Karl Landsteiner Institute for Health Promotion Research, 3062, Kirchstetten, Austria.
Academy for Ageing Research, House of Mercy, 1160, Vienna, Austria.
Wien Klin Wochenschr. 2023 Mar;135(5-6):113-124. doi: 10.1007/s00508-022-02146-4. Epub 2023 Jan 30.
Obesity is a multifactorial chronic disease that cannot be addressed by simply promoting better diets and more physical activity. To date, not a single country has successfully been able to curb the accumulating burden of obesity. One explanation for the lack of progress is that lifestyle intervention programs are traditionally implemented without a comprehensive evaluation of an individual's diagnostic biomarkers. Evidence from genome-wide association studies highlight the importance of genetic and epigenetic factors in the development of obesity and how they in turn affect the transcriptome, metabolites, microbiomes, and proteomes.
The purpose of this review is to provide an overview of the different types of omics data: genomics, epigenomics, transcriptomics, proteomics, metabolomics and illustrate how a multi-omics approach can be fundamental for the implementation of precision obesity management.
The different types of omics designs are grouped into two categories, the genotype approach and the phenotype approach. When applied to obesity prevention and management, each omics type could potentially help to detect specific biomarkers in people with risk profiles and guide healthcare professionals and decision makers in developing individualized treatment plans according to the needs of the individual before the onset of obesity.
Integrating multi-omics approaches will enable a paradigm shift from the one size fits all approach towards precision obesity management, i.e. (1) precision prevention of the onset of obesity, (2) precision medicine and tailored treatment of obesity, and (3) precision risk reduction and prevention of secondary diseases related to obesity.
肥胖是一种多因素的慢性疾病,单纯通过促进更好的饮食和更多的身体活动是无法解决的。迄今为止,没有一个国家能够成功遏制肥胖负担的不断增加。缺乏进展的一个解释是,生活方式干预计划传统上是在没有全面评估个体诊断生物标志物的情况下实施的。全基因组关联研究的证据强调了遗传和表观遗传因素在肥胖发展中的重要性,以及它们如何反过来影响转录组、代谢物、微生物组和蛋白质组。
本综述的目的是概述不同类型的组学数据:基因组学、表观基因组学、转录组学、蛋白质组学、代谢组学,并说明多组学方法如何成为实施精准肥胖管理的基础。
不同类型的组学设计分为两类,即基因型方法和表型方法。当应用于肥胖的预防和管理时,每种组学类型都有可能帮助检测具有风险特征的人群中的特定生物标志物,并根据个体的需求为医疗保健专业人员和决策者制定个体化的治疗计划,以在肥胖发生之前。
整合多组学方法将使我们从一刀切的方法转变为精准肥胖管理,即(1)精准预防肥胖的发生,(2)精准医学和个体化肥胖治疗,以及(3)精准降低肥胖相关的继发性疾病风险。