Evidence map›Paper›PMID 38343822›Full record

ArticlemedRxiv : the preprint server for health sciences2024

Association between allostatic load and accelerated white matter brain aging: findings from the UK Biobank.

Li Feng, Zhenyao Ye, Zewen Du, Yezhi Pan, Travis Canida, Hongjie Ke, Song Liu, Shuo Chen, L Elliot Hong, Peter Kochunov and 4 more

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors at 5 institutions in 2 countries.

Li FengDepartment of Nutrition and Food Science, College of Agriculture & Natural Resources, University of Maryland, College Park, Maryland, United States of America.ORCID 0000-0001-6991-6509
Zhenyao YeMaryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America.
Zewen DuDepartment of Biostatistics, School of Global Public Health, New York University, New York, New York, United States of America.
Yezhi PanMaryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America.
Travis CanidaDepartment of Mathematics, The college of Computer, Mathematical, and Natural Sciences, University of Maryland, College Park, Maryland, United States of America.
Hongjie KeDepartment of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America.
Song LiuSchool of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China.
Shuo ChenMaryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America.
L Elliot HongLouis A. Faillace Department of Psychiatry & Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, Texas, United States of America.
Peter KochunovLouis A. Faillace Department of Psychiatry & Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, Texas, United States of America.
Jie ChenDepartment of Health Policy and Management, School of Public Health, University of Maryland, College Park, Maryland, United States of America.
David K Y LeiDepartment of Nutrition and Food Science, College of Agriculture & Natural Resources, University of Maryland, College Park, Maryland, United States of America.
Edmond ShenassaDepartment of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America.
Tianzhou MaDepartment of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America.ORCID 0000-0003-3605-0811
University of Maryland, College Park · USUniversity of Maryland, Baltimore · USThe University of Texas Health Science Center at Houston · USNew York University · USQilu University of Technology · CN

Funding

SOLAR-Eclipse Computational Tools for Imaging GeneticsR01EB015611 · NIBIB · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI KOCHUNOV, PETER V. · 2012 to 2024
$5.0M
Towards Multisystem-Brain Successful Aging in Schizophrenia SpectrumR01MH116948 · NIMH · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI HONG, L ELLIOT ELLIOT · 2018 to 2022
$3.7M
Lifespan Vascular Biology on White MatterRF1NS114628 · NINDS · UNIVERSITY OF MARYLAND BALTIMORE · PI HONG, L ELLIOT ELLIOT, KOCHUNOV, PETER V. · 2020 to 2020
$3.1M
A Multivariate Mediation and Deep Learning Framework for Genome-Connectome -Substance Use ResearchDP1DA048968 · NIDA · UNIVERSITY OF MARYLAND BALTIMORE · PI CHEN, SHUO · 2019 to 2023
$2.3M
The Vascular Axis in Schizophrenia Brain-Body AgingR01MH133812 · NIMH · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI L Elliot Elliot Hong · 2024 to 2026
$2.2M
Redefine Trans-Neuropsychiatric Disorder Brain Patterns through Big-Data and Machine LearningRF1MH123163 · NIMH · UNIVERSITY OF MARYLAND BALTIMORE · PI KOCHUNOV, PETER V., THOMPSON, PAUL M · 2021 to 2021
$1.2M
Lifespan Vascular Biology on White MatterR01NS114628 · NINDS · UNIVERSITY OF MARYLAND BALTIMORE · PI HONG, L ELLIOT ELLIOT, KOCHUNOV, PETER V. · 2024 to 2024
$770k
Hybrid GPU/CPU Computing Resource to Support Connectomic and GenomicsS10OD023696 · OD · UNIVERSITY OF MARYLAND BALTIMORE · PI KOCHUNOV, PETER V. · 2018 to 2018
$593k
A novel transcriptome-connectome approach to study the neurogenetic mechanism of nicotine and cannabis addictionK01DA059603 · NIDA · UNIV OF MARYLAND, COLLEGE PARK · PI Tianzhou MA · 2024 to 2026
$579k
NIBIB NIH HHS R01 EB015611NIDA NIH HHS DP1 DA048968NIDA NIH HHS K01 DA059603NIH HHS S10 OD023696NIMH NIH HHS R01 MH116948NIMH NIH HHS R01 MH133812NIMH NIH HHS RF1 MH123163NINDS NIH HHS R01 NS114628NINDS NIH HHS RF1 NS114628
6 · The paper itself

Abstract

White matter (WM) brain age, a neuroimaging-derived biomarker indicating WM microstructural changes, helps predict dementia and neurodegenerative disorder risks. The cumulative effect of chronic stress on WM brain aging remains unknown. In this study, we assessed cumulative stress using a multi-system composite allostatic load (AL) index based on inflammatory, anthropometric, respiratory, lipidemia, and glucose metabolism measures, and investigated its association with WM brain age gap (BAG), computed from diffusion tensor imaging data using a machine learning model, among 22 951 European ancestries aged 40 to 69 (51.40% women) from UK Biobank. Linear regression, Mendelian randomization, along with inverse probability weighting and doubly robust methods, were used to evaluate the impact of AL on WM BAG adjusting for age, sex, socioeconomic, and lifestyle behaviors. We found increasing one AL score unit significantly increased WM BAG by 0.29 years in association analysis and by 0.33 years in Mendelian analysis. The age- and sex-stratified analysis showed consistent results among participants 45-54 and 55-64 years old, with no significant sex difference. This study demonstrated that higher chronic stress was significantly associated with accelerated brain aging, highlighting the importance of stress management in reducing dementia and neurodegenerative disease risks.

Indexed as

allostatic loadbrain agingchronic stressUK Biobankwhite matter

Identifiers

PMID38343822
PMCPMC10854327
OpenAlexW4391249395

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.