scholarly journals A semi-automated pipeline for fulfillment of resource requests from a longitudinal Alzheimer's disease registry

JAMIA Open ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 516-520
Author(s):  
Katelyn A McKenzie ◽  
Suzanne L Hunt ◽  
Genevieve Hulshof ◽  
Dinesh Pal Mudaranthakam ◽  
Kayla Meyer ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Data Management ◽  
Reproducible Research ◽  
University Of Kansas ◽  
Data Set ◽  
Data Accessibility ◽  
Monthly Data ◽  
Variable List ◽  
The University

Abstract Objective Managing registries with continual data collection poses challenges, such as following reproducible research protocols and guaranteeing data accessibility. The University of Kansas (KU) Alzheimer’s Disease Center (ADC) maintains one such registry: Curated Clinical Cohort Phenotypes and Observations (C3PO). We created an automated and reproducible process by which investigators have access to C3PO data. Materials and Methods Data was input into Research Electronic Data Capture. Monthly, data part of the Uniform Data Set (UDS), that is data also collected at other ADCs, was uploaded to the National Alzheimer’s Coordinating Center (NACC). Quarterly, NACC cleaned, curated, and returned the UDS to the KU Data Management and Statistics (DMS) Core, where it was stored in C3PO with other quarterly curated site-specific data. Investigators seeking to utilize C3PO submitted a research proposal and requested variables via the publicly accessible and searchable data dictionary. The DMS Core used this variable list and an automated SAS program to create a subset of C3PO. Results C3PO contained 1913 variables stored in 15 datasets. From 2017 to 2018, 38 data requests were completed for several KU departments and other research institutions. Completing data requests became more efficient; C3PO subsets were produced in under 10 seconds. Discussion The data management strategy outlined above facilitated reproducible research practices, which is fundamental to the future of research as it allows replication and verification to occur. Conclusion We created a transparent, automated, and efficient process of extracting subsets of data from a registry where data was changing daily.

scholarly journals Cognitive Effects of Aerobic Exercise in Alzheimer’s Disease: A Pilot Randomized Controlled Trial

2021 ◽  
pp. 1-12
Author(s):  
Fang Yu ◽  
David M. Vock ◽  
Lin Zhang ◽  
Dereck Salisbury ◽  
Nathaniel W. Nelson ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Older Adults ◽  
Alzheimer's Disease ◽  
Randomized Controlled Trial ◽  
Aerobic Exercise ◽  
Controlled Trial ◽  
Moderate Intensity ◽  
Cognitive Effects ◽  
Data Set ◽  
Randomized Controlled

Background: Aerobic exercise has shown inconsistent cognitive effects in older adults with Alzheimer’s disease (AD) dementia. Objective: To examine the immediate and longitudinal effects of 6-month cycling on cognition in older adults with AD dementia. Methods: This randomized controlled trial randomized 96 participants (64 to cycling and 32 to stretching for six months) and followed them for another six months. The intervention was supervised, moderate-intensity cycling for 20–50 minutes, 3 times a week for six months. The control was light-intensity stretching. Cognition was assessed at baseline, 3, 6, 9, and 12 months using the AD Assessment Scale-Cognition (ADAS-Cog). Discrete cognitive domains were measured using the AD Uniform Data Set battery. Results: The participants were 77.4±6.8 years old with 15.6±2.9 years of education, and 55%were male. The 6-month change in ADAS-Cog was 1.0±4.6 (cycling) and 0.1±4.1 (stretching), which were both significantly less than the natural 3.2±6.3-point increase observed naturally with disease progression. The 12-month change was 2.4±5.2 (cycling) and 2.2±5.7 (control). ADAS-Cog did not differ between groups at 6 (p = 0.386) and 12 months (p = 0.856). There were no differences in the 12-month rate of change in ADAS-Cog (0.192 versus 0.197, p = 0.967), memory (–0.012 versus –0.019, p = 0.373), executive function (–0.020 versus –0.012, p = 0.383), attention (–0.035 versus –0.033, p = 0.908), or language (–0.028 versus –0.026, p = 0.756). Conclusion: Exercise may reduce decline in global cognition in older adults with mild-to-moderate AD dementia. Aerobic exercise did not show superior cognitive effects to stretching in our pilot trial, possibly due to the lack of power.

