Evidence map›Paper›PMID 36824222›Full record

ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2022

Mediation Analysis and Mixed-Effects Models for the Identification of Stage-specific Imaging Genetics Patterns in Alzheimer's Disease.

Daniele Pala, Brian Lee, Xia Ning, Dokyoon Kim, Li Shen, ADNI

Open access · greenAbstract read
In one paragraph

Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.2field-weighted citation impact, top 10% 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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
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

6 authors at 2 institutions in 1 country.

Daniele PalaDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA.
Brian LeeDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA.
Xia NingDepartment of Biomedical Informatics, The Ohio State University, Columbus, USA.
Dokyoon KimDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA.
Li ShenDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA.
ADNI
University of Pennsylvania · USThe Ohio State University · US

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
National Centralized Repository for Alzheimer's Disease and Related Dementias (NCRAD)U24AG021886 · NIA · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI TATIANA M. FOROUD · 2002 to 2026
$119.8M
Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease BiobanksU01AG068057 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Christos Davatzikos, Heng Huang · 2020 to 2026
$20.7M
Artificial Intelligence Strategies for Alzheimer's Disease ResearchU01AG066833 · NIA · CEDARS-SINAI MEDICAL CENTER · PI MOORE, JASON H., RITCHIE, MARYLYN D · 2022 to 2025
$6.7M
Translational big data analytic approaches to advance drug repurposing for Alzheimer's diseaseR01AG071470 · NIA · UNIVERSITY OF PENNSYLVANIA · PI KIM, DOKYOON, NING, XIA · 2021 to 2025
$3.8M
Novel integrative imaging genetics analysis for Alzheimer's disease riskand progressionRF1AG068191 · NIA · YALE UNIVERSITY · PI ZHAO, YIZE · 2021 to 2021
$1.9M
Artificial Intelligence Strategies for Alzheimer's Disease ResearchR01AG066833 · NIA · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H., RITCHIE, MARYLYN D · 2021 to 2021
$1.6M
Informatics Algorithms for Genomic Analysis of Brain Imaging DataR01LM013463 · NLM · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H., SAYKIN, ANDREW J · 2020 to 2023
$1.4M
NIA NIH HHS R01 AG066833NIA NIH HHS R01 AG071470NIA NIH HHS RF1 AG068191NIA NIH HHS U01 AG024904NIA NIH HHS U01 AG066833NIA NIH HHS U01 AG068057NIA NIH HHS U24 AG021886NLM NIH HHS R01 LM013463
6 · The paper itself

Abstract

Alzheimer's disease (AD) is one of the most common and severe forms of Senile Dementia. Genome-wide association studies (GWAS) have identified dozens of AD susceptible loci. To better understand potential mechanism-of-action for AD, quantitative brain imaging features have been studied as mediators linking genetic variants to AD outcomes. In this study, Mediation analysis, Chow test and Mixed-effects Models are used to investigate the biological pathways by which genetic variants affect both brain structures/functions and disease diagnosis. We analyzed the imaging and genetics data collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project, including a Polygenic Hazard Score (PHS) and 13 imaging quantitative traits (QTs) extracted from the AV45 PET scans quantifying the amyloid deposition in different brain regions of subjects from four separate diagnostic groups. Mediation analysis assessed the mediating effects of image QTs between PHS and diagnosis, whereas Chow test and Linear Mixed-Effects models were used to characterize intra-group differences in the associations between genetic scores and imaging QTs for different disease stages. Results show that promising stage-specific imaging QTs that mediate the genetic effect of the studied PHS on disease status have been identified, providing novel insights into the predictive power of the PHS and the mediating power of amyloid imaging QTs with respect to multiple stages over the AD progression.

Indexed as

Alzheimer’s DiseaseChow testMediation AnalysisMixed-Effects ModelPolygenic Hazard Score

Identifiers

PMID36824222
PMCPMC9942815
OpenAlexW4313526634

What OpenQuestion holds

Textmetadata
LicenceTDM
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.