Brain white matter hyperintensities-predicted age reflects neurovascular health in middle-to-old aged subjects

Author:

Huang Chu-Chung12ORCID,Chou Kun-Hsien34,Lee Wei-Ju56ORCID,Yang Albert C78,Tsai Shih-Jen4910,Chen Liang-Kung51112,Chung Chih-Ping1314,Lin Ching-Po345

Affiliation:

1. Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China

2. Shanghai Changning Mental Health Center, Shanghai, China

3. Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan

4. Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan

5. Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan

6. Department of Family Medicine, Yuanshan Branch, Taipei Veterans General Hospital, Yi-Lan, Taiwan

7. Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA, USA

8. Institute of Brain Science, National Yang-Ming Chiao Tung University, Taipei, Taiwan

9. Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan

10. Division of Psychiatry, School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan

11. Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan

12. Taipei Municipal Gan-Dau Hospital (Managed by Taipei Veterans General Hospital), Taipei, Taiwan

13. Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan

14. School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan

Abstract

Abstract Background age-related neurovascular structural and functional impairment is a major aetiology of dementia and stroke in older people. There is no single marker representative of neurovascular biological age yet. Objective this study aims to develop and validate a white matter hyperintensities (WMH)-based model for characterising individuals’ neurovascular biological age. Methods in this prospective single-site study, the WMH-based age-prediction model was constructed based on WMH volumes of 491 healthy participants (21–89 years). In the training dataset, the constructed linear-regression model with log-transformed WMH volumes showed well-balanced complexity and accuracy (root mean squared error, RMSE = 10.20 and mean absolute error, MAE = 7.76 years). This model of neurovascular age estimation was then applied to a middle-to-old aged testing dataset (n = 726, 50–92 years) as the testing dataset for external validation. Results the established age estimator also had comparable generalizability with the testing dataset (RMSE = 7.76 and MAE = 6.38 years). In the testing dataset, the WMH-predicted age difference was negatively associated with visual executive function. Individuals with older predicted-age for their chronological age had greater cardiovascular burden and cardiovascular disease risks than individuals with normal or delayed predicted age. These associations were independent of chronological age. Conclusions our model is easy to use in clinical practice that helps to evaluate WMH severity objective to chronological age. Current findings support our WMH-based age measurement to reflect neurovascular health and have potential diagnostic and prognostic value for clinical or research purposes in age-related neurovascular disorders.

Funder

Ministry of Science and Technology, Taiwan

Taipei Veterans General Hospital

Ministry of Education

Publisher

Oxford University Press (OUP)

Subject

Geriatrics and Gerontology,Aging,General Medicine

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