Brain connectivity and genetics as predictors of opioid abuse treatment outcomes

大脑连接和遗传学作为阿片类药物滥用治疗结果的预测因素

• 批准号: 10316149
• 负责人: RAMIRO SALAS
• 金额: --
• 依托单位: MICHAEL E DEBAKEY VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10316149
• 关键词: Algorithms; Alleles; Americas; Anatomy; Big Data; Biological Markers; Brain; Brain imaging; Buprenorphine; Cessation of life; Characteristics; Clinic; Clinical; Complex; Corpus striatum structure; Data; Development; Diffusion Magnetic Resonance Imaging; Dose; Drops; Drug usage; Epidemic; Feeling suicidal; Future; Genes; Genetic; Genetic Risk; Genotype; Habenula; Human; Image; Insula of Reil; Knowledge; Machine Learning; Magnetic Resonance Imaging; Maintenance; Maintenance Therapy; Measures; Medicine; Mental Health; Methadone; Methods; Modality; Nicotinic Receptors; Opiate Addiction; Opioid; Opioid abuser; Outcome; Outcome Study; Pain management; Patients; Pharmaceutical Preparations; Play; Prediction of Response to Therapy; Psychiatry; Relapse; Resource Allocation; Resources; Rest; Rewards; Risk; Role; SNP genotyping; Scanning; Single Nucleotide Polymorphism; Structure; Suicide attempt; Testing; Therapeutic; Training; Treatment outcome; Urine; Variant; Veterans; Work; addiction; base; brain circuitry; buprenorphine treatment; compliance behavior; disorder risk; dosage; fentanyl overdose; follow-up; genetic variant; illicit opioid; imaging genetics; improved; interest; machine learning algorithm; machine learning model; methadone treatment; morphometry; mortality risk; mu opioid receptors; neural circuit; non-opioid analgesic; opioid abuse; opioid use; opioid use disorder; opioid user; outcome prediction; personalized medicine; prospective; rational design; success; suicidal; treatment duration; volunteer; white matter

Opioid use disorder (OUD) is a major problem in America, currently reaching epidemic levels. Unfortunately, OUD is especially prevalent among Veterans, as it is common that Veterans need pain treatment and the liberal use of opioids in medicine is one of the major reasons why the OUD problem keeps growing. There are good treatment options for OUD: Both buprenorphine and methadone can be used in maintenance therapies in which, as long as the patient stays in treatment, they will not likely truly abuse opioids. This is extremely important as one major reason for death in OUD is death by fentanyl overdose, and a patient in maintenance therapies will likely avoid that fate. However, it is very common that patients discontinue treatment. An important gap in knowledge arises from the fact that we have no means to predict which patients are more likely to drop from treatment. Such prediction would be of great interest as limited resources could be optimally allocated. In addition, an understanding of the brain circuitry behind both OUD and OUD treatment outcomes is necessary for the rational design of the next wave of therapeutic approaches. Big data approaches to scientific questions are increasingly common, however in psychiatry advances are (as usual, psychiatry likely being the most complex field in medicine) lagging. We have shown that using a machine learning approach to human brain imaging analysis, we can classify psychiatric patients according to past suicide attempt and high suicide ideation. We propose to use a similar (albeit improved) approach to the prediction of buprenorphine treatment in Veterans with OUD. We propose to use different MRI modalities (structure, white matter, resting state functional connectivity) and limited genotyping (two single nucleotide polymorphisms in the µ opioid receptor and the α 5 nicotinic acetylcholine receptor subunit known to be associated with OUD risk) in machine learning algorithms to predict OUD treatment outcomes. MRI and genetics will be collected before treatment and MRI again within 10 days of treatment initiation (with a smaller group imaged at 6 months also), and Veterans will be followed up to study outcomes. If successful, this proposal would provide both mechanistic data (brain circuitry and function, including a genetic component) about OUD and OUD treatment outcome, and an unbiased approach to OUD treatment prediction.

阿片类药物使用障碍（OUD）在美国是一个主要问题，目前处于流行水平。 不幸的是，在退伍军人中尤其普遍存在，因为退伍军人需要痛苦 治疗和对医学中阿片类药物的自由使用是OUD问题的主要原因之一 不断增长。 OUD有良好的治疗选择：丁丙诺啡和Meadadone都可以在 维护疗法，只要患者留在治疗中，他们就不会真正滥用 阿片类药物。这非常重要，因为在OUD中死亡的主要原因之一是芬太尼死亡 服用过量，维护疗法的患者可能会避免这种命运。但是，这很常见 患者停止治疗。 知识的一个重要差距来自以下事实：我们没有能够预测哪些患者 更有可能退出治疗。作为有限的资源，这种预测将引起极大的兴趣 可以最佳分配。此外，对Oud和Oud背后的大脑电路的理解 OUD治疗结果对于下一波治疗的合理设计是必需的 方法。 关于科学问题的大数据方法越来越普遍，但是在精神病学进步中 （像往常一样，精神病学可能是医学中最复杂的领域）落后。我们已经表明 使用机器学习方法进行人脑成像分析，我们可以对精神病进行分类 患者根据过去的自杀未遂和高自杀的想法。我们建议使用类似的（尽管 改进的方法可以预测OUD退伍军人的丁丙诺啡治疗方法。 我们建议使用不同的MRI模式（结构，白质，静止状态功能 连通性）和有限的基因分型（µ阿片受体中的两个单一核苷酸多态性和 α5烟碱乙酰胆碱受体亚基已知与机器中的OUD风险相关） 学习算法以预测OUD治疗结果。 MRI和遗传学将在之前收集 在治疗计划后的10天内再次进行治疗和MRI（在6个月时成像较小 同样），将跟踪退伍军人以研究结果。 如果成功，该建议将提供机械数据（脑电路和功能，包括 关于Oud和Oud治疗结果的遗传成分），以及一种公正的OUD方法 治疗预测。