scholarly journals RESTING HEART RATE MODERATES THE RELATIONSHIP BETWEEN NEUROPSYCHIATRIC SYMPTOMS, MCI, AND ALZHEIMER’S DISEASE

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S641-S641
Author(s):  
Shanna L Burke
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Heart Rate ◽  
Relative Risk ◽  
Neuropsychiatric Symptoms ◽  
Cognitive Status ◽  
Resting Heart Rate ◽  
Data Set ◽  
Increased Risk ◽  
The Relationship

Abstract Little is known about how resting heart rate moderates the relationship between neuropsychiatric symptoms and cognitive status. This study examined the relative risk of NPS on increasingly severe cognitive statuses and examined the extent to which resting heart rate moderates this relationship. A secondary analysis of the National Alzheimer’s Coordinating Center Uniform Data Set was undertaken, using observations from participants with normal cognition at baseline (13,470). The relative risk of diagnosis with a more severe cognitive status at a future visit was examined using log-binomial regression for each neuropsychiatric symptom. The moderating effect of resting heart rate among those who are later diagnosed with mild cognitive impairment (MCI) or Alzheimer’s disease (AD) was assessed. Delusions, hallucinations, agitation, depression, anxiety, elation, apathy, disinhibition, irritability, motor disturbance, nighttime behaviors, and appetite disturbance were all significantly associated (p<.001) with an increased risk of AD, and a reduced risk of MCI. Resting heart rate increased the risk of AD but reduced the relative risk of MCI. Depression significantly interacted with resting heart rate to increase the relative risk of MCI (RR: 1.07 (95% CI: 1.00-1.01), p<.001), but not AD. Neuropsychiatric symptoms increase the relative risk of AD but not MCI, which may mean that the deleterious effect of NPS is delayed until later and more severe stages of the disease course. Resting heart rate increases the relative risk of MCI among those with depression. Practitioners considering early intervention in neuropsychiatric symptomology may consider the downstream benefits of treatment considering the long-term effects of NPS.

scholarly journals MALCOLM EDWIN JEWELL

2010 ◽  
Vol 43 (03) ◽  
pp. 585-587
Author(s):  
Bradley C. Canon
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Political Parties ◽  
Political Science ◽  
The Political ◽  
Legislative Behavior ◽  
Science Department ◽  
University Of Kentucky ◽  
Political Science Department ◽  
The University

Malcolm “Mac” Jewell was a mainstay of the Political Science Department at the University of Kentucky (UK) for 36 years. For that same period and even longer, he was one of the profession's leading researchers in explaining legislative behavior (particularly in the states) and how state political parties worked. Mac retired from UK in 1994 but continued being active in our profession. Around 2004, he began suffering from Alzheimer's disease. He died on February 24, 2010, in Fairfield, Connecticut.

scholarly journals Advanced cognitive impairment among older nursing home residents

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tadeja Gracner ◽  
Patricia W. Stone ◽  
Mansi Agarwal ◽  
Mark Sorbero ◽  
Susan L Mitchell ◽  
...  
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Nursing Home ◽  
Cognitive Impairment ◽  
Mortality Risk ◽  
Advanced Disease ◽  
Nursing Home Residents ◽  
Functional Score ◽  
Data Set

Abstract Background Though work has been done studying nursing home (NH) residents with either advanced Alzheimer’s disease (AD) or Alzheimer’s disease related dementia (ADRD), none have distinguished between them; even though their clinical features affecting survival are different. In this study, we compared mortality risk factors and survival between NH residents with advanced AD and those with advanced ADRD. Methods This is a retrospective observational study, in which we examined a sample of 34,493 U.S. NH residents aged 65 and over in the Minimum Data Set (2011–2013). Incident assessment of advanced disease was defined as the first MDS assessment with severe cognitive impairment (Cognitive Functional Score equals to 4) and diagnoses of AD or ADRD. Demographics, functional limitations, and comorbidities were evaluated as mortality risk factors using Cox models. Survival was characterized with Kaplan-Maier functions. Results Of those with advanced cognitive impairment, 35 % had AD and 65 % ADRD. At the incident assessment of advanced disease, those with AD had better health compared to those with ADRD. Mortality risk factors were similar between groups (shortness of breath, difficulties eating, substantial weight-loss, diabetes mellitus, heart failure, chronic obstructive pulmonary disease, and pneumonia; all p < 0.01). However, stroke and difficulty with transfer (for women) were significant mortality risk factors only for those with advanced AD. Urinary tract infection, and hypertension (for women) only were mortality risk factors for those with advanced ADRD. Median survival was significantly shorter for the advanced ADRD group (194 days) compared to the advanced AD group (300 days). Conclusions There were distinct mortality and survival patterns of NH residents with advanced AD and ADRD. This may help with care planning decisions regarding therapeutic and palliative care.

