STM封面故事:如何在心脏病发生之前预见风险
2017/07/17
一项研究表明,研究人员已经开发出一种新的非侵入性的方法,可以在人的动脉探测到炎症,并可能避免心脏病严重到不能治疗之前才被发现。相关文章于7月12日发表在Science Translational Medicine上,并作为本期封面刊登。


一项研究表明,研究人员已经开发出一种新的非侵入性的方法,可以在人的动脉探测到炎症,并可能避免心脏病严重到不能治疗之前才被发现。相关文章于7月12日发表在Science Translational Medicine上,并作为本期封面刊登。

扫描冠状动脉狭窄无法预测心脏病

几十年来,医生们一直依赖CT扫描和造影来检测冠状动脉疾病——心脏病发作的主要原因。这些测试的重点是寻找由于胆固醇和其它被称为斑块的物质收缩而缩小的血管,从而限制血液流向心脏。但它们远非完美。通常情况下,冠状动脉狭窄的患者只有在病情严重时才能发现。动脉狭窄也不是心脏病发作的信号。

相反,当炎症在动脉中引发堵塞、导致心脏病发作,就成了真正的罪魁祸首。研究人员之一,牛津大学的心血管医学教授Keith Channon在文章发表在Science Translational Medicine之前,在电话会议上对记者说:“到目前为止,还没有办法检测出冠状动脉的炎症,这就是我们的研究成果创新的地方。”

CT脂肪衰减指数可以扫描血管炎症迹象

这个新研发出来的检测过程通过分析动脉周围脂肪组织的变化来工作。研究人员说,当脂肪靠近发炎的动脉时,这种脂肪会变得更稀,脂肪更少、水分含量更高。利用CT脂肪衰减指数(FAI),研究人员发现了现有CT扫描中的炎症迹象


血管周围的FAI使用冠状动脉周围脂肪组织的CT成像来评估脂肪细胞的大小和脂肪含量。更大、更成熟的脂肪细胞表现出更大的脂质积累,这与FAI呈负相关。炎症减少脂质积累和减缓脂肪细胞分化。心肌梗死后的不稳定斑块比稳定斑块有较大的血管周围的FAI值,直接与发炎的冠状动脉相邻FAI值是最大的。血管周围FAI检测可能是一种有用的、无创的监测血管炎症和冠状动脉疾病发展的方法。

这项指标即能表征出稳定的冠状动脉粥样硬化斑块,或者是最近心脏病发作过的患者中破裂的斑块。他们还发现,他们可以随着时间的推移跟踪血管周围脂肪的变化,从而使医生能够发现早期疾病征兆,用降低胆固醇的他汀类药物这样的干预措施就可以预防。

在研究人员能够掌握这种方法很好地预测未来的心脏病发作和挽救生命之前,还需要进行更多的研究。通讯作者,牛津大学心血管医学的副教授Charalambos Antoniades说:“如果这能在更大规模的研究中证实,那么将在标准的CT血管造影之外,提供一个额外的选择。我很乐观并且相信它能够预测未来的事件。然而,我们需要等待研究的完成。”据他介绍进一步的研究结果预计将在今年年底公布。

参考资料

Detecting human coronary inflammation by imaging perivascular fat

New way to see artery damage before heart disease sets in

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  • Detecting human coronary inflammation by imaging perivascular fat

    Early detection of vascular inflammation would allow deployment of targeted strategies for the prevention or treatment of multiple disease states. Because vascular inflammation is not detectable with commonly used imaging modalities, we hypothesized that phenotypic changes in perivascular adipose tissue (PVAT) induced by vascular inflammation could be quantified using a new computerized tomography (CT) angiography methodology. We show that inflamed human vessels release cytokines that prevent lipid accumulation in PVAT-derived preadipocytes in vitro, ex vivo, and in vivo. We developed a three-dimensional PVAT analysis method and studied CT images of human adipose tissue explants from 453 patients undergoing cardiac surgery, relating the ex vivo images with in vivo CT scan information on the biology of the explants. We developed an imaging metric, the CT fat attenuation index (FAI), that describes adipocyte lipid content and size. The FAI has excellent sensitivity and specificity for detecting tissue inflammation as assessed by tissue uptake of 18F-fluorodeoxyglucose in positron emission tomography. In a validation cohort of 273 subjects, the FAI gradient around human coronary arteries identified early subclinical coronary artery disease in vivo, as well as detected dynamic changes of PVAT in response to variations of vascular inflammation, and inflamed, vulnerable atherosclerotic plaques during acute coronary syndromes. Our study revealed that human vessels exert paracrine effects on the surrounding PVAT, affecting local intracellular lipid accumulation in preadipocytes, which can be monitored using a CT imaging approach. This methodology can be implemented in clinical practice to noninvasively detect plaque instability in the human coronary vasculature.

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