scholarly journals Breast Cancer, Alzheimer’s Disease, and APOE4 Allele in the UK Biobank Cohort

2021 ◽  
Vol 5 (1) ◽  
pp. 49-53
Author(s):  
Steven Lehrer ◽  
Peter H. Rheinstein
Keyword(s):  
Breast Cancer ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Apoe Genotype ◽  
Uk Biobank ◽  
Breast Cancer Patients ◽  
Apoe4 Allele ◽  
Significant Difference ◽  
The Uk ◽  
Cognitive Problems

Background: Cognitive problems are common in breast cancer patients. The apolipoprotein E4 (APOE4) gene, a risk factor for Alzheimer’s disease (AD), may be associated with cancer-related cognitive decline. Objective: To further evaluate the effects of the APOE4 allele, we studied a cohort of patients from the UK Biobank (UKB) who had breast cancer; some also had AD. Methods: Our analysis included all subjects with invasive breast cancer. Single nucleotide polymorphism (SNP) data for rs 429358 and rs 7412 was used to determine APOE genotypes. Cognitive function as numeric memory was assessed with an online test (UKB data field 20240). Results: We analyzed data from 2,876 women with breast cancer. Of the breast cancer subjects, 585 (20%) carried the APOE4 allele. Numeric memory scores were significantly lower in APOE4 carriers and APOE4 homozygotes than non-carriers (p = 0.046). 34 breast cancer subjects (1.1%) had AD. There was no significant difference in survival among genotypes ɛ3/ɛ3, ɛ3/ɛ4, and ɛ4/ɛ4. Conclusion: UKB data suggest that cognitive problems in women with breast cancer are, for the most part, mild, compared with other sequelae of the disease. AD, the worst cognitive problem, is relatively rare (1.1%) and, when it occurs, APOE genotype has little impact on survival.

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 Machine Learning Algorithms and Whole Exome Sequencing Data from Breast Cancer Patients in the UK Biobank Predict Survival

2020 ◽  
Author(s):  
Bum-Sup Jang ◽  
In Ah Kim
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Overall Survival ◽  
Cancer Patients ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Uk Biobank ◽  
Breast Cancer Patients ◽  
Gene Set ◽  
The Uk

Abstract Background: Using by machine learning algorithms, we aimed to identify the mutated gene set from the whole exome sequencing (WES) data of blood in the cancer, which is associated with overall survival in breast cancer patients.Methods: WES data from 1,181 female breast cancer patients within the UK Biobank cohort was collected. The number of mutations for each gene was summed and defined as the blood-based mutation burden per patient. Using by Long short-term memory (LSTM) machine learning algorithm and a XGBoost—a gradient-boosted tree algorithm, we developed the model to predict patient overall survival. Results: From the UK biobank-breast cancer cohort, most altered genes in blood samples were related with the TP53 pathway. In the LSTM model, the minimum 50 genes were found to predict high vs. low mutation burden. In the XGBoost survival model, the gene-set could predict overall survival showing the concordance index of 0.75 and the scaled Brier-score of 0.146 from the held-out testing set (20%, N=236). In older patients (≥ 56 years), the high mutation group based on this gene-set showed inferior overall survival compared to the low mutation group (log-rank test, P=0.042)Conclusion: The machine learning algorithms revealed the gene-signature in the UK biobank breast cancer cohort. Mutational burden observed in blood was associated with overall survival in relatively old patients. This gene-signature should be verified in prospective setting.

scholarly journals Heterogeneous effects of genetic risk for Alzheimer’s disease on the phenome

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hei Man Wu ◽  
Alison M. Goate ◽  
Paul F. O’Reilly
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genetic Risk ◽  
Familial Risk ◽  
Psychosocial Health ◽  
Uk Biobank ◽  
Multiple Forms ◽  
Heterogeneous Effects ◽  
The Difference ◽  
The Uk

AbstractHere we report how four major forms of Alzheimer’s disease (AD) genetic risk—APOE-ε4, APOE-ε2, polygenic risk and familial risk—are associated with 273 traits in ~500,000 individuals in the UK Biobank. The traits cover blood biochemistry and cell traits, metabolic and general health, psychosocial health, and cognitive function. The difference in the profile of traits associated with the different forms of AD risk is striking and may contribute to heterogenous presentation of the disease. However, we also identify traits significantly associated with multiple forms of AD genetic risk, as well as traits showing significant changes across ages in those at high risk of AD, which may point to their potential roles in AD etiology. Finally, we highlight how survivor effects, in particular those relating to shared risks of cardiovascular disease and AD, can generate associations that may mislead interpretation in epidemiological AD studies. The UK Biobank provides a unique opportunity to powerfully compare the effects of different forms of AD genetic risk on the phenome in the same cohort.

scholarly journals Sex and APOE Genotype Differences Related to Statin Use in The Aging Population

