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

Role of the metabolic syndrome profile in genetic susceptibility to Alzheimer's d
代谢综合征谱在阿尔茨海默病遗传易感性中的作用 d
批准号:
8702571
负责人:
Iryna Lobach
金额:
$21.09万
依托单位国家:
美国
项目类别:
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-05-15 至 2016-04-30

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项目成果

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中文摘要
翻译
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呈上升趋势,阐明这些可改变的行为在认知能力下降风险中的作用至关重要。阿尔茨海默病(AD)是最常见的进行性神经退行性疾病,会导致个体严重的认知、功能和行为障碍。识别以下因素:
英文摘要
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.
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