Discovery and CRISPR validation of genetic factors associated with antipsychotic-induced weight gain and cardiometabolic risk

项目摘要/摘要 抗精神病药物引起的体重增加(AIWG)在精神疾病人群中具有重要的公共卫生意义， 通过个性化、精准的医疗，潜在地可以解决。抗精神病药物会增加体重， 从而增加心脏代谢风险(CMR)情况，如2型糖尿病和心血管疾病， 与细胞加速老化相关的条件。这导致了10到15年的死亡率差距。 精神病患者和普通人群之间的关系。抗精神病药物是最常用的药物 年龄，但与不同的脂肪增加模式有关，即儿童增加更多，老年人增加 较少。大量全基因组关联研究(GWAS)已经确定了与以下疾病相关的关键遗传因素 AIWG，但受限于对身体脂肪的间接测量，如体重或身体质量指数(BMI)，这些都是 与代谢性疾病风险的相关性较差。此外，现有的研究还没有完全解决与年龄有关的问题 AIWG的差异。响应NIH PA-17-088“对现有队列、数据集和 储存生物样品以解决临床衰老研究问题，我们提出了一种新的方法，应用 基于人群的遗传学，现有的生物样本与包括精确测量的肥胖症在内的临床数据相关联 和胰岛素敏感性，以及先进的分子工具来识别和功能验证关键基因 年龄跨度的AIWG和CMR的决定因素。该方法利用1)现有的人口级别数据 来自大型生物库倡议和流行病学研究，包括约15,000名患有 遗传和相关的表型数据，2)现有的临床和生物谱数据，来自NIH资助的随机 抗精神病药物对儿童、成人和老年人代谢影响的临床试验或随机对照试验 通过直接和精确的身体脂肪测量，以及大约600名患有遗传疾病的人的数据 代谢风险的数据和其他生物标记物，以及3)基于体外药物暴露的CRISPR，其次是 细胞功能分析以表征抗精神病药物影响的分子机制。其他来源 的现有数据将在获得资金后可用，包括来自大型企业的约3000名个人的数据 行业资助的RCT，来自精神病学遗传学联盟(PGC，请参阅 支持信)，以及来自荷兰躁郁症队列研究的2,000多人的数据(见 支持)还将用于独立验证和复制。这项研究将结合无偏见的基因组 方法包括基于阵列的基因分型、GWAS和GWASMeta分析、基于CRISPR的基因 抑制/激活筛选(CRISPRi/a)，以及对优先的变异体的功能分子和细胞研究 兴趣，结合独特的临床数据，以确定遗传因素并生成体重预测模型 与加速衰老相关的生理变化。这套结合起来的分子技术将 允许我们在已知遗传联系的基础上，同时发现新的基因和遗传变异 在年轻和老年患者中，与治疗相关的脂肪增加的风险最大。这个项目将 有助于开发基于精确的治疗算法，可以准确地预测和预防 青年、青年、中年人和老年人的AIWG和心脏代谢风险。这项研究的结果将 同样重要的是，对公开可用的数据集做出贡献，并激励未来收集必要的类似数据 以进一步验证我们的结果。

PROJECT SUMMARY/ABSTRACT Antipsychotic-induced weight gain (AIWG) is of significant public health importance in mentally ill populations, potentially addressable with personalized, precision medicine. Antipsychotic medications increase body weight, thereby increasing cardiometabolic risk (CMR) conditions like type 2 diabetes and cardiovascular disease, conditions associated with accelerated cellular aging. This has contributed to a 10 to 15-year mortality gap between mentally ill individuals and the general population. Antipsychotic medications are commonly used at all ages, but are associated with differential patterns of fat gain, whereby children gain more and older adults gain less. Numerous genome-wide association studies (GWAS) have identified key genetic factors associated with AIWG, but are limited by the use of indirect measures of body fat, like weight or body mass index (BMI), that are less well correlated with metabolic disease risk. Additionally, existing research does not fully address age-related differences in AIWG. In response to NIH PA-17-088 “Secondary Analyses of Existing Cohorts, Data Sets and Stored Biospecimens to Address Clinical Aging Research Questions,” we propose a novel approach applying population-based genetics, existing biospecimen with linked clinical data including precisely-measured adiposity and insulin sensitivity, and advanced molecular tools to identify and functionally validate key genetic determinants of AIWG and CMR across the age-span. This approach leverages 1) existing population-level data from large biobanking initiatives and epidemiological studies inclusive of approximately 15,000 individuals with genetic and relevant phenotypic data, 2) existing clinical and biospecimen data from NIH funded randomized clinical trials or RCTs characterizing the metabolic effects of antipsychotics in children, adults and older adults with direct and precise measures of body fat, together with data from approximately 600 individuals with genetic data and additional biomarkers of metabolic risk, and 3) CRISPR based in vitro drug exposure, followed by cellular functional assays to characterize molecular mechanisms impacted by antipsychotic. Additional sources of existing data will be available upon funding, including data on approximately 3000 individuals from large industry funded RCTs, data on up to 250,000 individuals from the Psychiatric Genetics Consortium (PGC, see letter of support), and data from more than 2,000 individuals from the Dutch Bipolar Cohort Study (see letter of support) will also be used for independent validation and replication. This study will combine unbiased genomic methods, including array-based genotyping, GWAS and GWAS meta-analysis, CRISPR-based gene inhibition/activation screens (CRISPRi/a), and functional molecular and cellular studies on prioritized variants of interest, combined with unique clinical data to identify genetic factors and generate predictive models of weight related physiological changes associated with accelerated aging. This combined set of molecular techniques will allow us to build on known genetic associations, while discovering new genes and genetic variants that are associated with the greatest risk for treatment-related fat gain in younger and older patients. This project will contribute to the development of a precision-based treatment algorithm that can accurately predict and prevent AIWG and cardiometabolic risk in youth, young, middle-aged, and older adults. The results from this study will also importantly contribute to publicly available datasets, and motivate future collection of similar data necessary for further validation of our results.