Brain structure and allelic associations in Alzheimer's disease

Seok Woo Moon; Lu Zhao; William Matloff; Sam Hobel; Ryan Berger; Daehong Kwon; Jaebum Kim; Arthur W Toga; Ivo D Dinov; Alzheimer's Disease Neuroimaging Initiative

doi:10.1111/cns.14073

Brain structure and allelic associations in Alzheimer's disease

CNS Neurosci Ther. 2023 Apr;29(4):1034-1048. doi: 10.1111/cns.14073. Epub 2022 Dec 27.

Authors

Seok Woo Moon^{1

2}, Lu Zhao², William Matloff², Sam Hobel², Ryan Berger³, Daehong Kwon⁴, Jaebum Kim⁴, Arthur W Toga², Ivo D Dinov^{2

5

6}; Alzheimer's Disease Neuroimaging Initiative

Affiliations

¹ Department of Neuropsychiatry, Research Institute of Medical Science, Konkuk University School of Medicine, Seoul, Korea.
² USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, California, Los Angeles, USA.
³ Microbiology & Immunology, University of Michigan, Ann Arbor, Michigan, USA.
⁴ Department of Biomedical Science and Engineering, Konkuk University, Seoul, Korea.
⁵ Department of Health Behavior and Biological Sciences, Statistics Online Computational Resource (SOCR), Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, Michigan, USA.
⁶ Department of Statistics, University of California, Los Angeles, California, USA.

Abstract

Background: Alzheimer'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.

Aims: This paper examines late-onset dementia-related cognitive impairment utilizing neuroimaging-genetics biomarker associations.

Materials and methods: The 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.

Results: We 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.

Discussion: In 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.

Conclusion: This 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.

Keywords: ADNI; Alzheimer's disease; genetics; mild cognitive impairment; networking; neuroimaging.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Aged
Aged, 80 and over
Alzheimer Disease* / diagnostic imaging
Alzheimer Disease* / genetics
Brain / diagnostic imaging
Brain / pathology
Cognitive Dysfunction* / complications
Cognitive Dysfunction* / diagnostic imaging
Cognitive Dysfunction* / genetics
Disease Progression
Gray Matter / pathology
Humans
Neuroimaging / methods

Abstract

Publication types

MeSH terms

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