Evidence map›Paper›PMID 38889383›Full record

ArticleNeurology2024

Association of Cardiovascular Health With Brain Age Estimated Using Machine Learning Methods in Middle-Aged and Older Adults.

Huijie Huang, Jiao Wang, Michelle M Dunk, Jie Guo, Abigail Dove, Jun Ma, David A Bennett, Weili Xu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Metabolic syndrome is associated with accelerated brain aging.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Estimated glucose disposal rate is associated with brain aging and dementia among diabetes-free older adults.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Metabolic Dysfunction-Associated Steatotic Liver Disease Is Associated With Accelerated Brain Ageing: A Population-Based Study.Liver international : official journal of the International Association for the Study of the Liver · 2025
    Article
  14. Review
  15. Article
  16. Article
  17. 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

8 authors.

Huijie HuangFrom the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL.
Jiao WangFrom the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL.ORCID 0000-0001-6059-9279
Michelle M DunkFrom the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL.
Jie GuoFrom the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL.ORCID 0000-0001-6104-9957
Abigail DoveFrom the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL.ORCID 0000-0002-6890-0537
Jun MaFrom the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL.
David A BennettFrom the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL.ORCID 0000-0003-3689-554X
Weili XuFrom the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL.ORCID 0000-0001-6140-2968

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesCardiovascular health (CVH) has been associated with cognitive decline and dementia, but the extent to which CVH affects brain health remains unclear. We investigated the association of CVH, assessed using Life's Essential 8 (LE8), with neuroimaging-based brain age and brain-predicted age difference (brain-PAD).

methodsThis longitudinal community-based study was based on UK Biobank participants aged 40-69 years who were free from dementia and other neurologic diseases at baseline. LE8 score at baseline was assessed with 8 measures and tertiled as low, moderate, and high CVH. Structural and functional brain MRI scans were performed approximately 9 years after baseline, and 1,079 measures from 6 neuroimaging modalities were used to model brain age. A Least Absolute Shrinkage and Selection Operator regression model was trained in 4,355 healthy participants and then used to calculate brain age and brain-PAD in the whole population. Data were analyzed using linear regression models.

resultsThe study included 32,646 participants (mean age at baseline 54.74 years; 53.44% female; mean LE8 score: 71.90). In multivariable-adjusted linear regression, higher LE8 score was associated with younger brain age (β [95% CI] -0.037 [-0.043 to -0.031]) and more negative brain-PAD (β [95% CI] -0.043 [-0.048 to -0.038]) (brain looks younger for chronological age). Compared with high CVH, low/moderate CVH was associated with older brain age (β [95% CI] 1.030 [0.852-1.208]/0.475 [0.303-0.647]) and increased brain-PAD (β [95% CI] 1.193 [1.029-1.357]/0.528 [0.370-0.686]). The associations between low CVH and older brain age/brain-PAD remained similar and significant in both middle-aged (β [95% CI] 1.199 [0.992-1.405]/1.351 [1.159-1.542]) and older adults (β [95% CI] 0.764 [0.417-1.110]/0.948 [0.632-1.263]). DISCUSSION: Low CVH is associated with older brain age and greater brain-PAD, even among middle-aged adults. Our findings suggest that optimizing CVH could support brain health. The main limitation of our study is that the study sample was healthier than the general population, thus caution is required when generalizing our findings to other populations.

Indexed as

AgingBrainMachine LearningMagnetic Resonance ImagingAdultAgedCardiovascular DiseasesFemaleHumansLongitudinal StudiesMaleMiddle AgedNeuroimagingUnited Kingdom

Identifiers

PMID38889383
PMCPMC11226327

What OpenQuestion 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 OpenQuestion graph.