{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T00:42:44Z","timestamp":1792629764540,"version":"4.5.0"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T00:00:00Z","timestamp":1792627200000},"content-version":"vor","delay-in-days":87,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100000024","name":"Gouvernement du Canada | Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["PJT186290"],"award-info":[{"award-number":["PJT186290"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010523","name":"Centre for Addiction and Mental Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010523","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Transl Psychiatry"],"DOI":"10.1038\/s41398-026-04297-8","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T03:34:40Z","timestamp":1785123280000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Lifestyle and BrainAGE in adult depression"],"prefix":"10.1038","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4915-2537","authenticated-orcid":false,"given":"Nicole","family":"Sanford","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuetong","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5547-3935","authenticated-orcid":false,"given":"Ruiyang","family":"Ge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1385-1050","authenticated-orcid":false,"given":"Shalaila S.","family":"Haas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2874-9972","authenticated-orcid":false,"given":"Liisa A. M.","family":"Galea","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3210-6470","authenticated-orcid":false,"given":"Sophia","family":"Frangou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,27]]},"reference":[{"key":"4297_CR1","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1016\/j.tins.2017.10.001","volume":"40","author":"JH Cole","year":"2017","unstructured":"Cole JH, Franke K. Predicting age using neuroimaging: Innovative brain ageing biomarkers. Trends Neurosci. 2017;40:681\u201390.","journal-title":"Trends Neurosci"},{"key":"4297_CR2","doi-asserted-by":"publisher","first-page":"5124","DOI":"10.1038\/s41380-020-0754-0","volume":"26","author":"LKM Han","year":"2021","unstructured":"Han LKM, Dinga R, Hahn T, Ching CRK, Eyler LT, Aftanas L, et al. Brain aging in major depressive disorder: Results from the ENIGMA major depressive disorder working group. Mol Psychiatry. 2021;26:5124\u201339.","journal-title":"Mol Psychiatry"},{"key":"4297_CR3","first-page":"462","volume":"8","author":"MK Jha","year":"2023","unstructured":"Jha MK, Chin Fatt C, Minhajuddin A, Mayes TL, Trivedi MH. Accelerated brain aging in adults with major depressive disorder predicts poorer outcome with sertraline: Findings from the EMBARC study. Biol Psychiatry Cogn Neurosci Neuroimaging. 2023;8:462\u201370.","journal-title":"Biol Psychiatry Cogn Neurosci Neuroimaging"},{"key":"4297_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2021.102864","volume":"32","author":"PL Ballester","year":"2021","unstructured":"Ballester PL, Suh JS, Nogovitsyn N, Hassel S, Strother SC, Arnott SR, et al. Accelerated brain aging in major depressive disorder and antidepressant treatment response: A CAN-BIND report. Neuroimage Clin. 2021;32:102864.","journal-title":"Neuroimage Clin"},{"key":"4297_CR5","first-page":"786","volume":"9","author":"NCW Ho","year":"2024","unstructured":"Ho NCW, Bethlehem RAI, Seidlitz J, Nogovitsyn N, Metzak P, Ballester PL, et al. Atypical brain aging and its association with working memory performance in Major Depressive Disorder. Biol Psychiatry Cogn Neurosci Neuroimaging. 2024;9:786\u201399.","journal-title":"Biol Psychiatry Cogn Neurosci Neuroimaging"},{"key":"4297_CR6","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1038\/s41386-021-00967-x","volume":"46","author":"K Dunlop","year":"2021","unstructured":"Dunlop K, Victoria LW, Downar J, Gunning FM, Liston C. Accelerated brain aging predicts impulsivity and symptom severity in depression. Neuropsychopharmacology. 