Department of Biology I, Genetics, Ludwig-Maximilians University Munich, Biocenter, Munich, Germany.
Diabetes Metab Syndr Obes. 2012;5:247-82. doi: 10.2147/DMSO.S32923. Epub 2012 Aug 7.
Biomarkers are of tremendous importance for the prediction, diagnosis, and observation of the therapeutic success of common complex multifactorial metabolic diseases, such as type II diabetes and obesity. However, the predictive power of the traditional biomarkers used (eg, plasma metabolites and cytokines, body parameters) is apparently not sufficient for reliable monitoring of stage-dependent pathogenesis starting with the healthy state via its initiation and development to the established disease and further progression to late clinical outcomes. Moreover, the elucidation of putative considerable differences in the underlying pathogenetic pathways (eg, related to cellular/tissue origin, epigenetic and environmental effects) within the patient population and, consequently, the differentiation between individual options for disease prevention and therapy - hallmarks of personalized medicine - plays only a minor role in the traditional biomarker concept of metabolic diseases. In contrast, multidimensional and interdependent patterns of genetic, epigenetic, and phenotypic markers presumably will add a novel quality to predictive values, provided they can be followed routinely along the complete individual disease pathway with sufficient precision. These requirements may be fulfilled by small membrane vesicles, which are so-called exosomes and microvesicles (EMVs) that are released via two distinct molecular mechanisms from a wide variety of tissue and blood cells into the circulation in response to normal and stress/pathogenic conditions and are equipped with a multitude of transmembrane, soluble and glycosylphosphatidylinositol-anchored proteins, mRNAs, and microRNAs. Based on the currently available data, EMVs seem to reflect the diverse functional and dysfunctional states of the releasing cells and tissues along the complete individual pathogenetic pathways underlying metabolic diseases. A critical step in further validation of EMVs as biomarkers will rely on the identification of unequivocal correlations between critical disease states and specific EMV signatures, which in future may be determined in rapid and convenient fashion using nanoparticle-driven biosensors.
生物标志物对于预测、诊断和观察常见复杂多因素代谢疾病（如 2 型糖尿病和肥胖症）的治疗成功具有重要意义。然而，传统使用的生物标志物（如血浆代谢物和细胞因子、身体参数）的预测能力显然不足以可靠地监测从健康状态开始的阶段性发病机制，通过其启动和发展到既定疾病，再进一步发展到晚期临床结局。此外，在患者人群中，阐明潜在的发病机制途径（例如，与细胞/组织起源、表观遗传和环境影响相关）存在相当大的差异，并且，在疾病预防和治疗的个体化选择方面做出区分——这是个性化医学的标志——在代谢疾病的传统生物标志物概念中只起次要作用。相比之下，遗传、表观遗传和表型标志物的多维和相互依存模式可能会为预测值增加新的质量，前提是它们可以沿着完整的个体疾病途径以足够的精度进行常规跟踪。这些要求可以通过小膜泡来满足，这些小膜泡被称为外泌体和微泡（EMVs），它们通过两种不同的分子机制从各种组织和血细胞中释放到循环中，以响应正常和应激/病理条件，并配备了多种跨膜、可溶性和糖基磷脂酰肌醇锚定蛋白、mRNA 和 microRNAs。基于目前可用的数据，EMVs 似乎反映了代谢疾病相关的完整个体发病途径中释放细胞和组织的多样化功能和功能障碍状态。进一步验证 EMVs 作为生物标志物的关键步骤将依赖于确定关键疾病状态与特定 EMV 特征之间的明确相关性，未来可能使用纳米颗粒驱动的生物传感器以快速方便的方式确定这些相关性。
