human Serum allows measurement of clot-activated factors and gives an alternative matrix for biomarkers that anticoagulants can degrade.³ Human PBMCs and Human Leukopak support cellular studies that reveal how the immune system contributes to cardiovascular disease. Our nationwide collection network across the United States enables flexible protocols that meet specific biomarker requirements while maintaining rigorous quality standards from study design to receipt of samples.

Plasma Versus Serum Selection for Cardiovascular Biomarkers

Plasma and serum differ in one key way: plasma contains all clotting factors, kept from clotting through anticoagulation, while serum is the liquid left over after a clot forms. This distinction matters a lot for biomarker research. Plasma collection keeps things closer to their natural state, capturing circulating factors without exposing them to clot-formation. But the anticoagulants themselves can sometimes interfere with assays or bind to specific molecules, making them harder to detect.⁴ Serum collection lets the clot fully form, releasing clot-activated factors and platelet contents that can be valuable for some biomarkers. But clotting time and activation vary, adding variability to the process.

Plasma Advantages and Optimal Applications:

• Rapid processing possible (centrifugation immediately after collection)
• Preserves labile coagulation factors (if analyzing hemostasis)
• Higher yield (~15% more volume than serum from same blood draw)
• No clotting time variability affecting biomarker release
• Preferred for most protein biomarkers including troponins, natriuretic peptides, inflammatory cytokines
• Essential for metabolomics preserving small molecule stability
• Required for coagulation studies (fibrinogen, D-dimer, von Willebrand factor)
• Optimal for protease-sensitive biomarkers when protease inhibitors added
• Preferred matrix for most multiplexed cytokine/chemokine panels
• Standard choice for emerging biomarkers lacking established collection protocols

Serum Advantages and Optimal Applications:

• No anticoagulant interference with clinical chemistry assays
• Standard matrix for lipid panels (total cholesterol, HDL, LDL, triglycerides)
• Preferred for cardiac enzyme measurements in some clinical protocols
• Contains clot-activated platelet factors (PDGF, TGF-β, PF4) relevant to certain studies
• Historical clinical reference ranges established in serum
• Simpler collection (no anticoagulant mixing required)
• May show reduced hemolysis versus EDTA plasma in some applications
• Required for specific antibody measurements where anticoagulants interfere

Which anticoagulant you choose for plasma collection affects biomarker stability, assay compatibility, and the risk of cellular contamination. EDTA binds calcium and magnesium, preventing clotting while stabilizing most protein biomarkers and reducing platelet activation. But EDTA can interfere with assays that need divalent cations and may affect some enzyme activity.⁵ Heparin preserves calcium-dependent processes but varies from batch to batch, sometimes interferes with PCR when molecular analysis is planned downstream, and may affect certain immunoassays. Citrate stabilizes coagulation factors well but dilutes the sample by 10%, requiring adjustment. Our Human Plasma products come in all major anticoagulant formulations, so researchers can pick the best matrix for their specific application.

Emerging Biomarker Classes and Matrix Requirements

Cardiac Troponins:

High-sensitivity cardiac troponin assays can detect tiny troponin increases that signal subclinical heart muscle injury, enabling earlier diagnosis of heart attacks and better long-term risk assessment. EDTA plasma and lithium heparin plasma both work well, with minimal troponin breakdown when samples are processed within 4 hours and frozen at -80°C.⁶ Serum performs comparably but needs full clot formation (30-60 minutes), which delays processing. Repeated freeze-thaw cycles significantly degrade troponins, so longitudinal studies need single-use aliquots.

Natriuretic Peptides:

B-type natriuretic peptide (BNP) and N-terminal pro-BNP (NT-proBNP) are heart failure biomarkers; their plasma levels reflect stress on the heart and how well the ventricles are working. EDTA plasma provides the best stability — NT-proBNP stays stable for 72 hours at room temperature and indefinitely at -80°C. BNP is more fragile, degrading quickly in serum and needing prompt processing into EDTA plasma with protease inhibitors.⁷ Our Human Plasma from heart failure patients shows elevated natriuretic peptide levels suitable for assay validation.

