{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T05:15:49Z","timestamp":1771737349304,"version":"3.50.1"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T00:00:00Z","timestamp":1719878400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T00:00:00Z","timestamp":1719878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia (FCT) and FEDER, through COMPETE2020","award":["POCI-01-0145-FEDER-016428"],"award-info":[{"award-number":["POCI-01-0145-FEDER-016428"]}]},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia (FCT) and FEDER, through CENTRO2020","award":["CENTRO-08-5864-FSE-000039"],"award-info":[{"award-number":["CENTRO-08-5864-FSE-000039"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Hum Genomics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Aging represents a significant risk factor for the occurrence of cerebral small vessel disease, associated with white matter (WM) lesions, and to age-related cognitive alterations, though the precise mechanisms remain largely unknown. This study aimed to investigate the impact of polygenic risk scores (PRS) for WM integrity, together with age-related DNA methylation, and gene expression alterations, on cognitive aging in a cross-sectional healthy aging cohort. The PRSs were calculated using genome-wide association study (GWAS) summary statistics for magnetic resonance imaging (MRI) markers of WM integrity, including WM hyperintensities, fractional anisotropy (FA), and mean diffusivity (MD). These scores were utilized to predict age-related cognitive changes and evaluate their correlation with structural brain changes, which distinguish individuals with higher and lower cognitive scores. To reduce the dimensionality of the data and identify age-related DNA methylation and transcriptomic alterations, Sparse Partial Least Squares-Discriminant Analysis (sPLS-DA) was used. Subsequently, a canonical correlation algorithm was used to integrate the three types of omics data (PRS, DNA methylation, and gene expression data) and identify an individual \u201comics\u201d signature that distinguishes subjects with varying cognitive profiles.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We found a positive association between MD-PRS and long-term memory, as well as a correlation between MD-PRS and structural brain changes, effectively discriminating between individuals with lower and higher memory scores. Furthermore, we observed an enrichment of polygenic signals in genes related to both vascular and non-vascular factors. Age-related alterations in DNA methylation and gene expression indicated dysregulation of critical molecular features and signaling pathways involved in aging and lifespan regulation. The integration of multi-omics data underscored the involvement of synaptic dysfunction, axonal degeneration, microtubule organization, and glycosylation in the process of cognitive aging.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>These findings provide valuable insights into the biological mechanisms underlying the association between WM coherence and cognitive aging. Additionally, they highlight how age-associated DNA methylation and gene expression changes contribute to cognitive aging.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s40246-024-00640-6","type":"journal-article","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T20:42:13Z","timestamp":1719952933000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Unveiling the molecular landscape of cognitive aging: insights from polygenic risk scores, DNA methylation, and gene expression"],"prefix":"10.1186","volume":"18","author":[{"given":"Sonya","family":"Neto","sequence":"first","affiliation":[]},{"given":"Andreia","family":"Reis","sequence":"additional","affiliation":[]},{"given":"Miguel","family":"Pinheiro","sequence":"additional","affiliation":[]},{"given":"Margarida","family":"Ferreira","sequence":"additional","affiliation":[]},{"given":"Vasco","family":"Neves","sequence":"additional","affiliation":[]},{"given":"Teresa