Enhanced learning and retention of medical information in Alzheimer’s disease after differential outcomes training

The stigma attached to a diagnosis of Alzheimer’s Disease or a related dementia is enormous, and those living with dementia often speak of the negative, and almost immediate, social impact of the disease. According to Alzheimer’s Disease International, there were approximately 50 million people worldwide living with dementia in 2017 and this number could reach 131.5 million by 2050 (n.d.). The social isolation that affects many people living with dementia is best combatted by knowledge and understanding. There are many ways that libraries can put their mission statements into action with regard to this devastating disease. People living with dementia are coming into libraries every day. Library staff need training to recognize those who may be affected and to develop effective communication techniques to meet their special needs. In addition to purchasing books and other materials about the disease and how to cope with it, libraries can help those living with dementia and their caregivers find medical information available from underused sources such as Medline Plus from the National Library of Medicine. Libraries are uniquely suited to host educational events and community discussions. Outside organizations such as the Alzheimer’s Association can provide informational sessions held in the library. There are also several model projects developed by libraries across the country that demonstrate how libraries can provide direct programming and services to those living with dementia. Libraries can play a significant role in reducing social isolation among those living with dementia and improving the quality of their lives.

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A metabolite-based machine learning approach to diagnose Alzheimer’s-type dementia in blood: Results from the European Medical Information Framework for Alzheimer’s Disease biomarker discovery cohort

10.1101/19007146 ◽

2019 ◽

Author(s):

Daniel Stamate ◽

Min Kim ◽

Petroula Proitsi ◽

Sarah Westwood ◽

Alison Baird ◽

...

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Medical Information ◽

Biomarker Discovery ◽

Gradient Boosting ◽

Plasma Metabolites ◽

Csf Biomarkers ◽

Extreme Gradient Boosting ◽

And Gender

AbstractINTRODUCTIONMachine learning (ML) may harbor the potential to capture the metabolic complexity in Alzheimer’s Disease (AD). Here we set out to test the performance of metabolites in blood to categorise AD when compared to CSF biomarkers.METHODSThis study analysed samples from 242 cognitively normal (CN) people and 115 with AD-type dementia utilizing plasma metabolites (n=883). Deep Learning (DL), Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were used to differentiate AD from CN. These models were internally validated using Nested Cross Validation (NCV).RESULTSOn the test data, DL produced the AUC of 0.85 (0.80-0.89), XGBoost produced 0.88 (0.86-0.89) and RF produced 0.85 (0.83-0.87). By comparison, CSF measures of amyloid, p-tau and t-tau (together with age and gender) produced with XGBoost the AUC values of 0.78, 0.83 and 0.87, respectively.DISCUSSIONThis study showed that plasma metabolites have the potential to match the AUC of well-established AD CSF biomarkers in a relatively small cohort. Further studies in independent cohorts are needed to validate whether this specific panel of blood metabolites can separate AD from controls, and how specific it is for AD as compared with other neurodegenerative disorders

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Sex-Specific Metabolic Pathways Were Associated with Alzheimer’s Disease (AD) Endophenotypes in the European Medical Information Framework for AD Multimodal Biomarker Discovery Cohort

Biomedicines ◽

10.3390/biomedicines9111610 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1610

Author(s):

Jin Xu ◽

Rebecca Green ◽

Min Kim ◽

Jodie Lord ◽

Amera Ebshiana ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Metabolic Pathways ◽

Medical Information ◽

Biomarker Discovery ◽

Plasma Metabolites ◽

Csf Biomarkers ◽

Tryptophan Pathway ◽

Personalised Treatment ◽

Information Framework

Background: physiological differences between males and females could contribute to the development of Alzheimer's Disease (AD). Here, we examined metabolic pathways that may lead to precision medicine initiatives. Methods: We explored whether sex modifies the association of 540 plasma metabolites with AD endophenotypes including diagnosis, cerebrospinal fluid (CSF) biomarkers, brain imaging, and cognition using regression analyses for 695 participants (377 females), followed by sex-specific pathway overrepresentation analyses, APOE ε4 stratification and assessment of metabolites’ discriminatory performance in AD. Results: In females with AD, vanillylmandelate (tyrosine pathway) was increased and tryptophan betaine (tryptophan pathway) was decreased. The inclusion of these two metabolites (area under curve (AUC) = 0.83, standard error (SE) = 0.029) to a baseline model (covariates + CSF biomarkers, AUC = 0.92, SE = 0.019) resulted in a significantly higher AUC of 0.96 (SE = 0.012). Kynurenate was decreased in males with AD (AUC = 0.679, SE = 0.046). Conclusions: metabolic sex-specific differences were reported, covering neurotransmission and inflammation pathways with AD endophenotypes. Two metabolites, in pathways related to dopamine and serotonin, were associated to females, paving the way to personalised treatment.

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Improving delayed face recognition in Alzheimer's disease by differential outcomes.

Neuropsychology ◽

10.1037/a0028485 ◽

2012 ◽

Vol 26 (4) ◽

pp. 483-489 ◽

Cited By ~ 21

Author(s):

Victoria Plaza ◽

Ginesa López-Crespo ◽

Carmen Antúnez ◽

Luis J. Fuentes ◽

Angeles F. Estévez

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Face Recognition ◽

Differential Outcomes

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Leverage Real-world Longitudinal Data in Large Clinical Research Networks for Alzheimer's Disease and Related Dementia (ADRD)

10.1101/2020.08.03.20167619 ◽

2020 ◽

Author(s):

Rui Duan ◽

Zhaoyi Chen ◽

Jiayi Tong ◽

Chongliang Luo ◽

Tianchen Lyu ◽

...

Keyword(s):

Risk Factors ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Longitudinal Data ◽

Clinical Research ◽

Distributed Algorithms ◽

Real World ◽

Medical Information ◽

Patient Privacy ◽

Research Networks

With vast amounts of patients' medical information, electronic health records (EHRs) are becoming one of the most important data sources in biomedical and health care research. Effectively integrating data from multiple clinical sites can help provide more generalized real-world evidence that is clinically meaningful. To analyze the clinical data from multiple sites, distributed algorithms are developed to protect patient privacy without sharing individual-level medical information. In this paper, we applied the One-shot Distributed Algorithm for Cox proportional hazard model (ODAC) to the longitudinal data from the OneFlorida Clinical Research Consortium to demonstrate the feasibility of implementing the distributed algorithms in large research networks. We studied the associations between the clinical risk factors and Alzheimer's disease and related dementia (ADRD) onsets to advance clinical research on our understanding of the complex risk factors of ADRD and ultimately improve the care of ADRD patients.

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