2020 ◽  
Author(s):  
Arianna Dagliati ◽  
Niels Peek ◽  
Roberta Diaz Brinton ◽  
Nophar Geifman
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Function ◽  
Metabolic Diseases ◽  
Survival Rates ◽  
Apoe Genotype ◽  
Specific Area ◽  
Ageing Population ◽  
Uk Biobank ◽  
Statin Use

Abstract Background. Significant evidence suggests that the cholesterol-lowering statins can effect cognitive function, and reduce the risk for Alzheimer’s disease and dementia. These potential effects may be constrained by specific combinations of an individual’s sex and Apolipoprotein E (APOE) genotype. Methods. Here we examine data from 252,327 UK BioBank participants, aged 55 or over, and compare the effects of statin use in males and females. We identified that in this population, males were older, had a higher level of education, better cognitive scores, higher incidence of cardiovascular and metabolic diseases, and a higher rate of statin use.Results. We observed that males and those participants with an APOE4 (E4 variant of APOE) positive genotype had higher probabilities of being treated with statins; while participants with an Alzheimer’s diagnosis had slightly lower probabilities. We found that use of statins was not significantly associated with overall higher rates of survival. However, when considering the interaction of statin use with sex, the results suggest higher survival rates in males treated with statins. Finally, examination of cognitive function indicates a potential beneficial effect of statins, however this is limited to APOE4 positive genotypes. Conclusions. Our evaluation of the ageing population in a large cohort from the UK BioBank confirms sex and APOE genotype as funda mental risk stratifiers for Alzheimer’s disease and cognitive function, furthermore it extends them to the specific area of statin use, clarifying their specific interactions with treatments.

scholarly journals Identifying healthy individuals with Alzheimer neuroimaging phenotypes in the UK Biobank

2022 ◽  
Author(s):  
Tiago Azevedo ◽  
Richard A.I. Bethlehem ◽  
David J. Whiteside ◽  
Nol Swaddiwudhipong ◽  
James B. Rowe ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disease ◽  
Fluid Intelligence ◽  
Structural Mri ◽  
Critical Issue ◽  
Healthy Population ◽  
Uk Biobank ◽  
Sporadic Disease ◽  
The Uk

Identifying prediagnostic neurodegenerative disease is a critical issue in neurodegenerative disease research, and Alzheimer's disease (AD) in particular, to identify populations suitable for preventive and early disease modifying trials. Evidence from genetic studies suggest the neurodegeneration of Alzheimer's disease measured by brain atrophy starts many years before diagnosis, but it is unclear whether these changes can be detected in sporadic disease. To address this challenge we train a Bayesian machine learning neural network model to generate a neuroimaging phenotype and AD-score representing the probability of AD using structural MRI data in the Alzheimer's Disease Neuroimaging Cohort (cut-off 0.5, AUC 0.92, PPV 0.90, NPV 0.93). We go on to validate the model in an independent real world dataset of the National Alzheimer's Coordinating Centre (AUC 0.74, PPV 0.65, NPV 0.80), and demonstrate correlation of the AD-score with cognitive scores in those with an AD-score above 0.5. We then apply the model to a healthy population in the UK Biobank study to identify a cohort at risk for Alzheimer's disease. This cohort have a cognitive profile in keeping with Alzheimer's disease, with strong evidence for poorer fluid intelligence, and with some evidence of poorer performance on tests of numeric memory, reaction time, working memory and prospective memory. We found some evidence in the AD-score positive cohort for modifiable risk factors of hypertension and smoking. This approach demonstrates the feasibility of using AI methods to identify a potentially prediagnostic population at high risk for developing sporadic Alzheimer's disease.

scholarly journals Characterizing brain age in the Alzheimer's disease connectome project using a deep neural network pre‐trained on the UK Biobank

2021 ◽  
Vol 17 (S5) ◽  
Author(s):  
Nagesh Adluru ◽  
Veena A. Nair ◽  
Vivek Prabhakaran ◽  
Vishnu Bashyam ◽  
Shi‐Jiang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Neural Network ◽  
Uk Biobank ◽  
The Uk ◽  
Brain Age

scholarly journals Exploration of Alzheimer's Disease MRI Biomarkers Using APOE4 Carrier Status in the UK Biobank

2021 ◽  
Author(s):  
Jingnan Du ◽  
Zhaowen Liu ◽  
Lindsay C Hanford ◽  
Kevin M Anderson ◽  
Jianfeng Feng ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Large Scale ◽  
Vascular Dysfunction ◽  
Hippocampal Volume ◽  
Carrier Status ◽  
Amyloid Angiopathy ◽  
Uk Biobank ◽  
The Uk

