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Ding X, Yin L, Zhang L, Zhang Y, Zha T, Zhang W, Gui B. Diabetes accelerates Alzheimer's disease progression in the first year post mild cognitive impairment diagnosis. Alzheimers Dement 2024. [PMID: 38865281 DOI: 10.1002/alz.13882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/28/2024] [Accepted: 03/18/2024] [Indexed: 06/14/2024]
Abstract
BACKGROUND Mild cognitive impairment (MCI) heightens Alzheimer's disease (AD) risk, with diabetes mellitus (DM) potentially exacerbating this vulnerability. This study identifies the optimal intervention period and neurobiological targets in MCI to AD progression using the Alzheimer's Disease Neuroimaging Initiative dataset. METHODS Analysis of 980 MCI patients, categorized by DM status, used propensity score matching and inverse probability treatment weighting to assess rate of conversion from MCI to AD, neuroimaging, and cognitive changes. RESULTS DM significantly correlates with cognitive decline and an increased risk of progressing to AD, especially within the first year of MCI follow-up. It adversely affects specific brain structures, notably accelerating nucleus accumbens atrophy, decreasing gray matter volume and sulcal depth. DISCUSSION Findings suggest the first year after MCI diagnosis as the critical window for intervention. DM accelerates MCI-to-AD progression, targeting specific brain areas, underscoring the need for early therapeutic intervention. HIGHLIGHTS Diabetes mellitus (DM) accelerates mild cognitive impairment (MCI)-to-Alzheimer's disease (AD) progression within the first year after MCI diagnosis. DM leads to sharper cognitive decline within 12 months of follow-up. There is notable nucleus accumbens atrophy observed in MCI patients with DM. DM causes significant reductions in gray matter volume and sulcal depth. There are stronger correlations between cognitive decline and brain changes due to DM.
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Affiliation(s)
- Xiahao Ding
- Department of Anesthesiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Department of Anesthesiology and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Li Yin
- Department of Anesthesiology and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Lin Zhang
- Department of Anesthesiology and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yang Zhang
- Department of Anesthesiology and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Tianming Zha
- Department of Anesthesiology and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Wen Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Medical Imaging Center, the Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
| | - Bo Gui
- Department of Anesthesiology and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Hu Y, Zhu T, Zhang W. The characteristics of brain atrophy prior to the onset of Alzheimer's disease: a longitudinal study. Front Aging Neurosci 2024; 16:1344920. [PMID: 38863784 PMCID: PMC11165148 DOI: 10.3389/fnagi.2024.1344920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 05/02/2024] [Indexed: 06/13/2024] Open
Abstract
Objective We aimed to use the onset time of Alzheimer's disease (AD) as the reference time to longitudinally investigate the atrophic characteristics of brain structures prior to the onset of AD. Materials and methods A total of 328 participants from the ADNI database with clear onset of AD and structural imaging data were included in our study. The time before the onset of AD (abbreviated as BAD) was calculated. We investigated the longitudinal brain changes in 97 regions using multivariate linear mixed effects regression models. Results The average BAD was -28.15 months, with a range from -156 to 0 months. The 54 brain regions showed significant atrophy prior to the onset of AD, and these regions were mainly distributed in the frontal and temporal lobes. The parietal and occipital lobe exhibited relatively less atrophy than the other brain lobes. Sex, age, and magnetic field strength had greater direct impacts on structural indicators than APOE genotype and education. The analysis of interaction effects revealed that the APOE ε4 mutation carriers exhibited more severe structural changes in specific brain regions as the BAD increased. However, sex, age, and education had minimal regulatory influence on the structural changes associated with BAD. Conclusion Longitudinal analysis, with the onset time point of AD as the reference, can accurately describe the features of structural changes preceding the onset of AD and provide a comprehensive understanding of AD development.
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Affiliation(s)
- Ying Hu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Ting Zhu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Wei Zhang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Mental Health Center of West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
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Wang Z, Yang X, Li H, Wang S, Liu Z, Wang Y, Zhang X, Chen Y, Xu Q, Xu J, Wang Z, Wang J. Bidirectional two-sample Mendelian randomization analyses support causal relationships between structural and diffusion imaging-derived phenotypes and the risk of major neurodegenerative diseases. Transl Psychiatry 2024; 14:215. [PMID: 38806463 PMCID: PMC11133432 DOI: 10.1038/s41398-024-02939-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 05/10/2024] [Accepted: 05/16/2024] [Indexed: 05/30/2024] Open
Abstract
Previous observational investigations suggest that structural and diffusion imaging-derived phenotypes (IDPs) are associated with major neurodegenerative diseases; however, whether these associations are causal remains largely uncertain. Herein we conducted bidirectional two-sample Mendelian randomization analyses to infer the causal relationships between structural and diffusion IDPs and major neurodegenerative diseases using common genetic variants-single nucleotide polymorphism (SNPs) as instrumental variables. Summary statistics of genome-wide association study (GWAS) for structural and diffusion IDPs were obtained from 33,224 individuals in the UK Biobank cohort. Summary statistics of GWAS for seven major neurodegenerative diseases were obtained from the largest GWAS for each disease to date. The forward MR analyses identified significant or suggestively statistical causal effects of genetically predicted three structural IDPs on Alzheimer's disease (AD), frontotemporal dementia (FTD), and multiple sclerosis. For example, the reduction in the surface area of the left superior temporal gyrus was associated with a higher risk of AD. The reverse MR analyses identified significantly or suggestively statistical causal effects of genetically predicted AD, Lewy body dementia (LBD), and FTD on nine structural and diffusion IDPs. For example, LBD was associated with increased mean diffusivity in the right superior longitudinal fasciculus and AD was associated with decreased gray matter volume in the right ventral striatum. Our findings might contribute to shedding light on the prediction and therapeutic intervention for the major neurodegenerative diseases at the neuroimaging level.
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Affiliation(s)
- Zirui Wang
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Xuan Yang
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
- Department of Radiology, Jining No.1 People's Hospital, Jining, Shandong, 272000, China
| | - Haonan Li
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Siqi Wang
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Zhixuan Liu
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yaoyi Wang
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Xingyu Zhang
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yayuan Chen
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Qiang Xu
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Jiayuan Xu
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China.
| | - Zengguang Wang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, 300052, China.
| | - Junping Wang
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China.