Inflammatory Cytokines:

IL-6, TNF-α, IL-1β, and other inflammatory mediators are linked to cardiovascular disease progression and worse outcomes. These cytokines are quite fragile, requiring fast processing, protease inhibitors, and immediate freezing to prevent breakdown. EDTA or heparin plasma are the preferred matrices; serum shows more variability because cytokines keep being produced during clot formation.⁸ Samples need to reach freezers within 60 minutes of collection for the best preservation. Sanguine’s processing for Human Plasma includes optional protease inhibitor cocktails that stabilize fragile inflammatory biomarkers.

Metabolites and Lipids:

Changes in cardiovascular metabolism produce circulating metabolite signatures detectable through mass spectrometry-based metabolomics. These small molecules vary widely in stability — some break down within minutes, while others stay stable for days at room temperature. Processing immediately in the cold into EDTA plasma and freezing quickly at -80°C preserves most metabolite classes.⁹ Lipid measurements need fasting samples to avoid temporary triglyceride spikes after eating, with Human Serum providing the standard clinical matrix for lipid panels.

MicroRNAs:

Circulating microRNAs regulate gene expression and serve as cardiovascular disease biomarkers with remarkably stable levels in plasma. EDTA plasma preserves miRNAs best; RNase inhibitors are optional but recommended for storage beyond 6 months. Hemolysis dramatically raises red blood cell-derived miRNA levels, so careful collection technique and hemolysis checks are essential.¹⁰ Unlike most protein biomarkers, plasma miRNA levels stay stable through multiple freeze-thaw cycles.

Pre-Analytical Variables Impacting Biomarker Measurements

Collection Technique and Processing Timing

How blood is drawn affects biomarker profiles through mechanical stress, cell damage, and platelet activation. Keeping a tourniquet on for more than 2 minutes raises potassium, lactate dehydrogenase, and protein levels through fluid shifts and stress-induced release.¹¹ Shaking or foaming the sample during collection causes hemolysis, releasing cell contents that interfere with many assays. Underfilling collection tubes changes the blood-to-anticoagulant ratio and can allow microclots to form. Sanguine’s phlebotomy protocols specify standard tourniquet times, gentle mixing, and tube-fill verification to prevent these common mistakes.

Optimal Collection Best Practices:

• Apply tourniquets briefly (<1 minute optimal, <2 minutes maximum)
• Use appropriate needle gauge (21-22G typically) preventing hemolysis
• Fill tubes to manufacturer-specified volumes ensuring proper anticoagulant ratios
• Invert tubes gently (5-10 times) immediately after collection for anticoagulant mixing
• Avoid vigorous shaking or foaming
• Label tubes immediately preventing mix-ups
• Keep specimens at room temperature (not ice) during transport unless protocol specifies otherwise
• Process specimens as quickly as possible minimizing ex vivo changes
• Document exact collection times enabling time-course biomarker studies
• Use consistent fasting states (fasting vs. non-fasting) across research cohorts
• Standardize time-of-day collections when studying biomarkers exhibiting diurnal variation
• Avoid hemolyzed specimens for sensitive biomarkers

Centrifuge settings have a big impact on plasma/serum quality — too slow leaves cell contamination, too fast causes hemolysis. Standard protocols call for 1,500-2,000×g for 10-15 minutes at 4°C to separate plasma, though some biomarkers need adjusted settings.¹² Spinning more than once produces platelet-poor plasma, reducing platelet-derived biomarker contamination — useful when you want to study circulating rather than platelet-contained factors. Our Human Plasma goes through a standardized dual-spin process that removes over 99% of platelets, keeping cellular contamination to a minimum.

How long and how cold you store a sample determines biomarker stability, and breakdown rates vary a lot between biomarker classes. -80°C storage gives the best long-term stability for most biomarkers, while -20°C isn’t cold enough for many fragile factors. Freeze-thaw cycles cause proteins to gradually break down and microRNAs to degrade, so single-use aliquots are needed to avoid repeated thawing.¹³ Automated freezers that hold a steady temperature without manual defrost cycles prevent temperature swings that degrade stored samples. Sanguine tracks freeze-thaw history for every specimen, supporting quality checks down the line.

