Jani Bhautesh D, McLean Gary, Nicholl Barbara I, Barry Sarah J E, Sattar Naveed, Mair Frances S, Cavanagh Jonathan
General Practice and Primary Care, Institute of Health and Wellbeing, College of Medical, Veterinary and Life Sciences, University of Glasgow Glasgow, UK.
Robertson Centre for Biostatistics, Institute of Health and Well Being, College of Medical, Veterinary and Life Sciences, University of Glasgow Glasgow, UK.
Front Hum Neurosci. 2015 Feb 2;9:18. doi: 10.3389/fnhum.2015.00018. eCollection 2015.
Depression is one of the major global health challenges and a leading contributor of health related disability and costs. Depression is a heterogeneous disorder and current methods for assessing its severity in clinical practice rely on symptom count, however this approach is unreliable and inconsistent. The clinical evaluation of depressive symptoms is particularly challenging in primary care, where the majority of patients with depression are managed, due to the presence of co-morbidities. Current methods for risk assessment of depression do not accurately predict treatment response or clinical outcomes. Several biological pathways have been implicated in the pathophysiology of depression; however, accurate and predictive biomarkers remain elusive. We conducted a systematic review of the published evidence supporting the use of peripheral biomarkers to predict outcomes in depression, using Medline and Embase. Peripheral biomarkers in depression were found to be statistically significant predictors of mental health outcomes such as treatment response, poor outcome and symptom remission; and physical health outcomes such as increased incidence of cardiovascular events and deaths, and all-cause mortality. However, the available evidence has multiple methodological limitations which must be overcome to make any real clinical progress. Despite extensive research on the relationship of depression with peripheral biomarkers, its translational application in practice remains uncertain. In future, peripheral biomarkers identified with novel techniques and combining multiple biomarkers may have a potential role in depression risk assessment but further research is needed in this area.
抑郁症是全球主要的健康挑战之一,也是导致与健康相关的残疾和成本的主要因素。抑郁症是一种异质性疾病,目前临床实践中评估其严重程度的方法依赖于症状计数,但这种方法不可靠且不一致。在初级保健中,由于合并症的存在,对抑郁症状进行临床评估尤其具有挑战性,而大多数抑郁症患者在初级保健机构接受治疗。目前抑郁症风险评估方法无法准确预测治疗反应或临床结果。抑郁症的病理生理学涉及多种生物学途径;然而,准确且具有预测性的生物标志物仍然难以捉摸。我们使用Medline和Embase对已发表的支持使用外周生物标志物预测抑郁症预后的证据进行了系统综述。研究发现,抑郁症中的外周生物标志物是心理健康结果(如治疗反应、不良结局和症状缓解)以及身体健康结果(如心血管事件和死亡发生率增加以及全因死亡率)的统计学显著预测指标。然而,现有证据存在多个方法学局限性,必须克服这些局限性才能取得真正的临床进展。尽管对抑郁症与外周生物标志物的关系进行了广泛研究,但其在实践中的转化应用仍不确定。未来,通过新技术识别并结合多种生物标志物的外周生物标志物可能在抑郁症风险评估中发挥潜在作用,但该领域还需要进一步研究。