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

• Published in: BMC neurology Vol. 24; no. 1; p. 78
• Main Authors: Rajendrakumar, Aravind Lathika, Arbeev, Konstantin G, Bagley, Olivia, Yashin, Anatoliy I, Ukraintseva, Svetlana
• Format: Journal Article
• Language: English
• Published: England BioMed Central 27.02.2024; BMC
• Subjects: Age; Aged; Alleles; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - genetics; Alzheimer's disease; Biomarkers; Cell adhesion molecules; Cognition; Cognitive ability; Cognitive Dysfunction - diagnosis; Cross-Sectional Studies; Datasets; Dementia; Dementia disorders; Disease; Genes; Genetic diversity; Genetic variability; Heterogeneity; Humans; Latent class; Longitudinal studies; Maximum likelihood method; Medical imaging; MMSE; Multivariate analysis; Nectin; NECTIN2; Neurodegenerative diseases; Neuroimaging; Older people; Regression analysis; Retrospective Studies; Risk factors; rs6859; Single-nucleotide polymorphism; Trajectory analysis; Heterogeneity; rs6859; Latent class; Trajectory analysis; MMSE; NECTIN2
• ISSN: 1471-2377; 1471-2377
• DOI: 10.1186/s12883-024-03577-4

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). 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. 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). 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.