scholarly journals Vitamin D and Folate as Predictors of MMSE in Alzheimer’s Disease: A Machine Learning Analysis

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 940
Author(s):  
Giuseppe Murdaca ◽  
Sara Banchero ◽  
Alessandro Tonacci ◽  
Alessio Nencioni ◽  
Fiammetta Monacelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Vitamin D ◽  
Folic Acid ◽  
Machine Learning Approach ◽  
Low Levels ◽  
The Impact ◽  
Learning Analysis

Vitamin D (VD) and micronutrients, including folic acid, are able to modulate both the innate and the adaptive immune responses. Low VD and folic acid levels appear to promote cognitive decline as in Alzheimer’s disease (AD). A machine learning approach was applied to analyze the impact of various compounds, drawn from the blood of AD patients, including VD and folic acid levels, on the Mini-Mental State Exam (MMSE) in a cohort of 108 patients with AD. The first analysis was aimed at predicting the MMSE at recruitment, whereas a second investigation sought to predict the MMSE after a 4 year follow-up. The simultaneous presence of low levels of VD and folic acid allow to predict MMSE, suggestive of poorer cognitive function. Such results suggest that the low levels of VD and folic acid could be associated with more severe cases of cognitive impairment in AD. It could be hypothesized that simultaneous supplementation of VD and folic acid could slow down the progression of cerebral degeneration at least in a subset of AD individuals.

scholarly journals A Machine Learning Approach to Unmask Novel Gene Signatures and Prediction of Alzheimer’s Disease Within Different Brain Regions

2021 ◽  
Author(s):  
Abhibhav Sharma ◽  
Pinki Dey
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Brain Regions ◽  
Middle Temporal Gyrus ◽  
Non Coding Rna ◽  
Machine Learning Approach ◽  
Microarray Datasets ◽  
Rna Genes

AbstractAlzheimer’s disease (AD) is a progressive neurodegenerative disorder whose aetiology is currently unknown. Although numerous studies have attempted to identify the genetic risk factor(s) of AD, the interpretability and/or the prediction accuracies achieved by these studies remained unsatisfactory, reducing their clinical significance. Here, we employ the ensemble of random-forest and regularized regression model (LASSO) to the AD-associated microarray datasets from four brain regions - Prefrontal cortex, Middle temporal gyrus, Hippocampus, and Entorhinal cortex- to discover novel genetic biomarkers through a machine learning-based feature-selection classification scheme. The proposed scheme unrevealed the most optimum and biologically significant classifiers within each brain region, which achieved by far the highest prediction accuracy of AD in 5-fold cross-validation (99% average). Interestingly, along with the novel and prominent biomarkers including CORO1C, SLC25A46, RAE1, ANKIB1, CRLF3, PDYN, numerous non-coding RNA genes were also observed as discriminator, of which AK057435 and BC037880 are uncharacterized long non-coding RNA genes.

scholarly journals The Impact of a Long-Term Rivastigmine and Donepezil Treatment on All-Cause Mortality in Patients With Alzheimer’s Disease

2018 ◽  
Vol 33 (6) ◽  
pp. 385-393 ◽  
Author(s):  
Jakub Kazmierski ◽  
Chaido Messini-Zachou ◽  
Mara Gkioka ◽  
Magda Tsolaki
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cox Regression ◽  
Symptomatic Treatment ◽  
Risk Of Death ◽  
Increased Risk ◽  
Functional Assessments ◽  
The Impact

Cholinesterase inhibitors (ChEIs) are the mainstays of symptomatic treatment of Alzheimer’s disease (AD); however, their efficacy is limited, and their use was associated with deaths in some groups of patients. The aim of the current study was to assess the impact of the long-term use of ChEIs on mortality in patients with AD. This observational, longitudinal study included 1171 adult patients with a diagnosis of AD treated with donepezil or rivastigmine. Each patient was observed for 24 months or until death. The cognitive and functional assessments, the use of ChEIs, memantine, antipsychotics, antidepressants, and anxiolytics were recorded. The total number of deaths at the end of the observational period was 99 (8.45%). The patients who had received rivastigmine treatment were at an increased risk of death in the follow-up period. The higher risk of death in the rivastigmine group remained significant in multivariate Cox regression models.

