Yousaf Abughofah, Rachael Deardorff, Aaron Vosmeier, Savannah Hottle, Jeffrey L Dage, Desarae Dempsey, Liana G Apostolova, Jared Brosch, David Clark, Martin Farlow, Tatiana Foroud, Sujuan Gao, Sophia Wang, Henrik Zetterberg, Kaj Blennow, Andrew J Saykin, Shannon L Risacher
{"title":"BrainAGE 与阿尔茨海默病生物标志物之间的关联。","authors":"Yousaf Abughofah, Rachael Deardorff, Aaron Vosmeier, Savannah Hottle, Jeffrey L Dage, Desarae Dempsey, Liana G Apostolova, Jared Brosch, David Clark, Martin Farlow, Tatiana Foroud, Sujuan Gao, Sophia Wang, Henrik Zetterberg, Kaj Blennow, Andrew J Saykin, Shannon L Risacher","doi":"10.1002/dad2.70094","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>The brain age gap estimation (BrainAGE) method uses a machine learning model to generate an age estimate from structural magnetic resonance imaging (MRI) scans. The goal was to study the association of brain age with Alzheimer's disease (AD) imaging and plasma biomarkers.</p><p><strong>Methods: </strong>One hundred twenty-three individuals from the Indiana Memory and Aging Study underwent structural MRI, amyloid and tau positron emission tomography (PET), and plasma sampling. The MRI scans were processed using the software program BrainAgeR to receive a \"brain age\" estimate. Plasma biomarker concentrations were measured, and partial Pearson correlation models were used to evaluate their relationship with brain age gap (BAG) estimation (BrainAGE = chronological age - MRI estimated brain age).</p><p><strong>Results: </strong>Significant associations between BAG and amyloid and tau levels on PET and in plasma were observed depending on diagnostic categories.</p><p><strong>Discussion: </strong>These findings suggest that BAG is potentially a biomarker of pathology in AD which can be applied to routine brain imaging.</p><p><strong>Highlights: </strong>Novel research that uses an artificial intelligence learning tool to estimate brain age.Findings suggest that brain age gap is associated with plasma and positron emission tomography Alzheimer's disease (AD) biomarkers.Differential relationships are seen in different stages of disease (preclinical vs. clinical).Results could play a role in early AD diagnosis and treatment.</p>","PeriodicalId":53226,"journal":{"name":"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring","volume":"17 1","pages":"e70094"},"PeriodicalIF":4.0000,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11865712/pdf/","citationCount":"0","resultStr":"{\"title\":\"Association between BrainAGE and Alzheimer's disease biomarkers.\",\"authors\":\"Yousaf Abughofah, Rachael Deardorff, Aaron Vosmeier, Savannah Hottle, Jeffrey L Dage, Desarae Dempsey, Liana G Apostolova, Jared Brosch, David Clark, Martin Farlow, Tatiana Foroud, Sujuan Gao, Sophia Wang, Henrik Zetterberg, Kaj Blennow, Andrew J Saykin, Shannon L Risacher\",\"doi\":\"10.1002/dad2.70094\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>The brain age gap estimation (BrainAGE) method uses a machine learning model to generate an age estimate from structural magnetic resonance imaging (MRI) scans. The goal was to study the association of brain age with Alzheimer's disease (AD) imaging and plasma biomarkers.</p><p><strong>Methods: </strong>One hundred twenty-three individuals from the Indiana Memory and Aging Study underwent structural MRI, amyloid and tau positron emission tomography (PET), and plasma sampling. The MRI scans were processed using the software program BrainAgeR to receive a \\\"brain age\\\" estimate. Plasma biomarker concentrations were measured, and partial Pearson correlation models were used to evaluate their relationship with brain age gap (BAG) estimation (BrainAGE = chronological age - MRI estimated brain age).</p><p><strong>Results: </strong>Significant associations between BAG and amyloid and tau levels on PET and in plasma were observed depending on diagnostic categories.</p><p><strong>Discussion: </strong>These findings suggest that BAG is potentially a biomarker of pathology in AD which can be applied to routine brain imaging.</p><p><strong>Highlights: </strong>Novel research that uses an artificial intelligence learning tool to estimate brain age.Findings suggest that brain age gap is associated with plasma and positron emission tomography Alzheimer's disease (AD) biomarkers.Differential relationships are seen in different stages of disease (preclinical vs. clinical).Results could play a role in early AD diagnosis and treatment.</p>\",\"PeriodicalId\":53226,\"journal\":{\"name\":\"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring\",\"volume\":\"17 1\",\"pages\":\"e70094\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2025-02-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11865712/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/dad2.70094\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/dad2.70094","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Association between BrainAGE and Alzheimer's disease biomarkers.
Introduction: The brain age gap estimation (BrainAGE) method uses a machine learning model to generate an age estimate from structural magnetic resonance imaging (MRI) scans. The goal was to study the association of brain age with Alzheimer's disease (AD) imaging and plasma biomarkers.
Methods: One hundred twenty-three individuals from the Indiana Memory and Aging Study underwent structural MRI, amyloid and tau positron emission tomography (PET), and plasma sampling. The MRI scans were processed using the software program BrainAgeR to receive a "brain age" estimate. Plasma biomarker concentrations were measured, and partial Pearson correlation models were used to evaluate their relationship with brain age gap (BAG) estimation (BrainAGE = chronological age - MRI estimated brain age).
Results: Significant associations between BAG and amyloid and tau levels on PET and in plasma were observed depending on diagnostic categories.
Discussion: These findings suggest that BAG is potentially a biomarker of pathology in AD which can be applied to routine brain imaging.
Highlights: Novel research that uses an artificial intelligence learning tool to estimate brain age.Findings suggest that brain age gap is associated with plasma and positron emission tomography Alzheimer's disease (AD) biomarkers.Differential relationships are seen in different stages of disease (preclinical vs. clinical).Results could play a role in early AD diagnosis and treatment.
期刊介绍：
Alzheimer''s & Dementia: Diagnosis, Assessment & Disease Monitoring (DADM) is an open access, peer-reviewed, journal from the Alzheimer''s Association® that will publish new research that reports the discovery, development and validation of instruments, technologies, algorithms, and innovative processes. Papers will cover a range of topics interested in the early and accurate detection of individuals with memory complaints and/or among asymptomatic individuals at elevated risk for various forms of memory disorders. The expectation for published papers will be to translate fundamental knowledge about the neurobiology of the disease into practical reports that describe both the conceptual and methodological aspects of the submitted scientific inquiry. Published topics will explore the development of biomarkers, surrogate markers, and conceptual/methodological challenges. Publication priority will be given to papers that 1) describe putative surrogate markers that accurately track disease progression, 2) biomarkers that fulfill international regulatory requirements, 3) reports from large, well-characterized population-based cohorts that comprise the heterogeneity and diversity of asymptomatic individuals and 4) algorithmic development that considers multi-marker arrays (e.g., integrated-omics, genetics, biofluids, imaging, etc.) and advanced computational analytics and technologies.
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