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

与抗精神病药物引起的体重增加和心脏代谢风险相关的遗传因素的发现和 CRISPR 验证

• 批准号: 10350672
• 负责人: Anne Justice
• 金额: $ 73.06万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-12 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10350672
• 关键词: Address; Adipocytes; Adult; Affect; Age; Aging; Algorithms; Antipsychotic Agents; Bioinformatics; Biological; Biological Assay; Biological Markers; Body Composition; Body fat; Body mass index; Body measure procedure; CRISPR interference; CRISPR screen; CRISPR-mediated transcriptional activation; CRISPR/Cas technology; Candidate Disease Gene; Cardiovascular Diseases; Cell Aging; Cell Line; Cell physiology; Cells; Cellular Assay; Cellular biology; Characteristics; Child; Childhood; Chromatin; Clinical; Clinical Data; Clinical Trials Design; Clustered Regularly Interspaced Short Palindromic Repeats; Cohort Studies; Collection; Data; Data Set; Data Store; Decision Making; Development; Drug Exposure; Drug Targeting; Elderly; Engineering; Fatty acid glycerol esters; Funding; Future; Gene Expression; General Population; Generations; Genes; Genetic; Genetic Determinism; Genetic Risk; Genetic Variation; Genomic Segment; Genomics; Genotype; Geriatric Psychiatry; Hospitals; Human; In Vitro; Individual; Industry; Letters; Link; Lipids; Longevity; Measures; Mediating; Medical Genetics; Mental disorders; Mentally Ill Persons; Meta-Analysis; Metabolic; Metabolic Diseases; Methods; Methylation; Modeling; Molecular; Molecular Target; Neurons; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Pathway interactions; Pattern; Pharmaceutical Preparations; Pharmacological Treatment; Physiological; Population; Population Study; Premature Mortality; Public Health; Randomized Clinical Trials; Research; Risk; Sampling; Screening Result; Selection for Treatments; Source; Techniques; Testing; Tissues; Treatment outcome; United States National Institutes of Health; Validation; Variant; Weight; Weight Gain; Youth; age related; base; biobank; cardiometabolic risk; causal variant; cell type; cohort; disorder risk; diverse data; drug development; epidemiology study; gene discovery; genetic association; genetic variant; genome wide association study; genome wide screen; genomic locus; in silico; induced pluripotent stem cell; insight; insulin sensitivity; interest; metabolic phenotype; middle age; molecular phenotype; mortality; multiple omics; novel; novel strategies; older patient; phenotypic data; polygenic risk score; population based; population health; precision medicine; predictive modeling; prevent; psychogenetics; response; secondary analysis; side effect; tool; treatment response; treatment risk

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.

项目概要/摘要 抗精神病药引起的体重增加（AIWG）对于精神病人群具有重要的公共卫生重要性， 可以通过个性化的精准医学来解决，抗精神病药物会增加体重， 从而增加 2 型糖尿病和心血管疾病等心脏代谢风险 (CMR) 状况， 这导致了 10 至 15 年的死亡率差距。 精神病患者和普通人群之间普遍使用抗精神病药物。 年龄，但与脂肪增加的不同模式相关，因此儿童增加更多，老年人增加更多 许多全基因组关联研究（GWAS）已经确定了与此相关的关键遗传因素。 AIWG，但受到身体脂肪间接测量的限制，例如体重或体重指数（BMI）， 此外，现有研究并未完全解决与年龄相关的问题。 AIWG 的差异，以回应 NIH PA-17-088“现有队列、数据集和的二次分析” 存储生物样本来解决临床衰老研究问题”，我们提出了一种应用新方法 基于群体的遗传学、现有的生物样本以及相关的临床数据，包括精确测量的肥胖程度 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遗传 该方法利用了 1) 现有的人口水平数据。 来自大型生物样本库计划和流行病学研究，其中包括约 15,000 名患有此病的个体 遗传和相关表型数据，2) 来自 NIH 资助的随机化现有临床和生物样本数据 表征抗精神病药对儿童、成人和老年人代谢影响的临床试验或随机对照试验 直接、精确地测量身体脂肪，以及来自约 600 名患有遗传性遗传疾病的个体的数据 数据和其他代谢风险生物标志物，以及 3) 基于 CRISPR 的体外药物暴露，然后 细胞功能测定来表征抗精神病药物影响的分子机制。 现有数据将在资助后可用，包括来自大型机构的约 3000 名个人的数据 行业资助的随机对照试验，来自精神病遗传学联盟 (PGC，参见 支持信），以及来自荷兰双向情感障碍队列研究的 2,000 多人的数据（参见支持信） 支持）也将用于独立验证和复制，这项研究将结合公正的基因组。 方法，包括基于芯片的基因分型、GWAS 和 GWAS 荟萃分析、基于 CRISPR 的基因 抑制/激活筛选（CRISPRi/a），以及对优先变体的功能分子和细胞研究 兴趣，结合独特的临床数据来识别遗传因素并生成体重预测模型 与加速衰老相关的相关生理变化将通过这套组合的分子技术来实现。 使我们能够以已知的遗传关联为基础，同时发现新的基因和遗传变异 该项目将与年轻和老年患者治疗相关的脂肪增加的最大风险相关。 有助于开发基于精确的治疗算法，可以准确地预测和预防 AIWG 和青年、青年、中年和老年人的心脏代谢风险 这项研究的结果将。 还重要的是有助于公开可用的数据集，并激励未来收集必要的类似数据 进一步验证我们的结果。