VTT Technical Research Centre of Finland, Espoo, FI-02044 VTT, Finland.
Genome Med. 2009;1(11):83. doi: 10.1186/gm204. Epub 2010 Nov 15.
Because of the changes in demographic structure, the prevalence of Alzheimer's disease is expected to rise dramatically over the next decades. The progression of this degenerative and terminal disease is gradual, with the subclinical stage of illness believed to span several decades. Despite this, no therapy to prevent or cure Alzheimer's disease is currently available. Early disease detection is still important for delaying the onset of the disease with pharmacological treatment and/or lifestyle changes, assessing the efficacy of potential therapeutic agents, or monitoring disease progression more closely using medical imaging. Sensitive cerebrospinal-fluid-derived marker candidates exist, but given the invasiveness of sample collection their use in routine diagnostics may be limited. The pathogenesis of Alzheimer's disease is complex and poorly understood. There is thus a strong case for integrating information across multiple physiological levels, from molecular profiling (metabolomics, lipidomics, proteomics and transcriptomics) and brain imaging to cognitive assessments. To facilitate the integration of heterogeneous data, such as molecular and image data, sophisticated statistical approaches are needed to segment the image data and study their dependencies on molecular changes in the same individuals. Molecular profiling, combined with biophysical modeling of molecular assemblies associated with the disease, offer an opportunity to link the molecular pathway changes with cell- and tissue-level physiology and structure. Given that data acquired at different levels can carry complementary information about early Alzheimer's disease pathology, it is expected that their integration will improve early detection as well as our understanding of the disease.
由于人口结构的变化，预计在未来几十年里，阿尔茨海默病的患病率将大幅上升。这种退行性和终末期疾病的进展是渐进的，据信疾病的亚临床阶段跨越了几十年。尽管如此，目前还没有预防或治疗阿尔茨海默病的疗法。早期疾病检测仍然很重要，可以通过药物治疗和/或生活方式改变来延迟疾病的发作，评估潜在治疗药物的疗效，或使用医学成像更密切地监测疾病进展。存在敏感的脑脊液衍生标志物候选物，但鉴于样本采集的侵入性，其在常规诊断中的应用可能受到限制。阿尔茨海默病的发病机制复杂且了解甚少。因此，有充分的理由将信息整合到多个生理水平，从分子谱（代谢组学、脂质组学、蛋白质组学和转录组学）和脑成像到认知评估。为了促进分子和图像等异构数据的整合，需要复杂的统计方法来分割图像数据，并研究它们与同一个体中分子变化的依赖性。分子谱分析结合与疾病相关的分子组装的生物物理建模，提供了将分子途径变化与细胞和组织水平的生理学和结构联系起来的机会。鉴于在不同水平获得的数据可以携带有关早期阿尔茨海默病病理学的互补信息，预计它们的整合将改善早期检测以及我们对疾病的理解。
