{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T16:11:23Z","timestamp":1792685483680,"version":"4.5.0"},"reference-count":30,"publisher":"American Medical Association (AMA)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n Importance<\/jats:title>\n Little is known about structural brain changes in type 1 diabetes (T1D) and whether there are early manifestations of a neurodegenerative condition like Alzheimer disease (AD) or evidence of premature brain aging.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To evaluate neuroimaging markers of brain age and AD-like atrophy in participants with T1D in the Diabetes Control and Complications Trial (DCCT)\/Epidemiology of Diabetes Interventions and Complications (EDIC) study, identify which brain regions are associated with the greatest changes in patients with T1D, and assess the association between cognition and brain aging indices.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n This cohort study leveraged data collected during the combined DCCT (randomized clinical trial, 1983-1993) and EDIC (observational study, 1994 to present) studies at 27 clinical centers in the US and Canada. A total of 416 eligible EDIC participants and 99 demographically similar adults without diabetes were enrolled in the magnetic resonance imaging (MRI) ancillary study, which reports cross-sectional data collected in 2018 to 2019 and relates it to factors measured longitudinally in DCCT\/EDIC. Data analyses were performed between July 2020 and April 2022.<\/jats:p>\n <\/jats:sec>\n \n Exposure<\/jats:title>\n T1D diagnosis.<\/jats:p>\n <\/jats:sec>\n \n Main Outcomes and Measures<\/jats:title>\n Psychomotor and mental efficiency were evaluated using verbal fluency, digit symbol substitution test, trail making part B, and the grooved pegboard. Immediate memory scores were derived from the logical memory subtest of the Wechsler memory scale and the Wechsler digit symbol substitution test. MRI and machine learning indices were calculated to predict brain age and quantify AD-like atrophy.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n This study included 416 EDIC participants with a median (range) age of 60 (44-74) years (87 of 416 [21%]\u2009were older than\u200965 years) and a median (range) diabetes duration of 37 (30-51) years. EDIC participants had consistently higher brain age values compared with controls without diabetes, indicative of approximately 6 additional years of brain aging (EDIC participants: \u03b2, 6.16; SE, 0.71; control participants: \u03b2, 1.04; SE, 0.04;\n P<\/jats:italic>\n \u2009&amp;lt;\u2009.001). In contrast, AD regional atrophy was comparable between the 2 groups. Regions with atrophy in EDIC participants vs controls were observed mainly in the bilateral thalamus and putamen. Greater brain age was associated with lower psychomotor and mental efficiency among EDIC participants (\u03b2, \u22120.04; SE, 0.01;\n P<\/jats:italic>\n \u2009&amp;lt;\u2009.001), but not among controls.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n The findings of this study suggest an increase in brain aging among individuals with T1D without any early signs of AD-related neurodegeneration. These increases were associated with reduced cognitive performance, but overall, the abnormal patterns seen in this sample were modest, even after a mean of 38 years with T1D.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2023.16182","type":"journal-article","created":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T11:33:12Z","timestamp":1685619192000},"page":"e2316182","source":"Crossref","is-referenced-by-count":36,"title":["Patterns of Regional Brain Atrophy and Brain Aging in Middle- and Older-Aged Adults With Type 1 Diabetes"],"prefix":"10.1001","volume":"6","author":[{"given":"Mohamad","family":"Habes","sequence":"first","affiliation":[{"name":"Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio"},{"name":"Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alan M.","family":"Jacobson","sequence":"additional","affiliation":[{"name":"NYU Long Island School of Medicine, NYU Langone Hospital-Long Island, Mineola, New York"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barbara H.","family":"Braffett","sequence":"additional","affiliation":[{"name":"George Washington University, Biostatistics Center, Rockville, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tanweer","family":"Rashid","sequence":"additional","affiliation":[{"name":"Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio"},{"name":"Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher M.","family":"Ryan","sequence":"additional","affiliation":[{"name":"University of Pittsburgh, Pittsburgh, Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haochang","family":"Shou","sequence":"additional","affiliation":[{"name":"Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia"},{"name":"Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhan","family":"Cui","sequence":"additional","affiliation":[{"name":"Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christos","family":"Davatzikos","sequence":"additional","affiliation":[{"name":"Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose A.","family":"Luchsinger","sequence":"additional","affiliation":[{"name":"Columbia University Irving Medical Center, New York, New York"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geert