Hub for Metabolic Psychiatry

新陈代谢精神病学是一个关键但研究不足的领域。患有严重精神疾病(SMI)的人患肥胖、糖尿病、心血管疾病和过早死亡的风险增加。在SMI和代谢紊乱之间有既定的双向机制，最近的研究表明代谢干预可能对SMI有治疗作用。代谢精神病学中心将有三个总体目标：a)推动代谢精神病学的发现科学；b)与有SMI生活经验的人合作，开发和测试基于新陈代谢的新治疗方法；以及c)在英国范围内建立未来代谢精神病学研究和临床创新的能力。我们将有四个实验工作流：1.基因组学和因果推断：来自基因组学研究的数据已经确定，肥胖特征与严重抑郁障碍之间存在因果关系。糖尿病和SMI之间也有正的遗传相关性，但这些关联的因果方向尚不清楚。此外，根据族裔/血统的不同，这些联系可能存在重要差异。这一工作流程将使用因果推断方法，如孟德尔随机化，以更好地理解SMI和代谢紊乱的共同生物学2。健康信息学和数据科学：我们将使用多个国家和国际数据集中的数据链接来调查肥胖/糖尿病和SMI，重点是识别共病模式以及精神和代谢结果的预测因素。这些数据集包括苏格兰的SCI-糖尿病数据、来自NHS信息服务部、爱丁堡DataLoch、英国临床实践研究数据链的虚拟患者队列，以及来自Denmark的多个国家级数据集。SMI临床结果的代谢生物标记物：我们将确定SMI的代谢生物标记物，并进行一项为期12个月的前瞻性研究，以评估代谢特征的变化如何与临床和功能结果相关。这一工作流程将采用几种创新的数据收集方法，包括对代谢生物标志物的持续监测、基于雷达的睡眠和昼夜节律活动图评估、对精神状态的生态瞬时评估以及最先进的代谢组学分析。共同开发和测试针对SMI的基于代谢的新型干预措施：我们将与James Lind联盟和McPin基金会密切合作，共同开发和评估一系列代谢干预作为治疗SMI的新型疗法的可接受性和治疗潜力，包括数字方法、计时营养(限时进食)、生酮饮食、低碳水化合物饮食、二甲双胍和GLP1受体拮抗剂。5.数据分析和开放科学工作流：我们将为上述每一个工作流开发和测试统计方法，包括动态结构方程建模和机器学习，并创建一个数据管理和数据共享平台，以最大限度地利用这项工作的开放科学机会。患者和公众的参与、参与和传播工作流程：McPin基金会将帮助每个工作流程，以确保具有SMI生活经验的人是这项工作的核心，从实验设计到传播和知识交流活动。总体而言，代谢精神病学中心代表着一个创新的计划，在一个对SMI患者及其家人极其优先的领域内，提供生活体验参与、发现科学和临床研究。它填补了当前研究领域的一个重要空白，并将使英国处于未来代谢精神病学研究和创新的前沿。

Metabolic psychiatry is a critical but under-researched area. Individuals with severe mental illness (SMI) are at increased risk of obesity, diabetes, cardiovascular disease and premature mortality. There are established bidirectional mechanisms between SMI and metabolic disorders and recent research suggests that metabolic interventions may be therapeutic for SMI.The Hub for Metabolic Psychiatry will have three overarching objectives: a) to drive discovery science in metabolic psychiatry; b) to develop and test novel metabolism-based treatment approaches for SMI in collaboration with people with lived experience of SMI; and c) to build UK-wide capacity for future metabolic psychiatry research and clinical innovation.We will have four experimental workstreams:1. Genomics and causal inference: Data from genomics research has identified that adiposity traits are causally related to major depressive disorder. There are also positive genetic correlations between diabetes and SMI but the causal direction of these associations is unclear. Further, there may be important differences in these associations according to ethnicity/ancestry. This workstream will use causal inference methods such as Mendelian randomisation to better understand the shared biology of SMI and metabolic disorders.2. Health informatics and data science: We will use data linkage within multiple national and international datasets to investigate obesity/diabetes and SMI, with a focus on identifying patterns of comorbidity and predictors of both psychiatric and metabolic outcomes. These datasets include SCI-Diabetes data in Scotland, virtual patient cohorts from the NHS Information Services Division, Edinburgh DataLoch, UK-wide Clinical Practice Research Datalink, and multiple national-level datasets from Denmark.3. Metabolic biomarkers of clinical outcomes in SMI: We will identify metabolomic biomarkers of SMI and conduct a 12-month prospective study to assess how changes in metabolic profile relate to clinical and functional outcomes. This workstream will deploy several innovative data collection methods, including continuous monitoring of metabolic biomarkers, radar-based and actigraph assessments of sleep and circadian rhythms, ecological momentary assessments of mental state and state-of-the-art metabolomics analyses.4. Co-developing and testing novel metabolism-based interventions for SMI: Working closely with the James Lind alliance and The McPin Foundation, we will co-develop and assess the acceptability and therapeutic potential of a range of metabolic interventions as novel treatments for SMI, including digital approaches, chrononutrition (time restricted eating), the ketogenic diet, low carb diets, metformin and GLP1 receptor antagonists. This work will prioritise and co-design future clinical trials of metabolic interventions for SMI.Two additional cross-cutting workstreams will feed into the four experimental workstreams:5. Data analysis and open science workstream: We will develop and test statistical approaches - including dynamic structural equation modelling and machine learning - for each of the workstreams above and create a data curation and data sharing platform to maximise the open science opportunities of this work.6. Patient and public involvement, engagement and dissemination workstream: The McPin Foundation will help with each of the workstreams to ensure that people with lived experience of SMI are at the heart of this work, from experimental design to dissemination and knowledge exchange activities.Overall, the Hub for Metabolic Psychiatry represents an innovative programme of lived experience involvement, discovery science and clinical research within an area that is extremely high priority for people with SMI and their families. It fills an important gap in the current research landscape and will place the UK at the forefront of future metabolic psychiatry research and innovation.