scholarly journals Data Analysis With Shapley Values For Automatic Subject Selection in Alzheimer's Disease Data Sets Using Interpretable Machine Learning

2021 ◽  
Author(s):  
Louise Bloch ◽  
Christoph M. Friedrich
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Test Data ◽  
Noisy Data ◽  
Training Data ◽  
Data Sets ◽  
Data Set ◽  
Model Interpretation ◽  
Percentage Points ◽  
Shapley Values

Abstract Background: The prediction of whether Mild Cognitive Impaired (MCI) subjects will prospectively develop Alzheimer's Disease (AD) is important for the recruitment and monitoring of subjects for therapy studies. Machine Learning (ML) is suitable to improve early AD prediction. The etiology of AD is heterogeneous, which leads to noisy data sets. Additional noise is introduced by multicentric study designs and varying acquisition protocols. This article examines whether an automatic and fair data valuation method based on Shapley values can identify subjects with noisy data. Methods: An ML-workow was developed and trained for a subset of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The validation was executed for an independent ADNI test data set and for the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) cohort. The workow included volumetric Magnetic Resonance Imaging (MRI) feature extraction, subject sample selection using data Shapley, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) for model training and Kernel SHapley Additive exPlanations (SHAP) values for model interpretation. This model interpretation enables clinically relevant explanation of individual predictions. Results: The XGBoost models which excluded 116 of the 467 subjects from the training data set based on their Logistic Regression (LR) data Shapley values outperformed the models which were trained on the entire training data set and which reached a mean classification accuracy of 58.54 % by 14.13 % (8.27 percentage points) on the independent ADNI test data set. The XGBoost models, which were trained on the entire training data set reached a mean accuracy of 60.35 % for the AIBL data set. An improvement of 24.86 % (15.00 percentage points) could be reached for the XGBoost models if those 72 subjects with the smallest RF data Shapley values were excluded from the training data set. Conclusion: The data Shapley method was able to improve the classification accuracies for the test data sets. Noisy data was associated with the number of ApoEϵ4 alleles and volumetric MRI measurements. Kernel SHAP showed that the black-box models learned biologically plausible associations.

scholarly journals Identification of a simple and novel cut-point based CSF and MRI signature for predicting Alzheimer’s disease progression that reinforces the 2018 NIA-AA research framework

2018 ◽  
Author(s):  
Priya Devanarayan ◽  
Viswanath Devanarayan ◽  
Daniel A. Llano ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Multiple Reaction Monitoring ◽  
Pharmaceutical Research ◽  
Normal Subjects ◽  
Hippocampal Volume ◽  
National Institutes Of Health ◽  
Decision Threshold ◽  
Research Framework ◽  
The University

AbstractThe 2018 NIA-AA research framework proposes a classification system with beta-Amyloid deposition, pathologic Tau, and neurodegeneration (ATN) for the diagnosis and staging of Alzheimer’s Disease (AD). Data from the ADNI (AD neuroimaging initiative) database can be utilized to identify diagnostic signatures for predicting AD progression, and to determine the utility of this NIA-AA research framework. Profiles of 320 peptides from baseline cerebrospinal fluid (CSF) samples of 287 normal, mild cognitive impairment (MCI) and AD subjects followed over a 3-10 year period were measured via multiple reaction monitoring (MRM) mass spectrometry. CSF Aβ42, total-Tau (tTau), phosphorylated-Tau (pTau-181) and hippocampal volume were also measured. From these candidate markers, optimal diagnostic signatures with decision thresholds to separate AD and normal subjects were first identified via unbiased regression and tree-based algorithms. The best performing signature determined via cross-validation was then tested in an independent group of MCI subjects to predict future progression. This multivariate analysis yielded a simple diagnostic signature comprising CSF pTau-181 to Aβ42 ratio, MRI hippocampal volume and a novel PTPRN peptide, with a decision threshold on each marker. When applied to a separate MCI group at baseline, subjects meeting this signature criteria experience 4.3-fold faster progression to AD compared to a 2.2-fold faster progression using only conventional markers. This novel 4-marker signature represents an advance over the current diagnostics based on widely used marker, and is much easier to use in practice than recently published complex signatures. In addition, this signature reinforces the ATN construct from the 2018 NIA-AA research framework.DisclosuresViswanath Devanarayan is an employee of Charles River Laboratories, and as such owns equity in, receives salary and other compensation from Charles River Laboratories.Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.;Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