Hemolysis Assessment and Mitigation

Hemolysis — red blood cells rupturing and releasing their contents into plasma/serum — is the most common pre-analytical problem affecting cardiovascular biomarker measurements. Hemoglobin, potassium, lactate dehydrogenase, aspartate aminotransferase, cardiac troponins, and miRNAs all show artificially high levels in hemolyzed samples, which can distort disease associations.¹⁴ A visual check gives a rough sense of hemolysis, with pink or red coloration signaling a real problem. Quantitative methods — measuring free hemoglobin or absorbance at 414nm — give an objective hemolysis score for accepting, correcting, or rejecting a sample.

Common Hemolysis Causes:

• Small gauge needles (<23G) causing red cell shearing
• Excessive vacuum in collection tubes collapsing veins
• Vigorous shaking or mixing
• Prolonged tourniquet times (>2 minutes)
• Difficult draws requiring multiple needle repositioning
• Alcohol contamination from skin prep entering collection tubes
• Aspirating blood too rapidly through needles
• Processing delays allowing cellular degradation
• Insufficient anticoagulant from tube underfilling
• Freeze-thaw cycles
• Improper storage temperatures

Reporting a hemolysis index lets researchers correct biomarker results or exclude a sample if hemolysis is too severe. Many clinical labs automatically measure hemolysis and reject samples where interference is likely.¹⁵ Research applications need even stricter hemolysis control, since even small increases that wouldn’t matter clinically can distort biomarker associations in large studies. Sanguine’s quality control checks hemolysis on all plasma and serum products and documents the hemolysis index.

Disease-Specific Collection Protocols

Acute Coronary Syndrome Biomarker Studies

Biomarker levels change quickly after an acute coronary event, with cardiac troponins rising within 2-4 hours, peaking at 24-48 hours, and staying elevated for 7-14 days. Serial sampling protocols capture this pattern. Collections at presentation, 3 hours, 6 hours, 12 hours, 24 hours, and 48 hours document the troponin trajectory that distinguishes a heart attack from other causes of chest pain.¹⁶ Our Human Plasma from ACS patients, collected at multiple timepoints, supports assay development and validation studies that need serial samples.

ACS Serial Collection Protocol:

• Emergency department presentation (baseline)
• 3 hours post-presentation (early rise detection)
• 6 hours (standard diagnostic timepoint)
• 12 hours (peak or near-peak levels)
• 24 hours (definitive diagnosis, risk stratification)
• 48-72 hours (peak for late presenters)
• Pre-discharge (prognostic assessment)
• 30-day follow-up Human Serum (outcomes correlation)

Heart Failure Biomarker Research

Chronic heart failure keeps biomarker levels elevated, with dynamic changes during episodes of worsening symptoms. Natriuretic peptide levels track with symptom severity, stress on the heart, and prognosis, allowing treatment monitoring through repeated measurements.¹⁷ Collection protocols need consistent clinical conditions (resting, fasting), since BNP/NT-proBNP levels rise after eating and vary by time of day. Thorough protocols collect Human Plasma at diagnosis, during dose adjustment, at a stable chronic state, during acute worsening, and after stabilization, tracking how treatment is working.

Heart Failure Longitudinal Protocol:

• New diagnosis baseline (comprehensive biomarker panel)
• Monthly during initial treatment optimization
• Quarterly during stable chronic phase
• Hospitalization for decompensation (admission + daily + discharge)
• 30-day post-discharge (readmission risk assessment)
• Annual comprehensive reassessment

Atherosclerosis Progression Studies

Subclinical atherosclerosis progresses slowly over years before causing a clinical event. Inflammatory biomarkers, oxidized LDL, and markers of blood vessel dysfunction correlate with how much plaque has built up and how fast it’s progressing. Longitudinal studies that track biomarker changes need annual or twice-yearly Human Plasma and Human Serum collections paired with imaging that measures plaque volume and makeup.¹⁸ Population studies enrolling thousands of initially healthy people help identify biomarkers that predict cardiovascular events decades before they happen.

Cellular Biomarkers and Immunophenotyping

Cardiovascular disease involves complex immune responses, and the types of circulating immune cells present serve as both biomarkers and clues to disease mechanisms. Monocyte subsets (classical CD14++CD16-, intermediate CD14++CD16+, non-classical CD14+CD16++) occur in different proportions in cardiovascular disease patients versus healthy people, with more pro-inflammatory non-classical monocytes linked to worse outcomes.¹⁹ Flow cytometry on Human PBMCs measures these populations, requiring careful processing that preserves cell integrity and avoids activating them artificially.

