Quantifying Brain and Cognitive Maintenance as Key Indicators for Sustainable Cognitive Aging: Insights from the UK Biobank

Age-related cognitive decline is a global phenomenon that affects individuals worldwide. The course and extent of this decline are influenced by numerous factors, such as genetics, lifestyle, education, and cognitive engagement. The theory of brain and cognitive reserve/maintenance posits that these factors have a significant impact on the degree of cognitive decline and overall brain health. However, the absence of standardized definitions and measurements for these terms creates ambiguity in research. To address this issue, we utilized a robust and systematic experimental paradigm, employing a considerably large subject pool comprising 17,030 participants from the UK Biobank. Utilizing advanced machine learning methodologies, we were able to accurately quantify both brain maintenance (BM) and cognitive maintenance (CM), making use of six distinct MRI modalities and nine distinct cognitive capabilities. Our study successfully identified several significant features that were meaningfully associated with both BM and CM outcomes. The results of our study demonstrate that lifestyle factors play a significant role in influencing both BM and CM through unique and independent mechanisms. Specifically, our study found that health status is a critical determinant of BM, while diabetes was found to be moderately associated with CM. Furthermore, our study revealed a positive correlation between BM/CM and cognitive reserve. By carefully considering the unique and independent mechanisms that govern both BM and CM, as well as their correlation with cognitive reserve, our study has provided valuable insight into the various strategies that may be leveraged to promote sustainable interventions to enhance cognitive and brain health across the lifespan.

[1]  Shuicai Wu,et al.  Comparison of Machine Learning Models for Brain Age Prediction Using Six Imaging Modalities on Middle-Aged and Older Adults , 2023, Sensors.

[2]  Shuicai Wu,et al.  A review of the application of three-dimensional convolutional neural networks for the diagnosis of Alzheimer’s disease using neuroimaging , 2023, Reviews in the neurosciences.

[3]  Md. Iftekhar Tanveer,et al.  Deep Learning for Brain Age Estimation: A Systematic Review , 2022, Inf. Fusion.

[4]  S. Eickhoff,et al.  Brain-age prediction: A systematic comparison of machine learning workflows , 2022, NeuroImage.

[5]  Liu Yang,et al.  Associations of grip strength, walking pace, and the risk of incident dementia: A prospective cohort study of 340212 participants , 2022, Alzheimer's & dementia : the journal of the Alzheimer's Association.

[6]  U. Lindenberger,et al.  Linking Brain Age Gap to Mental and Physical Health in the Berlin Aging Study II , 2022, Frontiers in Aging Neuroscience.

[7]  W. Kremen,et al.  Cognitive Reserve and Related Constructs: A Unified Framework Across Cognitive and Brain Dimensions of Aging , 2022, Frontiers in Aging Neuroscience.

[8]  Shih-Yi Lin,et al.  The impact of heavy alcohol consumption on cognitive impairment in young old and middle old persons , 2022, Journal of translational medicine.

[9]  Klaus P. Ebmeier,et al.  Sex‐ and age‐specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort , 2021, Human brain mapping.

[10]  Yihong Yang,et al.  Dose‐dependent relationship between social drinking and brain aging , 2021, Neurobiology of Aging.

[11]  Christopher N. Kaufmann,et al.  Alcohol use and cognitive performance: a comparison between Greece and the United States , 2021, Aging & mental health.

[12]  Marnie E. Shaw,et al.  Optimal Blood Pressure Keeps Our Brains Younger , 2021, Frontiers in Aging Neuroscience.

[13]  A. Mechelli,et al.  Machine learning for brain age prediction: Introduction to methods and clinical applications , 2021, EBioMedicine.

[14]  J. Tavares,et al.  Explainable Deep Learning for Personalized Age Prediction With Brain Morphology , 2021, Frontiers in Neuroscience.

[15]  A. Vedaldi,et al.  Optimising a Simple Fully Convolutional Network for Accurate Brain Age Prediction in the PAC 2019 Challenge , 2020, bioRxiv.

[16]  V. Calhoun,et al.  Brain age prediction: A comparison between machine learning models using region‐ and voxel‐based morphometric data , 2021, Human brain mapping.

[17]  Shaylene E. Nancekivell,et al.  Perceptions of the malleability of fluid and crystallized intelligence. , 2020, Journal of experimental psychology. General.

