Diniz Breno Satler, Lin Chien-Wei, Sibille Etienne, Tseng George, Lotrich Francis, Aizenstein Howard J, Reynolds Charles F, Butters Meryl A
Department of Psychiatry & Behavioral Sciences, University of Texas Health Science Center at Houston, Houston, TX, USA.
Department of Biostatistics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA.
J Psychiatr Res. 2016 Nov;82:1-7. doi: 10.1016/j.jpsychires.2016.07.006. Epub 2016 Jul 11.
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.
关于老年期抑郁症（LLD）潜在的病理生理过程的信息匮乏。我们旨在通过结合大型、无偏倚的外周蛋白质组学面板和脑结构成像的多模态生物标志物方法，确定与LLD相关的神经生物学异常。我们检查了44名LLD患者和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分析。我们用随机截距模型评估组间蛋白质表达差异。我们进行了富集通路分析（EPA）以揭示与LLD相关的生物学通路和过程。将机器学习分析应用于合并数据集，以确定特定蛋白质正确区分LLD患者与对照参与者的准确性。61种蛋白质在LLD中差异表达（p < 0.05且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、细胞内信号传导有关。机器学习分析表明，由三种蛋白质（C肽、肝脏型脂肪酸结合蛋白、载脂蛋白A-IV）组成的一组能够100%准确地区分LLD患者和对照参与者。LLD患者的血浆蛋白质组图谱显示，在细胞和全身水平维持内环境稳态所必需的生物学过程中存在失调。这些异常增加了大脑和全身的应激负荷，导致LLD的下游负面结果，包括增加患医学合并症和痴呆症的风险。LLD的外周生物标志物具有预测能力，可能为LLD的预防、治疗和神经保护提示新的潜在治疗靶点。
