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Cai F, Xue S, Zhou Z, Zhang X, Kang Y, Zhang J, Zhang M. Exposure to coal dust exacerbates cognitive impairment by activating the IL6/ERK1/2/SP1 signaling pathway. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 946:174202. [PMID: 38925396 DOI: 10.1016/j.scitotenv.2024.174202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 06/06/2024] [Accepted: 06/20/2024] [Indexed: 06/28/2024]
Abstract
Coal dust (CD) is a common pollutant, and epidemiological surveys indicate that long-term exposure to coal dust not only leads to the occurrence of pulmonary diseases but also has certain impacts on cognitive abilities. However, there is little open-published literature on the effects and specific mechanisms of coal dust exposure on the cognition of patients with Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD). An animal model has been built in this study with clinical population samples to explore the changes in neuroinflammation and cognitive abilities with coal dust exposure. In the animal model, compared to C57BL/6 mice, APP/PS1 mice exposed to coal dust exhibited more severe cognitive impairment, accompanied by significantly elevated levels of neuroinflammatory factors Apolipoprotein E4 (AOPE4) and Interleukin-6 (IL6) in the hippocampus, and more severe neuronal damage. In clinical sample sequencing, it was found that there is significant upregulation of AOPE4, neutrophils, and IL6 expression in the peripheral blood of MCI patients compared to normal individuals. Mechanistically, cell experiments revealed that IL6 could promote the phosphorylation of ERK1/2 and enhance the expression of transcription factor SP1, thereby promoting AOPE4 expression. The results of this study suggest that coal dust can promote the upregulation of IL6 and AOPE4 in patients, exacerbating cognitive impairment.
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Affiliation(s)
- Fulin Cai
- The First Affiliated Hospital, Anhui University of Science and Technology, Huainan, Anhui, China; Anhui University of Science and Technology, Huainan 232001, China
| | - Sheng Xue
- Anhui University of Science and Technology, Huainan 232001, China.
| | - Zan Zhou
- Department of Physiology, Shihezi University Medical College, Xinjiang, Shihezi 832000, China
| | - Xin Zhang
- Department of Blood Transfusion, The People's Hospital of Rizhao, Shandong, Rizhao 276800, China
| | - Yingjie Kang
- Department of Physiology, Shihezi University Medical College, Xinjiang, Shihezi 832000, China
| | - Jing Zhang
- Department of Blood Transfusion, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Zhejiang, Hangzhou 310000, China
| | - Mei Zhang
- The First Affiliated Hospital, Anhui University of Science and Technology, Huainan, Anhui, China
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Association of Diabetes and Hypertension With Brain Structural Integrity and Cognition in the Boston Puerto Rican Health Study Cohort. Neurology 2024; 102:e209158. [PMID: 38320229 DOI: 10.1212/wnl.0000000000209158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2024] Open
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Gordon S, Lee JS, Scott TM, Bhupathiraju S, Ordovas J, Kelly RS, Bhadelia R, Koo BB, Bigornia S, Tucker KL, Palacios N. Metabolites and MRI-Derived Markers of AD/ADRD Risk in a Puerto Rican Cohort. RESEARCH SQUARE 2024:rs.3.rs-3941791. [PMID: 38410484 PMCID: PMC10896402 DOI: 10.21203/rs.3.rs-3941791/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Objective Several studies have examined metabolomic profiles in relation to Alzheimer's disease and related dementia (AD/ADRD) risk; however, few studies have focused on minorities, such as Latinos, or examined Magnetic-Resonance Imaging (MRI)-based outcomes. Methods We used multiple linear regression, adjusted for covariates, to examine the association between metabolite concentration and MRI-derived brain age deviation. Metabolites were measured at baseline with untargeted metabolomic profiling (Metabolon, Inc). Brain age deviation (BAD) was calculated at wave 4 (~ 9 years from Boston Puerto Rican Health Study (BPRHS) baseline) as chronologic age, minus MRI-estimated brain age, representing the rate of biological brain aging relative to chronologic age. We also examined if metabolites associated with BAD were similarly associated with hippocampal volume and global cognitive function at wave 4 in the BPRHS. Results Several metabolites, including isobutyrylcarnitine, propionylcarnitine, phenylacetylglutamine, phenylacetylcarnitine (acetylated peptides), p-cresol-glucuronide, phenylacetylglutamate, and trimethylamine N-oxide (TMAO) were inversely associated with brain age deviation. Taurocholate sulfate, a bile salt, was marginally associated with better brain aging. Most metabolites with negative associations with brain age deviation scores also were inversely associations with hippocampal volumes and wave 4 cognitive function. Conclusion The metabolites identifiedin this study are generally consistent with prior literature and highlight the role of BCAA, TMAO and microbially derived metabolites in cognitive decline.
