Brain change trajectories in healthy adults correlate with Alzheimer’s related genetic variation and memory decline across life
Sexton, C. E. et al. Accelerated changes in white matter microstructure during aging: a longitudinal diffusion tensor imaging study. J. Neurosci. 34, 15425–15436 (2014).
Google Scholar
Walhovd, K. B. et al. Genetic risk for Alzheimer disease predicts hippocampal volume through the human lifespan. Neurol. Genet. 6, e506 (2020).
Google Scholar
Vidal-Pineiro, D. et al. Cellular correlates of cortical thinning throughout the lifespan. Sci. Rep. 10, 1–14 (2020).
Google Scholar
Fjell, A. M., McEvoy, L., Holland, D., Dale, A. M. & Walhovd, K. B. What is normal in normal aging? Effects of aging, amyloid and Alzheimer’s disease on the cerebral cortex and the hippocampus. Prog. Neurobiol. 117, 20–40 (2014).
Google Scholar
Fjell, A. M., McEvoy, L., Holland, D., Dale, A. M. & Walhovd, K. B. Brain changes in older adults at very low risk for Alzheimer’s disease. J. Neurosci. 33, 8237–8242 (2013).
Google Scholar
Fjell, A. M. et al. One-year brain atrophy evident in healthy aging. 29, 15223–15231 (2009).
Roe, J. M. et al. Asymmetric thinning of the cerebral cortex across the adult lifespan is accelerated in Alzheimer’s disease. Nat. Commun. 12, 1–11 (2021).
Google Scholar
Braak, H. & Braak, E. Staging of Alzheimer-related cortical destruction. Rev. Clin. Neurosci. 33, 403–408 (1993).
Google Scholar
Jagust, W. Imaging the evolution and pathophysiology of Alzheimer disease. Nat. Rev. Neurosci. 19, 687–700 (2018).
Google Scholar
Bethlehem, R. A. I. et al. Brain charts for the human lifespan. Nature 604, 525–533 (2022).
Google Scholar
Rutherford, S. et al. Charting brain growth and aging at high spatial precision. Elife 11, 1–15 (2022).
Google Scholar
Walhovd, K. B. et al. Neurodevelopmental origins of lifespan changes in brain and cognition. Proc. Natl Acad. Sci. USA. 113, 9357–9362 (2016).
Google Scholar
Raz, N. et al. Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. Cereb. Cortex 15, 1676–1689 (2005).
Google Scholar
Fjell, A. M. et al. Accelerating cortical thinning: unique to dementia or universal in aging? Cereb. Cortex 24, 919–934 (2014).
Google Scholar
Corrada, M. M., Brookmeyer, R., Paganini-Hill, A., Berlau, D. & Kawas, C. H. Dementia incidence continues to increase with age in the oldest old the 90+ study. Ann. Neurol. 67, 114–121 (2010).
Google Scholar
Jorm, A. & Jolley, D. The incidence of dementia: a meta-analysis. Neurology 51, 728–733 (1998).
Google Scholar
Desikan, R. S. et al. Genetic assessment of age-associated Alzheimer disease risk: development and validation of a polygenic hazard score. PLoS Med. 14, 1–17 (2017).
Google Scholar
Altmann, A. et al. A comprehensive analysis of methods for assessing polygenic burden on Alzheimer’s disease pathology and risk beyond APOE. Brain Commun. 2, fcz047 (2020).
Google Scholar
Logue, M. W. et al. Use of an Alzheimer’s disease polygenic risk score to identify mild cognitive impairment in adults in their 50 s. Mol. Psychiatry 24, 421–430 (2019).
Google Scholar
Lyall, D. M. et al. Association between APOE e4 and white matter hyperintensity volume, but not total brain volume or white matter integrity. Brain Imaging Behav. 14, 1468–1476 (2020).
Machulda, M. M. et al. Effect of APOE? 4 status on intrinsic network connectivity in cognitively normal elderly subjects. Arch Neurol. 68, 1131–1136 (2011).
Habes, X. M. et al. Relationship between APOE genotype and structural MRI measures throughout adulthood in the study of health in Pomerania population-based cohort. AJNR Am. J. Neuroradiol. 37, 1636–42 (2016).
