JAMA ( IF 120.7 ) Pub Date : 2020-04-03 , DOI: 10.1001/jama.2020.2436 Jose F Figueroa 1, 2 , Austin B Frakt 1, 3, 4 , Ashish K Jha 1, 5
What determines health? For decades, researchers have attempted to quantify the exact contribution of social factors, such as income, education, race/ethnicity, and the community environment, to health. The motivation is simple: understanding which social factors most affect health would help prioritize societal investments in those areas. However, most efforts to precisely quantify the influence of individual social determinants of health have failed, largely because the causal pathways are numerous, interconnected, and complex. Most empirical evidence has found that social factors matter in aggregate, but quantifying their individual contributions is difficult. After decades of efforts in this area, it may be time for a new approach.
The challenges posed in studying social determinants of health today are similar to the challenges in studying human genetics not long ago. In 2001, many expected that sequencing the human genome would facilitate finding the causal links between individual genes and diseases. Some proponents assumed that once these links were identified, genetically customized therapies would surely follow. However, for most diseases, that has not occurred. Most efforts to identify gene-disease relationships have been limited by complexity, with all but a few diseases affected by multiple, interacting genes.
The field of genetics is now embracing a different approach, with use of the polygenic risk score. The basic notion is that the manifestation of certain diseases, such as cardiovascular disease and diabetes, is not mendelian but rather the aggregate result of multiple interacting genes. Derived from genome-wide association studies, an individual’s polygenic risk score for a disease is an aggregation of genetic risk that encompasses all known, relevant genetic factors for that disease.1 For example, no single gene “causes” coronary artery disease (CAD) but instead many genes are associated with the condition. The polygenic risk score estimates an individual’s likelihood of CAD, which can help guide clinical treatment and prevention decisions.
中文翻译:
解决健康的社会决定因素:进行多元社会风险评分的时间。
什么决定健康?几十年来,研究人员一直试图量化社会因素(例如收入,教育,种族/民族和社区环境)对健康的确切贡献。动机很简单:了解哪些社会因素对健康的影响最大,将有助于优先考虑那些领域的社会投资。但是,大多数精确量化各个社会健康决定因素影响的努力都以失败告终,这主要是因为因果途径众多,相互联系且复杂。大多数经验证据都发现,社会因素总体上很重要,但是很难量化其个人贡献。经过在这方面的数十年努力,也许是时候采用一种新方法了。
今天,研究健康的社会决定因素带来的挑战与不久前研究人类遗传学的挑战相似。在2001年,许多人期望对人类基因组进行测序将有助于发现单个基因与疾病之间的因果关系。一些支持者认为,一旦确定了这些联系,肯定会遵循基因定制疗法。但是,对于大多数疾病,这还没有发生。鉴定基因-疾病关系的大多数努力都受到复杂性的限制,除少数疾病外,所有疾病都受到多个相互作用基因的影响。
遗传学领域现在正在采用一种不同的方法,即利用多基因风险评分。基本概念是某些疾病(例如心血管疾病和糖尿病)的表现不是孟德尔,而是多种相互作用基因的总和。从全基因组关联研究得出的个体疾病的多基因风险评分是遗传风险的汇总,涵盖该疾病的所有已知相关遗传因素。1例如,没有单个基因“引起”冠状动脉疾病(CAD),而是许多基因与该病相关。多基因风险评分可估计个体发生CAD的可能性,这有助于指导临床治疗和预防决策。