Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring最新文献

{"title":"Dementia ascertainment in India and development of nation-specific cutoffs: A machine learning and diagnostic analysis.","authors":"Danny Maupin, Hongxin Gao, Emma Nichols, Alden Gross, Erik Meijer, Haomiao Jin","doi":"10.1002/dad2.70049","DOIUrl":"10.1002/dad2.70049","url":null,"abstract":"<p><strong>Introduction: </strong>Cognitive assessments are useful in ascertaining dementia but may be influenced by patient characteristics. India's distinct culture and demographics warrant investigation into population-specific cutoffs.</p><p><strong>Methods: </strong>Data were utilized from the Longitudinal Aging Study in India-Diagnostic Assessment of Dementia (<i>n</i> = 2528). Dementia ascertainment was conducted by an online panel. A machine learning (ML) model was trained on these classifications, with explainable artificial intelligence to assess feature importance and inform cutoffs that were assessed across demographic groups.</p><p><strong>Results: </strong>The Informant Questionnaire of Cognitive Decline in the Elderly (IQCODE) and Hindi Mini-Mental State Examination (HMSE) were identified as the most impactful assessments with optimal cutoffs of 3.8 and 25, respectively.</p><p><strong>Discussion: </strong>An ML assessment of clinician dementia ratings identified IQCODE and HMSE to be the most impactful assessments. Optimal cutoffs of 3.8 and 25 were identified and performed excellently in the overall sample, though did decrease in specific, more difficult-to-diagnose subgroups.</p><p><strong>Highlights: </strong>Pioneers use of explainable artificial intelligence in the diagnosis of dementia.Creates assessment cutoffs specific to the nation of India.Highlights differences in cutoffs across nations.</p>","PeriodicalId":53226,"journal":{"name":"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring","volume":"17 1","pages":"e70049"},"PeriodicalIF":4.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11952995/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plasma biomarker trajectories: Impact of AD genetic risk and clinical progression.","authors":"Corinne Pettigrew, Anja Soldan, Jiangxia Wang, Timothy Hohman, Logan Dumitrescu, Marilyn Albert, Kaj Blennow, Tobias Bittner, Abhay Moghekar","doi":"10.1002/dad2.70081","DOIUrl":"10.1002/dad2.70081","url":null,"abstract":"<p><strong>Introduction: </strong>We examined long-term plasma biomarker trajectories among participants who were cognitively unimpaired and primarily middle aged at baseline and whether trajectories differed by Alzheimer's disease (AD) genetic risk and among those who developed cognitive impairment.</p><p><strong>Methods: </strong>Plasma amyloid beta (Aβ)₄₂/Aβ₄₀, phosphorylated tau (p-tau)₁₈₁, neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), soluble triggering receptor expressed on myeloid cells, and chitinase 3-like protein 1 were measured longitudinally in 177 BIOCARD participants (<i>M</i> baseline age = 57.7 years; <i>M</i> follow-up = 15.8 years), including 57 who developed cognitive impairment. Measures of AD genetic risk included apolipoprotein E (<i>APOE</i>) ε4 and an AD polygenic risk score (AD-PRS).</p><p><strong>Results: </strong>Compared to non-carriers, <i>APOE</i> ε4 carriers had lower Aβ₄₂/Aβ₄₀ and greater longitudinal increases in p-tau₁₈₁ and GFAP; in contrast, the AD-PRS (excluding the <i>APOE</i> region) was associated with greater declines in Aβ₄₂/Aβ₄₀ among <i>APOE</i> ε4 non-carriers. Rates of increase in p-tau₁₈₁, NfL, and GFAP were greater among those who later developed cognitive impairment.</p><p><strong>Discussion: </strong>Monitoring changes in plasma p-tau₁₈₁, NfL, and GFAP may be particularly informative during preclinical AD.</p><p><strong>Highlights: </strong>We examined plasma biomarker changes in cognitively normal individuals over 15.8 years.Apolipoprotein E (<i>APOE</i>) ε4 was related to lower amyloid beta (Aβ)₄₂/Aβ₄₀ and greater increases in phosphorylated tau (p-tau)₁₈₁ and glial fibrillary acidic protein (GFAP).In <i>APOE</i> ε4 non-carriers, higher Alzheimer's disease (AD) polygenic risk score was related to greater Aβ₄₂/Aβ₄₀ declines.P-tau₁₈₁, NfL, and GFAP increases were greater among those who progressed to mild cognitive impairment.Results highlight the predictive value of plasma biomarkers during preclinical AD.</p>","PeriodicalId":53226,"journal":{"name":"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring","volume":"17 1","pages":"e70081"},"PeriodicalIF":4.4,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11947672/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cardiovascular rate pressure product is associated with NfL in older adults at risk for AD.","authors":"Chinenye C Odo, Joe Strong, Sarah R Lose, Yue Ma, Catherine L Gallagher, Barbara B Bendlin, Henrik Zetterberg, Kaj Blennow, Cynthia M Carlsson, Gwendlyn Kollmorgen, Clara Quijano-Rubio, Nathaniel A Chin, Sanjay Asthana, Sterling C Johnson, Jacqueline Pontes Monteiro, Ozioma C Okonkwo","doi":"10.1002/dad2.70086","DOIUrl":"10.1002/dad2.70086","url":null,"abstract":"<p><strong>Introduction: </strong>Elevated cardiovascular rate pressure product (RPP) has been shown to predict cardiovascular mortality and is associated with poor cognitive test performance among older adults. However, it is unclear how RPP is related to the cerebrospinal fluid (CSF) biomarkers of neurodegeneration and neuroinflammation.