Costa","family":"Castanho","sequence":"additional","affiliation":[]},{"given":"Nadine","family":"Santos","sequence":"additional","affiliation":[]},{"given":"Ana Jo\u00e3o","family":"Rodrigues","sequence":"additional","affiliation":[]},{"given":"Nuno","family":"Sousa","sequence":"additional","affiliation":[]},{"given":"Manuel A. S.","family":"Santos","sequence":"additional","affiliation":[]},{"given":"Gabriela R.","family":"Moura","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,2]]},"reference":[{"key":"640_CR1","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1038\/nrn3256","volume":"13","author":"C Grady","year":"2012","unstructured":"Grady C. The cognitive neuroscience of ageing (trends in neurocognitive aging). Nat Rev Neurosci. 2012;13:491\u2013505.","journal-title":"Nat Rev Neurosci"},{"issue":"1","key":"640_CR2","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1176\/appi.neuropsych.23.1.6","volume":"23","author":"AR Kaup","year":"2011","unstructured":"Kaup AR, Mirzakhanian H, Jeste DV, Eyler LT. A review of the brain structure correlates of successful cognitive aging. J Neuropsychiatry Clinic Neurosci. 2011;23(1):6\u201315.","journal-title":"J Neuropsychiatry Clinic Neurosci."},{"key":"640_CR3","doi-asserted-by":"publisher","DOI":"10.1515\/revneuro-2018-0096","author":"J Oschwald","year":"2019","unstructured":"Oschwald J, et al. Brain structure and cognitive ability in healthy aging: a review on longitudinal correlated change. Rev Neurosci. 2019. https:\/\/doi.org\/10.1515\/revneuro-2018-0096.","journal-title":"Rev Neurosci"},{"issue":"623","key":"640_CR4","first-page":"e15","volume":"33","author":"EC Klostermann","year":"2012","unstructured":"Klostermann EC, Braskie MN, Landau SM, O\u2019Neil JP, Jagust WJ. Dopamine and frontostriatal networks in cognitive aging. Neurobiol Aging. 2012;33(623):e15-623.e24.","journal-title":"Neurobiol Aging"},{"issue":"3","key":"640_CR5","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1037\/0882-7974.22.3.558","volume":"22","author":"D Finkel","year":"2007","unstructured":"Finkel D, Reynolds CA, McArdle JJ, Pedersen NL. Age changes in processing speed as a leading indicator of cognitive aging. Psychol Aging. 2007;22(3):558.","journal-title":"Psychol Aging"},{"key":"640_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroscience.2013.11.026","author":"IJ Bennett","year":"2014","unstructured":"Bennett IJ, Madden DJ. Disconnected aging: cerebral white matter integrity and age-related differences in cognition. Neuroscience. 2014. https:\/\/doi.org\/10.1016\/j.neuroscience.2013.11.026.","journal-title":"Neuroscience"},{"key":"640_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(19)30079-1","author":"JM Wardlaw","year":"2019","unstructured":"Wardlaw JM, Smith C, Dichgans M. Small vessel disease: mechanisms and clinical implications. Lancet Neurol. 2019. https:\/\/doi.org\/10.1016\/S1474-4422(19)30079-1.","journal-title":"Lancet Neurol"},{"key":"640_CR8","doi-asserted-by":"publisher","first-page":"001140","DOI":"10.1161\/JAHA.114.001140","volume":"4","author":"JM Wardlaw","year":"2015","unstructured":"Wardlaw JM, Vald\u00e9s Hern\u00e1ndez MC, Mu\u00f1oz-Maniega S. What are white matter hyperintensities made of? Relevance to vascular cognitive impairment. J Am Heart Assoc. 2015;4:001140.","journal-title":"J Am Heart Assoc"},{"key":"640_CR9","doi-asserted-by":"publisher","DOI":"10.1038\/s41576-019-0183-6","author":"D Melzer","year":"2019","unstructured":"Melzer D, Pilling LC, Ferrucci L. The genetics of human ageing. Nat Rev Genet. 