EPIDEMIOLOGY OF ALZHEIMERS DEMENTIA IN CACHE COUNTY UT

我们建议对阿尔茨海默病(AD)的发生和 其他痴呆症与基因、年龄和其他因素有关。绘图 在科学和技术的进步上，这项研究寻求重要的新 了解阿尔茨海默病的原因和可能的预防措施。最新数据 显示AD和等位基因epsilon 4之间存在关联。 载脂蛋白E(ApoE)是一种多态的脂质转运蛋白。等位基因 在AD患者中，epsilon 4的频率为0.40-0.50(参见最多0.014英镑 种群)。在平衡状态下，70%的AD流行病例将 至少一个epsilon 4等位基因(参见占大多数人口的26%。)新证据 提示在家族性AD中，epsilon 4足以诱发AD 家系所有epsilon 4纯合子在80岁前患上阿尔茨海默病；杂合子 到90岁时患上阿尔茨海默病，他们的疾病表现似乎最严重 快80岁了。因此，我们建议检验两个假设： 足以引发阿尔茨海默病，不仅在家族性AD家系中，而且 在所有携带等位基因的人中。如果epsilon 4占AD的70% 这些病例的疾病表现在接近80岁时达到最大值，然后： H2)阿尔茨海默病的发病率将在85岁之前最高，并呈下降趋势 之后。为了检验这些假设，我们提出了一个流行率和 一位长寿且合作的老人的AD发病率分析 将进行载脂蛋白E等位基因分型的5650名老年人的样本。 同时，我们将研究影响疾病风险的其他因素。 新的同卵双胞胎对照研究表明，长期接触抗肿瘤药物 炎症药物(AI‘s)与AD呈负相关(O.R.0.24，95% 中情局。0.07-0.74)。人工智能可能通过推迟AD的发病而起作用，但 只有在特定的年龄或性别中，才能看到AD与人工智能的联系 组。因此，我们建议测试三个额外的假设：H3) 人工智能与85岁之前AD发病率的降低有关；如果 风险的降低是因为人工智能推迟了AD的发作，然后 (令人惊讶的是)有人工智能暴露的人中AD的发病率将是 85岁以后增加；以及H4)人工智能与AD风险的关系显示 与载脂蛋白E基因、年龄或性别的交互作用。最后，我们 应研究载脂蛋白E基因、年龄和性别与 其他AD风险因素，如教育、头部损伤和家族史 痴呆症。 载脂蛋白E等位基因和风险因素将在第一年和第二年进行评估，因为 确定了痴呆症的可疑流行原因。在第二年和 3一个关键的信息网络将确定痴呆症的事件病例 将进行类似的评估。在第4年和第5年，出现了新一轮的筛选 评估将确定密钥未检测到的事件案例 告密者。然后将计算特定年龄段的发病率，并 这项研究的每一项假设都将得到检验。

We propose a study of the occurrence of Alzheimer's disease (AD) and other dementias in relation to genotype, age and other factors. Drawing on advances in both science and technology, the study seeks important new understanding of the causes and possible prevention of AD. Recent data from several groups show association between AD and allele epsilon4 of Apolipoprotein E (ApoE), a polymorphic lipid transporter. The allele frequency for epsilon4 in AD cases is 0.40 - 0.50 (cf. 0.014 in most populations). At equilibrium, 70% of prevalent AD cases would then bear at least one epsilon4 allele (cf. 26% of most populations.) New evidence suggests that epsilon4 is sufficient to provoke AD: in familial AD pedigrees all epsilon4 homozygotes develop AD by age 80; heterozygotes develop AD by age 90, and their disease expression appears to be maximal near age 80. We therefore propose to test two hypotheses: H1) epsilon4 is sufficient to provoke AD, not only within familial AD pedigrees but in all those bearing the allele. If epsilon4 accounts for 70% of AD cases, and disease expression in such cases is maximal near age 80, then: H2) the incidence of AD will be maximal before age 85 and decline thereafter. To test these hypotheses, we propose a prevalence and incidence analysis of AD in a remarkably long-lived and cooperative sample of 5650 elders who will have been genotyped for ApoE alleles. Simultaneously, we shall study other factors that influence disease risk. New co-twin control studies suggest that chronic exposure to anti- inflammatory drugs (AI's) is inversely associated with AD (o.r. 0.24, 95% c.i. 0.07 - 0.74). AI's may act by delaying the onset of AD, but the association of AD with AI's has been seen only in certain age or sex groups. We therefore propose to test three additional hypotheses: H3) AI's are associated with reduced incidence of AD before age 85; if the reduction in risk occurs because AI's delay the onset of AD, then (surprisingly) the incidence of AD among those with AI exposure will be increased after age 85; and H4) the relation of AI's to AD risk shows interaction with ApoE genotype, and with age or gender. Finally, we shall examine similar interactions of ApoE genotype, age and gender with other AD risk factors such as education, head injury, and family history of dementia. ApoE alleles and risk factors will be assessed in Years 1 and 2, as suspected prevalent causes of dementia are identified. In Years 2 and 3 a key informant network will identify incident cases of dementia who will be similarly evaluated. In Years 4 and 5 a new wave of screening and assessment will identify incident cases not detected by the key informants. Age-specific incidence rates will then be calculated, and each of the study's hypotheses will be tested.