University of Toronto, Ontario, Canada.
Curr Pharm Des. 2012;18(36):5976-89. doi: 10.2174/138161212803523635.
Identifying biological and clinical markers of treatment response in depression is an area of intense research that holds promise for increasing the efficiency and efficacy of resolving a major depressive episode and preventing future episodes. Collateral benefits include decreased healthcare costs and increased workplace productivity. Despite research advances in many areas, efforts to identify biomarkers have not revealed any consistently validated candidates. Studies of clinical characteristics, genetic, neuroimaging, and various biochemical markers have all shown promise in discrete studies, but these findings have not translated into a personalized medicine approach to treating individual patients in the clinic. We propose that an integrated study of a range of biomarker candidates from across different modalities is required. Furthermore, advanced mathematical modeling and pattern recognition methods are required to detect important biological signatures associated with treatment outcome. Through an informatics-based integration of the various clinical, molecular and imaging parameters that are known to be important in the pathophysiology of depression, it becomes possible to encompass the complexity of contributing factors and phenotypic presentations of depression, and identify the key signatures of treatment response.
识别抑郁症治疗反应的生物学和临床标志物是一个研究热点，有望提高解决重度抑郁发作和预防未来发作的效率和效果。附带的好处包括降低医疗成本和提高工作场所的生产力。尽管在许多领域都取得了研究进展，但识别生物标志物的努力并未发现任何经过一致验证的候选标志物。对临床特征、遗传、神经影像学和各种生化标志物的研究在离散研究中都显示出了希望，但这些发现并未转化为针对个体患者的个性化医疗方法。我们提出，需要对来自不同模式的一系列候选生物标志物进行综合研究。此外，还需要先进的数学建模和模式识别方法来检测与治疗结果相关的重要生物学特征。通过基于信息学的方法整合已知在抑郁症病理生理学中很重要的各种临床、分子和成像参数，就有可能包含导致抑郁症的复杂因素和表现形式，并确定治疗反应的关键特征。