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Shi J, Wang Z, Yi M, Xie S, Zhang X, Tao D, Liu Y, Yang Y. Evidence based on Mendelian randomization and colocalization analysis strengthens causal relationships between structural changes in specific brain regions and risk of amyotrophic lateral sclerosis. Front Neurosci 2024; 18:1333782. [PMID: 38505770 PMCID: PMC10948422 DOI: 10.3389/fnins.2024.1333782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 02/22/2024] [Indexed: 03/21/2024] Open
Abstract
Background Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by the degeneration of motor neurons in the brain and spinal cord with a poor prognosis. Previous studies have observed cognitive decline and changes in brain morphometry in ALS patients. However, it remains unclear whether the brain structural alterations contribute to the risk of ALS. In this study, we conducted a bidirectional two-sample Mendelian randomization (MR) and colocalization analysis to investigate this causal relationship. Methods Summary data of genome-wide association study were obtained for ALS and the brain structures, including surface area (SA), thickness and volume of subcortical structures. Inverse-variance weighted (IVW) method was used as the main estimate approach. Sensitivity analysis was conducted detect heterogeneity and pleiotropy. Colocalization analysis was performed to calculate the posterior probability of causal variation and identify the common genes. Results In the forward MR analysis, we found positive associations between the SA in four cortical regions (lingual, parahippocampal, pericalcarine, and middle temporal) and the risk of ALS. Additionally, decreased thickness in nine cortical regions (caudal anterior cingulate, frontal pole, fusiform, inferior temporal, lateral occipital, lateral orbitofrontal, pars orbitalis, pars triangularis, and pericalcarine) was significantly associated with a higher risk of ALS. In the reverse MR analysis, genetically predicted ALS was associated with reduced thickness in the bankssts and increased thickness in the caudal middle frontal, inferior parietal, medial orbitofrontal, and superior temporal regions. Colocalization analysis revealed the presence of shared causal variants between the two traits. Conclusion Our results suggest that altered brain morphometry in individuals with high ALS risk may be genetically mediated. The causal associations of widespread multifocal extra-motor atrophy in frontal and temporal lobes with ALS risk support the notion of a continuum between ALS and frontotemporal dementia. These findings enhance our understanding of the cortical structural patterns in ALS and shed light on potentially viable therapeutic targets.
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Affiliation(s)
| | | | | | | | | | | | | | - Yuan Yang
- Department of Medical Genetics, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
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Liu X, Shi L, Li E, Jia S. Associations of hearing loss and structural changes in specific cortical regions: a Mendelian randomization study. Cereb Cortex 2024; 34:bhae084. [PMID: 38494888 DOI: 10.1093/cercor/bhae084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 02/13/2024] [Accepted: 02/15/2024] [Indexed: 03/19/2024] Open
Abstract
INTRODUCTION Previous studies have suggested a correlation between hearing loss (HL) and cortical alterations, but the specific brain regions that may be affected are unknown. METHODS Genome-wide association study (GWAS) data for 3 subtypes of HL phenotypes, sensorineural hearing loss (SNHL), conductive hearing loss, and mixed hearing loss, were selected as exposures, and GWAS data for brain structure-related traits were selected as outcomes. The inverse variance weighted method was used as the main estimation method. RESULTS Negative associations were identified between genetically predicted SNHL and brain morphometric indicators (cortical surface area, cortical thickness, or volume of subcortical structures) in specific brain regions, including the bankssts (β = -0.006 mm, P = 0.016), entorhinal cortex (β = -4.856 mm2, P = 0.029), and hippocampus (β = -24.819 cm3, P = 0.045), as well as in brain regions functionally associated with visual perception, including the pericalcarine (β = -10.009 cm3, P = 0.013). CONCLUSION Adaptive changes and functional remodeling of brain structures occur in patients with genetically predicted HL. Brain regions functionally associated with auditory perception, visual perception, and memory function are the main brain regions vulnerable in HL.
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Affiliation(s)
- Xiaoduo Liu
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, 45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Lubo Shi
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing Digestive Disease Center, 95 Yong'an Road, Xicheng District, Beijing, 100050, China
| | - Enze Li
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, 2 Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Shuo Jia
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, 2 Anzhen Road, Chaoyang District, Beijing, 100029, China
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Stolicyn A, Lyall LM, Lyall DM, Høier NK, Adams MJ, Shen X, Cole JH, McIntosh AM, Whalley HC, Smith DJ. Comprehensive assessment of sleep duration, insomnia, and brain structure within the UK Biobank cohort. Sleep 2024; 47:zsad274. [PMID: 37889226 PMCID: PMC10851840 DOI: 10.1093/sleep/zsad274] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 09/22/2023] [Indexed: 10/28/2023] Open
Abstract
STUDY OBJECTIVES To assess for associations between sleeping more than or less than recommended by the National Sleep Foundation (NSF), and self-reported insomnia, with brain structure. METHODS Data from the UK Biobank cohort were analyzed (N between 9K and 32K, dependent on availability, aged 44 to 82 years). Sleep measures included self-reported adherence to NSF guidelines on sleep duration (sleeping between 7 and 9 hours per night), and self-reported difficulty falling or staying asleep (insomnia). Brain structural measures included global and regional cortical or subcortical morphometry (thickness, surface area, volume), global and tract-related white matter microstructure, brain age gap (difference between chronological age and age estimated from brain scan), and total volume of white matter lesions. RESULTS Longer-than-recommended sleep duration was associated with lower overall grey and white matter volumes, lower global and regional cortical thickness and volume measures, higher brain age gap, higher volume of white matter lesions, higher mean diffusivity globally and in thalamic and association fibers, and lower volume of the hippocampus. Shorter-than-recommended sleep duration was related to higher global and cerebellar white matter volumes, lower global and regional cortical surface areas, and lower fractional anisotropy in projection fibers. Self-reported insomnia was associated with higher global gray and white matter volumes, and with higher volumes of the amygdala, hippocampus, and putamen. CONCLUSIONS Sleeping longer than recommended by the NSF is associated with a wide range of differences in brain structure, potentially indicative of poorer brain health. Sleeping less than recommended is distinctly associated with lower cortical surface areas. Future studies should assess the potential mechanisms of these differences and investigate long sleep duration as a putative marker of brain health.