</p><p><strong>Methods: </strong>RPP was cross-sectionally evaluated as a predictor of CSF biomarker levels in a cohort of 310 cognitively unimpaired late-middle-aged adults at risk for Alzheimer's disease. The primary outcomes were CSF levels of α-Synuclein, glial fibrillary acidic protein, neurofilament light (NfL), soluble triggering receptor expressed in myeloid cells 2, and total tau. Further analyses examined amyloid beta (Aβ)42/Aβ40, phosphorylated tau 181 (pTau181), and pTau181/Aβ4.</p><p><strong>Results: </strong>RPP was positively associated with NfL (β = 0.006, <i>R</i> ² = 0.411, <i>p </i>= 0.012, but Bonferroni-corrected <i>p</i> ≤ 0.006) and not with other CSF biomarkers of neurodegeneration and neuroinflammation investigated in this sample.</p><p><strong>Discussion: </strong>A high myocardial oxygen demand at rest may be related to neuronal death and axonal degeneration in cognitively unimpaired late-middle-aged adults.</p><p><strong>Highlights: </strong>We explored the relationship between RPP and CSF analytes.Higher RPP was associated with higher NfL but not other measured CSF biomarkers.HR was positively associated with NfL, whereas SBP was not.</p>","PeriodicalId":53226,"journal":{"name":"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring","volume":"17 1","pages":"e70086"},"PeriodicalIF":4.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11934293/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning to discover factors predicting volume of white matter hyperintensities: Insights from the UK Biobank.","authors":"Yigizie Yeshaw, Iqbal Madakkatel, Anwar Mulugeta, Amanda Lumsden, Elina Hypponen","doi":"10.1002/dad2.70090","DOIUrl":"10.1002/dad2.70090","url":null,"abstract":"<p><strong>Introduction: </strong>Brain white matter hyperintensities (WMHs) reflect the risks of stroke, dementia, and overall mortality.</p><p><strong>Methods: </strong>We used a hypothesis-free gradient boosting decision tree (GBDT) approach and conventional statistical methods to discover risk factors associated with volume of WMHs. The GBDT models considered data on 2891 input features, collected ∼10 years prior to volume of WMH measurements from 44,053 participants. Top 3% of features, ranked by Shapley values, were taken forward to epidemiological analyses using linear regression.</p><p><strong>Results: </strong>Adiposity, lung function, and indicators of metabolic health (eg, glycated hemoglobin, hypertension, alkaline phosphatase, microalbumin, and urate) contribute to WMH prediction. Of lifestyle factors, smoking had the strongest association. Time spent outdoors, creatinine, and several red blood cell indices were among the identified less-known predictors of WMHs.</p><p><strong>Conclusions: </strong>Obesity, high blood pressure, lung function, metabolic abnormalities, and lifestyle are key contributors to WMHs, providing opportunities to prevent or reduce their development.</p><p><strong>Highlights: </strong>Obesity and related metabolic abnormalities were linked with WMHs.Associations with time spent outdoors, creatinine, some red blood cell indices and height were among the less-known risk factors identified.Action on blood pressure, metabolic abnormalities, and adequate oxygenation may help to prevent WMHs.Biomarker links may suggest simple blood tests could aid in early dementia prediction.</p>","PeriodicalId":53226,"journal":{"name":"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring","volume":"17 1","pages":"e70090"},"PeriodicalIF":4.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11934216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of cognitive, neuropsychiatric, and diagnostic factors on financial capacity: A longitudinal analysis of the ADNI cohort.","authors":"Milap A Nowrangi, Jeannie Marie Leoutsakos, Haijuan Yan, Arnold Bakker, Kevin J Manning, George W Rebok, Paul B Rosenberg, Vidyulata Kamath","doi":"10.1002/dad2.12583","DOIUrl":"10.1002/dad2.12583","url":null,"abstract":"<p><strong>Introduction: </strong>Financial capacity (FC) is the ability to independently manage finances in a manner consistent with one's self-interest. To investigate the relationship between FC, cognitive domains, neuropsychiatric symptoms, and transitions from normal cognition (cognitive normal [CN]) to mild cognitive impairment (MCI) or Alzheimer's disease (AD), we conducted a secondary analysis of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort using the Financial Capacity Instrument short form (FCI-SF).