Large-scale datasets enable novel strategies to refine and discover relations among biomarkers of disease. Here 30,863 individuals ages 44-82 from the UK Biobank were analyzed to explore MRI biomarkers associated with Alzheimer's disease (AD) genetic risk as contrast to general effects of aging. Individuals homozygotic for the E4 variant of apolipoprotein E (APOE4) overlapped non-carriers in their 50s but demonstrated neurodegenerative effects on the hippocampal system beginning in the seventh decade (reduced hippocampal volume, entorhinal thickness, and hippocampal cingulum integrity). Phenome-wide exploration further nominated the posterior thalamic radiation (PTR) as having a strong effect, as well as multiple diffusion MRI (dMRI) and white matter measures consistent with vascular dysfunction. Effects on the hippocampal system and white matter could be dissociated in the homozygotic APOE4 carriers supporting separation between AD and cerebral amyloid angiopathy (CAA) patterns. These results suggest new ways to combine and interrogate measures of neurodegeneration.

scholarly journals Variation in the concentration and regional distribution of magnetic nanoparticles in human brains, with and without Alzheimer’s disease, from the UK

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jessica Hammond ◽  
Barbara A. Maher ◽  
Imad A. M. Ahmed ◽  
David Allsop
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Nanoparticles ◽  
Senile Plaques ◽  
Regional Distribution ◽  
The Elderly ◽  
Brain Regions ◽  
Significant Difference ◽  
Fresh Frozen ◽  
The Uk

AbstractThe presence of magnetic nanoparticles (MNPs) in the human brain was attributed until recently to endogenous formation; associated with a putative navigational sense, or with pathological mishandling of brain iron within senile plaques. Conversely, an exogenous, high-temperature source of brain MNPs has been newly identified, based on their variable sizes/concentrations, rounded shapes/surface crystallites, and co-association with non-physiological metals (e.g., platinum, cobalt). Here, we examined the concentration and regional distribution of brain magnetite/maghemite, by magnetic remanence measurements of 147 samples of fresh/frozen tissues, from Alzheimer’s disease (AD) and pathologically-unremarkable brains (80–98 years at death) from the Manchester Brain Bank (MBB), UK. The magnetite/maghemite concentrations varied between individual cases, and different brain regions, with no significant difference between the AD and non-AD cases. Similarly, all the elderly MBB brains contain varying concentrations of non-physiological metals (e.g. lead, cerium), suggesting universal incursion of environmentally-sourced particles, likely across the geriatric blood–brain barrier (BBB). Cerebellar Manchester samples contained significantly lower (~ 9×) ferrimagnetic content compared with those from a young (29 years ave.), neurologically-damaged Mexico City cohort. Investigation of younger, variably-exposed cohorts, prior to loss of BBB integrity, seems essential to understand early brain impacts of exposure to exogenous magnetite/maghemite and other metal-rich pollution particles.

Characteristics of Insulin-degrading Enzyme in Alzheimer's Disease: A Meta-Analysis

2018 ◽  
Vol 15 (7) ◽  
pp. 610-617 ◽  
Author(s):  
Huifeng Zhang ◽  
Dan Liu ◽  
Huanhuan Huang ◽  
Yujia Zhao ◽  
Hui Zhou
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Enzyme Activity ◽  
Protein Level ◽  
Senile Plaque ◽  
Meta Analysis ◽  
Cochrane Library ◽  
Insulin Degrading Enzyme ◽  
Degrading Enzyme ◽  
Significant Difference

Background: β-amyloid (Aβ) accumulates abnormally to senile plaque which is the initiator of Alzheimer's disease (AD). As one of the Aβ-degrading enzymes, Insulin-degrading enzyme (IDE) remains controversial for its protein level and activity in Alzheimer's brain. Methods: The electronic databases PubMed, EMBASE, The Cochrane Library, OVID and Sinomed were systemically searched up to Sep. 20th, 2017. And the published case-control or cohort studies were retrieved to perform the meta-analysis. Results: Seven studies for IDE protein level (AD cases = 293; controls = 126), three for mRNA level (AD cases = 138; controls = 81), and three for enzyme activity (AD cases = 123; controls = 75) were pooling together. The IDE protein level was significantly lower in AD cases than in controls (SMD = - 0.47, 95% CI [-0.69, -0.24], p < 0.001), but IDE mRNA and enzyme activity had no significant difference (SMD = 0.02, 95% CI [-0.40, 0.43] and SMD = 0.06, 95% CI [-0.41, 0.53] respectively). Subgroup analyses found that IDE protein level was decreased in both cortex and hippocampus of AD cases (SMD = -0.43, 95% CI [-0.71, -0.16], p = 0.002 and SMD = -0.53, 95% CI [-0.91, -0.15], p = 0.006 respectively). However, IDE mRNA was higher in cortex of AD cases (SMD = 0.71, 95% CI [0.14, 1.29], p = 0.01), not in hippocampus (SMD = -0.26, 95% CI [-0.58, 0.06]). Conclusions: Our results indicate that AD patients may have lower IDE protease level. Further relevant studies are still needed to verify whether IDE is one of the factors affecting Aβ abnormal accumulation and throw new insights for AD detection or therapy.

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