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Affiliation(s)
- Aleks Stolicyn
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Laura M Lyall
- School of Health & Wellbeing, University of Glasgow, Glasgow, UK
| | - Donald M Lyall
- School of Health & Wellbeing, University of Glasgow, Glasgow, UK
| | - Nikolaj Kjær Høier
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Copenhagen Research Center for Mental Health CORE, Mental Health Center Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Mark J Adams
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Xueyi Shen
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - James H Cole
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Andrew M McIntosh
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Heather C Whalley
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Daniel J Smith
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
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Jia H, Li Z, Guo F, Hua Z, Zhou X, Li X, Li R, Liu Q, Liu Y, Dong H. Cortical structure and the risk of amyotrophic lateral sclerosis: A bidirectional Mendelian randomization study. Prog Neuropsychopharmacol Biol Psychiatry 2024; 129:110872. [PMID: 37827425 DOI: 10.1016/j.pnpbp.2023.110872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 09/06/2023] [Accepted: 10/08/2023] [Indexed: 10/14/2023]
Abstract
BACKGROUND Current observational studies indicate progressive brain atrophy is closely associated with the clinical feature of amyotrophic lateral sclerosis. However, it is unclear whether the changes in cortical structure are the cause or result of ALS. Our study aimed to investigate the causal relationship between cortical structure and ALS risk using a bidirectional two-sample MR study. METHODS We collected publicly available genome-wide association studies' summary statistics for cortical structure from UK Biobank and ENIGMA consortium (n = 33,992) and ALS from the Project MinE (n = 138,086). We used the inverse variance weighted method (IVW) as primary analysis in order to evaluate the causal effects. In addition, the weighted median and MR Egger methods were performed to ensure the robustness and reliability of the IVW results. RESULTS We found the decreased surface of the paracentral lobule and thickness of the frontal pole and middle temporal lobe were suggestively associated with an increased risk of ALS as well as the increased surface of medial orbitofrontal and middle temporal lobe. In another aspect, the causalities between the susceptibility to ALS and the volume of the transverse temporal gyrus and superior temporal gyrus were negative. Besides, the susceptibility to ALS might also contribute to an increased thickness of the postcentral gyrus and superior parietal gyrus. CONCLUSION In this two-sample MR analysis, we observed that multiple cortical brain regions are associated with a higher ALS risk. Further research into the underlying mechanisms is required to back up our findings.
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Affiliation(s)
- Hongning Jia
- Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China; The Key Laboratory of Clinical Neurology, Ministry of Education, Shijiazhuang, Hebei, China; Key Laboratory of Neurology of Hebei Province, Shijiazhuang, Hebei, China; Department of Neurology, Cangzhou Central Hospital, Cangzhou, Hebei, China
| | - Zhiguang Li
- Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China; Department of Neurology, Xingtai Third Hospital, Xingtai, China
| | - Fei Guo
- Department of Basic Medicine, Xingtai Medical College, Xingtai, China
| | - Zixin Hua
- Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Xiaomeng Zhou
- Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China; The Key Laboratory of Clinical Neurology, Ministry of Education, Shijiazhuang, Hebei, China; Key Laboratory of Neurology of Hebei Province, Shijiazhuang, Hebei, China
| | - Xin Li
- Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China; The Key Laboratory of Clinical Neurology, Ministry of Education, Shijiazhuang, Hebei, China; Key Laboratory of Neurology of Hebei Province, Shijiazhuang, Hebei, China
| | - Rui Li
- Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China; The Key Laboratory of Clinical Neurology, Ministry of Education, Shijiazhuang, Hebei, China; Key Laboratory of Neurology of Hebei Province, Shijiazhuang, Hebei, China
| | - Qi Liu
- Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China; The Key Laboratory of Clinical Neurology, Ministry of Education, Shijiazhuang, Hebei, China; Key Laboratory of Neurology of Hebei Province, Shijiazhuang, Hebei, China
| | - Yaling Liu
- Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China; The Key Laboratory of Clinical Neurology, Ministry of Education, Shijiazhuang, Hebei, China; Key Laboratory of Neurology of Hebei Province, Shijiazhuang, Hebei, China.
| | - Hui Dong
- Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China; The Key Laboratory of Clinical Neurology, Ministry of Education, Shijiazhuang, Hebei, China; Key Laboratory of Neurology of Hebei Province, Shijiazhuang, Hebei, China.
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Shui L, Shibata D, Chan KCG, Zhang W, Sung J, Haynor DR. Longitudinal Relationship Between Brain Atrophy Patterns, Cognitive Decline, and Cerebrospinal Fluid Biomarkers in Alzheimer's Disease Explored by Orthonormal Projective Non-Negative Matrix Factorization. J Alzheimers Dis 2024; 98:969-986. [PMID: 38517788 DOI: 10.3233/jad-231149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
Abstract
Background Longitudinal magnetic resonance imaging (MRI) has been proposed for tracking the progression of Alzheimer's disease (AD) through the assessment of brain atrophy. Objective Detection of brain atrophy patterns in patients with AD as the longitudinal disease tracker. Methods We used a refined version of orthonormal projective non-negative matrix factorization (OPNMF) to identify six distinct spatial components of voxel-wise volume loss in the brains of 83 subjects with AD from the ADNI3 cohort relative to healthy young controls from the ABIDE study. We extracted non-negative coefficients representing subject-specific quantitative measures of regional atrophy. Coefficients of brain atrophy were compared to subjects with mild cognitive impairment and controls, to investigate the cross-sectional and longitudinal associations between AD biomarkers and regional atrophy severity in different groups. We further validated our results in an independent dataset from ADNI2. Results The six non-overlapping atrophy components represent symmetric gray matter volume loss primarily in frontal, temporal, parietal and cerebellar regions. Atrophy in these regions was highly correlated with cognition both cross-sectionally and longitudinally, with medial temporal atrophy showing the strongest correlations. Subjects with elevated CSF levels of TAU and PTAU and lower baseline CSF Aβ42 values, demonstrated a tendency toward a more rapid increase of atrophy. Conclusions The present study has applied a transferable method to characterize the imaging changes associated with AD through six spatially distinct atrophy components and correlated these atrophy patterns with cognitive changes and CSF biomarkers cross-sectionally and longitudinally, which may help us better understand the underlying pathology of AD.