2019. https:\/\/doi.org\/10.1038\/s41576-019-0183-6.","journal-title":"Nat Rev Genet"},{"key":"640_CR10","doi-asserted-by":"publisher","first-page":"1339","DOI":"10.1038\/s41588-019-0481-0","volume":"51","author":"K Watanabe","year":"2019","unstructured":"Watanabe K, et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat Genet. 2019;51:1339\u201348.","journal-title":"Nat Genet"},{"key":"640_CR11","doi-asserted-by":"publisher","first-page":"2759","DOI":"10.1038\/s41596-020-0353-1","volume":"15","author":"SW Choi","year":"2021","unstructured":"Choi SW, Shin T, Mak H, Reilly PFO. A guide to performing polygenic risk score analyses Introduction to polygenic risk scores. Nat Protoc. 2021;15:2759\u201372.","journal-title":"Nat Protoc"},{"key":"640_CR12","doi-asserted-by":"publisher","unstructured":"International Schizophrenia Consortium; Purcell SM, Wray NR, Stone JL, Visscher PM, O'Donovan MC, Sullivan PF, Sklar P. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460(7256):748-52. https:\/\/doi.org\/10.1038\/nature08185.","DOI":"10.1038\/nature08185"},{"key":"640_CR13","doi-asserted-by":"publisher","first-page":"938","DOI":"10.1016\/j.biopsych.2013.01.011","volume":"73","author":"AM McIntosh","year":"2013","unstructured":"McIntosh AM, et al. Polygenic risk for schizophrenia is associated with cognitive change between childhood and old age. Biol Psychiatry. 2013;73:938\u201343.","journal-title":"Biol Psychiatry"},{"key":"640_CR14","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1093\/ije\/dyv354","volume":"45","author":"SP Hagenaars","year":"2016","unstructured":"Hagenaars SP, et al. Polygenic risk for coronary artery disease is associated with cognitive ability in older adults. Int J Epidemiol. 2016;45:433\u201340.","journal-title":"Int J Epidemiol"},{"key":"640_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2013.10.012","author":"SL Edwards","year":"2013","unstructured":"Edwards SL, Beesley J, French JD, Dunning M. Beyond GWASs: illuminating the dark road from association to function. Am J Hum Genet. 2013. https:\/\/doi.org\/10.1016\/j.ajhg.2013.10.012.","journal-title":"Am J Hum Genet"},{"key":"640_CR16","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2019.00107","author":"FH Xiao","year":"2019","unstructured":"Xiao FH, Wang HT, Kong QP. Dynamic DNA methylation during aging: a \u201cprophet\u201d of age-related outcomes. Front Genet. 2019. https:\/\/doi.org\/10.3389\/fgene.2019.00107.","journal-title":"Front Genet"},{"key":"640_CR17","doi-asserted-by":"publisher","first-page":"716722","DOI":"10.3389\/fpsyt.2021.716722","volume":"12","author":"X Song","year":"2021","unstructured":"Song X, et al. Transcriptomics analysis reveals shared pathways in peripheral blood mononuclear cells and brain tissues of patients with schizophrenia. Front Psychiatry. 2021;12:716722.","journal-title":"Front Psychiatry"},{"key":"640_CR18","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.cell.2022.11.001","volume":"186","author":"C L\u00f3pez-Ot\u00edn","year":"2023","unstructured":"L\u00f3pez-Ot\u00edn C, Blasco MA, Partridge L, Serrano M, Kroemer G. Hallmarks of aging: an expanding universe. Cell. 2023;186:243\u201378.","journal-title":"Cell"},{"issue":"11","key":"640_CR19","doi-asserted-by":"publisher","first-page":"e1005752","DOI":"10.1371\/journal.pcbi.1005752","volume":"13","author":"F Rohart","year":"2017","unstructured":"Rohart F, Gautier B, Singh A, L\u00ea Cao KA. mixOmics: an R package for \u2018omics feature selection and multiple data integration. PLoS Comput Biol. 2017;13(11):e1005752.","journal-title":"PLoS Comput Biol"},{"key":"640_CR20","doi-asserted-by":"publisher","first-page":"1523","DOI":"10.1038\/s41467-019-09234-6","volume":"10","author":"Y Zhou","year":"2019","unstructured":"Zhou Y, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10:1523.","journal-title":"Nat