J.","family":"Biessels","sequence":"additional","affiliation":[{"name":"Department of Neurology, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ionut","family":"Bebu","sequence":"additional","affiliation":[{"name":"George Washington University, Biostatistics Center, Rockville, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rose A.","family":"Gubitosi-Klug","sequence":"additional","affiliation":[{"name":"Case Western Reserve University School of Medicine, Rainbow Babies and Children's Hospital, Cleveland, Ohio"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R. Nick","family":"Bryan","sequence":"additional","affiliation":[{"name":"Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilya M.","family":"Nasrallah","sequence":"additional","affiliation":[{"name":"Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"DCCT\/EDIC Research Group","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10","published-online":{"date-parts":[[2023,6,1]]},"reference":[{"key":"zoi230493r1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nbd.2019.104608","article-title":"Diabetes mellitus in the young and the old: effects on cognitive functioning across the life span.","volume":"134","author":"van Duinkerken","year":"2020","journal-title":"Neurobiol Dis"},{"issue":"7","key":"zoi230493r2","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1016\/S2213-8587(21)00086-3","article-title":"Cognitive performance declines in older adults with type 1 diabetes: results from 32 years of follow-up in the DCCT and EDIC Study.","volume":"9","author":"Jacobson","year":"2021","journal-title":"Lancet Diabetes Endocrinol"},{"issue":"8","key":"zoi230493r3","doi-asserted-by":"publisher","first-page":"1779","DOI":"10.2337\/dc21-2438","article-title":"Brain structure among middle-aged and older adults with long-standing type 1 diabetes in the DCCT\/EDIC study.","volume":"45","author":"Jacobson","year":"2022","journal-title":"Diabetes Care"},{"issue":"6","key":"zoi230493r4","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1016\/j.jdiacomp.2013.07.001","article-title":"Frontal gray matter atrophy in middle aged adults with type 1 diabetes is independent of cardiovascular risk factors and diabetes complications.","volume":"27","author":"Hughes","year":"2013","journal-title":"J Diabetes Complications"},{"issue":"6","key":"zoi230493r5","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1055\/s-00000017","article-title":"Macrostructural brain changes in patients with longstanding type 1 diabetes mellitus - a cortical thickness analysis study.","volume":"121","author":"Fr\u00f8kj\u00e6r","year":"2013","journal-title":"Exp Clin Endocrinol Diabetes"},{"issue":"2","key":"zoi230493r6","doi-asserted-by":"publisher","first-page":"326","DOI":"10.2337\/diabetes.55.02.06.db05-0520","article-title":"Effects of type 1 diabetes on gray matter density as measured by voxel-based morphometry.","volume":"55","author":"Musen","year":"2006","journal-title":"Diabetes"},{"issue":"5","key":"zoi230493r7","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1159\/000100980","article-title":"Cognitive functioning and brain MRI in patients with type 1 and type 2 diabetes mellitus: a comparative study.","volume":"23","author":"Brands","year":"2007","journal-title":"Dement Geriatr Cogn Disord"},{"issue":"1","key":"zoi230493r8","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1002\/alz.v17.1","article-title":"The brain chart of aging: machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans.","volume":"17","author":"Habes","year":"2021","journal-title":"Alzheimers Dement"},{"issue":"4","key":"zoi230493r9","doi-asserted-by":"publisher","DOI":"10.1038\/tp.2016.39","article-title":"Advanced brain aging: relationship with epidemiologic and genetic risk factors, and overlap with Alzheimer disease atrophy patterns.","volume":"6","author":"Habes","year":"2016","journal-title":"Transl Psychiatry"},{"issue":"Pt 4","key":"zoi230493r10","doi-asserted-by":"publisher","first-page":"1164","DOI":"10.1093\/brain\/aww008","article-title":"White matter hyperintensities and imaging patterns of brain aging in the general population.","volume":"139","author":"Habes","year":"2016","journal-title":"Brain"},{"key":"zoi230493r11","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.neurobiolaging.2018.06.013","article-title":"Heterogeneity of structural and functional imaging patterns of advanced brain aging revealed via machine learning methods.","volume":"71","author":"Eavani","year":"2018","journal-title":"Neurobiol Aging"},{"key":"zoi230493r12","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.mri.2019.04.012","article-title":"Precision diagnostics based on machine learning-derived imaging signatures.","volume":"64","author":"Davatzikos","year":"2019","journal-title":"Magn Reson Imaging"},{"key":"zoi230493r13","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/j.dadm.2018.02.002","article-title":"Regional tract-specific white matter hyperintensities are associated with patterns to aging-related brain atrophy via vascular risk factors, but also independently.","volume":"10","author":"Habes","year":"2018","journal-title":"Alzheimers Dement (Amst)"},{"issue":"Pt 8","key":"zoi230493r14","doi-asserted-by":"publisher","first-page":"2026","DOI":"10.1093\/brain\/awp091","article-title":"Longitudinal progression of Alzheimer\u2019s-like patterns of atrophy in normal older adults: the SPARE-AD