scholarly journals Association between polygenic risk for Alzheimer’s disease, brain structure and cognitive abilities in UK Biobank

2021 ◽  
Author(s):  
Rachana Tank ◽  
Joey Ward ◽  
Kristin E. Flegal ◽  
Daniel J. Smith ◽  
Mark E. S. Bailey ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Fluid Intelligence ◽  
Late Onset ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Healthy Adults ◽  
Uk Biobank ◽  
Polygenic Risk ◽  
Structural Brain

AbstractPrevious studies testing associations between polygenic risk for late-onset Alzheimer’s disease (LOAD-PGR) and brain magnetic resonance imaging (MRI) measures have been limited by small samples and inconsistent consideration of potential confounders. This study investigates whether higher LOAD-PGR is associated with differences in structural brain imaging and cognitive values in a relatively large sample of non-demented, generally healthy adults (UK Biobank). Summary statistics were used to create PGR scores for n = 32,790 participants using LDpred. Outcomes included 12 structural MRI volumes and 6 concurrent cognitive measures. Models were adjusted for age, sex, body mass index, genotyping chip, 8 genetic principal components, lifetime smoking, apolipoprotein (APOE) e4 genotype and socioeconomic deprivation. We tested for statistical interactions between APOE e4 allele dose and LOAD-PGR vs. all outcomes. In fully adjusted models, LOAD-PGR was associated with worse fluid intelligence (standardised beta [β] = −0.080 per LOAD-PGR standard deviation, p = 0.002), matrix completion (β = −0.102, p = 0.003), smaller left hippocampal total (β = −0.118, p = 0.002) and body (β = −0.069, p = 0.002) volumes, but not other hippocampal subdivisions. There were no significant APOE x LOAD-PGR score interactions for any outcomes in fully adjusted models. This is the largest study to date investigating LOAD-PGR and non-demented structural brain MRI and cognition phenotypes. LOAD-PGR was associated with smaller hippocampal volumes and aspects of cognitive ability in healthy adults and could supplement APOE status in risk stratification of cognitive impairment/LOAD.

scholarly journals Association between polygenic risk for Alzheimer’s disease, brain structure and cognitive abilities in UK Biobank.

2021 ◽  
Author(s):  
Rachana Tank ◽  
Joey Ward ◽  
Kristin E. Flegal ◽  
Daniel Smith ◽  
Mark E.S. Bailey ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Fluid Intelligence ◽  
Late Onset ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Healthy Adults ◽  
Uk Biobank ◽  
Polygenic Risk ◽  
Structural Brain

Background and purpose: Previous studies testing associations between polygenic risk for late-onset Alzheimer’s disease (LOAD-PGR) and brain magnetic resonance imaging (MRI) measures have been limited by small samples and inconsistent consideration of potential confounders. This study investigates whether higher LOAD-PGR is associated with differences in structural brain imaging and cognitive values in a relatively large sample of non-demented, generally healthy adults (UK Biobank). Method: Summary statistics were used to create PGR scores for n=32,790 participants using LDpred. Outcomes included 12 structural MRI volumes and 6 concurrent cognitive measures. Models were adjusted for age, sex, body mass index, genotyping chip, 8 principal components, lifetime smoking, apolipoprotein (APOE) e4 genotype and socioeconomic deprivation. We tested for statistical interactions between APOE e4 allele dose and LOAD-PGR vs. all outcomes. Results: In fully adjusted models, LOAD-PGR was associated with worse fluid intelligence (standardised beta [β] = -0.080 per LOAD-PGR standard deviation, p = 0.002), matrix completion (β = -0.102, p = 0.003), smaller left hippocampal total (β = -0.118, p = 0.002) and body (β = -0.069, p = 0.002) volumes, but not other hippocampal subdivisions. There were no significant APOE x LOAD-PGR score interactions for any outcomes in fully adjusted models. Discussion: This is the largest study to date investigating LOAD-PGR and non-demented structural brain MRI and cognition phenotypes. LOAD-PGR was associated with smaller hippocampal volumes and aspects of cognitive ability in healthy adults, and could supplement APOE status in risk stratification of cognitive impairment/LOAD.