Commun"},{"key":"640_CR21","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1037\/0033-295X.103.3.403","volume":"103","author":"TA Salthouse","year":"1996","unstructured":"Salthouse TA. The processing-speed theory of adult age differences in cognition. Psychol Rev. 1996;103:403\u201328.","journal-title":"Psychol Rev"},{"key":"640_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-39809-8","volume":"9","author":"P Coup\u00e9","year":"2019","unstructured":"Coup\u00e9 P, Manj\u00f3n JV, Lanuza E, Catheline G. Lifespan changes of the human brain In Alzheimer\u2019s disease. Sci Rep. 2019;9:1\u201312.","journal-title":"Sci Rep"},{"key":"640_CR23","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1017\/S135561771000127X","volume":"17","author":"A Reuben","year":"2011","unstructured":"Reuben A, Brickman AM, Muraskin J, Steffener J, Stern Y. Hippocampal atrophy relates to fluid intelligence decline in the elderly. J Int Neuropsychol Soc. 2011;17:56\u201361.","journal-title":"J Int Neuropsychol Soc"},{"key":"640_CR24","doi-asserted-by":"publisher","first-page":"298","DOI":"10.3389\/fnagi.2016.00298","volume":"8","author":"A O\u2019Shea","year":"2016","unstructured":"O\u2019Shea A, Cohen RA, Porges EC, Nissim NR, Woods AJ. Cognitive aging and the hippocampus in older adults. Front Aging Neurosci. 2016;8:298.","journal-title":"Front Aging Neurosci"},{"key":"640_CR25","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1080\/13825585.2013.795513","volume":"21","author":"KV Papp","year":"2014","unstructured":"Papp KV, et al. Processing speed in normal aging: Effects of white matter hyperintensities and hippocampal volume loss. Aging Neuropsychol Cognit. 2014;21:197.","journal-title":"Aging Neuropsychol Cognit"},{"issue":"2","key":"640_CR26","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1212\/01.wnl.0000194256.15247.83","volume":"66","author":"RA Charlton","year":"2006","unstructured":"Charlton RA, et al. White matter damage on diffusion tensor imaging correlates with age-related cognitive decline. Neurology. 2006;66(2):217\u201322.","journal-title":"Neurology"},{"key":"640_CR27","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1161\/STROKEAHA.112.680223","volume":"44","author":"M De Groot","year":"2013","unstructured":"De Groot M, et al. Changes in normal-appearing white matter precede development of white matter lesions. Stroke. 2013;44:1037.","journal-title":"Stroke"},{"key":"640_CR28","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1016\/j.neurobiolaging.2021.06.021","volume":"106","author":"JL Merenstein","year":"2021","unstructured":"Merenstein JL, Corrada MM, Kawas CH, Bennett IJ. Age affects white matter microstructure and episodic memory across the older adult lifespan. Neurobiol Aging. 2021;106:282.","journal-title":"Neurobiol Aging"},{"key":"640_CR29","doi-asserted-by":"publisher","DOI":"10.1038\/nature03875","author":"P Carmeliet","year":"2005","unstructured":"Carmeliet P, Tessier-Lavigne M. Common mechanisms of nerve and blood vessel wiring. Nature. 2005. https:\/\/doi.org\/10.1038\/nature03875.","journal-title":"Nature"},{"key":"640_CR30","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1038\/ng1395","volume":"36","author":"L Cao","year":"2004","unstructured":"Cao L, et al. VEGF links hippocampal activity with neurogenesis, learning and memory. Nat Genet. 2004;36:827.","journal-title":"Nat Genet"},{"key":"640_CR31","doi-asserted-by":"publisher","first-page":"2239","DOI":"10.1016\/j.neurobiolaging.2013.04.006","volume":"34","author":"AM Fjell","year":"2013","unstructured":"Fjell AM, et al. Critical ages in the life course of the adult brain: nonlinear subcortical aging. Neurobiol Aging. 2013;34:2239\u201347.","journal-title":"Neurobiol Aging"},{"key":"640_CR32","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.physbeh.2017.03.040","volume":"176","author":"CWNSSJRB Usselman","year":"2017","unstructured":"Usselman CWNSSJRB. A meta-analysis of heritability of cognitive aging: minding the \u201cmissing heritability\u201d gap. Physiol Behav. 