scholarly journals Treatment of Sleep Disturbance May Reduce the Risk of Future Probable Alzheimer’s Disease

2018 ◽  
Vol 31 (2) ◽  
pp. 322-342 ◽  
Author(s):  
Shanna L. Burke ◽  
Tianyan Hu ◽  
Christine E. Spadola ◽  
Aaron Burgess ◽  
Tan Li ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sleep Disturbance ◽  
Proportional Hazards ◽  
Secondary Analysis ◽  
Cox Proportional Hazards ◽  
Data Set ◽  
Proportional Hazards Regression ◽  
Increased Risk ◽  
Research Questions

Objective: This study explored two research questions: (a) Does sleep medication neutralize or provide a protective effect against the hazard of Alzheimer’s disease (AD)? (b) Do apolipoprotein (APOE) e4 carriers reporting a sleep disturbance experience an increased risk of AD? Method: This study is a secondary analysis of the National Alzheimer’s Coordinating Center’s Uniform Data Set ( n = 6,782) using Cox proportional hazards regression. Results: Sleep disturbance was significantly associated with eventual AD development. Among the subset of participants taking general sleep medications, no relationship between sleep disturbance and eventual AD was observed. Among individuals not taking sleep medications, the increased hazard between the two variables remained. Among APOE e4 carriers, sleep disturbance and AD were significant, except among those taking zolpidem. Discussion: Our findings support the emerging link between sleep disturbance and AD. Our findings also suggest a continued need to elucidate the mechanisms that offer protective factors against AD development.

Immune-Related Hub Genes and the Competitive Endogenous RNA Network in Alzheimer’s Disease

2020 ◽  
Vol 77 (3) ◽  
pp. 1255-1265
Author(s):  
Hui Xu ◽  
Jianping Jia
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Data Set ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Immune Phenomena ◽  
Competitive Endogenous Rna

Background: The pathogenesis of Alzheimer’s disease (AD) involves various immune-related phenomena; however, the mechanisms underlying these immune phenomena and the potential hub genes involved therein are unclear. An understanding of AD-related immune hub genes and regulatory mechanisms would help develop new immunotherapeutic targets. Objective: The aim of this study was to explore the hub genes and the mechanisms underlying the regulation of competitive endogenous RNA (ceRNA) in immune-related phenomena in AD pathogenesis. Methods: We used the GSE48350 data set from the Gene Expression Omnibus database and identified AD immune-related differentially expressed RNAs (DERNAs). We constructed protein–protein interaction (PPI) networks for differentially expressed mRNAs and determined the degree for screening hub genes. By determining Pearson’s correlation coefficient and using StarBase, DIANA-LncBase, and Human MicroRNA Disease Database (HMDD), the AD immune-related ceRNA network was generated. Furthermore, we assessed the upregulated and downregulated ceRNA subnetworks to identify key lncRNAs. Results: In total, 552 AD immune-related DERNAs were obtained. Twenty hub genes, including PIK3R1, B2M, HLA-DPB1, HLA-DQB1, PIK3CA, APP, CDC42, PPBP, C3AR1, HRAS, PTAFR, RAB37, FYN, PSMD1, ACTR10, HLA-E, ARRB2, GGH, ALDOA, and VAMP2 were identified on PPI network analysis. Furthermore, upon microRNAs (miRNAs) inhibition, we identified LINC00836 and DCTN1-AS1 as key lncRNAs regulating the aforementioned hub genes. Conclusion: AD-related immune hub genes include B2M, FYN, PIK3R1, and PIK3CA, and lncRNAs LINC00836 and DCTN1-AS1 potentially contribute to AD immune-related phenomena by regulating AD-related hub genes.

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