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.

重性抑郁症（MDD）的一线治疗，包括心理和药物治疗， 有效性有限，1年后常规护理治疗成功率仅为29%。缓解率 如果能够优化治疗方法，并将其处方给那些最有可能受益的人，那么抑郁症就会加剧。 迫切需要开发和测试新型有效的MDD治疗方法，同时努力 优化他们的利益。抗阻运动训练（RET）是一种很有前途但研究不足的治疗方法 approach.我们最近的荟萃分析发现，在少数几个非常小的试验中，RET具有很大的抗抑郁作用， 临床抑郁样本（d=0.90），突出了RET治疗MDD的潜力。这些试验，虽然 确定临床有意义的效果的动力不足，显示了积极的结果，并为 更大的机械信息试验，以证实其有希望的早期效果。重要的是，脑血流量 在MDD成人中较低，与治疗反应差有关，RET可以改善脑血流量， 成年人了因此，RET可以通过改善脑血流来治疗MDD。然而， 将RET的抗抑郁作用与改善MDD患者的脑血流量联系起来尚未得到证实。此外，随着 机器学习的进步，识别临床和机制的可修改和稳定的预测因子 改变以及坚持可以为未来治疗MDD的精确医学计划提供信息。因此， 确认RET治疗MDD的疗效，了解其潜在的脑血管机制，并揭示 迫切需要对其影响作出可修改的预测。为此，我们提出了1：1的验证性功效 一项随机对照试验（n=200），在患有以下疾病的成人中进行16周的进行性RET或低剂量RET（SHAM） DSM-5诊断为MDD。目的1将证实RET与SHAM对16岁时抑郁症状的疗效 周，并在8，26和52周时评估RET的潜在更快和持久的效果。目标2将 确定RET与SHAM对脑血流速度和脉动性的机制目标的影响， 它们对抗抑郁疗效的潜在调节作用。Aim 3将使用监督机器学习工具来预测 抑郁变化、脑血管变化和参与者依从性。研究完成后， 建立我们的长期目标，确定和翻译机械驱动的行为治疗， 通过确定一个有希望的、可获得的、 可移植RET治疗MDD可通过改善脑血管功能实现。同时，该项目 将为未来的精准医学方法提供信息，这些方法将针对治疗反应的可修改预测因子 并坚持行为干预，以优化MDD治疗，并单独开出处方， 最有可能受益的人。如果RET能有效治疗MDD，本试验将为RET作为一种 MDD的独立治疗，并可能作为独立或增强治疗，以减少 情绪障碍和严重精神疾病的普遍负担。