Large-scale integrative studies of risk factors in coronary heart disease: from discovery to application

如果医学研究能够可靠地证明特定因素与心脏病相关，那么这可能对疾病的预测和预防具有重要影响，例如，现在测量和修改血液低密度脂蛋白(坏的)-胆固醇水平就是这样。不幸的是，试图使用传统方法来识别与心脏病有因果关系的风险因素的尝试往往会在新药试验中导致代价高昂的失败。同样，通过对传统方法的渐进修改，心脏病的预测只取得了适度的改善。我们的研究计划通过将分子测量的精确度与大规模人口健康研究的能力相结合，提供了一种解决这些问题的全新方法。基因和生化测量技术的尖端技术与对心脏病的极大和成熟的生物医学调查(“流行病学研究”)的深度结合将产生前所未有的力量和细节的研究。特别是，对于50,000名心脏病患者和50,000名对照组，我们已经为每个参与者记录了关于以下方面的广泛细节：-遗传构成(例如，多达100万个基因变异或“字母”)-血液生化(例如，多达数百个分析物)-生活方式和其他习惯(例如，饮食、体育活动、吸烟和饮酒)。在这些参与者中信息最丰富的子集，我们将进行最先进的测量，以补充这些广泛的现有信息。被称为“脂质组学”和“代谢组学”的血液测量方法的一个关键优势应该是，尽管它们与可能与心脏病发作的原因相关的生化过程有关(即分别是脂肪和糖的代谢)，但它们撒下了广泛的科学网。这应该避免过早地假设可能出现的与心脏病相关的确切因素的身份。此外，由于我们的一项研究(我们与一项新发2型糖尿病研究共享)中的15,000名“对照”参与者的“脂质组学”和“代谢组学”分析已经获得资金，我们将通过在新发心脏病患者中同时进行相同的分析来实现重大的科学协同和成本节约。为了帮助以严格和原则性的方式从我们的研究中获得复杂和丰富的数据，我们的团队包括生物统计学领域的世界领先者。我们将建立在我们以前开发的创新方法的基础上，帮助区分心脏病的因果因素和非因果因素。其目标将是发现心脏病的全新原因，并评估各种已被怀疑为心脏病的因素，如血脂、糖代谢、“炎症”(身体对伤害的反应)和营养因素。我们将使用相同的数据库(辅以额外的信息来源)来开发和测试一系列有可能改进对首发心脏病预测的方法，例如：-最大限度地提高对心脏病预测的准确性，例如包含遗传和生化信息的详细分数，这可能与考虑终身预防治疗的年轻人或有心脏病家族史的人特别相关-提高医疗服务的效率，例如，“序贯”筛查，最初只是对每个人进行相对简单的测试，然后将更昂贵和更详细的测量集中在最初被确定为最有可能从进一步评估中受益的个人。

If medical studies can reliably demonstrate that a particular factor is relevant to heart disease, then this could have important implications for the prediction and prevention of disease, as, for example, is now the case with measurement and modification of blood LDL("bad")-cholesterol levels.Unfortunately, attempts using conventional approaches to identify risk factors that have cause-and-effect relationships with heart disease have often yielded costly failures in drug trials of new medicines. Similarly, only modest improvements have been achieved in the prediction of heart disease with incremental modifications to conventional approaches.Our research plan offers a fundamentally new approach to address these problems by combining the precision of molecular measurements with the power of large-scale population health studies. The deep integration of cutting-edge technologies for genetic and biochemical measurement technologies with extremely large and mature biomedical surveys ("epidemiological studies") of heart disease will yield studies that combine unprecedented power and detail. In particular, for 50,000 people with heart attacks and 50,000 controls, we have for each participant, already recorded extensive detail about: -genetic make-up (eg, up to one million genetic variants or "letters")-blood biochemistry (eg, up to a few hundred analytes)-lifestyle and other habits (eg, diet, physical activity, tobacco and alcohol consumption).In the most informative subsets of these participants, we will conduct state-of-the-art measurements to supplement this extensive existing information. A key advantage of blood measurement methods called "lipidomics" and "metabonomics" should be that, although they relate to biochemical processes likely to be relevant to the causation of heart attacks (ie, fat and sugar metabolism, respectively), they cast wide scientific nets. This should avoid premature assumptions about the identity of the precise factors that might emerge to be relevant to heart disease. Furthermore, as funding has already been awarded for "lipidomics" and "metabonomics" assays in 15,000 "control" participants in one of our studies (which we share with a study of new-onset type 2 diabetes), we will achieve major scientific synergy and cost savings by conducting concurrently the same assays in patients with new onset heart disease.To help harvest the complex and rich data that will emerge from our studies in a rigorous and principled manner, our team includes world leaders in biostatistics. We will build on innovative approaches that we have previously developed to help distinguish causal from non-causal factors in heart disease. The objective will be to discover entirely new causes of heart disease as well as to evaluate a variety of factors already suspected in heart disease, such as blood fats, sugar metabolism, "inflammation" (which is the body's response to injury), and nutritional factors. We will use the same databases (supplemented by additional sources of information) to develop and test a range of approaches that have potential to improve the prediction of first-onset heart disease, such as those that: -maximise accuracy of the prediction of heart disease, such as detailed scores containing genetic and biochemical information, which may be especially relevant to young people contemplating a lifetime of preventive therapy or to people with a strong family history of heart disease-promote efficiency for health services, eg, "sequential" screening, which initially involves comparatively simple tests for everyone, then focusing more costly and detailed measurements on individuals initially identified as being most likely to benefit from further assessment.