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Affiliation(s)
- Lan Shui
- Department of Biostatistics, University of Washington, Seattle, WA, USA
- National Alzheimer's Coordinating Center, Seattle, WA, USA
- Department of Biostatistics, MD Anderson Cancer Center, Houston, TX, USA
| | - Dean Shibata
- Department of Radiology, University of Washington, Seattle, WA, USA
- National Alzheimer's Coordinating Center, Seattle, WA, USA
| | - Kwun Chuen Gary Chan
- Department of Biostatistics, University of Washington, Seattle, WA, USA
- National Alzheimer's Coordinating Center, Seattle, WA, USA
| | - Wenbo Zhang
- Department of Statistics, University of California Irvine, CA, USA
| | - Junhyoun Sung
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - David R Haynor
- Department of Biostatistics, MD Anderson Cancer Center, Houston, TX, USA
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Fedorov A, Geenjaar E, Wu L, Sylvain T, DeRamus TP, Luck M, Misiura M, Mittapalle G, Hjelm RD, Plis SM, Calhoun VD. Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links. Neuroimage 2024; 285:120485. [PMID: 38110045 PMCID: PMC10872501 DOI: 10.1016/j.neuroimage.2023.120485] [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: 08/24/2023] [Revised: 11/15/2023] [Accepted: 12/04/2023] [Indexed: 12/20/2023] Open
Abstract
In recent years, deep learning approaches have gained significant attention in predicting brain disorders using neuroimaging data. However, conventional methods often rely on single-modality data and supervised models, which provide only a limited perspective of the intricacies of the highly complex brain. Moreover, the scarcity of accurate diagnostic labels in clinical settings hinders the applicability of the supervised models. To address these limitations, we propose a novel self-supervised framework for extracting multiple representations from multimodal neuroimaging data to enhance group inferences and enable analysis without resorting to labeled data during pre-training. Our approach leverages Deep InfoMax (DIM), a self-supervised methodology renowned for its efficacy in learning representations by estimating mutual information without the need for explicit labels. While DIM has shown promise in predicting brain disorders from single-modality MRI data, its potential for multimodal data remains untapped. This work extends DIM to multimodal neuroimaging data, allowing us to identify disorder-relevant brain regions and explore multimodal links. We present compelling evidence of the efficacy of our multimodal DIM analysis in uncovering disorder-relevant brain regions, including the hippocampus, caudate, insula, - and multimodal links with the thalamus, precuneus, and subthalamus hypothalamus. Our self-supervised representations demonstrate promising capabilities in predicting the presence of brain disorders across a spectrum of Alzheimer's phenotypes. Comparative evaluations against state-of-the-art unsupervised methods based on autoencoders, canonical correlation analysis, and supervised models highlight the superiority of our proposed method in achieving improved classification performance, capturing joint information, and interpretability capabilities. The computational efficiency of the decoder-free strategy enhances its practical utility, as it saves compute resources without compromising performance. This work offers a significant step forward in addressing the challenge of understanding multimodal links in complex brain disorders, with potential applications in neuroimaging research and clinical diagnosis.
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Affiliation(s)
- Alex Fedorov
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA.
| | - Eloy Geenjaar
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Lei Wu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | | | - Thomas P DeRamus
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Margaux Luck
- Mila - Quebec AI Institute, Montréal, QC, Canada
| | - Maria Misiura
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Girish Mittapalle
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - R Devon Hjelm
- Mila - Quebec AI Institute, Montréal, QC, Canada; Apple Machine Learning Research, Seattle, WA, USA
| | - Sergey M Plis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
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Zhou M, Chen S, Chen Y, Wang C, Chen C. Causal associations between gut microbiota and regional cortical structure: a Mendelian randomization study. Front Neurosci 2023; 17:1296145. [PMID: 38196849 PMCID: PMC10774226 DOI: 10.3389/fnins.2023.1296145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 12/05/2023] [Indexed: 01/11/2024] Open
Abstract
Introduction Observational studies have reported associations between gut microbiota composition and central nervous system diseases. However, the potential causal relationships and underlying mechanisms remain unclear. Here, we applied Mendelian randomization (MR) to investigate the causal effects of gut microbiota on cortical surface area (SA) and thickness (TH) in the brain. Methods We used genome-wide association study summary statistics of gut microbiota abundance in 18,340 individuals from the MiBioGen Consortium to identify genetic instruments for 196 gut microbial taxa. We then analyzed data from 56,761 individuals from the ENIGMA Consortium to examine associations of genetically predicted gut microbiota with alterations in cortical SA and TH globally and across 34 functional brain regions. Inverse-variance weighted analysis was used as the primary MR method, with MR Egger regression, MR-PRESSO, Cochran's Q test, and leave-one-out analysis to assess heterogeneity and pleiotropy. Results At the functional region level, genetically predicted higher abundance of class Mollicutes was associated with greater SA of the medial orbitofrontal cortex (β = 8.39 mm2, 95% CI: 3.08-13.70 mm2, p = 0.002), as was higher abundance of phylum Tenericutes (β = 8.39 mm2, 95% CI: 3.08-13.70 mm2, p = 0.002). Additionally, higher abundance of phylum Tenericutes was associated with greater SA of the lateral orbitofrontal cortex (β = 10.51 mm2, 95% CI: 3.24-17.79 mm2, p = 0.0046). No evidence of heterogeneity or pleiotropy was detected. Conclusion Specific gut microbiota may causally influence cortical structure in brain regions involved in neuropsychiatric disorders. The findings provide evidence for a gut-brain axis influencing cortical development, particularly in the orbitofrontal cortex during adolescence.
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Affiliation(s)
- Maochao Zhou
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Song Chen
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Yan Chen
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, China
| | | | - Chunmei Chen
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, China
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11
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Jain PR, Yates M, de Celis CR, Drineas P, Jahanshad N, Thompson P, Paschou P. Multiomic approach and Mendelian randomization analysis identify causal associations between blood biomarkers and subcortical brain structure volumes. Neuroimage 2023; 284:120466. [PMID: 37995919 DOI: 10.1016/j.neuroimage.2023.120466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 10/17/2023] [Accepted: 11/20/2023] [Indexed: 11/25/2023] Open
Abstract
Alterations in subcortical brain structure volumes have been found to be associated with several neurodegenerative and psychiatric disorders. At the same time, genome-wide association studies (GWAS) have identified numerous common variants associated with brain structure. In this study, we integrate these findings, aiming to identify proteins, metabolites, or microbes that have a putative causal association with subcortical brain structure volumes via a two-sample Mendelian randomization approach. This method uses genetic variants as instrument variables to identify potentially causal associations between an exposure and an outcome. The exposure data that we analyzed comprised genetic associations for 2994 plasma proteins, 237 metabolites, and 103 microbial genera. The outcome data included GWAS data for seven subcortical brain structure volumes including accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Eleven proteins and six metabolites were found to have a significant association with subcortical structure volumes, with nine proteins and five metabolites replicated using independent exposure data. We found causal associations between accumbens volume and plasma protease c1 inhibitor as well as strong association between putamen volume and Agouti signaling protein. Among metabolites, urate had the strongest association with thalamic volume. No significant associations were detected between the microbial genera and subcortical brain structure volumes. We also observed significant enrichment for biological processes such as proteolysis, regulation of the endoplasmic reticulum apoptotic signaling pathway, and negative regulation of DNA binding. Our findings provide insights to the mechanisms through which brain volumes may be affected in the pathogenesis of neurodevelopmental and psychiatric disorders and point to potential treatment targets for disorders that are associated with subcortical brain structure volumes.