2021;46:911\u20139.","journal-title":"Neuropsychopharmacology"},{"key":"4297_CR7","doi-asserted-by":"publisher","first-page":"1617","DOI":"10.1038\/s41593-019-0471-7","volume":"22","author":"T Kaufmann","year":"2019","unstructured":"Kaufmann T, van der Meer D, Doan NT, Schwarz E, Lund MJ, Agartz I, et al. Common brain disorders are associated with heritable patterns of apparent aging of the brain. Nat Neurosci. 2019;22:1617\u201323.","journal-title":"Nat Neurosci"},{"key":"4297_CR8","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neurobiolaging.2020.03.014","volume":"92","author":"JH Cole","year":"2020","unstructured":"Cole JH. Multimodality neuroimaging brain-age in UK biobank: Relationship to biomedical, lifestyle, and cognitive factors. Neurobiol Aging. 2020;92:34\u201342.","journal-title":"Neurobiol Aging"},{"key":"4297_CR9","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.jpsychires.2018.05.020","volume":"103","author":"CA K\u00f6hler","year":"2018","unstructured":"K\u00f6hler CA, Evangelou E, Stubbs B, Solmi M, Veronese N, Belbasis L, et al. Mapping risk factors for depression across the lifespan: An umbrella review of evidence from meta-analyses and Mendelian randomization studies. J Psychiatr Res. 2018;103:189\u2013207.","journal-title":"J Psychiatr Res"},{"key":"4297_CR10","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-020-01813-5","volume":"18","author":"J Sarris","year":"2020","unstructured":"Sarris J, Thomson R, Hargraves F, Eaton M, de Manincor M, Veronese N, et al. Multiple lifestyle factors and depressed mood: A cross-sectional and longitudinal analysis of the UK Biobank (N = 84,860). BMC Med. 2020;18:354.","journal-title":"BMC Med"},{"key":"4297_CR11","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1016\/j.psychres.2017.04.020","volume":"253","author":"Y Li","year":"2017","unstructured":"Li Y, Lv MR, Wei YJ, Sun L, Zhang JX, Zhang HG, et al. Dietary patterns and depression risk: A meta-analysis. Psychiatry Res. 2017;253:373\u201382.","journal-title":"Psychiatry Res"},{"key":"4297_CR12","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1176\/appi.ajp.2018.17111194","volume":"175","author":"FB Schuch","year":"2018","unstructured":"Schuch FB, Vancampfort D, Firth J, Rosenbaum S, Ward PB, Silva ES, et al. Physical activity and incident depression: A meta-analysis of prospective cohort studies. Am J Psychiatry. 2018;175:631\u201348.","journal-title":"Am J Psychiatry"},{"key":"4297_CR13","doi-asserted-by":"publisher","unstructured":"Penninx BWJH, Milaneschi Y, Lamers F, Vogelzangs N Understanding the somatic consequences of depression: Biological mechanisms and the role of depression symptom profile. BMC Med 2013; 11. https:\/\/doi.org\/10.1186\/1741-7015-11-129.","DOI":"10.1186\/1741-7015-11-129"},{"key":"4297_CR14","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1002\/wps.20252","volume":"14","author":"D Vancampfort","year":"2015","unstructured":"Vancampfort D, Stubbs B, Mitchell AJ, De Hert M, Wampers M, Ward PB, et al. Risk of metabolic syndrome and its components in people with schizophrenia and related psychotic disorders, bipolar disorder and major depressive disorder: A systematic review and meta-analysis. World Psychiatry. 2015;14:339\u201347.","journal-title":"World Psychiatry"},{"key":"4297_CR15","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1038\/nri.2015.5","volume":"16","author":"AH Miller","year":"2016","unstructured":"Miller AH, Raison CL. The role of inflammation in depression: From evolutionary imperative to modern treatment target. Nat Rev Immunol. 