Circulating biosignatures of late-life depression (LLD): Towards a comprehensive, data-driven approach to understanding LLD pathophysiology
Circulating biosignatures of late-life depression (LLD): Towards a comprehensive, data-driven approach to understanding LLD pathophysiology
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DOI：
10.1016/j.jpsychires.2016.07.006
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发表时间：
2016-11-01
影响因子：
4.8
通讯作者：
Butters, Meryl A.
中科院分区：
文献类型：
--
作者：
Diniz, Breno Satler;Lin, Chien-Wei;Butters, Meryl A.
关于晚年抑郁症（LLD）的病理生理过程的信息很少。我们的目的是通过一个多模式的生物标志物的方法，结合一个大的，公正的外周蛋白质组学面板和结构脑成像，以确定与LLD相关的神经生物学异常。我们检查了44名LLD和31名对照参与者的数据。使用多重免疫测定法进行血浆蛋白质组学分析。我们用随机截距模型评估了组间差异蛋白质表达。我们进行了富集途径分析（EPA），以揭示与LLD相关的生物途径和过程。将机器学习分析应用于组合数据集，以确定特定蛋白质可以正确区分LLD与对照参与者的准确性。61个蛋白质在LLD中差异表达（p < 0.05和FDR < 0.01）。EPA显示这些蛋白质与异常的免疫炎症控制、细胞存活和增殖、蛋白质稳态控制、脂质代谢、细胞内信号转导等有关。机器学习分析表明，一组三种蛋白质（C肽，FABP-肝脏，ApoA-IV）以100%的准确率区分LLD和对照参与者。LLD的血浆蛋白质组学特征揭示了维持细胞和全身水平稳态所必需的生物过程的失调。这些异常增加了大脑和全身的非稳态负荷，导致LLD的下游负面结果，包括医学合并症和痴呆的风险增加。LLD的外周生物特征具有预测能力，并可能为LLD的预防、治疗和神经保护提供新的推定治疗靶点。(C)2016爱思唯尔有限公司版权所有
There is scarce information about the pathophysiological processes underlying Late-Life Depression (LLD). We aimed to determine the neurobiological abnormalities related to LLD through a multi-modal biomarker approach combining a large, unbiased peripheral proteomic panel and structural brain imaging. We examined data from 44 LLD and 31 control participants. Plasma proteomic analysis was performed using a multiplex immunoassay. We evaluated the differential protein expression between groups with random intercept models. We carried out enrichment pathway analyses (EPA) to uncover biological pathways and processes related to LLD. Machine learning analysis was applied to the combined dataset to determine the accuracy with which specific proteins could correctly discriminate LLD versus control participants. Sixty-one proteins were differentially expressed in LLD (p < 0.05 and FDR < 0.01). EPA showed that these proteins were related to abnormal immune-inflammatory control, cell survival and proliferation, proteostasis control, lipid metabolism, intracellular signaling. Machine learning analysis showed that a panel of three proteins (C-peptide, FABP-liver, ApoA-IV) discriminated LLD and control participants with 100% accuracy. The plasma proteomic profile in LLD revealed dysregulation in biological processes essential to the maintenance of homeostasis at cellular and systemic levels. These abnormalities increase brain and systemic allostatic load leading to the downstream negative outcomes of LLD, including increased risk of medical comorbidities and dementia. The peripheral biosignature of LLD has predictive power and may suggest novel putative therapeutic targets for prevention, treatment, and neuroprotection in LLD. (C) 2016 Elsevier Ltd. All rights reserved.