Key Immune Populations in Cardiovascular Disease:

• Monocyte subsets (differential functions in inflammation and healing)
• T cell subsets (CD4+ helper, CD8+ cytotoxic, regulatory T cells)
• B cells and antibody profiles (autoimmune contributions to atherosclerosis)
• Natural killer cells (potential protective roles)
• Neutrophils and neutrophil extracellular traps (thrombotic mechanisms)
• Circulating endothelial progenitor cells (vascular repair capacity)
• Platelet-leukocyte aggregates (inflammatory signaling)

Specialized collection tubes preserve RNA and protein levels for gene expression and protein studies. PAXgene tubes stabilize RNA within minutes of collection, enabling gene expression profiling that reveals activation states and inflammatory signatures. However, these tubes don’t work with flow cytometry, so a separate Human Whole Blood collection in EDTA tubes is needed when both molecular and cellular analyses are planned.²⁰ Sanguine coordinates multi-tube collections so every required matrix comes from a single blood draw, keeping the burden on patients low.

Specialized Cardiovascular Biospecimen Types

Circulating Endothelial Cells and Microparticles

Circulating endothelial cells (CECs), shed from damaged vessel walls, and endothelial microparticles (EMPs), released during cell activation, both serve as markers of cardiovascular injury. These rare cell populations need specialized collection and processing, and immediate processing is essential since EMPs keep forming outside the body.²¹ Flow cytometry counts them using endothelial markers (CD31, CD146, CD105), and higher levels signal acute vascular injury. Human Whole Blood collected in citrate tubes and processed within 4 hours gives the best CEC/EMP recovery.

Platelet Function Biomarkers

Platelet activation and clumping play a critical role in clot-related cardiovascular events, and testing platelet function outside the body can predict bleeding and clotting risk. But platelets activate quickly during blood collection and processing, so careful technique is needed to avoid falsely activating them. Citrate anticoagulation preserves platelet function better than EDTA, and processing immediately with temperature control prevents ongoing activation.²² Human Plasma collected with citrate anticoagulant supports aggregometry and other platelet function tests when processed from citrated whole blood within 2 hours of collection.

Extracellular Vesicles and Exosomes

Extracellular vesicles (EVs), including exosomes released from heart cells, endothelial cells, and immune cells, carry RNA, proteins, and lipids between cells, acting as messengers and biomarker sources. Isolating EVs from plasma requires careful processing to avoid contamination from cells that release EVs during handling rather than reflecting what’s actually circulating.²³ Sequential centrifugation removes cells and platelets before ultracentrifugation pellets the EVs. Standardized collection protocols specify anticoagulant type, processing timing, and centrifuge settings, all of which affect EV yield and characterization.

Multi-Omic Integration for Cardiovascular Biomarker Discovery

Modern cardiovascular biomarker discovery increasingly combines genomics, transcriptomics, proteomics, metabolomics, and lipidomics to build a full picture of disease states. These approaches generate huge molecular profiles from a single sample, revealing system-wide changes that single-biomarker studies would miss.²⁴ But multi-omic studies demand careful sample collection and processing, since different analytical platforms respond differently to pre-analytical variables.

Optimal Specimen Allocation for Multi-Omic Studies:

• Genomics: Human Whole Blood (10 mL) for germline DNA extraction
• Transcriptomics: PAXgene tubes preserving RNA or Human PBMCs processed <4 hours
• Proteomics: Human Plasma (5-10 mL) with protease inhibitors, depleted or undepleted
• Metabolomics: Human Plasma (2-5 mL) rapidly processed and frozen
• Lipidomics: Human Plasma or Human Serum (2-5 mL) fasting specimens
• Immune phenotyping: Human PBMCs (20-50 million cells) fresh or cryopreserved
• Single-cell omics: Human Leukopak providing abundant cells (billions)

Machine learning models trained on multi-omic datasets can identify biomarker signatures that distinguish disease states, predict outcomes, and reveal how disease mechanisms work. These integrated approaches have already identified new cardiovascular disease subtypes with distinct molecular profiles and treatment responses, supporting precision medicine.²⁵ But the computational complexity and sample requirements call for collaboration across institutions to pool specimens — something Sanguine supports through customized collection protocols across the United States.

