Evidence map›Paper›PMID 35738479›Full record

ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2023

Genetic and Environmental Influences on Structural and Diffusion-Based Alzheimer's Disease Neuroimaging Signatures Across Midlife and Early Old Age.

McKenna E Williams, Nathan A Gillespie, Tyler R Bell, Anders M Dale, Jeremy A Elman, Lisa T Eyler, Christine Fennema-Notestine, Carol E Franz, Donald J Hagler, Michael J Lyons and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

16 authors.

McKenna E WilliamsCenter for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California; Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, California. Electronic address: mewillia@health.ucsd.edu.
Nathan A GillespieVirginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, Virginia.
Tyler R BellCenter for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California.
Anders M DaleDepartment of Radiology, University of California San Diego, San Diego, California; Department of Neuroscience, University of California San Diego, San Diego, California.
Jeremy A ElmanCenter for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California.
Lisa T EylerDesert Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System, San Diego, California.
Christine Fennema-NotestineDepartment of Psychiatry, University of California San Diego, San Diego, California; Department of Radiology, University of California San Diego, San Diego, California.
Carol E FranzCenter for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California.
Donald J HaglerDepartment of Radiology, University of California San Diego, San Diego, California.
Michael J LyonsDepartment of Psychological and Brain Sciences, Boston University, Boston, Massachusetts.
Linda K McEvoyDepartment of Radiology, University of California San Diego, San Diego, California.
Michael C NealeVirginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, Virginia.
Matthew S PanizzonCenter for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California.
Chandra A ReynoldsDepartment of Psychology, University of California Riverside, Riverside, California.
Mark Sanderson-CiminoCenter for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California; Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, California.
William S KremenCenter for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California.

Funding

The VETSA Longitudinal Twin Study of Cognition and Aging (VETSA 4)R01AG050595 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ELMAN, JEREMY A, FRANZ, CAROL ELAINE · 2015 to 2024
$28.0M
The VETSA Longitudinal MRI Twin Study of AgingR01AG022381 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI KREMEN, WILLIAM S. · 2003 to 2019
$22.3M
The VETSA Longitudinal Twin Study of Cognition and AgingR01AG018384 · NIA · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI LYONS, MICHAEL J. · 2002 to 2012
$9.8M
The VETSA longitudinal twin study of cognition and agingR01AG018386 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI KREMEN, WILLIAM S. · 2002 to 2012
$9.7M
The VETSA Longitudinal MRI Twin Study of Aging (VETSA MRI 4)R01AG076838 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ANDERS M DALE, Jeremy A Elman · 2022 to 2026
$8.7M
Genetic Predictors, Transcriptomic Biomarkers, & Neurobiological Signatures of Resilience to Alzheimer's DiseaseR01AG064955 · NIA · UPSTATE MEDICAL UNIVERSITY · PI FENNEMA-NOTESTINE, CHRISTINE, GLATT, STEPHEN J · 2019 to 2023
$3.8M
The VETSA Longitudinal Twin Study of Cortisol and AgingR01AG022982 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI KREMEN, WILLIAM S. · 2004 to 2007
$2.6M
Linking genetic subtypes of Alzheimer's disease to biological and cognitive heterogeneityK01AG063805 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ELMAN, JEREMY A · 2021 to 2024
$516k
Testosterone-Androgen Receptor Genetics: Role in Cognitive and Biological AgingK08AG047903 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI PANIZZON, MATTHEW S · 2014 to 2017
$452k
Archiving the Vietnam Era Twin Studies of Aging (VETSA): New Uses for Old DataR03AG046413 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI FRANZ, CAROL ELAINE · 2013 to 2014
$247k
NIA NIH HHS K01 AG063805NIA NIH HHS K08 AG047903NIA NIH HHS R01 AG018384NIA NIH HHS R01 AG018386NIA NIH HHS R01 AG022381NIA NIH HHS R01 AG022982NIA NIH HHS R01 AG050595NIA NIH HHS R01 AG064955NIA NIH HHS R01 AG076838NIA NIH HHS R03 AG046413
6 · The paper itself

Abstract

backgroundComposite scores of magnetic resonance imaging-derived metrics in brain regions associated with Alzheimer's disease (AD), commonly termed AD signatures, have been developed to distinguish early AD-related atrophy from normal age-associated changes. Diffusion-based gray matter signatures may be more sensitive to early AD-related changes compared with thickness/volume-based signatures, demonstrating their potential clinical utility. The timing of early (i.e., midlife) changes in AD signatures from different modalities and whether diffusion- and thickness/volume-based signatures each capture unique AD-related phenotypic or genetic information remains unknown.

methodsOur validated thickness/volume signature, our novel mean diffusivity (MD) signature, and a magnetic resonance imaging-derived measure of brain age were used in biometrical analyses to examine genetic and environmental influences on the measures as well as phenotypic and genetic relationships between measures over 12 years. Participants were 736 men from 3 waves of the Vietnam Era Twin Study of Aging (VETSA) (baseline/wave 1: mean age [years] = 56.1, SD = 2.6, range = 51.1-60.2). Subsequent waves occurred at approximately 5.7-year intervals.

resultsMD and thickness/volume signatures were highly heritable (56%-72%). Baseline MD signatures predicted thickness/volume signatures over a decade later, but baseline thickness/volume signatures showed a significantly weaker relationship with future MD signatures. AD signatures and brain age were correlated, but each measure captured unique phenotypic and genetic variance.

conclusionsCortical MD and thickness/volume AD signatures are heritable, and each signature captures unique variance that is also not explained by brain age. Moreover, results are in line with changes in MD emerging before changes in cortical thickness, underscoring the utility of MD as a very early predictor of AD risk.

Indexed as

Alzheimer DiseaseBrainChildDiffusion Tensor ImagingHumansMagnetic Resonance ImagingMaleNeuroimagingAlzheimer’s diseaseBrain ageCortical thicknessEarly predictionGeneticsMean diffusivity

Identifiers

PMID35738479
PMCPMC9827615

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.