scholarly journals Classification of Alzheimer’s Disease Leveraging Multi-task Machine Learning Analysis of Speech and Eye-Movement Data

2021 ◽  
Vol 15 ◽  
Author(s):  
Hyeju Jang ◽  
Thomas Soroski ◽  
Matteo Rizzo ◽  
Oswald Barral ◽  
Anuj Harisinghani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Eye Movement ◽  
Memory Clinic ◽  
Subjective Memory ◽  
Discriminative Ability ◽  
Impaired Performance ◽  
Movement Data ◽  
Learning Analysis

Alzheimer’s disease (AD) is a progressive neurodegenerative condition that results in impaired performance in multiple cognitive domains. Preclinical changes in eye movements and language can occur with the disease, and progress alongside worsening cognition. In this article, we present the results from a machine learning analysis of a novel multimodal dataset for AD classification. The cohort includes data from two novel tasks not previously assessed in classification models for AD (pupil fixation and description of a pleasant past experience), as well as two established tasks (picture description and paragraph reading). Our dataset includes language and eye movement data from 79 memory clinic patients with diagnoses of mild-moderate AD, mild cognitive impairment (MCI), or subjective memory complaints (SMC), and 83 older adult controls. The analysis of the individual novel tasks showed similar classification accuracy when compared to established tasks, demonstrating their discriminative ability for memory clinic patients. Fusing the multimodal data across tasks yielded the highest overall AUC of 0.83 ± 0.01, indicating that the data from novel tasks are complementary to established tasks.

scholarly journals Antiherpetic drugs: a potential way to prevent Alzheimer’s disease?

2022 ◽  
Vol 14 (1) ◽  
Author(s):  
Morgane Linard ◽  
Julien Bezin ◽  
Emilie Hucteau ◽  
Pierre Joly ◽  
Isabelle Garrigue ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Protective Effect ◽  
Vascular Dementia ◽  
Sensitivity Analyses ◽  
Biological Plausibility ◽  
Potential Implication ◽  
Incident Cases ◽  
The Impact

Abstract Background Considering the growing body of evidence suggesting a potential implication of herpesviruses in the development of dementia, several authors have questioned a protective effect of antiherpetic drugs (AHDs) which may represent a new means of prevention, well tolerated and easily accessible. Subsequently, several epidemiological studies have shown a reduction in the risk of dementia in subjects treated with AHDs, but the biological plausibility of this association and the impact of potential methodological biases need to be discussed in more depth. Methods Using a French medico-administrative database, we assessed the association between the intake of systemic AHDs and the incidence of (i) dementia, (ii) Alzheimer’s disease (AD), and (iii) vascular dementia in 68,291 subjects over 65 who were followed between 2009 and 2017. Regarding potential methodological biases, Cox models were adjusted for numerous potential confounding factors (including proxies of sociodemographic status, comorbidities, and use of healthcare) and sensitivity analyses were performed in an attempt to limit the risk of indication and reverse causality biases. Results 9.7% of subjects (n=6642) had at least one intake of systemic AHD, and 8883 incident cases of dementia were identified. Intake of at least one systemic AHD during follow-up was significantly associated with a decreased risk of AD (aHR 0.85 95% confidence interval [0.75–0.96], p=0.009) and, to a lesser extent with respect to p values, to both dementia from any cause and vascular dementia. The association with AD remained significant in sensitivity analyses. The number of subjects with a regular intake was low and prevented us from studying its association with dementia. Conclusions Taking at least one systemic AHD during follow-up was significantly associated with a 15% reduced risk of developing AD, even after taking into account several potential methodological biases. Nevertheless, the low frequency of subjects with a regular intake questions the biological plausibility of this association and highlights the limits of epidemiological data to evaluate a potential protective effect of a regular treatment by systemic AHDs on the incidence of dementia

scholarly journals Trends in Alzheimer's Disease Research Based upon Machine Learning Analysis of PubMed Abstracts

