Rush Institute for Healthy Aging, Rush University Medical Center, Chicago, Illinois, USA.
Department of Internal Medicine, Rush University Medical Center, Chicago, Illinois, USA.
Alzheimers Dement. 2024 Nov;20(11):7913-7922. doi: 10.1002/alz.14280. Epub 2024 Oct 12.
Identifying people at high risk of Alzheimer's disease (AD) dementia allows for timely intervention, which, if successful, will result in preventing or delaying the onset of the disease.
Utilizing data from the Chicago Health and Aging Project (CHAP; n = 2130), we externally evaluated four risk-prediction models for AD dementia, including Cardiovascular Risk Factors, Aging, and Dementia (CAIDE), Australian National University Alzheimer's Disease Risk Index (ANU-ADRI), Brief Dementia Screening Indicator (BDSI), and Dementia Risk Score (DRS), in Black or African American and White adults.
BDSI had the highest discriminate abilities for AD dementia (c-statistics of 0.79 in Black and 0.77 in White adults), followed by ANU-ADRI, within the age range and follow-up period of the original development cohort. CAIDE had the lowest discriminating power (c-statistic ≤0.55). With increasing follow-up periods (i.e., 10-15 years), the discrimination abilities for all models declined.
Because of racial disparities in AD dementia and longer preclinical and prodromal stages of disease development, race-specific models are needed to predict AD risk over 10 years.
Utilizing risk-prediction models to identify individuals at higher risk of Alzheimer's disease (AD) dementia could benefit clinicians, patients, and policymakers. Clinicians could enroll high-risk individuals in clinical trials to test new risk-modifiable treatments or initiate lifestyle modifications, which, if successful, would slow cognitive decline and delay the onset of the disease. Current risk-prediction models had good discriminative power during the first 6 years of follow-up but decreased with longer follow-up time. Acknowledging the longer preclinical phase of AD dementia development and racial differences in dementia risk, there is a need to develop race-specific risk-prediction models that can predict 10 or 20 years of risk for AD and related dementias.
识别出患有阿尔茨海默病（AD）痴呆症风险较高的人群，可以及时进行干预，如果干预成功，将预防或延迟疾病的发作。
我们利用芝加哥健康与老龄化项目（CHAP；n=2130）的数据，对外评估了针对 AD 痴呆症的四个风险预测模型，包括心血管危险因素、衰老和痴呆症（CAIDE）、澳大利亚国立大学阿尔茨海默病风险指数（ANU-ADRI）、简短痴呆筛查指标（BDSI）和痴呆风险评分（DRS），这些模型适用于黑人和白人成年人。
BDSI 在黑人成年人中的 AD 痴呆症区分能力最高（原始开发队列的年龄范围和随访期内的 C 统计量为 0.79），其次是 ANU-ADRI。CAIDE 的区分能力最低（C 统计量≤0.55）。随着随访时间的延长（即 10-15 年），所有模型的判别能力均下降。
由于 AD 痴呆症在黑人和白人中的种族差异以及疾病发展的更长的临床前期和前驱期，因此需要针对不同种族的模型来预测 10 年以上的 AD 风险。
利用风险预测模型识别出患有阿尔茨海默病（AD）痴呆症风险较高的个体，将使临床医生、患者和决策者受益。临床医生可以让高风险个体参加临床试验，以测试新的可改变风险的治疗方法或启动生活方式的改变，如果成功，将减缓认知能力下降并延迟疾病的发作。目前的风险预测模型在最初 6 年的随访期间具有良好的区分能力，但随着随访时间的延长，其区分能力下降。鉴于 AD 痴呆症的更长的临床前期阶段以及痴呆症风险的种族差异，需要开发针对特定种族的风险预测模型，以预测 10 年或 20 年的 AD 和相关痴呆症风险。