Bunce, D. et al. APOE genotype and entorhinal cortex volume in non-demented community-dwelling adults in midlife and early old age. J. Alzheimer’s Dis. 30, 935–942 (2012).
Google Scholar
Henson, R. N. et al. Effect of apolipoprotein E polymorphism on cognition and brain in the Cambridge Centre for Ageing and Neuroscience cohort. Brain Neurosci. Adv. 4, 2398212820961704 (2020).
Jack, C. R. et al. Age, sex, and APOEε4 effects on memory, brain structure, and β-amyloid across the adult life span. JAMA Neurol. 72, 511-9 (2022).
Protas, H. D. et al. Posterior cingulate glucose metabolism, hippocampal glucose metabolism, and hippocampal volume in cognitively normal, late-middle-aged persons at 3 levels of genetic risk for Alzheimer disease. JAMA Neurol. 70, 320–325 (2013).
Foo, H. et al. Associations between Alzheimer’s disease polygenic risk scores and hippocampal subfield volumes in 17, 161 UK biobank participants. Neurobiol. Aging 98, 108–115 (2021).
Google Scholar
Du, J. et al. Exploration of Alzheimer’s disease MRI biomarkers using APOE4 carrier status in the UK biobank. medRxiv (2021).
Knickmeyer, R. C. et al. Common variants in psychiatric risk genes predict brain structure at birth. Cerebral Cortex. 24, 1230–1246 (2014).
Axelrud, L. K. et al. Polygenic risk score for Alzheimer’s disease: implications for memory performance and hippocampal volumes in early life. Am. J. Psychiatry 175, 555–563 (2018).
Google Scholar
Foley, S. F. et al. Multimodal brain imaging reveals structural differences in Alzheimer’s disease polygenic risk carriers: a study in healthy young adults. Biol. Psychiatry 81, 154–161 (2017).
Google Scholar
Mormino, E. C. et al. Polygenic risk of Alzheimer disease is associated with early- and late-life processes. Neurology 87, 481–488 (2016).
Google Scholar
Fjell, A. M. et al. Self-reported sleep relates to hippocampal atrophy across the adult lifespan—results from the lifebrain consortium. Sleep 43, zsz280 (2019).
Donix, M. et al. Longitudinal changes in medial temporal cortical thickness in normal subjects with the APOE-4 polymorphism. Neuroimage 53, 37–43 (2010).
Google Scholar
Lu, P. H. et al. Apolipoprotein e genotype is associated with temporal and hippocampal atrophy rates in healthy elderly adults: a tensor-based morphometry study. J. Alzheimer’s Dis. 23, 433–442 (2011).
Google Scholar
Harrison, T. M., Mahmood, Z., Lau, E. P., Karacozoff, A. M. & Alison, C. An Alzheimer’s disease genetic risk score predicts longitudinal thinning of hippocampal complex subregions in healthy older adults. eNeuro 3, ENEURO.0098-16.2016 (2016).
Taylor, J. L. et al. Neurobiology of Aging APOE-epsilon4 and aging of medial temporal lobe gray matter in healthy adults older than 50 years. NBA 35, 2479–2485 (2014).
Google Scholar
Gorbach, T. et al. Longitudinal association between hippocampus atrophy and episodic-memory decline in non-demented APOE ε4 carriers. Alzheimer’s Dement. Diagnosis. Assess. Dis. Monit. 12, 1–9 (2020).
Brouwer, R. M. et al. Genetic variants associated with longitudinal changes in brain structure across the lifespan. Nat. Neurosci. 25, 421–432 (2022).
Google Scholar
Braak, H. & Braak, E. Staging of Alzheimer’s disease-related neurofibrillary changes. Neurobiol. Aging 16, 271–284 (1995).
Google Scholar
La Joie, R. et al. Prospective longitudinal atrophy in Alzheimer’s disease correlates with the intensity and topography of baseline tau-PET. Sci. Transl. Med. 12, 1–13 (2020).
Roe, J. M. et al. Asymmetric thinning of the cerebral cortex across the adult lifespan is accelerated in Alzheimer’s disease. bioRxiv (2020).
