a Translational Metabolic Laboratory, Department of Laboratory Medicine and Radboud Institute for Molecular Life Sciences , Radboud University Medical Center , Nijmegen , The Netherlands.
b Department of Microbiology and Systems Biology , TNO , Zeist , The Netherlands.
Expert Rev Proteomics. 2019 Feb;16(2):105-115. doi: 10.1080/14789450.2018.1551134. Epub 2018 Nov 27.
The onset of type 2 diabetes mellitus (T2DM) is strongly associated with obesity and subsequent perturbations in immuno-metabolic responses. To understand the complexity of these systemic changes and better monitor the health status of people at risk, validated clinical biomarkers are needed. Omics technologies are increasingly applied to measure the interplay of genes, proteins and metabolites in biological systems, which is imperative in understanding molecular mechanisms of disease and selecting the best possible molecular biomarkers for clinical use. Areas covered: This review describes the complex onset of T2DM, the contribution of obesity and adipose tissue inflammation to the T2DM disease mechanism, and the output of current biomarker strategies. A new biomarker approach is described that combines published and new self-generated data to merge multiple -omes (i.e. genome, proteome, metabolome etc.) toward understanding of mechanism of disease on the individual level and design multiparameter biomarker panels that drive significant impacts on personalized healthcare. Expert commentary: We here propose an approach to use cross-omics analyses to contextualize published biomarker data and better understand molecular mechanisms of health and disease. This will improve the current and future innovation gaps in translation of discovered putative biomarkers to clinically applicable biomarker tests.
2 型糖尿病(T2DM)的发病与肥胖密切相关,随后会出现免疫代谢反应的紊乱。为了了解这些系统变化的复杂性,并更好地监测处于危险中的人群的健康状况,需要经过验证的临床生物标志物。组学技术越来越多地用于测量生物系统中基因、蛋白质和代谢物的相互作用,这对于理解疾病的分子机制以及为临床应用选择最佳的分子生物标志物至关重要。
本综述描述了 T2DM 的复杂发病机制、肥胖和脂肪组织炎症对 T2DM 发病机制的贡献,以及当前生物标志物策略的结果。本文描述了一种新的生物标志物方法,该方法结合了已发表和新生成的自数据集,将多个组学(即基因组、蛋白质组、代谢组等)合并,以了解个体水平上疾病的发病机制,并设计多参数生物标志物组合,对个性化医疗产生重大影响。
我们在此提出一种使用跨组学分析的方法,使已发表的生物标志物数据具有背景相关性,并更好地理解健康和疾病的分子机制。这将改善当前和未来在将发现的假定生物标志物转化为临床适用的生物标志物检测方面的创新差距。