Thrombosis, Translational Medicine, and Biomarker Research: Moving the Needle

Abstract

Arterial and venous thromboembolism are leading causes of morbidity and death worldwide. Despite significant advances in the diagnosis, prognostication, and treatment of thrombotic diseases over the past 3 decades, the adoption of findings stemming from translational biomarker research in clinical practice remains limited. Biomarkers provide an opportunity to enhance our understanding of pathophysiological processes and optimize treatment strategies. They hold the promise of revolutionizing patient care. Still, this potential remains untapped, and several factors impede their use for near-patient applications. We sought to provide an overview of biomarker research in arterial and venous thromboembolic disease. We then aimed to discuss key barriers to the broader clinical implementation of biomarker research and highlight promising strategies to overcome them. We emphasize the merits of translational and implementation science to bridge the gaps from bench to bedside. Innovative trial design, data sharing, and collaborative efforts between academia and industry will be essential. Purposeful regression methodology using rational conceptual framework design, causal mediation analysis, and artificial intelligence might better leverage the use of observational data. Dedicated translational science training programs geared toward educating physicians on the appropriate measurement, interpretation, and integration of biomarker data in clinical practice should foster endorsement by frontline physicians. Finally, we make the case in support of a paradigm shift in cardiovascular medicine. Improved recognition of biomarker research and a greater emphasis on mechanistic evidence can better equip clinicians to deal with the uncertainty that defines the practice of thrombosis medicine.

Keywords: biomarkers; cardiovascular diseases; evidence‐based medicine; implementation science; thrombosis; translational science—biomedical.

Figure 1. Categories of biomarkers used in thrombosis.

Biomarkers may be classified exclusively within each of the categories (mechanistic, clinical disease, or therapeutic). Some biomarkers may fall within 2 or even 3 categories. For example, TGAs continuously register thrombin activity using a fluorogenic substrate and provide information on the kinetics and quantitative aspects of thrombin generation. TGA parameters have been used to document and track clinical disease status (peak thrombin generation and the risk of recurrent VTE among patients with cancer ⁸⁴ ), but they have also been used to evaluate the therapeutic efficacy of anticoagulation (eg, use of the endogenous thrombin potential as a surrogate endpoint in a randomized clinical trial comparing warfarin to rivaroxaban in patients with antiphospholipid antibody syndrome. Similarly, genetic thrombophilia testing (eg, factor V Leiden) may play an important role as a mechanistic marker, but it may also be used in the context of VTE diagnosis and as a marker for an increased risk of clinical disease. Troponins, recognized markers of myocardial injury, are routinely used for diagnosing myocardial infarction. Moreover, troponin measurement guides management in patients with pulmonary embolism through stratification of disease severity. Adapted from Mayr et al under the terms and conditions of the Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/). TGA indicates thrombin generation assay; and VTE, venous thromboembolism.

Figure 2. Phases of biomarker development from discovery to clinical implementation.

Construct validity refers to how well, conceptually speaking, a biomarker might capture the therapeutic effect or pathophysiological process of interest. Criterion validity refers to how well a biomarker correlates with or predicts the construct it is supposed to be measuring (ie, criterion variables, such as a gold‐standard definition of disease status). Predictive criterion validity specifically refers to criterion validity when applied toward the future measurement of criterion variables. ROC indicates receiver operating characteristic; and RCT, randomized controlled trial. Adapted from Vasan et al with permission. Copyright ©2006, Wolters‐Kluwer Health, Inc.

Figure 3. Biomarker measurement—a conceptual framework for direct and indirect effects.

Therapeutic interventions and the natural history of disease can both produce effects on a clinical end point. Some effects can be captured directly through measurement of a biomarker, whereas other effects will indirectly impact the clinical end point but will not be captured by the biomarker in question. Adapted from Zeger et al with permission. Copyright ©2001, John Wiley and Sons.

Figure 4. Conceptual model—relationship between biomarkers, surrogate end points and clinical end points in the context of therapeutic interventions.

Only a subset of biomarkers should achieve the status of “surrogate end point.” “Provisional evaluation” refers to the fact that biomarkers and surrogate endpoints should generally provide preliminary evidence in support of the efficacy and safety of a therapeutic intervention. Final regulatory approval decisions are most often based on trials evaluating a “hard” clinical outcome. Biomarkers measured as surrogate end points in clinical trials are often extended to clinical practice and applied toward the assessment of disease responses. Adapted from Zeger et al with permission. Copyright ©2001, John Wiley and Sons.

Figure 5. Thrombosis biomarker research and implementation—timeline.

Red markers indicate time points at which combined biomarkers were either identified as independent predictors of an outcome, or both incorporated in a risk prediction model. aB2‐GP1 indicates anti‐β‐2 glycoprotein I; aCL, anticardiolipin; ACR/EULAR, American College of Rheumatology/European League Against Rheumatism; ACS, acute coronary syndrome; aPL, antiphospholipid antibody; DIC, disseminated intravascular coagulation; DVT, deep vein thrombosis; ETP, endogenous thrombin potential; FDP, fibrin degradation product; FVIII, factor VIII; LA, lupus anticoagulant; PE, pulmonary embolism; PF 1+2, prothrombin fragment 1+2; PL, phospholipid; PT, prothrombin time; RAPS, Rivaroxaban versus warfarin to treat patients with thrombotic antiphospholipid syndrome, with or without systemic lupus erythematosus; RCT, randomized controlled trial; TAT, thrombin–antithrombin complex; TGA, thrombin generation assay; VDRL, Venereal Disease Research Laboratory test; VTE, venous thromboembolism; and VWF, von Willebrand factor.

Figure 6. Key biomarkers for use in research or clinical applications in thrombosis.

Dark blue cells indicate that the referenced studies support the question referred to by each corresponding columns, whereas light blue cells indicate a lack of evidence in support of the question. APLAS indicates antiphospholipid antibody syndrome; aPTT, activated partial thromboplastin time; INR, international normalized ratio; NET, neutrophil extracellular traps; PF 1+2, prothrombin fragment 1+2; PT, prothrombin time; ROTEM, rotational thromboelastometry; TAT, thrombin–antithrombin complex; TEG, thromboelastography; TF, tissue factor; TGA, thrombin generation assay; and VWF, von Willebrand factor;