2017;176:139\u201348.","journal-title":"Physiol Behav"},{"key":"640_CR33","doi-asserted-by":"publisher","first-page":"e74578","DOI":"10.1371\/journal.pone.0074578","volume":"8","author":"K Cao","year":"2013","unstructured":"Cao K, Ryvkin P, Hwang YC, Johnson FB, Wang LS. Analysis of nonlinear gene expression progression reveals extensive pathway and age-specific transitions in aging human brains. PLoS ONE. 2013;8:e74578.","journal-title":"PLoS ONE"},{"key":"640_CR34","doi-asserted-by":"publisher","first-page":"1844","DOI":"10.1080\/15592294.2019.1626651","volume":"14","author":"G Sturm","year":"2019","unstructured":"Sturm G, et al. Human aging DNA methylation signatures are conserved but accelerated in cultured fibroblasts. Epigenetics. 2019;14:1844.","journal-title":"Epigenetics"},{"issue":"1","key":"640_CR35","doi-asserted-by":"publisher","first-page":"9201","DOI":"10.1038\/s41598-021-88504-0","volume":"11","author":"O Vershinina","year":"2021","unstructured":"Vershinina O, Bacalini MG, Zaikin A, Franceschi C, Ivanchenko M. Disentangling age-dependent DNA methylation: deterministic, stochastic, and nonlinear. Sci Rep. 2021;11(1):9201.","journal-title":"Sci Rep"},{"key":"640_CR36","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1038\/s41591-019-0673-2","volume":"25","author":"B Lehallier","year":"2019","unstructured":"Lehallier B, et al. Undulating changes in human plasma proteome profiles across the lifespan. Nat Med. 2019;25:327.","journal-title":"Nat Med"},{"key":"640_CR37","doi-asserted-by":"publisher","first-page":"1648","DOI":"10.1371\/journal.pcbi.0030170","volume":"3","author":"A Bergman","year":"2007","unstructured":"Bergman A, Atzmon G, Ye K, MacCarthy T, Barzilai N. Buffering mechanisms in aging: a systems approach toward uncovering the genetic component of aging. PLoS Comput Biol. 2007;3:1648\u201356.","journal-title":"PLoS Comput Biol"},{"key":"640_CR38","doi-asserted-by":"publisher","DOI":"10.3390\/cells13040339","author":"C Guerra-Espinosa","year":"2024","unstructured":"Guerra-Espinosa C, Jim\u00e9nez-Fern\u00e1ndez M, S\u00e1nchez-Madrid F, Serrador JM. ICAMs in immunity intercellular adhesion and communication. Cells. 2024. https:\/\/doi.org\/10.3390\/cells13040339.","journal-title":"Cells"},{"key":"640_CR39","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1038\/labinvest.3700241","volume":"85","author":"VG Gorgoulis","year":"2005","unstructured":"Gorgoulis VG, et al. p53-dependent ICAM-1 overexpression in senescent human cells identified in atherosclerotic lesions. Lab Invest. 2005;85:502.","journal-title":"Lab Invest"},{"key":"640_CR40","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1016\/S0960-9822(99)80420-5","volume":"9","author":"DN Shelton","year":"1999","unstructured":"Shelton DN, Chang E, Whittier PS, Choi D, Funk WD. Microarray analysis of replicative senescence. Curr Biol. 1999;9:939.","journal-title":"Curr Biol"},{"key":"640_CR41","doi-asserted-by":"publisher","first-page":"1541","DOI":"10.1161\/01.CIR.0000013836.85741.17","volume":"105","author":"T Minamino","year":"2002","unstructured":"Minamino T, et al. Endothelial cell senescence in human atherosclerosis: role of telomere in endothelial dysfunction. Circulation. 2002;105:1541.","journal-title":"Circulation"},{"key":"640_CR42","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1007\/BF00334673","volume":"85","author":"H Akiyama","year":"1993","unstructured":"Akiyama H, et al. Expression of intercellular adhesion molecule (ICAM)-1 by a subset of astrocytes in Alzheimer disease and some other degenerative neurological disorders. Acta Neuropathol. 