[18]  S. Malykh,et al.  Predicting Academic Achievement with Cognitive Abilities: Cross-Sectional Study across School Education , 2020, Behavioral sciences.

[19]  F. Cao,et al.  Subjective Cognitive Decline, Cognitive Reserve Indicators, and the Incidence of Dementia. , 2020, Journal of the American Medical Directors Association.

[20]  Mark Jenkinson,et al.  Learning patterns of the ageing brain in MRI using deep convolutional networks , 2020, NeuroImage.

[21]  Klaus P. Ebmeier,et al.  Prediction of brain age and cognitive age: Quantifying brain and cognitive maintenance in aging , 2020, Human brain mapping.

[22]  Meredith A. Shafto,et al.  Greater lifestyle engagement is associated with better age-adjusted cognitive abilities , 2020, PloS one.

[23]  Rachel J. Hopman,et al.  The Effect of Exercise Training on Brain Structure and Function in Older Adults: A Systematic Review Based on Evidence from Randomized Control Trials , 2020, Journal of clinical medicine.

[24]  J. Cole,et al.  Commentary: Correction procedures in brain-age prediction , 2020, NeuroImage: Clinical.

[25]  A. Toga,et al.  Association of relative brain age with tobacco smoking, alcohol consumption, and genetic variants , 2020, Scientific Reports.

[26]  D. Morris,et al.  Cognitive Genomics: Recent Advances and Current Challenges , 2020, Current Psychiatry Reports.

[27]  C. Barnes,et al.  Brain reserve, cognitive reserve, compensation, and maintenance: operationalization, validity, and mechanisms of cognitive resilience , 2019, Neurobiology of Aging.

[28]  H. Stefánsson,et al.  Brain age prediction using deep learning uncovers associated sequence variants , 2019, Nature Communications.

[29]  James H Cole,et al.  Multimodality neuroimaging brain-age in UK biobank: relationship to biomedical, lifestyle, and cognitive factors , 2019, Neurobiology of Aging.

[30]  Christian Gaser,et al.  Ten Years of BrainAGE as a Neuroimaging Biomarker of Brain Aging: What Insights Have We Gained? , 2019, Front. Neurol..

[31]  Ian J Deary,et al.  Reliability and validity of the UK Biobank cognitive tests , 2019, PloS one.

[32]  Stephen M. Smith,et al.  Estimation of brain age delta from brain imaging , 2019, NeuroImage.

[33]  J. Rehm,et al.  Alcohol use and dementia: a systematic scoping review , 2019, Alzheimer's Research & Therapy.

[34]  M. Kliegel,et al.  The role of cognitive reserve accumulated in midlife for the relation between chronic diseases and cognitive decline in old age: A longitudinal follow-up across six years , 2018, Neuropsychologia.

[35]  Soowon Park,et al.  Relationship between education, leisure activities, and cognitive functions in older adults , 2018, Aging & mental health.

[36]  M. N. Rajah,et al.  Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing , 2018, Nature Reviews Neuroscience.

[37]  J. Marchini,et al.  Genome-wide association studies of brain imaging phenotypes in UK Biobank , 2018, Nature.

[38]  Y. Stern,et al.  Whitepaper: Defining and investigating cognitive reserve, brain reserve, and brain maintenance , 2018, Alzheimer's & Dementia.

[39]  S. Blanchet,et al.  The benefits of physical activities on cognitive and mental health in healthy and pathological aging. , 2018, Geriatrie et psychologie neuropsychiatrie du vieillissement.

[40]  Bin Jing,et al.  Brain Structure Alterations in Respect to Tobacco Consumption and Nicotine Dependence: A Comparative Voxel-Based Morphometry Study , 2018, Front. Neuroanat..

[41]  John R. Petrie,et al.  Diabetes, Hypertension, and Cardiovascular Disease: Clinical Insights and Vascular Mechanisms , 2017, The Canadian journal of cardiology.

[42]  J. Castaldelli-Maia,et al.  Smoking and Cognition. , 2017, Current drug abuse reviews.

[43]  P. Vanhoutte,et al.  Macro‐ and microvascular endothelial dysfunction in diabetes , 2017, Journal of diabetes.

[44]  L. Ferrucci,et al.  Does a bit of alcohol turn off inflammation and improve health? , 2016, Age and ageing.

[45]  A. Woods,et al.  Current Heavy Alcohol Consumption is Associated with Greater Cognitive Impairment in Older Adults. , 2016, Alcoholism, clinical and experimental research.