Quality Control and Reference Standards

Standardizing biomarker assays requires well-characterized reference materials that allow labs to compare results and monitor quality. Pooled Human Plasma or Human Serum from healthy donors provides matrix-matched calibrators, though individual biomarker levels may not cover disease-relevant ranges.²⁶ Commercial reference materials with certified biomarker concentrations enable absolute quantification, though these standards sometimes behave differently than naturally occurring biomarkers in complex biological samples.

Essential Quality Control Elements:

• Pooled quality control samples included in every analytical batch
• Control charts tracking assay performance over time
• Coefficient of variation monitoring (intra- and inter-assay)
• Regular recalibration using certified reference materials
• Participation in external quality assessment schemes
• Hemolysis, icterus, and lipemia interference testing
• Matrix effects assessment comparing spiked recovery
• Freeze-thaw stability validation for banked specimens
• Long-term storage stability monitoring at -80°C
• Contamination screening (bacterial, viral if applicable)
• Documentation of all pre-analytical protocols in publications
• Complete reporting of biomarker measurement methods and assay details

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Sanguine’s comprehensive specimen menu includes Human Plasma in multiple anticoagulants, Human Serum, Human PBMCs, and Human Leukopak from cardiovascular disease patients and healthy controls. This is part of our broader respiratory & metabolic conditions biospecimen portfolio. All specimens include comprehensive genomic annotation supporting translational research from study design to receipt of samples.

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Ethical Sourcing and Regulatory Compliance

All Sanguine biospecimens are collected under IRB-approved protocols with full informed consent from every donor. Our HIPAA-compliant data systems protect donor privacy while giving researchers access to detailed genomic annotation. We maintain ISO 9001:2015 and ISO 13485:2016 certifications, reflecting our commitment to quality management across every part of our operations.

Donor compensation follows ethical guidelines set by professional societies, ensuring people participate voluntarily and without pressure. Our nationwide collection network supports health equity in research while giving access to underrepresented populations often left out of biomedical studies. Every specimen is designated Research Use Only (RUO), with clear documentation of its intended use.

