Sapkota Shraddha, McFall G Peggy, Masellis Mario, Dixon Roger A
Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.
Department of Psychology, University of Alberta, Edmonton, AB, Canada.
Front Aging Neurosci. 2021 Sep 16;13:621023. doi: 10.3389/fnagi.2021.621023. eCollection 2021.
Multiple modalities of Alzheimer's disease (AD) risk factors may operate through interacting networks to predict differential cognitive trajectories in asymptomatic aging. We test such a network in a series of three analytic steps. First, we test independent associations between three risk scores (functional-health, lifestyle-reserve, and a combined multimodal risk score) and cognitive [executive function (EF)] trajectories. Second, we test whether all three associations are moderated by the most penetrant AD genetic risk [ () ε4+ allele]. Third, we test whether a non- AD genetic risk score further moderates these × multimodal risk score associations. We assembled a longitudinal data set (spanning a 40-year band of aging, 53-95 years) with non-demented older adults (baseline = 602; age = 70.63(8.70) years; 66% female) from the Victoria Longitudinal Study (VLS). The measures included for each modifiable risk score were: (1) functional-health [pulse pressure (PP), grip strength, and body mass index], (2) lifestyle-reserve (physical, social, cognitive-integrative, cognitive-novel activities, and education), and (3) the combination of functional-health and lifestyle-reserve risk scores. Two AD genetic risk markers included (1) and (2) a combined AD-genetic risk score (AD-GRS) comprised of three single nucleotide polymorphisms (SNPs; [rs11136000], C[rs6656401], [rs3851179]). The analytics included confirmatory factor analysis (CFA), longitudinal invariance testing, and latent growth curve modeling. Structural path analyses were deployed to test and compare prediction models for EF performance and change. First, separate analyses showed that higher functional-health risk scores, lifestyle-reserve risk scores, and the combined score, predicted poorer EF performance and steeper decline. Second, and AD-GRS moderated the association between functional-health risk score and the combined risk score, on EF performance and change. Specifically, only older adults in the ε4- group showed steeper EF decline with high risk scores on both functional-health and combined risk score. Both associations were further magnified for adults with high AD-GRS. The present multimodal AD risk network approach incorporated both modifiable and genetic risk scores to predict EF trajectories. The results add an additional degree of precision to risk profile calculations for asymptomatic aging populations.
阿尔茨海默病(AD)风险因素的多种模式可能通过相互作用的网络发挥作用,以预测无症状衰老过程中的不同认知轨迹。我们通过三个分析步骤来测试这样一个网络。首先,我们测试三个风险评分(功能健康、生活方式储备和综合多模式风险评分)与认知[执行功能(EF)]轨迹之间的独立关联。其次,我们测试这三种关联是否都由最具渗透性的AD遗传风险[()ε4 +等位基因]调节。第三,我们测试非AD遗传风险评分是否进一步调节这些×多模式风险评分关联。我们从维多利亚纵向研究(VLS)中收集了一个纵向数据集(涵盖40年的衰老阶段,53 - 95岁),其中包括无痴呆的老年人(基线 = 602;年龄 = 70.63(8.70)岁;66%为女性)。每个可改变风险评分所包含的测量指标为:(1)功能健康[脉压(PP)、握力和体重指数],(2)生活方式储备(身体、社交、认知整合、认知新奇活动和教育),以及(3)功能健康和生活方式储备风险评分的组合。两个AD遗传风险标记包括(1)和(2)一个由三个单核苷酸多态性(SNP;[rs11136000]、C[rs6656401]、[rs3851179])组成的综合AD遗传风险评分(AD - GRS)。分析包括验证性因子分析(CFA)、纵向不变性测试和潜在增长曲线建模。采用结构路径分析来测试和比较EF表现和变化的预测模型。首先,单独分析表明,较高的功能健康风险评分、生活方式储备风险评分和综合评分,预测EF表现较差且下降更陡峭。其次,和AD - GRS调节了功能健康风险评分与综合风险评分之间关于EF表现和变化的关联。具体而言,只有ε4 -组的老年人在功能健康和综合风险评分上具有高风险评分时,EF下降更陡峭。对于AD - GRS高的成年人,这两种关联进一步放大。目前的多模式AD风险网络方法纳入了可改变和遗传风险评分来预测EF轨迹。结果为无症状衰老人群的风险概况计算增加了额外的精确程度。