scholarly journals Genetic overlap between Alzheimer’s disease and depression mapped onto the brain

2021 ◽  
Author(s):  
Jennifer Monereo Sánchez ◽  
Miranda T. Schram ◽  
Oleksandr Frei ◽  
Kevin O’Connell ◽  
Alexey A. Shadrin ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genome Wide Association Study ◽  
Brain Mri ◽  
Genetic Correlations ◽  
Gaussian Mixture ◽  
Brain Morphology ◽  
Uk Biobank ◽  
Genetic Overlap ◽  
The Uk

ABSTRACTBackgroundAlzheimer’s disease (AD) and depression are debilitating brain disorders that are often comorbid. Shared brain mechanisms have been implicated, yet findings are inconsistent, reflecting the complexity of the underlying pathophysiology. As both disorders are (partly) heritable, characterizing their genetic overlap may provide etiological clues. While previous studies have indicated negligible genetic correlations, this study aims to expose the genetic overlap that may remain hidden due to mixed directions of effects.MethodsWe applied Gaussian mixture modelling, through MiXeR, and conjunctional false discovery rate (cFDR) analysis, through pleioFDR, to genome-wide association study (GWAS) summary statistics of AD (n=79,145) and depression (n=450,619). The effects of identified overlapping loci on AD and depression were tested in 403,029 participants of the UK Biobank (mean age 57.21 52.0% female), and mapped onto brain morphology in 30,699 individuals with brain MRI data.ResultsMiXer estimated 98 causal genetic variants overlapping between the two disorders, with 0.44 concordant directions of effects. Through pleioFDR, we identified a SNP in the TMEM106B gene, which was significantly associated with AD (B=-0.002, p=9.1×10−4) and depression (B=0.007, p=3.2×10−9) in the UK Biobank. This SNP was also associated with several regions of the corpus callosum volume anterior (B>0.024, p<8.6×10−4), third ventricle volume ventricle (B=-0.025, p=5.0×10−6), and inferior temporal gyrus surface area (B=0.017, p=5.3×10−4).DiscussionOur results indicate there is substantial genetic overlap, with mixed directions of effects, between AD and depression. These findings illustrate the value of biostatistical tools that capture such overlap, providing insight into the genetic architectures of these disorders.

scholarly journals Associations between Alzheimer’s disease polygenic risk scores and hippocampal subfield volumes in 17,161 UK Biobank participants

2021 ◽  
Vol 98 ◽  
pp. 108-115
Author(s):  
Heidi Foo ◽  
Anbupalam Thalamuthu ◽  
Jiyang Jiang ◽  
Forrest Koch ◽  
Karen A. Mather ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Risk Scores ◽  
Uk Biobank ◽  
Polygenic Risk

scholarly journals Case Report: Usefulness of Biomarkers for Alzheimer's Disease in Two Cases With Very-Late-Onset Schizophrenia-Like Psychosis

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuto Satake ◽  
Hideki Kanemoto ◽  
Kenji Yoshiyama ◽  
Ryoko Nakahama ◽  
Keiko Matsunaga ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Psychotic Disorders ◽  
Late Onset ◽  
Single Photon ◽  
Photon Emission ◽  
Brain Magnetic Resonance Imaging ◽  
Later Life ◽  
Emission Computed Tomography ◽  
Amyloid Pet

The association between primary psychotic disorders emerging in later life and neurodegenerative diseases, including Alzheimer's disease (AD), is controversial. We present two female non-demented cases of psychosis with onset above the age of 60 years. Cases 1 and 2 were aged was 68 and 81 years, respectively. They suffered from persecutory delusions and scored 28 on the Mini-Mental State Examination (MMSE) at the first examination. Although detailed neuropsychological tests detected amnesia, they had preserved daily life function. Brain magnetic resonance imaging, N-isopropyl-p-[123I] iodoamphetamine (123I-IMP) single-photon emission computed tomography, and cardiac [123I]-metaiodobenzylguanidine (123I-MIBG) scintigraphy showed no specific abnormalities in either case. We diagnosed them with very-late-onset schizophrenia-like psychosis (VLOSLP) because there was no evidence that their psychoses were derived from organic diseases or affective disorders. Upon close inspection, the AD biomarkers, cerebrospinal fluid (CSF) testing and Florbetapir F 18 positron emission tomography (PET), were positive in Case 1 and negative in Case 2. Case 1 scored 25 1 year later and 23 2 years later on the MMSE and was finally diagnosed as AD dementia. These two cases suggest that some clinically diagnosed VLOSLPs may be a prodromal AD. Although VLOSLP is a disease entity supposed to be a primary psychotic disorder, some are probably secondary psychosis with insidious neurodegeneration. Advanced biomarkers such as amyloid PET and CSF may contribute to the detection of secondary psychosis from clinically diagnosed VLOSLP.