2016;16:22\u201334.","journal-title":"Nat Rev Immunol"},{"key":"4297_CR16","doi-asserted-by":"publisher","first-page":"e1001779","DOI":"10.1371\/journal.pmed.1001779","volume":"12","author":"C Sudlow","year":"2015","unstructured":"Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779.","journal-title":"PLoS Med"},{"key":"4297_CR17","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.neuroimage.2016.02.041","volume":"145","author":"E Varol","year":"2017","unstructured":"Varol E, Sotiras A, Davatzikos C. HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework. Neuroimage. 2017;145:346\u201364.","journal-title":"Neuroimage"},{"key":"4297_CR18","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1006\/pmed.2002.1064","volume":"35","author":"AJ Schuit","year":"2002","unstructured":"Schuit AJ, Van Loon AJM, Tijhuis M, Ock\u00e9 MC. Clustering of lifestyle risk factors in a general adult population. Prev Med (Baltim). 2002;35:219\u201324.","journal-title":"Prev Med (Baltim)"},{"key":"4297_CR19","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.26768","volume":"45","author":"Y Yu","year":"2024","unstructured":"Yu Y, Cui HQ, Haas SS, New F, Sanford N, Yu K, et al. Brain-age prediction: Systematic evaluation of site effects, and sample age range and size. Hum Brain Mapp. 2024;45:e26768.","journal-title":"Hum Brain Mapp"},{"key":"4297_CR20","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2021.761954","volume":"13","author":"SG Popescu","year":"2021","unstructured":"Popescu SG, Glocker B, Sharp DJ, Cole JH. Local brain-age: A U-Net model. Front Aging Neurosci. 2021;13:761954.","journal-title":"Front Aging Neurosci"},{"key":"4297_CR21","doi-asserted-by":"publisher","first-page":"1523","DOI":"10.1038\/nn.4393","volume":"19","author":"KL Miller","year":"2016","unstructured":"Miller KL, Alfaro-Almagro F, Bangerter NK, Thomas DL, Yacoub E, Xu J, et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci. 2016;19:1523\u201336.","journal-title":"Nat Neurosci"},{"key":"4297_CR22","doi-asserted-by":"publisher","first-page":"e2356787","DOI":"10.1001\/jamanetworkopen.2023.56787","volume":"7","author":"X Wang","year":"2024","unstructured":"Wang X, Hoffstaedter F, Kasper J, Eickhoff SB, Patil KR, Dukart J. Lifetime exposure to depression and neuroimaging measures of brain structure and function. JAMA Netw Open. 2024;7:e2356787.","journal-title":"JAMA Netw Open"},{"key":"4297_CR23","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.neuroimage.2007.07.007","volume":"38","author":"J Ashburner","year":"2007","unstructured":"Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage. 2007;38:95\u2013113.","journal-title":"Neuroimage"},{"key":"4297_CR24","doi-asserted-by":"publisher","first-page":"317","DOI":"10.3389\/fnagi.2018.00317","volume":"10","author":"TT Le","year":"2018","unstructured":"Le TT, Kuplicki RT, McKinney BA, Yeh H-W, Thompson WK, Paulus MP, et al. A nonlinear simulation framework supports adjusting for age when analyzing BrainAGE. Front Aging Neurosci. 2018;10:317.","journal-title":"Front Aging Neurosci"},{"key":"4297_CR25","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-99153-8","volume":"11","author":"A Salih","year":"2021","unstructured":"Salih A, Boscolo Galazzo I, Raisi-Estabragh Z, Rauseo E, Gkontra P, Petersen SE, et al. Brain age estimation at tract group level and its association with daily life measures, cardiac risk factors and genetic variants. Sci Rep. 2021;11:20563.","journal-title":"Sci Rep"},{"key":"4297_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v045.i03","volume":"45","author":"S van Buuren","year":"2011","unstructured":"van Buuren S, Groothuis-Oudshoorn K. mice: Multivariate Imputation by Chained Equations in R. J Stat Softw. 2011;45:1\u201367.","journal-title":"J Stat Softw"},{"key":"4297_CR27","unstructured":"The MathWorks Inc. MATLAB version: 9.7 (R2019b). 