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: 10595492
• 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/10595492
• 关键词: Algorithms; Alleles; Anatomy; Big Data; Biological Markers; Brain; Brain imaging; Buprenorphine; Cessation of life; Characteristics; Classification; 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 Receptor; Opioid abuser; Outcome; Outcome Study; Pain management; Patients; Pharmaceutical Preparations; Prediction of Response to Therapy; Psychiatry; Relapse; Resource Allocation; Resources; Rest; Rewards; Risk; SNP genotyping; Scanning; Single Nucleotide Polymorphism; Structure; Suicide attempt; Testing; Therapeutic; Training; Treatment outcome; Urine; Variant; Veterans; Work; addiction; brain circuitry; buprenorphine treatment; compliance behavior; disorder risk; dosage; fentanyl overdose; follow-up; genetic variant; illicit opioid; improved; interest; machine learning algorithm; machine learning model; methadone treatment; morphometry; mortality risk; neural circuit; 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) 是美国的一个主要问题，目前已达到流行水平。 不幸的是，OUD 在退伍军人中尤其普遍，因为退伍军人需要疼痛是很常见的 治疗和在医学上自由使用阿片类药物是 OUD 问题的主要原因之一 不断增长。 OUD 有很好的治疗选择：丁丙诺啡和美沙酮都可以用于治疗 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药物 这非常重要，因为芬太尼导致 OUD 死亡的主要原因之一。 服用过量，接受维持治疗的患者可能会避免这种命运，但这种情况很常见。 使患者停止治疗。 知识上的一个重要差距是由于我们无法预测哪些患者 由于资源有限，这种预测将引起人们极大的兴趣。 此外，还可以了解 OUD 和 OUD 背后的大脑回路。 OUD 治疗结果对于合理设计下一波治疗方案是必要的 接近。 解决科学问题的大数据方法越来越普遍，但在精神病学方面取得了进展 （像往常一样，精神病学可能是医学中最复杂的领域）我们已经证明了这一点。 使用机器学习方法进行人脑成像分析，我们可以对精神疾病进行分类 根据过去的自杀企图和高自杀意念的患者，我们建议使用类似的方法（尽管）。 改进）预测 OUD 退伍军人丁丙诺啡治疗的方法。 我们建议使用不同的 MRI 模式（结构、白质、静息态功能） 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和 已知与 OUD 风险相关的 α 5 烟碱乙酰胆碱受体亚基）在机器中 之前将收集预测 OUD 治疗结果的学习算法。 治疗开始后 10 天内再次进行治疗和 MRI（较小的一组在 6 个月时进行成像） 也），并且将对退伍军人进行跟踪研究结果。 如果成功，该提案将提供机械数据（脑电路和功能，包括 遗传成分）关于 OUD 和 OUD 治疗结果，以及 OUD 的公正方法 治疗预测。