. 2025 May:115:105723.

doi: 10.1016/j.ebiom.2025.105723. Epub 2025 Apr 24.

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

Li Feng¹, Zhenyao Ye², Yezhi Pan³, Rozalina G McCoy⁴, Braxton D Mitchell⁵, Peter Kochunov⁶, Paul M Thompson⁷, Jie Chen⁸, Menglu Liang⁹, Thu T Nguyen¹⁰, Edmond Shenassa¹¹, Yan Li⁹, Travis Canida¹², Hongjie Ke⁹, Hwiyoung Lee², Song Liu¹³, L Elliot Hong⁶, Chixiang Chen¹⁴, David K Y Lei¹⁵, Shuo Chen¹⁶, Tianzhou Ma¹⁷

Affiliations

¹ Department 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.
² Maryland 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.
³ Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, USA.
⁴ Division 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.
⁵ Division of Endocrinology, Diabetes, & Nutrition, Department of Medicine, School of Medicine, University of Maryland, Baltimore, MD, USA.
⁶ Louis A. Faillace Department of Psychiatry & Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
⁷ Imaging Genetics Center, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
⁸ Department of Health Policy and Management, School of Public Health, University of Maryland, College Park, MD, USA.
⁹ Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD, USA.
¹⁰ Department 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.
¹¹ Department 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.
¹² Department 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.
¹³ School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China.
¹⁴ 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.
¹⁵ Department of Nutrition and Food Science, College of Agriculture & Natural Resources, University of Maryland, College Park, MD, USA.
¹⁶ Maryland 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.
¹⁷ Department 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.

PMID: 40280025
PMCID: PMC12242590
DOI: 10.1016/j.ebiom.2025.105723

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

Li Feng et al. EBioMedicine. 2025 May.

. 2025 May:115:105723.

doi: 10.1016/j.ebiom.2025.105723. Epub 2025 Apr 24.

Authors

Affiliations

¹ Department 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.
² Maryland 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.
³ Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, USA.
⁴ Division 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.
⁵ Division of Endocrinology, Diabetes, & Nutrition, Department of Medicine, School of Medicine, University of Maryland, Baltimore, MD, USA.
⁶ Louis A. Faillace Department of Psychiatry & Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
⁷ Imaging Genetics Center, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
⁸ Department of Health Policy and Management, School of Public Health, University of Maryland, College Park, MD, USA.
⁹ Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD, USA.
¹⁰ Department 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.
¹¹ Department 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.
¹² Department 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.
¹³ School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China.
¹⁴ 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.
¹⁵ Department of Nutrition and Food Science, College of Agriculture & Natural Resources, University of Maryland, College Park, MD, USA.
¹⁶ Maryland 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.
¹⁷ Department 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.

PMID: 40280025
PMCID: PMC12242590
DOI: 10.1016/j.ebiom.2025.105723

Abstract

Background: The 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.

Methods: This 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.

Findings: The 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.

Interpretation: Adherence 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.

Funding: US National Institute of Health, University of Maryland, Montgomery County of Maryland.

Keywords: APOE4; Brain age gap; Life’s essential 8; Machine learning; White matter.

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Conflict of interest statement

Declaration of interests None.

Figures

**Fig. 1**
Directed acyclic graph (DAG): Illustrating the association between life’s essential 8 (LE8) and white matter brain age gap (WM BAG), controlling for confounders and modulated by the effect of *APOE4* genotype.

**Fig. 2**
Flow chart of participants selection and data processing procedures. Flowchart of our analysis procedures and the number of subjects included at each step of the analysis. It details three key analytical processes: (1) Utilizing a machine learning model to estimate age bias-corrected white matter (WM) brain age gap (BAG) (shown in grey); (2) Analysing the linear relationship between Life’s Essential 8 (LE8) and WM BAG (highlighted in pink); (3) Analysing the main effect of *APOE4* carriers on WM BAG (highlighted in yellow); and (4) Assessing *APOE4* (gene) by LE8 (environment) interaction on WM BAG as the primary analysis (indicated in green).

**Fig. 3**
Forest plot of the effect sizes with 95% confidence intervals for joint and individual life’s essential 8 (LE8) scores on white matter brain age gap (BAG) in models 2 and 3. Model 2: model 1+ education, Townsend deprivation index, moderate drink, sedentary behaviour, social connection. Model 3: model 2+ medical conditions (hypertension, cardiovascular disease, cancer, brain diseases). BMI: body mass index. BP: blood pressure.

**Fig. 4**
Interaction effects of *APOE4* and Life’s Essential 8 (LE8) scores on white matter (WM) brain age gap (BAG) across different age and sex groups. The graphs present both unadjusted p-values and FDR-adjusted p-values (Benjamini-Hochberg correction) for testing the interaction between LE8 and *APOE4*. Additionally, the regression effects of LE8 on WM BAG are shown separately for *APOE4* carriers and non-carriers at the bottom of each section (β coefficients are derived from models adjusted for age, education, Townsend Deprivation Index, moderate alcohol consumption, sedentary behaviour, social connections, and medical conditions). Statistical significance is denoted as follows: NS (non-significant), ∗ (Adjusted p < 0.05), ∗∗ (Adjusted p < 0.01), and ∗∗∗ (Adjusted p < 0.001).

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Update of

Adherence to Life's Essential 8 is associated with delayed white matter aging.
Feng L, Ye Z, Pan Y, McCoy RG, Mitchell BD, Kochunov P, Thompson PM, Chen J, Liang M, Nguyen TT, Shenassa E, Li Y, Canida T, Ke H, Lee H, Liu S, Hong LE, Chen C, Lei DKY, Chen S, Ma T. Feng L, et al. medRxiv [Preprint]. 2024 Dec 3:2024.12.02.24318274. doi: 10.1101/2024.12.02.24318274. medRxiv. 2024. Update in: EBioMedicine. 2025 May;115:105723. doi: 10.1016/j.ebiom.2025.105723. PMID: 39677426 Free PMC article. Updated. Preprint.

References

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

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

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