Marioni, R. E. et al. Genetic stratification to identify risk groups for Alzheimer’s disease. J. Alzheimer’s Dis. 57, 275–283 (2017).
Google Scholar
Hayden, K. M., Lutz, M. W., Kuchibhatla, M., Germain, C. & Plassman, B. L. Effect of APOE and CD33 on cognitive decline. PLoS ONE 10, 1–10 (2015).
Caselli, R. J. et al. Longitudinal modeling of age-related memory decline and the APOE ε4 Effect. N. Engl. J. Med. 361, 255–263 (2009).
Google Scholar
Kauppi, K., Rönnlund, M., Nordin Adolfsson, A., Pudas, S. & Adolfsson, R. Effects of polygenic risk for Alzheimer’s disease on rate of cognitive decline in normal aging. Transl. Psychiatry 10, 250 (2020).
Google Scholar
Salthouse, T. A. What and when of cognitive aging. Curr. Dir. Psychol. Sci. 13, 140–144 (2004).
Google Scholar
Salthouse, T. A. Why are there different age relations in cross-sectional and longitudinal comparisons of cognitive functioning? Curr. Dir. Psychol. Sci. 23, 252–256 (2014).
Google Scholar
Harris, S. E. et al. Polygenic risk for Alzheimer’s disease is not associated with cognitive ability or cognitive aging in non-demented older people. J. Alzheimer’s Dis. 39, 565–574 (2014).
Google Scholar
Braak, H. & Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82, 239–259 (1991).
Google Scholar
Simonsohn, U., Simmons, J. P. & Nelson, L. D. Specification curve analysis. Nat. Hum. Behav. 4, 1208–1214 (2020).
Google Scholar
Steegen, S., Tuerlinckx, F., Gelman, A. & Vanpaemel, W. Increasing transparency through a multiverse analysis. Perspect Psychol. Sci. 11, 702–712 (2016).
Heijer, T. D. et al. Magnetic resonance imaging in early dementia and cognitive decline. Cochrane Database Syst. Rev. 3, CD009628 (2010).
Jack, C. R. et al. Comparison of different MRI brain athrophy rate measures with clinical disease progression in AD. Neurology 62, 591–600 (2004).
Google Scholar
Lupton, M. K. et al. The effect of increased genetic risk for Alzheimer’s disease on hippocampal and amygdala volume. Neurobiol. Aging 40, 68–77 (2016).
Google Scholar
Chauhan, G. et al. Association of Alzheimer’s disease GWAS loci with MRI markers of brain aging. Neurobiol. Aging 36, 1765.e7–1765.e16 (2015).
Google Scholar
Walhovd, K. B., Lövden, M. & Fjell, A. M. Timing of lifespan influences on brain and cognition. Trends Cogn. Sci. 27, 901–915 (2023).
Storsve, A. B. et al. Differential longitudinal changes in cortical thickness, surface area and volume across the adult life span: regions of accelerating and decelerating change. J. Neurosci. 34, 8488–8498 (2014).
Google Scholar
Salthouse, T. A. Attrition in longitudinal data is pimarily selective with respect to level rather than rate of change. J. Int. Neuropsychol. Soc. 25, 618–623 (2019).
Google Scholar
Lambert, J. C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 45, 1452–1458 (2013).
Google Scholar
Papenberg, G., Lindenberger, U. & Bäckman, L. Aging-related magnification of genetic effects on cognitive and brain integrity. Trends Cogn. Sci. 19, 506–514 (2015).
Google Scholar
Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).
Google Scholar
Wei, Y. et al. Statistical testing in transcriptomic-neuroimaging studies: A how-to and evaluation of methods assessing spatial and gene specificity. Hum. Brain Mapp. 43, 885–901 (2022).
Google Scholar
Franzmeier, N. et al. The BIN1 rs744373 SNP is associated with increased tau-PET levels and impaired memory. Nat. Commun. 10, 1–12 (2019).
Google Scholar
Therriault, J. et al. Association of apolipoprotein e ϵ4 with medial temporal tau independent of amyloid-β. JAMA Neurol. 77, 470–479 (2020).
