Pharmacogenetic Prediction of Antipsychotic Induced Weight Gain

描述（由申请人提供）：NIMH战略目标强调制定个性化治疗策略的重要性，但目前还没有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（SGA）。虽然大多数SGAs对精神分裂症阳性症状的治疗有足够的疗效（尽管远非完美），但患者、家属和临床医生的主要担忧是抗精神病药物诱导的体重增加（AIWG）的负担。此外，AIWG的问题是一个日益增长的公共卫生问题，由于快速加速使用SGAs治疗儿童，青少年和成人的非精神病性疾病。由于以下几个原因，迫切需要鉴定预测AIWG易感性的生物标志物：1）AIWG的程度可能是极端的（>基线体重的14%）; 2）AIWG可导致代谢综合征和相关的发病率; 3）AIWG可使患者蒙受耻辱并导致进一步的心理痛苦;和4）AIWG经常导致药物不依从性，导致复发和再住院的风险增加。此外，对AIWG的潜在机制知之甚少，预测性生物标志物的鉴定可以帮助阐明潜在的药理学，从而有助于开发具有降低AIWG负担的新型药物。拟定的研究旨在通过分析6个队列（总n~1200）前瞻性表征的SGA治疗患者（抗精神病药物初治或既往暴露非常有限）的全基因组关联研究（GWAS）数据，确定AIWG的药物遗传学生物标志物。为了适当控制研究特定变量，例如 研究持续时间、治疗类型和人口统计学、GWAS（具有适当的协变量）将在每个队列中单独进行，结果通过荟萃分析合并。基因型与受试者内BMI变化的关系将是关注的主要结局;次要结局将包括抗精神病药诱导的其他代谢参数变化，包括SGA治疗诱导的甘油三酯水平、胆固醇水平、臀围和腰围以及脂肪量。请注意，拟定研究中的资金不用于受试者招募、数据收集或基因分型。这项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，该生物标志物可重复预测风险基因型携带者的AIWG加倍（12周内体重增加约20磅，相比之下，约10磅），利用基于小得多的样本量（n=139）的GWAS。

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).