2019 ◽  
Vol 15 (10) ◽  
pp. 2065-2074 ◽  
Author(s):  
Renchu Guan ◽  
Xiaojing Wen ◽  
Yanchun Liang ◽  
Dong Xu ◽  
Baorun He ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Research ◽  
Learning Analysis

scholarly journals Neuroinflammation and Alzheimer’s Disease: A Machine Learning Approach to CSF Proteomics

Cells ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 1930
Author(s):  
Lorenzo Gaetani ◽  
Giovanni Bellomo ◽  
Lucilla Parnetti ◽  
Kaj Blennow ◽  
Henrik Zetterberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Biomarker Discovery ◽  
Neurological Diseases ◽  
Univariate Analysis ◽  
Learning Approach ◽  
Barrier Dysfunction ◽  
Csf Analysis ◽  
Machine Learning Approach

In Alzheimer’s disease (AD), the contribution of pathophysiological mechanisms other than amyloidosis and tauopathy is now widely recognized, although not clearly quantifiable by means of fluid biomarkers. We aimed to identify quantifiable protein biomarkers reflecting neuroinflammation in AD using multiplex proximity extension assay (PEA) testing. Cerebrospinal fluid (CSF) samples from patients with mild cognitive impairment due to AD (AD-MCI) and from controls, i.e., patients with other neurological diseases (OND), were analyzed with the Olink Inflammation PEA biomarker panel. A machine-learning approach was then used to identify biomarkers discriminating AD-MCI (n: 34) from OND (n: 25). On univariate analysis, SIRT2, HGF, MMP-10, and CXCL5 showed high discriminatory performance (AUC 0.809, p = 5.2 × 10−4, AUC 0.802, p = 6.4 × 10−4, AUC 0.793, p = 3.2 × 10−3, AUC 0.761, p = 2.3 × 10−3, respectively), with higher CSF levels in AD-MCI patients as compared to controls. These same proteins were the best contributors to the penalized logistic regression model discriminating AD-MCI from controls (AUC of the model 0.906, p = 2.97 × 10−7). The biological processes regulated by these proteins include astrocyte and microglia activation, amyloid, and tau misfolding modulation, and blood-brain barrier dysfunction. Using a high-throughput multiplex CSF analysis coupled with a machine-learning statistical approach, we identified novel biomarkers reflecting neuroinflammation in AD. Studies confirming these results by means of different assays are needed to validate PEA as a multiplex technique for CSF analysis and biomarker discovery in the field of neurological diseases.

scholarly journals Predicting future regional tau accumulation in asymptomatic and early Alzheimer’s disease

2020 ◽  
Author(s):  
Joseph Giorgio ◽  
William J Jagust ◽  
Suzanne Baker ◽  
Susan M. Landau ◽  
Peter Tino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trial ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Learning Approach ◽  
Multimodal Data ◽  
Machine Learning Approach ◽  
Β Amyloid

AbstractThe earliest stages of Alzheimer’s disease (AD) involve interactions between multiple pathophysiological processes. Although these processes are well studied, we still lack robust tools to predict individualised trajectories of disease progression. Here, we employ a robust and interpretable machine learning approach to combine multimodal biological data and predict future tau accumulation, translating predictive information from deep phenotyping cohorts at early stages of AD to cognitively normal individuals. In particular, we use machine learning to quantify interactions between key pathological markers (β-amyloid, medial temporal atrophy, tau and APOE 4) at early and asymptomatic stages of AD. We next derive a predictive index that stratifies individuals based on future pathological tau accumulation, highlighting two critical features for optimal clinical trial design. First, future tau accumulation provides a better outcome measure compared to changes in cognition. Second, stratification based on multimodal data compared to β-amyloid alone reduces the sample size required to detect a clinically meaningful change in tau accumulation. Further, we extend our machine learning approach to derive individualised trajectories of future pathological tau accumulation in early AD patients and accurately predict regional future rate of tau accumulation in an independent sample of cognitively unimpaired individuals. Our results propose a robust approach for fine scale stratification and prognostication with translation impact for clinical trial design at asymptomatic and early stages of AD.One Sentence SummaryOur machine learning approach combines baseline multimodal data to make individualised predictions of future pathological tau accumulation at prodromal and asymptomatic stages of Alzheimer’s disease with high accuracy and regional specificity.

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