Pharmacogenetic Prediction of Antipsychotic Induced Weight Gain

抗精神病药物引起的体重增加的药物遗传学预测

• 批准号: 8532495
• 负责人: TODD LENCZ
• 金额: $ 25.28万
• 依托单位: FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-01 至 2015-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8532495
• 关键词: Adolescence; Adolescent; Adult; Adverse effects; Affect; Aftercare; Age; Antipsychotic Agents; Anxiety Disorders; Autistic Disorder; Awareness; Behavioral; Biological Markers; Child; Cholesterol; Clinical; Code; Colorectal Cancer; Complex; Controlled Study; Coronary Arteriosclerosis; Data; Data Collection; Development; Diet Monitoring; Discrimination; Epidemiologic Studies; Family; Fatty acid glycerol esters; Funding; General Population; Generations; Genes; Genetic Markers; Genotype; Haloperidol; Health; Healthcare; Height; Hip region structure; Hospitalization; Individual; Institution; Lead; Melanocortin 4 Receptor; Mental disorders; Meta-Analysis; Metabolic; Metabolic syndrome; Molecular; Mood Disorders; Morbidity - disease rate; Motor; National Institute of Mental Health; Obesity; Patients; Pattern; Pharmaceutical Preparations; Pharmacogenetics; Pharmacology; Phenotype; Population Study; Psychiatry; Public Health; Quality Control; Quantitative Trait Loci; Relapse; Research; Risk; Risk Factors; Risk Marker; Risperidone; Sample Size; Sampling; Schizophrenia; Source; Symptoms; Time; Treatment Efficacy; Triglycerides; Variant; Weight; Weight Gain; Work; aripiprazole; base; clinical practice; cohort; critical period; demographics; design; experience; genetic variant; genome wide association study; genome-wide; interest; novel; patient population; primary outcome; psychologic; public health relevance; quetiapine; secondary outcome; treatment duration; treatment strategy; waist circumference

DESCRIPTION (provided by applicant): The NIMH strategic objectives emphasize the importance of developing strategies for personalized treatment, yet there are currently no widely utilized biomarkers to guide clinicians in the usage of second generation antipsychotic (SGAs). While most SGAs have adequate (though far from perfect) efficacy for the treatment of positive symptoms in schizophrenia, a major concern for patients, families, and clinicians is the burden of antipsychotic-induced weight gain (AIWG). Moreover, the problem of AIWG is a growing public health concern due to the rapidly accelerating use of SGAs for the treatment of non-psychotic conditions in children, adolescents, and adults. Identification of biomarkers predicting AIWG liability are urgently needed for several reasons: 1) the degree of AIWG can be extreme (>14% of baseline weight) in as many as one-fifth of all patients; 2) AIWG can lead to metabolic syndrome and related morbidity; 3) AIWG can be stigmatizing and cause further psychological suffering in patients; and 4) AIWG often leads to medication nonadherence resulting in increased risk of relapse and re-hospitalization. Additionally, the mechanisms underlying AIWG are poorly understood, and the identification of predictive biomarkers can help illuminate the underlying pharmacology, thereby aiding the development of novel medications with reduced AIWG burden. The proposed study aims to identify pharmacogenetic biomarkers for AIWG by analyzing genomewide association study (GWAS) data obtained on 6 cohorts (total n~1200) of prospectively-characterized, SGA- treated patients who are either antipsychotic-na¿ve or have very limited prior exposure. In order to appropriately control for study-specific variables such as study duration, treatment type, and demographics, GWAS (with appropriate covariates) will be performed in each cohort separately, with results combined via meta-analysis. The relationship of genotype to within-subject changes in BMI will be the primary outcome of interest; secondary outcomes will include antipsychotic-induced changes in other metabolic parameters, including triglyceride levels, cholesterol levels, hip and waist circumference, and fat mass, induced by SGA treatment. Note that no funds in the proposed study are used for subject recruitment, data collection, or genotyping. The proposed study builds upon our recent identification of a genetic biomarker that replicably predicts a doubling of AIWG in carriers of the risk genotype (~20 lbs of weight gain in 12 weeks, as compared to ~10 lbs), utilizing a GWAS based on a much smaller sample size (n=139).

描述（由申请人提供）：NIMH 的战略目标强调制定个性化治疗策略的重要性，但目前尚无广泛使用的生物标志物来指导临床医生使用第二代抗精神病药物 (SGA)。虽然大多数 SGA 对于治疗精神分裂症的阳性症状具有足够的（尽管远非完美）疗效，但患者、家属和临床医生的主要担忧是抗精神病药物引起的体重增加 (AIWG) 的负担。此外，由于 SGA 快速加速用于治疗儿童、青少年和成人的非精神病状况，AIWG 问题已成为日益严重的公共卫生问题。出于以下几个原因，迫切需要识别预测 AIWG 责任的生物标志物：1) 在多达五分之一的患者中，AIWG 的程度可能非常严重（> 基线体重的 14%）； 2）AIWG可导致代谢综合征及相关发病； 3）AIWG可能会给患者带来污名化并造成进一步的心理痛苦； 4) AIWG 通常会导致药物不依从，导致复发和再住院的风险增加。此外，人们对 AIWG 的机制知之甚少，而预测生物标志物的识别可以帮助阐明潜在的药理学，从而有助于开发减少 AIWG 负担的新药物。拟议的研究旨在通过分析全基因组关联研究 (GWAS) 数据来确定 AIWG 的药物遗传学生物标志物，这些数据是从 6 个队列（总共 n~1200 名）前瞻性表征的 SGA 治疗患者中获得的，这些患者要么从未接受过抗精神病药物治疗，要么既往接触过非常有限的抗精神病药物。为了适当控制研究特定的变量，例如 研究持续时间、治疗类型和人口统计数据、GWAS（具有适当的协变量）将在每个队列中分别进行，并通过荟萃分析合并结果。基因型与受试者体内 BMI 变化的关系将是主要的关注结果；次要结果将包括抗精神病药物引起的其他代谢参数的变化，包括由 SGA 治疗引起的甘油三酯水平、胆固醇水平、臀围和腰围以及脂肪量。请注意，拟议研究中没有资金用于受试者招募、数据收集或基因分型。拟议的研究建立在我们最近鉴定的一种遗传生物标志物的基础上，该生物标志物利用基于小得多的样本量（n=139）的 GWAS，可重复地预测风险基因型携带者的 AIWG 倍增（12 周内体重增加约 20 磅，而相比之下约 10 磅）。