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Affiliation(s)
- Pritesh R Jain
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, United States
| | - Madison Yates
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, United States
| | - Carlos Rubin de Celis
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, United States
| | - Petros Drineas
- Department of Computer Science, Purdue University, United States
| | - Neda Jahanshad
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of South California, United States
| | - Paul Thompson
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of South California, United States
| | - Peristera Paschou
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, United States.
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12
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Gao C, Li B, He Y, Huang P, Du J, He G, Zhang P, Tang H, Chen S. Early changes of fecal short-chain fatty acid levels in patients with mild cognitive impairments. CNS Neurosci Ther 2023; 29:3657-3666. [PMID: 37144597 PMCID: PMC10580335 DOI: 10.1111/cns.14252] [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: 01/18/2023] [Revised: 03/21/2023] [Accepted: 04/24/2023] [Indexed: 05/06/2023] Open
Abstract
AIMS To compare the fecal levels of short-chain fatty acids (SCFAs) in patients with mild cognitive impairment (MCI) and normal controls (NCs) and to examine whether fecal SCFAs could be used as the biomarker for the identification of patients with MCI. To examine the relationship between fecal SCFAs and amyloid-β (Aβ) deposition in the brain. METHODS A cohort of 32 MCI patients, 23 Parkinson's disease (PD) patients, and 27 NC were recruited in our study. Fecal levels of SCFAs were measured using chromatography and mass spectrometry. Disease duration, ApoE genotype, body mass index, constipation, and diabetes were evaluated. To assess cognitive impairment, we used the Mini-Mental Status Examination (MMSE). To assess brain atrophy, the degree of medial temporal atrophy (MTA score, Grade 0-4) was measured by structural MRI. Aβ positron emission tomography with 18 F-florbetapir (FBP) was performed in seven MCI patients at the time of stool sampling and in 28 MCI patients at an average of 12.3 ± 0.4 months from the time of stool sampling to detect and quantify Aβ deposition in the brain. RESULTS Compared with NC, MCI patients had significantly lower fecal levels of acetic acid, butyric acid, and caproic acid. Among fecal SCFAs, acetic acid performed the best in discriminating MCI from NC, achieved an AUC of 0.752 (p = 0.001, 95% CI: 0.628-0.876), specificity of 66.7%, and sensitivity of 75%. By combining fecal levels of acetic acid, butyric acid, and caproic acid, the diagnostic specificity was significantly improved, reaching 88.9%. To better verify the diagnostic performance of SCFAs, we randomly assigned 60% of participants into training dataset and 40% into testing dataset. Only acetic acid showed significantly difference between these two groups in the training dataset. Based on the fecal levels of acetic acid, we achieved the ROC curve. Next, the ROC curve was evaluated in the independent test data and 61.5% (8 in 13) of patients with MCI, and 72.7% (8 in 11) of NC could be identified correctly. Subgroup analysis showed that reduced fecal SCFAs in MCI group were negatively associated with Aβ deposition in cognition-related brain regions. CONCLUSION Reductions in fecal SCFAs were observed in patients with MCI compared with NC. Reduced fecal SCFAs were negatively associated with Aβ deposition in cognition-related brain regions in MCI group. Our findings suggest that gut metabolite SCFAs have the potential to serve as early diagnostic biomarkers for distinguishing patients with MCI from NC and could serve as potential targets for preventing AD.
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Affiliation(s)
- Chao Gao
- Department of Neurology and Institute of Neurology, Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Binyin Li
- Department of Neurology and Institute of Neurology, Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Yixi He
- Department of Neurology and Institute of Neurology, Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Pai Huang
- Department of Neurology and Institute of Neurology, Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Juanjuan Du
- Department of Neurology and Institute of Neurology, Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Guiying He
- Department of Neurology and Institute of Neurology, Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Pingchen Zhang
- Department of Neurology and Institute of Neurology, Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Huidong Tang
- Department of Neurology and Institute of Neurology, Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Shengdi Chen
- Department of Neurology and Institute of Neurology, Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
- Lab for Translational Research of Neurodegenerative Diseases, Shanghai Institute for Advanced Immunochemical Studies (SIAIS)Shanghai Tech UniversityShanghaiChina
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13
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Gao X, Wei T, Xu S, Sun W, Zhang B, Li C, Sui R, Fei N, Li Y, Xu W, Han D. Sleep disorders causally affect the brain cortical structure: A Mendelian randomization study. Sleep Med 2023; 110:243-253. [PMID: 37657176 DOI: 10.1016/j.sleep.2023.08.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 07/14/2023] [Accepted: 08/13/2023] [Indexed: 09/03/2023]
Abstract
BACKGROUND s: Previous studies have reported that patients with sleep disorders have altered brain cortical structures. However, the causality has not been determined. We performed a two-sample Mendelian randomization (MR) to reveal the causal effect of sleep disorders on brain cortical structure. METHODS We included as exposures 11 phenotypes of sleep disorders including subjective and objective sleep duration, insomnia symptom and poor sleep efficiency, daytime sleepiness (narcolepsy)/napping, morning/evening preference, and four sleep breathing related traits from nine European-descent genome-wide association studies (GWASs). Further, outcome variables were provided by ENIGMA Consortium GWAS for full brain and 34 region-specific cortical thickness (TH) and surface area (SA) of grey matter. Inverse-variance weighted (IVW) was used as the primary estimate whereas alternative MR methods were implemented as sensitivity analysis approaches to ensure results robustness. RESULTS At the global level, both self-reported or accelerometer-measured shorter sleep duration decreases the thickness of full brain both derived from self-reported data (βIVW = 0.03 mm, standard error (SE) = 0.02, P = 0.038; βIVW = 0.02 mm, SE = 0.01, P = 0.010). At the functional level, there were 66 associations of suggestive evidence of causality. Notably, one robust evidence after multiple testing correction (1518 tests) suggests the without global weighted SA of superior parietal lobule was influenced significantly by sleep efficiency (βIVW = -285.28 mm2, SE = 68.59, P = 3.2 × 10-5). CONCLUSIONS We found significant evidence that shorter sleep duration, as estimated by self-reported interview and accelerometer measurements, was causally associated with atrophy in the entire human brain.