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Kalaria RN, Akinyemi RO, Paddick SM, Ihara M. Current perspectives on prevention of vascular cognitive impairment and promotion of vascular brain health. Expert Rev Neurother 2024; 24:25-44. [PMID: 37916306 PMCID: PMC10872925 DOI: 10.1080/14737175.2023.2273393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 10/17/2023] [Indexed: 11/03/2023]
Abstract
INTRODUCTION The true global burden of vascular cognitive impairment (VCI) is unknown. Reducing risk factors for stroke and cardiovascular disease would inevitably curtail VCI. AREAS COVERED The authors review current diagnosis, epidemiology, and risk factors for VCI. VCI increases in older age and by inheritance of known genetic traits. They emphasize modifiable risk factors identified by the 2020 Lancet Dementia Commission. The most profound risks for VCI also include lower education, cardiometabolic factors, and compromised cognitive reserve. Finally, they discuss pharmacological and non-pharmacological interventions. EXPERT OPINION By virtue of the high frequencies of stroke and cardiovascular disease the global prevalence of VCI is expectedly higher than prevalent neurodegenerative disorders causing dementia. Since ~ 90% of the global burden of stroke can be attributed to modifiable risk factors, a formidable opportunity arises to reduce the burden of not only stroke but VCI outcomes including progression from mild to the major in form of vascular dementia. Strict control of vascular risk factors and secondary prevention of cerebrovascular disease via pharmacological interventions will impact on burden of VCI. Non-pharmacological measures by adopting healthy diets and encouraging physical and cognitive activities and urging multidomain approaches are important for prevention of VCI and preservation of vascular brain health.
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Affiliation(s)
- Raj N Kalaria
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Rufus O Akinyemi
- Neuroscience and Ageing Research Unit, Institute for Advanced Medical Research and Training, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Stella-Maria Paddick
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Masafumi Ihara
- Department of Neurology, National Cerebral and Cardiovascular Centre, Osaka, Japan
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Veitch DP, Weiner MW, Miller M, Aisen PS, Ashford MA, Beckett LA, Green RC, Harvey D, Jack CR, Jagust W, Landau SM, Morris JC, Nho KT, Nosheny R, Okonkwo O, Perrin RJ, Petersen RC, Rivera Mindt M, Saykin A, Shaw LM, Toga AW, Tosun D. The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022. Alzheimers Dement 2024; 20:652-694. [PMID: 37698424 PMCID: PMC10841343 DOI: 10.1002/alz.13449] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/27/2023] [Accepted: 08/01/2023] [Indexed: 09/13/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co-pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI-4 aims to address by enrolling a diverse cohort.