</p><p><strong>Methods: </strong>To examine these longitudinal relationships, we fit two models, a random effects (random intercept) \"time-averaged\" model and a \"time since previous visit\" model, where we regressed each of the five component financial scores on each of the cognitive composite scores. To examine the effect of baseline FCI-SF performance on conversion rates from normal to MCI or AD, we computed a survival model.</p><p><strong>Results: </strong>A total of 874 participants (diagnostic group, <i>N</i>, mean age: CN: 501, 74.4; MCI: 319, 74.6; and AD 54, 74.9) were included in the analyses. In time since previous visit models, we found that lower executive function composite scores were related to decline in the complex checkbook score (<i>ß</i> = 1.35 (0.55), <i>p</i> = 0.016) and total completion time of the FCI-SF (<i>ß</i> = 1.85 (9.36), <i>p</i> = 0.025). In addition, lower composite visuospatial score was significantly related to poorer performance on financial conceptual knowledge, complex checkbook, and total completion time. Lower composite memory score was highly related to decline in financial conceptual knowledge, single checkbook, and bank statement subscale scores. ADNI participants in the lowest tertile of total completion time, at any point in time, were four times more likely to receive a diagnosis of MCI or AD compared to participants in the highest tertile with a hazard ratio of 4.22 ([2.29] <i>p</i> = 008).</p><p><strong>Discussion: </strong>There is a multifaceted interaction between poorer cognition and everyday financial function where executive function, memory, and visuospatial cognition are related to FC. The strongest predictor of conversion from normal to either MCI or AD, appears to be time to completion.</p><p><strong>Highlights: </strong>Decline in financial capacity (FC) is observed during transition to dementia and increases the risk of negative outcomes.Executive function, memory, and visuospatial cognition are related to FC.The strongest predictor of conversion from normal to either mild cognitive impairment (MCI) or Alzheimer's disease (AD) is time to completion or processing speed.</p>","PeriodicalId":53226,"journal":{"name":"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring","volume":"17 1","pages":"e12583"},"PeriodicalIF":4.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926251/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to \"A survey among experts on the future role of tau-PET in clinical practice and trials\".","authors":"","doi":"10.1002/dad2.70058","DOIUrl":"https://doi.org/10.1002/dad2.70058","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1002/dad2.70033.].</p>","PeriodicalId":53226,"journal":{"name":"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring","volume":"17 1","pages":"e70058"},"PeriodicalIF":4.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926248/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of cerebrovascular reactivity on brain activation during a working memory task in type 2 diabetes.","authors":"Yarden Oliel, Ramit Ravona-Springer, Maayan Harel, Joseph Azuri, Chen Botvin Moshe, David Tanne, Salo Haratz, Barbara B Bendlin, Michal Schnaider Beeri, Abigail Livny","doi":"10.1002/dad2.70045","DOIUrl":"10.1002/dad2.70045","url":null,"abstract":"<p><strong>Introduction: </strong>Impaired cerebrovascular reactivity (CVR) is common in type 2 diabetes (T2D) patients and is a risk factor for dementia. However, most prior functional magnetic resonance imaging (fMRI) studies in T2D disregarded the impact of impaired CVR on brain activation patterns. This study investigated the relationship between CVR and brain activation during an fMRI task in T2D patients.</p><p><strong>Methods: </strong>Seventy-four T2D patients underwent a working-memory (WM) fMRI task. CVR was measured by the breath-holding index test using transcranial Doppler (TCD). Regression analyses examined associations between CVR and brain activation and between glycated hemoglobin (HbA1c) and activation with/without adjusting for CVR.</p><p><strong>Results: </strong>An association between CVR and brain activation was found in the left middle and inferior frontal gyri. Adjusting for CVR led to a different pattern of HbA1c-related activation.</p><p><strong>Discussion: </strong>The findings highlight methodological implications, emphasizing the importance of accounting for impaired CVR when analyzing and interpreting fMRI data in T2D patients.</p><p><strong>Highlights: </strong>The study found that cerebrovascular reactivity impacts brain activation patterns during a working memory task in type 2 diabetes patients.Accounting for cerebrovascular reactivity altered the brain regions showing activation related to working memory and glycemic control.The findings highlight the importance of considering vascular factors when interpreting fMRI data in populations with vascular dysfunction.</p>","PeriodicalId":53226,"journal":{"name":"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring","volume":"17 1","pages":"e70045"},"PeriodicalIF":4.