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Affiliation(s)
- Xiang Gao
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, People's Republic of China; Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, 100730, People's Republic of China; Key Laboratory of Otolaryngology Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, 100730, People's Republic of China
| | - Tao Wei
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, 100053, People's Republic of China
| | - Shenglong Xu
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, People's Republic of China; Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, 100730, People's Republic of China; Key Laboratory of Otolaryngology Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, 100730, People's Republic of China
| | - Wei Sun
- Department of Neurology, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, 100053, People's Republic of China
| | - Bowen Zhang
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, People's Republic of China; Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, 100730, People's Republic of China; Key Laboratory of Otolaryngology Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, 100730, People's Republic of China
| | - Cancan Li
- Department of Epidemiology and Health Statistics, School of Public Halth, Capital Medical University, Beijing, 100069, People's Republic of China
| | - Rongcui Sui
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, People's Republic of China; Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, 100730, People's Republic of China; Key Laboratory of Otolaryngology Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, 100730, People's Republic of China
| | - Nanxi Fei
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, People's Republic of China
| | - Yanru Li
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, People's Republic of China; Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, 100730, People's Republic of China; Key Laboratory of Otolaryngology Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, 100730, People's Republic of China.
| | - Wen Xu
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, People's Republic of China; Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, 100730, People's Republic of China; Key Laboratory of Otolaryngology Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, 100730, People's Republic of China
| | - Demin Han
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, People's Republic of China; Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, 100730, People's Republic of China; Key Laboratory of Otolaryngology Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, 100730, People's Republic of China.
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14
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Chen Y, Lyu S, Xiao W, Yi S, Liu P, Liu J. Sleep Traits Causally Affect the Brain Cortical Structure: A Mendelian Randomization Study. Biomedicines 2023; 11:2296. [PMID: 37626792 PMCID: PMC10452307 DOI: 10.3390/biomedicines11082296] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 08/01/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
Background: Brain imaging results in sleep deprived patients showed structural changes in the cerebral cortex; however, the reasons for this phenomenon need to be further explored. Methods: This MR study evaluated causal associations between morningness, ease of getting up, insomnia, long sleep, short sleep, and the cortex structure. Results: At the functional level, morningness increased the surface area (SA) of cuneus with global weighted (beta(b) (95% CI): 32.63 (10.35, 54.90), p = 0.004). Short sleep increased SA of the lateral occipital with global weighted (b (95% CI): 394.37(107.89, 680.85), p = 0.007. Short sleep reduced cortical thickness (TH) of paracentral with global weighted (OR (95% CI): -0.11 (-0.19, -0.03), p = 0.006). Short sleep reduced TH of parahippocampal with global weighted (b (95% CI): -0.25 (-0.42, -0.07), p = 0.006). No pleiotropy was detected. However, none of the Bonferroni-corrected p values of the causal relationship between cortical structure and the five types of sleep traits met the threshold. Conclusions: Our results potentially show evidence of a higher risk association between neuropsychiatric disorders and not only paracentral and parahippocampal brain areas atrophy, but also an increase in the middle temporal zone. Our findings shed light on the associations of cortical structure with the occurrence of five types of sleep traits.
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Affiliation(s)
- Yanjing Chen
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha 410011, China; (Y.C.); (S.L.); (S.Y.); (P.L.)
| | - Shiyi Lyu
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha 410011, China; (Y.C.); (S.L.); (S.Y.); (P.L.)
| | - Wang Xiao
- Department of General Surgery, Second Xiangya Hospital, Central South University, Changsha 410011, China;
| | - Sijie Yi
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha 410011, China; (Y.C.); (S.L.); (S.Y.); (P.L.)
| | - Ping Liu
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha 410011, China; (Y.C.); (S.L.); (S.Y.); (P.L.)
| | - Jun Liu
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha 410011, China; (Y.C.); (S.L.); (S.Y.); (P.L.)
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha 410011, China
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15
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He XY, Wu BS, Kuo K, Zhang W, Ma Q, Xiang ST, Li YZ, Wang ZY, Dong Q, Feng JF, Cheng W, Yu JT. Association between polygenic risk for Alzheimer's disease and brain structure in children and adults. Alzheimers Res Ther 2023; 15:109. [PMID: 37312172 DOI: 10.1186/s13195-023-01256-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 06/01/2023] [Indexed: 06/15/2023]
Abstract
BACKGROUND The correlations between genetic risk for Alzheimer's disease (AD) with comprehensive brain regions at a regional scale are still not well understood. We aim to explore whether these associations vary across different age stages. METHODS This study used large existing genome-wide association datasets to calculate polygenic risk score (PRS) for AD in two populations from the UK Biobank (N ~ 23 000) and Adolescent Brain Cognitive Development Study (N ~ 4660) who had multimodal macrostructural and microstructural magnetic resonance imaging (MRI) metrics. We used linear mixed-effect models to assess the strength of the association between AD PRS and multiple MRI metrics of regional brain structures at different stages of life. RESULTS Compared to those with lower PRSs, adolescents with higher PRSs had thinner cortex in the caudal anterior cingulate and supramarginal. In the middle-aged and elderly population, AD PRS had correlations with regional structure shrink primarily located in the cingulate, prefrontal cortex, hippocampus, thalamus, amygdala, and striatum, whereas the brain expansion was concentrated near the occipital lobe. Furthermore, both adults and adolescents with higher PRSs exhibited widespread white matter microstructural changes, indicated by decreased fractional anisotropy (FA) or increased mean diffusivity (MD). CONCLUSIONS In conclusion, our results suggest genetic loading for AD may influence brain structures in a highly dynamic manner, with dramatically different patterns at different ages. This age-specific change is consistent with the classical pattern of brain impairment observed in AD patients.
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Affiliation(s)
- Xiao-Yu He
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Kevin Kuo
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Qing Ma
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Shi-Tong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Yu-Zhu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Zi-Yi Wang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Qiang Dong
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai, China
- ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China.
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai, China.
- ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China.