Google Scholar
Mesulam, M. A. Plasticity-based theory of the pathogenesis of Alzheimer’s disease. Ann. N. Y. Acad. Sci. 924, 42–52 (2000).
Walhovd, K. B. et al. Premises of plasticity—and the loneliness of the medial temporal lobe. Neuroimage 131, 48–54 (2016).
Google Scholar
Douaud, G. et al. A common brain network links development, aging, and vulnerability to disease. Proc. Natl Acad. Sci. USA. 111, 17648–17653 (2014).
Jack, C. R. et al. Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 12, 207–216 (2013).
Google Scholar
Hardy, J. A., Higgins, G. A., Hardy, J. A. & Higgins, G. A. Alzheimer’s disease: the amyloid cascade hypothesis. Science 256, 184–185 (1992).
Google Scholar
Herrup, K. The case for rejecting the amyloid cascade hypothesis. Nat. Neurosci. 18, 794–799 (2015).
Google Scholar
Korologou-Linden, R. et al. The causes and consequences of Alzheimer’s disease: phenome-wide evidence from mendelian randomization. Nat. Commun. 13, 4726 (2022).
Google Scholar
Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51, 404–413 (2019).
Google Scholar
Kunkle, B. W. et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 51, 414–430 (2019).
Google Scholar
Zhang, Q. et al. Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture. Nat. Commun. 11, 1–11 (2020).
Google Scholar
de Rojas, I. et al. Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores. Nat. Commun. 12, 3417 (2021).
Google Scholar
Escott-Price, V. & Hardy, J. Genome-wide association studies for Alzheimer’s disease: bigger is not always better. Brain Commun. 4, 1–7 (2022).
Google Scholar
Korczyn, A. D. & Grinberg, L. T. Is Alzheimer disease a disease? Nat. Rev. Neurol. 20, 245–251 (2024).
Google Scholar
Tucker-Drob, E. M. Cognitive aging and dementia: a life-span perspective. Annu. Rev. Dev. Psychol. 1, 177–196 (2019).
Google Scholar
Cole, J. H. & Franke, K. Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends Neurosci. 40, 681–690 (2017).
Google Scholar
Vidal-Pineiro, D. et al. Individual variations in ‘brain age’ relate to early-life factors more than to longitudinal brain change. Elife 10, 1–19 (2021).
Google Scholar
Demidenko, E. Mixed Models: Theory and Applications With R. (John Wiley & Sons, 2013).
Pinheiro, J. & Bates, D. Mixed-Effects Models in S and S-PLUS 1st edn, Vol. 528 (Springer-Verlag, 2000).
Nelson, E. A. & Dannefer, D. Aged heterogeneity: fact or fiction? the fate of diversity in gerontological research. Gerontologist 32, 17–23 (1992).
Google Scholar
Ferreira, D., Nordberg, A. & Westman, E. Biological subtypes of Alzheimer disease a systematic review and meta-analysis. Neurology. 94, 436–448 (2020).
Vogel, J. W. et al. Four distinct trajectories of tau deposition identified in Alzheimer’s disease. Nat. Med. 27, 871–881 (2021).
Google Scholar
Mohanty, R., Ferreira, D., Nordberg, A. & Westman, E. Associations between different tau ‑ PET patterns and longitudinal atrophy in the Alzheimer’s disease continuum: biological and methodological perspectives from disease heterogeneity. Alzheimers. Res. Ther. 15, 37 (2023).
Cardinale, F. et al. Validation of FreeSurfer-estimated brain cortical thickness: comparison with histologic measurements. Neuroinformatics 12, 535–542 (2014).
Google Scholar
Han, X. et al. Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer. Neuroimage 32, 180–194 (2006).
Google Scholar
Leng, Y., Ng, K. E. T., Vogrin, S. J., Meade, C. & Ngo, M. Comparative utility of manual versus automated segmentation of hippocampus and entorhinal cortex volumes in a memory clinic sample. J. Alzheimers Dis. 68, 159–171 (2019).