scholarly journals Resting state brain signal complexity of young healthy adults reflects genetic risk for developing Alzheimer’s Disease

2020 ◽  
Author(s):  
Xiaojing Li ◽  
Yadwinder Kaur ◽  
Oliver Wilhelm ◽  
Martin Reuter ◽  
Christian Montag ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Performance ◽  
Time Scales ◽  
Fluid Intelligence ◽  
Brain Activity ◽  
Apoe Genotype ◽  
Healthy Adults ◽  
Signal Complexity ◽  
E4 Allele

AbstractThe e4 allele of the APOE gene is strongly associated with impaired brain functionality and cognitive decline in humans at older age. It is controversial whether and how the APOE e4 allele is affecting brain activity among young healthy individuals and how such effects may contribute to individual differences in cognitive performance. Signal complexity is a critical aspect of brain activity that has been shown to be associated with brain function. In this study, we analyzed multiscale entropy (MSE) of EEG signals among young healthy adults as an indicator of brain signal complexity and investigated how MSE is predicted by APOE genotype groups. Furthermore, by means of structural equation modeling, we investigated whether MSE predicts fluid intelligence. Results indicate larger MSE in young healthy e4 carriers across all time scales. Moreover, better fluid intelligence (gf) is associated with smaller MSE at low time scales and larger MSE at higher scales. However, MSE does not account for better cognitive performance among APOE e4 carriers by mediating the APOE genotype effect on fluid intelligence. The present results shed further light on the neural mechanisms underlying gene-behavior association relevant for Alzheimer’s Disease risk.

scholarly journals Polygenic risk score of sporadic late-onset Alzheimer's disease reveals a shared architecture with the familial and early-onset forms

2017 ◽  
Vol 14 (2) ◽  
pp. 205-214 ◽  
Author(s):  
Carlos Cruchaga ◽  
Jorge L. Del-Aguila ◽  
Benjamin Saef ◽  
Kathleen Black ◽  
Maria Victoria Fernandez ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Risk Score ◽  
Early Onset ◽  
Late Onset ◽  
Polygenic Risk Score ◽  
Polygenic Risk

O1-03-06: Polygenic Risk of Alzheimer's Disease and Neurodegenerative Related Traits and Their Association With Cognitive Function: Uk Biobank and the Dementias Platform Uk

2016 ◽  
Vol 12 ◽  
pp. P179-P179 ◽  
Author(s):  
Chloe Fawns-Ritchie ◽  
Saskia P. Hagenaars ◽  
Sarah E. Harris ◽  
Gail Davies ◽  
David C. Liewald ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Function ◽  
Uk Biobank ◽  
Polygenic Risk

scholarly journals Associations between Alzheimer’s disease polygenic risk scores and hippocampal subfield volumes in 17,161 UK Biobank participants

2020 ◽  
Author(s):  
Heidi Foo ◽  
Anbupalam Thalamuthu ◽  
Jiyang Jiang ◽  
Forrest Koch ◽  
Karen A. Mather ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Layer ◽  
Community Dwelling ◽  
Granule Cell Layer ◽  
Risk Scores ◽  
Uniform Structure ◽  
Polygenic Risk Score ◽  
Uk Biobank ◽  
Polygenic Risk

