Brain age gap as a predictive biomarker that links aging, lifestyle, and neuropsychiatric health

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  • Badhwar, A. et al. Assessment of brain-derived extracellular vesicle enrichment for blood biomarker analysis in age-related neurodegenerative diseases: an international overview. Alzheimers Dement 20, 4411–4422 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Gonzales M. M. et al. Biological aging processes underlying cognitive decline and neurodegenerative disease. J. Clin. Investig. 132. (2022).

  • Van Hove L. I. et al. Converging cross-modal evidence for a phylogenetic age effect in neurodegenerative susceptibility. Brain, published online Feb 5. (2025).

  • de Fátima Dias M., Duarte J. V., de Carvalho P., Castelo-Branco M. Unravelling pathological ageing with brain age gap estimation in Alzheimer’s disease, diabetes, and schizophrenia. Brain Commun. published online March 11. (2025).

  • Hatos, A. Anxiety in the age of AI: constructing a tool to assess public perceptions. Brain 16, 415 (2025).

    Article 

    Google Scholar 

  • Yi, F. et al. Genetically supported targets and drug repurposing for brain aging: a systematic study in the UK Biobank. Sci. Adv. 11, eadr3757 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bellantuono, L. et al. Predicting brain age with complex networks: from adolescence to adulthood. Neuroimage 225, 117458 (2021).

    Article 
    PubMed 

    Google Scholar 

  • Hong, J. et al. Brain age prediction of children using routine brain MR images via deep learning. Front Neurol. 11, 584682 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Nemati, S., Arjmandi, M., Busby, N., Bonilha, L. & Fridriksson, J. The impact of age-related hearing loss on cognitive decline: the mediating role of brain age gap. Neuroscience 551, 185–195 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Teselink, J. et al. Efficacy of non-invasive brain stimulation on global cognition and neuropsychiatric symptoms in Alzheimer’s disease and mild cognitive impairment: a meta-analysis and systematic review. Ageing Res Rev. 72, 101499 (2021).

    Article 
    PubMed 

    Google Scholar 

  • Tian, Y. E. et al. Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality. Nat. Med. 29, 1221–1231 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Ballester, P. L. et al. Gray matter volume drives the brain age gap in schizophrenia: a SHAP study. Schizophrenia 9, 3 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Siddiqi et al. Brain stimulation and brain lesions converge on common causal circuits in neuropsychiatric disease. Nat. Hum. Behav. 5, 1707–1716 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Almeida, F. C. et al. Lewy body co-pathology in Alzheimer’s disease and primary age-related tauopathy contributes to differential neuropathological, cognitive, and brain atrophy patterns. Alzheimers Dement 21, e14191 (2025).

    Article 
    PubMed 

    Google Scholar 

  • Dinsdale, N. K. et al. Learning patterns of the ageing brain in MRI using deep convolutional networks. Neuroimage 224, 117401 (2021).

    Article 
    PubMed 

    Google Scholar 

  • He, S., Feng, Y., Grant, P. E. & Ou, Y. Deep relation learning for regression and its application to brain age estimation. IEEE Trans. Med. Imaging 41, 2304–2317 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Cai, H., Gao, Y. & Liu, M. Graph transformer geometric learning of brain networks using multimodal MR images for brain age estimation. IEEE Trans. Med. Imaging 42, 456–466 (2023).

    Article 
    PubMed 

    Google Scholar 

  • Honea, R. A. et al. TOMM40 may mediate GFAP, neurofilament light Protein, pTau181, and brain morphometry in aging. Aging Brain 7, 100134 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Hernandez, A. R. et al. Microbiome-driven alterations in metabolic pathways and impaired cognition in aged female TgF344-AD rats. Aging Brain 5, 100119 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Fadadu, R. P., Bozack, A. K. & Cardenas, A. Chemical and climatic environmental exposures and epigenetic aging: a systematic review. Environ. Res. 274, 121347 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Condello, G. et al. Energy balance and active lifestyle: potential mediators of health and quality of life perception in aging. Nutrients 11, 2122 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ploughman, M., Wallack, E. M., Chatterjee, T., Kirkland, M. C. & Curtis, M. E. Health Lifestyle and Aging with MS Consortium. Under-treated depression negatively impacts lifestyle behaviors, participation and health-related quality of life among older people with multiple sclerosis. Mult. Scler. Relat. Disord. 40, 101919 (2020).

