Evidence map›Paper›PMID 36575854›Full record

ArticleCNS neuroscience & therapeutics2023

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

Open access · goldFull text read
In one paragraph

Article in CNS neuroscience & therapeutics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.8field-weighted citation impact, top 27% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 7 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 4 institutions in 2 countries.

Seok Woo MoonDepartment of Neuropsychiatry, Research Institute of Medical Science, Konkuk University School of Medicine, Seoul, Korea.ORCID 0000-0002-1074-8122
Lu ZhaoUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, California, Los Angeles, USA.
William MatloffUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, California, Los Angeles, USA.
Sam HobelUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, California, Los Angeles, USA.
Ryan BergerMicrobiology & Immunology, University of Michigan, Ann Arbor, Michigan, USA.
Daehong KwonDepartment of Biomedical Science and Engineering, Konkuk University, Seoul, Korea.
Jaebum KimDepartment of Biomedical Science and Engineering, Konkuk University, Seoul, Korea.
Arthur W TogaUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, California, Los Angeles, USA.
Ivo D DinovUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, California, Los Angeles, USA.ORCID 0000-0003-3825-4375
Alzheimer's Disease Neuroimaging Initiative
Keck Hospital of USC · USKonkuk University · KRUniversity of California, Los Angeles · USUniversity of Michigan · US

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
Michigan Institute for Clinical and Health Research (MICHR)UL1TR002240 · NCATS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LUMENG, JULIE C, MASHOUR, GEORGE ALEXANDER · 2017 to 2022
$54.9M
USCADRC Diversity Supplement PachicanoP30AG066530 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI HELENA Chang CHUI, ARTHUR W TOGA · 2020 to 2026
$27.8M
COVID-19 supplement to a computational examination of threat and reward constructs in a predominantly low-income, longitudinal sample at increased risk for internalizing disordersR01MH121079 · NIMH · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI HYDE, LUKE WILLIAMSON, MITCHELL, COLTER M.S. · 2019 to 2024
$7.2M
Digital behavioral phenotyping and multi-region electrophysiology to determine behavioral and neural network changes underlying the stress response in miceR01MH126137 · NIMH · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI WATSON, BRENDON O · 2021 to 2025
$3.3M
Biomedical Informatics and Data Science Training Program (BIDS-TP)T32GM141746 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Ivo D Dinov, RYAN E MILLS · 2021 to 2026
$2.6M
Optimal Decision Making in Radiotherapy Using Panomics AnalyticsR01CA233487 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI EL NAQA, ISSAM M. · 2019 to 2023
$2.4M
NCATS NIH HHS UL1 TR002240NCI NIH HHS R01 CA233487NIA NIH HHS P30 AG066530NIA NIH HHS U01 AG024904NIGMS NIH HHS T32 GM141746NIMH NIH HHS R01 MH121079NIMH NIH HHS R01 MH126137
6 · The paper itself

Abstract

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. 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.

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.

Indexed as

Alzheimer DiseaseCognitive DysfunctionAgedAged, 80 and overBrainDisease ProgressionGray MatterHumansNeuroimagingADNIAlzheimer's diseasegeneticsmild cognitive impairmentnetworkingneuroimaging

Identifiers

PMID36575854
PMCPMC10018103
OpenAlexW4313237692

What OpenQuestion holds

Textfull text, public
LicenceCC BY
reference markers read1
measurements read13
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.