Association of peripheral immune markers with brain age and dementia risk estimated using deep learning methods

Background: The peripheral immune system is essential for maintaining central nervous system homeostasis. This study investigates the effects of peripheral immune markers on accelerated brain aging and dementia using brain-predicted age difference based on neuroimaging. Methods: By leveraging data from the UK Biobank, Cox regression was used to explore the relationship between peripheral immune markers and dementia, and multivariate linear regression to assess associations between peripheral immune biomarkers and brain structure. Additionally, we established a brain age prediction model using simple fully convolutional network (SFCN) deep learning architecture. Analysis of the resulting brain-predicted age difference (PAD) revealed relationships between accelerated brain aging, peripheral immune markers, and dementia. Results: During the median follow-up period of 14.3 years, 4277 dementia cases were observed among 322 761 participants. Both innate and adaptive immune markers correlated with dementia risk. NLR showed the strongest association with dementia risk (hazard ratio = 1.14; 95% CI: 1.11–1.18, P < 0.001). Multivariate linear regression revealed significant associations between peripheral immune markers and brain regional structural indices. Utilizing the deep learning-based SFCN model, the estimated brain age of dementia subjects (mean absolute error = 5.63, r 2 = −0.46, R = 0.22) was determined. PAD showed significant correlation with dementia risk and certain peripheral immune markers, particularly in individuals with positive brain age increment. Conclusion: This study employs brain age as a quantitative marker of accelerated brain aging to investigate its potential associations with peripheral immunity and dementia, highlighting the importance of early intervention targeting peripheral immune markers to delay brain aging and prevent dementia.