2019. https:\/\/www.mathworks.com."},{"key":"4297_CR28","doi-asserted-by":"publisher","first-page":"e0232447","DOI":"10.1371\/journal.pone.0232447","volume":"15","author":"HL Rippin","year":"2020","unstructured":"Rippin HL, Hutchinson J, Greenwood DC, Jewell J, Breda JJ, Martin A, et al. Inequalities in education and national income are associated with poorer diet: Pooled analysis of individual participant data across 12 European countries. PLoS One. 2020;15:e0232447.","journal-title":"PLoS One"},{"key":"4297_CR29","doi-asserted-by":"publisher","first-page":"3070","DOI":"10.3390\/ijerph19053070","volume":"19","author":"SU Ismail","year":"2022","unstructured":"Ismail SU, Asamane EA, Osei-Kwasi HA, Boateng D. Socioeconomic determinants of cardiovascular diseases, obesity, and diabetes among migrants in the United Kingdom: A systematic review. Int J Environ Res Public Health. 2022;19:3070.","journal-title":"Int J Environ Res Public Health"},{"key":"4297_CR30","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1146\/annurev.soc.012809.102529","volume":"36","author":"FC Pampel","year":"2010","unstructured":"Pampel FC, Krueger PM, Denney JT. Socioeconomic disparities in health behaviors. Annu Rev Sociol. 2010;36:349\u201370.","journal-title":"Annu Rev Sociol"},{"key":"4297_CR31","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1177\/1090198102251036","volume":"30","author":"NW Burton","year":"2003","unstructured":"Burton NW, Turrell G, Oldenburg B. Participation in recreational physical activity: Why do socioeconomic groups differ?. Health Education & Behavior. 2003;30:225\u201344.","journal-title":"Health Education & Behavior"},{"key":"4297_CR32","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1093\/ajcn\/87.5.1107","volume":"87","author":"N Darmon","year":"2008","unstructured":"Darmon N, Drewnowski A. Does social class predict diet quality?. American Journal of Clinical Nutrition. 2008;87:1107\u201317.","journal-title":"American Journal of Clinical Nutrition"},{"key":"4297_CR33","doi-asserted-by":"publisher","first-page":"487","DOI":"10.3389\/fpsyg.2018.00487","volume":"9","author":"KL Brookie","year":"2018","unstructured":"Brookie KL, Best GI, Conner TS. Intake of raw fruits and vegetables is associated with better mental health than intake of processed fruits and vegetables. Front Psychol. 2018;9:487.","journal-title":"Front Psychol"},{"key":"4297_CR34","doi-asserted-by":"publisher","first-page":"1790","DOI":"10.3390\/nu13061790","volume":"13","author":"S Berkins","year":"2021","unstructured":"Berkins S, Schi\u00f6th HB, Rukh G. Depression and vegetarians: Association between dietary vitamin b6, b12 and folate intake and global and subcortical brain volumes. Nutrients. 2021;13:1790.","journal-title":"Nutrients"},{"key":"4297_CR35","doi-asserted-by":"publisher","first-page":"6686","DOI":"10.3390\/ijerph17186686","volume":"17","author":"D Nucci","year":"2020","unstructured":"Nucci D, Fatigoni C, Amerio A, Odone A, Gianfredi V. Red and processed meat consumption and risk of depression: A systematic review and meta-analysis. Int J Environ Res Public Health. 2020;17:6686.","journal-title":"Int J Environ Res Public Health"},{"key":"4297_CR36","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1093\/nutrit\/nuz052","volume":"78","author":"KB Miller","year":"2020","unstructured":"Miller KB. Review of whole grain and dietary fiber recommendations and intake levels in different countries. Nutr Rev. 2020;78:29\u201336.","journal-title":"Nutr Rev"},{"key":"4297_CR37","first-page":"127","volume":"14","author":"R Ghodsi","year":"2021","unstructured":"Ghodsi R, Rostami H, Parastouei K, Taghdir M. Associations between whole and low-fat dairy products consumption, physical performance and mental health. Med J Nutrition Metab. 