References

1. Roth GA, Mensah GA, Johnson CO, et al. Global burden of cardiovascular diseases and risk factors, 1990-2019. J Am Coll Cardiol. 2020;76(25):2982-3021. doi:10.1016/j.jacc.2020.11.010
2. Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints. Clin Pharmacol Ther. 2001;69(3):89-95. doi:10.1067/mcp.2001.113989
3. Simundic AM, Cornes M, Grankvist K, et al. Standardization of collection requirements for fasting samples. Clin Chim Acta. 2014;432:33-37. doi:10.1016/j.cca.2013.11.008
4. Lippi G, Salvagno GL, Montagnana M, et al. Influence of the needle bore size on platelet count and routine coagulation testing. Blood Coagul Fibrinolysis. 2006;17(7):557-561. doi:10.1097/01.mbc.0000245301.31387.ab
5. Hallworth MJ, Epner PL, Ebert C, et al. Current evidence and future perspectives on the effective practice of patient-centered laboratory medicine. Clin Chem. 2015;61(4):589-599. doi:10.1373/clinchem.2014.232629
6. Apple FS, Sandoval Y, Jaffe AS, Ordonez-Llanos J. Cardiac troponin assays: guide to understanding analytical characteristics. Clin Chem. 2017;63(1):73-81. doi:10.1373/clinchem.2016.255109
7. Mueller T, Gegenhuber A, Poelz W, Haltmayer M. Preliminary evaluation of the AxSYM B-type natriuretic peptide assay. Clin Chem. 2004;50(7):1104-1106. doi:10.1373/clinchem.2003.030403
8. Zhou X, Fragala MS, McElhaney JE, Kuchel GA. Conceptual and methodological issues relevant to cytokine and inflammatory marker measurements. Curr Opin Clin Nutr Metab Care. 2010;13(5):541-547. doi:10.1097/MCO.0b013e32833cf3bc
9. Breier M, Wahl S, Prehn C, et al. Targeted metabolomics identifies reliable and stable metabolites in human serum and plasma samples. PLoS One. 2014;9(2):e89728. doi:10.1371/journal.pone.0089728
10. Cheng HH, Yi HS, Kim Y, et al. Plasma processing conditions substantially influence circulating microRNA biomarker levels. PLoS One. 2013;8(6):e64795. doi:10.1371/journal.pone.0064795
11. Lima-Oliveira G, Lippi G, Salvagno GL, et al. Processing of diagnostic blood specimens: is it really the first step of the total testing process? Lab Med. 2017;48(2):e21-e26. doi:10.1093/labmed/lmw062
12. Bowen RA, Remaley AT. Interferences from blood collection tube components on clinical chemistry assays. Biochem Med. 2014;24(1):31-44. doi:10.11613/BM.2014.006
13. Mitchell AJ, Gray WD, Hayek SS, et al. Platelets confound the measurement of extracellular miRNA in archived plasma. Sci Rep. 2016;6:32651. doi:10.1038/srep32651
14. Lippi G, Blanckaert N, Bonini P, et al. Haemolysis: an overview of the leading cause of unsuitable specimens. Clin Chem Lab Med. 2008;46(6):764-772. doi:10.1515/CCLM.2008.170
15. Snozek CL, Karon BS, Scott R, et al. Evaluation of the role of hemolysis on assay precision in the clinical setting. Am J Clin Pathol. 2009;131(5):734-739. doi:10.1309/AJCPBVP4UGVVWAGG
16. Thygesen K, Alpert JS, Jaffe AS, et al. Fourth universal definition of myocardial infarction. J Am Coll Cardiol. 2018;72(18):2231-2264. doi:10.1016/j.jacc.2018.08.1038
17. Januzzi JL Jr, van Kimmenade R, Lainchbury J, et al. NT-proBNP testing for diagnosis and short-term prognosis in acute destabilized heart failure. J Am Coll Cardiol. 2006;47(11):2197-2203. doi:10.1016/j.jacc.2006.03.005
18. Ingelsson E, Schaefer EJ, Contois JH, et al. Clinical utility of different lipid measures for prediction of coronary heart disease. JAMA. 2007;298(7):776-785. doi:10.1001/jama.298.7.776
19. Rogacev KS, Cremers B, Zawada AM, et al. CD14++CD16+ monocytes independently predict cardiovascular events. J Am Coll Cardiol. 2012;60(16):1512-1520. doi:10.1016/j.jacc.2012.07.019
20. Rainen L, Oelmueller U, Jurgensen S, et al. Stabilization of mRNA expression in whole blood samples. Clin Chem. 2002;48(11):1883-1890.
21. Dignat-George F, Boulanger CM. The many faces of endothelial microparticles. Arterioscler Thromb Vasc Biol. 2011;31(1):27-33. doi:10.1161/ATVBAHA.110.218123
22. Frelinger AL 3rd, Furman MI, Linden MD, et al. Residual arachidonic acid-induced platelet activation. Circulation. 2006;113(25):2888-2896. doi:10.1161/CIRCULATIONAHA.105.596502
23. Théry C, Witwer KW, Aikawa E, et al. Minimal information for studies of extracellular vesicles 2018 (MISEV2018). J Extracell Vesicles. 2018;7(1):1535750. doi:10.1080/20013078.2018.1535750
24. Shah SH, Kraus WE, Newgard CB. Metabolomic profiling for the identification of novel biomarkers and mechanisms related to common cardiovascular diseases. Circulation. 2012;126(9):1110-1120. doi:10.1161/CIRCULATIONAHA.111.060368
25. Leon BM, Maddox TM. Diabetes and cardiovascular disease: epidemiology, biological mechanisms, treatment recommendations. Curr Probl Cardiol. 2015;40(1):11-41. doi:10.1016/j.cpcardiol.2014.09.003
26. Miller WG, Myers GL, Ashwood ER, et al. State of the art in trueness and interlaboratory harmonization for 10 analytes. Arch Pathol Lab Med. 2008;132(5):838-846. doi:10.5858/2008-132-838-SOTAIT