    Article 
    PubMed 

    Google Scholar 

  • Zhang, X.-H., Huang, H.-W., Zeng, J.-Y., Chen, H.-J. & Lin, Y.-J. The beneficial influence of night-shift napping on brain core cognition networks in nurses experiencing sleep deprivation: A preliminary resting-state fMRI study. Sleep. Med. 131, 106503 (2025).

    Article 
    PubMed 

    Google Scholar 

  • Zhang, R. et al. Associations of dietary patterns with brain health from behavioral, neuroimaging, biochemical and genetic analyses. Nat. Ment. Health 2, 535–552 (2024).

    Article 

    Google Scholar 

  • Tian Y. E., Cole J. H., Bullmore E. T., Zalesky A. Brain, lifestyle and environmental pathways linking physical and mental health. Nat. Ment. Health published online Aug 9. (2024).

  • Seitz-Holland, J., Haas, S. S., Penzel, N., Reichenberg, A. & Pasternak, O. BrainAGE, brain health, and mental disorders: a systematic review. Neurosci. Biobehav Rev. 159, 105581 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Guo, J. et al. Mendelian randomization analyses support causal relationships between brain imaging-derived phenotypes and risk of psychiatric disorders. Nat. Neurosci. 25, 1519–1527 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Leonardsen, E. H. et al. Genetic architecture of brain age and its causal relations with brain and mental disorders. Mol. Psychiatry 28, 3111–3120 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bravo-Ortiz, M. A. et al. A systematic review of vision transformers and convolutional neural networks for Alzheimer’s disease classification using 3D MRI images. Neural Comput. Appl. 36, 21985–22012 (2024).

    Article 

    Google Scholar 

  • Das B. K. et al. VIViT: variable-input vision transformer framework for 3D MR image segmentation. arXiv [eess.IV]. published online May 13. (2025).

  • Gibbon S., Breen D. P., MacGillivray T. J., UK Biobank Eye & Vision Consortium. Optic disc pallor in Parkinson’s disease: a UK Biobank study. Mov. Disord published online Jan 30. (2025).

  • Hanazawa, R., Sato, H. & Hirakawa, A. Alzheimer’s Disease Neuroimaging Initiative. Mixture disease progression model to predict and cluster the long-term trajectory of cognitive decline in Alzheimer’s disease. Ther. Innov. Regul. Sci. 59, 264–277 (2025).

    Article 
    PubMed 

    Google Scholar 

  • Nudelman K. N. H., Brumm M. C., Marek K., Foroud T. M., for the Parkinson’s Progression Markers Initiative (PPMI) Study. TREM2 variants in Parkinson’s disease: results from the Parkinson’s progression markers initiative (PPMI) study. Alzheimers Dement. 18. (2022).

  • Giff, A. et al. Spatial normalization discrepancies between native and MNI152 brain template scans in gamma ventral capsulotomy patients. Psychiatry Res Neuroimaging 329, 111595 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Giff, A. et al. 19. Spatial normalization discrepancies between native and MNI152 brain template scans in gamma ventral capsulotomy patients. Biol. Psychiatry 93, S101–S102 (2023).

    Article 

    Google Scholar 

  • Shen Q., Xiao B., Mi H., Yu J., Xiao L. Adaptive learning filters–embedded vision transformer for pixel-level segmentation of low-light concrete cracks. J. Perform Constr. Facil 39. (2025).

  • Sadeghi B., Alesheikh A. A., Jafari A., Rezaie F. Performance evaluation of convolutional neural network and vision transformer models for groundwater potential mapping. J. Hydrol. 132840 (2025).

