Use of Hormone Therapy in Postmenopausal Women with Alzheimer’s Disease: A Systematic Review

Drugs & Aging ◽  
2021 ◽  
Author(s):  
Camila A. E. F. Cardinali ◽  
Yandara A. Martins ◽  
Andréa S. Torrão
Keyword(s):  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Hormone Therapy ◽  
Postmenopausal Women

scholarly journals Use of postmenopausal hormone therapy and risk of Alzheimer’s disease in Finland: nationwide case-control study

BMJ ◽  
2019 ◽  
pp. l665 ◽  
Author(s):  
Hanna Savolainen-Peltonen ◽  
Päivi Rahkola-Soisalo ◽  
Fabian Hoti ◽  
Pia Vattulainen ◽  
Mika Gissler ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Hormone Therapy ◽  
Postmenopausal Women ◽  
Case Control Study ◽  
Case Control ◽  
Drug Register ◽  
National Drug ◽  
Increased Risk ◽  
Control Study

Abstract Objectives To compare the use of hormone therapy between Finnish postmenopausal women with and without a diagnosis for Alzheimer’s disease. Design Nationwide case-control study. Setting Finnish national population and drug register, between 1999 and 2013. Participants All postmenopausal women (n=84 739) in Finland who, between 1999 and 2013, received a diagnosis of Alzheimer’s disease from a neurologist or geriatrician, and who were identified from a national drug register. Control women without a diagnosis (n=84 739), matched by age and hospital district, were traced from the Finnish national population register. Interventions Data on hormone therapy use were obtained from the Finnish national drug reimbursement register. Main outcome measures Odds ratios and 95% confidence intervals for Alzheimer’s disease, calculated with conditional logistic regression analysis. Results In 83 688 (98.8%) women, a diagnosis for Alzheimer’s disease was made at the age of 60 years or older, and 47 239 (55.7%) women had been over 80 years of age at diagnosis. Use of systemic hormone therapy was associated with a 9-17% increased risk of Alzheimer’s disease. The risk of the disease did not differ significantly between users of estradiol only (odds ratio 1.09, 95% confidence interval 1.05 to 1.14) and those of oestrogen-progestogen (1.17, 1.13 to 1.21). The risk increases in users of oestrogen-progestogen therapy were not related to different progestogens (norethisterone acetate, medroxyprogesterone acetate, or other progestogens); but in women younger than 60 at hormone therapy initiation, these risk increases were associated with hormone therapy exposure over 10 years. Furthermore, the age at initiation of systemic hormone therapy was not a decisive determinant for the increase in risk of Alzheimer’s disease. The exclusive use of vaginal estradiol did not affect the risk of the disease (0.99, 0.96 to 1.01). Conclusions Long term use of systemic hormone therapy might be accompanied with an overall increased risk of Alzheimer’s disease, which is not related to the type of progestogen or the age at initiation of systemic hormone therapy. By contrast, use of vaginal estradiol shows no such risk. Even though the absolute risk increase for Alzheimer’s disease is small, our data should be implemented into information for present and future users of hormone therapy.

Evaluating the Role of Hormone Therapy in Postmenopausal Women with Alzheimer’s Disease

Drugs & Aging ◽  
2016 ◽  
Vol 33 (11) ◽  
pp. 787-808 ◽  
Author(s):  
Jelena Osmanovic-Barilar ◽  
Melita Salkovic-Petrisi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Hormone Therapy ◽  
Postmenopausal Women

Economic Evaluations of Treatment Options in Patients with Alzheimer's Disease – A Systematic Review

2011 ◽  
Vol 73 (08/09) ◽  
Author(s):  
L Pouryamout ◽  
A Neumann ◽  
J Dams ◽  
J Wasem ◽  
R Dodel
Keyword(s):  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Treatment Options ◽  
Economic Evaluations

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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