Hedges, E. P. et al. Reliability of structural MRI measurements: the effects of scan session, head tilt, inter-scan interval, acquisition sequence, FreeSurfer version and processing stream. Neuroimage 246, 118751 (2022).
Google Scholar
McGuire, S. A. et al. Reproducibility of quantitative structural and physiological MRI measurements. Brain Behav. 7, 1–17 (2017).
Google Scholar
Ossenkoppele, R. et al. Amyloid and tau PET-positive cognitively unimpaired individuals are at high risk for future cognitive decline. Nat. Med. 28, 2381–2387 (2022).
Google Scholar
Folstein, M. F., Folstein, S. E. & McHugh, P. R. Mini-mental state’. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12, 189–198 (1975).
Google Scholar
Beck, A. T., Ward, C., Mendelson, M., Mock, J. & Erbaugh, J. An inventory for measuring depression. Arch. Gen. Psychiatry 4, 561 (1961).
Google Scholar
Yesavage, J. A. et al. Development and validation of a geriatric depression screening scale: a preliminary report. J. Psychiatr. Res. 17, 37–49 (1982).
Google Scholar
Nilsson, L. G. et al. The Betula prospective cohort study: memory, health, and aging. Aging Neuropsychol. Cogn. 4, 1–32 (1997).
Google Scholar
Penninx, B. W. J. H. et al. Cohort profile of the longitudinal Netherlands study of depression and anxiety (NESDA) on etiology, course and consequences of depressive and anxiety disorders. J. Affect. Disord. 287, 69–77 (2021).
Google Scholar
Weiner, M. W. et al. The Alzheimer’s disease neuroimaging initiative: a review of papers published since its inception. Alzheimer’s Dement. 8, S1–S68 (2012).
Google Scholar
Chow, N. et al. Comparing 3T and 1.5T MRI for mapping hippocampal atrophy in the Alzheimer’s disease neuroimaging initiative. Am. J. Neuroradiol. 36, 653–660 (2015).
Google Scholar
Ellis, K. A. et al. The Australian imaging, biomarkers and lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease. Int. Psychogeriatr. 21, 672–687 (2009).
Google Scholar
Grasby, K. L. et al. The genetic architecture of the human cerebral cortex. Science 367, eaay6690 (2020).
Google Scholar
Hong, S. et al. TMEM106B and CPOX are genetic determinants of cerebrospinal fluid Alzheimer’s disease biomarker levels. Alzheimer’s Dement. 17, 1628–1640 (2021).
Google Scholar
Wightman, D. P. et al. A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease. Nat. Genet. 53, 1276–1282 (2021).
Google Scholar
Purcell, S. et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
Google Scholar
Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Google Scholar
Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS Genet. 2, 2074–2093 (2006).
Google Scholar
Reuter, M., Rosas, H. D. & Fischl, B. Highly accurate inverse consistent registration: a robust approach. Neuroimage 53, 1181–1196 (2010).
Google Scholar
Fischl, B., Sereno, M. I., Tootell, R. B. & Dale, A. M. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8, 272–284 (1999).
Google Scholar
Fischl, B., Sereno, M. I. & Dale, A. M. Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system. Neuroimage 9, 195–207 (1999).
Google Scholar
Schöll, M. et al. PET imaging of Tau deposition in the aging human brain. Neuron 89, 971–982 (2016).
Google Scholar
Franzmeier, N. et al. Functional brain architecture is associated with the rate of tau accumulation in Alzheimer’s disease. Nat. Commun. 11, 1–17 (2020).
Google Scholar
Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006).
Google Scholar
Wood, S. & Scheipl, F. gamm4: Generalized Additive Mixed Models Using ‘mgcv’ and ‘lme4’. R Package Version 0.2-5. (2017).
Chen, T. & Guestrin, C. XGBoost: A scalable tree boosting system. Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. (2016).
Delis, D. C., Kramer, J. H., Kaplan, E. & Thompkins, B. A. O. CVLT: California Verbal Learning Test-Adult Version: Manual, Vol. 91 (Psychological Corporation, 1987).
Roe, J. M. Brain Change Trajectories in Healthy Adults Correlate with Alzheimer’s Related Genetic Variation and Memory Decline Across Life. (2024).
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