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Affiliation(s)
- Dallas P. Veitch
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of MedicineUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of NeurologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Melanie Miller
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
| | - Paul S. Aisen
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Miriam A. Ashford
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | - Laurel A. Beckett
- Division of BiostatisticsDepartment of Public Health SciencesUniversity of CaliforniaDavisCaliforniaUSA
| | - Robert C. Green
- Division of GeneticsDepartment of MedicineBrigham and Women's HospitalBroad Institute Ariadne Labs and Harvard Medical SchoolBostonMassachusettsUSA
| | - Danielle Harvey
- Division of BiostatisticsDepartment of Public Health SciencesUniversity of CaliforniaDavisCaliforniaUSA
| | | | - William Jagust
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - John C. Morris
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
- Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Kwangsik T. Nho
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Center for Computational Biology and BioinformaticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Rachel Nosheny
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Ozioma Okonkwo
- Wisconsin Alzheimer's Disease Research Center and Department of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Richard J. Perrin
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
- Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | | | - Monica Rivera Mindt
- Department of PsychologyLatin American and Latino Studies InstituteAfrican and African American StudiesFordham UniversityNew YorkNew YorkUSA
- Department of NeurologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Andrew Saykin
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine and the PENN Alzheimer's Disease Research CenterCenter for Neurodegenerative ResearchPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Arthur W. Toga
- Laboratory of Neuro ImagingInstitute of Neuroimaging and InformaticsKeck School of Medicine of University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Duygu Tosun
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
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Guan Y, Cheng CH, Bellomo LI, Narain S, Bigornia SJ, Garelnabi MO, Scott T, Ordovás JM, Tucker KL, Bhadelia R, Koo BB. APOE4 allele-specific associations between diet, multimodal biomarkers, and cognition among Puerto Rican adults in Massachusetts. Front Aging Neurosci 2023; 15:1285333. [PMID: 38035273 PMCID: PMC10684694 DOI: 10.3389/fnagi.2023.1285333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 10/25/2023] [Indexed: 12/02/2023] Open
Abstract
Background Apolipoprotein E (APOE) is the strongest genetic risk factor for sporadic Alzheimer's Disease (AD), and the ε4 allele (APOE4) may interact with lifestyle factors that relate to brain structural changes, underlying the increased risk of AD. However, the exact role of APOE4 in mediating interactions between the peripheral circulatory system and the central nervous system, and how it may link to brain and cognitive aging requires further elucidation. In this analysis, we investigated the association between APOE4 carrier status and multimodal biomarkers (diet, blood markers, clinical diagnosis, brain structure, and cognition) in the context of gene-environment interactions. Methods Participants were older adults from a longitudinal observational study, the Boston Puerto Rican Health Study (BPRHS), who self-identified as of Puerto Rican descent. Demographics, APOE genotype, diet, blood, and clinical data were collected at baseline and at approximately 12th year, with the addition of multimodal brain magnetic resonance imaging (MRI) (T1-weighted and diffusion) and cognitive testing acquired at 12-year. Measures were compared between APOE4 carriers and non-carriers, and associations between multimodal variables were examined using correlation and multivariate network analyses within each group. Results A total of 156 BPRHS participants (mean age at imaging = 68 years, 77% female, mean follow-up 12.7 years) with complete multimodal data were included in the current analysis. APOE4 carriers (n = 43) showed reduced medial temporal lobe (MTL) white matter (WM) microstructural integrity and lower mini-mental state examination (MMSE) score than non-carriers (n = 113). This pattern was consistent with an independent sample from the Alzheimer's Disease Neuroimaging Initiative (ADNI) of n = 283 non-Hispanic White adults without dementia (mean age = 75, 40% female). Within BPRHS, carriers showed distinct connectivity patterns between multimodal biomarkers, characterized by stronger direct network connections between baseline diet/blood markers with 12-year blood/clinical measures, and between blood markers (especially lipids and cytokines) and WM. Cardiovascular burden (i.e., hypertension and diabetes status) was associated with WM integrity for both carriers and non-carriers. Conclusion APOE4 carrier status affects interactions between dietary factors, multimodal blood biomarkers, and MTL WM integrity across ~12 years of follow-up, which may reflect increased peripheral-central systems crosstalk following blood-brain barrier breakdown in carriers.