AbstractHippocampal volume is an important biomarker of Alzheimer’s disease (AD), and genetic risk of AD is associated with hippocampal atrophy. However, the hippocampus is not a uniform structure and has a number of subfields, the associations of which with age, sex, and polygenic risk score for AD (PRSAD) have been inadequately investigated. We examined these associations in 17,161 cognitively normal UK Biobank participants (44-80 years). Age was negatively associated with all the hippocampal subfield volumes and females had smaller volumes than men. Higher PRSAD was associated with lower volumes in the bilateral whole hippocampus, hippocampal-amygdala-transition-area (HATA), and hippocampal tail; right subiculum; left cornu ammonis (CA)1, CA4, molecular layer, and granule cell layer of dentate gyrus (CG-DG), with associations being greater on the left side. Older individuals (median age 63 years, n=8984) showed greater subfield vulnerability to high PRSAD compared to the younger group (n=8177), but the effect did not differ by sex. The pattern of subfield involvement in relation to the PRSAD in community dwelling healthy individuals sheds additional light on the pathogenesis of AD.

scholarly journals Incidence of Epilepsy among elderly Sudanese patients with Alzheimer’s disease: A prospective cohort study on 480 patients, Khartoum, Sudan, 2019

2021 ◽  
Author(s):  
Abbasher Hussien Mohamed Ahmed ◽  
Khabab Abbasher Hussien Mohamed Ahmed ◽  
Mohammed Eltahier Abdalla Omer ◽  
Amira Siddig
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cohort Study ◽  
Prospective Cohort Study ◽  
Prospective Cohort ◽  
Late Onset ◽  
Brain Mri ◽  
Cerebral Atrophy ◽  
Incidence Rates ◽  
Increased Risk

Abstract Background: An increased prevalence of epilepsy had been documented with dementia. Alzheimer’s disease and epilepsy often coexist. Objective: The aim of this study to assess incidence rates of epilepsy among Sudanese patients with Alzheimer’s disease. Methods: This is a prospective cohort study. We followed 480 patients aged more than 65 years with diagnosis of Alzheimer’s disease between May 2006 and May 2019 looking for coexist epilepsy. Results: Regarding Alzheimer’s disease female were affected more than male (60%). 10% of our patients have epilepsy. Generalize epilepsy was the most common type (62%). Epilepsy was more common with late onset Alzheimer’s disease. Abnormal EEG was detected in 20% of our studied group. Abnormal Brain MRI in form of cerebral atrophy was observed in 60 % of patients with Alzheimer’s disease and epilepsy. Conclusion: Patients with Alzheimer’s disease have an increased risk of developing epilepsy. There is strong relation between disease duration and development of epilepsy.

scholarly journals Diagnosis and Classification of Alzheimer's Disease by Using a Convolution Neural Network Algorithm

2021 ◽  
Author(s):  
Mosleh Hmoud Al-Adhaileh
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Stage ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Classification Systems ◽  
Detection Accuracy ◽  
Learning Approaches

Abstract Alzheimer's disease (AD) is a high-risk and atrophic neurological illness that slowly and gradually destroys brain cells (i.e. neurons). As the most common type of amentia, AD affects 60–65% of all people with amentia and poses major health dangers to middle-aged and elderly people. For classification of AD in the early stage, classification systems and computer-aided diagnostic techniques have been developed. Previously, machine learning approaches were applied to develop diagnostic systems by extracting features from neural images. Currently, deep learning approaches have been used in many real-time medical imaging applications. In this study, two deep neural network techniques, AlexNet and Restnet50, were applied for the classification and recognition of AD. The data used in this study to evaluate and test the proposed model included those from brain magnetic resonance imaging (MRI) images collected from the Kaggle website. A convolutional neural network (CNN) algorithm was applied to classify AD efficiently. CNNs were pre-trained using AlexNet and Restnet50 transfer learning models. The results of this experimentation showed that the proposed method is superior to the existing systems in terms of detection accuracy. The AlexNet model achieved outstanding performance based on five evaluation metrics (accuracy, F1 score, precision, sensitivity and specificity) for the brain MRI datasets. AlexNet displayed an accuracy of 94.53%, specificity of 98.21%, F1 score of 94.12% and sensitivity of 100%, outperforming Restnet50. The proposed method can help improve CAD methods for AD in medical investigations.

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