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）是美国的一个主要问题，目前已达到流行病的水平。