[46]  P. Matthews,et al.  Multimodal population brain imaging in the UK Biobank prospective epidemiological study , 2016, Nature Neuroscience.

[47]  Andrew Trehearne Genetics, lifestyle and environment , 2016, Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz.

[48]  Maria Giulia Preti,et al.  Cigarette smoking leads to persistent and dose‐dependent alterations of brain activity and connectivity in anterior insula and anterior cingulate , 2015, Addiction biology.

[49]  H. Nilsson,et al.  Mindful Sustainable Aging: Advancing a Comprehensive Approach to the Challenges and Opportunities of Old Age , 2015, Europe's journal of psychology.

[50]  D. Murman The Impact of Age on Cognition , 2015, Seminars in Hearing.

[51]  P. Elliott,et al.  UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age , 2015, PLoS medicine.

[52]  J. Dare,et al.  Social engagement, setting and alcohol use among a sample of older Australians. , 2014, Health & social care in the community.

[53]  Christian Gaser,et al.  Advanced BrainAGE in older adults with type 2 diabetes mellitus , 2013, Front. Aging Neurosci..

[54]  C. Harada,et al.  Normal cognitive aging. , 2013, Clinics in geriatric medicine.

[55]  Y. Stern,et al.  Efficiency, capacity, compensation, maintenance, plasticity: emerging concepts in cognitive reserve , 2013, Trends in Cognitive Sciences.

[56]  Xiangfei Meng,et al.  Education and Dementia in the Context of the Cognitive Reserve Hypothesis: A Systematic Review with Meta-Analyses and Qualitative Analyses , 2012, PloS one.

[57]  M. Kivimaki,et al.  Impact of smoking on cognitive decline in early old age: the Whitehall II cohort study. , 2012, Archives of general psychiatry.

[58]  L. Nyberg,et al.  Memory aging and brain maintenance , 2012, Trends in Cognitive Sciences.

[59]  E. McAuley,et al.  Exercise training increases size of hippocampus and improves memory , 2011, Proceedings of the National Academy of Sciences.

[60]  A. Kaufman,et al.  How do educational attainment and gender relate to fluid intelligence, crystallized intelligence, and academic skills at ages 22-90 years? , 2009, Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists.

[61]  C. Iadecola,et al.  Hypertension and cerebrovascular dysfunction. , 2008, Cell metabolism.

[62]  R. Lamuela-Raventós,et al.  Down-regulation of adhesion molecules and other inflammatory biomarkers after moderate wine consumption in healthy women: a randomized trial. , 2007, The American journal of clinical nutrition.

[63]  P. Crome,et al.  Moderate alcohol consumption in older adults is associated with better cognition and well-being than abstinence. , 2007, Age and ageing.

[64]  Gary E. Swan,et al.  The Effects of Tobacco Smoke and Nicotine on Cognition and the Brain , 2007, Neuropsychology Review.

[65]  Annelies E. M. Van Vianen,et al.  Score gains on g-loaded tests: No g , 2007 .

[66]  Frank Seifert,et al.  Smoking and structural brain deficits: a volumetric MR investigation , 2006, The European journal of neuroscience.

[67]  Y. Stern What is cognitive reserve? Theory and research application of the reserve concept , 2002, Journal of the International Neuropsychological Society.

[68]  Monique Ernst,et al.  Smoking History and Nicotine Effects on Cognitive Performance , 2001, Neuropsychopharmacology.

[69]  P. Satz Brain reserve capacity on symptom onset after brain injury: A formulation and review of evidence for threshold theory. , 1993 .

[70]  R. Katzman.,et al.  Clinical, pathological, and neurochemical changes in dementia: A subgroup with preserved mental status and numerous neocortical plaques , 1988, Annals of neurology.

[71]  J. Gallacher,et al.  The relationship between alcohol use and long-term cognitive decline in middle and late life: a longitudinal analysis using UK Biobank. , 2018, Journal of public health.

[72]  M. Piano Alcohol’s Effects on the Cardiovascular System , 2017, Alcohol research : current reviews.

[73]  R. Chang,et al.  Neuropathology of cigarette smoking , 2013, Acta Neuropathologica.

[74]  Yaakov Stern,et al.  Cognitive Reserve: Implications for Assessment and Intervention , 2013, Folia Phoniatrica et Logopaedica.

[75]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[76]  F. Galton Regression Towards Mediocrity in Hereditary Stature. , 1886 .