1993;85:628.","journal-title":"Acta Neuropathol"},{"key":"640_CR43","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1097\/FPC.0b013e3283559b22","volume":"22","author":"L Gong","year":"2012","unstructured":"Gong L, Goswami S, Giacomini KM, Altman RB, Klein TE. Metformin pathways: pharmacokinetics and pharmacodynamics. Pharmacogenet Genom. 2012;22:820.","journal-title":"Pharmacogenet Genom"},{"key":"640_CR44","doi-asserted-by":"publisher","first-page":"970","DOI":"10.14336\/AD.2021.1213","volume":"13","author":"FF Cheng","year":"2022","unstructured":"Cheng FF, Liu YL, Du J, Lin JT. Metformin\u2019s mechanisms in attenuating hallmarks of aging and age-related disease. Aging Dis. 2022;13:970\u201386.","journal-title":"Aging Dis"},{"key":"640_CR45","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2021.718942","author":"I Mohammed","year":"2021","unstructured":"Mohammed I, Hollenberg MD, Ding H, Triggle CR. A critical review of the evidence that metformin is a putative anti-aging drug that enhances healthspan and extends lifespan. Front Endocrinol. 2021. https:\/\/doi.org\/10.3389\/fendo.2021.718942.","journal-title":"Front Endocrinol"},{"key":"640_CR46","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.cell.2016.02.035","volume":"165","author":"L Chantranupong","year":"2016","unstructured":"Chantranupong L, et al. The CASTOR proteins are arginine sensors for the mTORC1 pathway. Cell. 2016;165:153.","journal-title":"Cell"},{"key":"640_CR47","doi-asserted-by":"publisher","DOI":"10.1038\/nature11861","author":"SC Johnson","year":"2013","unstructured":"Johnson SC, Rabinovitch PS, Kaeberlein M. MTOR is a key modulator of ageing and age-related disease. Nature. 2013. https:\/\/doi.org\/10.1038\/nature11861.","journal-title":"Nature"},{"key":"640_CR48","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1038\/ncb3225","volume":"17","author":"N Herranz","year":"2015","unstructured":"Herranz N, et al. mTOR regulates MAPKAPK2 translation to control the senescence-associated secretory phenotype. Nat Cell Biol. 2015;17:1205.","journal-title":"Nat Cell Biol"},{"key":"640_CR49","doi-asserted-by":"publisher","DOI":"10.1038\/nrn1809","author":"SN Burke","year":"2006","unstructured":"Burke SN, Barnes CA. Neural plasticity in the ageing brain. Nat Rev Neurosci. 2006. https:\/\/doi.org\/10.1038\/nrn1809.","journal-title":"Nat Rev Neurosci"},{"key":"640_CR50","doi-asserted-by":"publisher","DOI":"10.1186\/1749-8104-8-17","author":"A Prokop","year":"2013","unstructured":"Prokop A. The intricate relationship between microtubules and their associated motor proteins during axon growth and maintenance. Neural Dev. 2013. https:\/\/doi.org\/10.1186\/1749-8104-8-17.","journal-title":"Neural Dev"},{"key":"640_CR51","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neuron.2008.11.013","volume":"61","author":"J Jaworski","year":"2009","unstructured":"Jaworski J, et al. Dynamic microtubules regulate dendritic spine morphology and synaptic plasticity. Neuron. 2009;61:85.","journal-title":"Neuron"},{"key":"640_CR52","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1093\/glycob\/cww086","volume":"27","author":"A Varki","year":"2017","unstructured":"Varki A. Biological roles of glycans. Glycobiology. 2017;27:3.","journal-title":"Glycobiology"},{"key":"640_CR53","doi-asserted-by":"publisher","DOI":"10.1042\/BST20180330","author":"S Iqbal","year":"2018","unstructured":"Iqbal S, Fard MG, Everest-Dass A, Packer NH, Parker LM. Understanding cellular glycan surfaces in the central nervous system. Biochem Soc Trans. 2018. https:\/\/doi.org\/10.1042\/BST20180330.","journal-title":"Biochem Soc Trans"},{"key":"640_CR54","doi-asserted-by":"publisher","DOI":"10.1093\/glycob\/cwu015","author":"H Scott","year":"2014","unstructured":"Scott H, Panin VM. The role of protein N-glycosylation in neural transmission. Glycobiology. 