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16
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Jain P, Yates M, de Celis CR, Drineas P, Jahanshad N, Thompson P, Paschou P. Multiomic approach and Mendelian randomization analysis identify causal associations between blood biomarkers and subcortical brain structure volumes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.30.23287968. [PMID: 37066330 PMCID: PMC10104218 DOI: 10.1101/2023.03.30.23287968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Alterations in subcortical brain structure volumes have been found to be associated with several neurodegenerative and psychiatric disorders. At the same time, genome-wide association studies (GWAS) have identified numerous common variants associated with brain structure. In this study, we integrate these findings, aiming to identify proteins, metabolites, or microbes that have a putative causal association with subcortical brain structure volumes via a two-sample Mendelian randomization approach. This method uses genetic variants as instrument variables to identify potentially causal associations between an exposure and an outcome. The exposure data that we analyzed comprised genetic associations for 2,994 plasma proteins, 237 metabolites, and 103 microbial genera. The outcome data included GWAS data for seven subcortical brain structure volumes including accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Eleven proteins and six metabolites were found to have a significant association with subcortical structure volumes. We found causal associations between amygdala volume and granzyme A as well as association between accumbens volume and plasma protease c1 inhibitor. Among metabolites, urate had the strongest association with thalamic volume. No significant associations were detected between the microbial genera and subcortical brain structure volumes. We also observed significant enrichment for biological processes such as proteolysis, regulation of the endoplasmic reticulum apoptotic signaling pathway, and negative regulation of DNA binding. Our findings provide insights to the mechanisms through which brain volumes may be affected in the pathogenesis of neurodevelopmental and psychiatric disorders and point to potential treatment targets for disorders that are associated with subcortical brain structure volumes.
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Affiliation(s)
- Pritesh Jain
- Department of Biological Sciences, Purdue University
| | - Madison Yates
- Department of Biological Sciences, Purdue University
| | | | | | - Neda Jahanshad
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of South California
| | - Paul Thompson
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of South California
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Alban SL, Lynch KM, Ringman JM, Toga AW, Chui HC, Sepehrband F, Choupan J. The association between white matter hyperintensities and amyloid and tau deposition. Neuroimage Clin 2023; 38:103383. [PMID: 36965457 PMCID: PMC10060905 DOI: 10.1016/j.nicl.2023.103383] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 02/09/2023] [Accepted: 03/16/2023] [Indexed: 03/22/2023]
Abstract
White matter hyperintensities (WMHs) frequently occur in Alzheimer's Disease (AD) and have a contribution from ischemia, though their relationship with β-amyloid and cardiovascular risk factors (CVRFs) is not completely understood. We used AT classification to categorize individuals based on their β-amyloid and tau pathologies, then assessed the effects of β-amyloid and tau on WMH volume and number. We then determined regions in which β-amyloid and WMH accumulation were related. Last, we analyzed the effects of various CVRFs on WMHs. As secondary analyses, we observed effects of age and sex differences, atrophy, cognitive scores, and APOE genotype. PET, MRI, FLAIR, demographic, and cardiovascular health data was collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI-3) (N = 287, 48 % male). Participants were categorized as A + and T + if their Florbetapir SUVR and Flortaucipir SUVR were above 0.79 and 1.25, respectively. WMHs were mapped on MRI using a deep convolutional neural network (Sepehrband et al., 2020). CVRF scores were based on history of hypertension, systolic and diastolic blood pressure, pulse rate, respiration rate, BMI, and a cumulative score with 6 being the maximum score. Regression models and Pearson correlations were used to test associations and correlations between variables, respectively, with age, sex, years of education, and scanner manufacturer as covariates of no interest. WMH volume percent was significantly associated with global β-amyloid (r = 0.28, p < 0.001), but not tau (r = 0.05, p = 0.25). WMH volume percent was higher in individuals with either A + or T + pathology compared to controls, particularly within in the A+/T + group (p = 0.007, Cohen's d = 0.4, t = -2.5). Individual CVRFs nor cumulative CVRF scores were associated with increased WMH volume. Finally, the regions where β-amyloid and WMH count were most positively associated were the middle temporal region in the right hemisphere (r = 0.18, p = 0.002) and the fusiform region in the left hemisphere (r = 0.017, p = 0.005). β-amyloid and WMH have a clear association, though the mechanism facilitating this association is still not fully understood. The associations found between β-amyloid and WMH burden emphasizes the relationship between β-amyloid and vascular lesion formation while factors like CVRFs, age, and sex affect AD development through various mechanisms. These findings highlight potential causes and mechanisms of AD as targets for future preventions and treatments. Going forward, a larger emphasis may be placed on β-amyloid's vascular effects and the implications of impaired brain clearance in AD.
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Affiliation(s)
- Sierra L Alban
- Laboratory of NeuroImaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Kirsten M Lynch
- Laboratory of NeuroImaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - John M Ringman
- Alzheimer's Disease Research Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Arthur W Toga
- Laboratory of NeuroImaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Alzheimer's Disease Research Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Helena C Chui
- Alzheimer's Disease Research Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Farshid Sepehrband
- Laboratory of NeuroImaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jeiran Choupan
- Laboratory of NeuroImaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; NeuroScope Inc., Scarsdale, NY, USA
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Regulatory role of melatonin in Notch1 signaling pathway in cerebral cortex of Aβ 1-42-induced Alzheimer's disease rat model. Mol Biol Rep 2023; 50:2463-2469. [PMID: 36602704 DOI: 10.1007/s11033-022-08213-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 12/14/2022] [Indexed: 01/06/2023]
Abstract
BACKGROUND Soluble Amyloid-beta (Aβ) oligomers are thought to play a key role in the pathogenesis of Alzheimer's disease (AD), which is the most common age-associated neurodegenerative diseases with obvious neuropathological changes and functional decline in both cortical and subcortical regions. Melatonin is ubiquitously distributed and multifunctioning indoleamine. Accumulating studies support that melatonin is potential therapeutic molecule for AD through modulating a broad variety of signaling pathways. In recent years, Notch1 signaling pathway is been known involved in dynamic changes in the cellular architecture and function of adult brain, as well as associated with the pathophysiology of AD and other neurodegenerative disorders. METHODS AND RESULTS In this study, we performed real-time polymerase chain reaction, immunohistochemistry and western blotting analyses using the cerebral cortical tissues of Aβ1-42 oligomers-induced AD rats with or without melatonin treatment. Our results showed that soluble Aβ1-42 oligomers decreased the expression of the main components of Notch1 signaling pathway, Notch1, NICD and Hes1 in the cerebral cortex, and melatonin could restore the level of Notch1, NICD and Hes1. CONCLUSION This observation suggests that targeting of Notch1 signaling might be a promising therapeutic approach for AD and other age-associated neurodegenerative diseases, and melatonin might serve as a potential therapeutic agent for AD and other age-associated neurodegenerative diseases.