  • Kundu B., Khanal B., Simon R., Linte C. A. Assessing the performance of the DINOv2 self-supervised learning vision transformer model for the segmentation of the left atrium from MRI images. In: Rettmann M. E., Siewerdsen J. H., eds. Medical Imaging 2025: Image-Guided Procedures, Robotic Interventions, and Modeling. 19 (SPIE, 2025).

  • Beheshti, I., Mishra, S., Sone, D., Khanna, P. & Matsuda, H. T1-weighted MRI-driven brain age estimation in Alzheimer’s disease and Parkinson’s disease. Aging Dis. 11, 618–628 (2020).

    Article 
    PubMed 

    Google Scholar 

  • Zuo, Q. et al. Associations of metabolic syndrome with cognitive function and dementia risk: Evidence from the UK Biobank cohort. Diab. Obes. Metab. 26, 6023–6033 (2024).

    Article 
    CAS 

    Google Scholar 

  • Schulz, C.-A., Weinhold, L., Schmid, M., Nöthen, M. M. & Nöthlings, U. Analysis of associations between dietary patterns, genetic disposition, and cognitive function in data from UK Biobank. Eur. J. Nutr. 62, 511–521 (2023).

    Article 
    PubMed 

    Google Scholar 

  • Susetyo, B. & Fitrianto, A. Estimating missing panel data with regression and multivariate imputation by chained equations (MICE). CAUCHY 9, 94–105 (2024).

    Article 

    Google Scholar 

  • Austin, P. C. Graphical methods to illustrate the nature of the relation between a continuous variable and the outcome when using restricted cubic splines with a Cox proportional hazards model. Stat. Methods Med Res. 34, 277–285 (2025).

    Article 
    PubMed 

    Google Scholar 

  • Erratum to “A dose-effect network meta-analysis model with application in antidepressants using restricted cubic splines.” Stat. Methods Med. Res. 33, NP1 (2024).

  • Fuh, C.-D., Kao, C.-L. M. & Pang, T. Kullback-Leibler divergence and Akaike information criterion in general hidden Markov models. IEEE Trans. Inf. Theory 70, 5888–5909 (2024).

    Article 

    Google Scholar 

  • Saumard, A. & Navarro, F. Finite sample improvement of Akaike’s information criterion. IEEE Trans. Inf. Theory 67, 6328–6343 (2021).

    Article 

    Google Scholar 

  • Xia, L., Nan, B. & Li, Y. Statistical inference for Cox proportional hazards models with a diverging number of covariates. Scand. Stat. Theory Appl. 50, 550–571 (2023).

    Article 
    PubMed 

    Google Scholar 

  • Hahn, G. et al. Polygenic hazard score models for the prediction of Alzheimer’s free survival using the lasso for Cox’s proportional hazards model. Genet. Epidemiol. 49, e22581 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Liu, W., Leung, D. & Shao, Q.-M. Asymptotic false discovery control of the Benjamini-Hochberg procedure for pairwise comparisons. Sci. China Math. (2024).

    Article 

    Google Scholar 

  • Hepsomali, P. & Groeger, J. A. Diet, sleep, and mental health: insights from the UK Biobank study. Nutrients 13, 2573 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Huang, S.-Y. et al. Sleep, physical activity, sedentary behavior, and risk of incident dementia: a prospective cohort study of 431,924 UK Biobank participants. Mol. Psychiatry 27, 4343–4354 (2022).

    Article 
    PubMed 

    Google Scholar 

  • Chudasama, Y. V. et al. Healthy lifestyle and life expectancy in people with multimorbidity in the UK Biobank: A longitudinal cohort study. PLoS Med. 17, e1003332 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Zhang, Y.-B. et al. Associations of healthy lifestyle and socioeconomic status with mortality and incident cardiovascular disease: two prospective cohort studies. BMJ 373, n604 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Rodriguez D., Sued M., Valdora M. A Kruskal-Wallis type test for functional data. Commun Stat. Simul. Comput. 1–15 (2025).

