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: 10012446
• 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/10012446
• 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; Modeling; 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; design; disorder risk; dosage; fentanyl overdose; follow-up; genetic variant; illicit opioid; imaging genetics; improved; interest; machine learning algorithm; 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; 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）是美国的一个主要问题，目前已达到流行病的水平。 不幸的是，OUD在退伍军人中特别普遍，因为退伍军人需要疼痛是很常见的 治疗和医学中大量使用阿片类药物是OUD问题的主要原因之一 一直在增长 对于OUD有很好的治疗选择：丁丙诺啡和美沙酮都可以用于 维持疗法，只要患者继续接受治疗，他们就不太可能真正滥用 阿片类药物这是非常重要的，因为OUD死亡的一个主要原因是芬太尼死亡 过量，维持治疗的患者可能会避免这种命运。然而， 患者停止治疗。 一个重要的知识缺口来自于我们无法预测哪些患者 更有可能放弃治疗。由于资源有限， 可以优化配置。此外，对OUD和OUD背后的大脑回路的理解， OUD治疗结果对于下一波治疗的合理设计是必要的 接近。 科学问题的大数据方法越来越普遍，但在精神病学的进步中， （像往常一样，精神病学可能是医学中最复杂的领域）落后。我们已经证明 使用机器学习方法对人脑成像分析，我们可以将精神病分类为 患者根据过去的自杀企图和高自杀意念。我们建议使用类似的（尽管 改进的）方法来预测退伍军人中的丁丙诺啡治疗。 我们建议使用不同的MRI模式（结构，白色物质，静息状态功能 连接性）和有限的基因分型（μ阿片受体中的两个单核苷酸多态性， 已知与OUD风险相关α 5烟碱乙酰胆碱受体亚单位） 学习算法来预测OUD治疗结果。之前将收集MRI和遗传学信息 在治疗开始后10天内再次进行治疗和MRI（较小的一组在6个月时成像 此外），退伍军人将随访研究结果。 如果成功的话，这项提议将提供两个机械数据（大脑电路和功能，包括 遗传成分）和OUD治疗结果，以及对OUD的无偏倚方法 治疗预测