Epileptic Seizures in Alzheimer’s Disease: What Are the Implications of SANAD II?

2021 ◽  
pp. 1-3
Author(s):  
Andrew J. Larner ◽  
Anthony G. Marson
Keyword(s):  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Phenotype ◽  
Focal Epilepsy ◽  
Epileptic Seizures ◽  
Treatment Decisions ◽  
Evidence Based ◽  
Newly Diagnosed ◽  
Pragmatic Study

Epileptic seizures are increasingly recognized as part of the clinical phenotype of patients with Alzheimer’s disease (AD). However, the evidence based on which to make treatment decisions for such patients is slim, there being no clear recommendation based on systematic review of the few existing studies of anti-seizure drugs in AD patients. Here the authors examine the potential implications for the treatment of seizures in AD of the results of the recently published SANAD II pragmatic study, which examined the effectiveness of levetiracetam, zonisamide, or lamotrigine in newly diagnosed focal epilepsy, and of valproate and levetiracetam in generalized and unclassifiable epilepsy.

scholarly journals Evidence-based prevention of Alzheimer's disease: systematic review and meta-analysis of 243 observational prospective studies and 153 randomised controlled trials

2020 ◽  
Vol 91 (11) ◽  
pp. 1201-1209 ◽  
Author(s):  
Jin-Tai Yu ◽  
Wei Xu ◽  
Chen-Chen Tan ◽  
Sandrine Andrieu ◽  
John Suckling ◽  
...  
Keyword(s):  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prospective Studies ◽  
Randomised Controlled Trials ◽  
Meta Analysis ◽  
Current Evidence ◽  
Controlled Trials ◽  
Evidence Based ◽  
Randomised Controlled

BackgroundEvidence on preventing Alzheimer’s disease (AD) is challenging to interpret due to varying study designs with heterogeneous endpoints and credibility. We completed a systematic review and meta-analysis of current evidence with prospective designs to propose evidence-based suggestions on AD prevention.MethodsElectronic databases and relevant websites were searched from inception to 1 March 2019. Both observational prospective studies (OPSs) and randomised controlled trials (RCTs) were included. The multivariable-adjusted effect estimates were pooled by random-effects models, with credibility assessment according to its risk of bias, inconsistency and imprecision. Levels of evidence and classes of suggestions were summarised.ResultsA total of 44 676 reports were identified, and 243 OPSs and 153 RCTs were eligible for analysis after exclusion based on pre-decided criteria, from which 104 modifiable factors and 11 interventions were included in the meta-analyses. Twenty-one suggestions are proposed based on the consolidated evidence, with Class I suggestions targeting 19 factors: 10 with Level A strong evidence (education, cognitive activity, high body mass index in latelife, hyperhomocysteinaemia, depression, stress, diabetes, head trauma, hypertension in midlife and orthostatic hypotension) and 9 with Level B weaker evidence (obesity in midlife, weight loss in late life, physical exercise, smoking, sleep, cerebrovascular disease, frailty, atrial fibrillation and vitamin C). In contrast, two interventions are not recommended: oestrogen replacement therapy (Level A2) and acetylcholinesterase inhibitors (Level B).InterpretationEvidence-based suggestions are proposed, offering clinicians and stakeholders current guidance for the prevention of AD.

scholarly journals Systematic review of the literature on vitamin A and memory

2012 ◽  
Vol 6 (4) ◽  
pp. 219-222 ◽  
Author(s):  
Yara Dadalti Fragoso ◽  
Niklas Söderberg Campos ◽  
Breno Faria Tenrreiro ◽  
Fernanda Jussio Guillen
Keyword(s):  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Vitamin A ◽  
Evidence Based ◽  
Review Of The Literature ◽  
Vitamin A Supplementation ◽  
Treatment And Prevention ◽  
The Subject

ABSTRACT Background: Over the last 30 years, a variety of studies reporting the effects of vitamin A on memory have been published. Objective: To perform a rigorous systematic review of the literature on vitamin A and memory in order to organize evidence-based data on the subject. Methods: Four authors carried out the systematic review in accordance with strict guidelines. The terms "vitamin A" OR "retinol" OR "retinoic acid" AND "memory" OR "cognition" OR "Alzheimer" were searched in virtually all medical research databases. Results: From 236 studies containing the key words, 44 were selected for this review, numbering 10 reviews and 34 original articles. Most studies used animal models for studying vitamin A and cognition. Birds, mice and rats were more frequently employed whereas human studies accounted for only two reports on brain tissue from autopsies and one on the role of isotretinoin in cognition among individuals taking this medication to treat acne. Conclusion: Vitamin A may be an important and viable complement in the treatment and prevention of Alzheimer's disease. Clinical trials are imperative and, at present, there is no evidence-based data to recommend vitamin A supplementation for the prevention or treatment of Alzheimer's disease.

Leveraging Technology in Detection of Alzheimer’s Disease: A Systematic Review (Preprint)

2019 ◽  
Author(s):  
Clemens Kruse ◽  
Britney Larson ◽  
Reagan Wilkinson ◽  
Roger Samson ◽  
Taylor Castillo
Keyword(s):  
Magnetic Resonance Imaging ◽  
United States ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
The United States ◽  
Diagnostic Process ◽  
Resonance Imaging ◽  
Common Cause ◽  
Key Terms

BACKGROUND Incidence of AD continues to increase, making it the most common cause of dementia and the sixth-leading cause of death in the United States. 2018 numbers are expected to double by 2030. OBJECTIVE We examined the benefits of utilizing technology to identify and detect Alzheimer’s disease in the diagnostic process. METHODS We searched PubMed and CINAHL using key terms and filters to identify 30 articles for review. We analyzed these articles and reported them in accordance with the PRISMA guidelines. RESULTS We identified 11 technologies used in the detection of Alzheimer’s disease: 66% of which used some form of MIR. Functional, structural, and 7T magnetic resonance imaging were all used with structural being the most prevalent. CONCLUSIONS MRI is the best form of current technology being used in the detection of Alzheimer’s disease. MRI is a noninvasive approach that provides highly accurate results in the diagnostic process of Alzheimer’s disease.

scholarly journals Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1071
Author(s):  
Lucia Billeci ◽  
Asia Badolato ◽  
Lorenzo Bachi ◽  
Alessandro Tonacci
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Classification Problem ◽  
Computer Algorithms ◽  
Imaging Data

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

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