Combining biological and non-biological markers to develop a model predictive of treatment response for individuals with depression

该学生进行了一项研究，调查炎症生物标志物和临床标志物是否能预测抑郁症和焦虑症心理治疗的治疗反应，这是他们MRes轮转项目的一部分。研究结果表明，较高的儿童创伤率和精神药物的使用预示着对焦虑和抑郁的治疗无效。因此，该研究得出结论，需要进一步的研究，以确定治疗无反应风险较高的患者，并先发制人地将他们分配到不同或更强烈的治疗中。两位导师在转化研究方面都有丰富的专业知识，可以更广泛地研究抑郁症和情感障碍。更具体地说，Cleare教授进行了研究，检查了反应的临床预测因素，特别是治疗耐药性或严重程度的标志物（Fekadu et al., 2009）和临床结果的各种生物标志物预测因素。这些包括炎症蛋白（Strawbridge等人，2015;2019）、生长因子（Pisoni等人，2018）和遗传多态性（Fischer等人，2018）。Cleare教授和Juruena博士都研究了患者的童年创伤史和预测抑郁功能缓解未来严重程度的下丘脑-垂体-肾上腺（HPA）轴生物标志物（例如Fischer等人，2018；Tunnard等人，2014；Juruena等人，2009;2010;2013）。这些信息也可以被博士研究中心用于博士培训补助金的电子提交。该项目的第一部分包括对已发表的研究进行系统回顾（如果合适的话，还包括随机效应荟萃分析），这些研究招募了接受心理治疗的个体样本，并结合心理和药物治疗抑郁症，测量治疗前潜在的非生物学（临床、人口统计学或社会心理）和生物学（如炎症和内分泌生物标志物）对后续治疗反应的预测。最近，全面的系统综述研究了抗抑郁药治疗反应的生物和非生物预测因子（Perlman等人，2019）和心理治疗反应的生物标志物预测因子（Cristea等人，2019）。这些结论表明，总体而言，反应预测因素并非针对评估的个别治疗，可能代表抑郁症预后不良的一般危险因素。然而，心理治疗是一个相对被忽视的领域，最近没有评论评估心理治疗反应的临床预测因素。将这个系统的回顾作为项目的第一部分，将允许学生确定哪些因素最有可能在预测模型开发阶段进行测试。整个审查过程分配了一年的时间，包括文献范围，最后确定审查问题和具体方法(10月至12月；系统搜索和筛选纳入研究（1月至2月），最终确定纳入研究的资格（3月至4月），提取数据为审查结果做准备并进行质量评估（5月至6月），完成结果（包括任何数据分析；7月至8月），解释结果的含义并撰写论文发表（9月至10月）。在第一年，学生还将参加由生物统计部门组织的各种讲习班，这些讲习班将发展他们在纵向预测建模和统计规划方面的技能和理解（见下文；培训）。第二年将着重于：(1)因应上述检讨的结果，发展模型；(2)帮助完成第二队列（LQD研究）的数据收集；(3)为分析准备所有数据。

The student conducted a study investigating whether inflammatory biomarkers and clinical markers would predict treatment response to psychological therapy in depression and anxiety as part of their MRes rotation project. The study's findings suggested that higher rates of both childhood trauma and use of psychotropic medications were predictive of treatment non-response for both anxiety and depression. Thus, the study concluded that further research is needed in order to identify patients who are at higher risk for treatment non-response and pre-emptively allocate them to different or more intense treatments. Both supervisors have substantial expertise in translational research into major depressive disorder and affective disorders more broadly. More specifically, Professor Cleare has conducted research examining clinical predictors of response, particularly markers of treatment resistance or severity (Fekadu et al., 2009) and various biomarker predictors of clinical outcomes. These include inflammatory proteins (Strawbridge et al., 2015; 2019), growth factors (Pisoni et al., 2018) and genetic polymorphisms (Fischer et al., 2018). Both Professor Cleare and Dr Juruena have examined patients' history of childhood trauma and biomarkers of hypothalamic-pituitary-adrenal (HPA) axis predicting future severity of remission of depression function (e.g. Fischer et al., 2018; Tunnard et al., 2014; Juruena et al., 2009; 2010; 2013). This information may also be used by the Centre for Doctoral Studies for the electronic submission of Doctoral Training Grants. The first part of the project constitutes a systematic review (and if appropriate, with random-effects meta-analysis) of published studies recruiting a sample of individuals undertaking a psychological therapy and combined psychological and pharmacological treatments for depression which measure potential non-biological (clinical, demographic or psychosocial) and biological (e.g. inflammatory and endocrine biomarkers) pre-treatment predictors of subsequent therapy response. Recently, comprehensive systematic reviews have examined biological and non-biological predictors of antidepressant treatment response (Perlman et al., 2019) and biomarker predictors of psychological therapy response (Cristea et al., 2019). These conclude that response predictors are, overall, not specific to individual treatments assessed and may represent generalised risk factors for poor depression prognosis. However, psychological therapies are an area of relative neglect and no recent review has assessed clinical predictors of psychotherapy response. Undertaking this systematic review as the first part of the project will allow the student to determine which factors have the most potential to be tested during a predictive model development stage. One year is allocated for the entire review process, which covers literature scoping, finalising the review question and specific methodology (October - December; protocol to be registered by January), running the systematic search and screening studies for inclusion (January - February), finalising the eligibility of studies for inclusion (March - April), extracting data in preparation for review results and undertaking quality assessment (May - June), completing results (including any data analyses; July - August), interpreting the meaning of results and writing up a manuscript for publication (September - October). During the first year the student will also attend various workshops organised by the biostatistics department which will develop their skills and understanding in longitudinal prediction modelling and statistical programming (see below section; Training). The second year will be focused on: (1) model development, in response to the results of the above review; (2) contributing to the completion of data collection for the second cohort (the LQD study); and (3) preparing all data for analyses.