期刊
CNS NEUROSCIENCE & THERAPEUTICS
卷 29, 期 4, 页码 1034-1048出版社
WILEY
DOI: 10.1111/cns.14073
关键词
ADNI; Alzheimer's disease; genetics; mild cognitive impairment; networking; neuroimaging
This study examined the association between genetic and neuroimaging biomarkers in late-onset dementia-related cognitive impairment. The results showed significant correlations between specific genomic markers and neuroimaging markers, and identified key markers for distinguishing Alzheimer's disease and mild cognitive impairment.
BackgroundAlzheimer's disease (AD), the most prevalent form of dementia, affects 6.5 million Americans and over 50 million people globally. Clinical, genetic, and phenotypic studies of dementia provide some insights of the observed progressive neurodegenerative processes, however, the mechanisms underlying AD onset remain enigmatic. AimsThis paper examines late-onset dementia-related cognitive impairment utilizing neuroimaging-genetics biomarker associations. Materials and MethodsThe participants, ages 65-85, included 266 healthy controls (HC), 572 volunteers with mild cognitive impairment (MCI), and 188 Alzheimer's disease (AD) patients. Genotype dosage data for AD-associated single nucleotide polymorphisms (SNPs) were extracted from the imputed ADNI genetics archive using sample-major additive coding. Such 29 SNPs were selected, representing a subset of independent SNPs reported to be highly associated with AD in a recent AD meta-GWAS study by Jansen and colleagues. ResultsWe identified the significant correlations between the 29 genomic markers (GMs) and the 200 neuroimaging markers (NIMs). The odds ratios and relative risks for AD and MCI (relative to HC) were predicted using multinomial linear models. DiscussionIn the HC and MCI cohorts, mainly cortical thickness measures were associated with GMs, whereas the AD cohort exhibited different GM-NIM relations. Network patterns within the HC and AD groups were distinct in cortical thickness, volume, and proportion of White to Gray Matter (pct), but not in the MCI cohort. Multinomial linear models of clinical diagnosis showed precisely the specific NIMs and GMs that were most impactful in discriminating between AD and HC, and between MCI and HC. ConclusionThis study suggests that advanced analytics provide mechanisms for exploring the interrelations between morphometric indicators and GMs. The findings may facilitate further clinical investigations of phenotypic associations that support deep systematic understanding of AD pathogenesis.
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