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19
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Pölsterl S, Wachinger C. Identification of causal effects of neuroanatomy on cognitive decline requires modeling unobserved confounders. Alzheimers Dement 2022; 19:1994-2005. [PMID: 36419215 DOI: 10.1002/alz.12825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 09/16/2022] [Accepted: 09/19/2022] [Indexed: 11/27/2022]
Abstract
INTRODUCTION Carrying out a randomized controlled trial to estimate the causal effects of regional brain atrophy due to Alzheimer's disease (AD) is impossible. Instead, we must estimate causal effects from observational data. However, this generally requires knowing and having recorded all confounders, which is often unrealistic. METHODS We provide an approach that leverages the dependencies among multiple neuroanatomical measures to estimate causal effects from observational neuroimaging data without the need to know and record all confounders. RESULTS Our analyses of N = 732 $N=732$ subjects from the Alzheimer's Disease Neuroimaging Initiative demonstrate that using our approach results in biologically meaningful conclusions, whereas ignoring unobserved confounding yields results that conflict with established knowledge on cognitive decline due to AD. DISCUSSION The findings provide evidence that the impact of unobserved confounding can be substantial. To ensure trustworthy scientific insights, future AD research can account for unobserved confounding via the proposed approach.
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Affiliation(s)
- Sebastian Pölsterl
- The Lab for Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany
| | - Christian Wachinger
- The Lab for Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany.,Technical University of Munich, School of Medicine, Department of Radiology, Munich, Germany
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20
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Taschler B, Smith SM, Nichols TE. Causal inference on neuroimaging data with Mendelian randomisation. Neuroimage 2022; 258:119385. [PMID: 35714886 PMCID: PMC10933777 DOI: 10.1016/j.neuroimage.2022.119385] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 05/30/2022] [Accepted: 06/12/2022] [Indexed: 10/18/2022] Open
Abstract
While population-scale neuroimaging studies offer the promise of discovery and characterisation of subtle risk factors, massive sample sizes increase the power for both meaningful associations and those attributable to confounds. This motivates the need for causal modelling of observational data that goes beyond statements of association and towards deeper understanding of complex relationships between individual traits and phenotypes, clinical biomarkers, genetic variation, and brain-related measures of health. Mendelian randomisation (MR) presents a way to obtain causal inference on the basis of genetic data and explicit assumptions about the relationship between genetic variables, exposure and outcome. In this work, we provide an introduction to and overview of causal inference methods based on Mendelian randomisation, with examples involving imaging-derived phenotypes from UK Biobank to make these methods accessible to neuroimaging researchers. We motivate the use of MR techniques, lay out the underlying assumptions, introduce common MR methods and focus on several scenarios in which modelling assumptions are potentially violated, resulting in biased effect estimates. Importantly, we give a detailed account of necessary steps to increase the reliability of MR results with rigorous sensitivity analyses.
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Affiliation(s)
- Bernd Taschler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, City Oxford, UK
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21
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Liu H, Hu Y, Zhang Y, Zhang H, Gao S, Wang L, Wang T, Han Z, Sun BL, Liu G. Mendelian randomization highlights significant difference and genetic heterogeneity in clinically diagnosed Alzheimer's disease GWAS and self-report proxy phenotype GWAX. Alzheimers Res Ther 2022; 14:17. [PMID: 35090530 PMCID: PMC8800228 DOI: 10.1186/s13195-022-00963-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 01/13/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Until now, Mendelian randomization (MR) studies have investigated the causal association of risk factors with Alzheimer's disease (AD) using large-scale AD genome-wide association studies (GWAS), GWAS by proxy (GWAX), and meta-analyses of GWAS and GWAX (GWAS+GWAX) datasets. However, it currently remains unclear about the consistency of MR estimates across these GWAS, GWAX, and GWAS+GWAX datasets. METHODS Here, we first selected 162 independent educational attainment genetic variants as the potential instrumental variables (N = 405,072). We then selected one AD GWAS dataset (N = 63,926), two AD GWAX datasets (N = 314,278 and 408,942), and three GWAS+GWAX datasets (N = 388,324, 455,258, and 472,868). Finally, we conducted a MR analysis to evaluate the impact of educational attainment on AD risk across these datasets. Meanwhile, we tested the genetic heterogeneity of educational attainment genetic variants across these datasets. RESULTS In AD GWAS dataset, MR analysis showed that each SD increase in years of schooling (about 3.6 years) was significantly associated with 29% reduced AD risk (OR=0.71, 95% CI: 0.60-0.84, and P=1.02E-04). In AD GWAX dataset, MR analysis highlighted that each SD increase in years of schooling significantly increased 84% AD risk (OR=1.84, 95% CI: 1.59-2.13, and P=4.66E-16). Meanwhile, MR analysis suggested the ambiguous findings in AD GWAS+GWAX datasets. Heterogeneity test indicated evidence of genetic heterogeneity in AD GWAS and GWAX datasets. CONCLUSIONS We highlighted significant difference and genetic heterogeneity in clinically diagnosed AD GWAS and self-report proxy phenotype GWAX. Our MR findings are consistent with recent findings in AD genetic variants. Hence, the GWAX and GWAS+GWAX findings and MR findings from GWAX and GWAS+GWAX should be carefully interpreted and warrant further investigation using the AD GWAS dataset.
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Affiliation(s)
- Haijie Liu
- grid.413259.80000 0004 0632 3337Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053 China
| | - Yang Hu
- grid.19373.3f0000 0001 0193 3564School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150080 China
| | - Yan Zhang
- grid.268079.20000 0004 1790 6079Department of Pathology, The Affiliated Hospital of Weifang Medical University, Weifang, 261053 China
| | - Haihua Zhang
- grid.24696.3f0000 0004 0369 153XBeijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, 100069 China
| | - Shan Gao
- grid.24696.3f0000 0004 0369 153XBeijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, 100069 China
| | - Longcai Wang
- grid.268079.20000 0004 1790 6079Department of Anesthesiology, The Affiliated Hospital of Weifang Medical University, Weifang, 261053 China
| | - Tao Wang
- grid.510934.a0000 0005 0398 4153Chinese Institute for Brain Research, Beijing, China
| | - Zhifa Han
- grid.506261.60000 0001 0706 7839State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China
| | - Bao-liang Sun
- grid.415440.0Key Laboratory of Cerebral Microcirculation in Universities of Shandong, Department of Neurology, Second Affiliated Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000 Shandong China
| | - Guiyou Liu
- Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, 100069, China. .,Chinese Institute for Brain Research, Beijing, China. .,Key Laboratory of Cerebral Microcirculation in Universities of Shandong, Department of Neurology, Second Affiliated Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China. .,Beijing Key Laboratory of Hypoxia Translational Medicine, National Engineering Laboratory of Internet Medical Diagnosis and Treatment Technology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
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