2014. https:\/\/doi.org\/10.1093\/glycob\/cwu015.","journal-title":"Glycobiology"},{"key":"640_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.tem.2021.09.006","author":"LR Conroy","year":"2021","unstructured":"Conroy LR, Hawkinson TR, Young LEA, Gentry MS, Sun RC. Emerging roles of N-linked glycosylation in brain physiology and disorders. Trends Endocrinol Metabol. 2021. https:\/\/doi.org\/10.1016\/j.tem.2021.09.006.","journal-title":"Trends Endocrinol Metabol"},{"key":"640_CR56","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1038\/nchembio.797","volume":"8","author":"SA Yuzwa","year":"2012","unstructured":"Yuzwa SA, et al. Increasing O-GlcNAc slows neurodegeneration and stabilizes tau against aggregation. Nat Chem Biol. 2012;8:393.","journal-title":"Nat Chem Biol"},{"key":"640_CR57","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1038\/nchem.2361","volume":"7","author":"NP Marotta","year":"2015","unstructured":"Marotta NP, et al. O-GlcNAc modification blocks the aggregation and toxicity of the protein \u03b1-synuclein associated with Parkinson\u2019s disease. Nat Chem. 2015;7:913.","journal-title":"Nat Chem"},{"key":"640_CR58","doi-asserted-by":"publisher","DOI":"10.1007\/s11357-012-9482-y","author":"NC Santos","year":"2013","unstructured":"Santos NC, et al. Mood is a key determinant of cognitive performance in community-dwelling older adults: a cross-sectional analysis. Age. 2013. https:\/\/doi.org\/10.1007\/s11357-012-9482-y.","journal-title":"Age"},{"key":"640_CR59","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0024553","author":"AC Paulo","year":"2011","unstructured":"Paulo AC, et al. Patterns of cognitive performance in healthy ageing in northern Portugal: a cross-sectional analysis. PLoS ONE. 2011. https:\/\/doi.org\/10.1371\/journal.pone.0024553.","journal-title":"PLoS ONE"},{"key":"640_CR60","first-page":"9","volume":"1","author":"M Guerreiro","year":"1994","unstructured":"Guerreiro M, et al. Adapta\u00e7\u00e3o \u00e0 popula\u00e7\u00e3o portuguesa da tradu\u00e7\u00e3o do \u201cmini mental state examination\u201d (MMSE). Rev Port Neurol. 1994;1:9\u201310.","journal-title":"Rev Port Neurol"},{"key":"640_CR61","volume-title":"WAIS-III: wechsler adult intelligence scale","author":"D Wechsler","year":"1997","unstructured":"Wechsler D. WAIS-III: wechsler adult intelligence scale. 3rd ed. San Antonio: The Psychological Corporation Pearson; 1997.","edition":"3"},{"issue":"5","key":"640_CR62","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1017\/S1355617700000576","volume":"1","author":"H Buschke","year":"1995","unstructured":"Buschke H, Sliwinski M, Kuslansky G, Lipton RB. Aging, encoding specificity, and memory change in the double memory test. J Int Neuropsychol Soc. 1995;1(5):483\u201393.","journal-title":"J Int Neuropsychol Soc"},{"key":"640_CR63","unstructured":"Strauss E, Sherman EMS, Spreen O. A compendium of neuropsychological tests: Administration, norms, and commentary 3rd ed. Oxford University Press; 2006."},{"key":"640_CR64","volume-title":"Neuropsychological assessment","author":"M Lezak","year":"2004","unstructured":"Lezak M, Howieson D, Loring D. Neuropsychological assessment. New York: Oxford University Press; 2004."},{"key":"640_CR65","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2014.00330","author":"JM Soares","year":"2014","unstructured":"Soares JM, Marques P, Magalh\u00e3es R, Santos NC, Sousa N. Brain structure across the lifespan: the influence of stress and mood. Front Aging Neurosci. 