  • Yap, S. M., Dillon, M., Crowley, R. K. & McGuigan, C. Alemtuzumab-related thyroid disease in people with multiple sclerosis is associated with age and brainstem phenotype at disease onset. Mult. Scler. J. Exp. Transl. Clin. 6, 2055217320933928 (2020).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Sharma S., Dhakal S., Bhavsar M. Transfer learning for wildlife classification: Evaluating YOLOv8 against DenseNet, ResNet, and VGGNet on a custom dataset. arXiv [cs.CV]. published online July 10. (2024).

  • Arnob, A. S., Kausik, A. K., Islam, Z., Khan, R. & Bin Rashid, A. Comparative result analysis of cauliflower disease classification based on deep learning approach VGG16, inception v3, ResNet, and a custom CNN model. Hybrid. Adv. 10, 100440 (2025).

    Article 

    Google Scholar 

  • Lee, J. et al. Deep learning-based brain age prediction in normal aging and dementia. Nat. Aging 2, 412–424 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Poloni, K. M. & Ferrari, R. J. A deep ensemble hippocampal CNN model for brain age estimation applied to Alzheimer’s diagnosis. Expert Syst. Appl. 195, 116622 (2022).

    Article 

    Google Scholar 

  • Baecker, L., Garcia-Dias, R., Vieira, S., Scarpazza, C. & Mechelli, A. Machine learning for brain age prediction: Introduction to methods and clinical applications. EBioMedicine 72, 103600 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • He, S., Grant, P. E. & Ou, Y. Global-local transformer for brain age estimation. IEEE Trans. Med. Imaging 41, 213–224 (2022).

    Article 
    PubMed 

    Google Scholar 

  • Pilli, R., Goel, T., Murugan, R. & Tanveer, M. Brain age estimation using universum learning-based kernel random vector functional link regression network. Cogn. Comput. 16, 3186–3199 (2024).

    Article 

    Google Scholar 

  • Pilli, R., Goel, T. & Murugan, R. Unveiling Alzheimer’s disease through brain age estimation using multi-kernel regression network and magnetic resonance imaging. Comput. Methods Prog. Biomed. 261, 108617 (2025).

    Article 

    Google Scholar 

  • Liu, W. et al. Risk prediction of Alzheimer’s disease conversion in mild cognitive impaired population based on brain age estimation. IEEE Trans. Neural Syst. Rehabil. Eng. 31, 2468–2476 (2023).

    Article 
    PubMed 

    Google Scholar 

  • Koohsari, S. et al. Relationships of in vivo brain norepinephrine transporter and age, BMI, and gender. Synapse 77, e22279 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Cadena, E. J., White, D. M., Kraguljac, N. V., Reid, M. A. & Lahti, A. C. Evaluation of fronto-striatal networks during cognitive control in unmedicated patients with schizophrenia and the effect of antipsychotic medication. NPJ Schizophr. 4, 8 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Averbeck, B. & O’Doherty, J. P. Reinforcement-learning in fronto-striatal circuits. Neuropsychopharmacology 47, 147–162 (2022).

    Article 
    PubMed 

    Google Scholar 

  • Tang, Y., Yan, Y., Mao, J., Ni, J. & Qing, H. The hippocampus associated GABAergic neural network impairment in early-stage of Alzheimer’s disease. Ageing Res Rev. 86, 101865 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Pal, G. et al. Global cognitive function and processing speed are associated with gait and balance dysfunction in Parkinson’s disease. J. Neuroeng. Rehabil. 13, 94 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ebaid, D., Crewther, S. G., MacCalman, K., Brown, A. & Crewther, D. P. Cognitive processing speed across the lifespan: beyond the influence of motor speed. Front. Aging Neurosci. 9, 62 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Albrecht, F. et al. Investigating underlying brain structures and influence of mild and subjective cognitive impairment on dual-task performance in people with Parkinson’s disease. Sci. Rep. 14, 9513 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Abd-Alrazaq, A., Ahmed, A., Alali, H., Aldardour, A. M. & Househ, M. The effectiveness of serious games on cognitive processing speed among older adults with cognitive impairment: systematic review and meta-analysis. JMIR Serious Games 10, e36754 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Liu, Y. et al. The interaction between ageing and Alzheimer’s disease: insights from the hallmarks of ageing. Transl. Neurodegener. 13, 7 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bogdanova I. et al. The effectiveness of rehabilitation programs for the mobilization of compensatory-adaptive neuroplasticity processes in patients with Parkinson’s disease according to indicators of neurotrophic factors. Ukrains’kyi Visnyk Psykhonevrolohii 18–23 (2022).

