超越GWAS对抗高血压:微生物组和人工智能是精准医学的机遇

Cambridge prisms, Precision medicine Pub Date : 2023-05-16 eCollection Date: 2023-01-01 DOI:10.1017/pcm.2023.13
Sachin Aryal, Ishan Manandhar, Xue Mei, Beng S Yeoh, Ramakumar Tummala, Piu Saha, Islam Osman, Jasenka Zubcevic, David J Durgan, Matam Vijay-Kumar, Bina Joe
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引用次数: 0

摘要

导致人类死亡的最大因素是心血管疾病,而心血管疾病的首要风险因素是高血压(HTN)。在过去的二十年里,人们一直非常重视确定导致高血压的遗传因素。因此,超过 1,500 个遗传等位基因与人类高血压有关。利用高血压的遗传模型进行的图谱研究发现了数百个血压(BP)基因位点,但这些基因位点对血压的单独影响很小,这限制了在临床中针对这些基因位点进行研究的机会。收集全基因组关联数据的价值在正在进行的研究中显而易见,这些研究正开始利用这些数据,结合人工智能(AI)策略,在个体层面的遗传差异中开发出预测高血压的多基因风险评分(PRS)。然而,仅靠多基因风险评分可能不足以解释高血压的发病率和进展情况,因为遗传是导致高血压的原因之一。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Combating hypertension beyond genome-wide association studies: Microbiome and artificial intelligence as opportunities for precision medicine.

The single largest contributor to human mortality is cardiovascular disease, the top risk factor for which is hypertension (HTN). The last two decades have placed much emphasis on the identification of genetic factors contributing to HTN. As a result, over 1,500 genetic alleles have been associated with human HTN. Mapping studies using genetic models of HTN have yielded hundreds of blood pressure (BP) loci but their individual effects on BP are minor, which limits opportunities to target them in the clinic. The value of collecting genome-wide association data is evident in ongoing research, which is beginning to utilize these data at individual-level genetic disparities combined with artificial intelligence (AI) strategies to develop a polygenic risk score (PRS) for the prediction of HTN. However, PRS alone may or may not be sufficient to account for the incidence and progression of HTN because genetics is responsible for <30% of the risk factors influencing the etiology of HTN pathogenesis. Therefore, integrating data from other nongenetic factors influencing BP regulation will be important to enhance the power of PRS. One such factor is the composition of gut microbiota, which constitute a more recently discovered important contributor to HTN. Studies to-date have clearly demonstrated that the transition from normal BP homeostasis to a state of elevated BP is linked to compositional changes in gut microbiota and its interaction with the host. Here, we first document evidence from studies on gut dysbiosis in animal models and patients with HTN followed by a discussion on the prospects of using microbiota data to develop a metagenomic risk score (MRS) for HTN to be combined with PRS and a clinical risk score (CRS). Finally, we propose that integrating AI to learn from the combined PRS, MRS and CRS may further enhance predictive power for the susceptibility and progression of HTN.

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