Dunster Joanne L, Wright Joy R, Samani Nilesh J, Goodall Alison H
School of Biological Sciences, Institute for Cardiovascular and Metabolic Research, Reading, United Kingdom.
Department of Cardiovascular Sciences, University of Leicester & NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom.
Front Cardiovasc Med. 2022 Jun 30;9:919394. doi: 10.3389/fcvm.2022.919394. eCollection 2022.
Advancing understanding of key factors that determine the magnitude of the hemostatic response may facilitate the identification of individuals at risk of generating an occlusive thrombus as a result of an atherothrombotic event such as an acute Myocardial Infarction (MI). While fibrinogen levels are a recognized risk factor for MI, the association of thrombotic risk with other coagulation proteins is inconsistent. This is likely due to the complex balance of pro- and anticoagulant factors in any individual.
We compared measured levels of pro- and anticoagulant proteins in plasma from 162 patients who suffered an MI at an early age (MI <50 y) and 186 age- and gender-matched healthy controls with no history of CAD. We then used the measurements from these individuals as inputs for an established mathematical model to investigate how small variations in hemostatic factors affect the overall amplitude of the hemostatic response and to identify differential key drivers of the hemostatic response in male and female patients and controls.
Plasma from the MI patients contained significantly higher levels of Tissue Factor ( = 0.007), the components of the tenase (FIX and FVIII; < 0.0001 for both) and the prothrombinase complexes (FX; = 0.003), and lower levels of Tissue Factor Pathway Inhibitor (TFPI; = 0.033) than controls. The mathematical model, which generates time-dependent predictions describing the depletion, activation, and interaction of the main procoagulant factors and inhibitors, identified different patterns of hemostatic response between MI patients and controls, and additionally, between males and females. Whereas, in males, TF, FVIII, FIX, and the inhibitor TFPI contribute to the differences seen between case and controls, and in females, FII, FVIII, and FIX had the greatest influence on the generation of thrombin. We additionally show that further donor stratification may be possible according to the predicted donor response to anticoagulant therapy.
We suggest that modeling could be of value in enhancing our prediction of risk of premature MI, recurrent risk, and therapeutic efficacy.
深入了解决定止血反应强度的关键因素，可能有助于识别因动脉粥样硬化血栓形成事件（如急性心肌梗死（MI））而有形成闭塞性血栓风险的个体。虽然纤维蛋白原水平是公认的心肌梗死风险因素，但血栓形成风险与其他凝血蛋白的关联并不一致。这可能是由于任何个体中促凝血和抗凝血因子的复杂平衡所致。
我们比较了162例早年发生心肌梗死（心肌梗死<50岁）患者和186例年龄和性别匹配、无冠心病病史的健康对照者血浆中促凝血和抗凝血蛋白的测量水平。然后，我们将这些个体的测量结果作为输入，用于一个既定的数学模型，以研究止血因子的微小变化如何影响止血反应的总体幅度，并识别男性和女性患者及对照者止血反应的差异关键驱动因素。
与对照组相比，心肌梗死患者血浆中组织因子水平显著升高（P = 0.007），凝血酶原酶复合物（FIX和FVIII；两者P均<0.0001）和凝血酶原酶复合物（FX；P = 0.003）的成分水平升高，而组织因子途径抑制物（TFPI）水平降低（P = 0.033）。该数学模型生成描述主要促凝血因子和抑制剂的消耗、激活及相互作用的时间依赖性预测，确定了心肌梗死患者与对照组之间以及男性和女性之间不同的止血反应模式。在男性中，TF、FVIII、FIX和抑制剂TFPI导致病例组与对照组之间的差异，而在女性中，FII、FVIII和FIX对凝血酶的生成影响最大。我们还表明，根据预测的供体对抗凝治疗的反应，进一步进行供体分层可能是可行的。
我们认为，建模可能有助于提高我们对过早发生心肌梗死风险、复发风险和治疗效果的预测。