  • Passaretti, M. et al. Neurophysiological markers of motor compensatory mechanisms in early Parkinson’s disease. Brain 147, 3714–3726 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Rademacher, K. & Nakamura, K. Role of dopamine neuron activity in Parkinson’s disease pathophysiology. Exp. Neurol. 373, 114645 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Yegorov, Y. E., Poznyak, A. V., Nikiforov, N. G., Sobenin, I. A. & Orekhov, A. N. The link between chronic stress and accelerated aging. Biomedicines 8, 198 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bobba-Alves, N. et al. Cellular allostatic load is linked to increased energy expenditure and accelerated biological aging. Psychoneuroendocrinology 155, 106322 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Jamea, A. A. et al. Altered default mode network activity and cortical thickness as vulnerability indicators for SCZ: a preliminary resting state MRI study. Eur. Rev. Med. Pharm. Sci. 25, 669–677 (2021).

    CAS 

    Google Scholar 

  • Wang, H. et al. Shared genetic architecture of cortical thickness alterations in major depressive disorder and schizophrenia. Prog. Neuropsychopharmacol. Biol. Psychiatry 135, 111121 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Klotz, L., Antel, J. & Kuhlmann, T. Inflammation in multiple sclerosis: consequences for remyelination and disease progression. Nat. Rev. Neurol. 19, 305–320 (2023).

    Article 
    PubMed 

    Google Scholar 

  • Psenicka, M. W., Smith, B. C., Tinkey, R. A. & Williams, J. L. Connecting neuroinflammation and neurodegeneration in multiple sclerosis: Are oligodendrocyte precursor cells a nexus of disease?. Front Cell Neurosci. 15, 654284 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Nian, K., Harding, I. C., Herman, I. M. & Ebong, E. E. Blood-brain barrier damage in ischemic stroke and its regulation by endothelial mechanotransduction. Front. Physiol. 11, 605398 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Li, X. et al. Endothelial cells and the blood-brain barrier: Critical determinants of ineffective reperfusion in stroke. Eur. J. Neurosci. 61, e16663 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Sone, D. et al. Neuroimaging-based brain-age prediction in diverse forms of epilepsy: a signature of psychosis and beyond. Mol. Psychiatry 26, 825–834 (2021).

    Article 
    PubMed 

    Google Scholar 

  • Hwang, G. et al. Brain aging in temporal lobe epilepsy: chronological, structural, and functional. NeuroImage Clin. 25, 102183 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Gong Y. et al. Progression of frailty and cardiovascular outcomes among Medicare beneficiaries. medRxiv 2024; published online Feb 13 (2024).

  • Bernal J. et al. Longitudinal evidence for a mutually reinforcing relationship between white matter hyperintensities and cortical thickness in cognitively unimpaired older adults. medRxiv. published online July 10. (2024).

  • Jiménez-Balado, J., Habeck, C., Stern, Y. & Eich, T. The relationship between cortical thickness and white matter hyperintensities in mid to late life. Neurobiol. Aging 141, 129–139 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Sanford N. et al Lifestyle and BrainAGE in adult depression. medRxiv. 2025; published online March 28. https://doi.org/10.1101/2025.03.27.25324698.

  • Turpin, A.-L. et al. Association between lifestyle at different life periods and brain integrity in older adults. Neurology 104, e213347 (2025).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Zhang, R. & Yi, F. Brain age gap model. Zenodo (2025).

    Article 

    Google Scholar 

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