Evidence map›Paper›PMID 40280025›Full record

ArticleEBioMedicine2025

Adherence to life's essential 8 is associated with delayed white matter aging.

Li Feng, Zhenyao Ye, Yezhi Pan, Rozalina G McCoy, Braxton D Mitchell, Peter Kochunov, Paul M Thompson, Jie Chen, Menglu Liang, Thu T Nguyen and 11 more

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PubMed Full text at PMC Publisher
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Article in EBioMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
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  7. Reimagining Resilience in Aging: Leveraging AI/ML, Big Data Analytics, and Systems Innovation.The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry · 2025
    Article
  8. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

21 authors.

Li FengDepartment of Nutrition and Food Science, College of Agriculture & Natural Resources, University of Maryland, College Park, MD, USA; Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD, USA.
Zhenyao YeMaryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, USA; Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, USA.
Yezhi PanMaryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, USA.
Rozalina G McCoyDivision of Endocrinology, Diabetes, and Nutrition, Department of Medicine, School of Medicine, University of Maryland, Baltimore, MD, USA; Division of Gerontology, Department of Medicine, School of Medicine, University of Maryland, Baltimore, MD, USA; University of Maryland Institute for Health Computing, North Bethesda, MD, USA; Department of Health Policy and Management, School of Public Health, University of Maryland, College Park, MD, USA; Division of Endocrinology, Diabetes, & Nutrition, Department of Medicine, School of Medicine, University of Maryland, Baltimore, MD, USA.
Braxton D MitchellDivision of Endocrinology, Diabetes, & Nutrition, Department of Medicine, School of Medicine, University of Maryland, Baltimore, MD, USA.
Peter KochunovLouis A. Faillace Department of Psychiatry & Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
Paul M ThompsonImaging Genetics Center, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
Jie ChenDepartment of Health Policy and Management, School of Public Health, University of Maryland, College Park, MD, USA.
Menglu LiangDepartment of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD, USA.
Thu T NguyenDepartment of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD, USA; University of Maryland Institute for Health Computing, North Bethesda, MD, USA.
Edmond ShenassaDepartment of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD, USA; Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, USA; Maternal & Child Health Program, School of Public Health, University of Maryland, College Park, MD, USA; Department of Epidemiology, School of Public Health, Brown University, RI, USA.
Yan LiDepartment of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD, USA.
Travis CanidaDepartment of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD, USA; Department of Mathematics, The College of Computer, Mathematical, and Natural Sciences, University of Maryland, College Park, MD, USA.
Hongjie KeDepartment of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD, USA.
Hwiyoung LeeMaryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, USA; Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, USA.
Song LiuSchool of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China.
L Elliot HongLouis A. Faillace Department of Psychiatry & Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
Chixiang ChenDivision of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, USA; University of Maryland Institute for Health Computing, North Bethesda, MD, USA.
David K Y LeiDepartment of Nutrition and Food Science, College of Agriculture & Natural Resources, University of Maryland, College Park, MD, USA.
Shuo ChenMaryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, USA; Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, USA; University of Maryland Institute for Health Computing, North Bethesda, MD, USA. Electronic address: shuochen@som.umaryland.edu.
Tianzhou MaDepartment of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD, USA; Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, USA. Electronic address: tma0929@umd.edu.

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
Amish Connectome Project on Mental IllnessU01MH108148 · NIMH · UNIVERSITY OF MARYLAND BALTIMORE · PI HONG, L ELLIOT ELLIOT, KOCHUNOV, PETER V. · 2015 to 2018
$4.3M
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
Risk and strength: determining the impact of area-level sentiment and protective factors on birth outcomesR01MD015716 · NIMHD · UNIV OF MARYLAND, COLLEGE PARK · PI NGUYEN, THU · 2021 to 2025
$3.6M
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 OD023696NIMHD NIH HHS R01 MD015716NIMH NIH HHS R01 MH116948NIMH NIH HHS R01 MH133812NIMH NIH HHS RF1 MH123163NIMH NIH HHS U01 MH108148NINDS NIH HHS R01 NS114628NINDS NIH HHS RF1 NS114628
6 · The paper itself

Abstract

backgroundThe American Heart Association introduced Life's Essential 8 (LE8) to promote cardiovascular health and longevity. However, its impact on brain ageing and interactions with genetic risk factors of dementia, such as APOE4, remains unclear. This study investigates the relationship between LE8 and white matter brain ageing and evaluates the moderating effects of the APOE4 allele.

methodsThis cross-sectional study utilized data from the UK Biobank, including genetic, neuroimaging, and health-related data from touchscreen questionnaires, physical examinations, and biological samples. Participants were non-pregnant whites with LE8 variables, diffusion tensor imaging (DTI) data, and APOE4 genetic information available, excluding those with extreme white matter hyperintensities. Regional fractional anisotropy measures from DTI data were used to predict white matter brain age via random forest regression. The white matter brain age gap (BAG) was calculated by subtracting chronological age from predicted brain age.

findingsThe analysis included 18,817 participants (9430 women and 9387 men; mean age 55.45 years [SD: 7.46]). Higher LE8 scores were associated with a lower white matter BAG, indicating delayed brain ageing. The effect was more pronounced in non-APOE4 carriers (124 days younger per 10-point increase, 95% CI: 102-146 days; p < 0.001) compared to APOE4 carriers (84 days younger per 10-point increase, 95% CI: 47-120 days; p < 0.001). Potential interaction between APOE4 and LE8 on brain ageing was observed for some age and sex groups but with only borderline significance, further investigation in larger and more targeted studies is needed to validate the finding.

interpretationAdherence to LE8 is associated with delayed brain ageing, with genetic factors such as APOE4 potentially moderating this effect in specific age and sex groups. The overall benefit from a healthier lifestyle in individuals' brain ageing across genetic, sex, and age groups underscore the importance and broad applicability of behavioural lifestyle interventions in promoting brain health.

fundingUS National Institute of Health, University of Maryland, Montgomery County of Maryland.

Indexed as

AgingWhite MatterAdultAgedApolipoprotein E4BrainCross-Sectional StudiesDiffusion Tensor ImagingFemaleHumansMaleMiddle AgedApolipoprotein E4APOE4Brain age gapLife’s essential 8Machine learningWhite matter

Identifiers

PMID40280025
PMCPMC12242590

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Registered trials

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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.