2021;14:127\u201336.","journal-title":"Med J Nutrition Metab"},{"key":"4297_CR38","doi-asserted-by":"publisher","first-page":"847","DOI":"10.1007\/s00127-016-1333-1","volume":"52","author":"Y Cui","year":"2017","unstructured":"Cui Y, Huang C, Momma H, Ren Z, Sugiyama S, Guan L, et al. Consumption of low-fat dairy, but not whole-fat dairy, is inversely associated with depressive symptoms in Japanese adults. Soc Psychiatry Psychiatr Epidemiol. 2017;52:847\u201353.","journal-title":"Soc Psychiatry Psychiatr Epidemiol"},{"key":"4297_CR39","doi-asserted-by":"publisher","first-page":"165","DOI":"10.3109\/15622970903131571","volume":"11","author":"S Rigucci","year":"2010","unstructured":"Rigucci S, Serafini G, Pompili M, Kotzalidis GD, Tatarelli R. Anatomical and functional correlates in major depressive disorder: The contribution of neuroimaging studies. The World Journal of Biological Psychiatry. 2010;11:165\u201380.","journal-title":"The World Journal of Biological Psychiatry"},{"key":"4297_CR40","doi-asserted-by":"publisher","first-page":"3214","DOI":"10.1002\/hbm.23235","volume":"37","author":"JW Murrough","year":"2016","unstructured":"Murrough JW, Abdallah CG, Anticevic A, Collins KA, Geha P, Averill LA, et al. Reduced global functional connectivity of the medial prefrontal cortex in major depressive disorder. Hum Brain Mapp. 2016;37:3214\u201323.","journal-title":"Hum Brain Mapp"},{"key":"4297_CR41","doi-asserted-by":"publisher","first-page":"1946","DOI":"10.1093\/brain\/awaa106","volume":"143","author":"S Rupprechter","year":"2020","unstructured":"Rupprechter S, Romaniuk L, Series P, Hirose Y, Hawkins E, Sandu AL, et al. Blunted medial prefrontal cortico-limbic reward-related effective connectivity and depression. Brain. 2020;143:1946\u201356.","journal-title":"Brain"},{"key":"4297_CR42","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2024.1462919","volume":"15","author":"F Buchholz","year":"2024","unstructured":"Buchholz F, Meffert M, Bazin PL, Trampel R, Turner R, Sch\u00f6nknecht P. Highfield imaging of the subgenual anterior cingulate cortex in uni- and bipolar depression. Front Psychiatry. 2024;15:1462919.","journal-title":"Front Psychiatry"},{"key":"4297_CR43","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1176\/appi.ajp.2016.16050518","volume":"174","author":"BW Dunlop","year":"2017","unstructured":"Dunlop BW, Rajendra JK, Craighead WE, Kelley ME, McGrath CL, Choi KS, et al. Functional connectivity of the subcallosal cingulate cortex and differential outcomes to treatment with cognitive-behavioral therapy or antidepressant medication for major depressive disorder. American Journal of Psychiatry. 2017;174:533\u201345.","journal-title":"American Journal of Psychiatry"},{"key":"4297_CR44","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-024-03117-1","volume":"14","author":"Y Wang","year":"2024","unstructured":"Wang Y, Wang C, Zhou J, Chen X, Liu R, Zhang Z, et al. Contribution of resting-state functional connectivity of the subgenual anterior cingulate to prediction of antidepressant efficacy in patients with major depressive disorder. Transl Psychiatry. 2024;14:399.","journal-title":"Transl Psychiatry"},{"key":"4297_CR45","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1177\/0271678X221074279","volume":"42","author":"SA Teixeira","year":"2022","unstructured":"Teixeira SA, de Moreira JLS, Sousa NRT, Pereira AM, de C, Rodrigues PN, Santos ASD, et al. Molecular basis and clinical perspectives of deep brain stimulation for major depressive disorder. Journal of Cerebral Blood Flow & Metabolism. 