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11899760/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143617840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The prognosis of mild cognitive impairment: A systematic review and meta-analysis.","authors":"Simone Salemme, Flavia Lucia Lombardo, Eleonora Lacorte, Francesco Sciancalepore, Giulia Remoli, Ilaria Bacigalupo, Paola Piscopo, Giovanna Zamboni, Paolo Maria Rossini, Stefano Francesco Cappa, Daniela Perani, Patrizia Spadin, Fabrizio Tagliavini, Nicola Vanacore, Antonio Ancidoni","doi":"10.1002/dad2.70074","DOIUrl":"10.1002/dad2.70074","url":null,"abstract":"<p><strong>Introduction: </strong>Knowledge gaps remain about the prognosis of mild cognitive impairment (MCI). Conversion rates to dementia vary widely, and reversion to normal cognition has gained attention. This review updates evidence on MCI conversion risk and probability of stability and reversion.</p><p><strong>Methods: </strong>We searched databases for studies on MCI prognosis with ≥3 years of follow-up, established criteria for MCI and dementia, and performed a meta-analysis using a random-effects model to assess conversion risk, reversion, and stability probability. Meta-regressions identified sources of heterogeneity and guided subgroup analysis.</p><p><strong>Results: </strong>From 89 studies (mean follow-up: 5.2 years), conversion risk was 41.5% (38.3%-44.7%) in clinical and 27.0% (22.0%-32.0%) in population-based studies, with Alzheimer's dementia as the most common outcome. Stability rates were 49.3% (clinical) and 49.8% (population). Reversion was 8.7% (clinical) and 28.2% (population).</p><p><strong>Discussion: </strong>Our findings highlight higher conversion in clinical settings and 30% reversion in population studies, calling for sustainable care pathway development.</p><p><strong>Highlights: </strong>Prognosis for mild cognitive impairment (MCI) varies by setting; dementia risk is higher and the probability of reversion is lower in clinical-based studies.In both clinical and population settings, cognitive stability is ≈50%.A reorganization of health services could ensure sustainable care for individuals with MCI.Significant heterogeneity in MCI studies impacts data interpretation; follow-up length is crucial.Long-term prognosis studies on MCI in low- and middle-income countries are urgently needed.</p>","PeriodicalId":53226,"journal":{"name":"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring","volume":"17 1","pages":"e70074"},"PeriodicalIF":4.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11898010/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143617839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Opportunities to encourage adoption of a biomarker-enabled care pathway for Alzheimer's in primary care.","authors":"Soo Borson, Rhoda Au, Anna H Chodos, Sam Gandy, Holly Jain, Amy Alagor, Kristi Cohn, Diana R Kerwin, Jacobo Mintzer, Stephanie Monroe, Delecia Robinson, Michelle M Mielke, Donna M Wilcock","doi":"10.1002/dad2.70095","DOIUrl":"10.1002/dad2.70095","url":null,"abstract":"<p><p>Identification of early-stage Alzheimer's disease (AD) remains a challenge due to limited specialist availability, diagnostic access, disease awareness, and cultural factors. Blood-based biomarkers (BBBM) could play a critical role in the identification and referral of patients suspected of AD to specialty care. A multidisciplinary AD Biomarker Task Force was convened to evaluate current biomarker use cases, define an optimal biomarker-enabled AD diagnostic care pathway, and understand factors impacting adoption. The Task Force identified opportunities to support biomarker-enabled AD diagnostic care pathway adoption, including streamlining risk assessment and screening by leveraging digital tools, activating primary care providers through education, generating data to expand applicability to diverse populations, and advocating for aligned policies and quality measures. Adoption of BBBMs in the primary care setting will be critical to improve early AD detection. However, challenges to pathway adoption persist and will require action from clinicians, payers, policy makers, and patients to address.</p><p><strong>Highlights: </strong>Blood-based biomarkers can streamline the identification of AD in primary care.Future biomarker-enabled diagnostic care pathways will leverage digital assessments.Education, data generation, and policy advocacy are vital to encourage BBBM use.Implementation of AD care pathways requires the activation of diverse stakeholders.</p>","PeriodicalId":53226,"journal":{"name":"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring","volume":"17 1","pages":"e70095"},"PeriodicalIF":4.0,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11895972/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"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":"10.1002/dad2.70094","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":4.4,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11865712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}