index.","volume":"132","author":"Davatzikos","year":"2009","journal-title":"Brain"},{"issue":"14","key":"zoi230493r15","doi-asserted-by":"publisher","first-page":"977","DOI":"10.1056\/NEJM199309303291401","article-title":"The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus.","volume":"329","author":"Nathan","year":"1993","journal-title":"N Engl J Med"},{"issue":"1","key":"zoi230493r16","doi-asserted-by":"publisher","first-page":"99","DOI":"10.2337\/diacare.22.1.99","article-title":"Epidemiology of Diabetes Interventions and Complications (EDIC): design, implementation, and preliminary results of a long-term follow-up of the Diabetes Control and Complications Trial cohort.","volume":"22","author":"Epidemiology of Diabetes Interventions and Complications (EDIC) Research Group","year":"1999","journal-title":"Diabetes Care"},{"issue":"5","key":"zoi230493r17","doi-asserted-by":"publisher","first-page":"883","DOI":"10.2337\/dc18-2062","article-title":"Risk factors for kidney disease in type 1 diabetes.","volume":"42","author":"Perkins","year":"2019","journal-title":"Diabetes Care"},{"issue":"5","key":"zoi230493r18","doi-asserted-by":"publisher","first-page":"875","DOI":"10.2337\/dc18-2308","article-title":"Risk factors for retinopathy in type 1 diabetes: the DCCT\/EDIC study.","volume":"42","author":"Hainsworth","year":"2019","journal-title":"Diabetes Care"},{"issue":"5","key":"zoi230493r19","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.2337\/dc09-1941","article-title":"Effect of prior intensive insulin treatment during the Diabetes Control and Complications Trial (DCCT) on peripheral neuropathy in type 1 diabetes during the Epidemiology of Diabetes Interventions and Complications (EDIC) Study.","volume":"33","author":"Albers","year":"2010","journal-title":"Diabetes Care"},{"issue":"25","key":"zoi230493r20","doi-asserted-by":"publisher","first-page":"2643","DOI":"10.1056\/NEJMoa052187","article-title":"Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes.","volume":"353","author":"Nathan","year":"2005","journal-title":"N Engl J Med"},{"issue":"4","key":"zoi230493r21","doi-asserted-by":"publisher","first-page":"379","DOI":"10.7326\/0003-4819-124-4-199602150-00001","article-title":"Effects of intensive diabetes therapy on neuropsychological function in adults in the Diabetes Control and Complications Trial.","volume":"124","year":"1996","journal-title":"Ann Intern Med"},{"issue":"18","key":"zoi230493r22","doi-asserted-by":"publisher","first-page":"1842","DOI":"10.1056\/NEJMoa066397","article-title":"Long-term effect of diabetes and its treatment on cognitive function.","volume":"356","author":"Jacobson","year":"2007","journal-title":"N Engl J Med"},{"issue":"7","key":"zoi230493r23","doi-asserted-by":"publisher","first-page":"940","DOI":"10.1007\/s00125-003-1128-2","article-title":"Cognitive efficiency declines over time in adults with Type 1 diabetes: effects of micro- and macrovascular complications.","volume":"46","author":"Ryan","year":"2003","journal-title":"Diabetologia"},{"issue":"1","key":"zoi230493r24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1515\/revneuro-2018-0096","article-title":"Brain structure and cognitive ability in healthy aging: a review on longitudinal correlated change.","volume":"31","author":"Oschwald","year":"2019","journal-title":"Rev Neurosci"},{"issue":"6","key":"zoi230493r25","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","article-title":"N4ITK: improved N3 bias correction.","volume":"29","author":"Tustison","year":"2010","journal-title":"IEEE Trans Med Imaging"},{"issue":"12","key":"zoi230493r26","doi-asserted-by":"publisher","first-page":"1566","DOI":"10.1016\/j.acra.2013.09.010","article-title":"Multi-atlas skull-stripping.","volume":"20","author":"Doshi","year":"2013","journal-title":"Acad Radiol"},{"key":"zoi230493r27","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.neuroimage.2015.11.073","article-title":"MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection.","volume":"127","author":"Doshi","year":"2016","journal-title":"Neuroimage"},{"key":"zoi230493r28","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.116450","article-title":"Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan.","volume":"208","author":"Pomponio","year":"2020","journal-title":"Neuroimage"},{"issue":"4","key":"zoi230493r29","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1007\/s11682-014-9348-2","article-title":"Meta-analyses of structural regional cerebral effects in type 1 and type 2 diabetes.","volume":"9","author":"Moulton","year":"2015","journal-title":"Brain Imaging Behav"},{"key":"zoi230493r30","unstructured":"Pomponio R. NeuroHarmonize. GitHub. Accessed May 1, 2023. https:\/\/github.com\/rpomponio\/neuroHarmonize"}],"container-title":["JAMA Network Open"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/jamanetwork.com\/journals\/jamanetworkopen\/articlepdf\/2805469\/habes_2023_oi_230493_1684773129.59808.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T11:33:14Z","timestamp":1685619194000},"score":1,"resource":{"primary":{"URL":"https:\/\/jamanetwork.com\/journals\/jamanetworkopen\/fullarticle\/2805469"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,1]]},"references-count":30,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,6,1]]}},"URL":"https:\/\/doi.org\/10.1001\/jamanetworkopen.2023.16182","relation":{},"ISSN":["2574-3805"],"issn-type":[{"value":"2574-3805","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,1]]}}}