2022;42:683\u20135.","journal-title":"Journal of Cerebral Blood Flow & Metabolism"},{"key":"4297_CR46","doi-asserted-by":"publisher","first-page":"3260","DOI":"10.3390\/jcm9103260","volume":"9","author":"S Khairuddin","year":"2020","unstructured":"Khairuddin S, Ngo FY, Lim WL, Aquili L, Khan NA, Fung ML, et al. A decade of progress in deep brain stimulation of the subcallosal cingulate for the treatment of depression. J Clin Med. 2020;9:3260.","journal-title":"J Clin Med"},{"key":"4297_CR47","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1017\/S1461145711001088","volume":"15","author":"D Puigdemont","year":"2012","unstructured":"Puigdemont D, P\u00e9rez-Egea R, Portella MJ, Molet J, De Diego-Adeli\u00f1o J, Gironell A, et al. Deep brain stimulation of the subcallosal cingulate gyrus: Further evidence in treatment-resistant major depression. Int J Neuropsychopharmacol. 2012;15:121\u201333.","journal-title":"Int J Neuropsychopharmacol"},{"key":"4297_CR48","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1556\/2060.106.2019.10","volume":"106","author":"PD Loprinzi","year":"2019","unstructured":"Loprinzi PD. The effects of physical exercise on parahippocampal function. Physiol Int. 2019;106:114\u201327.","journal-title":"Physiol Int"},{"key":"4297_CR49","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.neures.2021.07.006","volume":"174","author":"S Frangou","year":"2022","unstructured":"Frangou S, Abbasi F, Watson K, Haas SS, Antoniades M, Modabbernia A, et al. Hippocampal volume reduction is associated with direct measure of insulin resistance in adults. Neurosci Res. 2022;174:19\u201324.","journal-title":"Neurosci Res"},{"key":"4297_CR50","doi-asserted-by":"publisher","first-page":"1908","DOI":"10.1111\/jdi.13554","volume":"12","author":"Y Adachi","year":"2021","unstructured":"Adachi Y, Ota K, Minami I, Yamada T, Watanabe T. Lower insulin secretion is associated with hippocampal and parahippocampal gyrus atrophy in elderly patients with type 2 diabetes mellitus. J Diabetes Investig. 2021;12:1908\u201313.","journal-title":"J Diabetes Investig"},{"key":"4297_CR51","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2024.1367563","volume":"16","author":"N Can\u00e1rio","year":"2024","unstructured":"Can\u00e1rio N, Cris\u00f3stomo J, Duarte JV, Moreno C, Quental H, Gomes L, et al. Irreversible atrophy in memory brain regions over 7 years is predicted by glycemic control in type 2 diabetes without mild cognitive impairment. Front Aging Neurosci. 2024;16:1367563.","journal-title":"Front Aging Neurosci"},{"key":"4297_CR52","unstructured":"R Core Team. R: A language and environment for statistical computing. 2021. https:\/\/www.R-project.org\/."},{"key":"4297_CR53","unstructured":"Herve M. RVAideMemoire: Testing and Plotting Procedures for Biostatistics. 2025. https:\/\/CRAN.R-project.org\/package=RVAideMemoire."},{"key":"4297_CR54","unstructured":"Kassambara A. rstatix: Pipe-Friendly Framework for Basic Statistical Tests. 2025. https:\/\/CRAN.R-project.org\/package=rstatix."}],"container-title":["Translational Psychiatry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41398-026-04297-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41398-026-04297-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41398-026-04297-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T00:08:57Z","timestamp":1792627737000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41398-026-04297-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,27]]},"references-count":54,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["4297"],"URL":"https:\/\/doi.org\/10.1038\/s41398-026-04297-8","relation":{},"ISSN":["2158-3188"],"issn-type":[{"value":"2158-3188","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,27]]},"assertion":[{"value":"12 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 July 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interest.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"579"}}