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Affiliation(s)
- Yi Guan
- Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Chia Hsin Cheng
- Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Luis I. Bellomo
- Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Sriman Narain
- Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Sherman J. Bigornia
- Department of Agriculture, Nutrition, and Food Systems, College of Life Sciences and Agriculture, University of New Hampshire, Durham, NH, United States
| | - Mahdi O. Garelnabi
- Department of Public Health, Zuckerberg College of Health Sciences, University of Massachusetts Lowell, Lowell, MA, United States
| | - Tammy Scott
- School of Medicine, Tufts University, Boston, MA, United States
| | - José M. Ordovás
- Nutrition and Genomics Laboratory, J.M.-US Department of Agriculture Human Nutrition Research Center on Aging at Tufts University, Boston, MA, United States
- IMDEA Alimentacion, Madrid, Spain
- CIBER Fisiopatologia de la Obesidad y la Nutricion (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Katherine L. Tucker
- Department of Biomedical and Nutritional Sciences, Zuckerberg College of Health Sciences, University of Massachusetts Lowell, Lowell, MA, United States
- Center for Population Health, University of Massachusetts Lowell, Lowell, MA, United States
| | - Rafeeque Bhadelia
- Neuroradiology Section, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Bang-Bon Koo
- Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
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He S, Guan Y, Cheng CH, Moore TL, Luebke JI, Killiany RJ, Rosene DL, Koo BB, Ou Y. Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age. Front Aging Neurosci 2023; 15:1249415. [PMID: 38020785 PMCID: PMC10646581 DOI: 10.3389/fnagi.2023.1249415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 10/10/2023] [Indexed: 12/01/2023] Open
Abstract
The application of artificial intelligence (AI) to summarize a whole-brain magnetic resonance image (MRI) into an effective "brain age" metric can provide a holistic, individualized, and objective view of how the brain interacts with various factors (e.g., genetics and lifestyle) during aging. Brain age predictions using deep learning (DL) have been widely used to quantify the developmental status of human brains, but their wider application to serve biomedical purposes is under criticism for requiring large samples and complicated interpretability. Animal models, i.e., rhesus monkeys, have offered a unique lens to understand the human brain - being a species in which aging patterns are similar, for which environmental and lifestyle factors are more readily controlled. However, applying DL methods in animal models suffers from data insufficiency as the availability of animal brain MRIs is limited compared to many thousands of human MRIs. We showed that transfer learning can mitigate the sample size problem, where transferring the pre-trained AI models from 8,859 human brain MRIs improved monkey brain age estimation accuracy and stability. The highest accuracy and stability occurred when transferring the 3D ResNet [mean absolute error (MAE) = 1.83 years] and the 2D global-local transformer (MAE = 1.92 years) models. Our models identified the frontal white matter as the most important feature for monkey brain age predictions, which is consistent with previous histological findings. This first DL-based, anatomically interpretable, and adaptive brain age estimator could broaden the application of AI techniques to various animal or disease samples and widen opportunities for research in non-human primate brains across the lifespan.
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Affiliation(s)
- Sheng He
- Harvard Medical School, Boston Children's Hospital, Boston, MA, United States
| | - Yi Guan
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Chia Hsin Cheng
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Tara L. Moore
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Jennifer I. Luebke
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Ronald J. Killiany
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Douglas L. Rosene
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Bang-Bon Koo
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Yangming Ou
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
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Seyedsalehi A, Warrier V, Bethlehem RAI, Perry BI, Burgess S, Murray GK. Educational attainment, structural brain reserve and Alzheimer's disease: a Mendelian randomization analysis. Brain 2023; 146:2059-2074. [PMID: 36310536 PMCID: PMC10151197 DOI: 10.1093/brain/awac392] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 09/01/2022] [Accepted: 09/19/2022] [Indexed: 11/13/2022] Open
Abstract
