Testing an Imaging Biomarker for Treatment Stratification in Major Depression

描述（由申请人提供）：重度抑郁症（MDD）的一线药物治疗显示缓解率低于40%。对于许多患者来说，由于持续的痛苦、自杀风险、生产力损失、浪费资源和与2-3个月无效策略相关的潜在神经影响，“错误”治疗具有显著的个人和社会成本。虽然治疗对某些人非常有效，但没有可靠的方法将患者与最佳选择相匹配。因此，长期目标是开发临床上可行的算法，该算法选择最佳治疗并避免无效治疗，同时还识别需要标准一线选择的替代方案的患者。该提案的目的是测试一种新的成像生物标志物的有效性，该生物标志物用于将患者分为两种亚型，预测认知行为治疗（CBT）或艾司西酞普兰（sCIT）单药治疗缓解的可能性-两种标准的MDD一线治疗。我们的中心假设是，使用β代谢作为治疗选择生物标志物（TSB）来分配治疗将使缓解率增加到至少50%，超过常规治疗通常报告的35-40%。来自上一个资助期的数据确定了CBT和sCIT缓解/无应答的最佳治疗方案。这些数据进一步表明了其他潜在的生物标志物，这些生物标志物可以识别无法缓解CBT和sCIT联合治疗的患者。我们将研究两个具体目标：1）前瞻性地测试CRTSB将个体MDD患者分配到CBT或sCIT治疗的有效性; 2）进一步表征预测联合治疗失败的脑亚型。将通过正电子发射断层扫描（FDG PET）测量的右前额叶葡萄糖代谢水平确定治疗分配。我们将使用一个简单的区域/全脑比率方法和一个固定的截止值来推导每个患者的BTTSB。患者将被分配到一个为期12周的CBT或sCIT治疗过程中使用的CBT TSB值。未缓解至TSB分配的首次治疗的患者将接受第二次为期12周的CBT和sCIT联合治疗。将在单药治疗和联合治疗后均未缓解的患者中评价基线全脑代谢，以确定双重衰竭的生物标志物。最后，我们将检查治疗前12周内的局部代谢变化，以表征介导不同亚型缓解的机制。该提议是创新的，因为它测试了一种新的成像生物标志物策略，该策略专门用于将患者前瞻性地分层为两种不同的治疗特异性亚型， 对重度抑郁发作的两种标准一线治疗的反应可能性。此外，这项研究将进一步表征第二种生物标志物，用于识别失败的患者。 对两种治疗都有效。这项研究意义重大，因为它直接解决了为个体抑郁症患者选择最佳治疗方法的循证方法的需求，最终目标是改善治疗效果。

DESCRIPTION (provided by applicant): Usual first-line treatments for major depressive disorder (MDD) show less than a 40% remission rate. For many patients, the "wrong" treatment has significant individual and societal costs due to continued distress, risk of suicide, loss of productivity, wasted resources and potential neural effects associated with 2-3 months of an ineffective strategy. Though treatments are highly effective in some individuals, there is no reliable way to match patients to their best option. Therefore, a long term goal is to develop a clinically viable algorithm that selects the best treatment and avoids ineffective treatments, whil also identifying patients that require alternatives to standard first-line options. The objective o this proposal is to test the efficacy of a novel imaging biomarker developed to stratify patients into two subtypes that predict the likelihood of remission to monotherapy with cognitive behavioral therapy (CBT) or escitalopram (sCIT)--two standard first-line treatments for MDD. Our central hypothesis is that use of insula metabolism as a treatment selection biomarker (TSB) to assign treatment will increase remission rates to at least 50%, exceeding the usual 35-40% commonly reported with usual care. Data from the previous funding period identified the insula as the best discriminator of remission/non response to CBT and sCIT. The data further indicated other potential biomarkers that identify patients who will fail to remit to combined treatment with both CBT and sCIT. We will examine two specific aims: 1) To prospectively test the efficacy of the insula TSB to assign individual MDD patients to treatment with either CBT or sCIT; and 2) To further characterize brain subtypes predictive of combined treatment failure., Treatment assignment will be determined by the level of glucose metabolism in the right anterior insula measured with positron emission tomography (FDG PET). We will derive each patient's insula TSB using a simple region/whole brain ratio approach and a fixed cut-off. Patients will be assigned to a 12-week treatment course of CBT or sCIT using the insula TSB value. Patients who do not remit to their TSB-assigned first treatment will receive a second 12-week course of combined CBT and sCIT. Baseline whole brain metabolism will be evaluated in patients who do not remit after both monotherapy and combination therapy to determine biomarkers of dual failure. Lastly, we will examine regional metabolic changes over the first 12 weeks of treatment to characterize mechanisms mediating remission across insula subtypes. This proposal is innovative because it tests a novel imaging biomarker strategy developed specifically to prospectively stratify patients into two distinct treatment specific subtypes that will predict the likelihood of response to two standard first line therapies for a major depressive episode. In addition, this study will further characterize a second biomarker that identifies patients who fail to remit to both treatments. The proposed research is significant because it directly addresses the need for evidence-based methods for selecting optimal treatments for individual depressed patients with the ultimate goal of improving treatment outcomes.