2014. https:\/\/doi.org\/10.3389\/fnagi.2014.00330.","journal-title":"Front Aging Neurosci"},{"key":"640_CR66","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","volume":"31","author":"RS Desikan","year":"2006","unstructured":"Desikan RS, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31:968.","journal-title":"Neuroimage"},{"key":"640_CR67","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neuroimage.2010.06.010","volume":"53","author":"C Destrieux","year":"2010","unstructured":"Destrieux C, Fischl B, Dale A, Halgren E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage. 2010;53:1.","journal-title":"Neuroimage"},{"key":"640_CR68","doi-asserted-by":"publisher","DOI":"10.1038\/nprot.2010.116","author":"CA Anderson","year":"2010","unstructured":"Anderson CA, et al. Data quality control in genetic case-control association studies. Nat Protoc. 2010. https:\/\/doi.org\/10.1038\/nprot.2010.116.","journal-title":"Nat Protoc"},{"key":"640_CR69","doi-asserted-by":"publisher","DOI":"10.1038\/ng.3656","author":"S Das","year":"2016","unstructured":"Das S, et al. Next-generation genotype imputation service and methods. Nat Genet. 2016. https:\/\/doi.org\/10.1038\/ng.3656.","journal-title":"Nat Genet"},{"key":"640_CR70","doi-asserted-by":"publisher","first-page":"e47","DOI":"10.1093\/nar\/gkv007","volume":"43","author":"ME Ritchie","year":"2015","unstructured":"Ritchie ME, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.","journal-title":"Nucleic Acids Res"},{"key":"640_CR71","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-13-86","volume":"13","author":"EA Houseman","year":"2012","unstructured":"Houseman EA, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012;13:1\u20136.","journal-title":"BMC Bioinformatics"},{"key":"640_CR72","doi-asserted-by":"publisher","first-page":"082","DOI":"10.1093\/gigascience\/giz082","volume":"8","author":"SW Choi","year":"2019","unstructured":"Choi SW, O\u2019Reilly PF. PRSice-2: polygenic risk score software for biobank-scale data. Gigascience. 2019;8:082.","journal-title":"Gigascience"},{"key":"640_CR73","doi-asserted-by":"publisher","first-page":"2175","DOI":"10.1038\/s41467-020-15932-3","volume":"11","author":"E Persyn","year":"2020","unstructured":"Persyn E, et al. Genome-wide association study of MRI markers of cerebral small vessel disease in 42,310 participants. Nat Commun. 2020;11:2175.","journal-title":"Nat Commun"},{"key":"640_CR74","doi-asserted-by":"publisher","first-page":"3055","DOI":"10.1093\/bioinformatics\/bty1054","volume":"35","author":"A Singh","year":"2019","unstructured":"Singh A, et al. DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics. 2019;35:3055\u201362.","journal-title":"Bioinformatics"}],"container-title":["Human Genomics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40246-024-00640-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40246-024-00640-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40246-024-00640-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T20:50:29Z","timestamp":1719953429000},"score":1,"resource":{"primary":{"URL":"https:\/\/humgenomics.biomedcentral.com\/articles\/10.1186\/s40246-024-00640-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,2]]},"references-count":74,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["640"],"URL":"https:\/\/doi.org\/10.1186\/s40246-024-00640-6","relation":{},"ISSN":["1479-7364"],"issn-type":[{"value":"1479-7364","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,2]]},"assertion":[{"value":"23 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The cohort was established according to the ethical principles of the Declaration of Helsinki and experiments were approved by the Portuguese ethical committee (Comiss\u00e3o Nacional de Prote\u00e7\u00e3o de Dados) and local ethic review boards (Hospital de Braga, Braga; Centro Hospitalar do Alto Ave, Guimar\u00e3es; and Unidade Local de Sa\u00fade do Alto Minho, Viana do Castelo\/Ponte de Lima). All participants gave voluntary and informed written consent. Medical and\/or research professionals signed a Statement of Responsibility and Confidentiality.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"75"}}