背景:与痴呆相关的功能减退,包括阿尔茨海默病(AD),在个人之间并不统一,和各自的异质性尚未完全解释。这种异质性可能部分与个体之间的遗传变异性有关。在这项研究中,我们调查了nectin细胞粘附分子2(NECTIN2)基因(AD的主要危险因素)中的SNPrs6859是否影响阿尔茨海默病神经影像学计划(ADNI)中老年参与者的认知功能减退轨迹.
方法:我们回顾性分析了ADNI数据库中1310名参与者的记录,用于多变量分析。我们对参与者使用了简易精神状态检查(MMSE)分数的纵向测量,认知正常的人,或者有AD,或其他认知缺陷来研究认知变化的轨迹。多元线性回归,进行线性混合模型和潜在类别分析,以研究SNPrs6859与MMSE的相关性.
结果:在横截面(-2.23,p<0.01)和线性混合模型(-2.26,p<0.01)分析中,SNPrs6859每个等位基因剂量的回归系数与MMSE独立相关。具有三个不同子群的潜在类模型(类1:稳定和逐渐下降,2类:中后期下降,和第3类:最低和不规则)在后验分类中表现最好,42.67%(n=559),21.45%(n=281),35.88%(n=470)被分类为1级,2级和3级。在异质线性混合模型中,rs6859-A风险等位基因每一个等位基因剂量的回归系数与MMSE1级和2级成员和相关下降显著相关;1级(-2.28,95%CI:-4.05,-0.50,p<0.05),第2类(-5.56,95%CI:-9.61,-1.51,p<0.01)和第3类(-0.37,95%CI:-1.62,0.87,p=0.55)。
结论:本研究发现了支持ADNI队列中代表复杂MMSE轨迹的三个潜在亚类组的分类的统计学证据。SNPrs6859可以作为MMSE轨迹建模中变异的候选遗传预测因子,以及用于识别具有较高基线MMSE的潜在类别。功能研究可能有助于进一步阐明这种关系。
BACKGROUND: Functional decline associated with dementia, including in Alzheimer\'s disease (AD), is not uniform across individuals, and respective heterogeneity is not yet fully explained. Such heterogeneity may in part be related to genetic variability among individuals. In this study, we investigated whether the SNP rs6859 in nectin cell adhesion molecule 2 (NECTIN2) gene (a major risk factor for AD) influences trajectories of cognitive decline in older participants from the Alzheimer\'s Disease Neuroimaging Initiative (ADNI).
METHODS: We retrospectively analyzed records on 1310 participants from the ADNI database for the multivariate analysis. We used longitudinal measures of Mini-Mental State Examination (MMSE) scores in participants, who were cognitively normal, or having AD, or other cognitive deficits to investigate the trajectories of cognitive changes. Multiple linear regression, linear mixed models and latent class analyses were conducted to investigate the association of the SNP rs6859 with MMSE.
RESULTS: The regression coefficient per one allele dose of the SNP rs6859 was independently associated with MMSE in both cross-sectional (-2.23, p < 0.01) and linear mixed models (-2.26, p < 0.01) analyses. The latent class model with three distinct subgroups (class 1: stable and gradual decline, class 2: intermediate and late decline, and class 3: lowest and irregular) performed best in the posterior classification, 42.67% (n = 559), 21.45% (n = 281), 35.88% (n = 470) were classified as class 1, class 2, and class 3. In the heterogeneous linear mixed model, the regression coefficient per one allele dose of rs6859 - A risk allele was significantly associated with MMSE class 1 and class 2 memberships and related decline; Class 1 (-2.28, 95% CI: -4.05, -0.50, p < 0.05), Class 2 (-5.56, 95% CI: -9.61, -1.51, p < 0.01) and Class 3 (-0.37, 95% CI: -1.62, 0.87, p = 0.55).
CONCLUSIONS: This study found statistical evidence supporting the classification of three latent subclass groups representing complex MMSE trajectories in the ADNI cohort. The SNP rs6859 can be suggested as a candidate genetic predictor of variation in modeling MMSE trajectory, as well as for identifying latent classes with higher baseline MMSE. Functional studies may help further elucidate this relationship.