Resistance Exercise to Treat Major Depression via Cerebrovascular Mechanisms: Confirming Efficacy and Informing Precision Medicine

通过脑血管机制进行抗阻运动治疗重度抑郁症：证实疗效并为精准医学提供信息

• 批准号: 10724799
• 负责人: Jacob D Meyer
• 金额: $ 72.13万
• 依托单位: IOWA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-22 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10724799
• 关键词: Acute; Adherence; Adult; Antidepressive Agents; Behavior Therapy; Behavioral; Behavioral Model; Blood; Blood Flow Velocity; Cerebrovascular Circulation; Cerebrum; Classification; Clinical; DSM-V; Data; Depressed mood; Diagnosis; Disease remission; Dose; Effectiveness; Exercise; Foundations; Future; Goals; Health Care Costs; Human; Individual; Intervention; Intervention Studies; Investigational Therapies; Link; Machine Learning; Major Depressive Disorder; Measures; Mediating; Mediation; Mental Depression; Mental disorders; Meta-Analysis; Methods; Modeling; Mood Disorders; Outcome; Participant; Pathway interactions; Patients; Persons; Pharmaceutical Preparations; Pharmacotherapy; Phase; Pilot Projects; Population; Precision Medicine Initiative; Prediction of Response to Therapy; Prevalence; Productivity; Psychiatry; Psychotherapy; Randomized, Controlled Trials; Recording of previous events; Research; Rest; Sampling; Societies; Source; Testing; Training Programs; Translating; Treatment Cost; Variant; Work; antidepressant effect; arm; base; cerebrovascular; clinical predictors; confirmatory trial; depressive symptoms; disability; efficacious treatment; exercise training; improved; insight; machine learning model; middle cerebral artery; novel; precision medicine; programs; random forest; reduce symptoms; resistance exercise; response; severe mental illness; social; standard care; success; supervised learning; therapy development; tool; treatment adherence; treatment as usual; treatment optimization; treatment program; treatment response

Frontline treatments for major depressive disorder (MDD), including psycho- and pharmacotherapy, have limited effectiveness, with usual care treatment success at just 29% after 1 year. Remission rates for depression would be enhanced if treatments could be optimized and prescribed to those most likely to benefit. There is a critical need to develop and test novel, efficacious treatments for MDD and simultaneously work to optimize their benefits. Resistance exercise training (RET) is a promising but understudied treatment approach. Our recent meta-analysis found a large antidepressant effect of RET in the few very small trials with clinically depressed samples (d=0.90), highlighting the potential of RET for treating MDD. These trials, while underpowered to determine clinically meaningful effects, showed positive results and provide the foundation for larger mechanistically-informed trials to confirm their promising early effects. Importantly, cerebral blood flow is lower in adults with MDD, linked with a poor treatment response, and RET can improve cerebral blood flow in adults. As such, RET may treat MDD via improving cerebral blood flow. However, the mechanistic pathway linking RET’s antidepressant effects to improved cerebral blood flow in MDD is as-of-yet untested. Further, with advances in machine learning, the identification of modifiable and stable predictors of clinical and mechanistic change as well as adherence can inform future precision medicine initiatives for treating MDD. Thus, a trial to confirm the efficacy of RET for MDD, understand its potential cerebrovascular mechanisms, and uncover the modifiable predictors of its effects is urgently needed. Toward this end, we propose a confirmatory efficacy 1:1 randomized controlled trial (n=200) of 16 weeks of progressive RET or low dose RET (SHAM) in adults with DSM-5 diagnosed MDD. Aim 1 will confirm the efficacy of RET vs SHAM on depressive symptoms at 16 weeks, and evaluate both potentially quicker and enduring effects of RET at 8, 26 and 52 weeks. Aim 2 will determine the effect of RET vs. SHAM on the mechanistic target of cerebral blood velocity and pulsatility and their potential mediation of antidepressant efficacy. Aim 3 will use supervised machine learning tools to predict depression changes, cerebrovascular changes, and participant adherence. Upon completion, this study will build towards our long-term goal of identifying and translating mechanistically-driven behavioral treatments to reduce the global burden of mental illness by determining the extent to which a promising, accessible, translatable RET approach can treat MDD by improving cerebrovascular function. Simultaneously, this project will inform future precision medicine approaches that will target modifiable predictors of treatment response and adherence to behavioral interventions to optimize MDD treatments and individually prescribe them to those most likely to benefit. If RET effectively treats MDD, this trial would lay the foundation to apply RET as a standalone treatment for MDD, and potentially as a standalone or augmentation treatment to reduce the widespread burden of mood disorders and serious mental illnesses.

主要抑郁症（MDD）的一线治疗，包括心理和药物治疗，具有 有限的有效性，通常的护理治疗成功率在1年后仅29％。缓解率的 如果可以优化治疗并规定最有可能受益的治疗方法，将会增加抑郁症。 对于MDD开发和测试新颖，有效治疗的迫切需要 优化他们的利益。抵抗运动训练（RET）是一个承诺，但可以理解 方法。我们最近的荟萃分析发现，在少数非常小的试验中，RET具有很大的抗抑郁作用， 临床下降的样品（d = 0.90），突出了RET治疗MDD的潜力。这些试验，而 无法确定临床意义的影响，表现出积极的结果并为 较大的机械信息试验以确认其承诺的早期影响。重要的是，脑血流量是 MDD的成年人较低，与治疗反应不佳有关，而RET可以改善脑血流 成年人。因此，RET可以通过改善脑血流来治疗MDD。但是，机械途径 将RET的抗抑郁作用与改善MDD中的脑血流相关联的抗抑郁作用是未经测试的。此外，与 机器学习的进步，鉴定可修改和稳定的临床和机械预测指标 变化以及依从性可以为未来的精确医学计划提供治疗MDD的倡议。那是一个试验 确认MDD的RET效率，了解其潜在的脑血管机制，并发现 迫切需要修改其影响的预测指标。为此，我们提出了确认有效性1：1 成年人的随机对照试验（n = 200）为16周的进行性RET或低剂量RET（假） DSM-5诊断为MDD。 AIM 1将确认RET与假手术在16时的抑郁症状的效率 周，在8、26和52周时，RET可能会更快地评估RET的效果。 AIM 2意志 确定RET与假对脑血液速度和脉动性的机械靶标的影响 它们的抗抑郁效率的潜在调解。 AIM 3将使用监督的机器学习工具来预测 抑郁症的变化，脑血管变化和参与依从性。完成后，这项研究将 建立我们的长期目标，即确定和翻译机械驱动的行为治疗 通过确定承诺，可及的诺言的程度，减轻全球精神疾病负担 可翻译的RET方法可以通过改善脑血管功能来治疗MDD。同时，这个项目 将告知未来的精确医学方法，该方法将针对可修改的治疗反应预测指标 并遵守行为干预措施以优化MDD治疗并单独规定它们 那些最有可能受益的人。如果RET有效治疗MDD，则该试验将奠定基础，以将RET应用于 独立治疗MDD，并有可能作为独立或增强处理来减少 情绪障碍和严重精神疾病的广泛伯恩。