Higher educational attainment is observationally associated with lower risk of Alzheimer's disease. However, the biological mechanisms underpinning this association remain unclear. The protective effect of education on Alzheimer's disease may be mediated via increased brain reserve. We used two-sample Mendelian randomization to explore putative causal relationships between educational attainment, structural brain reserve as proxied by MRI phenotypes and Alzheimer's disease. Summary statistics were obtained from genome-wide association studies of educational attainment (n = 1 131 881), late-onset Alzheimer's disease (35 274 cases, 59 163 controls) and 15 measures of grey or white matter macro- or micro-structure derived from structural or diffusion MRI (nmax = 33 211). We conducted univariable Mendelian randomization analyses to investigate bidirectional associations between (i) educational attainment and Alzheimer's disease; (ii) educational attainment and imaging-derived phenotypes; and (iii) imaging-derived phenotypes and Alzheimer's disease. Multivariable Mendelian randomization was used to assess whether brain structure phenotypes mediated the effect of education on Alzheimer's disease risk. Genetically proxied educational attainment was inversely associated with Alzheimer's disease (odds ratio per standard deviation increase in genetically predicted years of schooling = 0.70, 95% confidence interval 0.60, 0.80). There were positive associations between genetically predicted educational attainment and four cortical metrics (standard deviation units change in imaging phenotype per one standard deviation increase in genetically predicted years of schooling): surface area 0.30 (95% confidence interval 0.20, 0.40); volume 0.29 (95% confidence interval 0.20, 0.37); intrinsic curvature 0.18 (95% confidence interval 0.11, 0.25); local gyrification index 0.21 (95% confidence interval 0.11, 0.31)]; and inverse associations with cortical intracellular volume fraction [-0.09 (95% confidence interval -0.15, -0.03)] and white matter hyperintensities volume [-0.14 (95% confidence interval -0.23, -0.05)]. Genetically proxied levels of surface area, cortical volume and intrinsic curvature were positively associated with educational attainment [standard deviation units change in years of schooling per one standard deviation increase in respective genetically predicted imaging phenotype: 0.13 (95% confidence interval 0.10, 0.16); 0.15 (95% confidence interval 0.11, 0.19) and 0.12 (95% confidence interval 0.04, 0.19)]. We found no evidence of associations between genetically predicted imaging-derived phenotypes and Alzheimer's disease. The inverse association of genetically predicted educational attainment with Alzheimer's disease did not attenuate after adjusting for imaging-derived phenotypes in multivariable analyses. Our results provide support for a protective causal effect of educational attainment on Alzheimer's disease risk, as well as potential bidirectional causal relationships between education and brain macro- and micro-structure. However, we did not find evidence that these structural markers affect risk of Alzheimer's disease. The protective effect of education on Alzheimer's disease may be mediated via other measures of brain reserve not included in the present study, or by alternative mechanisms.
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Affiliation(s)
- Aida Seyedsalehi
- Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0SR, UK
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford OX3 7JX, UK
| | - Varun Warrier
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
| | - Richard A I Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Benjamin I Perry
- Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
- CAMEO, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge CB4 1PX, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0BB, UK
| | - Graham K Murray
- Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
- CAMEO, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge CB4 1PX, UK
- Institute for Molecular Bioscience, University of Queensland, Brisbane 4072, Australia
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Kawada T. Reader Response: Association of Diabetes and Hypertension With Brain Structural Integrity and Cognition in the Boston Puerto Rican Health Study Cohort. Neurology 2023; 100:164. [PMID: 36646469 DOI: 10.1212/wnl.0000000000206741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 11/14/2022] [Indexed: 01/17/2023] Open
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Koo BB, Guan Y. Author Response: Association of Diabetes and Hypertension With Brain Structural Integrity and Cognition in the Boston Puerto Rican Health Study Cohort. Neurology 2023; 100:164-165. [PMID: 36646472 DOI: 10.1212/wnl.0000000000206742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 11/14/2022] [Indexed: 01/18/2023] Open
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Lee A, Hamilton R. Vascular Risk Factors and Cognition in Individuals From Puerto Rico: Moving Away From Monolithic Racial and Ethnic Categories in Research. Neurology 2022; 98:609-610. [PMID: 35354580 DOI: 10.1212/wnl.0000000000200232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Andrew Lee
- Centre for Neuroscience Innovation, Flinders University College of Medicine and Public Health, Bedford Park, South Australia, Australia; 2. Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
| | - Roy Hamilton
- Centre for Neuroscience Innovation, Flinders University College of Medicine and Public Health, Bedford Park, South Australia, Australia; 2. Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
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