The SNP rs6859 in NECTIN2 gene is associated with underlying heterogeneous trajectories of cognitive changes in older adults

Abstract

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.

Keywords: Heterogeneity; Latent class; MMSE; NECTIN2; Trajectory analysis; rs6859.

Fig. 1

Longitudinal measures of individual MMSE in the ADNI cohort for clinical visits stratified by rs6859 genotype

Fig. 2

Forest plot showing the result of the covariate adjusted linear mixed models for the normalized MMSE response. Note. Clinical conditions such as diabetes, hypertension, dyslipidemia, and cardiovascular disease (CVD) were discerned from the medication files, indirectly reflecting the impact of relevant medications on the longitudinal MMSE.

Fig. 3

Effect plots depicting the association between rs6859 and clinical visits with normalized MMSE are represented by Figure (a) and Figure (b) respectively. Note. The plot was generated from a covariate adjusted linear mixed model. The dark blue line and surrounding light blue colour show the relationship and associated 95% confidence intervals. The black colour lines in the x-axis for figures show the density of the observed values

Fig. 4

Individual variable contribution towards linear mixed model variance identified using hierarchical partitioning

Fig. 5

Weighted mean marginal predictions showing the trajectory of MMSE for the 3 latent classes identified from the grid search across time (n = 1310, observations = 6864). Note. The green, red, and black dots represent observations corresponding to classes 1, 2, and 3 respectively. The solid and dashed lines represent model-fitted lines and confidence intervals for the three latent subclasses. The x-axis shows the order of MMSE measurements from the baseline visit. The class membership probabilities were used as weights to generate the mean trajectories [40].