Role of the metabolic syndrome profile in genetic susceptibility to Alzheimer's d

描述(由申请人提供)：来自人类流行病学研究的越来越多的证据表明，代谢异常与年龄相关的认知能力下降和损害有关。由于肥胖和久坐不动的生活方式在美国可能正在上升，因此阐明这些可改变的行为对认知能力下降的风险所起的作用至关重要。阿尔茨海默病(AD)是一种最常见的进行性神经退行性疾病，可导致个体严重的认知、功能和行为障碍。确定以下因素 增强自身对阿尔茨海默病的易感性可以为其预防、治疗和管理提供依据。几种常见疾病的聚集性，包括腹型肥胖、高甘油三酯血症、低高密度脂蛋白水平、高血压和高血糖定义了代谢综合征的特征。现有文献表明，阿尔茨海默病的一个主要易感因素是遗传易感性，占归因风险的60-80%。最近的一些遗传和基因组研究证明了代谢综合征的遗传易感性。这项建议的目标是应用先进的统计技术来研究遗传和代谢综合征风险因素在AD中的独立、累积和交互影响。这项拟议的研究有可能发现新的遗传风险因素，并更好地表征代谢综合征风险因素在AD中的作用。这个项目的另一个创新特点是这种方法是灵活的和 可应用于其他复杂的疾病，如帕金森氏症、药物滥用或精神分裂症，这些疾病存在其他大型遗传和临床数据集。

DESCRIPTION (provided by applicant): Increasing evidence from human epidemiologic studies suggests that metabolic abnormalities are associated with age-related cognitive decline and impairment. Because obesity and sedentary lifestyle might be on rise in the United States, it is critical to elucidate the role of these modifiable behaviors on risk for cognitive decline. Alzheimer's disease (AD) is the most common progressive neurodegenerative illness that leads to severe cognitive, functional and behavioral impairments on individuals. Identifying factors that enhance one's vulnerability to AD can provide a basis for its prevention, treatment and management. A clustering of several commonly occurring disorders, including abdominal obesity, hypertriglyceridemia, low high-density lipoprotein level, hypertension and hyperglycemia define the metabolic syndrome profile. The extant literature suggests that a major vulnerability factor to AD is genetic predisposition, accounting for 60-80% of the attributable risk. A number of recent genetic and genomic studies demonstrated evidence for genetic predisposition to the metabolic syndrome profile. The goal of this proposal is to apply advanced statistical techniques to study the independent, cumulative and interactive effects of genetic and metabolic syndrome risk factors in AD. The proposed study has the potential to uncover new genetic risk factors and to better characterize role of the metabolic syndrome risk factors for AD. Another innovative feature of this project is that this methodology is flexible and can be applied to other complex diseases, such as Parkinson's, substance abuse or Schizophrenia where other large genetic and clinical datasets exist.